From f395f05945af8cd6dc8305282e0b72c70841b7be Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Mon, 24 Apr 2023 16:19:14 -0600 Subject: [PATCH 01/32] Add reopt_multi_solutions.jl With run_reopt_multi_solutions for running REopt and generating results summary --- src/core/reopt_multi_solutions.jl | 149 ++++++++++++++++++++++++++++++ 1 file changed, 149 insertions(+) create mode 100644 src/core/reopt_multi_solutions.jl diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl new file mode 100644 index 000000000..00c5afc6d --- /dev/null +++ b/src/core/reopt_multi_solutions.jl @@ -0,0 +1,149 @@ +""" +run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel + +Run REopt to get optimal tech sizes, and then run REopt multiple times with scaling factor size_scale applied to + the optimal sizes to look at sensitivity of tech size on key results parameters + + fp is the inputs .json file path + size_scale is the vector of scaling factors to apply to each optimal tech size + ms is a vector of identical JuMP model objects and the length has to be larger than the max possible REopt runs + +""" +function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel + # Run REopt the first time for optimal tech sizes + n = 1 # Indexing into the ms vector of JuMP models + input_data = JSON.parsefile(fp) + p = REoptInputs(input_data) + results = run_reopt(ms[n], p) + n += 1 + + # Check results to see which techs are sized, to inform number of solutions + techs_possible = ["PV", "ElectricStorage", "Generator", "CHP"] + techs_considered = intersect(techs_possible, keys(input_data)) + techs_sized = intersect(techs_possible, keys(results)) + techs_to_zero = setdiff(techs_considered, techs_sized) + + # TODO if length(techs_sized) == 0, no need to run BAU or any other cases, so skip! + # Only need to run BAU once, and then run "combine_results()" N times to get Financial results for all + bau_inputs = REopt.BAUInputs(p) + bau_results = run_reopt(ms[n], bau_inputs) + n += 1 + + # Combine results for BAU-Opt to get e.g. NPV + results_dict = REopt.combine_results(p, bau_results, results, bau_inputs.s) + results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) + + # Now create number of solutions based on the # of techs sized in optimal case + # TODO?? need to include where both/N techs are scaled together and in opposite directions + results_all = Dict("optimal" => results_dict) + results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[1], techs_sized)) + for i in eachindex(size_scale) + for tech in techs_sized + # Copy input_data so all techs start at optimal size (size_scale = 1.0) + # Remove techs which were considered but not sized (size=0) in optimal case + input_data_s = deepcopy(input_data) + for t in techs_to_zero + delete!(input_data_s, t) + end + # Force size based on size_scale factor + input_data_s[tech]["min_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] + input_data_s[tech]["max_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] + if tech == "ElectricStorage" + input_data_s[tech]["min_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] + input_data_s[tech]["max_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] + end + + # Use modified inputs to run_reopt and get results + local p = REoptInputs(input_data_s) + local results = run_reopt(ms[n], input_data_s) + + # Combine with BAU to get e.g. NPV + local results_dict = REopt.combine_results(p, bau_results, results, bau_inputs.s) + results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) + results_all[tech*"_size_scale_"*string(size_scale[i])] = results_dict + + # Build results summary, a select number of outputs for Eaton + results_summary[tech*"_size_scale_"*string(size_scale[i])] = get_multi_solutions_results_summary(results_dict, p, ms[n], techs_sized) + n += 1 + end + end + return results_all, results_summary +end + +""" + get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}) + +Get the results summary dictionary which is a selected number of outputs for Financial, Emissions, and + the relevant techs + +""" +function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}) + # results_summary always has Financial and emissions, and then add techs as needed + results_summary = Dict( + "Financial" => + Dict("Net Present Value" => results["Financial"]["npv"], + "Simple payback period" => results["Financial"]["simple_payback_years"], + "Net capital cost" => results["Financial"]["lifecycle_capital_costs"], + "Internal Rate of Return %" => results["Financial"]["internal_rate_of_return"] * 100.0), + "emissions" => + Dict("Site life cycle CO2 tonnes" => results["Site"]["lifecycle_emissions_tonnes_CO2"], + "Site life cycle NOx tonnes" => results["Site"]["lifecycle_emissions_tonnes_NOx"], + "Site life cycle SO2 tonnes" => results["Site"]["lifecycle_emissions_tonnes_SO2"], + "Site life cycle PM25 tonnes" => results["Site"]["lifecycle_emissions_tonnes_PM25"]) + ) + + # Add techs if they have been sized in the optimal case + for key in techs_sized + if key == "PV" + # Note, capital cost is "net" of incentives, and Maintenance cost is year-one + results_summary["PV"] = + Dict("Rated capacity" => results[key]["size_kw"], + "Average annual energy produced" => results[key]["year_one_energy_produced_kwh"], + "Capital cost" => p.cap_cost_slope[key] * results[key]["size_kw"], + "Annual maintenance cost" => p.s.pvs[1].om_cost_per_kw * results[key]["size_kw"]) + elseif key == "ElectricStorage" + # Note, capital cost is "net" of incentives, and Maintenance cost is year-one + # Estimate the installed upfront cost versus the replacement cost by scaling inputs + # TODO this has become complicated and might be easier to just leverage effective_cost REopt function + net_installed_plus_replace_cost = value(m[Symbol("TotalStorageCapCosts")]) + total_input_cost_per_kw = p.s.storage.attr[key].installed_cost_per_kw + p.s.storage.attr[key].replace_cost_per_kw + total_input_cost_per_kwh = p.s.storage.attr[key].installed_cost_per_kwh + p.s.storage.attr[key].replace_cost_per_kwh + replacement_fraction_per_kw = p.s.storage.attr[key].replace_cost_per_kw / total_input_cost_per_kw + replacement_fraction_per_kwh = p.s.storage.attr[key].replace_cost_per_kwh / total_input_cost_per_kwh + replacement_cost_fraction = (replacement_fraction_per_kw * results[key]["size_kw"] + + replacement_fraction_per_kwh * results[key]["size_kwh"]) / + (results[key]["size_kw"] + results[key]["size_kwh"]) + net_replacement_cost = net_installed_plus_replace_cost * replacement_cost_fraction + net_installed_cost = net_installed_plus_replace_cost - net_replacement_cost + results_summary["Storage"] = + Dict("Rated energy capacity" => results[key]["size_kwh"], + "Rated inverter capacity" => results[key]["size_kw"], + "Capital cost" => net_installed_cost, + "Total replacement cost" => net_replacement_cost) + elseif key == "Generator" + results_summary["Generator"] = + Dict("Rated capacity" => results[key]["size_kw"], + "Capital cost" => p.cap_cost_slope[key] * results[key]["size_kw"], + "Annual maintenance cost" => p.s.generator.om_cost_per_kw * results[key]["size_kw"], + "Life cycle fuel cost after tax" => results[key]["lifecycle_fuel_cost_after_tax"], + "Annual fuel used gallons" => results[key]["annual_fuel_consumption_gal"], + "Annual energy produced" => results[key]["annual_energy_produced_kwh"]) + elseif key == "CHP" + cap_cost = p.third_party_factor * ( + sum(p.cap_cost_slope[key][s] * value(m[Symbol("dvSegmentSystemSize"*key)][s]) + + p.seg_yint[key][s] * value(m[Symbol("binSegment"*key)][s]) for s in 1:p.n_segs_by_tech[key])) + var_om_cost = (value(m[Symbol("TotalCHPPerUnitProdOMCosts")]) + value(m[Symbol("TotalHourlyCHPOMCosts")])) / + p.third_party_factor * p.pwf_om + fixed_om_cost = p.s.chp.om_cost_per_kw * results[key]["size_kw"] + results_summary["Fuel Cell"] = + Dict("Rated capacity" => results[key]["size_kw"], + "Capital cost" => cap_cost, + "Annual maintenance cost" => var_om_cost + fixed_om_cost, + "Annual energy produced" => results[key]["annual_electric_production_kwh"], + "Life cycle fuel cost after tax" => results[key]["lifecycle_fuel_cost_after_tax"], + "Annual fuel used MMBtu" => results[key]["annual_fuel_consumption_mmbtu"], + "Life cycle standby cost after tax" => results[key]["lifecycle_standby_cost_after_tax"]) + end + end + return results_summary +end \ No newline at end of file From ae657143fba0d7bf1c52df75ff87819efab0ddbe Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Mon, 24 Apr 2023 16:19:33 -0600 Subject: [PATCH 02/32] Add reopt_multi_solutions code to REopt module NOT exported yet, so can call by REopt.run_reopt_multi_solutions --- src/REopt.jl | 1 + 1 file changed, 1 insertion(+) diff --git a/src/REopt.jl b/src/REopt.jl index 05a7d7a84..ac1501cd9 100644 --- a/src/REopt.jl +++ b/src/REopt.jl @@ -160,6 +160,7 @@ include("core/reopt_inputs.jl") include("core/bau_inputs.jl") include("core/cost_curve.jl") include("core/simulated_load.jl") +include("core/reopt_multi_solutions.jl") include("constraints/outage_constraints.jl") include("constraints/storage_constraints.jl") From d8e249480dae7d06c9b9bdf0716c09f8a8639086 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 27 Apr 2023 10:46:05 -0600 Subject: [PATCH 03/32] Add readme for reopt multi solution --- multi_solution_readme.md | 28 ++++++++++++++++++++++++++++ src/core/reopt_multi_solutions.jl | 12 ++++++------ 2 files changed, 34 insertions(+), 6 deletions(-) create mode 100644 multi_solution_readme.md diff --git a/multi_solution_readme.md b/multi_solution_readme.md new file mode 100644 index 000000000..4b1a5d328 --- /dev/null +++ b/multi_solution_readme.md @@ -0,0 +1,28 @@ +# Summary +The multiple solutions functionality runs REopt (like `run_reopt`) multiple times to look at the +sensitivity of a number of key results, such as Net Present Value (NPV) and capital cost, on +sizing variations around the optimal technology sizes. The function first runs the optimal case to +identify the cost-effective technologies recommended by REopt. It then re-runs a number of times with scaling factors applied to each of the cost-optimal techs, and it forces those sizes into the +solution. + +# Example use-case for multi-solutions +fp = "scenarios/eaton_multi.json" +# Fractions/ratios of size relative to the optimal size +# For each technology which is sized, multiply the size by the size_scale for a different solution +# The other techs will all be at 1.0/optimal size for the scaling of other techs +size_scale = [0.5, 1.5] +# Need to know max number of JuMP model to create, so identify the number of technologies considered +n_techs = 2 +# Max models is 2 for optimal+BAU and then however many scenarios are possible +max_models = 2 + length(size_scale) * n_techs +# Hard to duplicate JuMP models dynamically, so conservatively instantiate max possible models +ms = [Model(optimizer_with_attributes(HiGHS.Optimizer, + "output_flag" => false, "mip_rel_gap" => 0.001, "log_to_console" => true)) for _ in 1:max_models] + +results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms) + +# Print some interesting data from all the solutions +for s in keys(results_summary) + println("NPV for scenario "*s*" = ", results_summary[s]["Financial"]["Net Present Value"]) + println("Capital cost for scenario "*s*" = ", results_summary[s]["Financial"]["Net capital cost"]) +end \ No newline at end of file diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 00c5afc6d..d24bf85ad 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -1,12 +1,12 @@ """ -run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel + run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel Run REopt to get optimal tech sizes, and then run REopt multiple times with scaling factor size_scale applied to - the optimal sizes to look at sensitivity of tech size on key results parameters +the optimal sizes to look at sensitivity of tech size on key results parameters - fp is the inputs .json file path - size_scale is the vector of scaling factors to apply to each optimal tech size - ms is a vector of identical JuMP model objects and the length has to be larger than the max possible REopt runs +fp is the inputs .json file path +size_scale is the vector of scaling factors to apply to each optimal tech size +ms is a vector of identical JuMP model objects and the length has to be larger than the max possible REopt runs """ function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel @@ -74,7 +74,7 @@ end get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}) Get the results summary dictionary which is a selected number of outputs for Financial, Emissions, and - the relevant techs +the relevant techs """ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}) From 2240f9e71580c5cd3d5b9888e423acd32b607d1c Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Mon, 1 May 2023 10:40:42 -0600 Subject: [PATCH 04/32] Add parallel multi-threaded multi solns function --- multi_solution_readme.md | 4 ++ src/core/reopt_multi_solutions.jl | 91 +++++++++++++++++++++++++++++++ 2 files changed, 95 insertions(+) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index 4b1a5d328..e8b182425 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -19,8 +19,12 @@ max_models = 2 + length(size_scale) * n_techs ms = [Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "mip_rel_gap" => 0.001, "log_to_console" => true)) for _ in 1:max_models] +# In-series/sequential run_reopt: results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms) +# Parallel run_reopt: +results_all, results_summary = REopt.run_reopt_multi_solutions_parallel(fp, size_scale, ms) + # Print some interesting data from all the solutions for s in keys(results_summary) println("NPV for scenario "*s*" = ", results_summary[s]["Financial"]["Net Present Value"]) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index d24bf85ad..4f24c9336 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -146,4 +146,95 @@ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::J end end return results_summary +end + +""" + run_reopt_multi_solutions_parallel(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel + +Run REopt to get optimal tech sizes, and then run REopt multiple times with scaling factor size_scale applied to +the optimal sizes to look at sensitivity of tech size on key results parameters + +fp is the inputs .json file path +size_scale is the vector of scaling factors to apply to each optimal tech size +ms is a vector of identical JuMP model objects and the length has to be larger than the max possible REopt runs + +For this multi-threading version, but set JULIA_NUM_THREADS="auto" or set to max on computer +export JULIA_NUM_THREADS="auto" +or +export JULIA_NUM_THREADS=8 + +""" + +function run_reopt_multi_solutions_parallel(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel + # Load in input_data from .json to dictionary + input_data = JSON.parsefile(fp) + # Create optimal and BAU inputs structs for initial 2 runs + p = REoptInputs(input_data) + bau_inputs = BAUInputs(p) + # Note, ms[1] and rs[1] are for the BAU case which we'll need to combine with all other solutions too + # and ms[2] and rs[2] are for the optimal case + inputs = ((ms[1], bau_inputs), (ms[2], p)) + rs = Any[0, 0] + Threads.@threads for i = 1:2 + rs[i] = run_reopt(inputs[i]) + end + + # Combine results for BAU-Opt to get e.g. NPV + results_dict = REopt.combine_results(p, rs[1], rs[2], bau_inputs.s) + results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) + + # Check results to see which techs are sized, to inform number of solutions + techs_possible = ["PV", "ElectricStorage", "Generator", "CHP"] + techs_considered = intersect(techs_possible, keys(input_data)) + techs_sized = intersect(techs_possible, keys(results_dict)) + techs_to_zero = setdiff(techs_considered, techs_sized) + n_solutions = length(size_scale) * length(techs_sized) + + # Create the first entry for "optimal" solution in the results dictionary + results_all = Dict("optimal" => results_dict) + results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized)) + + # Now create number of inputs based on the # of techs sized in optimal case + ps = [] + for i in eachindex(size_scale) + for tech in techs_sized + # Copy input_data so all techs start at optimal size (size_scale = 1.0) + # Remove techs which were considered but not sized (size=0) in optimal case + input_data_s = deepcopy(input_data) + for t in techs_to_zero + delete!(input_data_s, t) + end + # Force size based on size_scale factor + input_data_s[tech]["min_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] + input_data_s[tech]["max_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] + if tech == "ElectricStorage" + input_data_s[tech]["min_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] + input_data_s[tech]["max_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] + end + + # Create named entry for REoptInputs + append!(ps, [(tech*"_size_scale_"*string(size_scale[i]), REoptInputs(input_data_s))]) + end + end + + # Run extra n_solutions with multi-threading + rs_solns = Vector{Any}(nothing, n_solutions) + Threads.@threads for i in 1:n_solutions # Threads doesn't like enumerate with for loop + # JuMP model index starts at 3 because 1 and 2 were used by BAU and optimal sceanarios + n = 2 + i + rs_solns[i] = run_reopt((ms[n], ps[i][2])) + end + + # Combine BAU with each extra solution and get results summary + for (i, p) in enumerate(ps) + n = 2 + i + # Combine with BAU to get e.g. NPV + local results_dict = REopt.combine_results(p[2], rs[1], rs_solns[i], bau_inputs.s) + results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p[2], results_dict)) + results_all[p[1]] = results_dict + # Build results summary, a select number of outputs for Eaton + results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized) + end + + return results_all, results_summary end \ No newline at end of file From a93e5ce0fee623464759095426cb76865f22d336 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Tue, 30 May 2023 21:48:50 -0600 Subject: [PATCH 05/32] Add exception handling and combine parallel and series run options into same function Use kwarg parallel=true/false for using @threads for multi-solutions --- multi_solution_readme.md | 7 +- src/core/reopt_multi_solutions.jl | 222 ++++++++++++------------------ 2 files changed, 94 insertions(+), 135 deletions(-) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index e8b182425..5dcadafa3 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -16,14 +16,17 @@ n_techs = 2 # Max models is 2 for optimal+BAU and then however many scenarios are possible max_models = 2 + length(size_scale) * n_techs # Hard to duplicate JuMP models dynamically, so conservatively instantiate max possible models +# Solver options for Cbc.jl: https://github.com/jump-dev/Cbc.jl +# Solver options for HiGHS.jl: https://ergo-code.github.io/HiGHS/dev/options/definitions/ +# Increasing optimality gap (allowableGap for Cbc and mip_rel_gap for HiGHS) to 0.01 (1%) is particularly useful if REopt is taking a long time to solver ms = [Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "mip_rel_gap" => 0.001, "log_to_console" => true)) for _ in 1:max_models] # In-series/sequential run_reopt: -results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms) +results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=false) # Parallel run_reopt: -results_all, results_summary = REopt.run_reopt_multi_solutions_parallel(fp, size_scale, ms) +results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true) # Print some interesting data from all the solutions for s in keys(results_summary) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 4f24c9336..82d6f0da1 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -1,72 +1,118 @@ """ - run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel + run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}; parallel::Bool=false) where T <: JuMP.AbstractModel Run REopt to get optimal tech sizes, and then run REopt multiple times with scaling factor size_scale applied to -the optimal sizes to look at sensitivity of tech size on key results parameters + the optimal sizes to look at sensitivity of tech size on key results parameters fp is the inputs .json file path size_scale is the vector of scaling factors to apply to each optimal tech size ms is a vector of identical JuMP model objects and the length has to be larger than the max possible REopt runs +if kwarg `parallel`==`true` + Uses multi-threading for multiple-solutions, but set JULIA_NUM_THREADS="auto" or set to max on computer + export JULIA_NUM_THREADS="auto" + or + export JULIA_NUM_THREADS=8 +else + Runs multiple solutions in series, but still uses 2 @threads for 2x parallel BAU and Optimal + so still at least need JULIA_NUM_THREADS=2 + """ -function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel - # Run REopt the first time for optimal tech sizes - n = 1 # Indexing into the ms vector of JuMP models + +function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}; parallel=true) where T <: JuMP.AbstractModel + # Load in input_data from .json to dictionary input_data = JSON.parsefile(fp) - p = REoptInputs(input_data) - results = run_reopt(ms[n], p) - n += 1 + # Create optimal and BAU inputs structs for initial 2 runs + s = Scenario(input_data) + p = REoptInputs(s) + bau_inputs = BAUInputs(p) + # Note, ms[1] and rs[1] are for the BAU case which we'll need to combine with all other solutions too + # and ms[2] and rs[2] are for the optimal case + inputs = ((ms[1], bau_inputs), (ms[2], p)) + rs = Any[0, 0] + Threads.@threads for i = 1:2 + rs[i] = run_reopt(inputs[i]) + end + + # Combine results for BAU-Opt to get e.g. NPV + # This may still error ungracefully if the scenario is infeasible, same as REopt.jl does + results_dict = REopt.combine_results(p, rs[1], rs[2], bau_inputs.s) + results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) # Check results to see which techs are sized, to inform number of solutions - techs_possible = ["PV", "ElectricStorage", "Generator", "CHP"] + techs_possible = ["PV", "ElectricStorage", "Generator", "CHP"] # Eaton's techs of interest techs_considered = intersect(techs_possible, keys(input_data)) - techs_sized = intersect(techs_possible, keys(results)) + techs_sized = intersect(techs_possible, keys(results_dict)) techs_to_zero = setdiff(techs_considered, techs_sized) + n_solutions = length(size_scale) * length(techs_sized) - # TODO if length(techs_sized) == 0, no need to run BAU or any other cases, so skip! - # Only need to run BAU once, and then run "combine_results()" N times to get Financial results for all - bau_inputs = REopt.BAUInputs(p) - bau_results = run_reopt(ms[n], bau_inputs) - n += 1 - - # Combine results for BAU-Opt to get e.g. NPV - results_dict = REopt.combine_results(p, bau_results, results, bau_inputs.s) - results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) - - # Now create number of solutions based on the # of techs sized in optimal case - # TODO?? need to include where both/N techs are scaled together and in opposite directions + # Create the first entry for "optimal" solution in the results dictionary results_all = Dict("optimal" => results_dict) - results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[1], techs_sized)) + results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized)) + + # Now create number of inputs based on the # of techs sized in optimal case + ps = [] for i in eachindex(size_scale) for tech in techs_sized - # Copy input_data so all techs start at optimal size (size_scale = 1.0) - # Remove techs which were considered but not sized (size=0) in optimal case input_data_s = deepcopy(input_data) + + # Remove techs which were considered but not sized (size=0) in optimal case for t in techs_to_zero delete!(input_data_s, t) end - # Force size based on size_scale factor - input_data_s[tech]["min_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] - input_data_s[tech]["max_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] - if tech == "ElectricStorage" - input_data_s[tech]["min_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] - input_data_s[tech]["max_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] + + # Force size based on size_scale factor for tech, and set other techs to optimal size + for t in techs_sized + size_scale_tech = 1.0 + if t == tech + size_scale_tech = size_scale[i] + end + input_data_s[t]["min_kw"] = results_all["optimal"][t]["size_kw"] * size_scale_tech + input_data_s[t]["max_kw"] = results_all["optimal"][t]["size_kw"] * size_scale_tech + if t == "ElectricStorage" + input_data_s[t]["min_kwh"] = results_all["optimal"][t]["size_kwh"] * size_scale_tech + input_data_s[t]["max_kwh"] = results_all["optimal"][t]["size_kwh"] * size_scale_tech + end end - # Use modified inputs to run_reopt and get results - local p = REoptInputs(input_data_s) - local results = run_reopt(ms[n], input_data_s) - - # Combine with BAU to get e.g. NPV - local results_dict = REopt.combine_results(p, bau_results, results, bau_inputs.s) - results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) - results_all[tech*"_size_scale_"*string(size_scale[i])] = results_dict - + # Create named entry for REoptInputs + append!(ps, [(tech*"_size_scale_"*string(size_scale[i]), REoptInputs(Scenario(input_data_s)))]) + end + end + + # Run extra n_solutions with multi-threading + rs_solns = Vector{Any}(nothing, n_solutions) + + if parallel + Threads.@threads for i in 1:n_solutions # Threads doesn't like enumerate with for loop + # JuMP model index starts at 3 because 1 and 2 were used by BAU and optimal sceanarios + n = 2 + i + rs_solns[i] = run_reopt((ms[n], ps[i][2])) + end + else + for i in eachindex(rs_solns) + n = 2 + i + rs_solns[i] = run_reopt((ms[n], ps[i][2])) + end + end + + # Combine BAU with each extra solution and get results summary + for (i, p) in enumerate(ps) + n = 2 + i + # Combine with BAU to get e.g. NPV + if typeof(rs_solns[i]) <: Dict + local results_dict = REopt.combine_results(p[2], rs[1], rs_solns[i], bau_inputs.s) + results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p[2], results_dict)) + results_all[p[1]] = results_dict # Build results summary, a select number of outputs for Eaton - results_summary[tech*"_size_scale_"*string(size_scale[i])] = get_multi_solutions_results_summary(results_dict, p, ms[n], techs_sized) - n += 1 + results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized) + else + @warn "REopt did not solve successully (infeasible) for extra run number $i" + results_all[p[1]] = Dict("status"=>Dict("error" => "infeasible")) + results_summary[p[1]] = Dict("status"=>Dict("error" => "infeasible")) end end + return results_all, results_summary end @@ -80,6 +126,7 @@ the relevant techs function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}) # results_summary always has Financial and emissions, and then add techs as needed results_summary = Dict( + "status" => results["status"], "Financial" => Dict("Net Present Value" => results["Financial"]["npv"], "Simple payback period" => results["Financial"]["simple_payback_years"], @@ -146,95 +193,4 @@ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::J end end return results_summary -end - -""" - run_reopt_multi_solutions_parallel(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel - -Run REopt to get optimal tech sizes, and then run REopt multiple times with scaling factor size_scale applied to -the optimal sizes to look at sensitivity of tech size on key results parameters - -fp is the inputs .json file path -size_scale is the vector of scaling factors to apply to each optimal tech size -ms is a vector of identical JuMP model objects and the length has to be larger than the max possible REopt runs - -For this multi-threading version, but set JULIA_NUM_THREADS="auto" or set to max on computer -export JULIA_NUM_THREADS="auto" -or -export JULIA_NUM_THREADS=8 - -""" - -function run_reopt_multi_solutions_parallel(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}) where T <: JuMP.AbstractModel - # Load in input_data from .json to dictionary - input_data = JSON.parsefile(fp) - # Create optimal and BAU inputs structs for initial 2 runs - p = REoptInputs(input_data) - bau_inputs = BAUInputs(p) - # Note, ms[1] and rs[1] are for the BAU case which we'll need to combine with all other solutions too - # and ms[2] and rs[2] are for the optimal case - inputs = ((ms[1], bau_inputs), (ms[2], p)) - rs = Any[0, 0] - Threads.@threads for i = 1:2 - rs[i] = run_reopt(inputs[i]) - end - - # Combine results for BAU-Opt to get e.g. NPV - results_dict = REopt.combine_results(p, rs[1], rs[2], bau_inputs.s) - results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) - - # Check results to see which techs are sized, to inform number of solutions - techs_possible = ["PV", "ElectricStorage", "Generator", "CHP"] - techs_considered = intersect(techs_possible, keys(input_data)) - techs_sized = intersect(techs_possible, keys(results_dict)) - techs_to_zero = setdiff(techs_considered, techs_sized) - n_solutions = length(size_scale) * length(techs_sized) - - # Create the first entry for "optimal" solution in the results dictionary - results_all = Dict("optimal" => results_dict) - results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized)) - - # Now create number of inputs based on the # of techs sized in optimal case - ps = [] - for i in eachindex(size_scale) - for tech in techs_sized - # Copy input_data so all techs start at optimal size (size_scale = 1.0) - # Remove techs which were considered but not sized (size=0) in optimal case - input_data_s = deepcopy(input_data) - for t in techs_to_zero - delete!(input_data_s, t) - end - # Force size based on size_scale factor - input_data_s[tech]["min_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] - input_data_s[tech]["max_kw"] = results_all["optimal"][tech]["size_kw"] * size_scale[i] - if tech == "ElectricStorage" - input_data_s[tech]["min_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] - input_data_s[tech]["max_kwh"] = results_all["optimal"][tech]["size_kwh"] * size_scale[i] - end - - # Create named entry for REoptInputs - append!(ps, [(tech*"_size_scale_"*string(size_scale[i]), REoptInputs(input_data_s))]) - end - end - - # Run extra n_solutions with multi-threading - rs_solns = Vector{Any}(nothing, n_solutions) - Threads.@threads for i in 1:n_solutions # Threads doesn't like enumerate with for loop - # JuMP model index starts at 3 because 1 and 2 were used by BAU and optimal sceanarios - n = 2 + i - rs_solns[i] = run_reopt((ms[n], ps[i][2])) - end - - # Combine BAU with each extra solution and get results summary - for (i, p) in enumerate(ps) - n = 2 + i - # Combine with BAU to get e.g. NPV - local results_dict = REopt.combine_results(p[2], rs[1], rs_solns[i], bau_inputs.s) - results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p[2], results_dict)) - results_all[p[1]] = results_dict - # Build results summary, a select number of outputs for Eaton - results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized) - end - - return results_all, results_summary end \ No newline at end of file From aaeafac2ffd7b94510f7ef2db7dae15776bf55aa Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Sun, 11 Jun 2023 14:30:19 -0600 Subject: [PATCH 06/32] Add resilience runs with simulate_outages to run_reopt_multi_solutions --- multi_solution_readme.md | 52 ++++++++++++++++------------ src/core/reopt_multi_solutions.jl | 57 +++++++++++++++++++++++++++---- test/scenarios/eaton_multi.json | 20 +++++++++++ test/scenarios/eaton_voll.json | 35 +++++++++++++++++++ 4 files changed, 135 insertions(+), 29 deletions(-) create mode 100644 test/scenarios/eaton_multi.json create mode 100644 test/scenarios/eaton_voll.json diff --git a/multi_solution_readme.md b/multi_solution_readme.md index 5dcadafa3..9582126a1 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -5,31 +5,39 @@ sizing variations around the optimal technology sizes. The function first runs t identify the cost-effective technologies recommended by REopt. It then re-runs a number of times with scaling factors applied to each of the cost-optimal techs, and it forces those sizes into the solution. -# Example use-case for multi-solutions -fp = "scenarios/eaton_multi.json" -# Fractions/ratios of size relative to the optimal size -# For each technology which is sized, multiply the size by the size_scale for a different solution -# The other techs will all be at 1.0/optimal size for the scaling of other techs -size_scale = [0.5, 1.5] -# Need to know max number of JuMP model to create, so identify the number of technologies considered -n_techs = 2 -# Max models is 2 for optimal+BAU and then however many scenarios are possible -max_models = 2 + length(size_scale) * n_techs -# Hard to duplicate JuMP models dynamically, so conservatively instantiate max possible models -# Solver options for Cbc.jl: https://github.com/jump-dev/Cbc.jl -# Solver options for HiGHS.jl: https://ergo-code.github.io/HiGHS/dev/options/definitions/ -# Increasing optimality gap (allowableGap for Cbc and mip_rel_gap for HiGHS) to 0.01 (1%) is particularly useful if REopt is taking a long time to solver -ms = [Model(optimizer_with_attributes(HiGHS.Optimizer, - "output_flag" => false, "mip_rel_gap" => 0.001, "log_to_console" => true)) for _ in 1:max_models] +If outages are modeled for a resilience analysis, the `resilience` key in the results summary gets populated which includes results from the stochastic outages modeled in the optimization as well as the results from the `simulate_outages()` function which is run to evaluate the resilience performance for all outage start times of the year. -# In-series/sequential run_reopt: -results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=false) +## Example use-case for multi-solutions +### Specify path to input file: +`fp = "scenarios/eaton_multi.json"` -# Parallel run_reopt: -results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true) +or for resilience: -# Print some interesting data from all the solutions +`fp = "scenarios/eaton_voll.json"` + +### Specify fractions/ratios of size relative to the optimal size to run. The other techs will all be forced to the optimal size for each run: +`size_scale = [0.5, 1.5]` +### Need to know the maximum number of JuMP models to create, so first identify the number of technologies considered. So for the eaton_multi.json scenario with PV and Battery (ElectricStorage): +`n_techs = 2` +### This equation uses 2 for optimal+BAU plus however many scenario combinations are possible: +`max_models = 2 + length(size_scale) * n_techs` +### Define an array of models of length `max_models` along with the specified solver and desired parameters: +`ms = [Model(optimizer_with_attributes(HiGHS.Optimizer, + "output_flag" => false, "mip_rel_gap" => 0.001, "log_to_console" => true)) for _ in 1:max_models]` +- Solver options for Cbc.jl: https://github.com/jump-dev/Cbc.jl +- Solver options for HiGHS.jl: https://ergo-code.github.io/HiGHS/dev/options/definitions/ +- Increasing optimality gap (ratioGap for Cbc and mip_rel_gap for HiGHS) to 0.01 (1%) is particularly useful if REopt is taking a long time to solver +### In-series/sequential runs to reduce number of threads required to 2: +`results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=false)` + +## Parallel runs, requiring multiple cores for the multiple threads (see function docstring for more information): +`results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true)` + +### Print some interesting data from all the solutions from the results_summary which is created: +``` for s in keys(results_summary) println("NPV for scenario "*s*" = ", results_summary[s]["Financial"]["Net Present Value"]) println("Capital cost for scenario "*s*" = ", results_summary[s]["Financial"]["Net capital cost"]) -end \ No newline at end of file + println("Resilience duration average (hours) for scenario "*s*" = ", results_summary[s]["resilience"]["Average hours of load served during outage"]) +end +``` \ No newline at end of file diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 82d6f0da1..ce10fab2d 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -19,7 +19,8 @@ else """ -function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}; parallel=true) where T <: JuMP.AbstractModel +function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}; + parallel=true) where T <: JuMP.AbstractModel # Load in input_data from .json to dictionary input_data = JSON.parsefile(fp) # Create optimal and BAU inputs structs for initial 2 runs @@ -40,15 +41,25 @@ function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms:: results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p, results_dict)) # Check results to see which techs are sized, to inform number of solutions - techs_possible = ["PV", "ElectricStorage", "Generator", "CHP"] # Eaton's techs of interest + techs_possible = ["PV", "ElectricStorage", "Generator", "CHP"] # Eaton's techs of interest, where CHP really for power-only fuel cell techs_considered = intersect(techs_possible, keys(input_data)) - techs_sized = intersect(techs_possible, keys(results_dict)) + techs_sized = String[] + for tech in techs_considered + if results_dict[tech]["size_kw"] > 0.0 + append!(techs_sized, [tech]) + end + end techs_to_zero = setdiff(techs_considered, techs_sized) n_solutions = length(size_scale) * length(techs_sized) # Create the first entry for "optimal" solution in the results dictionary + if !isempty(p.s.electric_utility.outage_start_time_steps) + simresults = simulate_outages(results_dict, p) + else + simresults = Dict() + end results_all = Dict("optimal" => results_dict) - results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized)) + results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized, simresults)) # Now create number of inputs based on the # of techs sized in optimal case ps = [] @@ -104,8 +115,13 @@ function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms:: local results_dict = REopt.combine_results(p[2], rs[1], rs_solns[i], bau_inputs.s) results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p[2], results_dict)) results_all[p[1]] = results_dict + if !isempty(p[2].s.electric_utility.outage_start_time_steps) + simresults = simulate_outages(rs_solns[i], p[2]) + else + simresults = Dict() + end # Build results summary, a select number of outputs for Eaton - results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized) + results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized, simresults) else @warn "REopt did not solve successully (infeasible) for extra run number $i" results_all[p[1]] = Dict("status"=>Dict("error" => "infeasible")) @@ -117,13 +133,13 @@ function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms:: end """ - get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}) + get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict) Get the results summary dictionary which is a selected number of outputs for Financial, Emissions, and the relevant techs """ -function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}) +function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict) # results_summary always has Financial and emissions, and then add techs as needed results_summary = Dict( "status" => results["status"], @@ -139,6 +155,33 @@ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::J "Site life cycle PM25 tonnes" => results["Site"]["lifecycle_emissions_tonnes_PM25"]) ) + if !isempty(simresults) + # WIP find BAU max_cost_outage to get the delta/% between optimal and BAU + # General Q?: How could dvMaxOutageCost be anything other than the largest outage_duration? + BAUOutageCost = Array{Array{Float64, 1}}(undef, length(p.s.electric_utility.scenarios)) + for s in p.s.electric_utility.scenarios + BAUOutageCost[s] = [p.pwf_e * sum(p.value_of_lost_load_per_kwh[tz+ts-1] * p.s.electric_load.critical_loads_kw[tz+ts-1] + for ts in 1:p.s.electric_utility.outage_durations[s]) + for tz in p.s.electric_utility.outage_start_time_steps] + end + BAUMaxOutageCost = [maximum(BAUOutageCost[s]) for s in p.s.electric_utility.scenarios] + BAUExpectedOutageCost = sum(BAUMaxOutageCost[s] * p.s.electric_utility.outage_probabilities[s] for s in p.s.electric_utility.scenarios) + + # % Improvement is (1 - unserved/total) * 100 + # Cost improvement ($/year) is expected_outage_cost/25 - (critical load kWh) * VoLL *pwf_e/25 + annual_outage_cost = results["Outages"]["expected_outage_cost"] / p.s.financial.analysis_years + annual_outage_cost_bau = BAUExpectedOutageCost / p.s.financial.analysis_years + annual_cost_reduction_percent = (1 - annual_outage_cost / annual_outage_cost_bau) * 100.0 + results_summary["resilience"] = Dict("Input outage duration (hours)" => sum(p.s.electric_utility.outage_durations) / + length(p.s.electric_utility.outage_durations), + "Average hours of load served during outage" => simresults["resilience_hours_avg"], + "Annual value of load served" => annual_outage_cost_bau - annual_outage_cost, + "Value of load served as a percent of total unserved load" => annual_cost_reduction_percent + ) + else + results_summary["resilience"] = Dict("No resilience simulation was run" => 0.0) + end + # Add techs if they have been sized in the optimal case for key in techs_sized if key == "PV" diff --git a/test/scenarios/eaton_multi.json b/test/scenarios/eaton_multi.json new file mode 100644 index 000000000..a4dbd21d5 --- /dev/null +++ b/test/scenarios/eaton_multi.json @@ -0,0 +1,20 @@ +{ + "Site": { + "longitude": -118.1164613, + "latitude": 34.5794343 + }, + "ElectricLoad": { + "doe_reference_name": "LargeOffice" + }, + "PV": { + }, + "ElectricStorage": { + }, + "ElectricUtility": { + "outage_start_time_step": 2, + "outage_end_time_step": 4 + }, + "ElectricTariff": { + "urdb_label": "5ed6c1a15457a3367add15ae" + } +} \ No newline at end of file diff --git a/test/scenarios/eaton_voll.json b/test/scenarios/eaton_voll.json new file mode 100644 index 000000000..1d75b6b9f --- /dev/null +++ b/test/scenarios/eaton_voll.json @@ -0,0 +1,35 @@ +{ + "Site": { + "longitude": -118.1164613, + "latitude": 34.5794343 + }, + "ElectricLoad": { + "doe_reference_name": "LargeOffice", + "critical_load_fraction": 1.0 + }, + "PV": { + }, + "ElectricStorage": { + }, + "Generator": { + "max_kw": 1000000.0, + "fuel_avail_gal": 1e8, + "min_turn_down_fraction": 0.0, + "only_runs_during_grid_outage": true, + "sells_energy_back_to_grid": false + }, + "ElectricUtility": { + "outage_start_time_steps": [4600, 5000, 5400, 5800], + "outage_durations": [10] + }, + "ElectricTariff": { + "urdb_label": "5ed6c1a15457a3367add15ae" + }, + "Financial": { + "value_of_lost_load_per_kwh": 5.0, + "microgrid_upgrade_cost_fraction": 0.3, + "owner_discount_rate_fraction": 0.1, + "elec_cost_escalation_rate_fraction": 0.0, + "om_cost_escalation_rate_fraction": 0.0 + } +} \ No newline at end of file From f6ee54fd3264f5adb090c965ecce5a4eef9b82a9 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 22 Jun 2023 16:15:23 -0600 Subject: [PATCH 07/32] Update resilience to be just post-process instead of sizing with model --- src/core/reopt_multi_solutions.jl | 90 ++++++++++++++++++++----------- 1 file changed, 60 insertions(+), 30 deletions(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index ce10fab2d..93642e410 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -1,5 +1,7 @@ """ - run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}; parallel::Bool=false) where T <: JuMP.AbstractModel + run_reopt_multi_solutions(fp::String, size_scale::Vector{Union{Any,Float64}}, ms::AbstractVector{T}; + parallel::Bool=false, resilience=false, outage_start_hour::Int64=1, + outage_duration_hours::Int64=0)) where T <: JuMP.AbstractModel Run REopt to get optimal tech sizes, and then run REopt multiple times with scaling factor size_scale applied to the optimal sizes to look at sensitivity of tech size on key results parameters @@ -18,9 +20,9 @@ else so still at least need JULIA_NUM_THREADS=2 """ - -function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms::AbstractVector{T}; - parallel=true) where T <: JuMP.AbstractModel +# TODO allow empty vector for size_scale to bypass multiple solutions while leveraging results summary stuff (currently errors with []=Vector[Any]) +function run_reopt_multi_solutions(fp::String, size_scale::Vector{Union{Any,Float64}}, ms::AbstractVector{T}; + parallel=false, resilience=false, outage_start_hour=1, outage_duration_hours=0) where T <: JuMP.AbstractModel # Load in input_data from .json to dictionary input_data = JSON.parsefile(fp) # Create optimal and BAU inputs structs for initial 2 runs @@ -53,13 +55,13 @@ function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms:: n_solutions = length(size_scale) * length(techs_sized) # Create the first entry for "optimal" solution in the results dictionary - if !isempty(p.s.electric_utility.outage_start_time_steps) + if resilience simresults = simulate_outages(results_dict, p) else simresults = Dict() end results_all = Dict("optimal" => results_dict) - results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized, simresults)) + results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized, simresults, outage_start_hour, outage_duration_hours)) # Now create number of inputs based on the # of techs sized in optimal case ps = [] @@ -115,13 +117,13 @@ function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms:: local results_dict = REopt.combine_results(p[2], rs[1], rs_solns[i], bau_inputs.s) results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p[2], results_dict)) results_all[p[1]] = results_dict - if !isempty(p[2].s.electric_utility.outage_start_time_steps) + if resilience simresults = simulate_outages(rs_solns[i], p[2]) else simresults = Dict() end # Build results summary, a select number of outputs for Eaton - results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized, simresults) + results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized, simresults, outage_start_hour, outage_duration_hours) else @warn "REopt did not solve successully (infeasible) for extra run number $i" results_all[p[1]] = Dict("status"=>Dict("error" => "infeasible")) @@ -133,13 +135,15 @@ function run_reopt_multi_solutions(fp::String, size_scale::Vector{Float64}, ms:: end """ - get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict) + get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict, + outage_start_hour::Int64, outage_duration_hours::Int64) Get the results summary dictionary which is a selected number of outputs for Financial, Emissions, and the relevant techs """ -function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict) +function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict, + outage_start_hour::Int64, outage_duration_hours::Int64) # results_summary always has Financial and emissions, and then add techs as needed results_summary = Dict( "status" => results["status"], @@ -156,27 +160,53 @@ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::J ) if !isempty(simresults) - # WIP find BAU max_cost_outage to get the delta/% between optimal and BAU - # General Q?: How could dvMaxOutageCost be anything other than the largest outage_duration? - BAUOutageCost = Array{Array{Float64, 1}}(undef, length(p.s.electric_utility.scenarios)) - for s in p.s.electric_utility.scenarios - BAUOutageCost[s] = [p.pwf_e * sum(p.value_of_lost_load_per_kwh[tz+ts-1] * p.s.electric_load.critical_loads_kw[tz+ts-1] - for ts in 1:p.s.electric_utility.outage_durations[s]) - for tz in p.s.electric_utility.outage_start_time_steps] + # User specified outage_start_hour and outage_duration_hours ONLY for this post-processing for Eaton + # Also VoLL only specified for this post-processing, NOT using it in the model + # Must specify critical_load_fraction of 1.0 if you want to use the whole load (default 0.5) in REopt inputs + # even though the model is NOT going to use that if no outage is input + num_outage_time_steps = outage_duration_hours * p.s.settings.time_steps_per_hour + + # Get BAU and Optimal outage lost load and cost for every hour of the year to get average and max values + BAUOutageLostLoadKWH = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) + BAUOutageCost = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) + OptimalOutageLostLoadKWH = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) + OptimalOutageCost = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) + for tz in 1:((8760-outage_duration_hours) * p.s.settings.time_steps_per_hour) # Avoid wrapping around the year + BAUOutageLostLoadKWH[tz] = sum(p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour + for ts in 1:num_outage_time_steps; init=0.0) + BAUOutageCost[tz] = sum(p.value_of_lost_load_per_kwh[tz+ts-1] * p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour + for ts in 1:num_outage_time_steps; init=0.0) + OptimalOutageLostLoadKWH[tz] = sum(p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour + for ts in (convert(Int, simresults["resilience_by_time_step"][tz])+1):outage_duration_hours; init=0.0) + OptimalOutageCost[tz] = sum(p.value_of_lost_load_per_kwh[tz+ts-1] * p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour + for ts in (convert(Int, simresults["resilience_by_time_step"][tz])+1):outage_duration_hours; init=0.0) end - BAUMaxOutageCost = [maximum(BAUOutageCost[s]) for s in p.s.electric_utility.scenarios] - BAUExpectedOutageCost = sum(BAUMaxOutageCost[s] * p.s.electric_utility.outage_probabilities[s] for s in p.s.electric_utility.scenarios) - - # % Improvement is (1 - unserved/total) * 100 - # Cost improvement ($/year) is expected_outage_cost/25 - (critical load kWh) * VoLL *pwf_e/25 - annual_outage_cost = results["Outages"]["expected_outage_cost"] / p.s.financial.analysis_years - annual_outage_cost_bau = BAUExpectedOutageCost / p.s.financial.analysis_years - annual_cost_reduction_percent = (1 - annual_outage_cost / annual_outage_cost_bau) * 100.0 - results_summary["resilience"] = Dict("Input outage duration (hours)" => sum(p.s.electric_utility.outage_durations) / - length(p.s.electric_utility.outage_durations), - "Average hours of load served during outage" => simresults["resilience_hours_avg"], - "Annual value of load served" => annual_outage_cost_bau - annual_outage_cost, - "Value of load served as a percent of total unserved load" => annual_cost_reduction_percent + avg_lost_load_bau_kwh = sum(BAUOutageLostLoadKWH) / length(BAUOutageLostLoadKWH) + avg_year_one_outage_cost_bau = sum(BAUOutageCost) / length(BAUOutageCost) + avg_lost_load_optimal_kwh = sum(OptimalOutageLostLoadKWH) / length(OptimalOutageLostLoadKWH) + avg_year_one_outage_cost_optimal = sum(OptimalOutageCost) / length(OptimalOutageCost) + avg_outage_load_served = avg_lost_load_bau_kwh - avg_lost_load_optimal_kwh + avg_outage_cost_savings = avg_year_one_outage_cost_bau - avg_year_one_outage_cost_optimal + avg_outage_hours_served = sum(simresults["resilience_by_time_step"]) / length(simresults["resilience_by_time_step"]) + specific_outage_lost_load_bau_kwh = BAUOutageLostLoadKWH[outage_start_hour * p.s.settings.time_steps_per_hour] + specific_outage_cost_bau = BAUOutageCost[outage_start_hour * p.s.settings.time_steps_per_hour] + specific_outage_lost_load_optimal_kwh = OptimalOutageLostLoadKWH[outage_start_hour * p.s.settings.time_steps_per_hour] + specific_outage_cost_optimal = OptimalOutageCost[outage_start_hour * p.s.settings.time_steps_per_hour] + specific_outage_load_served_kwh = specific_outage_lost_load_bau_kwh - specific_outage_lost_load_optimal_kwh + specific_outage_hours_served = simresults["resilience_by_time_step"][outage_start_hour] + year_one_outage_cost_savings = specific_outage_cost_bau - specific_outage_cost_optimal + # Outage cost as percentage of year one utility bill to put into perspective? + year_one_utility_bill_bau = results["ElectricTariff"]["year_one_bill_before_tax"] + year_one_outage_cost_savings_percent_of_utility_bill = year_one_outage_cost_savings / year_one_utility_bill_bau * 100.0 + + results_summary["resilience"] = Dict("Input outage duration (hours)" => outage_duration_hours, + "Average outage hours of load served (hours)" => round(avg_outage_hours_served, digits=0), + "Average outage load served (kWh)" => round(avg_outage_load_served, sigdigits=3), + "Average outage cost savings per year" => round(avg_outage_cost_savings, sigdigits=3), + "Specified outage hours of load served" => round(specific_outage_hours_served, digits=0), + "Specified outage load served with DERs (kWh)" => round(specific_outage_load_served_kwh, sigdigits=3), + "Specified outage cost savings per year with DERs" => round(year_one_outage_cost_savings, sigdigits=3), + "Specified outage cost savings as percent of annual utility bill" => round(year_one_outage_cost_savings_percent_of_utility_bill, sigdigits=3) ) else results_summary["resilience"] = Dict("No resilience simulation was run" => 0.0) From 58dd838e2c885dc352f287157d101b85efdb2046 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 22 Jun 2023 16:15:51 -0600 Subject: [PATCH 08/32] Update the eaton_multi.json input file for use with example in readme --- test/scenarios/eaton_multi.json | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/test/scenarios/eaton_multi.json b/test/scenarios/eaton_multi.json index a4dbd21d5..48afa281c 100644 --- a/test/scenarios/eaton_multi.json +++ b/test/scenarios/eaton_multi.json @@ -4,16 +4,16 @@ "latitude": 34.5794343 }, "ElectricLoad": { - "doe_reference_name": "LargeOffice" - }, + "doe_reference_name": "LargeOffice", + "critical_load_fraction": 1.0 + }, + "Financial": { + "value_of_lost_load_per_kwh": 5000.0 + }, "PV": { }, "ElectricStorage": { }, - "ElectricUtility": { - "outage_start_time_step": 2, - "outage_end_time_step": 4 - }, "ElectricTariff": { "urdb_label": "5ed6c1a15457a3367add15ae" } From 12052e199e3e227af5d879203ac629a8123b9ed6 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 22 Jun 2023 16:16:07 -0600 Subject: [PATCH 09/32] Update multi_solution_readme.md --- multi_solution_readme.md | 80 ++++++++++++++++++++++++++++++---------- 1 file changed, 61 insertions(+), 19 deletions(-) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index 9582126a1..48bf86dd9 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -1,22 +1,32 @@ # Summary The multiple solutions functionality runs REopt (like `run_reopt`) multiple times to look at the sensitivity of a number of key results, such as Net Present Value (NPV) and capital cost, on -sizing variations around the optimal technology sizes. The function first runs the optimal case to -identify the cost-effective technologies recommended by REopt. It then re-runs a number of times with scaling factors applied to each of the cost-optimal techs, and it forces those sizes into the -solution. +sizing variations around the optimal technology sizes. The `run_reopt_multi_solutions()` function first runs the optimal case to +identify the cost-effective technologies recommended by REopt. It then re-runs a number of times with user-specified scaling factors applied to each of the cost-optimal techs, and it forces those DER sizes into the solution. -If outages are modeled for a resilience analysis, the `resilience` key in the results summary gets populated which includes results from the stochastic outages modeled in the optimization as well as the results from the `simulate_outages()` function which is run to evaluate the resilience performance for all outage start times of the year. +## Resilience evaluation option +If resilience metrics and an estimate of the value of avoided lost load are desired, modify the `resilience` keyword argument to `resilience=true`. Additionally, the user must input the `outage_start_hour` and the `outage_duration_hours` keyword arguments to the function to evaluate the value of avoided lost load for that specific outage period. The `simulate_outages()` function gets evaluated after running REopt to calculate the number of hours the DERs serve the critical load for all possible outage start times of the year to get an average, and it outputs the specified outage from the user inputs. -## Example use-case for multi-solutions -### Specify path to input file: -`fp = "scenarios/eaton_multi.json"` +Note, this resilience evaluation is not considering the user-specified outage for sizing the DERs in the model. The evaluation first runs REopt to get DER size recommendations based on economics. Then the resilience benefit of the recommended DERs are evaluated using a post-processing algorithm from `simulate_outages()` for dispatching the DERs during outages to serve the critical load (determined by `ElectricLoad.critical_load_fraction`). Therefore, the DERs may not serve any or all of the critical load during the user specified outage. The calculations describe how much of the critical load is served compared to not having DERs (where none of the critical load is served), and it estimates the value of avoided lost load based on the user input `Financial.value_of_lost_load`. -or for resilience: +The `resilience` key in the results summary gets populated with the following information: +``` + "Input outage duration (hours)" # The user-input outage duration + "Average outage hours of load served (hours)" # Average number of hours of critical load served for all (8760) start times of the year + "Average outage load served (kWh)" # The average amount of critical load served for all (8760) start times of the year + "Average outage cost savings per year" # The average yearly cost savings for all outage start times of the year + "Specified outage hours of load served" # The number of hours of critical load served for the user-input outage duration + "Specified outage load served with DERs (kWh)" # The critical load served for the user-input outage start time + "Specified outage cost savings per year with DERs" # The yearly savings from serving load during the user-input outage start time + "Specified outage cost savings as percent of annual utility bill" # The value of avoided lost load as a percent of the annual electric bill +``` -`fp = "scenarios/eaton_voll.json"` +# Example use-case for multi-solutions +### Specify path to input file: +`fp = "scenarios/eaton_multi.json"` -### Specify fractions/ratios of size relative to the optimal size to run. The other techs will all be forced to the optimal size for each run: -`size_scale = [0.5, 1.5]` +### Specify fractions/ratios of size relative to the optimal size to run. The other techs will all be forced to the optimal size for each run. If you run with an empty array for size_scale, it will just run the optimal scenario. +`size_scale = [0.8, 1.2]` ### Need to know the maximum number of JuMP models to create, so first identify the number of technologies considered. So for the eaton_multi.json scenario with PV and Battery (ElectricStorage): `n_techs = 2` ### This equation uses 2 for optimal+BAU plus however many scenario combinations are possible: @@ -30,14 +40,46 @@ or for resilience: ### In-series/sequential runs to reduce number of threads required to 2: `results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=false)` -## Parallel runs, requiring multiple cores for the multiple threads (see function docstring for more information): -`results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true)` +### Parallel runs with resilience, requiring multiple cores for the multiple threads (see function docstring for more information): +`results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true, resilience=true, outage_start_hour=4000, outage_duration_hours=10)` -### Print some interesting data from all the solutions from the results_summary which is created: +### Here is an example `results_summary` output for `PV` and Battery (`ElectricStorage`) with `resilience=true`: ``` -for s in keys(results_summary) - println("NPV for scenario "*s*" = ", results_summary[s]["Financial"]["Net Present Value"]) - println("Capital cost for scenario "*s*" = ", results_summary[s]["Financial"]["Net capital cost"]) - println("Resilience duration average (hours) for scenario "*s*" = ", results_summary[s]["resilience"]["Average hours of load served during outage"]) -end + "optimal": { + "status": "optimal", + "resilience": { + "Specified outage hours of load served": 3.0, + "Average outage hours of load served (hours)": 15.0, + "Average outage load served (kWh)": 4940.0, + "Specified outage load served with DERs (kWh)": 4090.0, + "Specified outage cost savings per year with DERs": 2.05e7, + "Specified outage cost savings as percent of annual utility bill": 6640.0, + "Input outage duration (hours)": 10, + "Average outage cost savings per year": 2.47e7 + }, + "PV": { + "Capital cost": 2.9387651486586295e6, + "Annual maintenance cost": 61478.709899999994, + "Average annual energy produced": 6.131804e6, + "Rated capacity": 3616.3947 + }, + "Financial": { + "Simple payback period": 6.66, + "Net Present Value": 4.1934961e6, + "Net capital cost": 5.0679701147e6, + "Internal Rate of Return %": 13.0 + }, + "Storage": { + "Capital cost": 1.3586723305624602e6, + "Rated inverter capacity": 961.82, + "Rated energy capacity": 5350.36, + "Total replacement cost": 770532.6255763071 + }, + "emissions": { + "Site life cycle PM25 tonnes": 1.08, + "Site life cycle SO2 tonnes": 1.43, + "Site life cycle NOx tonnes": 1.43, + "Site life cycle CO2 tonnes": 18582.68 + } + } ``` \ No newline at end of file From 7d26ab40c679a7d46ef9223eaaadb345454904e7 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Tue, 27 Jun 2023 14:50:31 -0600 Subject: [PATCH 10/32] Fix Vector{Any} and Vector{Float64} size_scale issue --- src/core/reopt_multi_solutions.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 93642e410..9642ef7c1 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -1,5 +1,5 @@ """ - run_reopt_multi_solutions(fp::String, size_scale::Vector{Union{Any,Float64}}, ms::AbstractVector{T}; + run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vector{Float64}}, ms::AbstractVector{T}; parallel::Bool=false, resilience=false, outage_start_hour::Int64=1, outage_duration_hours::Int64=0)) where T <: JuMP.AbstractModel @@ -21,7 +21,7 @@ else """ # TODO allow empty vector for size_scale to bypass multiple solutions while leveraging results summary stuff (currently errors with []=Vector[Any]) -function run_reopt_multi_solutions(fp::String, size_scale::Vector{Union{Any,Float64}}, ms::AbstractVector{T}; +function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vector{Float64}}, ms::AbstractVector{T}; parallel=false, resilience=false, outage_start_hour=1, outage_duration_hours=0) where T <: JuMP.AbstractModel # Load in input_data from .json to dictionary input_data = JSON.parsefile(fp) From e542b2054aa3145079dc1f69220b0166fea43744 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 13 Jul 2023 11:26:23 -0600 Subject: [PATCH 11/32] Add results_summary[s]["resilience"] to results_all dictionary --- src/core/reopt_multi_solutions.jl | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 9642ef7c1..79113ed98 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -20,7 +20,6 @@ else so still at least need JULIA_NUM_THREADS=2 """ -# TODO allow empty vector for size_scale to bypass multiple solutions while leveraging results summary stuff (currently errors with []=Vector[Any]) function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vector{Float64}}, ms::AbstractVector{T}; parallel=false, resilience=false, outage_start_hour=1, outage_duration_hours=0) where T <: JuMP.AbstractModel # Load in input_data from .json to dictionary @@ -60,8 +59,10 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec else simresults = Dict() end - results_all = Dict("optimal" => results_dict) results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized, simresults, outage_start_hour, outage_duration_hours)) + # Add custom resilience output to results_all, per Eaton's request + results_dict["resilience"] = results_summary["optimal"]["resilience"] + results_all = Dict("optimal" => results_dict) # Now create number of inputs based on the # of techs sized in optimal case ps = [] @@ -116,7 +117,6 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec if typeof(rs_solns[i]) <: Dict local results_dict = REopt.combine_results(p[2], rs[1], rs_solns[i], bau_inputs.s) results_dict["Financial"] = merge(results_dict["Financial"], REopt.proforma_results(p[2], results_dict)) - results_all[p[1]] = results_dict if resilience simresults = simulate_outages(rs_solns[i], p[2]) else @@ -124,6 +124,8 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec end # Build results summary, a select number of outputs for Eaton results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized, simresults, outage_start_hour, outage_duration_hours) + results_dict["resilience"] = results_summary[p[1]]["resilience"] + results_all[p[1]] = results_dict else @warn "REopt did not solve successully (infeasible) for extra run number $i" results_all[p[1]] = Dict("status"=>Dict("error" => "infeasible")) From 7facf3a8bbbece7e6f823a58844864b9934b6efe Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 13 Jul 2023 11:26:56 -0600 Subject: [PATCH 12/32] Fix null/undef issue with outage lost load --- src/core/reopt_multi_solutions.jl | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 79113ed98..98a2dbd1f 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -169,11 +169,12 @@ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::J num_outage_time_steps = outage_duration_hours * p.s.settings.time_steps_per_hour # Get BAU and Optimal outage lost load and cost for every hour of the year to get average and max values - BAUOutageLostLoadKWH = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) - BAUOutageCost = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) - OptimalOutageLostLoadKWH = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) - OptimalOutageCost = Array{Float64}(undef, 8760 * p.s.settings.time_steps_per_hour) - for tz in 1:((8760-outage_duration_hours) * p.s.settings.time_steps_per_hour) # Avoid wrapping around the year + last_time_step = 8760-outage_duration_hours # Avoid wrapping around the year + BAUOutageLostLoadKWH = Array{Float64}(undef, last_time_step * p.s.settings.time_steps_per_hour) + BAUOutageCost = Array{Float64}(undef, last_time_step * p.s.settings.time_steps_per_hour) + OptimalOutageLostLoadKWH = Array{Float64}(undef, last_time_step * p.s.settings.time_steps_per_hour) + OptimalOutageCost = Array{Float64}(undef, last_time_step * p.s.settings.time_steps_per_hour) + for tz in 1:(last_time_step * p.s.settings.time_steps_per_hour) BAUOutageLostLoadKWH[tz] = sum(p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour for ts in 1:num_outage_time_steps; init=0.0) BAUOutageCost[tz] = sum(p.value_of_lost_load_per_kwh[tz+ts-1] * p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour From 6317d8d07aca4ae6b48cd04c94d72816869fe240 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Wed, 26 Jul 2023 09:51:51 -0600 Subject: [PATCH 13/32] Add DSIRE incentives processing Add SQLite package to REopt.jl dependencies --- Manifest.toml | 430 ++++++++++++++++++++--------------- Project.toml | 3 +- data/incentives/DSIRE.db | Bin 0 -> 21200896 bytes src/REopt.jl | 2 + src/core/dsire_incentives.jl | 126 ++++++++++ 5 files changed, 379 insertions(+), 182 deletions(-) create mode 100644 data/incentives/DSIRE.db create mode 100644 src/core/dsire_incentives.jl diff --git a/Manifest.toml b/Manifest.toml index fa6ceafb5..639298ddd 100644 --- a/Manifest.toml +++ b/Manifest.toml @@ -1,20 +1,30 @@ # This file is machine-generated - editing it directly is not advised -julia_version = "1.8.3" +julia_version = "1.9.1" manifest_format = "2.0" -project_hash = "2e3f73051e60a5c2ffca2d998f6043d51c7f6c2a" +project_hash = "eaefbe85d2808ab8023d870938aec8e0132fca30" [[deps.AbstractFFTs]] -deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "69f7020bd72f069c219b5e8c236c1fa90d2cb409" +deps = ["LinearAlgebra"] +git-tree-sha1 = "cad4c758c0038eea30394b1b671526921ca85b21" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.2.1" +version = "1.4.0" +weakdeps = ["ChainRulesCore"] + + [deps.AbstractFFTs.extensions] + AbstractFFTsChainRulesCoreExt = "ChainRulesCore" [[deps.Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "0310e08cb19f5da31d08341c6120c047598f5b9c" +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "76289dc51920fdc6e0013c872ba9551d54961c24" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "3.5.0" +version = "3.6.2" + + [deps.Adapt.extensions] + AdaptStaticArraysExt = "StaticArrays" + + [deps.Adapt.weakdeps] + StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" [[deps.ArchGDAL]] deps = ["CEnum", "ColorTypes", "Dates", "DiskArrays", "Extents", "GDAL", "GDAL_jll", "GeoFormatTypes", "GeoInterface", "GeoInterfaceRecipes", "ImageCore", "Tables"] @@ -50,10 +60,10 @@ uuid = "a74b3585-a348-5f62-a45c-50e91977d574" version = "0.7.3" [[deps.Blosc_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Lz4_jll", "Pkg", "Zlib_jll", "Zstd_jll"] -git-tree-sha1 = "e94024822c0a5b14989abbdba57820ad5b177b95" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Lz4_jll", "Zlib_jll", "Zstd_jll"] +git-tree-sha1 = "6aa2d0b8db41ab860bbbf61b9587e3b957683fd3" uuid = "0b7ba130-8d10-5ba8-a3d6-c5182647fed9" -version = "1.21.2+0" +version = "1.21.4+0" [[deps.Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -67,22 +77,16 @@ uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" version = "0.4.2" [[deps.CSV]] -deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "SentinelArrays", "SnoopPrecompile", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"] -git-tree-sha1 = "c700cce799b51c9045473de751e9319bdd1c6e94" +deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"] +git-tree-sha1 = "44dbf560808d49041989b8a96cae4cffbeb7966a" uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -version = "0.10.9" +version = "0.10.11" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "c6d890a52d2c4d55d326439580c3b8d0875a77d9" +git-tree-sha1 = "e30f2f4e20f7f186dc36529910beaedc60cfa644" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "1.15.7" - -[[deps.ChangesOfVariables]] -deps = ["ChainRulesCore", "LinearAlgebra", "Test"] -git-tree-sha1 = "844b061c104c408b24537482469400af6075aae4" -uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" -version = "0.1.5" +version = "1.16.0" [[deps.CodecBzip2]] deps = ["Bzip2_jll", "Libdl", "TranscodingStreams"] @@ -92,9 +96,9 @@ version = "0.7.2" [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] -git-tree-sha1 = "9c209fb7536406834aa938fb149964b985de6c83" +git-tree-sha1 = "02aa26a4cf76381be7f66e020a3eddeb27b0a092" uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.7.1" +version = "0.7.2" [[deps.ColorTypes]] deps = ["FixedPointNumbers", "Random"] @@ -115,9 +119,9 @@ uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" version = "0.12.10" [[deps.CommonSolve]] -git-tree-sha1 = "9441451ee712d1aec22edad62db1a9af3dc8d852" +git-tree-sha1 = "0eee5eb66b1cf62cd6ad1b460238e60e4b09400c" uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2" -version = "0.2.3" +version = "0.2.4" [[deps.CommonSubexpressions]] deps = ["MacroTools", "Test"] @@ -126,21 +130,39 @@ uuid = "bbf7d656-a473-5ed7-a52c-81e309532950" version = "0.3.0" [[deps.Compat]] -deps = ["Dates", "LinearAlgebra", "UUIDs"] -git-tree-sha1 = "61fdd77467a5c3ad071ef8277ac6bd6af7dd4c04" +deps = ["UUIDs"] +git-tree-sha1 = "5ce999a19f4ca23ea484e92a1774a61b8ca4cf8e" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "4.6.0" +version = "4.8.0" +weakdeps = ["Dates", "LinearAlgebra"] + + [deps.Compat.extensions] + CompatLinearAlgebraExt = "LinearAlgebra" [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "0.5.2+0" +version = "1.0.2+0" + +[[deps.ConcurrentUtilities]] +deps = ["Serialization", "Sockets"] +git-tree-sha1 = "5372dbbf8f0bdb8c700db5367132925c0771ef7e" +uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" +version = "2.2.1" [[deps.ConstructionBase]] deps = ["LinearAlgebra"] -git-tree-sha1 = "fb21ddd70a051d882a1686a5a550990bbe371a95" +git-tree-sha1 = "fe2838a593b5f776e1597e086dcd47560d94e816" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" -version = "1.4.1" +version = "1.5.3" + + [deps.ConstructionBase.extensions] + ConstructionBaseIntervalSetsExt = "IntervalSets" + ConstructionBaseStaticArraysExt = "StaticArrays" + + [deps.ConstructionBase.weakdeps] + IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953" + StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" [[deps.CoolProp]] deps = ["CoolProp_jll", "Markdown", "Unitful"] @@ -159,22 +181,27 @@ git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15" uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" version = "4.1.1" +[[deps.DBInterface]] +git-tree-sha1 = "9b0dc525a052b9269ccc5f7f04d5b3639c65bca5" +uuid = "a10d1c49-ce27-4219-8d33-6db1a4562965" +version = "2.5.0" + [[deps.DataAPI]] -git-tree-sha1 = "e8119c1a33d267e16108be441a287a6981ba1630" +git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" -version = "1.14.0" +version = "1.15.0" [[deps.DataFrames]] -deps = ["Compat", "DataAPI", "Future", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SnoopPrecompile", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"] -git-tree-sha1 = "d4f69885afa5e6149d0cab3818491565cf41446d" +deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"] +git-tree-sha1 = "04c738083f29f86e62c8afc341f0967d8717bdb8" uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -version = "1.4.4" +version = "1.6.1" [[deps.DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] -git-tree-sha1 = "d1fff3a548102f48987a52a2e0d114fa97d730f0" +git-tree-sha1 = "cf25ccb972fec4e4817764d01c82386ae94f77b4" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.18.13" +version = "0.18.14" [[deps.DataValueInterfaces]] git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" @@ -187,7 +214,9 @@ uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" [[deps.DelimitedFiles]] deps = ["Mmap"] +git-tree-sha1 = "9e2f36d3c96a820c678f2f1f1782582fcf685bae" uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab" +version = "1.9.1" [[deps.DiffResults]] deps = ["StaticArraysCore"] @@ -197,15 +226,15 @@ version = "1.1.0" [[deps.DiffRules]] deps = ["IrrationalConstants", "LogExpFunctions", "NaNMath", "Random", "SpecialFunctions"] -git-tree-sha1 = "c5b6685d53f933c11404a3ae9822afe30d522494" +git-tree-sha1 = "23163d55f885173722d1e4cf0f6110cdbaf7e272" uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" -version = "1.12.2" +version = "1.15.1" [[deps.DiskArrays]] deps = ["OffsetArrays"] -git-tree-sha1 = "27ebdcf03ca847fa484f28273db57de3c8514920" +git-tree-sha1 = "52253d45971d96d24c33e0cf89bba41c127ee3b9" uuid = "3c3547ce-8d99-4f5e-a174-61eb10b00ae3" -version = "0.3.8" +version = "0.3.14" [[deps.Distributed]] deps = ["Random", "Serialization", "Sockets"] @@ -222,11 +251,17 @@ deps = ["ArgTools", "FileWatching", "LibCURL", "NetworkOptions"] uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6" version = "1.6.0" +[[deps.ExceptionUnwrapping]] +deps = ["Test"] +git-tree-sha1 = "e90caa41f5a86296e014e148ee061bd6c3edec96" +uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4" +version = "0.1.9" + [[deps.Expat_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "bad72f730e9e91c08d9427d5e8db95478a3c323d" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "4558ab818dcceaab612d1bb8c19cee87eda2b83c" uuid = "2e619515-83b5-522b-bb60-26c02a35a201" -version = "2.4.8+0" +version = "2.5.0+0" [[deps.Extents]] git-tree-sha1 = "5e1e4c53fa39afe63a7d356e30452249365fba99" @@ -235,9 +270,9 @@ version = "0.1.1" [[deps.FileIO]] deps = ["Pkg", "Requires", "UUIDs"] -git-tree-sha1 = "7be5f99f7d15578798f338f5433b6c432ea8037b" +git-tree-sha1 = "299dc33549f68299137e51e6d49a13b5b1da9673" uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" -version = "1.16.0" +version = "1.16.1" [[deps.FilePaths]] deps = ["FilePathsBase", "MacroTools", "Reexport", "Requires"] @@ -260,17 +295,17 @@ git-tree-sha1 = "335bfdceacc84c5cdf16aadc768aa5ddfc5383cc" uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" version = "0.8.4" -[[deps.Formatting]] -deps = ["Printf"] -git-tree-sha1 = "8339d61043228fdd3eb658d86c926cb282ae72a8" -uuid = "59287772-0a20-5a39-b81b-1366585eb4c0" -version = "0.4.2" - [[deps.ForwardDiff]] -deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions", "StaticArrays"] -git-tree-sha1 = "a69dd6db8a809f78846ff259298678f0d6212180" +deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions"] +git-tree-sha1 = "00e252f4d706b3d55a8863432e742bf5717b498d" uuid = "f6369f11-7733-5829-9624-2563aa707210" -version = "0.10.34" +version = "0.10.35" + + [deps.ForwardDiff.extensions] + ForwardDiffStaticArraysExt = "StaticArrays" + + [deps.ForwardDiff.weakdeps] + StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" [[deps.Future]] deps = ["Random"] @@ -289,10 +324,10 @@ uuid = "a7073274-a066-55f0-b90d-d619367d196c" version = "300.500.200+0" [[deps.GEOS_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "f4c0cafb093b62d5a5d8447a9b2306555385c0d9" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "818ab247de98d8848a022c7be084b1283d912326" uuid = "d604d12d-fa86-5845-992e-78dc15976526" -version = "3.11.0+0" +version = "3.11.2+0" [[deps.GeoFormatTypes]] git-tree-sha1 = "434166198434a5c2fcc0a1a59d22c3b0ad460889" @@ -301,20 +336,20 @@ version = "0.4.1" [[deps.GeoInterface]] deps = ["Extents"] -git-tree-sha1 = "e07a1b98ed72e3cdd02c6ceaab94b8a606faca40" +git-tree-sha1 = "bb198ff907228523f3dee1070ceee63b9359b6ab" uuid = "cf35fbd7-0cd7-5166-be24-54bfbe79505f" -version = "1.2.1" +version = "1.3.1" [[deps.GeoInterfaceRecipes]] deps = ["GeoInterface", "RecipesBase"] -git-tree-sha1 = "29e1ec25cfb6762f503a19495aec347acf867a9e" +git-tree-sha1 = "0e26e1737e94de57c858649dc28e482b6c87d341" uuid = "0329782f-3d07-4b52-b9f6-d3137cf03c7a" -version = "1.0.0" +version = "1.0.1" [[deps.Glob]] -git-tree-sha1 = "4df9f7e06108728ebf00a0a11edee4b29a482bb2" +git-tree-sha1 = "97285bbd5230dd766e9ef6749b80fc617126d496" uuid = "c27321d9-0574-5035-807b-f59d2c89b15c" -version = "1.3.0" +version = "1.3.1" [[deps.Graphics]] deps = ["Colors", "LinearAlgebra", "NaNMath"] @@ -330,9 +365,9 @@ version = "0.1.1" [[deps.HDF5]] deps = ["Compat", "HDF5_jll", "Libdl", "Mmap", "Random", "Requires", "UUIDs"] -git-tree-sha1 = "b5df7c3cab3a00c33c2e09c6bd23982a75e2fbb2" +git-tree-sha1 = "c73fdc3d9da7700691848b78c61841274076932a" uuid = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" -version = "0.16.13" +version = "0.16.15" [[deps.HDF5_jll]] deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "Libdl", "OpenSSL_jll", "Pkg", "Zlib_jll"] @@ -341,10 +376,10 @@ uuid = "0234f1f7-429e-5d53-9886-15a909be8d59" version = "1.12.2+2" [[deps.HTTP]] -deps = ["Base64", "CodecZlib", "Dates", "IniFile", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "37e4657cd56b11abe3d10cd4a1ec5fbdb4180263" +deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] +git-tree-sha1 = "cb56ccdd481c0dd7f975ad2b3b62d9eda088f7e2" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.7.4" +version = "1.9.14" [[deps.ImageCore]] deps = ["AbstractFFTs", "ColorVectorSpace", "Colors", "FixedPointNumbers", "Graphics", "MappedArrays", "MosaicViews", "OffsetArrays", "PaddedViews", "Reexport"] @@ -354,14 +389,9 @@ version = "0.9.4" [[deps.InfrastructureModels]] deps = ["JuMP", "Memento"] -git-tree-sha1 = "cca034ca1ae73e08bf4db63f158474d40d7dad9a" +git-tree-sha1 = "dc1e2eba1a98aa457b629fe1723d9078ecb74340" uuid = "2030c09a-7f63-5d83-885d-db604e0e9cc0" -version = "0.7.5" - -[[deps.IniFile]] -git-tree-sha1 = "f550e6e32074c939295eb5ea6de31849ac2c9625" -uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f" -version = "0.5.1" +version = "0.7.7" [[deps.InlineStrings]] deps = ["Parsers"] @@ -373,21 +403,15 @@ version = "1.4.0" deps = ["Markdown"] uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" -[[deps.InverseFunctions]] -deps = ["Test"] -git-tree-sha1 = "49510dfcb407e572524ba94aeae2fced1f3feb0f" -uuid = "3587e190-3f89-42d0-90ee-14403ec27112" -version = "0.1.8" - [[deps.InvertedIndices]] -git-tree-sha1 = "82aec7a3dd64f4d9584659dc0b62ef7db2ef3e19" +git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038" uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f" -version = "1.2.0" +version = "1.3.0" [[deps.IrrationalConstants]] -git-tree-sha1 = "7fd44fd4ff43fc60815f8e764c0f352b83c49151" +git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2" uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" -version = "0.1.1" +version = "0.2.2" [[deps.IteratorInterfaceExtensions]] git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" @@ -408,21 +432,21 @@ version = "1.4.1" [[deps.JSON]] deps = ["Dates", "Mmap", "Parsers", "Unicode"] -git-tree-sha1 = "3c837543ddb02250ef42f4738347454f95079d4e" +git-tree-sha1 = "31e996f0a15c7b280ba9f76636b3ff9e2ae58c9a" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -version = "0.21.3" +version = "0.21.4" [[deps.JpegTurbo_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "b53380851c6e6664204efb2e62cd24fa5c47e4ba" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "6f2675ef130a300a112286de91973805fcc5ffbc" uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" -version = "2.1.2+0" +version = "2.1.91+0" [[deps.JuMP]] deps = ["LinearAlgebra", "MathOptInterface", "MutableArithmetics", "OrderedCollections", "Printf", "SnoopPrecompile", "SparseArrays"] -git-tree-sha1 = "8ebcb407c28617ea075563c550ec766dddf25a2e" +git-tree-sha1 = "b0ded4f36829f7cfd4400b11289faf9b4f0b795a" uuid = "4076af6c-e467-56ae-b986-b466b2749572" -version = "1.7.0" +version = "1.12.0" [[deps.Kerberos_krb5_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] @@ -488,7 +512,7 @@ uuid = "bf674bac-ffe4-48d3-9f32-72124ffa9ede" version = "0.2.0" [[deps.LinearAlgebra]] -deps = ["Libdl", "libblastrampoline_jll"] +deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"] uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" [[deps.LittleCMS_jll]] @@ -498,10 +522,20 @@ uuid = "d3a379c0-f9a3-5b72-a4c0-6bf4d2e8af0f" version = "2.12.0+0" [[deps.LogExpFunctions]] -deps = ["ChainRulesCore", "ChangesOfVariables", "DocStringExtensions", "InverseFunctions", "IrrationalConstants", "LinearAlgebra"] -git-tree-sha1 = "680e733c3a0a9cea9e935c8c2184aea6a63fa0b5" +deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] +git-tree-sha1 = "c3ce8e7420b3a6e071e0fe4745f5d4300e37b13f" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" -version = "0.3.21" +version = "0.3.24" + + [deps.LogExpFunctions.extensions] + LogExpFunctionsChainRulesCoreExt = "ChainRulesCore" + LogExpFunctionsChangesOfVariablesExt = "ChangesOfVariables" + LogExpFunctionsInverseFunctionsExt = "InverseFunctions" + + [deps.LogExpFunctions.weakdeps] + ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" + ChangesOfVariables = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" + InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" [[deps.Logging]] uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" @@ -525,19 +559,19 @@ uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" version = "0.5.10" [[deps.MappedArrays]] -git-tree-sha1 = "e8b359ef06ec72e8c030463fe02efe5527ee5142" +git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" uuid = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900" -version = "0.4.1" +version = "0.4.2" [[deps.Markdown]] deps = ["Base64"] uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" [[deps.MathOptInterface]] -deps = ["BenchmarkTools", "CodecBzip2", "CodecZlib", "DataStructures", "ForwardDiff", "JSON", "LinearAlgebra", "MutableArithmetics", "NaNMath", "OrderedCollections", "Printf", "SnoopPrecompile", "SparseArrays", "SpecialFunctions", "Test", "Unicode"] -git-tree-sha1 = "b577d6c6b484f35fc27c1e767dc32458815da0e5" +deps = ["BenchmarkTools", "CodecBzip2", "CodecZlib", "DataStructures", "ForwardDiff", "JSON", "LinearAlgebra", "MutableArithmetics", "NaNMath", "OrderedCollections", "PrecompileTools", "Printf", "SparseArrays", "SpecialFunctions", "Test", "Unicode"] +git-tree-sha1 = "5c5cd501ae1d76d3ccd7c7e6b4325a15dde7f31c" uuid = "b8f27783-ece8-5eb3-8dc8-9495eed66fee" -version = "1.11.5" +version = "1.18.0" [[deps.MbedTLS]] deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "Random", "Sockets"] @@ -548,7 +582,7 @@ version = "1.1.7" [[deps.MbedTLS_jll]] deps = ["Artifacts", "Libdl"] uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" -version = "2.28.0+0" +version = "2.28.2+0" [[deps.Memento]] deps = ["Dates", "Distributed", "Requires", "Serialization", "Sockets", "Test", "UUIDs"] @@ -573,25 +607,25 @@ version = "0.3.4" [[deps.MozillaCACerts_jll]] uuid = "14a3606d-f60d-562e-9121-12d972cd8159" -version = "2022.2.1" +version = "2022.10.11" [[deps.MutableArithmetics]] deps = ["LinearAlgebra", "SparseArrays", "Test"] -git-tree-sha1 = "aa532179d4a643d4bd9f328589ca01fa20a0d197" +git-tree-sha1 = "964cb1a7069723727025ae295408747a0b36a854" uuid = "d8a4904e-b15c-11e9-3269-09a3773c0cb0" -version = "1.1.0" +version = "1.3.0" [[deps.NaNMath]] deps = ["OpenLibm_jll"] -git-tree-sha1 = "a7c3d1da1189a1c2fe843a3bfa04d18d20eb3211" +git-tree-sha1 = "0877504529a3e5c3343c6f8b4c0381e57e4387e4" uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3" -version = "1.0.1" +version = "1.0.2" [[deps.NetCDF_jll]] -deps = ["Artifacts", "HDF5_jll", "JLLWrappers", "LibCURL_jll", "Libdl", "Pkg", "XML2_jll", "Zlib_jll"] -git-tree-sha1 = "072f8371f74c3b9e1b26679de7fbf059d45ea221" +deps = ["Artifacts", "HDF5_jll", "JLLWrappers", "LibCURL_jll", "Libdl", "XML2_jll", "Zlib_jll"] +git-tree-sha1 = "7f5a03e6712f5447c9c344430b8d1927a4777483" uuid = "7243133f-43d8-5620-bbf4-c2c921802cf3" -version = "400.902.5+1" +version = "400.902.206+0" [[deps.NetworkOptions]] uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" @@ -599,14 +633,14 @@ version = "1.2.0" [[deps.OffsetArrays]] deps = ["Adapt"] -git-tree-sha1 = "82d7c9e310fe55aa54996e6f7f94674e2a38fcb4" +git-tree-sha1 = "2ac17d29c523ce1cd38e27785a7d23024853a4bb" uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881" -version = "1.12.9" +version = "1.12.10" [[deps.OpenBLAS_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" -version = "0.3.20+0" +version = "0.3.21+4" [[deps.OpenJpeg_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libtiff_jll", "LittleCMS_jll", "Pkg", "libpng_jll"] @@ -621,15 +655,15 @@ version = "0.8.1+0" [[deps.OpenSSL]] deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] -git-tree-sha1 = "6503b77492fd7fcb9379bf73cd31035670e3c509" +git-tree-sha1 = "51901a49222b09e3743c65b8847687ae5fc78eb2" uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" -version = "1.3.3" +version = "1.4.1" [[deps.OpenSSL_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "f6e9dba33f9f2c44e08a020b0caf6903be540004" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1aa4b74f80b01c6bc2b89992b861b5f210e665b5" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "1.1.19+0" +version = "1.1.21+0" [[deps.OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] @@ -638,9 +672,9 @@ uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e" version = "0.5.5+0" [[deps.OrderedCollections]] -git-tree-sha1 = "85f8e6578bf1f9ee0d11e7bb1b1456435479d47c" +git-tree-sha1 = "2e73fe17cac3c62ad1aebe70d44c963c3cfdc3e3" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" -version = "1.4.1" +version = "1.6.2" [[deps.PROJ_jll]] deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "Libdl", "Libtiff_jll", "Pkg", "SQLite_jll"] @@ -650,20 +684,20 @@ version = "900.100.0+0" [[deps.PaddedViews]] deps = ["OffsetArrays"] -git-tree-sha1 = "03a7a85b76381a3d04c7a1656039197e70eda03d" +git-tree-sha1 = "0fac6313486baae819364c52b4f483450a9d793f" uuid = "5432bcbf-9aad-5242-b902-cca2824c8663" -version = "0.5.11" +version = "0.5.12" [[deps.Parsers]] -deps = ["Dates", "SnoopPrecompile"] -git-tree-sha1 = "18f84637e00b72ba6769034a4b50d79ee40c84a9" +deps = ["Dates", "PrecompileTools", "UUIDs"] +git-tree-sha1 = "4b2e829ee66d4218e0cef22c0a64ee37cf258c29" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "2.5.5" +version = "2.7.1" [[deps.Pkg]] -deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] +deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" -version = "1.8.0" +version = "1.9.0" [[deps.PolyhedralRelaxations]] deps = ["DataStructures", "ForwardDiff", "JuMP", "Logging", "LoggingExtras"] @@ -678,22 +712,28 @@ uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720" version = "1.4.2" [[deps.PowerModelsDistribution]] -deps = ["CSV", "Dates", "FilePaths", "Glob", "InfrastructureModels", "JSON", "JuMP", "LinearAlgebra", "Logging", "LoggingExtras", "PolyhedralRelaxations", "SpecialFunctions", "Statistics"] -git-tree-sha1 = "7eeb7a58472845870cc9ea7d505a4aabf0067299" +deps = ["CSV", "Dates", "FilePaths", "Glob", "InfrastructureModels", "JSON", "JuMP", "LinearAlgebra", "Logging", "LoggingExtras", "PolyhedralRelaxations", "SparseArrays", "SpecialFunctions", "Statistics"] +git-tree-sha1 = "60b4d3446f0ddce0f86ec779438c7fa3772ca216" uuid = "d7431456-977f-11e9-2de3-97ff7677985e" -version = "0.14.6" +version = "0.14.9" + +[[deps.PrecompileTools]] +deps = ["Preferences"] +git-tree-sha1 = "9673d39decc5feece56ef3940e5dafba15ba0f81" +uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" +version = "1.1.2" [[deps.Preferences]] deps = ["TOML"] -git-tree-sha1 = "47e5f437cc0e7ef2ce8406ce1e7e24d44915f88d" +git-tree-sha1 = "7eb1686b4f04b82f96ed7a4ea5890a4f0c7a09f1" uuid = "21216c6a-2e73-6563-6e65-726566657250" -version = "1.3.0" +version = "1.4.0" [[deps.PrettyTables]] -deps = ["Crayons", "Formatting", "LaTeXStrings", "Markdown", "Reexport", "StringManipulation", "Tables"] -git-tree-sha1 = "96f6db03ab535bdb901300f88335257b0018689d" +deps = ["Crayons", "LaTeXStrings", "Markdown", "Printf", "Reexport", "StringManipulation", "Tables"] +git-tree-sha1 = "ee094908d720185ddbdc58dbe0c1cbe35453ec7a" uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" -version = "2.2.2" +version = "2.2.7" [[deps.Printf]] deps = ["Unicode"] @@ -712,10 +752,10 @@ deps = ["SHA", "Serialization"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [[deps.RecipesBase]] -deps = ["SnoopPrecompile"] -git-tree-sha1 = "261dddd3b862bd2c940cf6ca4d1c8fe593e457c8" +deps = ["PrecompileTools"] +git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff" uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" -version = "1.3.3" +version = "1.3.4" [[deps.Reexport]] git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" @@ -730,25 +770,41 @@ version = "1.3.0" [[deps.Roots]] deps = ["ChainRulesCore", "CommonSolve", "Printf", "Setfield"] -git-tree-sha1 = "a3db467ce768343235032a1ca0830fc64158dadf" +git-tree-sha1 = "de432823e8aab4dd1a985be4be768f95acf152d4" uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" -version = "2.0.8" +version = "2.0.17" + + [deps.Roots.extensions] + RootsForwardDiffExt = "ForwardDiff" + RootsIntervalRootFindingExt = "IntervalRootFinding" + RootsSymPyExt = "SymPy" + + [deps.Roots.weakdeps] + ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" + IntervalRootFinding = "d2bf35a9-74e0-55ec-b149-d360ff49b807" + SymPy = "24249f21-da20-56a4-8eb1-6a02cf4ae2e6" [[deps.SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" +[[deps.SQLite]] +deps = ["DBInterface", "Random", "SQLite_jll", "Serialization", "Tables", "WeakRefStrings"] +git-tree-sha1 = "eb9a473c9b191ced349d04efa612ec9f39c087ea" +uuid = "0aa819cd-b072-5ff4-a722-6bc24af294d9" +version = "1.6.0" + [[deps.SQLite_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "2c761a91fb503e94bd0130fcf4352166c3c555bc" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = "4619dd3363610d94fb42a95a6dc35b526a26d0ef" uuid = "76ed43ae-9a5d-5a62-8c75-30186b810ce8" -version = "3.40.0+1" +version = "3.42.0+0" [[deps.SentinelArrays]] deps = ["Dates", "Random"] -git-tree-sha1 = "c02bd3c9c3fc8463d3591a62a378f90d2d8ab0f3" +git-tree-sha1 = "04bdff0b09c65ff3e06a05e3eb7b120223da3d39" uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c" -version = "1.3.17" +version = "1.4.0" [[deps.Serialization]] uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" @@ -775,19 +831,23 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc" [[deps.SortingAlgorithms]] deps = ["DataStructures"] -git-tree-sha1 = "a4ada03f999bd01b3a25dcaa30b2d929fe537e00" +git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee" uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" -version = "1.1.0" +version = "1.1.1" [[deps.SparseArrays]] -deps = ["LinearAlgebra", "Random"] +deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" [[deps.SpecialFunctions]] -deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] -git-tree-sha1 = "d75bda01f8c31ebb72df80a46c88b25d1c79c56d" +deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] +git-tree-sha1 = "7beb031cf8145577fbccacd94b8a8f4ce78428d3" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "2.1.7" +version = "2.3.0" +weakdeps = ["ChainRulesCore"] + + [deps.SpecialFunctions.extensions] + SpecialFunctionsChainRulesCoreExt = "ChainRulesCore" [[deps.StackViews]] deps = ["OffsetArrays"] @@ -795,30 +855,30 @@ git-tree-sha1 = "46e589465204cd0c08b4bd97385e4fa79a0c770c" uuid = "cae243ae-269e-4f55-b966-ac2d0dc13c15" version = "0.1.1" -[[deps.StaticArrays]] -deps = ["LinearAlgebra", "Random", "StaticArraysCore", "Statistics"] -git-tree-sha1 = "6954a456979f23d05085727adb17c4551c19ecd1" -uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "1.5.12" - [[deps.StaticArraysCore]] -git-tree-sha1 = "6b7ba252635a5eff6a0b0664a41ee140a1c9e72a" +git-tree-sha1 = "36b3d696ce6366023a0ea192b4cd442268995a0d" uuid = "1e83bf80-4336-4d27-bf5d-d5a4f845583c" -version = "1.4.0" +version = "1.4.2" [[deps.Statistics]] deps = ["LinearAlgebra", "SparseArrays"] uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +version = "1.9.0" [[deps.StringManipulation]] git-tree-sha1 = "46da2434b41f41ac3594ee9816ce5541c6096123" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" version = "0.3.0" +[[deps.SuiteSparse_jll]] +deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] +uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" +version = "5.10.1+6" + [[deps.TOML]] deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" -version = "1.0.0" +version = "1.0.3" [[deps.TableTraits]] deps = ["IteratorInterfaceExtensions"] @@ -828,14 +888,14 @@ version = "1.0.1" [[deps.Tables]] deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits", "Test"] -git-tree-sha1 = "c79322d36826aa2f4fd8ecfa96ddb47b174ac78d" +git-tree-sha1 = "1544b926975372da01227b382066ab70e574a3ec" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -version = "1.10.0" +version = "1.10.1" [[deps.Tar]] deps = ["ArgTools", "SHA"] uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e" -version = "1.10.1" +version = "1.10.0" [[deps.TensorCore]] deps = ["LinearAlgebra"] @@ -849,20 +909,20 @@ uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.TestEnv]] deps = ["Pkg"] -git-tree-sha1 = "b5a483bcc8c9f0df569101df166601e5e699f346" +git-tree-sha1 = "c7c98d2bb491261207459c55ce041a68fd1b477b" uuid = "1e6cf692-eddd-4d53-88a5-2d735e33781b" -version = "1.9.3" +version = "1.100.0" [[deps.TranscodingStreams]] deps = ["Random", "Test"] -git-tree-sha1 = "94f38103c984f89cf77c402f2a68dbd870f8165f" +git-tree-sha1 = "9a6ae7ed916312b41236fcef7e0af564ef934769" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.9.11" +version = "0.9.13" [[deps.URIs]] -git-tree-sha1 = "ac00576f90d8a259f2c9d823e91d1de3fd44d348" +git-tree-sha1 = "074f993b0ca030848b897beff716d93aca60f06a" uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" -version = "1.4.1" +version = "1.4.2" [[deps.UUIDs]] deps = ["Random", "SHA"] @@ -872,10 +932,18 @@ uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" [[deps.Unitful]] -deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"] -git-tree-sha1 = "d670a70dd3cdbe1c1186f2f17c9a68a7ec24838c" +deps = ["Dates", "LinearAlgebra", "Random"] +git-tree-sha1 = "c4d2a349259c8eba66a00a540d550f122a3ab228" uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" -version = "1.12.2" +version = "1.15.0" + + [deps.Unitful.extensions] + ConstructionBaseUnitfulExt = "ConstructionBase" + InverseFunctionsUnitfulExt = "InverseFunctions" + + [deps.Unitful.weakdeps] + ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9" + InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" [[deps.WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] @@ -897,18 +965,18 @@ version = "2.10.3+0" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.12+3" +version = "1.2.13+0" [[deps.Zstd_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "49ce682769cd5de6c72dcf1b94ed7790cd08974c" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" -version = "1.5.2+0" +version = "1.5.5+0" [[deps.libblastrampoline_jll]] -deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] +deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" -version = "5.1.1+0" +version = "5.8.0+0" [[deps.libgeotiff_jll]] deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "Libdl", "Libtiff_jll", "PROJ_jll", "Pkg"] diff --git a/Project.toml b/Project.toml index 156e8992a..72047da41 100644 --- a/Project.toml +++ b/Project.toml @@ -20,13 +20,14 @@ Logging = "56ddb016-857b-54e1-b83d-db4d58db5568" MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee" Requires = "ae029012-a4dd-5104-9daa-d747884805df" Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" +SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" TestEnv = "1e6cf692-eddd-4d53-88a5-2d735e33781b" [compat] ArchGDAL = "0.8, 0.9" -CoolProp = "0.1" CSV = "0.10" +CoolProp = "0.1" DataFrames = "1.4" HTTP = "0.8, 0.9, 1" JLD = "0.13" diff --git a/data/incentives/DSIRE.db b/data/incentives/DSIRE.db 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zhIcyULAW1Ror2efcRJ-J+>g?PcRJ+;+_!jg6};0a*WtdI)`oXF^KJUMr$e+sj*`>CpHtp5#* zcRJ-;;XWDc>wghbe-5^ScRJ-;;64%Tqwdc}`}fbh(<$E!_witFUsNSs!8@JuO>iFz z_EIvYa*-0=>6CATTM_K(e+lvKUU{cez5(v3U=M$8-j@*G>6EXBdotMF|K_7=xC-9s zl&^z(!k3Yip72hmd@bDL!EXNCmMB?MU6YotfqN`isKy!Xe^=q1PWfuMM}u9}IH!B# zolf~GxJT0Vns++oE8!jvc2T{J@xOBNPN#eY+(W_6{*HPl$2* 7, "ElectricStorage" => 207, "FuelCellsNonRenewable" => 15, "FuelCellsRenewable" => 124, "Wind" => 8) + tech_number = tech_map[tech] + state = DBInterface.execute(db, "SELECT * FROM state WHERE abbreviation='$state_abbr'") |> DataFrame + state_id = state[1,1] + todays_date = Dates.today() + todays_date_str = Dates.format(todays_date, "yyyymmdd") + state_programs = DBInterface.execute(db, "SELECT * FROM program WHERE (state_id=$state_id) + AND (is_entire_state=1) + AND ((end_date > $todays_date_str) OR (end_date IS NULL))") |> DataFrame + tech_programs = DBInterface.execute(db, "SELECT * FROM program_technology WHERE technology_id=$tech_number") |> DataFrame + tech_state_program_ids = intersect(state_programs[!,"id"],tech_programs[!,"program_id"]) + tech_state_program_ids_tuple = tuple(tech_state_program_ids...) + parameter_set = sql_select_helper_variable_length(db; table_name="parameter_set", column_name="program_id", data_tuple=tech_state_program_ids_tuple) + # parameter_set = DBInterface.execute(db, "SELECT * FROM parameter_set WHERE program_id IN $tech_state_program_ids_tuple") |> DataFrame + parameter_set_technology = DBInterface.execute(db, "SELECT * FROM parameter_set_technology WHERE technology_id=$tech_number") |> DataFrame + tech_state_parameter_set = intersect(parameter_set[!,"id"], parameter_set_technology[!,"set_id"]) + parameter_set_ids_tuple = tuple(tech_state_parameter_set...) + parameters = sql_select_helper_variable_length(db; table_name="parameter", column_name="parameter_set_id", data_tuple=parameter_set_ids_tuple) + + data = Dict() + for set_id in unique(parameters[!, "parameter_set_id"]) + program_id = parameter_set[parameter_set[!, "id"].==set_id, "program_id"][1] + data[program_id] = Dict() + param_sub = parameters[parameters.parameter_set_id.==set_id, :] + for param in eachrow(param_sub) + if !ismissing(param["units"]) && !ismissing(param["source"]) + if occursin("kWh", param["units"]) || occursin("MWh", param["units"]) + # Production based incentive + divide_by = 1.0 + if occursin("MWh", param["units"]) + divide_by = 1000.0 + end + data[program_id]["production_based_incentive_per_kwh"] = param["amount"] / divide_by + if occursin("year", param["units"]) + # Limited number of years of production incentive + for i in 1:30 + if occursin("i", param["units"]) + data[program_id]["production_incentive_years"] = i + break + end + end + end + elseif occursin(r"\$/W", param["units"]) || occursin(r"\$/kW", param["units"]) + # Capacity-based incentive ($/kW), could be AC or DC basis + if occursin(r"\$/W", param["units"]) + incentive_per_kw = param["amount"] * 1000.0 + else + incentive_per_kw = param["amount"] + end + data[program_id]["capacity_based_incentive_per_kw"] = incentive_per_kw + if occursin("AC", param["units"]) + data[program_id]["capacity_based_incentive_basis_ac_or_dc"] = "AC" + else + data[program_id]["capacity_based_incentive_basis_ac_or_dc"] = "DC" + end + elseif ismissing(param["qualifier"]) && occursin(r"\%", param["units"]) + # %-based incentives + data[program_id]["percent_cost_based_incentive"] = param["amount"] + elseif param["source"] == "System" + # Size limits (min/max kW), could be AC or DC basis + divide_by = 1000.0 # For W (Watts) + if occursin("kW", param["units"]) + divide_by = 1.0 + elseif occursin("MW", param["units"]) + divide_by = 0.001 + end + if param["qualifier"] == "max" + data[program_id]["max_kw"]= param["amount"] / divide_by + elseif param["qualifier"] == "min" + data[program_id]["min_kw"] = param["amount"] / divide_by + end + if occursin("AC", param["units"]) + data[program_id]["size_limit_basis_ac_or_dc"] = "AC" + else + data[program_id]["size_limit_basis_ac_or_dc"] = "DC" + end + elseif !ismissing(param["qualifier"]) + if param["source"] == "Incentive" && param["units"] == "\$" + # Max incentive value in $; some have NULL for qualifier but we can't know if it's max or min, so ignoring those + if param["qualifier"] == "max" + data[program_id]["max_incentive_amount_dollar"] = param["amount"] + elseif param["qualifier"] == "min" + data[program_id]["min_incentive_amount_dollar"] = param["amount"] + end + elseif param["qualifier"] == "max" && occursin(r"\%", param["units"]) + # Max incentive as a percentage of cost + data[program_id]["max_percent_of_cost"] = param["amount"] + end + end + end + end + end + + for program_id in keys(data) + if isempty(data[program_id]) + delete!(data, program_id) + elseif !("capacity_based_incentive_per_kw" in keys(data[program_id])) && !("production_based_incentive_per_kwh" in keys(data[program_id])) + delete!(data, program_id) + end + end + + return data +end + + +function sql_select_helper_variable_length(db::SQLite.DB; table_name::String="", column_name::String="", data_tuple::Tuple=()) + if length(data_tuple) >= 2 + data_df = DBInterface.execute(db, "SELECT * FROM $table_name WHERE $column_name IN $data_tuple") |> DataFrame + elseif length(data_tuple) == 1 + data_df = DBInterface.execute(db, "SELECT * FROM $table_name WHERE $column_name=$(data_tuple[1])") |> DataFrame + else + data_df = DBInterface.execute(db, "SELECT * FROM $table_name WHERE $column_name=1") |> DataFrame + delete!(data_df, :) + end + return data_df +end \ No newline at end of file From 2b6b23056128389d3d091782c4524d9ed9405f10 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 27 Jul 2023 11:17:39 -0600 Subject: [PATCH 14/32] Add net metering and function to generate REopt incentives scenarios --- src/core/dsire_incentives.jl | 125 ++++++++++++++++++++++++++++++++--- 1 file changed, 117 insertions(+), 8 deletions(-) diff --git a/src/core/dsire_incentives.jl b/src/core/dsire_incentives.jl index 24c424a9b..daa46e407 100644 --- a/src/core/dsire_incentives.jl +++ b/src/core/dsire_incentives.jl @@ -2,11 +2,107 @@ # The program description sometimes describes that the parameters do not fully define the program constraints, # e.g. the DC production incentive of $0.5/kWh has a tiered value after 2023, and only applies to 10 kW or less, but # the parameter_set_id does not have a parameter of source=System, qualifier=max, and units=kW +# Assuming the incentives are state based, as opposed to utility-based - may have tax implications # ONLY TX has a somewhat competitive capacity-based vs production-based Value # Most states only have one or the other, and if they do have both, the production is typically the best by far # Some states have different sizing classes for incentives, but I think that can be done with multiply REopt runs -function get_incentive_data(db::SQLite.DB; state_abbr::String="CO", tech::String="PV") + +# Some states have a 100% of cost incentive, which is not realistic so need to look into those + +# TODO these data from DSIRE are not directly used in REopt, but need to be post-processed +# with the incentives to modify the REopt inputs, e.g. convert +# max_percent_of_cost # Convert to state_ibi_max (dollars) (how???) +# size_limit_basis_ac_or_dc # If AC, convert to incentive per kW-DC based on AC/DC ratio + + +all_state_abbrev = ["AK", "AL", "AR", "AZ", "CA", "CO", "CT", + "DC", "DE", "FL", "GA", "GU", "HI", "IA", "ID", "IL", "IN", + "KS", "KY", "LA", "MA", "MD", "ME", "MI", "MN", "MO", "MP", + "MS", "MT", "NC", "ND", "NE", "NH", "NJ", "NM", "NV", "NY", + "OH", "OK", "OR", "PA", "PR", "RI", "SC", "SD", "TN", "TX", + "UT", "VA", "VI", "VT", "WA", "WI", "WV", "WY"] + +inputs_map = Dict("capacity_based" => Dict("capacity_based_incentive_per_kw" => "state_rebate_per_kw", + "max_incentive_amount_dollar" => "state_rebate_max"), + "production_based" => Dict("production_based_incentive_per_kwh" => "production_incentive_per_kwh", + "production_incentive_years" => "production_incentive_years", + "max_kw" => "production_incentive_max_kw"), + "percent_based" => Dict("percent_cost_based_incentive" => "state_ibi_fraction", + "max_incentive_amount_dollar" => "state_ibi_max")) + +# TODO loop across all techs +function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tech::String="", inputs_map::Dict=inputs_map) + db = SQLite.DB(joinpath(dirname(@__FILE__), "..", "..", "data", "incentives", "DSIRE.db")) + reopt_inputs_to_assign = Dict() + can_net_meter = false + possible_techs = ["PV", "ElectricStorage", "FuelCellsNonRenewable", "FuelCellsRenewable", "Wind"] + # try + state_incentive_program_data = get_incentive_data(db; state_abbr=state_abbr, tech=tech) + if state_incentive_program_data["can_net_meter"] + can_net_meter = true + end + for program in setdiff(keys(state_incentive_program_data), ["can_net_meter"]) + basis = "" + count = 0 + # Find the basis of the program + for key in keys(state_incentive_program_data[program]) + if occursin("capacity", key) + basis = "capacity" + count += 1 + elseif occursin("production", key) + basis = "production" + count += 1 + elseif occursin("percent", key) + basis = "percent" + count += 1 + end + end + # TODO find the highest value program if multiple of the same type + # if count > 1 + # # Multiple programs of the same type, find the max value + # best_program = program + # for key in keys(state_incentive_program_data[program]) + reopt_inputs_to_assign[basis] = Dict() + if !isempty(basis) + # Assign REopt inputs from DSIRE.db data for that program + program_data = inputs_map[basis*"_based"] + for key in keys(program_data) + if key in keys(state_incentive_program_data[program]) + # Skipping unused keys for now + reopt_inputs_to_assign[basis][program_data[key]] = state_incentive_program_data[program][key] + end + end + end + end + # catch e + # println("Errored on state = ", state_abbr) + # println(showerror(stdout, e)) + # # or + # # println(e.msg) + # end + + reopt_inputs_scenarios = Dict() + for basis in keys(reopt_inputs_to_assign) + reopt_inputs_scenarios[basis] = deepcopy(reopt_inputs) + for input in keys(reopt_inputs_to_assign[basis]) + reopt_inputs_scenarios[basis][tech][input] = reopt_inputs_to_assign[basis][input] + end + # By default, even if a tech "can_net_meter", the net_metering_limit_kw is zero, so need to increase that if can_net_meter + if can_net_meter + reopt_inputs_scenarios[basis][tech]["can_net_meter"] = can_net_meter # Just to make sure it's true even though some techs default to true + if haskey(reopt_inputs_scenarios[basis], "ElectricUtility") + reopt_inputs_scenarios[basis]["ElectricUtility"]["net_metering_limit_kw"] = 99999.0 + else + reopt_inputs_scenarios[basis]["ElectricUtility"] = Dict("net_metering_limit_kw" => 99999.0) + end + end + end + + return reopt_inputs_scenarios +end + +function get_incentive_data(db::SQLite.DB; state_abbr::String="", tech::String="") # Filter incentives database by technology and state, only keeping state-wide and non-expired incentives tech_map = Dict("PV" => 7, "ElectricStorage" => 207, "FuelCellsNonRenewable" => 15, "FuelCellsRenewable" => 124, "Wind" => 8) tech_number = tech_map[tech] @@ -17,17 +113,21 @@ function get_incentive_data(db::SQLite.DB; state_abbr::String="CO", tech::String state_programs = DBInterface.execute(db, "SELECT * FROM program WHERE (state_id=$state_id) AND (is_entire_state=1) AND ((end_date > $todays_date_str) OR (end_date IS NULL))") |> DataFrame + net_metering = "Net Metering" in state_programs[!, "name"] + if ismissing(net_metering) + net_metering = true + end tech_programs = DBInterface.execute(db, "SELECT * FROM program_technology WHERE technology_id=$tech_number") |> DataFrame tech_state_program_ids = intersect(state_programs[!,"id"],tech_programs[!,"program_id"]) tech_state_program_ids_tuple = tuple(tech_state_program_ids...) parameter_set = sql_select_helper_variable_length(db; table_name="parameter_set", column_name="program_id", data_tuple=tech_state_program_ids_tuple) - # parameter_set = DBInterface.execute(db, "SELECT * FROM parameter_set WHERE program_id IN $tech_state_program_ids_tuple") |> DataFrame parameter_set_technology = DBInterface.execute(db, "SELECT * FROM parameter_set_technology WHERE technology_id=$tech_number") |> DataFrame tech_state_parameter_set = intersect(parameter_set[!,"id"], parameter_set_technology[!,"set_id"]) parameter_set_ids_tuple = tuple(tech_state_parameter_set...) parameters = sql_select_helper_variable_length(db; table_name="parameter", column_name="parameter_set_id", data_tuple=parameter_set_ids_tuple) data = Dict() + data["can_net_meter"] = net_metering for set_id in unique(parameters[!, "parameter_set_id"]) program_id = parameter_set[parameter_set[!, "id"].==set_id, "program_id"][1] data[program_id] = Dict() @@ -65,7 +165,7 @@ function get_incentive_data(db::SQLite.DB; state_abbr::String="CO", tech::String end elseif ismissing(param["qualifier"]) && occursin(r"\%", param["units"]) # %-based incentives - data[program_id]["percent_cost_based_incentive"] = param["amount"] + data[program_id]["percent_cost_based_incentive"] = param["amount"] / 100.0 # Fraction elseif param["source"] == "System" # Size limits (min/max kW), could be AC or DC basis divide_by = 1000.0 # For W (Watts) @@ -102,10 +202,19 @@ function get_incentive_data(db::SQLite.DB; state_abbr::String="CO", tech::String end for program_id in keys(data) - if isempty(data[program_id]) - delete!(data, program_id) - elseif !("capacity_based_incentive_per_kw" in keys(data[program_id])) && !("production_based_incentive_per_kwh" in keys(data[program_id])) - delete!(data, program_id) + useful_data = ["capacity_based_incentive_per_kw", "production_based_incentive_per_kwh", "percent_cost_based_incentive"] + has_useful_data = length(intersect(useful_data, keys(data[program_id]))) >= 1 + if !(program_id == "can_net_meter") + if isempty(data[program_id]) + delete!(data, program_id) + elseif !has_useful_data + delete!(data, program_id) + elseif haskey(data[program_id], "percent_cost_based_incentive") + if data[program_id]["percent_cost_based_incentive"] > 0.75 + # Likely a tax exemption and not a percent of cost rebate + delete!(data, program_id) + end + end end end @@ -123,4 +232,4 @@ function sql_select_helper_variable_length(db::SQLite.DB; table_name::String="", delete!(data_df, :) end return data_df -end \ No newline at end of file +end From 01665db63950cf0217fcabac83052eeda854aea0 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 27 Jul 2023 11:18:12 -0600 Subject: [PATCH 15/32] Integrate DSIRE incentives into multi_solutions --- src/core/reopt_multi_solutions.jl | 37 ++++++++++++++++++++++++++----- 1 file changed, 31 insertions(+), 6 deletions(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 98a2dbd1f..c786a5fc7 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -21,16 +21,41 @@ else """ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vector{Float64}}, ms::AbstractVector{T}; - parallel=false, resilience=false, outage_start_hour=1, outage_duration_hours=0) where T <: JuMP.AbstractModel + parallel=false, resilience=false, outage_start_hour=1, outage_duration_hours=0, + state="") where T <: JuMP.AbstractModel # Load in input_data from .json to dictionary input_data = JSON.parsefile(fp) + # Count number of JuMP models used + ms_count = 1 + # Decide which state incentives to apply + if !isempty(state) && haskey(input_data, "PV") + tech = "PV" + reopt_inputs_scenarios = REopt.get_incentives_scenarios(input_data; state_abbr=state, tech=tech) + results_scenarios = Dict() + best_incentives = "" + for (i, scenario) in enumerate(keys(reopt_inputs_scenarios)) + s = Scenario(reopt_inputs_scenarios[scenario]) + inputs = REoptInputs(s) + m = ms[i] + ms_count += 1 + results_scenarios[scenario] = run_reopt(m, inputs) + if i == 1 + best_incentives = scenario + elseif results_scenarios[scenario]["Financial"]["lcc"] < results_scenarios[best_incentives]["Financial"]["lcc"] + best_incentives = scenario + end + end + input_data = reopt_inputs_scenarios[best_incentives] + end + # Create optimal and BAU inputs structs for initial 2 runs s = Scenario(input_data) p = REoptInputs(s) bau_inputs = BAUInputs(p) # Note, ms[1] and rs[1] are for the BAU case which we'll need to combine with all other solutions too # and ms[2] and rs[2] are for the optimal case - inputs = ((ms[1], bau_inputs), (ms[2], p)) + inputs = ((ms[ms_count], bau_inputs), (ms[ms_count+1], p)) + ms_count += 2 rs = Any[0, 0] Threads.@threads for i = 1:2 rs[i] = run_reopt(inputs[i]) @@ -59,7 +84,7 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec else simresults = Dict() end - results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[2], techs_sized, simresults, outage_start_hour, outage_duration_hours)) + results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[ms_count-1], techs_sized, simresults, outage_start_hour, outage_duration_hours)) # Add custom resilience output to results_all, per Eaton's request results_dict["resilience"] = results_summary["optimal"]["resilience"] results_all = Dict("optimal" => results_dict) @@ -100,19 +125,19 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec if parallel Threads.@threads for i in 1:n_solutions # Threads doesn't like enumerate with for loop # JuMP model index starts at 3 because 1 and 2 were used by BAU and optimal sceanarios - n = 2 + i + n = ms_count + (i - 1) rs_solns[i] = run_reopt((ms[n], ps[i][2])) end else for i in eachindex(rs_solns) - n = 2 + i + n = ms_count + (i - 1) rs_solns[i] = run_reopt((ms[n], ps[i][2])) end end # Combine BAU with each extra solution and get results summary for (i, p) in enumerate(ps) - n = 2 + i + n = ms_count + (i-1) # Combine with BAU to get e.g. NPV if typeof(rs_solns[i]) <: Dict local results_dict = REopt.combine_results(p[2], rs[1], rs_solns[i], bau_inputs.s) From c1142be596dcde3b187ff61e3d9f48b7d2797cda Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Fri, 28 Jul 2023 11:07:48 -0600 Subject: [PATCH 16/32] Add incentives to the README --- multi_solution_readme.md | 19 +++++++++++++++++-- src/core/dsire_incentives.jl | 2 +- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index 48bf86dd9..86bee56e1 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -21,6 +21,19 @@ The `resilience` key in the results summary gets populated with the following in "Specified outage cost savings as percent of annual utility bill" # The value of avoided lost load as a percent of the annual electric bill ``` +## Incentives +Incentives are pulled from the data/incentives/DSIRE.db database for the specified kwarg `state` (e.g. = "CO" for Colorado) in the `run_reopt_multi_solutions()` function. + +**The user must now input a kwarg for `state` into the `run_reopt_multi_solutions()` to evaluate the state incentives. + +Incentives are filtered by those that apply to the entire state and have an end date of later than today or Null. The availability of Net metering is found and applied, as well as the following three incentives to choose from: +1. Capacity-based rebate ($/kW) +2. Production incentive ($/kWh) +3. Percent-of-cost based state tax credit (% of installed cost) +REopt is run with any of these three incentive options which are avaialable, and the one that produces the lowest lifecycle cost of energy is chosen. All of the solutions are then run with the best incentives option. + +If REopt identified a state incentive to use, the user should verify that that incentive program is available. + # Example use-case for multi-solutions ### Specify path to input file: `fp = "scenarios/eaton_multi.json"` @@ -29,8 +42,10 @@ The `resilience` key in the results summary gets populated with the following in `size_scale = [0.8, 1.2]` ### Need to know the maximum number of JuMP models to create, so first identify the number of technologies considered. So for the eaton_multi.json scenario with PV and Battery (ElectricStorage): `n_techs = 2` +# Incentives scenarios, max 3 for 1. capacity-based, 2. production-based, and 3. percent cost based incentives to choose from +`n_incentives_scenarios = 3` ### This equation uses 2 for optimal+BAU plus however many scenario combinations are possible: -`max_models = 2 + length(size_scale) * n_techs` +`max_models = 2 + length(size_scale) * n_techs + n_incentives_scenarios` ### Define an array of models of length `max_models` along with the specified solver and desired parameters: `ms = [Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "mip_rel_gap" => 0.001, "log_to_console" => true)) for _ in 1:max_models]` @@ -41,7 +56,7 @@ The `resilience` key in the results summary gets populated with the following in `results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=false)` ### Parallel runs with resilience, requiring multiple cores for the multiple threads (see function docstring for more information): -`results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true, resilience=true, outage_start_hour=4000, outage_duration_hours=10)` +`results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true, resilience=true, outage_start_hour=4000, outage_duration_hours=10, state="CO")` ### Here is an example `results_summary` output for `PV` and Battery (`ElectricStorage`) with `resilience=true`: ``` diff --git a/src/core/dsire_incentives.jl b/src/core/dsire_incentives.jl index daa46e407..9e97a7b51 100644 --- a/src/core/dsire_incentives.jl +++ b/src/core/dsire_incentives.jl @@ -1,4 +1,4 @@ -# Really only meaningful incentives for PV for relevant techs; none for fuel cells or battery +# Really only meaningful incentives for PV for relevant techs; none for battery, couple for fuel cells # The program description sometimes describes that the parameters do not fully define the program constraints, # e.g. the DC production incentive of $0.5/kWh has a tiered value after 2023, and only applies to 10 kW or less, but # the parameter_set_id does not have a parameter of source=System, qualifier=max, and units=kW From 075cca70f15755ad27fb6f1dab6ba9e5ce88789b Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Fri, 28 Jul 2023 11:58:17 -0600 Subject: [PATCH 17/32] Add Incentives PPT --- Eaton DSIRE Incentives in REopt.pptx | Bin 0 -> 44259 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Eaton DSIRE Incentives in REopt.pptx diff --git a/Eaton DSIRE Incentives in REopt.pptx b/Eaton DSIRE Incentives in REopt.pptx new file mode 100644 index 0000000000000000000000000000000000000000..e1f09c282460c13ffe0423d7af9482cf807bccb4 GIT binary patch literal 44259 zcmeF2Wl$tryQK?vcXt|S+}*v=#@*eeaBJM%-D%vRaT<4bY24i%hI77qXYS0Lh;RPi zsURwAlUYA1W53T{Ywb4`zJYxR|x_Dh{FQ_XaHzXEfG6gXA@gz 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    !$Ng*#F3***P-kCZ9OTRem;nH(vZcF{sUC!9dq~+EQ^T zZrrxL|45$l$hqIj9mI0>c(J~_jej$JwA75~H}>>tew`Zf%!0;XlLw0K3wL*T{`aj| z`@7qNSprAs-NyDMtMP`F32#>nDrdd8t@|%a1TU=s(G2bxD?5tx6f1}^Uobz7# zLaa6@|GePJpakxou$DcM_7tbVdAi40Xg_uo znDUwT>^|7Z!RK7m*Q|*y5IEUnomlv`dHhyrT4XD?_Y@yVI+7Pg8Sy4hFAK`}@R=9f|Jp>p916SsxFoVH(0AE+vsldh2IBN&&Mwi|;W>FIe z9Io?Lfx&=M#S{$>RQy=eA9WFTv9q?okdKr@o!BcTL%##?z6J0VIKf8IBmtk65i1ca zFPSM_*)Sq$7Jj;N0I{qKB|w3K!F;&Z`9q-(1)lAPy5{!!&?RPI5icdXQuqJ_UI&g2 zu0^*h=|zr_Ld*+_3q=-pz<{ap6bBU700h2vCZ+Wb;$0DAMF8-^gnEPBO_b=!wfbLgHGXwLs& z#eCfk7StTNodTMZV*a@a>o-@Rfc0Azpg9bd0KDV!(*_3AeEmZurMdh=3iE#+F?BSz U0un|r7y|gw0v5m56PN${8wz{%C;$Ke literal 0 HcmV?d00001 From 82d1c6e14a4b811e08a0124569a44032032541e3 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Tue, 22 Aug 2023 22:43:23 -0600 Subject: [PATCH 18/32] Avoid error if no state incentives found --- src/core/reopt_multi_solutions.jl | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index c786a5fc7..eb46c3e11 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -45,7 +45,9 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec best_incentives = scenario end end - input_data = reopt_inputs_scenarios[best_incentives] + if !isempty(best_incentives) + input_data = reopt_inputs_scenarios[best_incentives] + end end # Create optimal and BAU inputs structs for initial 2 runs From e815b7259b4e1dea5e7f4b3362b4f904d73b0040 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Tue, 29 Aug 2023 22:06:42 -0600 Subject: [PATCH 19/32] Add the best chosen incentive in the results_summary --- multi_solution_readme.md | 5 ++++- src/core/dsire_incentives.jl | 8 +++++++- src/core/reopt_multi_solutions.jl | 7 +++++++ 3 files changed, 18 insertions(+), 2 deletions(-) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index 86bee56e1..db071f5e8 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -32,7 +32,7 @@ Incentives are filtered by those that apply to the entire state and have an end 3. Percent-of-cost based state tax credit (% of installed cost) REopt is run with any of these three incentive options which are avaialable, and the one that produces the lowest lifecycle cost of energy is chosen. All of the solutions are then run with the best incentives option. -If REopt identified a state incentive to use, the user should verify that that incentive program is available. +If REopt identified a state incentive to use, the "incentive_used" will be a key at the top level of the `results_summary` and it will contain the `best_incentive_program_name`. The user should verify that that incentive program is available. # Example use-case for multi-solutions ### Specify path to input file: @@ -62,6 +62,9 @@ If REopt identified a state incentive to use, the user should verify that that i ``` "optimal": { "status": "optimal", + "incentive_used": { + "best_incentive_program_name" => "Austin Energy - Commercial Solar PV Rebate Program" + }, "resilience": { "Specified outage hours of load served": 3.0, "Average outage hours of load served (hours)": 15.0, diff --git a/src/core/dsire_incentives.jl b/src/core/dsire_incentives.jl index 9e97a7b51..e11c8b1f1 100644 --- a/src/core/dsire_incentives.jl +++ b/src/core/dsire_incentives.jl @@ -10,6 +10,9 @@ # Some states have a 100% of cost incentive, which is not realistic so need to look into those +# Net metering - this assigns net metering, but only if it's available state-wide, and +# assigns an arbitrary high net metering limit (kW) because there is no parameter_set for net metering + # TODO these data from DSIRE are not directly used in REopt, but need to be post-processed # with the incentives to modify the REopt inputs, e.g. convert # max_percent_of_cost # Convert to state_ibi_max (dollars) (how???) @@ -67,8 +70,9 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec if !isempty(basis) # Assign REopt inputs from DSIRE.db data for that program program_data = inputs_map[basis*"_based"] + reopt_inputs_to_assign[basis]["incentive_program_name"] = state_incentive_program_data[program]["name"] for key in keys(program_data) - if key in keys(state_incentive_program_data[program]) + if key in setdiff(keys(state_incentive_program_data[program]), ["name"]) # Skipping unused keys for now reopt_inputs_to_assign[basis][program_data[key]] = state_incentive_program_data[program][key] end @@ -85,6 +89,7 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec reopt_inputs_scenarios = Dict() for basis in keys(reopt_inputs_to_assign) reopt_inputs_scenarios[basis] = deepcopy(reopt_inputs) + reopt_inputs_scenarios[basis]["incentive_program_name"] = reopt_inputs_to_assign[basis]["incentive_program_name"] for input in keys(reopt_inputs_to_assign[basis]) reopt_inputs_scenarios[basis][tech][input] = reopt_inputs_to_assign[basis][input] end @@ -131,6 +136,7 @@ function get_incentive_data(db::SQLite.DB; state_abbr::String="", tech::String=" for set_id in unique(parameters[!, "parameter_set_id"]) program_id = parameter_set[parameter_set[!, "id"].==set_id, "program_id"][1] data[program_id] = Dict() + data[program_id]["name"] = state_programs[state_programs[!, "id"].==program_id, "name"][1] param_sub = parameters[parameters.parameter_set_id.==set_id, :] for param in eachrow(param_sub) if !ismissing(param["units"]) && !ismissing(param["source"]) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index eb46c3e11..21108487a 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -28,12 +28,15 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec # Count number of JuMP models used ms_count = 1 # Decide which state incentives to apply + best_incentive_program_name = "None" if !isempty(state) && haskey(input_data, "PV") tech = "PV" reopt_inputs_scenarios = REopt.get_incentives_scenarios(input_data; state_abbr=state, tech=tech) results_scenarios = Dict() best_incentives = "" for (i, scenario) in enumerate(keys(reopt_inputs_scenarios)) + incentive_name = pop!(reopt_inputs_scenarios[scenario], "incentive_program_name") + delete!(get(reopt_inputs_scenarios[scenario], "PV", Dict()), "incentive_program_name") s = Scenario(reopt_inputs_scenarios[scenario]) inputs = REoptInputs(s) m = ms[i] @@ -41,12 +44,15 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec results_scenarios[scenario] = run_reopt(m, inputs) if i == 1 best_incentives = scenario + best_incentive_program_name = incentive_name elseif results_scenarios[scenario]["Financial"]["lcc"] < results_scenarios[best_incentives]["Financial"]["lcc"] best_incentives = scenario + best_incentive_program_name = incentive_name end end if !isempty(best_incentives) input_data = reopt_inputs_scenarios[best_incentives] + delete!(input_data, "incentive_program_name") end end @@ -87,6 +93,7 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec simresults = Dict() end results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[ms_count-1], techs_sized, simresults, outage_start_hour, outage_duration_hours)) + results_summary["incentive_used"] = Dict("best_incentive_program_name" => best_incentive_program_name) # Add custom resilience output to results_all, per Eaton's request results_dict["resilience"] = results_summary["optimal"]["resilience"] results_all = Dict("optimal" => results_dict) From b9f34b7107abc06e561ddfbecbfae9c83a1cafc3 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Wed, 30 Aug 2023 07:25:10 -0600 Subject: [PATCH 20/32] Add net metering indicator from DSIRE --- multi_solution_readme.md | 5 +++-- src/core/dsire_incentives.jl | 5 ++++- src/core/reopt_multi_solutions.jl | 5 ++++- 3 files changed, 11 insertions(+), 4 deletions(-) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index db071f5e8..3021de3db 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -32,7 +32,7 @@ Incentives are filtered by those that apply to the entire state and have an end 3. Percent-of-cost based state tax credit (% of installed cost) REopt is run with any of these three incentive options which are avaialable, and the one that produces the lowest lifecycle cost of energy is chosen. All of the solutions are then run with the best incentives option. -If REopt identified a state incentive to use, the "incentive_used" will be a key at the top level of the `results_summary` and it will contain the `best_incentive_program_name`. The user should verify that that incentive program is available. +If REopt identified a state incentive to use, the `incentive_used` key at the top level of the `results_summary` will contain the `best_incentive_program_name` (`None` if none where found) and if net metering was enabled from the DSIRE database from `net_metering_from_dsire`. The user should verify that that incentive program is available. # Example use-case for multi-solutions ### Specify path to input file: @@ -63,7 +63,8 @@ If REopt identified a state incentive to use, the "incentive_used" will be a key "optimal": { "status": "optimal", "incentive_used": { - "best_incentive_program_name" => "Austin Energy - Commercial Solar PV Rebate Program" + "net_metering_from_dsire: "false", + "best_incentive_program_name": "Austin Energy - Commercial Solar PV Rebate Program" }, "resilience": { "Specified outage hours of load served": 3.0, diff --git a/src/core/dsire_incentives.jl b/src/core/dsire_incentives.jl index e11c8b1f1..b8511ac70 100644 --- a/src/core/dsire_incentives.jl +++ b/src/core/dsire_incentives.jl @@ -86,7 +86,7 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec # # println(e.msg) # end - reopt_inputs_scenarios = Dict() + reopt_inputs_scenarios = Dict() for basis in keys(reopt_inputs_to_assign) reopt_inputs_scenarios[basis] = deepcopy(reopt_inputs) reopt_inputs_scenarios[basis]["incentive_program_name"] = reopt_inputs_to_assign[basis]["incentive_program_name"] @@ -104,6 +104,9 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec end end + # Report if net metering was enabled from DSIRE, for knowledge + reopt_inputs_scenarios["net_metering_from_dsire"] = can_net_meter ? "true" : "false" + return reopt_inputs_scenarios end diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 21108487a..76d931e6c 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -29,9 +29,11 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec ms_count = 1 # Decide which state incentives to apply best_incentive_program_name = "None" + net_metering_from_dsire = "false" if !isempty(state) && haskey(input_data, "PV") tech = "PV" reopt_inputs_scenarios = REopt.get_incentives_scenarios(input_data; state_abbr=state, tech=tech) + net_metering_from_dsire = pop!(reopt_inputs_scenarios, "net_metering_from_dsire") results_scenarios = Dict() best_incentives = "" for (i, scenario) in enumerate(keys(reopt_inputs_scenarios)) @@ -93,7 +95,8 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec simresults = Dict() end results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[ms_count-1], techs_sized, simresults, outage_start_hour, outage_duration_hours)) - results_summary["incentive_used"] = Dict("best_incentive_program_name" => best_incentive_program_name) + results_summary["incentive_used"] = Dict("best_incentive_program_name" => best_incentive_program_name, + "net_metering_from_dsire" => net_metering_from_dsire) # Add custom resilience output to results_all, per Eaton's request results_dict["resilience"] = results_summary["optimal"]["resilience"] results_all = Dict("optimal" => results_dict) From 65e709ce7ccf59ec09f4a8b652fd84f05c327639 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Fri, 15 Sep 2023 16:30:09 -0600 Subject: [PATCH 21/32] Set net_metering_limit_kw to 1.0E9 which is equal to the default interconnection_limit_kw to avoid the binary variable that some open source solvers are unable to do. --- multi_solution_readme.md | 2 +- src/core/dsire_incentives.jl | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index 3021de3db..c3356cd31 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -32,7 +32,7 @@ Incentives are filtered by those that apply to the entire state and have an end 3. Percent-of-cost based state tax credit (% of installed cost) REopt is run with any of these three incentive options which are avaialable, and the one that produces the lowest lifecycle cost of energy is chosen. All of the solutions are then run with the best incentives option. -If REopt identified a state incentive to use, the `incentive_used` key at the top level of the `results_summary` will contain the `best_incentive_program_name` (`None` if none where found) and if net metering was enabled from the DSIRE database from `net_metering_from_dsire`. The user should verify that that incentive program is available. +If REopt identified a state incentive to use, the `incentive_used` key at the top level of the `results_summary` will contain the `best_incentive_program_name` (`None` if none where found) and if net metering was enabled from the DSIRE database from `net_metering_from_dsire`. The user should verify that that incentive program is available and what the net metering capacity (kW) limit is. Because the net meter capacity limit is not retrieved from the DSIRE database, an arbitrarily large number is used for the net metering limit if net metering is available. # Example use-case for multi-solutions ### Specify path to input file: diff --git a/src/core/dsire_incentives.jl b/src/core/dsire_incentives.jl index b8511ac70..8860a9d3a 100644 --- a/src/core/dsire_incentives.jl +++ b/src/core/dsire_incentives.jl @@ -97,9 +97,9 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec if can_net_meter reopt_inputs_scenarios[basis][tech]["can_net_meter"] = can_net_meter # Just to make sure it's true even though some techs default to true if haskey(reopt_inputs_scenarios[basis], "ElectricUtility") - reopt_inputs_scenarios[basis]["ElectricUtility"]["net_metering_limit_kw"] = 99999.0 + reopt_inputs_scenarios[basis]["ElectricUtility"]["net_metering_limit_kw"] = 1.0E9 else - reopt_inputs_scenarios[basis]["ElectricUtility"] = Dict("net_metering_limit_kw" => 99999.0) + reopt_inputs_scenarios[basis]["ElectricUtility"] = Dict("net_metering_limit_kw" => 1.0E9) end end end From 822061d74a5fa1c131d0b8082a8ffb36ee8fd371 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 19 Oct 2023 14:59:09 -0600 Subject: [PATCH 22/32] Update Manifest.toml --- Manifest.toml | 89 +++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 80 insertions(+), 9 deletions(-) diff --git a/Manifest.toml b/Manifest.toml index 478eb6f05..ff45dc83c 100644 --- a/Manifest.toml +++ b/Manifest.toml @@ -5,16 +5,25 @@ manifest_format = "2.0" project_hash = "eaefbe85d2808ab8023d870938aec8e0132fca30" [[deps.AbstractFFTs]] -deps = ["ChainRulesCore", "LinearAlgebra", "Test"] +deps = ["LinearAlgebra"] git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" version = "1.5.0" +weakdeps = ["ChainRulesCore", "Test"] + + [deps.AbstractFFTs.extensions] + AbstractFFTsChainRulesCoreExt = "ChainRulesCore" + AbstractFFTsTestExt = "Test" [[deps.Adapt]] deps = ["LinearAlgebra", "Requires"] git-tree-sha1 = "76289dc51920fdc6e0013c872ba9551d54961c24" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" version = "3.6.2" +weakdeps = ["StaticArrays"] + + [deps.Adapt.extensions] + AdaptStaticArraysExt = "StaticArrays" [[deps.ArchGDAL]] deps = ["CEnum", "ColorTypes", "Dates", "DiskArrays", "Extents", "GDAL", "GDAL_jll", "GeoFormatTypes", "GeoInterface", "GeoInterfaceRecipes", "ImageCore", "Tables"] @@ -79,10 +88,14 @@ uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" version = "1.16.0" [[deps.ChangesOfVariables]] -deps = ["InverseFunctions", "LinearAlgebra", "Test"] +deps = ["LinearAlgebra", "Test"] git-tree-sha1 = "2fba81a302a7be671aefe194f0525ef231104e7f" uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" version = "0.1.8" +weakdeps = ["InverseFunctions"] + + [deps.ChangesOfVariables.extensions] + ChangesOfVariablesInverseFunctionsExt = "InverseFunctions" [[deps.CodecBzip2]] deps = ["Bzip2_jll", "Libdl", "TranscodingStreams"] @@ -126,15 +139,19 @@ uuid = "bbf7d656-a473-5ed7-a52c-81e309532950" version = "0.3.0" [[deps.Compat]] -deps = ["Dates", "LinearAlgebra", "UUIDs"] +deps = ["UUIDs"] git-tree-sha1 = "8a62af3e248a8c4bad6b32cbbe663ae02275e32c" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" version = "4.10.0" +weakdeps = ["Dates", "LinearAlgebra"] + + [deps.Compat.extensions] + CompatLinearAlgebraExt = "LinearAlgebra" [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "0.5.2+0" +version = "1.0.2+0" [[deps.ConcurrentUtilities]] deps = ["Serialization", "Sockets"] @@ -148,6 +165,14 @@ git-tree-sha1 = "c53fc348ca4d40d7b371e71fd52251839080cbc9" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" version = "1.5.4" + [deps.ConstructionBase.extensions] + ConstructionBaseIntervalSetsExt = "IntervalSets" + ConstructionBaseStaticArraysExt = "StaticArrays" + + [deps.ConstructionBase.weakdeps] + IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953" + StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" + [[deps.CoolProp]] deps = ["CoolProp_jll", "Markdown", "Unitful"] git-tree-sha1 = "94062163b5656b1351f7f7a784341b8fe13c1ca1" @@ -280,10 +305,14 @@ uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" version = "0.8.4" [[deps.ForwardDiff]] -deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions", "StaticArrays"] +deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "LinearAlgebra", "LogExpFunctions", "NaNMath", "Preferences", "Printf", "Random", "SpecialFunctions"] git-tree-sha1 = "cf0fe81336da9fb90944683b8c41984b08793dad" uuid = "f6369f11-7733-5829-9624-2563aa707210" version = "0.10.36" +weakdeps = ["StaticArrays"] + + [deps.ForwardDiff.extensions] + ForwardDiffStaticArraysExt = "StaticArrays" [[deps.Future]] deps = ["Random"] @@ -432,6 +461,12 @@ git-tree-sha1 = "3700a700bc80856fe673b355123ae4574f2d5dfe" uuid = "4076af6c-e467-56ae-b986-b466b2749572" version = "1.15.1" + [deps.JuMP.extensions] + JuMPDimensionalDataExt = "DimensionalData" + + [deps.JuMP.weakdeps] + DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0" + [[deps.LERC_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "bf36f528eec6634efc60d7ec062008f171071434" @@ -498,10 +533,16 @@ uuid = "d3a379c0-f9a3-5b72-a4c0-6bf4d2e8af0f" version = "2.12.0+0" [[deps.LogExpFunctions]] -deps = ["ChainRulesCore", "ChangesOfVariables", "DocStringExtensions", "InverseFunctions", "IrrationalConstants", "LinearAlgebra"] +deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] git-tree-sha1 = "7d6dd4e9212aebaeed356de34ccf262a3cd415aa" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" version = "0.3.26" +weakdeps = ["ChainRulesCore", "ChangesOfVariables", "InverseFunctions"] + + [deps.LogExpFunctions.extensions] + LogExpFunctionsChainRulesCoreExt = "ChainRulesCore" + LogExpFunctionsChangesOfVariablesExt = "ChangesOfVariables" + LogExpFunctionsInverseFunctionsExt = "InverseFunctions" [[deps.Logging]] uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" @@ -764,6 +805,18 @@ git-tree-sha1 = "06b5ac80ff1b88bd82df92c1c1875eea3954cd6e" uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665" version = "2.0.20" + [deps.Roots.extensions] + RootsForwardDiffExt = "ForwardDiff" + RootsIntervalRootFindingExt = "IntervalRootFinding" + RootsSymPyExt = "SymPy" + RootsSymPyPythonCallExt = "SymPyPythonCall" + + [deps.Roots.weakdeps] + ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" + IntervalRootFinding = "d2bf35a9-74e0-55ec-b149-d360ff49b807" + SymPy = "24249f21-da20-56a4-8eb1-6a02cf4ae2e6" + SymPyPythonCall = "bc8888f7-b21e-4b7c-a06a-5d9c9496438c" + [[deps.SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" version = "0.7.0" @@ -820,10 +873,14 @@ deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" [[deps.SpecialFunctions]] -deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] +deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] git-tree-sha1 = "e2cfc4012a19088254b3950b85c3c1d8882d864d" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" version = "2.3.1" +weakdeps = ["ChainRulesCore"] + + [deps.SpecialFunctions.extensions] + SpecialFunctionsChainRulesCoreExt = "ChainRulesCore" [[deps.StackViews]] deps = ["OffsetArrays"] @@ -832,10 +889,14 @@ uuid = "cae243ae-269e-4f55-b966-ac2d0dc13c15" version = "0.1.1" [[deps.StaticArrays]] -deps = ["LinearAlgebra", "Random", "StaticArraysCore", "Statistics"] +deps = ["LinearAlgebra", "Random", "StaticArraysCore"] git-tree-sha1 = "0adf069a2a490c47273727e029371b31d44b72b2" uuid = "90137ffa-7385-5640-81b9-e52037218182" version = "1.6.5" +weakdeps = ["Statistics"] + + [deps.StaticArrays.extensions] + StaticArraysStatisticsExt = "Statistics" [[deps.StaticArraysCore]] git-tree-sha1 = "36b3d696ce6366023a0ea192b4cd442268995a0d" @@ -853,6 +914,11 @@ git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5" uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" version = "0.3.4" +[[deps.SuiteSparse_jll]] +deps = ["Artifacts", "Libdl", "Pkg", "libblastrampoline_jll"] +uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" +version = "5.10.1+6" + [[deps.TOML]] deps = ["Dates"] uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" @@ -910,10 +976,15 @@ uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" [[deps.Unitful]] -deps = ["ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "Random"] +deps = ["Dates", "LinearAlgebra", "Random"] git-tree-sha1 = "a72d22c7e13fe2de562feda8645aa134712a87ee" uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" version = "1.17.0" +weakdeps = ["ConstructionBase", "InverseFunctions"] + + [deps.Unitful.extensions] + ConstructionBaseUnitfulExt = "ConstructionBase" + InverseFunctionsUnitfulExt = "InverseFunctions" [[deps.WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] From a8e1ac96f9e8c3d25219fd207af8ebe13c48f506 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 19 Oct 2023 16:09:06 -0600 Subject: [PATCH 23/32] Update Git Workflows to Julia 1.9 --- .github/workflows/CI.yml | 2 +- .github/workflows/build_docs.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 26f63108a..352f60d58 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -11,7 +11,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - julia-version: ['1.8'] + julia-version: ['1.9'] julia-arch: [x64] os: [ubuntu-latest, windows-latest, macOS-latest] diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index b6365e57b..2510d4936 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -14,7 +14,7 @@ jobs: - uses: actions/checkout@v2 - uses: julia-actions/setup-julia@latest with: - version: '1.8' + version: '1.9' - name: Install dependencies run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()' - name: Build and deploy From d35d9560cd88b85850fcde717981569b944fdc54 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 19 Oct 2023 16:10:17 -0600 Subject: [PATCH 24/32] Add Julia v1.9 to Project.toml [compat] --- Project.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project.toml b/Project.toml index da4d50c73..ad664fb01 100644 --- a/Project.toml +++ b/Project.toml @@ -38,4 +38,4 @@ MathOptInterface = "0.9, 0.10, 1" Requires = "1.3" Roots = "1.3, 2" TestEnv = "1.7, 1.8, 1.9" -julia = "1.4, 1.5, 1.6, 1.7, 1.8" +julia = "1.4, 1.5, 1.6, 1.7, 1.8, 1.9" From 1192138d22c954aa22c0982df0045bbf45c5e3dd Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 19 Oct 2023 21:55:16 -0600 Subject: [PATCH 25/32] Add incentive_used to results_all under optimal case --- src/core/reopt_multi_solutions.jl | 1 + 1 file changed, 1 insertion(+) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 76d931e6c..67fb2edf2 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -99,6 +99,7 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec "net_metering_from_dsire" => net_metering_from_dsire) # Add custom resilience output to results_all, per Eaton's request results_dict["resilience"] = results_summary["optimal"]["resilience"] + results_dict["incentive_used"] = results_summary["incentive_used"] results_all = Dict("optimal" => results_dict) # Now create number of inputs based on the # of techs sized in optimal case From ff8b47610428e10096948eba4b5b941575fce648 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Fri, 10 Nov 2023 16:14:25 -0700 Subject: [PATCH 26/32] Add state, type, and value for incentive_used --- src/core/dsire_incentives.jl | 8 +++++++- src/core/reopt_multi_solutions.jl | 11 ++++++++++- 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/src/core/dsire_incentives.jl b/src/core/dsire_incentives.jl index 8860a9d3a..a17e0148c 100644 --- a/src/core/dsire_incentives.jl +++ b/src/core/dsire_incentives.jl @@ -70,11 +70,15 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec if !isempty(basis) # Assign REopt inputs from DSIRE.db data for that program program_data = inputs_map[basis*"_based"] - reopt_inputs_to_assign[basis]["incentive_program_name"] = state_incentive_program_data[program]["name"] + reopt_inputs_to_assign[basis]["incentive_program_name"] = state_incentive_program_data[program]["name"] for key in keys(program_data) if key in setdiff(keys(state_incentive_program_data[program]), ["name"]) # Skipping unused keys for now reopt_inputs_to_assign[basis][program_data[key]] = state_incentive_program_data[program][key] + if occursin("based_incentive", key) + reopt_inputs_to_assign[basis]["type"] = key + reopt_inputs_to_assign[basis]["value"] = state_incentive_program_data[program][key] + end end end end @@ -90,6 +94,8 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec for basis in keys(reopt_inputs_to_assign) reopt_inputs_scenarios[basis] = deepcopy(reopt_inputs) reopt_inputs_scenarios[basis]["incentive_program_name"] = reopt_inputs_to_assign[basis]["incentive_program_name"] + reopt_inputs_scenarios[basis]["type"] = reopt_inputs_to_assign[basis]["type"] + reopt_inputs_scenarios[basis]["value"] = reopt_inputs_to_assign[basis]["value"] for input in keys(reopt_inputs_to_assign[basis]) reopt_inputs_scenarios[basis][tech][input] = reopt_inputs_to_assign[basis][input] end diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 67fb2edf2..4148953ba 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -29,6 +29,8 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec ms_count = 1 # Decide which state incentives to apply best_incentive_program_name = "None" + incentive_type = "None" + incentive_value = 0.0 net_metering_from_dsire = "false" if !isempty(state) && haskey(input_data, "PV") tech = "PV" @@ -38,7 +40,11 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec best_incentives = "" for (i, scenario) in enumerate(keys(reopt_inputs_scenarios)) incentive_name = pop!(reopt_inputs_scenarios[scenario], "incentive_program_name") + incentive_type = pop!(reopt_inputs_scenarios[scenario], "type") + incentive_value = pop!(reopt_inputs_scenarios[scenario], "value") delete!(get(reopt_inputs_scenarios[scenario], "PV", Dict()), "incentive_program_name") + delete!(get(reopt_inputs_scenarios[scenario], "PV", Dict()), "type") + delete!(get(reopt_inputs_scenarios[scenario], "PV", Dict()), "value") s = Scenario(reopt_inputs_scenarios[scenario]) inputs = REoptInputs(s) m = ms[i] @@ -96,7 +102,10 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec end results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[ms_count-1], techs_sized, simresults, outage_start_hour, outage_duration_hours)) results_summary["incentive_used"] = Dict("best_incentive_program_name" => best_incentive_program_name, - "net_metering_from_dsire" => net_metering_from_dsire) + "net_metering_from_dsire" => net_metering_from_dsire, + "state" => state, + "type" => incentive_type, + "value" => incentive_value) # Add custom resilience output to results_all, per Eaton's request results_dict["resilience"] = results_summary["optimal"]["resilience"] results_dict["incentive_used"] = results_summary["incentive_used"] From a682729a4f001ac509a27b79a12f22a300d428c2 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Wed, 6 Dec 2023 11:36:38 -0700 Subject: [PATCH 27/32] Update Production Based Incentives for BAU with Existing Techs --- src/core/bau_inputs.jl | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/src/core/bau_inputs.jl b/src/core/bau_inputs.jl index 3cb43c9d0..e7afa543d 100644 --- a/src/core/bau_inputs.jl +++ b/src/core/bau_inputs.jl @@ -60,9 +60,6 @@ function BAUInputs(p::REoptInputs) levelization_factor[pvname] = p.levelization_factor[pvname] cap_cost_slope[pvname] = 0.0 tech_renewable_energy_fraction[pvname] = 1.0 - if pvname in p.techs.pbi - push!(pbi_techs, pvname) - end pv = get_pv_by_name(pvname, p.s.pvs) fillin_techs_by_exportbin(techs_by_exportbin, pv, pv.name) if !pv.can_curtail @@ -78,15 +75,14 @@ function BAUInputs(p::REoptInputs) om_cost_per_kw["Generator"] = p.s.generator.om_cost_per_kw production_factor["Generator", :] = p.production_factor["Generator", :] fillin_techs_by_exportbin(techs_by_exportbin, p.s.generator, "Generator") - if "Generator" in p.techs.pbi - push!(pbi_techs, "Generator") - end if !p.s.generator.can_curtail push!(techs.no_curtail, "Generator") end fuel_cost_per_kwh["Generator"] = p.fuel_cost_per_kwh["Generator"] end + pbi_pwf, pbi_max_benefit, pbi_max_kw, pbi_benefit_per_kwh = setup_pbi_inputs(bau_scenario, techs) + if "ExistingBoiler" in techs.all setup_existing_boiler_inputs(bau_scenario, max_sizes, min_sizes, existing_sizes, cap_cost_slope, boiler_efficiency, tech_renewable_energy_fraction, tech_emissions_factors_CO2, tech_emissions_factors_NOx, tech_emissions_factors_SO2, tech_emissions_factors_PM25, fuel_cost_per_kwh) @@ -164,10 +160,10 @@ function BAUInputs(p::REoptInputs) seg_min_size, seg_max_size, seg_yint, - p.pbi_pwf, - p.pbi_max_benefit, - p.pbi_max_kw, - p.pbi_benefit_per_kwh, + pbi_pwf, + pbi_max_benefit, + pbi_max_kw, + pbi_benefit_per_kwh, boiler_efficiency, fuel_cost_per_kwh, ghp_options, From 7056aa61fcee1582959962ee123c3fd855abd5e1 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 7 Dec 2023 22:44:20 -0700 Subject: [PATCH 28/32] Fix 15-minute interval for resilience output --- src/core/reopt_multi_solutions.jl | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 4148953ba..3e1673e88 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -226,10 +226,11 @@ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::J for ts in 1:num_outage_time_steps; init=0.0) BAUOutageCost[tz] = sum(p.value_of_lost_load_per_kwh[tz+ts-1] * p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour for ts in 1:num_outage_time_steps; init=0.0) + resil_by_time_step = simresults["resilience_by_time_step"][tz] * p.s.settings.time_steps_per_hour OptimalOutageLostLoadKWH[tz] = sum(p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour - for ts in (convert(Int, simresults["resilience_by_time_step"][tz])+1):outage_duration_hours; init=0.0) + for ts in (convert(Int, resil_by_time_step)+1):outage_duration_hours; init=0.0) OptimalOutageCost[tz] = sum(p.value_of_lost_load_per_kwh[tz+ts-1] * p.s.electric_load.critical_loads_kw[tz+ts-1] / p.s.settings.time_steps_per_hour - for ts in (convert(Int, simresults["resilience_by_time_step"][tz])+1):outage_duration_hours; init=0.0) + for ts in (convert(Int, resil_by_time_step)+1):outage_duration_hours; init=0.0) end avg_lost_load_bau_kwh = sum(BAUOutageLostLoadKWH) / length(BAUOutageLostLoadKWH) avg_year_one_outage_cost_bau = sum(BAUOutageCost) / length(BAUOutageCost) From 854ad70df7c9fda03d58f01172b8fb4e3e88a102 Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Wed, 20 Dec 2023 18:41:35 -0700 Subject: [PATCH 29/32] Add all considered techs to results_summary even if sized to zero for optimal --- src/core/reopt_multi_solutions.jl | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/core/reopt_multi_solutions.jl b/src/core/reopt_multi_solutions.jl index 3e1673e88..1a664156d 100644 --- a/src/core/reopt_multi_solutions.jl +++ b/src/core/reopt_multi_solutions.jl @@ -100,7 +100,7 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec else simresults = Dict() end - results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[ms_count-1], techs_sized, simresults, outage_start_hour, outage_duration_hours)) + results_summary = Dict("optimal" => get_multi_solutions_results_summary(results_dict, p, ms[ms_count-1], techs_considered, simresults, outage_start_hour, outage_duration_hours)) results_summary["incentive_used"] = Dict("best_incentive_program_name" => best_incentive_program_name, "net_metering_from_dsire" => net_metering_from_dsire, "state" => state, @@ -119,7 +119,8 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec # Remove techs which were considered but not sized (size=0) in optimal case for t in techs_to_zero - delete!(input_data_s, t) + # delete!(input_data_s, t) + input_data_s[t]["max_kw"] = 0.0 end # Force size based on size_scale factor for tech, and set other techs to optimal size @@ -170,7 +171,7 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec simresults = Dict() end # Build results summary, a select number of outputs for Eaton - results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_sized, simresults, outage_start_hour, outage_duration_hours) + results_summary[p[1]] = get_multi_solutions_results_summary(results_dict, p[2], ms[n], techs_considered, simresults, outage_start_hour, outage_duration_hours) results_dict["resilience"] = results_summary[p[1]]["resilience"] results_all[p[1]] = results_dict else @@ -184,14 +185,14 @@ function run_reopt_multi_solutions(fp::String, size_scale::Union{Vector{Any},Vec end """ - get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict, + get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_considered::Vector{String}, simresults::Dict, outage_start_hour::Int64, outage_duration_hours::Int64) Get the results summary dictionary which is a selected number of outputs for Financial, Emissions, and the relevant techs """ -function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_sized::Vector{String}, simresults::Dict, +function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::JuMP.AbstractModel, techs_considered::Vector{String}, simresults::Dict, outage_start_hour::Int64, outage_duration_hours::Int64) # results_summary always has Financial and emissions, and then add techs as needed results_summary = Dict( @@ -264,7 +265,7 @@ function get_multi_solutions_results_summary(results::Dict, p::REoptInputs, m::J end # Add techs if they have been sized in the optimal case - for key in techs_sized + for key in techs_considered if key == "PV" # Note, capital cost is "net" of incentives, and Maintenance cost is year-one results_summary["PV"] = From 90cc06c42b4db7681eda827fe3477d986f6a9dba Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Fri, 8 Mar 2024 19:36:28 -0700 Subject: [PATCH 30/32] Update Readme and TODO notes --- multi_solution_readme.md | 18 +++++++++--------- src/core/dsire_incentives.jl | 3 +++ 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/multi_solution_readme.md b/multi_solution_readme.md index c3356cd31..94a71fdba 100644 --- a/multi_solution_readme.md +++ b/multi_solution_readme.md @@ -35,30 +35,30 @@ REopt is run with any of these three incentive options which are avaialable, and If REopt identified a state incentive to use, the `incentive_used` key at the top level of the `results_summary` will contain the `best_incentive_program_name` (`None` if none where found) and if net metering was enabled from the DSIRE database from `net_metering_from_dsire`. The user should verify that that incentive program is available and what the net metering capacity (kW) limit is. Because the net meter capacity limit is not retrieved from the DSIRE database, an arbitrarily large number is used for the net metering limit if net metering is available. # Example use-case for multi-solutions -### Specify path to input file: +#### Specify path to input file: `fp = "scenarios/eaton_multi.json"` -### Specify fractions/ratios of size relative to the optimal size to run. The other techs will all be forced to the optimal size for each run. If you run with an empty array for size_scale, it will just run the optimal scenario. +#### Specify fractions/ratios of size relative to the optimal size to run. The other techs will all be forced to the optimal size for each run. If you run with an empty array for size_scale, it will just run the optimal scenario. `size_scale = [0.8, 1.2]` -### Need to know the maximum number of JuMP models to create, so first identify the number of technologies considered. So for the eaton_multi.json scenario with PV and Battery (ElectricStorage): +#### Need to know the maximum number of JuMP models to create, so first identify the number of technologies considered. So for the eaton_multi.json scenario with PV and Battery (ElectricStorage): `n_techs = 2` -# Incentives scenarios, max 3 for 1. capacity-based, 2. production-based, and 3. percent cost based incentives to choose from +#### Incentives scenarios, max 3 for 1. capacity-based, 2. production-based, and 3. percent cost based incentives to choose from `n_incentives_scenarios = 3` -### This equation uses 2 for optimal+BAU plus however many scenario combinations are possible: +#### This equation uses 2 for optimal+BAU plus however many scenario combinations are possible: `max_models = 2 + length(size_scale) * n_techs + n_incentives_scenarios` -### Define an array of models of length `max_models` along with the specified solver and desired parameters: +#### Define an array of models of length `max_models` along with the specified solver and desired parameters: `ms = [Model(optimizer_with_attributes(HiGHS.Optimizer, "output_flag" => false, "mip_rel_gap" => 0.001, "log_to_console" => true)) for _ in 1:max_models]` - Solver options for Cbc.jl: https://github.com/jump-dev/Cbc.jl - Solver options for HiGHS.jl: https://ergo-code.github.io/HiGHS/dev/options/definitions/ - Increasing optimality gap (ratioGap for Cbc and mip_rel_gap for HiGHS) to 0.01 (1%) is particularly useful if REopt is taking a long time to solver -### In-series/sequential runs to reduce number of threads required to 2: +#### In-series/sequential runs to reduce number of threads required to 2: `results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=false)` -### Parallel runs with resilience, requiring multiple cores for the multiple threads (see function docstring for more information): +#### Parallel runs with resilience, requiring multiple cores for the multiple threads (see function docstring for more information): `results_all, results_summary = REopt.run_reopt_multi_solutions(fp, size_scale, ms; parallel=true, resilience=true, outage_start_hour=4000, outage_duration_hours=10, state="CO")` -### Here is an example `results_summary` output for `PV` and Battery (`ElectricStorage`) with `resilience=true`: +#### Here is an example `results_summary` output for `PV` and Battery (`ElectricStorage`) with `resilience=true`: ``` "optimal": { "status": "optimal", diff --git a/src/core/dsire_incentives.jl b/src/core/dsire_incentives.jl index a17e0148c..64ccf93b8 100644 --- a/src/core/dsire_incentives.jl +++ b/src/core/dsire_incentives.jl @@ -103,6 +103,7 @@ function get_incentives_scenarios(reopt_inputs::Dict; state_abbr::String="", tec if can_net_meter reopt_inputs_scenarios[basis][tech]["can_net_meter"] = can_net_meter # Just to make sure it's true even though some techs default to true if haskey(reopt_inputs_scenarios[basis], "ElectricUtility") + # TODO replace big number 1.0E9 with a practical limit like 5 MW which is the max for all states - however, this makes it harder to solve sometimes reopt_inputs_scenarios[basis]["ElectricUtility"]["net_metering_limit_kw"] = 1.0E9 else reopt_inputs_scenarios[basis]["ElectricUtility"] = Dict("net_metering_limit_kw" => 1.0E9) @@ -128,6 +129,8 @@ function get_incentive_data(db::SQLite.DB; state_abbr::String="", tech::String=" AND (is_entire_state=1) AND ((end_date > $todays_date_str) OR (end_date IS NULL))") |> DataFrame net_metering = "Net Metering" in state_programs[!, "name"] + # TODO CA, AZ, and UT, as well as recently AL, TN, and SD have net billing instead of net metering, so + # ideally we'd assign a wholesale_rate equal to the avoided electricity cost credit those states would receive if ismissing(net_metering) net_metering = true end From 3389c84641f73ed65d5f6796864d3aaf853a8697 Mon Sep 17 00:00:00 2001 From: Bill Becker <42586683+Bill-Becker@users.noreply.github.com> Date: Thu, 25 Jul 2024 21:43:53 -0600 Subject: [PATCH 31/32] Remove MacOS from GitHub Actions runner list for CI testing MacOS-13 is still frequently freezing and getting cancelled after 6+ hours. --- .github/workflows/CI.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 9a10a9557..cd14bb7a6 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -13,7 +13,7 @@ jobs: matrix: julia-version: ['1.9'] julia-arch: [x64] - os: [windows-latest, macOS-13] + os: [windows-latest] steps: - uses: actions/checkout@v2 @@ -23,4 +23,4 @@ jobs: - uses: julia-actions/julia-buildpkg@latest # - uses: mxschmitt/action-tmate@v3 # for interactive debugging - run: julia --project=. -e 'using Pkg; Pkg.activate("test"); Pkg.rm("Xpress"); Pkg.activate("."); using TestEnv; TestEnv.activate(); cd("test"); include("runtests.jl")' - shell: bash \ No newline at end of file + shell: bash From b5592f1f65257f09b0da1252e2b3c66b68c23cfb Mon Sep 17 00:00:00 2001 From: Bill Becker Date: Thu, 12 Sep 2024 22:36:08 -0600 Subject: [PATCH 32/32] Update expected Solar dataset test from updated NSRDB --- test/runtests.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/runtests.jl b/test/runtests.jl index 4aee1d809..9cc9d893e 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -54,7 +54,7 @@ else # run HiGHS tests latitude, longitude = 3.8603988398663125, 11.528880303663136 radius = 0 dataset, distance, datasource = REopt.call_solar_dataset_api(latitude, longitude, radius) - @test dataset ≈ "intl" + @test dataset ≈ "nsrdb" # 4. Fairbanks, AK site = "Fairbanks"

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