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Add coincident peak pricing and other updates for MG-RAVENS
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@Bill-Becker
Bill-Becker committed Jan 2, 2025
1 parent e5c2b16 commit 2e04b10
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Showing 2 changed files with 90 additions and 50 deletions.
10 changes: 5 additions & 5 deletions data/mgravens_fields_defaults.json
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Original file line number Diff line number Diff line change
Expand Up @@ -23,18 +23,18 @@
"installed_cost_per_kw": 1500.0,
"om_cost_per_kw": 10.0,
"array_type": 0,
"federal_itc_fraction": 0.0,
"macrs_option_years": 0,
"federal_itc_fraction": 0.0,
"macrs_option_years": 0,
"macrs_bonus_fraction": 0.0,
"macrs_itc_reduction": 0.0
},
"ElectricStorage": {
"installed_cost_per_kw": 600.0,
"installed_cost_per_kwh": 400.0,
"can_grid_charge": true,
"installed_cost_per_kwh": 400.0,
"can_grid_charge": true,
"macrs_option_years": 0,
"macrs_bonus_fraction": 0.0,
"macrs_itc_reduction": 0.0,
"macrs_itc_reduction": 0.0,
"total_itc_fraction": 0.0
}
}
130 changes: 85 additions & 45 deletions src/core/mgravens.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -5,13 +5,11 @@
"""
convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
- Load in the starting-point REopt inputs .json file (not a MG-Ravens user input) which has:
1. All possible fields that may be extracted from the MG-Ravens .json schema into the REopt .json schema
2. Default values for:
a) Non-required but possible Ravens-input fields
b) REopt inputs which are not exposed in Ravens (yet), customized to be for utility-scale MG's, different than standard REopt (C&I-scale)
- Load in the starting-point REopt inputs .json file (not a MG-Ravens user input) which has default values for:
1. Non-required but possible Ravens-input fields
2. REopt inputs which are not exposed in Ravens (yet), customized to be for utility-scale MG's, different than standard REopt (C&I-scale)
- Build out REopt inputs by overwritting defaults or adding MG-Ravens inputs to REopt inputs
- Return the REopt inputs Dictionary
- Return the REopt inputs Dictionary for using with run_reopt() function
"""

# Notes for REopt
Expand All @@ -20,7 +18,41 @@
# Summing up the load profiles of the list of LoadGroup.EnergyConsumers to get the total load profile
# Only using value1 for real power entries in the EnergyConsumerSchedule.RegularTimePoints list of dictionaries for load profile

# TODO the Location.670a_residential2_Loc has it's own Lat/Long and in this case it's different from the ProposedSiteLocation, even though I'm not using the Location key/dict for anything
# TODO ask David/Juan: the Location.670a_residential2_Loc has it's own Lat/Long and in this case it's different from the ProposedSiteLocation, even though I'm not using the Location key/dict for anything

"""
build_timeseries_array(list_of_dict, y_value_name, timestep_sec=3600)
- Create array depending on interval and length of timeseries data
- If less than year-long data, build a year by repeating the partial load data
- Return the populated loads_kw array
"""
function build_timeseries_array(list_of_dict, y_value_name, timestep_sec)
# Validate timestep_sec options
if !(timestep_sec in [900, 3600])
throw(@error("Valid EnergyConsumerSchedule.timeStep for BasicIntervalSchedule LoadForecast is 900 and 3600, input of $timestep_sec"))
end
# Adjust scale_factor for repeating data to fill a year-long array
if timestep_sec == 3600
scale_factor = 8760 / length(list_of_dict)
elseif timestep_sec == 900
scale_factor = 8760 * 4 / length(list_of_dict)
else
throw(@error("Invalid timestep_sec: $timestep_sec"))
end
# Build timeseries array
reopt_array = []
repeated_loads = [data[y_value_name] for data in list_of_dict]
for _ in 1:convert(Int, ceil(scale_factor))
append!(reopt_array, repeated_loads)
extra_ts = length(reopt_array) - (timestep_sec == 3600 ? 8760 : 8760 * 4)
if extra_ts > 0
reopt_array = reopt_array[1:end-extra_ts]
break
end
end
return reopt_array
end

function convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
reopt_inputs = JSON.parsefile(joinpath(@__DIR__, "..", "..", "data", "mgravens_fields_defaults.json"))
Expand All @@ -36,7 +68,7 @@ function convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
reopt_inputs["Financial"]["analysis_years"] = parse(Int64, split(split(lifetime_str, "P")[2], "Y")[1])
end

# Major assumptions: every ProposedEnergyProducerOption has the same SiteLocation, Region, and LoadGroup
# Major assumptions: every ProposedEnergyProducerOption has the same SiteLocation
# TODO add error checking in case above is not true
techs_to_include = []
for (i, name) in enumerate(tech_names)
Expand All @@ -47,11 +79,13 @@ function convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
load_group_names = [] # May be one or more than one, e.g. ["ResidentialGroup", "IndustrialGroup"]
energy_consumer_names = [] # One set (1+) for each LoadGroup, e.g. ["670a_residential2", "670b_residential2"]
load_forecast_names = [] # One-to-one with energy_consumer_names
length_load_input = 0
region_name = ""
lmp_name = ""
capacity_prices_name = ""
timestep_sec = 0

# Assign site, load, and energy prices attributes, using only the first ProducerOption because they **should** all be the same
# Assign site, load, and energy prices attributes, using only the FIRST ProposedEnergyProducerOption because they **should** all be the same
# TODO track all missing required inputs, and key optional inputs that rely on defaults
if i == 1
# Site data (lat, long, area) - lat/long is only needed if relying on PV or Wind APIs; default area is a Big Number
Expand All @@ -63,7 +97,7 @@ function convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
position_points = mgravens["ProposedSiteLocations"][site_name]["Location.PositionPoints"][1]
reopt_inputs["Site"]["latitude"] = get(position_points, "PositionPoint.yPosition", nothing)
reopt_inputs["Site"]["longitude"] = get(position_points, "PositionPoint.xPosition", nothing)
# Also from SiteLocation, get needed references for 1) LoadGroup for load profile and 2) SubGeographicalRegion for energy prices
# Also from SiteLocation, get needed references for LoadGroup
load_groups_lumped = mgravens["ProposedSiteLocations"][site_name]["ProposedSiteLocation.LoadGroup"]
# Have to extract just the name we want from lumped string value, e.g. "SubGeographicalRegion::'County1'" (want just 'County1')
# Need to assume only one/first EnergyConsumer which is tied to a LoadForecast
Expand All @@ -82,37 +116,23 @@ function convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
# Need timestep_sec from ONE forecast for initializing loads_kw, but we do validation of timestep_sec and length of EACH load profile in the loop below
timestep_sec = mgravens["BasicIntervalSchedule"][load_forecast_names[1]]["EnergyConsumerSchedule.timeStep"]
reopt_inputs["Settings"]["time_steps_per_hour"] = 3600 / timestep_sec
reopt_inputs["ElectricLoad"]["loads_kw"] = zeros(8760 * convert(Int64, reopt_inputs["Settings"]["time_steps_per_hour"]))
# Sum up the loads in all load forecasts to aggregate into a single load profile
# This may only matter if relying on a URDB rate structure but this is not currently being used in REopt:
# "670a_residential2_shape": {
# "EnergyConsumerSchedule.startDay": "Monday",
for load_forecast_name in load_forecast_names
load_forecast_dict = get(mgravens["BasicIntervalSchedule"], load_forecast_name, nothing)
if !isnothing(load_forecast_dict)
# Currently allowing 24 hour, 168 hour, and 8760 hour load profile inputs and scaling to 1-year if not the full 8760
# TODO make sure it's consistent with prices by creating a "time_interval_length" variable to cross-check with all time-series input data
timestep_sec = load_forecast_dict["EnergyConsumerSchedule.timeStep"]
if !(timestep_sec in [900, 3600])
throw(@error("Valid EnergyConsumerSchedule.timeStep for BasicIntervalSchedule LoadForecast is 900 and 3600, input of $timestep_sec"))
# Currently allowing 15-min and hourly intervals with length of N timesteps and scaling to 1-year if not the full year
# Note, we also do this with LMPs but we still require 12 months for capacity prices, and optional-input PV profiles
timestep_sec_i = load_forecast_dict["EnergyConsumerSchedule.timeStep"]
if !(timestep_sec_i == timestep_sec)
throw(@error("All EnergyConsumerSchedule.timeStep for BasicIntervalSchedule LoadForecast must be the same"))
end
# Allow for 15-minute (900 timestep_sec) or hourly (3600 timestep_sec) time intervals, and time windows of 1, 2, 7, and 365 days, and scale to year-long time window arrays (365 days)
load_list_of_dict = load_forecast_dict["EnergyConsumerSchedule.RegularTimePoints"]
# TODO make appending shorter horizon load data to make 8760 more efficient than below which is appending many many dictionaries
# instead, make an array of e.g. length 24, and then append just the numbers to get 8760 before assigning to loads_kw
# if length(load_list_of_dict) == 24
# for _ in 1:364
# append!(load_list_of_dict, load_forecast_dict["EnergyConsumerSchedule.RegularTimePoints"])
# end
# elseif length(load_list_of_dict) == 168
# for _ in 1:52
# append!(load_list_of_dict, load_forecast_dict["EnergyConsumerSchedule.RegularTimePoints"])
# end
# # Have to add one more day's worth of time, so take the first 24 hours of the week again for the last 24 hours of the 8760
# append!(load_list_of_dict, load_forecast_dict["EnergyConsumerSchedule.RegularTimePoints"][1:24])
# elseif !(length(load_list_of_dict) in [8760, 35040])
# throw(@error("Valid length of load forecast data is 24, 168, or 8760"))
# end
for (ts, data) in enumerate(load_list_of_dict)
# Ignoring value2 which is "VAr" (reactive power)
reopt_inputs["ElectricLoad"]["loads_kw"][ts] += data["RegularTimePoint.value1"]
end
length_load_input = length(load_list_of_dict) # Used for validating against LMP data length below
reopt_inputs["ElectricLoad"]["loads_kw"] = build_timeseries_array(load_list_of_dict, "RegularTimePoint.value1", timestep_sec_i)
else
throw(@error("No $load_forecast_name load_forecast_name found in BasicIntervalSchedule"))
end
Expand Down Expand Up @@ -141,16 +161,11 @@ function convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
# LMP - energy prices
lmp_list_of_dict = lmp_dict["LocationalMarginalPrices.LMPCurve"]["PriceCurve.CurveDatas"]
# Note, if 15-minute interval analysis, must supply LMPs in 15-minute interval, so they have one-to-one data
reopt_inputs["ElectricTariff"]["tou_energy_rates_per_kwh"] = zeros(8760 * convert(Int64, reopt_inputs["Settings"]["time_steps_per_hour"]))
# reopt_inputs["ElectricTariff"]["wholesale_rate"] = zeros(8760 * convert(Int64, reopt_inputs["Settings"]["time_steps_per_hour"]))
# TODO allow e.g. list of 24 (1-day) and 168 hour (1-week) for LMP if the load forecast is also 24/168, and just replicate e.g. 365/52 times
if length(lmp_list_of_dict) == length(reopt_inputs["ElectricLoad"]["loads_kw"])
for (ts, data) in enumerate(lmp_list_of_dict)
reopt_inputs["ElectricTariff"]["tou_energy_rates_per_kwh"][ts] = data["CurveData.y1value"]
# reopt_inputs["ElectricTariff"]["wholesale_rate"][ts] = data["CurveData.y1value"]
end
if length(lmp_list_of_dict) == length_load_input
reopt_inputs["ElectricTariff"]["tou_energy_rates_per_kwh"] = build_timeseries_array(lmp_list_of_dict, "CurveData.y1value", timestep_sec)
# reopt_inputs["ElectricTariff"]["wholesale_rate"] = build_timeseries_array(lmp_list_of_dict, "CurveData.y1value", timestep_sec) .- 0.001
else
throw(@error("Interval of Load RegularIntervalSchedule.TimePoints must match the interval of LMP PriceCurve.CurveDatas"))
throw(@error("LMP PriceCurve.CurveDatas must match the interval and length of the Load Profile RegularIntervalSchedule.TimePoints array"))
end
else
throw(@error("No LMP name $lmp_name found in EnergyPrices.LocationalMarginalPrices"))
Expand All @@ -173,6 +188,23 @@ function convert_mgravens_inputs_to_reopt_inputs(mgravens::Dict)
throw(@error("No Capacity name $capacity_prices_name found in EnergyPrices.CapacityPrices"))
end

# Coincident peak prices (monthly)
# TODO allow EnergyPrices.CoincidentPeakPrices to be optional
coincident_peak_prices_name = replace(split(region_dict["Regions.EnergyPrices"]["EnergyPrices.CoincidentPeakPrices"], "::")[2], "'" => "")
coincident_peak_dict = get(mgravens["EnergyPrices"]["CoincidentPeakPrices"], coincident_peak_prices_name, nothing)
if !isnothing(coincident_peak_dict)
coincident_peak_list_of_dict = coincident_peak_dict["CoincidentPeakPrices.CoincidentPeakPriceCurve"]["PriceCurve.CurveDatas"]
prices, ts_array = [], []
for (i, price) in enumerate(coincident_peak_list_of_dict)
append!(prices, [price["CurveData.y1value"]])
append!(ts_array, [price["CurveData.xvalue"]])
end
reopt_inputs["ElectricTariff"]["coincident_peak_load_charge_per_kw"] = prices
reopt_inputs["ElectricTariff"]["coincident_peak_load_active_time_steps"] = ts_array
else
throw(@error("No Coincident Peak name $coincident_peak_prices_name found in EnergyPrices.CoincidentPeakPrices"))
end

# Printing for debugging
# println("")
# println("site_name = $site_name")
Expand Down Expand Up @@ -320,6 +352,10 @@ function update_mgravens_with_reopt_results!(reopt_results::Dict, mgravens::Dict
# 3) ProposedAssets.[Each Technology]

# Add any warning or error messages in the top-level "Message" list of dictionaries
if isnothing(get(reopt_results, "Messages", nothing))
reopt_results["Messages"] = Dict("warnings" => "", "errors" => "")
end

mgravens["Message"] = [
Dict(
"IdentifiedObject.mRID" => string(uuid4()),
Expand Down Expand Up @@ -369,13 +405,17 @@ function update_mgravens_with_reopt_results!(reopt_results::Dict, mgravens::Dict
npv = reopt_results["Financial"]["npv"]
lcc_capital_costs = reopt_results["Financial"]["lifecycle_capital_costs"]
end

# Include demand charges and coincident peak charges in the capacity cost
capacity_cost = (reopt_results["ElectricTariff"]["lifecycle_demand_cost_after_tax"*bau_suffix] +
reopt_results["ElectricTariff"]["lifecycle_coincident_peak_cost_after_tax"*bau_suffix])

estimated_asset_costs_uuid = string(uuid4())
mgravens["EstimatedCost"][scenario_name] = Dict{String, Any}(
"IdentifiedObject.name" => scenario_name,
"IdentifiedObject.mRID" => estimated_asset_costs_uuid,
"Ravens.cimObjectType" => "EstimatedCost",
"EstimatedCost.lifecycleCapacityCost" => reopt_results["ElectricTariff"]["lifecycle_demand_cost_after_tax"*bau_suffix],
"EstimatedCost.lifecycleCapacityCost" => capacity_cost,
"EstimatedCost.lifecycleEnergyCost" => reopt_results["ElectricTariff"]["lifecycle_energy_cost_after_tax"*bau_suffix],
"EstimatedCost.lifecycleCapitalCost" => lcc_capital_costs,
"EstimatedCost.lifecycleCost" => reopt_results["Financial"]["lcc"*bau_suffix],
Expand Down

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