From e79bb53f0d9e68574d613b506138c31a41fccc3a Mon Sep 17 00:00:00 2001 From: michaelavs Date: Wed, 14 Feb 2024 13:32:51 -0700 Subject: [PATCH 01/17] Updates for PySAM>3 --- dgen_os/python/financial_functions.py | 313 +++++++------------------ dgen_os/python/input_data_functions.py | 5 +- 2 files changed, 88 insertions(+), 230 deletions(-) diff --git a/dgen_os/python/financial_functions.py b/dgen_os/python/financial_functions.py index ef6961d..872765d 100644 --- a/dgen_os/python/financial_functions.py +++ b/dgen_os/python/financial_functions.py @@ -8,12 +8,8 @@ import utility_functions as utilfunc import agent_mutation -import pyarrow as pa -import pyarrow.parquet as pq - -import PySAM -import PySAM.Battwatts as battery -import PySAM.BatteryTools as batt_tools +import PySAM.Battery as battery +import PySAM.BatteryTools as battery_tools import PySAM.Utilityrate5 as utility import PySAM.Cashloan as cashloan @@ -25,7 +21,7 @@ #%% -def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_switch_table, en_batt=True, batt_simple_dispatch=0): +def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_switch_table, en_batt=True, batt_dispatch='peak_shaving'): """ Executes Battwatts, Utilityrate5, and Cashloan PySAM modules with system sizes (kw) as input @@ -43,7 +39,6 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ batt_simple_dispatch: batt.Battery.batt_simple_dispatch - batt_simple_dispatch = 0 (peak shaving look ahead) - batt_simple_dispatch = 1 (peak shaving look behind) - Returns ------- -loan.Outputs.npv: the negative net present value of system + storage to be optimized for system sizing @@ -60,14 +55,17 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ # Set up battery, with system generation conditional on the battery generation being included if en_batt: - batt.Battery.dc = dc - batt.Battery.ac = ac - batt.Battery.batt_simple_enable = 1 - batt.Battery.batt_simple_chemistry = 1 # default value is 1: li ion for residential - batt.Battery.batt_simple_dispatch = batt_simple_dispatch - batt.Battery.batt_simple_meter_position = 0 # default value - batt.Battery.inverter_efficiency = 100 # recommended by Darice for dc-connected - batt.Battery.load = load_hourly + #batt.Battery.dc = dc + #batt.Battery.ac = ac + batt.BatterySystem.en_batt = 1 + batt.BatterySystem.batt_ac_or_dc = 1 # AC connected + batt.BatteryCell.batt_chem = 1 # default value is 1: li ion for residential + batt.BatterySystem.batt_meter_position = 0 # behind the meter + #batt.Battery.load = load_hourly + + # need to consider lifetime since pysam needs profiles for all years if considering replacement. + batt.Lifetime.system_use_lifetime_output = 0 + batt.BatterySystem.batt_replacement_option = 0 # PV to Battery ratio (kW) - From Ashreeta, 02/08/2020 pv_to_batt_ratio = 1.31372 @@ -75,41 +73,33 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ desired_size = kw / pv_to_batt_ratio # Default SAM value for residential systems is 10 desired_power = desired_size / batt_capacity_to_power_ratio + desired_voltage = 500 if agent.loc['sector_abbr'] != 'res' else 240 + + # Size battery using desired parameters + battery_tools.battery_model_sizing(batt, desired_power, desired_size, desired_voltage=desired_voltage, tol=1e38) - batt_inputs = { - 'batt_chem': batt.Battery.batt_simple_chemistry, - 'batt_Qfull': 2.5, # default SAM value - 'batt_Vnom_default': 3.6, # default SAM value - 'batt_ac_or_dc': 0, # dc-connected - 'desired_power': desired_power, - 'desired_capacity': desired_size, - 'desired_voltage': 500, - 'size_by_ac_not_dc': 0, # dc-connected - 'inverter_eff': batt.Battery.inverter_efficiency - # 'batt_dc_dc_efficiency': (optional) - } - - # Default values for lead acid batteries - if batt.Battery.batt_simple_chemistry == 0: - batt_inputs['LeadAcid_q10'] = 93.2 - batt_inputs['LeadAcid_q20'] = 100 - batt_inputs['LeadAcid_qn'] = 58.12 - # batt_inputs['LeadAcid_tn']: (optional) - - batt_outputs = batt_tools.size_li_ion_battery(batt_inputs) - - computed_size = batt_outputs['batt_computed_bank_capacity'] - computed_power = batt_outputs['batt_power_discharge_max_kwdc'] - - batt.Battery.batt_simple_kwh = computed_size - batt.Battery.batt_simple_kw = computed_power + # copy over gen and load + batt.Load.load = load_hourly #kw + batt.SystemOutput.gen = gen - batt.execute() + # Set dispatch option and associated parameters in detailed battery model + # Only peak shaving and price signal forecast options are supported + if batt_dispatch =='peak_shaving': + batt.BatteryDispatch.batt_dispatch_choice = 0 + else: + batt.BatteryDispatch.batt_dispatch_choice = 4 + batt.BatteryDispatch.batt_dispatch_auto_can_charge = 1 + batt.BatteryDispatch.batt_dispatch_auto_can_clipcharge = 1 + batt.BatteryDispatch.batt_dispatch_auto_can_gridcharge = 1 + cycle_cost_list = [0.1] + batt.BatteryDispatch.batt_cycle_cost = cycle_cost_list + batt.BatteryDispatch.batt_cycle_cost_choice = 0 + batt.execute() # apply storage rate switch if computed_size is nonzero - if computed_size > 0.: - agent, one_time_charge = agent_mutation.elec.apply_rate_switch(rate_switch_table, agent, computed_size, tech='storage') + if batt.BatterySystem.batt_computed_bank_capacity > 0.: + agent, one_time_charge = agent_mutation.elec.apply_rate_switch(rate_switch_table, agent, batt.BatterySystem.batt_computed_bank_capacity, tech='storage') else: one_time_charge = 0. @@ -120,22 +110,11 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ net_billing_sell_rate = agent.loc['wholesale_elec_price_dollars_per_kwh'] * agent.loc['elec_price_multiplier'] utilityrate = process_tariff(utilityrate, agent.loc['tariff_dict'], net_billing_sell_rate) - utilityrate.SystemOutput.gen = batt.Outputs.gen + utilityrate.SystemOutput.gen = batt.SystemOutput.gen #not sure if this should be battery output + pv output or only pv output loan.BatterySystem.en_batt = 1 loan.BatterySystem.batt_computed_bank_capacity = batt.Outputs.batt_bank_installed_capacity loan.BatterySystem.batt_bank_replacement = batt.Outputs.batt_bank_replacement - - # #loan.BatterySystem.battery_per_kWh = costs['batt_capex_per_kwh'] - - # # Battery capacity-based System Costs amount [$/kWcap] - # loan.SystemCosts.om_capacity1 = [costs['batt_om_per_kw']] - - # # Battery production-based System Costs amount [$/MWh] - # # loan.SystemCosts.om_production1 = [costs['batt_om_per_kwh'] * 1000] - # loan.SystemCosts.om_production1 = [costs['batt_om_per_kwh'] * 1000.] - - # specify number of O&M types (1 = PV+batt) loan.SystemCosts.add_om_num_types = 1 @@ -144,9 +123,10 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ loan.BatterySystem.battery_per_kWh = costs['batt_capex_per_kwh_combined'] loan.SystemCosts.om_capacity = [costs['system_om_per_kw_combined'] + costs['system_variable_om_per_kw_combined']] - loan.SystemCosts.om_capacity1 = [costs['batt_om_per_kw_combined']] - loan.SystemCosts.om_production1 = [costs['batt_om_per_kwh_combined'] * 1000.] - loan.SystemCosts.om_replacement_cost1 = [0.] + loan.SystemCosts.om_batt_capacity_cost = [costs['batt_om_per_kw_combined']] + loan.SystemCosts.om_batt_variable_cost = [costs['batt_om_per_kwh_combined'] * 1000.] + loan.SystemCosts.om_batt_replacement_cost = [0.] + loan.SystemCosts.om_batt_nameplate = batt.Outputs.batt_bank_installed_capacity system_costs = costs['system_capex_per_kw_combined'] * kw @@ -158,9 +138,10 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ loan.BatterySystem.battery_per_kWh = costs['batt_capex_per_kwh'] loan.SystemCosts.om_capacity = [costs['system_om_per_kw'] + costs['system_variable_om_per_kw']] - loan.SystemCosts.om_capacity1 = [costs['batt_om_per_kw']] - loan.SystemCosts.om_production1 = [costs['batt_om_per_kwh'] * 1000.] - loan.SystemCosts.om_replacement_cost1 = [0.] + loan.SystemCosts.om_batt_capacity_cost = [costs['batt_om_per_kw']] + loan.SystemCosts.om_batt_variable_cost = [costs['batt_om_per_kwh'] * 1000.] + loan.SystemCosts.om_batt_replacement_cost = [0.] + loan.SystemCosts.om_batt_nameplate = batt.Outputs.batt_bank_installed_capacity system_costs = costs['system_capex_per_kw'] * kw @@ -168,21 +149,23 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ linear_constant = agent.loc['linear_constant'] - # Battery capacity for System Costs values [kW] - loan.SystemCosts.om_capacity1_nameplate = batt.Battery.batt_simple_kw # Battery production for System Costs values [kWh] - loan.SystemCosts.om_production1_values = [batt.Battery.batt_simple_kwh] # should this be batt.Outputs.batt_bank_installed_capacity? - - #batt_costs = (costs['batt_capex_per_kw']*batt.Battery.batt_simple_kw) + (costs['batt_capex_per_kwh'] * batt.Battery.batt_simple_kwh) - batt_costs = ((costs['batt_capex_per_kw_combined']*batt.Battery.batt_simple_kw) + - (costs['batt_capex_per_kwh_combined'] * batt.Battery.batt_simple_kwh)) - + #loan.SystemCosts.om_production1_values = [batt.Outputs.batt_bank_installed_capacity] # Use actual production from battery run for variable O&M + loan.SystemCosts.om_production1_values = batt.Outputs.batt_annual_discharge_energy + + batt_costs = ((costs['batt_capex_per_kw_combined']* batt.BatterySystem.batt_power_charge_max_kwdc) + + (costs['batt_capex_per_kwh_combined'] * batt.Outputs.batt_bank_installed_capacity)) value_of_resiliency = agent.loc['value_of_resiliency_usd'] else: - batt.Battery.batt_simple_enable = 0 + batt.BatterySystem.en_batt = 0 loan.BatterySystem.en_batt = 0 - computed_power = computed_size = 0 + loan.LCOS.batt_annual_charge_energy = [0] + loan.LCOS.batt_annual_charge_from_system = [0] + loan.LCOS.batt_annual_discharge_energy = [0] + loan.LCOS.batt_capacity_percent = [0] + loan.LCOS.batt_salvage_percentage = 0 + loan.LCOS.battery_total_cost_lcos = 0 # apply solar rate switch if computed_size is nonzero if kw > 0: @@ -198,14 +181,13 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ utilityrate = process_tariff(utilityrate, agent.loc['tariff_dict'], net_billing_sell_rate) utilityrate.SystemOutput.gen = gen - - loan.BatterySystem.en_batt = 1 # specify number of O&M types (0 = PV only) loan.SystemCosts.add_om_num_types = 0 # since battery system size is zero, specify standalone PV O&M costs loan.SystemCosts.om_capacity = [costs['system_om_per_kw'] + costs['system_variable_om_per_kw']] - loan.SystemCosts.om_replacement_cost1 = [0.] + loan.SystemCosts.om_batt_replacement_cost = [0.] + loan.SystemCosts.om_batt_nameplate = 0 system_costs = costs['system_capex_per_kw'] * kw @@ -219,13 +201,13 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ # Execute utility rate module utilityrate.Load.load = load_hourly #utilityrate.ElectricityRates.ur_metering_option = ur_metering_option - + utilityrate.execute() - loan = process_incentives(loan, kw, computed_power, computed_size, gen_hourly, agent) + loan = process_incentives(loan, kw, batt.BatterySystem.batt_power_discharge_max_kwdc, batt.Outputs.batt_bank_installed_capacity, gen_hourly, agent) - # Specify final Cashloan parameters loan.FinancialParameters.system_capacity = kw + # Add value_of_resiliency -- should only apply from year 1 onwards, not to year 0 annual_energy_value = ([utilityrate.Outputs.annual_energy_value[0]] + [x + value_of_resiliency for i,x in enumerate(utilityrate.Outputs.annual_energy_value) if i!=0]) @@ -255,12 +237,10 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): ---------- agent : 'pd.df' individual agent object. - Returns ------- agent: 'pd.df' Adds several features to the agent dataframe: - - agent_id - system_kw - system capacity selected by agent - batt_kw - battery capacity selected by agent @@ -306,15 +286,15 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): # Battwatts if agent.loc['sector_abbr'] == 'res': - batt = battery.default("PVWattsBatteryResidential") + batt = battery.default("GenericBatteryResidential") else: - batt = battery.default("PVWattsBatteryCommercial") + batt = battery.default("GenericBatteryCommercial") # Instantiate utilityrate5 model based on agent sector if agent.loc['sector_abbr'] == 'res': - utilityrate = utility.default("PVWattsBatteryResidential") + utilityrate = utility.from_existing(batt, "GenericBatteryResidential") else: - utilityrate = utility.default("PVWattsBatteryCommercial") + utilityrate = utility.from_existing(batt, "GenericBatteryCommercial") tariff_dict = agent.loc['tariff_dict'] @@ -341,10 +321,6 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): # Annual energy degradation [%] utilityrate.SystemOutput.degradation = [agent.loc['pv_degradation_factor'] * 100] # convert decimal to % - # old method - # degradation = [agent.loc['pv_degradation_factor'] * 100] # convert decimal to % - # utilityrate.SystemOutput.degradation = degradation - # Annual electricity rate escalation [%/year] utilityrate.ElectricityRates.rate_escalation = [agent.loc['elec_price_escalator'] * 100] # convert decimal to % @@ -365,119 +341,6 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): net_billing_sell_rate = 0. else: net_billing_sell_rate = agent.loc['wholesale_elec_price_dollars_per_kwh'] * agent.loc['elec_price_multiplier'] - - - # ###################################### - # ###--------- UTILITYRATE5 ---------### - # ###--- FIXED AND ANNUAL CHARGES ---### - # ###################################### - - # # Monthly fixed charge [$] - # utilityrate.ElectricityRates.ur_monthly_fixed_charge = tariff_dict['fixed_charge'] - # # Annual minimum charge [$] - # utilityrate.ElectricityRates.ur_annual_min_charge = 0. # not currently tracked in URDB rate attribute downloads - # # Monthly minimum charge [$] - # utilityrate.ElectricityRates.ur_monthly_min_charge = 0. # not currently tracked in URDB rate attribute downloads - - - # ###################################### - # ###--------- UTILITYRATE5 ---------### - # ###-------- DEMAND CHARGES --------### - # ###################################### - - # # Enable demand charge - # utilityrate.ElectricityRates.ur_dc_enable = (tariff_dict['d_flat_exists']) | (tariff_dict['d_tou_exists']) - - # if utilityrate.ElectricityRates.ur_dc_enable: - - # if tariff_dict['d_flat_exists']: - - # # Reformat demand charge table from dGen format - # n_periods = len(tariff_dict['d_flat_levels'][0]) - # n_tiers = len(tariff_dict['d_flat_levels']) - # ur_dc_flat_mat = [] - # for period in range(n_periods): - # for tier in range(n_tiers): - # row = [period, tier+1, tariff_dict['d_flat_levels'][tier][period], tariff_dict['d_flat_prices'][tier][period]] - # ur_dc_flat_mat.append(row) - - # # Demand rates (flat) table - # utilityrate.ElectricityRates.ur_dc_flat_mat = ur_dc_flat_mat - - - # if tariff_dict['d_tou_exists']: - - # # Reformat demand charge table from dGen format - # n_periods = len(tariff_dict['d_tou_levels'][0]) - # n_tiers = len(tariff_dict['d_tou_levels']) - # ur_dc_tou_mat = [] - # for period in range(n_periods): - # for tier in range(n_tiers): - # row = [period+1, tier+1, tariff_dict['d_tou_levels'][tier][period], tariff_dict['d_tou_prices'][tier][period]] - # ur_dc_tou_mat.append(row) - - # # Demand rates (TOU) table - # utilityrate.ElectricityRates.ur_dc_tou_mat = ur_dc_tou_mat - - - # # Reformat 12x24 tables - original are indexed to 0, PySAM needs index starting at 1 - # d_wkday_12by24 = [] - # for m in range(len(tariff_dict['d_wkday_12by24'])): - # row = [x+1 for x in tariff_dict['d_wkday_12by24'][m]] - # d_wkday_12by24.append(row) - - # d_wkend_12by24 = [] - # for m in range(len(tariff_dict['d_wkend_12by24'])): - # row = [x+1 for x in tariff_dict['d_wkend_12by24'][m]] - # d_wkend_12by24.append(row) - - # # Demand charge weekday schedule - # utilityrate.ElectricityRates.ur_dc_sched_weekday = d_wkday_12by24 - # # Demand charge weekend schedule - # utilityrate.ElectricityRates.ur_dc_sched_weekend = d_wkend_12by24 - - - # ###################################### - # ###--------- UTILITYRATE5 ---------### - # ###-------- ENERGY CHARGES --------### - # ###################################### - - # if tariff_dict['e_exists']: - - # # Dictionary to map dGen max usage units to PySAM options - # max_usage_dict = {'kWh':0, 'kWh/kW':1, 'kWh daily':2, 'kWh/kW daily':3} - - # # If max usage units are 'kWh daily', divide max usage by 30 -- rate download procedure converts daily to monthly - # modifier = 30. if tariff_dict['energy_rate_unit'] == 'kWh daily' else 1. - - # # Reformat energy charge table from dGen format - # n_periods = len(tariff_dict['e_levels'][0]) - # n_tiers = len(tariff_dict['e_levels']) - # ur_ec_tou_mat = [] - # for period in range(n_periods): - # for tier in range(n_tiers): - # row = [period+1, tier+1, tariff_dict['e_levels'][tier][period]/modifier, - # max_usage_dict[tariff_dict['energy_rate_unit']], tariff_dict['e_prices'][tier][period], net_billing_sell_rate] - # ur_ec_tou_mat.append(row) - - # # Energy rates table - # utilityrate.ElectricityRates.ur_ec_tou_mat = ur_ec_tou_mat - - # # Reformat 12x24 tables - original are indexed to 0, PySAM needs index starting at 1 - # e_wkday_12by24 = [] - # for m in range(len(tariff_dict['e_wkday_12by24'])): - # row = [x+1 for x in tariff_dict['e_wkday_12by24'][m]] - # e_wkday_12by24.append(row) - - # e_wkend_12by24 = [] - # for m in range(len(tariff_dict['e_wkend_12by24'])): - # row = [x+1 for x in tariff_dict['e_wkend_12by24'][m]] - # e_wkend_12by24.append(row) - - # # Energy charge weekday schedule - # utilityrate.ElectricityRates.ur_ec_sched_weekday = e_wkday_12by24 - # # Energy charge weekend schedule - # utilityrate.ElectricityRates.ur_ec_sched_weekend = e_wkend_12by24 ###################################### @@ -522,11 +385,11 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): # Assume res agents do not evaluate depreciation at all # Assume non-res agents only evaluate federal depreciation (not state) if agent.loc['sector_abbr'] == 'res': - loan = cashloan.default("PVWattsBatteryResidential") + loan = cashloan.from_existing(utilityrate, "GenericBatteryResidential") loan.FinancialParameters.market = 0 else: - loan = cashloan.default("PVWattsBatteryCommercial") + loan = cashloan.from_existing(utilityrate, "GenericBatteryCommercial") loan.FinancialParameters.market = 1 loan.FinancialParameters.analysis_period = agent.loc['economic_lifetime_yrs'] @@ -594,7 +457,7 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): ###----- TAX CREDIT INCENTIVES ----### ###################################### - loan.TaxCreditIncentives.itc_fed_percent = agent.loc['itc_fraction_of_capex'] * 100 + loan.TaxCreditIncentives.itc_fed_percent = [agent.loc['itc_fraction_of_capex'] * 100] ###################################### ###----------- CASHLOAN -----------### @@ -604,7 +467,7 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): loan.BatterySystem.batt_replacement_option = 2 # user schedule batt_replacement_schedule = [0 for i in range(0, agent.loc['batt_lifetime_yrs'] - 1)] + [1] - loan.BatterySystem.batt_replacement_schedule = batt_replacement_schedule + loan.BatterySystem.batt_replacement_schedule_percent = batt_replacement_schedule ###################################### @@ -634,31 +497,33 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): # set tolerance for minimize_scalar based on max_system_kw value tol = min(0.25 * max_system_kw, 0.5) + min_system_kw = min(0.3, max_system_kw) #tol = 0.25 * max_system_kw - # Calculate the PV system size that maximizes the agent's NPV, to a tolerance of 0.5 kW. - # Note that the optimization is technically minimizing negative NPV - # ! As is, because of the tolerance this function would not necessarily return a system size of 0 or max PV size if those are optimal + # # Calculate the PV system size that maximizes the agent's NPV, to a tolerance of 0.5 kW. + # # Note that the optimization is technically minimizing negative NPV + # # ! As is, because of the tolerance this function would not necessarily return a system size of 0 or max PV size if those are optimal + batt_dispatch = 'peak_shaving' if agent.loc['sector_abbr'] != 'res' else 'price_signal_forecast' res_with_batt = optimize.minimize_scalar(calc_system_performance, - args = (pv, utilityrate, loan, batt, system_costs, agent, rate_switch_table, True, 0), - bounds = (0, max_system_kw), + args = (pv, utilityrate, loan, batt, system_costs, agent, rate_switch_table, True, batt_dispatch), + bounds = (min_system_kw, max_system_kw), method = 'bounded', options={'xatol':tol}) - #tol = tol) # PySAM Module outputs with battery batt_loan_outputs = loan.Outputs.export() batt_util_outputs = utilityrate.Outputs.export() batt_annual_energy_kwh = np.sum(utilityrate.SystemOutput.gen) - batt_kw = batt.Battery.batt_simple_kw - batt_kwh = batt.Battery.batt_simple_kwh - batt_dispatch_profile = batt.Outputs.batt_power + batt_kw = batt.BatterySystem.batt_power_charge_max_kwdc + batt_kwh = batt.Outputs.batt_bank_installed_capacity + batt_dispatch_profile = batt.Outputs.batt_power # ? + npv_w_batt = batt_loan_outputs['npv'] # Run without battery res_no_batt = optimize.minimize_scalar(calc_system_performance, args = (pv, utilityrate, loan, batt, system_costs, agent, rate_switch_table, False, 0), - bounds = (0, max_system_kw), + bounds = (0.0, max_system_kw), method = 'bounded', options={'xatol':tol}) # tol = tol) @@ -669,10 +534,8 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): no_batt_annual_energy_kwh = np.sum(utilityrate.SystemOutput.gen) # Retrieve NPVs of system with batt and system without batt - npv_w_batt = batt_loan_outputs['npv'] npv_no_batt = no_batt_loan_outputs['npv'] - # Choose the system with the higher NPV if npv_w_batt >= npv_no_batt: system_kw = res_with_batt.x annual_energy_production_kwh = batt_annual_energy_kwh @@ -680,9 +543,9 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): first_year_elec_bill_without_system = batt_util_outputs['elec_cost_without_system_year1'] npv = npv_w_batt - payback = batt_loan_outputs['payback'] - cash_flow = list(batt_loan_outputs['cf_payback_with_expenses']) # ? + cash_flow = list(batt_loan_outputs['cf_payback_with_expenses']) # ? + payback = batt_loan_outputs['payback'] cbi_total = batt_loan_outputs['cbi_total'] cbi_total_fed = batt_loan_outputs['cbi_total_fed'] cbi_total_oth = batt_loan_outputs['cbi_total_oth'] @@ -732,8 +595,7 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): pbi_total_fed = no_batt_loan_outputs['cf_pbi_total_fed'] pbi_total_oth = no_batt_loan_outputs['cf_pbi_total_oth'] pbi_total_sta = no_batt_loan_outputs['cf_pbi_total_sta'] - pbi_total_uti = no_batt_loan_outputs['cf_pbi_total_uti'] - + pbi_total_uti = no_batt_loan_outputs['cf_pbi_total_uti'] # change 0 value to 1 to avoid divide by zero errors if first_year_elec_bill_without_system == 0: @@ -1086,12 +948,10 @@ def process_incentives(loan, kw, batt_kw, batt_kwh, generation_hourly, agent): def calc_financial_performance(dataframe): """ Function to calculate the payback period and join it on the agent dataframe - Parameters ---------- dataframe : "pd.df" Agent dataframe - Returns ------- - dataframe: 'pd.df' - Agent dataframe with payback period joined on dataframe @@ -1116,14 +976,12 @@ def calc_payback_vectorized(cfs, tech_lifetime): """ Calculate the payback period in years for a given cash flow. Payback is defined as the first year where cumulative cash flows are positive. Cash flows that do not result in payback are given a period of 30.1 - Parameters ---------- cfs : "numpy.ndarray" Annual cash flows of investment, where 0th index refers to 0th year of investment tech_lifetime : "numpy.ndarray" Number of years to assume for technology lifetime - Returns ------- pp_final : 'numpy.ndarray' @@ -1443,4 +1301,3 @@ def eqn_linear_decay_to_zero(incentive_info, info_params, default_params,additio #%% def eqn_flat_rate(incentive_info, info_params, default_params,additional_params): return eqn_builder('flat_rate', incentive_info, info_params, default_params,additional_params) - diff --git a/dgen_os/python/input_data_functions.py b/dgen_os/python/input_data_functions.py index 107786e..885dc74 100644 --- a/dgen_os/python/input_data_functions.py +++ b/dgen_os/python/input_data_functions.py @@ -2,6 +2,7 @@ import numpy as np import os import sqlalchemy +from sqlalchemy import text import data_functions as datfunc import utility_functions as utilfunc import agent_mutation @@ -132,11 +133,11 @@ def df_to_psql(df, engine, schema, owner, name, if_exists='replace', append_tran if if_exists == 'append': fields = [i.lower() for i in get_psql_table_fields(engine, schema, name)] for f in list(set(df.columns.values) - set(fields)): - sql = "ALTER TABLE {}.{} ADD COLUMN {} {}".format(schema, name, f, sql_type[f]) + sql = text("ALTER TABLE {}.{} ADD COLUMN {} {}".format(schema, name, f, sql_type[f])) conn.execute(sql) df.to_sql(name, engine, schema=schema, index=False, dtype=d_types, if_exists=if_exists) - sql = 'ALTER TABLE {}."{}" OWNER to "{}"'.format(schema, name, owner) + sql = text('ALTER TABLE {}."{}" OWNER to "{}"'.format(schema, name, owner)) conn.execute(sql) conn.close() From 3178a0c19562970bf21f483f24fd1b8d3ee9e0b9 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Wed, 14 Feb 2024 13:38:36 -0700 Subject: [PATCH 02/17] Add new environment file --- dgen_os/python/dg3n.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/dgen_os/python/dg3n.yml b/dgen_os/python/dg3n.yml index c0ab273..3dbf7a8 100644 --- a/dgen_os/python/dg3n.yml +++ b/dgen_os/python/dg3n.yml @@ -12,11 +12,10 @@ dependencies: - numpy - openpyxl - pandas - - pyarrow - psutil - psycopg2 - scipy - spyder - sqlalchemy - - nrel-pysam 2.1.4 - - nrel-pysam-stubs 2.1.4 + - nrel-pysam=4.2.0 + From 00fe5f4d68a41f38382b4cec76962c78f2b74893 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Wed, 14 Feb 2024 14:13:31 -0700 Subject: [PATCH 03/17] Update env file to use pip to install pysam-stubs --- dgen_os/python/dg3n.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/dgen_os/python/dg3n.yml b/dgen_os/python/dg3n.yml index 3dbf7a8..3afad1a 100644 --- a/dgen_os/python/dg3n.yml +++ b/dgen_os/python/dg3n.yml @@ -1,4 +1,4 @@ -name: dg3n +name: dg3n_test channels: - defaults - conda-forge @@ -12,10 +12,13 @@ dependencies: - numpy - openpyxl - pandas + - pip - psutil - psycopg2 - scipy - spyder - sqlalchemy - nrel-pysam=4.2.0 + - pip: + - nrel-pysam-stubs From 64b90a964134e7fa17d6524dc4607cba15055f73 Mon Sep 17 00:00:00 2001 From: Michaela Sizemore <43652875+michaelavs@users.noreply.github.com> Date: Wed, 14 Feb 2024 14:18:02 -0700 Subject: [PATCH 04/17] Update dg3n.yml --- dgen_os/python/dg3n.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dgen_os/python/dg3n.yml b/dgen_os/python/dg3n.yml index 3afad1a..9681156 100644 --- a/dgen_os/python/dg3n.yml +++ b/dgen_os/python/dg3n.yml @@ -1,4 +1,4 @@ -name: dg3n_test +name: dg3n channels: - defaults - conda-forge From e2a42c637ec516c1d3bd91601cdf6096577be638 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Fri, 26 Apr 2024 10:42:55 -0600 Subject: [PATCH 05/17] Updating input data to add 2023 cases --- .../batt_prices/batt_prices_FY23_high.csv | 38 + .../batt_prices/batt_prices_FY23_low.csv | 38 + .../batt_prices/batt_prices_FY23_mid.csv | 38 + .../ATB22_High_NG_Price_retail.csv | 4959 +++++++++++++++++ .../elec_prices/ATB22_Low_NG_Price_retail.csv | 4959 +++++++++++++++++ .../elec_prices/ATB22_Mid_Case_retail.csv | 4959 +++++++++++++++++ .../elec_prices/ATB23_High_NG_retail.csv | 4959 +++++++++++++++++ .../elec_prices/ATB23_High_RE_retail.csv | 4959 +++++++++++++++++ .../elec_prices/ATB23_Low_NG_retail.csv | 4959 +++++++++++++++++ .../elec_prices/ATB23_Low_RE_retail.csv | 4959 +++++++++++++++++ .../elec_prices/ATB23_Mid_Case_retail.csv | 4959 +++++++++++++++++ .../financing_terms/financing_atb_FY22.csv | 20 + .../financing_terms/financing_atb_FY23.csv | 20 + ...served_deployment_by_state_sector_2022.csv | 589 ++ ...served_deployment_by_state_sector_2023.csv | 736 +++ .../pv_plus_batt_prices_FY23_low.csv | 38 + .../pv_plus_batt_prices_FY23_mid.csv | 38 + .../pv_prices/pv_price_atb22_high.csv | 38 + .../pv_prices/pv_price_atb22_low.csv | 38 + .../pv_prices/pv_price_atb22_mid.csv | 38 + .../pv_prices/pv_price_atb23_high.csv | 38 + .../pv_prices/pv_price_atb23_low.csv | 38 + .../pv_prices/pv_price_atb23_mid.csv | 38 + .../ATB22_Mid_Case_wholesale.csv | 135 + .../ATB23_Mid_Case_wholesale.csv | 135 + 25 files changed, 41725 insertions(+) create mode 100644 dgen_os/input_data/batt_prices/batt_prices_FY23_high.csv create mode 100644 dgen_os/input_data/batt_prices/batt_prices_FY23_low.csv create mode 100644 dgen_os/input_data/batt_prices/batt_prices_FY23_mid.csv create mode 100644 dgen_os/input_data/elec_prices/ATB22_High_NG_Price_retail.csv create mode 100644 dgen_os/input_data/elec_prices/ATB22_Low_NG_Price_retail.csv create mode 100644 dgen_os/input_data/elec_prices/ATB22_Mid_Case_retail.csv create mode 100644 dgen_os/input_data/elec_prices/ATB23_High_NG_retail.csv create mode 100644 dgen_os/input_data/elec_prices/ATB23_High_RE_retail.csv create mode 100644 dgen_os/input_data/elec_prices/ATB23_Low_NG_retail.csv create mode 100644 dgen_os/input_data/elec_prices/ATB23_Low_RE_retail.csv create mode 100644 dgen_os/input_data/elec_prices/ATB23_Mid_Case_retail.csv create mode 100755 dgen_os/input_data/financing_terms/financing_atb_FY22.csv create mode 100644 dgen_os/input_data/financing_terms/financing_atb_FY23.csv create mode 100644 dgen_os/input_data/observed_deployment_by_state_sector_2022.csv create mode 100644 dgen_os/input_data/observed_deployment_by_state_sector_2023.csv create mode 100644 dgen_os/input_data/pv_plus_batt_prices/pv_plus_batt_prices_FY23_low.csv create mode 100644 dgen_os/input_data/pv_plus_batt_prices/pv_plus_batt_prices_FY23_mid.csv create mode 100755 dgen_os/input_data/pv_prices/pv_price_atb22_high.csv create mode 100755 dgen_os/input_data/pv_prices/pv_price_atb22_low.csv create mode 100755 dgen_os/input_data/pv_prices/pv_price_atb22_mid.csv create mode 100644 dgen_os/input_data/pv_prices/pv_price_atb23_high.csv create mode 100644 dgen_os/input_data/pv_prices/pv_price_atb23_low.csv create mode 100644 dgen_os/input_data/pv_prices/pv_price_atb23_mid.csv create mode 100644 dgen_os/input_data/wholesale_electricity_prices/ATB22_Mid_Case_wholesale.csv create mode 100644 dgen_os/input_data/wholesale_electricity_prices/ATB23_Mid_Case_wholesale.csv diff --git a/dgen_os/input_data/batt_prices/batt_prices_FY23_high.csv b/dgen_os/input_data/batt_prices/batt_prices_FY23_high.csv new file mode 100644 index 0000000..5ce8b40 --- /dev/null +++ b/dgen_os/input_data/batt_prices/batt_prices_FY23_high.csv @@ -0,0 +1,38 @@ +year,batt_capex_per_kwh_res,batt_capex_per_kw_res,batt_capex_per_kwh_nonres,batt_capex_per_kw_nonres,linear_constant_res,linear_constant_nonres,batt_om_per_kw_res,batt_om_per_kwh_res,batt_om_per_kw_nonres,batt_om_per_kwh_nonres,batt_replace_frac_kw,batt_replace_frac_kwh +2014,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2015,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2016,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2017,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2018,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2019,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2020,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2021,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 +2022,597.3337942,646.4052909,264.3236332,957.8767111,7834.848561,215911.2606,92.66773721,0,59.37558362,0,0.174,1 +2023,619.457268,670.3462276,274.1133974,993.3536263,8125.028137,223907.974,96.09987563,0,61.57467931,0,0.174,1 +2024,623.8819628,675.1344149,276.0713502,1000.449009,8183.064052,225507.3166,96.78630331,0,62.01449845,0,0.174,1 +2025,615.0325732,665.5580402,272.1554445,986.2582433,8066.992222,222308.6313,95.41344794,0,61.13486017,0,0.174,1 +2026,592.0241604,640.6594661,261.9740898,949.3622514,7765.205462,213992.0494,91.84402399,0,58.84780066,0,0.174,1 +2027,569.0157476,615.7608919,251.792735,912.4662596,7463.418703,205675.4675,88.27460004,0,56.56074114,0,0.174,1 +2028,546.0073348,590.8623177,241.6113803,875.5702678,7161.631944,197358.8856,84.70517609,0,54.27368162,0,0.174,1 +2029,522.998922,565.9637436,231.4300255,838.6742759,6859.845184,189042.3037,81.13575213,0,51.9866221,0,0.174,1 +2030,499.9905092,541.0651694,221.2486707,801.7782841,6558.058425,180725.7218,77.56632818,0,49.69956259,0,0.174,1 +2031,498.5627364,539.5201038,220.6168731,799.4887262,6539.331235,180209.6415,77.3448298,0,49.55764053,0,0.174,1 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+2048,474.2905994,513.2539894,209.8763131,760.5662427,6220.969007,171436.276,73.57935723,0,47.14496555,0,0.174,1 +2049,472.8628266,511.7089239,209.2445155,758.2766848,6202.241817,170920.1957,73.35785885,0,47.00304349,0,0.174,1 +2050,471.4350539,510.1638583,208.6127178,755.987127,6183.514627,170404.1154,73.13636046,0,46.86112143,0,0.174,1 diff --git a/dgen_os/input_data/batt_prices/batt_prices_FY23_low.csv b/dgen_os/input_data/batt_prices/batt_prices_FY23_low.csv new file mode 100644 index 0000000..cc3b3c4 --- /dev/null +++ b/dgen_os/input_data/batt_prices/batt_prices_FY23_low.csv @@ -0,0 +1,38 @@ +year,batt_capex_per_kwh_res,batt_capex_per_kw_res,batt_capex_per_kwh_nonres,batt_capex_per_kw_nonres,linear_constant_res,linear_constant_nonres,batt_om_per_kw_res,batt_om_per_kwh_res,batt_om_per_kw_nonres,batt_om_per_kwh_nonres,batt_replace_frac_kw,batt_replace_frac_kwh +2014,717.9439627,554.8244171,234.6229377,327.5022644,6657.420662,397856.729,92.02921141,0,48.22721409,0,0.174,1 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zFu$K1^OWz*apx6%7&@9>aUso}6pbMbitLAfkP0;3p59-3z^Aa1+R6EsPBvzy{{G z{v>}{(te2!;3Nd-^>=c7ieJ=YXk%ymJNJJzez>6Gne{Fp)A?#ECtL}bZF_M(KSN`wsgg=wmeWqr=lKmP?{c&ULEu23S zy8ZK{Q0*!1&-89zfW0&P8N%(KVF}0-0oDI{4Jry)yZ1T~u*|T4JaUE+_yGM6O0S-A literal 0 HcmV?d00001 From ae81e0b3a9d08787d412876c167b36b929db48f9 Mon Sep 17 00:00:00 2001 From: Michaela Sizemore <43652875+michaelavs@users.noreply.github.com> Date: Fri, 26 Apr 2024 10:57:20 -0600 Subject: [PATCH 07/17] remove comments --- dgen_os/python/financial_functions.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/dgen_os/python/financial_functions.py b/dgen_os/python/financial_functions.py index 872765d..e19a2ba 100644 --- a/dgen_os/python/financial_functions.py +++ b/dgen_os/python/financial_functions.py @@ -110,8 +110,7 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_ net_billing_sell_rate = agent.loc['wholesale_elec_price_dollars_per_kwh'] * agent.loc['elec_price_multiplier'] utilityrate = process_tariff(utilityrate, agent.loc['tariff_dict'], net_billing_sell_rate) - utilityrate.SystemOutput.gen = batt.SystemOutput.gen #not sure if this should be battery output + pv output or only pv output - + utilityrate.SystemOutput.gen = batt.SystemOutput.gen loan.BatterySystem.en_batt = 1 loan.BatterySystem.batt_computed_bank_capacity = batt.Outputs.batt_bank_installed_capacity loan.BatterySystem.batt_bank_replacement = batt.Outputs.batt_bank_replacement @@ -364,7 +363,7 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): ###################################### # Use single monthly peak for TOU demand charge; options: 0=use TOU peak,1=use flat peak - utilityrate.ElectricityRates.TOU_demand_single_peak = 0 # ? + utilityrate.ElectricityRates.TOU_demand_single_peak = 0 # Optionally enable/disable electricity_rate [years] utilityrate.ElectricityRates.en_electricity_rates = 1 @@ -517,7 +516,7 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): batt_kw = batt.BatterySystem.batt_power_charge_max_kwdc batt_kwh = batt.Outputs.batt_bank_installed_capacity - batt_dispatch_profile = batt.Outputs.batt_power # ? + batt_dispatch_profile = batt.Outputs.batt_power npv_w_batt = batt_loan_outputs['npv'] # Run without battery @@ -544,7 +543,7 @@ def calc_system_size_and_performance(agent, sectors, rate_switch_table=None): npv = npv_w_batt - cash_flow = list(batt_loan_outputs['cf_payback_with_expenses']) # ? + cash_flow = list(batt_loan_outputs['cf_payback_with_expenses']) payback = batt_loan_outputs['payback'] cbi_total = batt_loan_outputs['cbi_total'] cbi_total_fed = batt_loan_outputs['cbi_total_fed'] @@ -1070,7 +1069,7 @@ def calc_max_market_share(dataframe, max_market_share_df): #%% -# system_costs --> ssytem_cost look for error in later commits? + def check_incentive_constraints(incentive_data, incentive_value, system_cost): # Reduce the incentive if is is more than the max allowable payment (by percent total costs) if not pd.isnull(incentive_data['max_incentive_usd']): From 3e4086ed9facbb6d5c624847ea1412ea958f83b8 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Fri, 26 Apr 2024 11:38:46 -0600 Subject: [PATCH 08/17] Add code to drop duplicate values to prevent errors --- dgen_os/python/agent_mutation/elec.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/dgen_os/python/agent_mutation/elec.py b/dgen_os/python/agent_mutation/elec.py index d20e591..133d2d1 100644 --- a/dgen_os/python/agent_mutation/elec.py +++ b/dgen_os/python/agent_mutation/elec.py @@ -115,7 +115,7 @@ def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_ut dataframe['nem_system_kw_limit'].fillna(0, inplace=True) dataframe = dataframe.set_index('agent_id') - + dataframe = dataframe.drop_duplicates(subset='pgid') return dataframe @@ -376,7 +376,7 @@ def apply_financial_params(dataframe, financing_terms, itc_options, inflation_ra dataframe['inflation_rate'] = inflation_rate dataframe = dataframe.set_index('agent_id') - + dataframe = dataframe.drop_duplicates(subset='pgid') return dataframe @@ -387,9 +387,11 @@ def apply_load_growth(dataframe, load_growth_df): dataframe = dataframe.reset_index() dataframe["county_id"] = dataframe.county_id.astype(int) - dataframe = pd.merge(dataframe, load_growth_df, how='left', on=['year', 'sector_abbr', 'county_id']) - + + # Drop any duplicated rows based on pgid + dataframe = dataframe.drop_duplicates(subset='pgid') + # for res, load growth translates to kwh_per_customer change dataframe['load_kwh_per_customer_in_bin'] = np.where(dataframe['sector_abbr']=='res', dataframe['load_kwh_per_customer_in_bin_initial'] * dataframe['load_multiplier'], @@ -399,10 +401,10 @@ def apply_load_growth(dataframe, load_growth_df): dataframe['customers_in_bin'] = np.where(dataframe['sector_abbr']!='res', dataframe['customers_in_bin_initial'] * dataframe['load_multiplier'], dataframe['customers_in_bin_initial']) - + # for all sectors, total kwh_in_bin changes dataframe['load_kwh_in_bin'] = dataframe['load_kwh_in_bin_initial'] * dataframe['load_multiplier'] - + dataframe = dataframe.set_index('agent_id') return dataframe @@ -518,7 +520,7 @@ def get_and_apply_agent_load_profiles(con, agent): AND state_abbr = '{state_abbr}';""".format(**inputs) df = pd.read_sql(sql, con, coerce_float=False) - + df = df[['consumption_hourly']] df['load_kwh_per_customer_in_bin'] = agent.loc['load_kwh_per_customer_in_bin'] From bc321387ec3bd6d2b2104ff3d26acc29e2dc6846 Mon Sep 17 00:00:00 2001 From: Michaela Sizemore <43652875+michaelavs@users.noreply.github.com> Date: Tue, 30 Apr 2024 08:33:22 -0600 Subject: [PATCH 09/17] Delete dgen_os/python/agent_mutation/elec.py Removes as changes were not necessary --- dgen_os/python/agent_mutation/elec.py | 1028 ------------------------- 1 file changed, 1028 deletions(-) delete mode 100644 dgen_os/python/agent_mutation/elec.py diff --git a/dgen_os/python/agent_mutation/elec.py b/dgen_os/python/agent_mutation/elec.py deleted file mode 100644 index 133d2d1..0000000 --- a/dgen_os/python/agent_mutation/elec.py +++ /dev/null @@ -1,1028 +0,0 @@ -import psycopg2 as pg -import numpy as np -import pandas as pd -import decorators -import datetime -import utility_functions as utilfunc -from io import StringIO -import os -import gc -import multiprocessing as mp -import concurrent.futures as concur_f -import tariff_functions as tFuncs -import sqlalchemy - -# GLOBAL SETTINGS - -# load logger -logger = utilfunc.get_logger() - -# configure psycopg2 to treat numeric values as floats (improves performance of pulling data from the database) -DEC2FLOAT = pg.extensions.new_type(pg.extensions.DECIMAL.values, - 'DEC2FLOAT', - lambda value, curs: float(value) if value is not None else None) -pg.extensions.register_type(DEC2FLOAT) - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_elec_price_multiplier_and_escalator(dataframe, year, elec_price_change_traj): - ''' - Obtain a single scalar multiplier for each agent, that is the cost of - electricity relative to 2016 (when the tariffs were curated). - Also calculate the compound annual growth rate (CAGR) for the price of - electricity from present year to 2050, which will be the escalator that - agents use to project electricity changes in their bill calculations. - - elec_price_multiplier = change in present-year elec cost to 2016 - elec_price_escalator = agent's assumption about future price changes - Note that many customers will not differentiate between real and nominal, - and therefore many would overestimate the real escalation of electriicty - prices. - - - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - year : 'int' - The year for which you want multiplier values for - elec_price_change_traj : 'pd.DataFrame' - Dataframe of electricity prices' trajectories over time. See the - 'input_elec_prices_user_defined' table in the database. - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with elec_price_multiplier and elec_price_escalator data merged in. - - ''' - - dataframe = dataframe.reset_index() - - # get current year multiplier values - elec_price_multiplier_df = elec_price_change_traj[elec_price_change_traj['year']==year].reset_index(drop=True) - - # copy to the multiplier_df for escalator calcs - year_cap = min(year, 2040) - elec_price_escalator_df = elec_price_change_traj[elec_price_change_traj['year']==year_cap].reset_index(drop=True) - - # get final year multiplier values and attach to escalator_df - final_year = np.max(elec_price_change_traj['year']) - final_year_df = elec_price_change_traj[elec_price_change_traj['year']==final_year].reset_index(drop=True) - elec_price_escalator_df['final_year_values'] = final_year_df['elec_price_multiplier'].reset_index(drop=True) - - # calculate CAGR for time period between final year and current year - elec_price_escalator_df['elec_price_escalator'] = (elec_price_escalator_df['final_year_values'] / elec_price_escalator_df['elec_price_multiplier'])**(1.0/(final_year-year_cap)) - 1.0 - - # et upper and lower bounds of escalator at 1.0 and -1.0, based on historic elec price growth rates - elec_price_escalator_df['elec_price_escalator'] = np.clip(elec_price_escalator_df['elec_price_escalator'], -.01, .01) - - # merge multiplier and escalator values back to agent dataframe - dataframe = pd.merge(dataframe, elec_price_multiplier_df[['elec_price_multiplier', 'sector_abbr', 'county_id']], how='left', on=['sector_abbr', 'county_id']) - dataframe = pd.merge(dataframe, elec_price_escalator_df[['sector_abbr', 'county_id', 'elec_price_escalator']], - how='left', on=['sector_abbr', 'county_id']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_utility_df): - - dataframe = dataframe.reset_index() - - # specify relevant NEM columns - nem_columns = ['compensation_style','nem_system_kw_limit'] - - # check if utility-specific NEM parameters apply to any agents - need to join on state too (e.g. Pacificorp UT vs Pacificorp ID) - temp_df = pd.merge(dataframe, net_metering_utility_df[ - ['eia_id','sector_abbr','state_abbr']+nem_columns], how='left', on=['eia_id','sector_abbr','state_abbr']) - - # filter agents with non-null nem_system_kw_limit - these are agents WITH utility NEM - agents_with_utility_nem = temp_df[pd.notnull(temp_df['nem_system_kw_limit'])] - - # filter agents with null nem_system_kw_limit - these are agents WITHOUT utility NEM - agents_without_utility_nem = temp_df[pd.isnull(temp_df['nem_system_kw_limit'])].drop(nem_columns, axis=1) - # merge agents with state-specific NEM parameters - agents_without_utility_nem = pd.merge(agents_without_utility_nem, net_metering_state_df[ - ['state_abbr', 'sector_abbr']+nem_columns], how='left', on=['state_abbr', 'sector_abbr']) - - # re-combine agents list and fill nan's - dataframe = pd.concat([agents_with_utility_nem, agents_without_utility_nem], sort=False) - dataframe['compensation_style'].fillna('none', inplace=True) - dataframe['nem_system_kw_limit'].fillna(0, inplace=True) - - dataframe = dataframe.set_index('agent_id') - dataframe = dataframe.drop_duplicates(subset='pgid') - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_pv_tech_performance(dataframe, pv_tech_traj): - - ''' - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - pv_tech_traj : 'pd.DataFrame' - Dataframe of PV tech performance over time. See the - 'input_pv_tech_performance_user_defined' table in the database. - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with pv tech performance parameters merged in. - - ''' - - dataframe = dataframe.reset_index() - - dataframe = pd.merge(dataframe, pv_tech_traj, how='left', on=['sector_abbr', 'year']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_depreciation_schedule(dataframe, deprec_sch): - - dataframe = dataframe.reset_index() - - dataframe = pd.merge(dataframe, deprec_sch[['sector_abbr', 'deprec_sch', 'year']], - how='left', on=['sector_abbr', 'year']) - - dataframe = dataframe.set_index('agent_id') - - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_pv_prices(dataframe, pv_price_traj): - - ''' - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - pv_price_traj : 'pd.DataFrame' - Dataframe of PV price trajectories over time. See the - 'input_pv_prices_user_defined' table in the database. - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with pv price parameters merged in. - - ''' - - dataframe = dataframe.reset_index() - - # join the data - dataframe = pd.merge(dataframe, pv_price_traj, how='left', on=['sector_abbr', 'year']) - - # apply the capital cost multipliers - #dataframe['system_capex_per_kw'] = (dataframe['system_capex_per_kw'] * dataframe['cap_cost_multiplier']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger = logger, tab_level = 2, prefix = '') -def apply_batt_prices(dataframe, batt_price_traj, batt_tech_traj, year): - - ''' - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - batt_price_traj : 'pd.DataFrame' - Dataframe of battery price trajectories over time. See the - 'input_batt_prices_user_defined' table in the database. - batt_tech_traj : 'pd.DataFrame' - Dataframe of battery tech trajectories over time. See the - 'input_batt_tech_performance_user_defined' table in the database. - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with battery price and tech parameters merged in. - - ''' - - dataframe = dataframe.reset_index() - - # Merge on prices - dataframe = pd.merge(dataframe, batt_price_traj[['year','sector_abbr', - 'batt_capex_per_kwh','batt_capex_per_kw','linear_constant', - 'batt_om_per_kwh','batt_om_per_kw']], - how = 'left', on = ['sector_abbr', 'year']) - - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_pv_plus_batt_prices(dataframe, pv_plus_batt_price_traj, batt_tech_traj, year): - - ''' - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - pv_plus_batt_price_traj : 'pd.DataFrame' - Dataframe of battery price trajectories over time. See the - 'input_pv_plus_batt_prices_user_defined' table in the database. - batt_tech_traj : 'pd.DataFrame' - Dataframe of battery tech trajectories over time. See the - 'input_batt_tech_performance_user_defined' table in the database. - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with pv plus battery price parameters and battery tech parameters merged in. - - ''' - - dataframe = dataframe.reset_index() - - # rename cost columns -- PV+batt configuration has distinct costs - pv_plus_batt_price_traj.rename(columns={'system_capex_per_kw':'system_capex_per_kw_combined', - 'batt_capex_per_kwh':'batt_capex_per_kwh_combined', - 'batt_capex_per_kw':'batt_capex_per_kw_combined', - 'linear_constant':'linear_constant_combined', - 'batt_om_per_kw':'batt_om_per_kw_combined', - 'batt_om_per_kwh':'batt_om_per_kwh_combined'}, inplace=True) - - # Merge on prices - dataframe = pd.merge(dataframe, pv_plus_batt_price_traj[['year','sector_abbr', - 'system_capex_per_kw_combined', - 'batt_capex_per_kwh_combined','batt_capex_per_kw_combined', - 'linear_constant_combined', - 'batt_om_per_kw_combined','batt_om_per_kwh_combined']], - how = 'left', on = ['year', 'sector_abbr']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_value_of_resiliency(dataframe, value_of_resiliency): - - ''' - - Note, value of resiliency (VOR) is not currently used in the open source version of the model. - - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - vaule_of_resiliency : 'pd.DataFrame' - Dataframe of financials pertaining to the value of resiliency. See the - 'input_value_of_resiliency_user_defined' table in the database. - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with value of resiliency parameters merged in. - - ''' - - dataframe = dataframe.reset_index() - - dataframe = dataframe.merge(value_of_resiliency[['state_abbr','sector_abbr','value_of_resiliency_usd']], how='left', on=['state_abbr', 'sector_abbr']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_batt_tech_performance(dataframe, batt_tech_traj): - - ''' - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - batt_tech_traj : 'pd.DataFrame' - Dataframe of battery tech trajectories over time. See the - 'input_batt_tech_performance_user_defined' table in the database. - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with battery tech parameters merged in. - - ''' - - dataframe = dataframe.reset_index() - - dataframe = dataframe.merge(batt_tech_traj, how='left', on=['year', 'sector_abbr']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_financial_params(dataframe, financing_terms, itc_options, inflation_rate): - - ''' - - Applies financial parameters specified in input sheet to agent dataframe. - - Parameters - ---------- - dataframe : 'pd.DataFrame' - Agent dataframe - financing_terms : 'pd.DataFrame' - Dataframe of financing terms. - itc_options : 'pd.DataFrame' - Dataframe of different ITC (investment tax credit) parameters, namely 'itc_fraction_of_capex' - that is merged to the agent dataframe on year, technology, and sector. - inflation_rate : 'float' - rate of inflation specified in the input sheet as a percentage (e.g. 2.5%). - - Returns - ------- - dataframe : 'pd.DataFrame' - Agent DataFrame with financial parameters merged in. - - ''' - - dataframe = dataframe.reset_index() - - dataframe = dataframe.merge(financing_terms, how='left', on=['year', 'sector_abbr']) - - dataframe = dataframe.merge(itc_options[['itc_fraction_of_capex', 'year', 'tech', 'sector_abbr']], - how='left', on=['year', 'tech', 'sector_abbr']) - - dataframe['inflation_rate'] = inflation_rate - - dataframe = dataframe.set_index('agent_id') - dataframe = dataframe.drop_duplicates(subset='pgid') - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_load_growth(dataframe, load_growth_df): - - dataframe = dataframe.reset_index() - - dataframe["county_id"] = dataframe.county_id.astype(int) - dataframe = pd.merge(dataframe, load_growth_df, how='left', on=['year', 'sector_abbr', 'county_id']) - - # Drop any duplicated rows based on pgid - dataframe = dataframe.drop_duplicates(subset='pgid') - - # for res, load growth translates to kwh_per_customer change - dataframe['load_kwh_per_customer_in_bin'] = np.where(dataframe['sector_abbr']=='res', - dataframe['load_kwh_per_customer_in_bin_initial'] * dataframe['load_multiplier'], - dataframe['load_kwh_per_customer_in_bin_initial']) - - # for C&I, load growth translates to customer count change - dataframe['customers_in_bin'] = np.where(dataframe['sector_abbr']!='res', - dataframe['customers_in_bin_initial'] * dataframe['load_multiplier'], - dataframe['customers_in_bin_initial']) - - # for all sectors, total kwh_in_bin changes - dataframe['load_kwh_in_bin'] = dataframe['load_kwh_in_bin_initial'] * dataframe['load_multiplier'] - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def calculate_developable_customers_and_load(dataframe): - - dataframe = dataframe.reset_index() - - dataframe['developable_agent_weight'] = dataframe['pct_of_bldgs_developable'] * dataframe['customers_in_bin'] - dataframe['developable_load_kwh_in_bin'] = dataframe['pct_of_bldgs_developable'] * dataframe['load_kwh_in_bin'] - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -def get_electric_rates_json(con, unique_rate_ids): - - inputs = locals().copy() - - # updated urdb3_rate_jsons_201905 to 20200721 version - - # reformat the rate list for use in postgres query - inputs['rate_id_list'] = utilfunc.pylist_2_pglist(unique_rate_ids) - inputs['rate_id_list'] = inputs['rate_id_list'].replace("L", "") - - # get (only the required) rate jsons from postgres - sql = """SELECT a.rate_id_alias, a.rate_name, a.eia_id, a.json as rate_json - FROM diffusion_shared.urdb3_rate_jsons_20200721 a - WHERE a.rate_id_alias in ({rate_id_list});""".format(**inputs) - df = pd.read_sql(sql, con, coerce_float=False) - - return df - - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def filter_nem_year(df, year): - - # Filter by Sector Specific Sunset Years - df = df.loc[(df['first_year'] <= year) & (df['sunset_year'] >= year)] - - return df - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def get_nem_settings(state_limits, state_by_sector, utility_by_sector, selected_scenario, year, state_capacity_by_year, cf_during_peak_demand): - - # Find States That Have Not Sunset - valid_states = filter_nem_year(state_limits, year) - - # Filter States to Those That Have Not Exceeded Cumulative Capacity Constraints - valid_states['filter_year'] = pd.to_numeric(valid_states['max_reference_year'], errors='coerce') - valid_states['filter_year'][valid_states['max_reference_year'] == 'previous'] = year - 2 - valid_states['filter_year'][valid_states['max_reference_year'] == 'current'] = year - valid_states['filter_year'][pd.isnull(valid_states['filter_year'])] = year - - state_df = pd.merge(state_capacity_by_year, valid_states , how='left', on=['state_abbr']) - state_df = state_df[state_df['year'] == state_df['filter_year'] ] - state_df = state_df.merge(cf_during_peak_demand, on = 'state_abbr') - - state_df = state_df.loc[ pd.isnull(state_df['max_cum_capacity_mw']) | ( pd.notnull( state_df['max_cum_capacity_mw']) & (state_df['cum_system_mw'] < state_df['max_cum_capacity_mw']))] - # Calculate the maximum MW of solar capacity before reaching the NEM cap. MW are determine on a generation basis during the period of peak demand, as determined by ReEDS. - # CF during peak period is based on ReEDS H17 timeslice, assuming average over south-facing 15 degree tilt systems (so this could be improved by using the actual tilts selected) - state_df['max_mw'] = (state_df['max_pct_cum_capacity']/100) * state_df['peak_demand_mw'] / state_df['solar_cf_during_peak_demand_period'] - state_df = state_df.loc[ pd.isnull(state_df['max_pct_cum_capacity']) | ( pd.notnull( state_df['max_pct_cum_capacity']) & (state_df['max_mw'] > state_df['cum_system_mw']))] - - # Filter state and sector data to those that have not sunset - selected_state_by_sector = state_by_sector.loc[state_by_sector['scenario'] == selected_scenario] - valid_state_sector = filter_nem_year(selected_state_by_sector, year) - - # Filter state and sector data to those that match states which have not sunset/reached peak capacity - valid_state_sector = valid_state_sector[valid_state_sector['state_abbr'].isin(state_df['state_abbr'].values)] - - # Filter utility and sector data to those that have not sunset - selected_utility_by_sector = utility_by_sector.loc[utility_by_sector['scenario'] == selected_scenario] - valid_utility_sector = filter_nem_year(selected_utility_by_sector, year) - - # Filter out utility/sector combinations in states where capacity constraints have been reached - # Assumes that utilities adhere to broader state capacity constraints, and not their own - valid_utility_sector = valid_utility_sector[valid_utility_sector['state_abbr'].isin(state_df['state_abbr'].values)] - - # Return State/Sector data (or null) for all combinations of states and sectors - full_state_list = state_by_sector.loc[ state_by_sector['scenario'] == 'BAU' ].loc[:, ['state_abbr', 'sector_abbr']] - state_result = pd.merge( full_state_list.drop_duplicates(), valid_state_sector, how='left', on=['state_abbr','sector_abbr'] ) - state_result['nem_system_kw_limit'].fillna(0, inplace=True) - - # Return Utility/Sector data (or null) for all combinations of utilities and sectors - full_utility_list = utility_by_sector.loc[ utility_by_sector['scenario'] == 'BAU' ].loc[:, ['eia_id','sector_abbr','state_abbr']] - utility_result = pd.merge( full_utility_list.drop_duplicates(), valid_utility_sector, how='left', on=['eia_id','sector_abbr','state_abbr'] ) - utility_result['nem_system_kw_limit'].fillna(0, inplace=True) - - return state_result, utility_result - - -def get_and_apply_agent_load_profiles(con, agent): - inputs = locals().copy() - inputs['bldg_id'] = agent.loc['bldg_id'] - inputs['sector_abbr'] = agent.loc['sector_abbr'] - inputs['state_abbr'] = agent.loc['state_abbr'] - - sql = """SELECT bldg_id, sector_abbr, state_abbr, - kwh_load_profile as consumption_hourly - FROM diffusion_load_profiles.{sector_abbr}stock_load_profiles - WHERE bldg_id = {bldg_id} - AND sector_abbr = '{sector_abbr}' - AND state_abbr = '{state_abbr}';""".format(**inputs) - - df = pd.read_sql(sql, con, coerce_float=False) - - df = df[['consumption_hourly']] - - df['load_kwh_per_customer_in_bin'] = agent.loc['load_kwh_per_customer_in_bin'] - - # scale the normalized profile to sum to the total load - df = df.apply(scale_array_sum, axis=1, args=( - 'consumption_hourly', 'load_kwh_per_customer_in_bin')) - - - return df - -#%% -def get_and_apply_normalized_hourly_resource_solar(con, agent): - - inputs = locals().copy() - - inputs['solar_re_9809_gid'] = agent.loc['solar_re_9809_gid'] - inputs['tilt'] = agent.loc['tilt'] - inputs['azimuth'] = agent.loc['azimuth'] - - sql = """SELECT solar_re_9809_gid, tilt, azimuth, - cf as generation_hourly, - 1e6 as scale_offset - FROM diffusion_resource_solar.solar_resource_hourly - WHERE solar_re_9809_gid = '{solar_re_9809_gid}' - AND tilt = '{tilt}' - AND azimuth = '{azimuth}';""".format(**inputs) - - df = pd.read_sql(sql, con, coerce_float=False) - - df = df[['generation_hourly', 'scale_offset']] - - # rename the column generation_hourly to solar_cf_profile - df.rename(columns={'generation_hourly':'solar_cf_profile'}, inplace=True) - - return df - - -#%% -def scale_array_precision(row, array_col, prec_offset_col): - - row[array_col] = np.array( - row[array_col], dtype='float64') / row[prec_offset_col] - - return row - - -#%% -def scale_array_sum(row, array_col, scale_col): - - hourly_array = np.array(row[array_col], dtype='float64') - row[array_col] = hourly_array / \ - hourly_array.sum() * np.float64(row[scale_col]) - - return row - - -#%% -def interpolate_array(row, array_1_col, array_2_col, interp_factor_col, out_col): - - if row[interp_factor_col] != 0: - interpolated = row[interp_factor_col] * \ - (row[array_2_col] - row[array_1_col]) + row[array_1_col] - else: - interpolated = row[array_1_col] - row[out_col] = interpolated - - return row - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_carbon_intensities(dataframe, carbon_intensities): - - dataframe = dataframe.reset_index() - - dataframe = pd.merge(dataframe, carbon_intensities, how='left', on=['state_abbr', 'year']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_wholesale_elec_prices(dataframe, wholesale_elec_prices): - - dataframe = dataframe.reset_index() - - dataframe = pd.merge(dataframe, wholesale_elec_prices, how='left', on=['county_id', 'year']) - - dataframe = dataframe.set_index('agent_id') - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def get_state_starting_capacities(con, schema): - - inputs = locals().copy() - - # sql = """SELECT * - # FROM {schema}.state_starting_capacities_to_model;""".format(**inputs) - # df = pd.read_sql(sql, con) - - - # get state starting capacities for both solar and storage, distinguished by column names - sql = """WITH all_combos AS( - SELECT state_abbr, unnest(ARRAY['res','com','ind']) as sector_abbr - FROM diffusion_shared.state_abbr_lkup - WHERE state_abbr NOT IN ('AK','HI','PR') - ), solar AS( - SELECT sector_abbr, state_abbr, system_mw, systems_count AS pv_systems_count - FROM {schema}.state_starting_capacities_to_model - WHERE tech = 'solar' - ), storage AS( - SELECT sector_abbr, state_abbr, system_mw AS batt_mw, system_mwh as batt_mwh, systems_count AS batt_systems_count - FROM {schema}.state_starting_capacities_to_model - WHERE tech = 'storage' - ) - SELECT a.state_abbr, a.sector_abbr, b.system_mw, c.batt_mw, c.batt_mwh, b.pv_systems_count, c.batt_systems_count - FROM all_combos a - LEFT JOIN solar b - ON a.state_abbr = b.state_abbr AND a.sector_abbr = b.sector_abbr - LEFT JOIN storage c - ON a.state_abbr = c.state_abbr AND a.sector_abbr = c.sector_abbr;""".format(**inputs) - df = pd.read_sql(sql, con).fillna(0.) - - return df - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_state_incentives(dataframe, state_incentives, year, start_year, state_capacity_by_year, end_date = datetime.date(2029, 1, 1)): - - dataframe = dataframe.reset_index() - - # Fill in missing end_dates - if bool(end_date): - state_incentives['end_date'][pd.isnull(state_incentives['end_date'])] = end_date - - #Adjust incenctives to account for reduced values as adoption increases - yearly_escalation_function = lambda value, end_year: max(value - value * (1.0 / (end_year - start_year)) * (year-start_year), 0) - for field in ['pbi_usd_p_kwh','cbi_usd_p_w','ibi_pct','cbi_usd_p_wh']: - state_incentives[field] = state_incentives.apply(lambda row: yearly_escalation_function(row[field], row['end_date'].year), axis=1) - - # Filter Incentives by the Years in which they are valid - state_incentives = state_incentives.loc[ - pd.isnull(state_incentives['start_date']) | (pd.to_datetime(state_incentives['start_date']).dt.year <= year)] - state_incentives = state_incentives.loc[ - pd.isnull(state_incentives['end_date']) | (pd.to_datetime(state_incentives['end_date']).dt.year >= year)] - - # Combine valid incentives with the cumulative metrics for each state up until the current year - state_incentives_mg = state_incentives.merge(state_capacity_by_year.loc[state_capacity_by_year['year'] == year], - how='left', on=["state_abbr"]) - - # Filter where the states have not exceeded their cumulative installed capacity (by mw or pct generation) or total program budget - #state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['incentive_cap_total_pct']) | (state_incentives_mg['cum_capacity_pct'] < state_incentives_mg['incentive_cap_total_pct'])] - state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['incentive_cap_total_mw']) | (state_incentives_mg['cum_system_mw'] < state_incentives_mg['incentive_cap_total_mw'])] - state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['budget_total_usd']) | (state_incentives_mg['cum_incentive_spending_usd'] < state_incentives_mg['budget_total_usd'])] - - output =[] - for i in state_incentives_mg.groupby(['state_abbr', 'sector_abbr']): - row = i[1] - state, sector = i[0] - output.append({'state_abbr':state, 'sector_abbr':sector,"state_incentives":row}) - - state_inc_df = pd.DataFrame(columns=['state_abbr', 'sector_abbr', 'state_incentives']) - state_inc_df = pd.concat([state_inc_df, pd.DataFrame.from_records(output)], sort=False) - - dataframe = pd.merge(dataframe, state_inc_df, on=['state_abbr','sector_abbr'], how='left') - - dataframe = dataframe.set_index('agent_id') - - return dataframe - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def estimate_initial_market_shares(dataframe, state_starting_capacities_df): - - # record input columns - in_cols = list(dataframe.columns) - - # find the total number of customers in each state (by technology and - # sector) - state_total_developable_customers = dataframe[['state_abbr', 'sector_abbr', 'tech', 'developable_agent_weight']].groupby( - ['state_abbr', 'sector_abbr', 'tech']).sum().reset_index() - state_total_agents = dataframe[['state_abbr', 'sector_abbr', 'tech', 'developable_agent_weight']].groupby( - ['state_abbr', 'sector_abbr', 'tech']).count().reset_index() - # rename the final columns - state_total_developable_customers.columns = state_total_developable_customers.columns.str.replace( - 'developable_agent_weight', 'developable_customers_in_state') - state_total_agents.columns = state_total_agents.columns.str.replace( - 'developable_agent_weight', 'agent_count') - # merge together - state_denominators = pd.merge(state_total_developable_customers, state_total_agents, how='left', on=[ - 'state_abbr', 'sector_abbr', 'tech']) - - # merge back to the main dataframe - dataframe = pd.merge(dataframe, state_denominators, how='left', on=[ - 'state_abbr', 'sector_abbr', 'tech']) - - # merge in the state starting capacities - dataframe = pd.merge(dataframe, state_starting_capacities_df, how='left', - on=['state_abbr', 'sector_abbr']) - - # dataframe = pd.merge(dataframe, state_starting_capacities_df, how='left', - # on=['tech', 'state_abbr', 'sector_abbr']) - - # determine the portion of initial load and systems that should be allocated to each agent - # (when there are no developable agnets in the state, simply apportion evenly to all agents) - dataframe['portion_of_state'] = np.where(dataframe['developable_customers_in_state'] > 0, - dataframe[ - 'developable_agent_weight'] / dataframe['developable_customers_in_state'], - 1. / dataframe['agent_count']) - - # apply the agent's portion to the total to calculate starting capacity and systems - dataframe['adopters_cum_last_year'] = dataframe['portion_of_state'] * dataframe['pv_systems_count'] - dataframe['batt_adopters_cum_last_year'] = dataframe['portion_of_state'] * dataframe['batt_systems_count'] - dataframe['system_kw_cum_last_year'] = dataframe['portion_of_state'] * dataframe['system_mw'] * 1000. - dataframe['batt_kw_cum_last_year'] = dataframe['portion_of_state'] * dataframe['batt_mw'] * 1000.0 - dataframe['batt_kwh_cum_last_year'] = dataframe['portion_of_state'] * dataframe['batt_mwh'] * 1000.0 - - dataframe['market_share_last_year'] = np.where(dataframe['developable_agent_weight'] == 0, 0, - dataframe['adopters_cum_last_year'] / dataframe['developable_agent_weight']) - - dataframe['market_value_last_year'] = dataframe['system_capex_per_kw'] * dataframe['system_kw_cum_last_year'] - - # reproduce these columns as "initial" columns too - dataframe['initial_number_of_adopters'] = dataframe['adopters_cum_last_year'] - dataframe['initial_pv_kw'] = dataframe['system_kw_cum_last_year'] - dataframe['initial_batt_kw'] = dataframe['batt_kw_cum_last_year'] - dataframe['initial_batt_kwh'] = dataframe['batt_kwh_cum_last_year'] - dataframe['initial_market_share'] = dataframe['market_share_last_year'] - dataframe['initial_market_value'] = 0 - - # isolate the return columns - return_cols = ['initial_number_of_adopters','initial_pv_kw','initial_batt_kw','initial_batt_kwh','initial_market_share','initial_market_value', - 'adopters_cum_last_year','system_kw_cum_last_year','batt_kw_cum_last_year','batt_kwh_cum_last_year','market_share_last_year','market_value_last_year'] - - # return_cols = ['initial_number_of_adopters', 'initial_pv_kw', 'initial_market_share', 'initial_market_value', - # 'adopters_cum_last_year', 'system_kw_cum_last_year', 'batt_kw_cum_last_year', 'batt_kwh_cum_last_year', 'market_share_last_year', 'market_value_last_year'] - - dataframe[return_cols] = dataframe[return_cols].fillna(0) - - out_cols = in_cols + return_cols - - return dataframe[out_cols] - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def apply_market_last_year(dataframe, market_last_year_df): - - dataframe = dataframe.merge(market_last_year_df, on=['agent_id'], how='left') - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def estimate_total_generation(dataframe): - - dataframe['total_gen_twh'] = ((dataframe['number_of_adopters'] - dataframe['initial_number_of_adopters']) - * dataframe['annual_energy_production_kwh'] * 1e-9) + (0.23 * 8760 * dataframe['initial_pv_kw'] * 1e-6) - - return dataframe - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def calc_state_capacity_by_year(con, schema, load_growth, peak_demand_mw, is_first_year, year,solar_agents, last_year_installed_capacity): - - if is_first_year: - # get state starting capacities for solar & storage and sum by state - df = last_year_installed_capacity.groupby('state_abbr')[['system_mw','batt_mw','batt_mwh']].sum().reset_index() - df.rename(columns={'system_mw':'cum_system_mw', 'batt_mw':'cum_batt_mw', 'batt_mwh':'cum_batt_mwh'}, inplace=True) - - # Not all states have starting capacity, don't want to drop any states thus left join on peak_demand - df = peak_demand_mw.merge(df, how='left').fillna(0) - - # rename columns - df.rename(columns={'peak_demand_mw_2014':'peak_demand_mw'}, inplace=True) - - - else: - df = last_year_installed_capacity.copy() - df['cum_system_mw'] = df['system_kw_cum']/1000 - df['cum_batt_mw'] = df['batt_kw_cum']/1000 - df['cum_batt_mwh'] = df['batt_kwh_cum']/1000 -# # Load growth is resolved by census region, so a lookup table is needed -# df = df.merge(census_division_lkup, on = 'state_abbr') - load_growth_this_year = load_growth.loc[(load_growth['year'] == year) & (load_growth['sector_abbr'] == 'res')] - load_growth_this_year = pd.merge(solar_agents.df[['state_abbr', 'county_id']], load_growth_this_year, how='left', on=['county_id']) - load_growth_this_year = load_growth_this_year.groupby('state_abbr')['load_multiplier'].mean().reset_index() - df = df.merge(load_growth_this_year, on = 'state_abbr') - - df = peak_demand_mw.merge(df, how='left', on='state_abbr').fillna(0) - df['peak_demand_mw'] = df['peak_demand_mw_2014'] * df['load_multiplier'] - - # TODO: drop cum_capacity_pct from table (misnomer) - df['cum_capacity_pct'] = 0 - # TODO: enforce program spending cap - df['cum_incentive_spending_usd'] = 0 - df['year'] = year - - df = df[['state_abbr','cum_system_mw','cum_batt_mw','cum_batt_mwh','cum_capacity_pct','cum_incentive_spending_usd','peak_demand_mw','year']] - - return df - - -#%% -def get_rate_switch_table(con): - - # get rate switch table from database - sql = """SELECT * FROM diffusion_shared.rate_switch_lkup_2020;""" - # sql = """SELECT * FROM diffusion_shared.rate_switch_lkup_2019;""" - rate_switch_table = pd.read_sql(sql, con, coerce_float=False) - rate_switch_table = rate_switch_table.reset_index(drop=True) - - return rate_switch_table - -def apply_rate_switch(rate_switch_table, agent, system_size_kw, tech='solar'): - - rate_switch_table = rate_switch_table.loc[rate_switch_table['tech'] == tech] - rate_switch_table.rename(columns={'rate_id_alias':'tariff_id', 'json':'tariff_dict'}, inplace=True) - rate_switch_table = rate_switch_table[(rate_switch_table['eia_id'] == agent.loc['eia_id']) & - (rate_switch_table['res_com'] == str(agent.loc['sector_abbr']).upper()[0]) & - (rate_switch_table['min_kw_limit'] <= system_size_kw) & - (rate_switch_table['max_kw_limit'] > system_size_kw)] - - rate_switch_table = rate_switch_table.reset_index(drop=True) - - # check if a DG rate is applicable to agent - if (system_size_kw > 0) & (len(rate_switch_table) == 1): - # if valid DG rate available to agent, force NEM on - agent['nem_system_kw_limit'] = 1e6 - # update agent attributes to DG rate - agent['tariff_id'] = rate_switch_table['tariff_id'][0] - agent['tariff_dict'] = rate_switch_table['tariff_dict'][0] - # return any one time charges (e.g., interconnection fees) - one_time_charge = rate_switch_table['one_time_charge'][0] - else: - # don't update agent attributes, return one time charge of $0 - one_time_charge = 0. - - - return agent, one_time_charge - - -#%% -@decorators.fn_timer(logger=logger, tab_level=2, prefix='') -def reassign_agent_tariffs(dataframe, con): - - # define rates to use in replacement of incorrect tariffs - - # map res/com tariffs based on most likely tariff in state - res_tariffs = { - 'AL':17279, # Family Dwelling Service - 'AR':16671, # Optional Residential Time-Of-Use (RT) Single Phase - 'AZ':15704, # Residential Time of Use (Saver Choice) TOU-E - 'CA':15747, # E-1 -Residential Service Baseline Region P - 'CO':17078, # Residential Service (Schedule R) - 'CT':16678, # Rate 1 - Residential Electric Service - 'DC':16806, # Residential - Schedule R - 'DE':11569, # Residential Service - 'FL':16986, # RS-1 Residential Service - 'GA':16649, # SCHEDULE R-22 RESIDENTIAL SERVICE - 'IA':11693, # Optional Residential Service - 'ID':16227, # Schedule 1: Residential Rates - 'IL':16045, # DS-1 Residential Zone 1 - 'IN':15491, # RS - Residential Service - 'KS':8178, # M System Residential Service - 'KY':16566, # Residential Service - 'LA':16352, # Residential and Farm Service - Single Phase (RS-L) - 'MA':15953, # Greater Boston Residential R-1 (A1) - 'MD':14779, # Residential Service (R) - 'ME':15984, # A Residential Standard Offer Service (Bundled) - 'MI':16265, # Residential Service - Secondary (Rate RS) - 'MN':15556, # Residential Service - Overhead Standard (A01) - 'MO':17207, # 1(M) Residential Service Rate - 'MS':16788, # Residential Service Single Phase (RS-38C) - 'MT':5216, # Single Phase - 'NC':16938, # Residential Service (RES-41) Single Phase - 'ND':14016, # Residential Service Rate 10 - 'NE':13817, # Residential Service - 'NH':16605, # Residential Service - 'NJ':16229, # RS - Residential Service - 'NM':8692, # 1A (Residential Service) - 'NV':16701, # D-1 (Residential Service) - 'NY':16902, # SC1- Zone A - 'OH':16892, # RS (Residential Service) - 'OK':15258, # Residential Service (R-1) - 'OR':15847, # Schedule 4 - Residential (Single Phase) - 'PA':17237, # RS (Residential Service) - 'RI':16598, # A-16 (Residential Service) - 'SC':15744, # Residential - RS (SC) - 'SD':1216, # Town and Rural Residential Rate - 'TN':15149, # Residential Electric Service - 'TX':16710, # Residential Service - Time Of Day - 'UT':15847, # Schedule 4 - Residential (Single Phase) - 'VA':17067, # Residential Schedule 1 - 'VT':16544, # Rate 01 Residential Service - 'WA':16305, # 10 (Residential and Farm Primary General Service) - 'WI':15543, # Residential Rg-1 - 'WV':15515, # Residential Service A - 'WY':15847 # Schedule 4 - Residential (Single Phase) - } - - com_tariffs = { - 'AL':15494, # BTA - BUSINESS TIME ADVANTAGE (OPTIONAL) - Primary - 'AR':16674, # Small General Service (SGS) - 'AZ':10742, # LGS-TOU- N - Large General Service Time-of-Use - 'CA':17057, # A-10 Medium General Demand Service (Secondary Voltage) - 'CO':17102, # Commercial Service (Schedule C) - 'CT':16684, # Rate 35 Intermediate General Electric Service - 'DC':15336, # General Service (Schedule GS) - 'DE':1199, # Schedule LC-P Large Commercial Primary - 'FL':13790, # SDTR-1 (Option A) - 'GA':1905, # SCHEDULE TOU-MB-4 TIME OF USE - MULTIPLE BUSINESS - 'IA':11705, # Three Phase Farm - 'ID':14782, # Large General Service (3 Phase)-Schedule 21 - 'IL':1567, # General Service Three Phase standard - 'IN':15492, # CS - Commercial Service - 'KS':14736, # Generation Substitution Service - 'KY':17179, # General Service (Single Phase) - 'LA':17220, # Large General Service (LGS-L) - 'MA':16005, # Western Massachusetts Primary General Service G-2 - 'MD':2659, # Commercial - 'ME':16125, # General Service Rate - 'MI':5355, # Large Power Service (LP4) - 'MN':15566, # General Service (A14) Secondary Voltage - 'MO':17208, # 2(M) Small General Service - Single phase - 'MS':13427, # General Service - Low Voltage Single-Phase (GS-LVS-14) - 'MT':10707, # Three Phase - 'NC':16947, # General Service (GS-41) - 'ND':14035, # Small General Electric Service rate 20 (Demand Metered; Non-Demand) - 'NE':13818, # General Service Single-Phase - 'NH':16620, # GV Commercial and Industrial Service - 'NJ':17095, # AGS Secondary- BGS-RSCP - 'NM':15769, # 2A (Small Power Service) - 'NV':13724, # OGS-2-TOU - 'NY':15940, # SC-9 - General Large High Tension Service [Westchester] - 'OH':16873, # GS (General Service-Secondary) - 'OK':17144, # GS-TOU (General Service Time-Of-Use) - 'OR':15829, # Small Non-Residential Direct Access Service, Single Phase (Rate 532) - 'PA':7066, # Large Power 2 (LP2) - 'RI':16600, # G-02 (General C & I Rate) - 'SC':16207, # 3 (Municipal Power Service) - 'SD':3650, # Small Commercial - 'TN':15154, # Medium General Service (Primary) - 'TX':6001, # Medium Non-Residential LSP POLR - 'UT':3478, # SCHEDULE GS - 3 Phase General Service - 'VA':16557, # Small General Service Schedule 5 - 'VT':16543, # Rate 06: General Service - 'WA':16306, # 40 (Large Demand General Service over 3MW - Primary) - 'WI':6620, # Cg-7 General Service Time-of-Day (Primary) - 'WV':15518, # General Service C - 'WY':3878 # General Service (GS)-Three phase - } - - # map industrial tariffs based on census division - ind_tariffs = { - 'SA':16657, # Georgia Power Co, Schedule TOU-GSD-10 Time Of Use - General Service Demand - 'WSC':15919, # Southwestern Public Service Co (Texas), Large General Service - Inside City Limits 115 KV - 'PAC':15864, # PacifiCorp (Oregon), Schedule 47 - Secondary (Less than 4000 kW) - 'MA':16525, # New York State Elec & Gas Corp, All Regions - SERVICE CLASSIFICATION NO. 7-1 Large General Service TOU - Secondary -ESCO - 'MTN':17101, # Public Service Co of Colorado, Secondary General Service (Schedule SG) - 'ENC':15526, # Wisconsin Power & Light Co, Industrial Power Cp-1 (Secondary) - 'NE':16635, # Delmarva Power, General Service - Primary - 'ESC':15490, # Alabama Power Co, LPM - LIGHT AND POWER SERVICE - MEDIUM - 'WNC':6642 # Northern States Power Co - Wisconsin, Cg-9.1 Large General Time-of-Day Primary Mandatory Customers - } - - dataframe = dataframe.reset_index() - - # separate agents with incorrect and correct rates - bad_rates = dataframe.loc[np.in1d(dataframe['tariff_id'], [4145, 7111, 8498, 10953, 10954, 12003])] - good_rates = dataframe.loc[~np.in1d(dataframe['tariff_id'], [4145, 7111, 8498, 10953, 10954, 12003])] - - # if incorrect rates exist, grab the correct ones from the rates table - if len(bad_rates) > 0: - - # set new tariff_id based on location - bad_rates['tariff_id'] = np.where(bad_rates['sector_abbr']=='res', - bad_rates['state_abbr'].map(res_tariffs), - np.where(bad_rates['sector_abbr']=='com', - bad_rates['state_abbr'].map(com_tariffs), - bad_rates['census_division_abbr'].map(ind_tariffs))) - - # get json objects for new rates and rename columns in preparation for merge - new_rates_json_df = get_electric_rates_json(con, bad_rates['tariff_id'].tolist()) - new_rates_json_df = (new_rates_json_df - .drop(['rate_name','eia_id'], axis='columns') - .rename(columns={'rate_id_alias':'tariff_id','rate_json':'tariff_dict'}) - ) - - # drop bad tariff_dict from agent dataframe and merge correct one - bad_rates = bad_rates.drop(['tariff_dict'], axis='columns') - bad_rates = bad_rates.merge(new_rates_json_df, how='left', on='tariff_id') - - # reconstruct full agent dataframe - dataframe = pd.concat([good_rates, bad_rates], ignore_index=True, sort=False) - dataframe = dataframe.set_index('agent_id') - - return dataframe \ No newline at end of file From ee45cba65961f579d2b6f7a69c279f3b0a187e71 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Tue, 30 Apr 2024 08:40:44 -0600 Subject: [PATCH 10/17] Minor updates to comments/formatting in elec.py, ensure file stays in repo after merge --- dgen_os/python/agent_mutation/elec.py | 48 ++++++++++----------------- 1 file changed, 18 insertions(+), 30 deletions(-) diff --git a/dgen_os/python/agent_mutation/elec.py b/dgen_os/python/agent_mutation/elec.py index 133d2d1..f61fc9a 100644 --- a/dgen_os/python/agent_mutation/elec.py +++ b/dgen_os/python/agent_mutation/elec.py @@ -95,10 +95,9 @@ def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_ut # specify relevant NEM columns nem_columns = ['compensation_style','nem_system_kw_limit'] - + net_metering_utility_df = net_metering_utility_df[['eia_id','sector_abbr','state_abbr']+nem_columns] # check if utility-specific NEM parameters apply to any agents - need to join on state too (e.g. Pacificorp UT vs Pacificorp ID) - temp_df = pd.merge(dataframe, net_metering_utility_df[ - ['eia_id','sector_abbr','state_abbr']+nem_columns], how='left', on=['eia_id','sector_abbr','state_abbr']) + temp_df = pd.merge(dataframe, net_metering_utility_df, how='left', on=['eia_id','sector_abbr','state_abbr']) # filter agents with non-null nem_system_kw_limit - these are agents WITH utility NEM agents_with_utility_nem = temp_df[pd.notnull(temp_df['nem_system_kw_limit'])] @@ -106,8 +105,8 @@ def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_ut # filter agents with null nem_system_kw_limit - these are agents WITHOUT utility NEM agents_without_utility_nem = temp_df[pd.isnull(temp_df['nem_system_kw_limit'])].drop(nem_columns, axis=1) # merge agents with state-specific NEM parameters - agents_without_utility_nem = pd.merge(agents_without_utility_nem, net_metering_state_df[ - ['state_abbr', 'sector_abbr']+nem_columns], how='left', on=['state_abbr', 'sector_abbr']) + net_metering_state_df = net_metering_state_df[['state_abbr', 'sector_abbr']+nem_columns] + agents_without_utility_nem = pd.merge(agents_without_utility_nem, net_metering_state_df, how='left', on=['state_abbr', 'sector_abbr']) # re-combine agents list and fill nan's dataframe = pd.concat([agents_with_utility_nem, agents_without_utility_nem], sort=False) @@ -115,7 +114,7 @@ def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_ut dataframe['nem_system_kw_limit'].fillna(0, inplace=True) dataframe = dataframe.set_index('agent_id') - dataframe = dataframe.drop_duplicates(subset='pgid') + # dataframe = dataframe.drop_duplicates(subset=dataframe.columns.difference(['tariff_dict'])) return dataframe @@ -365,18 +364,17 @@ def apply_financial_params(dataframe, financing_terms, itc_options, inflation_ra Agent DataFrame with financial parameters merged in. ''' - dataframe = dataframe.reset_index() dataframe = dataframe.merge(financing_terms, how='left', on=['year', 'sector_abbr']) dataframe = dataframe.merge(itc_options[['itc_fraction_of_capex', 'year', 'tech', 'sector_abbr']], how='left', on=['year', 'tech', 'sector_abbr']) + dataframe['inflation_rate'] = inflation_rate - dataframe = dataframe.set_index('agent_id') - dataframe = dataframe.drop_duplicates(subset='pgid') + return dataframe @@ -389,9 +387,6 @@ def apply_load_growth(dataframe, load_growth_df): dataframe["county_id"] = dataframe.county_id.astype(int) dataframe = pd.merge(dataframe, load_growth_df, how='left', on=['year', 'sector_abbr', 'county_id']) - # Drop any duplicated rows based on pgid - dataframe = dataframe.drop_duplicates(subset='pgid') - # for res, load growth translates to kwh_per_customer change dataframe['load_kwh_per_customer_in_bin'] = np.where(dataframe['sector_abbr']=='res', dataframe['load_kwh_per_customer_in_bin_initial'] * dataframe['load_multiplier'], @@ -406,7 +401,6 @@ def apply_load_growth(dataframe, load_growth_df): dataframe['load_kwh_in_bin'] = dataframe['load_kwh_in_bin_initial'] * dataframe['load_multiplier'] dataframe = dataframe.set_index('agent_id') - return dataframe @@ -495,12 +489,12 @@ def get_nem_settings(state_limits, state_by_sector, utility_by_sector, selected_ # Return State/Sector data (or null) for all combinations of states and sectors full_state_list = state_by_sector.loc[ state_by_sector['scenario'] == 'BAU' ].loc[:, ['state_abbr', 'sector_abbr']] - state_result = pd.merge( full_state_list.drop_duplicates(), valid_state_sector, how='left', on=['state_abbr','sector_abbr'] ) + state_result = pd.merge( full_state_list, valid_state_sector, how='left', on=['state_abbr','sector_abbr'] ) state_result['nem_system_kw_limit'].fillna(0, inplace=True) # Return Utility/Sector data (or null) for all combinations of utilities and sectors full_utility_list = utility_by_sector.loc[ utility_by_sector['scenario'] == 'BAU' ].loc[:, ['eia_id','sector_abbr','state_abbr']] - utility_result = pd.merge( full_utility_list.drop_duplicates(), valid_utility_sector, how='left', on=['eia_id','sector_abbr','state_abbr'] ) + utility_result = pd.merge( full_utility_list, valid_utility_sector, how='left', on=['eia_id','sector_abbr','state_abbr'] ) utility_result['nem_system_kw_limit'].fillna(0, inplace=True) return state_result, utility_result @@ -672,11 +666,12 @@ def apply_state_incentives(dataframe, state_incentives, year, start_year, state_ pd.isnull(state_incentives['start_date']) | (pd.to_datetime(state_incentives['start_date']).dt.year <= year)] state_incentives = state_incentives.loc[ pd.isnull(state_incentives['end_date']) | (pd.to_datetime(state_incentives['end_date']).dt.year >= year)] - + # Combine valid incentives with the cumulative metrics for each state up until the current year - state_incentives_mg = state_incentives.merge(state_capacity_by_year.loc[state_capacity_by_year['year'] == year], + state_capacity_by_year = state_capacity_by_year.loc[state_capacity_by_year['year'] == year] + state_incentives_mg = state_incentives.merge(state_capacity_by_year, how='left', on=["state_abbr"]) - + # Filter where the states have not exceeded their cumulative installed capacity (by mw or pct generation) or total program budget #state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['incentive_cap_total_pct']) | (state_incentives_mg['cum_capacity_pct'] < state_incentives_mg['incentive_cap_total_pct'])] state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['incentive_cap_total_mw']) | (state_incentives_mg['cum_system_mw'] < state_incentives_mg['incentive_cap_total_mw'])] @@ -690,11 +685,10 @@ def apply_state_incentives(dataframe, state_incentives, year, start_year, state_ state_inc_df = pd.DataFrame(columns=['state_abbr', 'sector_abbr', 'state_incentives']) state_inc_df = pd.concat([state_inc_df, pd.DataFrame.from_records(output)], sort=False) - + dataframe = pd.merge(dataframe, state_inc_df, on=['state_abbr','sector_abbr'], how='left') dataframe = dataframe.set_index('agent_id') - return dataframe #%% @@ -726,12 +720,10 @@ def estimate_initial_market_shares(dataframe, state_starting_capacities_df): # merge in the state starting capacities dataframe = pd.merge(dataframe, state_starting_capacities_df, how='left', on=['state_abbr', 'sector_abbr']) - - # dataframe = pd.merge(dataframe, state_starting_capacities_df, how='left', - # on=['tech', 'state_abbr', 'sector_abbr']) + # determine the portion of initial load and systems that should be allocated to each agent - # (when there are no developable agnets in the state, simply apportion evenly to all agents) + # (when there are no developable agents in the state, simply apportion evenly to all agents) dataframe['portion_of_state'] = np.where(dataframe['developable_customers_in_state'] > 0, dataframe[ 'developable_agent_weight'] / dataframe['developable_customers_in_state'], @@ -761,9 +753,6 @@ def estimate_initial_market_shares(dataframe, state_starting_capacities_df): return_cols = ['initial_number_of_adopters','initial_pv_kw','initial_batt_kw','initial_batt_kwh','initial_market_share','initial_market_value', 'adopters_cum_last_year','system_kw_cum_last_year','batt_kw_cum_last_year','batt_kwh_cum_last_year','market_share_last_year','market_value_last_year'] - # return_cols = ['initial_number_of_adopters', 'initial_pv_kw', 'initial_market_share', 'initial_market_value', - # 'adopters_cum_last_year', 'system_kw_cum_last_year', 'batt_kw_cum_last_year', 'batt_kwh_cum_last_year', 'market_share_last_year', 'market_value_last_year'] - dataframe[return_cols] = dataframe[return_cols].fillna(0) out_cols = in_cols + return_cols @@ -810,8 +799,7 @@ def calc_state_capacity_by_year(con, schema, load_growth, peak_demand_mw, is_fir df['cum_system_mw'] = df['system_kw_cum']/1000 df['cum_batt_mw'] = df['batt_kw_cum']/1000 df['cum_batt_mwh'] = df['batt_kwh_cum']/1000 -# # Load growth is resolved by census region, so a lookup table is needed -# df = df.merge(census_division_lkup, on = 'state_abbr') + load_growth_this_year = load_growth.loc[(load_growth['year'] == year) & (load_growth['sector_abbr'] == 'res')] load_growth_this_year = pd.merge(solar_agents.df[['state_abbr', 'county_id']], load_growth_this_year, how='left', on=['county_id']) load_growth_this_year = load_growth_this_year.groupby('state_abbr')['load_multiplier'].mean().reset_index() @@ -836,7 +824,7 @@ def get_rate_switch_table(con): # get rate switch table from database sql = """SELECT * FROM diffusion_shared.rate_switch_lkup_2020;""" - # sql = """SELECT * FROM diffusion_shared.rate_switch_lkup_2019;""" + rate_switch_table = pd.read_sql(sql, con, coerce_float=False) rate_switch_table = rate_switch_table.reset_index(drop=True) From 872cc32b45aa10e213a3ad30569c38fd6ba5ec13 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Tue, 30 Apr 2024 08:44:59 -0600 Subject: [PATCH 11/17] re-add elec.py, includes minor formatting/clean up changes --- dgen_os/python/agent_mutation/elec.py | 1016 +++++++++++++++++++++++++ 1 file changed, 1016 insertions(+) create mode 100644 dgen_os/python/agent_mutation/elec.py diff --git a/dgen_os/python/agent_mutation/elec.py b/dgen_os/python/agent_mutation/elec.py new file mode 100644 index 0000000..f61fc9a --- /dev/null +++ b/dgen_os/python/agent_mutation/elec.py @@ -0,0 +1,1016 @@ +import psycopg2 as pg +import numpy as np +import pandas as pd +import decorators +import datetime +import utility_functions as utilfunc +from io import StringIO +import os +import gc +import multiprocessing as mp +import concurrent.futures as concur_f +import tariff_functions as tFuncs +import sqlalchemy + +# GLOBAL SETTINGS + +# load logger +logger = utilfunc.get_logger() + +# configure psycopg2 to treat numeric values as floats (improves performance of pulling data from the database) +DEC2FLOAT = pg.extensions.new_type(pg.extensions.DECIMAL.values, + 'DEC2FLOAT', + lambda value, curs: float(value) if value is not None else None) +pg.extensions.register_type(DEC2FLOAT) + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_elec_price_multiplier_and_escalator(dataframe, year, elec_price_change_traj): + ''' + Obtain a single scalar multiplier for each agent, that is the cost of + electricity relative to 2016 (when the tariffs were curated). + Also calculate the compound annual growth rate (CAGR) for the price of + electricity from present year to 2050, which will be the escalator that + agents use to project electricity changes in their bill calculations. + + elec_price_multiplier = change in present-year elec cost to 2016 + elec_price_escalator = agent's assumption about future price changes + Note that many customers will not differentiate between real and nominal, + and therefore many would overestimate the real escalation of electriicty + prices. + + + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + year : 'int' + The year for which you want multiplier values for + elec_price_change_traj : 'pd.DataFrame' + Dataframe of electricity prices' trajectories over time. See the + 'input_elec_prices_user_defined' table in the database. + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with elec_price_multiplier and elec_price_escalator data merged in. + + ''' + + dataframe = dataframe.reset_index() + + # get current year multiplier values + elec_price_multiplier_df = elec_price_change_traj[elec_price_change_traj['year']==year].reset_index(drop=True) + + # copy to the multiplier_df for escalator calcs + year_cap = min(year, 2040) + elec_price_escalator_df = elec_price_change_traj[elec_price_change_traj['year']==year_cap].reset_index(drop=True) + + # get final year multiplier values and attach to escalator_df + final_year = np.max(elec_price_change_traj['year']) + final_year_df = elec_price_change_traj[elec_price_change_traj['year']==final_year].reset_index(drop=True) + elec_price_escalator_df['final_year_values'] = final_year_df['elec_price_multiplier'].reset_index(drop=True) + + # calculate CAGR for time period between final year and current year + elec_price_escalator_df['elec_price_escalator'] = (elec_price_escalator_df['final_year_values'] / elec_price_escalator_df['elec_price_multiplier'])**(1.0/(final_year-year_cap)) - 1.0 + + # et upper and lower bounds of escalator at 1.0 and -1.0, based on historic elec price growth rates + elec_price_escalator_df['elec_price_escalator'] = np.clip(elec_price_escalator_df['elec_price_escalator'], -.01, .01) + + # merge multiplier and escalator values back to agent dataframe + dataframe = pd.merge(dataframe, elec_price_multiplier_df[['elec_price_multiplier', 'sector_abbr', 'county_id']], how='left', on=['sector_abbr', 'county_id']) + dataframe = pd.merge(dataframe, elec_price_escalator_df[['sector_abbr', 'county_id', 'elec_price_escalator']], + how='left', on=['sector_abbr', 'county_id']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_utility_df): + + dataframe = dataframe.reset_index() + + # specify relevant NEM columns + nem_columns = ['compensation_style','nem_system_kw_limit'] + net_metering_utility_df = net_metering_utility_df[['eia_id','sector_abbr','state_abbr']+nem_columns] + # check if utility-specific NEM parameters apply to any agents - need to join on state too (e.g. Pacificorp UT vs Pacificorp ID) + temp_df = pd.merge(dataframe, net_metering_utility_df, how='left', on=['eia_id','sector_abbr','state_abbr']) + + # filter agents with non-null nem_system_kw_limit - these are agents WITH utility NEM + agents_with_utility_nem = temp_df[pd.notnull(temp_df['nem_system_kw_limit'])] + + # filter agents with null nem_system_kw_limit - these are agents WITHOUT utility NEM + agents_without_utility_nem = temp_df[pd.isnull(temp_df['nem_system_kw_limit'])].drop(nem_columns, axis=1) + # merge agents with state-specific NEM parameters + net_metering_state_df = net_metering_state_df[['state_abbr', 'sector_abbr']+nem_columns] + agents_without_utility_nem = pd.merge(agents_without_utility_nem, net_metering_state_df, how='left', on=['state_abbr', 'sector_abbr']) + + # re-combine agents list and fill nan's + dataframe = pd.concat([agents_with_utility_nem, agents_without_utility_nem], sort=False) + dataframe['compensation_style'].fillna('none', inplace=True) + dataframe['nem_system_kw_limit'].fillna(0, inplace=True) + + dataframe = dataframe.set_index('agent_id') + # dataframe = dataframe.drop_duplicates(subset=dataframe.columns.difference(['tariff_dict'])) + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_pv_tech_performance(dataframe, pv_tech_traj): + + ''' + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + pv_tech_traj : 'pd.DataFrame' + Dataframe of PV tech performance over time. See the + 'input_pv_tech_performance_user_defined' table in the database. + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with pv tech performance parameters merged in. + + ''' + + dataframe = dataframe.reset_index() + + dataframe = pd.merge(dataframe, pv_tech_traj, how='left', on=['sector_abbr', 'year']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_depreciation_schedule(dataframe, deprec_sch): + + dataframe = dataframe.reset_index() + + dataframe = pd.merge(dataframe, deprec_sch[['sector_abbr', 'deprec_sch', 'year']], + how='left', on=['sector_abbr', 'year']) + + dataframe = dataframe.set_index('agent_id') + + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_pv_prices(dataframe, pv_price_traj): + + ''' + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + pv_price_traj : 'pd.DataFrame' + Dataframe of PV price trajectories over time. See the + 'input_pv_prices_user_defined' table in the database. + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with pv price parameters merged in. + + ''' + + dataframe = dataframe.reset_index() + + # join the data + dataframe = pd.merge(dataframe, pv_price_traj, how='left', on=['sector_abbr', 'year']) + + # apply the capital cost multipliers + #dataframe['system_capex_per_kw'] = (dataframe['system_capex_per_kw'] * dataframe['cap_cost_multiplier']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger = logger, tab_level = 2, prefix = '') +def apply_batt_prices(dataframe, batt_price_traj, batt_tech_traj, year): + + ''' + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + batt_price_traj : 'pd.DataFrame' + Dataframe of battery price trajectories over time. See the + 'input_batt_prices_user_defined' table in the database. + batt_tech_traj : 'pd.DataFrame' + Dataframe of battery tech trajectories over time. See the + 'input_batt_tech_performance_user_defined' table in the database. + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with battery price and tech parameters merged in. + + ''' + + dataframe = dataframe.reset_index() + + # Merge on prices + dataframe = pd.merge(dataframe, batt_price_traj[['year','sector_abbr', + 'batt_capex_per_kwh','batt_capex_per_kw','linear_constant', + 'batt_om_per_kwh','batt_om_per_kw']], + how = 'left', on = ['sector_abbr', 'year']) + + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_pv_plus_batt_prices(dataframe, pv_plus_batt_price_traj, batt_tech_traj, year): + + ''' + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + pv_plus_batt_price_traj : 'pd.DataFrame' + Dataframe of battery price trajectories over time. See the + 'input_pv_plus_batt_prices_user_defined' table in the database. + batt_tech_traj : 'pd.DataFrame' + Dataframe of battery tech trajectories over time. See the + 'input_batt_tech_performance_user_defined' table in the database. + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with pv plus battery price parameters and battery tech parameters merged in. + + ''' + + dataframe = dataframe.reset_index() + + # rename cost columns -- PV+batt configuration has distinct costs + pv_plus_batt_price_traj.rename(columns={'system_capex_per_kw':'system_capex_per_kw_combined', + 'batt_capex_per_kwh':'batt_capex_per_kwh_combined', + 'batt_capex_per_kw':'batt_capex_per_kw_combined', + 'linear_constant':'linear_constant_combined', + 'batt_om_per_kw':'batt_om_per_kw_combined', + 'batt_om_per_kwh':'batt_om_per_kwh_combined'}, inplace=True) + + # Merge on prices + dataframe = pd.merge(dataframe, pv_plus_batt_price_traj[['year','sector_abbr', + 'system_capex_per_kw_combined', + 'batt_capex_per_kwh_combined','batt_capex_per_kw_combined', + 'linear_constant_combined', + 'batt_om_per_kw_combined','batt_om_per_kwh_combined']], + how = 'left', on = ['year', 'sector_abbr']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_value_of_resiliency(dataframe, value_of_resiliency): + + ''' + + Note, value of resiliency (VOR) is not currently used in the open source version of the model. + + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + vaule_of_resiliency : 'pd.DataFrame' + Dataframe of financials pertaining to the value of resiliency. See the + 'input_value_of_resiliency_user_defined' table in the database. + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with value of resiliency parameters merged in. + + ''' + + dataframe = dataframe.reset_index() + + dataframe = dataframe.merge(value_of_resiliency[['state_abbr','sector_abbr','value_of_resiliency_usd']], how='left', on=['state_abbr', 'sector_abbr']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_batt_tech_performance(dataframe, batt_tech_traj): + + ''' + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + batt_tech_traj : 'pd.DataFrame' + Dataframe of battery tech trajectories over time. See the + 'input_batt_tech_performance_user_defined' table in the database. + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with battery tech parameters merged in. + + ''' + + dataframe = dataframe.reset_index() + + dataframe = dataframe.merge(batt_tech_traj, how='left', on=['year', 'sector_abbr']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_financial_params(dataframe, financing_terms, itc_options, inflation_rate): + + ''' + + Applies financial parameters specified in input sheet to agent dataframe. + + Parameters + ---------- + dataframe : 'pd.DataFrame' + Agent dataframe + financing_terms : 'pd.DataFrame' + Dataframe of financing terms. + itc_options : 'pd.DataFrame' + Dataframe of different ITC (investment tax credit) parameters, namely 'itc_fraction_of_capex' + that is merged to the agent dataframe on year, technology, and sector. + inflation_rate : 'float' + rate of inflation specified in the input sheet as a percentage (e.g. 2.5%). + + Returns + ------- + dataframe : 'pd.DataFrame' + Agent DataFrame with financial parameters merged in. + + ''' + dataframe = dataframe.reset_index() + + dataframe = dataframe.merge(financing_terms, how='left', on=['year', 'sector_abbr']) + + dataframe = dataframe.merge(itc_options[['itc_fraction_of_capex', 'year', 'tech', 'sector_abbr']], + how='left', on=['year', 'tech', 'sector_abbr']) + + + dataframe['inflation_rate'] = inflation_rate + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_load_growth(dataframe, load_growth_df): + + dataframe = dataframe.reset_index() + + dataframe["county_id"] = dataframe.county_id.astype(int) + dataframe = pd.merge(dataframe, load_growth_df, how='left', on=['year', 'sector_abbr', 'county_id']) + + # for res, load growth translates to kwh_per_customer change + dataframe['load_kwh_per_customer_in_bin'] = np.where(dataframe['sector_abbr']=='res', + dataframe['load_kwh_per_customer_in_bin_initial'] * dataframe['load_multiplier'], + dataframe['load_kwh_per_customer_in_bin_initial']) + + # for C&I, load growth translates to customer count change + dataframe['customers_in_bin'] = np.where(dataframe['sector_abbr']!='res', + dataframe['customers_in_bin_initial'] * dataframe['load_multiplier'], + dataframe['customers_in_bin_initial']) + + # for all sectors, total kwh_in_bin changes + dataframe['load_kwh_in_bin'] = dataframe['load_kwh_in_bin_initial'] * dataframe['load_multiplier'] + + dataframe = dataframe.set_index('agent_id') + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def calculate_developable_customers_and_load(dataframe): + + dataframe = dataframe.reset_index() + + dataframe['developable_agent_weight'] = dataframe['pct_of_bldgs_developable'] * dataframe['customers_in_bin'] + dataframe['developable_load_kwh_in_bin'] = dataframe['pct_of_bldgs_developable'] * dataframe['load_kwh_in_bin'] + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +def get_electric_rates_json(con, unique_rate_ids): + + inputs = locals().copy() + + # updated urdb3_rate_jsons_201905 to 20200721 version + + # reformat the rate list for use in postgres query + inputs['rate_id_list'] = utilfunc.pylist_2_pglist(unique_rate_ids) + inputs['rate_id_list'] = inputs['rate_id_list'].replace("L", "") + + # get (only the required) rate jsons from postgres + sql = """SELECT a.rate_id_alias, a.rate_name, a.eia_id, a.json as rate_json + FROM diffusion_shared.urdb3_rate_jsons_20200721 a + WHERE a.rate_id_alias in ({rate_id_list});""".format(**inputs) + df = pd.read_sql(sql, con, coerce_float=False) + + return df + + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def filter_nem_year(df, year): + + # Filter by Sector Specific Sunset Years + df = df.loc[(df['first_year'] <= year) & (df['sunset_year'] >= year)] + + return df + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def get_nem_settings(state_limits, state_by_sector, utility_by_sector, selected_scenario, year, state_capacity_by_year, cf_during_peak_demand): + + # Find States That Have Not Sunset + valid_states = filter_nem_year(state_limits, year) + + # Filter States to Those That Have Not Exceeded Cumulative Capacity Constraints + valid_states['filter_year'] = pd.to_numeric(valid_states['max_reference_year'], errors='coerce') + valid_states['filter_year'][valid_states['max_reference_year'] == 'previous'] = year - 2 + valid_states['filter_year'][valid_states['max_reference_year'] == 'current'] = year + valid_states['filter_year'][pd.isnull(valid_states['filter_year'])] = year + + state_df = pd.merge(state_capacity_by_year, valid_states , how='left', on=['state_abbr']) + state_df = state_df[state_df['year'] == state_df['filter_year'] ] + state_df = state_df.merge(cf_during_peak_demand, on = 'state_abbr') + + state_df = state_df.loc[ pd.isnull(state_df['max_cum_capacity_mw']) | ( pd.notnull( state_df['max_cum_capacity_mw']) & (state_df['cum_system_mw'] < state_df['max_cum_capacity_mw']))] + # Calculate the maximum MW of solar capacity before reaching the NEM cap. MW are determine on a generation basis during the period of peak demand, as determined by ReEDS. + # CF during peak period is based on ReEDS H17 timeslice, assuming average over south-facing 15 degree tilt systems (so this could be improved by using the actual tilts selected) + state_df['max_mw'] = (state_df['max_pct_cum_capacity']/100) * state_df['peak_demand_mw'] / state_df['solar_cf_during_peak_demand_period'] + state_df = state_df.loc[ pd.isnull(state_df['max_pct_cum_capacity']) | ( pd.notnull( state_df['max_pct_cum_capacity']) & (state_df['max_mw'] > state_df['cum_system_mw']))] + + # Filter state and sector data to those that have not sunset + selected_state_by_sector = state_by_sector.loc[state_by_sector['scenario'] == selected_scenario] + valid_state_sector = filter_nem_year(selected_state_by_sector, year) + + # Filter state and sector data to those that match states which have not sunset/reached peak capacity + valid_state_sector = valid_state_sector[valid_state_sector['state_abbr'].isin(state_df['state_abbr'].values)] + + # Filter utility and sector data to those that have not sunset + selected_utility_by_sector = utility_by_sector.loc[utility_by_sector['scenario'] == selected_scenario] + valid_utility_sector = filter_nem_year(selected_utility_by_sector, year) + + # Filter out utility/sector combinations in states where capacity constraints have been reached + # Assumes that utilities adhere to broader state capacity constraints, and not their own + valid_utility_sector = valid_utility_sector[valid_utility_sector['state_abbr'].isin(state_df['state_abbr'].values)] + + # Return State/Sector data (or null) for all combinations of states and sectors + full_state_list = state_by_sector.loc[ state_by_sector['scenario'] == 'BAU' ].loc[:, ['state_abbr', 'sector_abbr']] + state_result = pd.merge( full_state_list, valid_state_sector, how='left', on=['state_abbr','sector_abbr'] ) + state_result['nem_system_kw_limit'].fillna(0, inplace=True) + + # Return Utility/Sector data (or null) for all combinations of utilities and sectors + full_utility_list = utility_by_sector.loc[ utility_by_sector['scenario'] == 'BAU' ].loc[:, ['eia_id','sector_abbr','state_abbr']] + utility_result = pd.merge( full_utility_list, valid_utility_sector, how='left', on=['eia_id','sector_abbr','state_abbr'] ) + utility_result['nem_system_kw_limit'].fillna(0, inplace=True) + + return state_result, utility_result + + +def get_and_apply_agent_load_profiles(con, agent): + inputs = locals().copy() + inputs['bldg_id'] = agent.loc['bldg_id'] + inputs['sector_abbr'] = agent.loc['sector_abbr'] + inputs['state_abbr'] = agent.loc['state_abbr'] + + sql = """SELECT bldg_id, sector_abbr, state_abbr, + kwh_load_profile as consumption_hourly + FROM diffusion_load_profiles.{sector_abbr}stock_load_profiles + WHERE bldg_id = {bldg_id} + AND sector_abbr = '{sector_abbr}' + AND state_abbr = '{state_abbr}';""".format(**inputs) + + df = pd.read_sql(sql, con, coerce_float=False) + + df = df[['consumption_hourly']] + + df['load_kwh_per_customer_in_bin'] = agent.loc['load_kwh_per_customer_in_bin'] + + # scale the normalized profile to sum to the total load + df = df.apply(scale_array_sum, axis=1, args=( + 'consumption_hourly', 'load_kwh_per_customer_in_bin')) + + + return df + +#%% +def get_and_apply_normalized_hourly_resource_solar(con, agent): + + inputs = locals().copy() + + inputs['solar_re_9809_gid'] = agent.loc['solar_re_9809_gid'] + inputs['tilt'] = agent.loc['tilt'] + inputs['azimuth'] = agent.loc['azimuth'] + + sql = """SELECT solar_re_9809_gid, tilt, azimuth, + cf as generation_hourly, + 1e6 as scale_offset + FROM diffusion_resource_solar.solar_resource_hourly + WHERE solar_re_9809_gid = '{solar_re_9809_gid}' + AND tilt = '{tilt}' + AND azimuth = '{azimuth}';""".format(**inputs) + + df = pd.read_sql(sql, con, coerce_float=False) + + df = df[['generation_hourly', 'scale_offset']] + + # rename the column generation_hourly to solar_cf_profile + df.rename(columns={'generation_hourly':'solar_cf_profile'}, inplace=True) + + return df + + +#%% +def scale_array_precision(row, array_col, prec_offset_col): + + row[array_col] = np.array( + row[array_col], dtype='float64') / row[prec_offset_col] + + return row + + +#%% +def scale_array_sum(row, array_col, scale_col): + + hourly_array = np.array(row[array_col], dtype='float64') + row[array_col] = hourly_array / \ + hourly_array.sum() * np.float64(row[scale_col]) + + return row + + +#%% +def interpolate_array(row, array_1_col, array_2_col, interp_factor_col, out_col): + + if row[interp_factor_col] != 0: + interpolated = row[interp_factor_col] * \ + (row[array_2_col] - row[array_1_col]) + row[array_1_col] + else: + interpolated = row[array_1_col] + row[out_col] = interpolated + + return row + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_carbon_intensities(dataframe, carbon_intensities): + + dataframe = dataframe.reset_index() + + dataframe = pd.merge(dataframe, carbon_intensities, how='left', on=['state_abbr', 'year']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_wholesale_elec_prices(dataframe, wholesale_elec_prices): + + dataframe = dataframe.reset_index() + + dataframe = pd.merge(dataframe, wholesale_elec_prices, how='left', on=['county_id', 'year']) + + dataframe = dataframe.set_index('agent_id') + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def get_state_starting_capacities(con, schema): + + inputs = locals().copy() + + # sql = """SELECT * + # FROM {schema}.state_starting_capacities_to_model;""".format(**inputs) + # df = pd.read_sql(sql, con) + + + # get state starting capacities for both solar and storage, distinguished by column names + sql = """WITH all_combos AS( + SELECT state_abbr, unnest(ARRAY['res','com','ind']) as sector_abbr + FROM diffusion_shared.state_abbr_lkup + WHERE state_abbr NOT IN ('AK','HI','PR') + ), solar AS( + SELECT sector_abbr, state_abbr, system_mw, systems_count AS pv_systems_count + FROM {schema}.state_starting_capacities_to_model + WHERE tech = 'solar' + ), storage AS( + SELECT sector_abbr, state_abbr, system_mw AS batt_mw, system_mwh as batt_mwh, systems_count AS batt_systems_count + FROM {schema}.state_starting_capacities_to_model + WHERE tech = 'storage' + ) + SELECT a.state_abbr, a.sector_abbr, b.system_mw, c.batt_mw, c.batt_mwh, b.pv_systems_count, c.batt_systems_count + FROM all_combos a + LEFT JOIN solar b + ON a.state_abbr = b.state_abbr AND a.sector_abbr = b.sector_abbr + LEFT JOIN storage c + ON a.state_abbr = c.state_abbr AND a.sector_abbr = c.sector_abbr;""".format(**inputs) + df = pd.read_sql(sql, con).fillna(0.) + + return df + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_state_incentives(dataframe, state_incentives, year, start_year, state_capacity_by_year, end_date = datetime.date(2029, 1, 1)): + + dataframe = dataframe.reset_index() + + # Fill in missing end_dates + if bool(end_date): + state_incentives['end_date'][pd.isnull(state_incentives['end_date'])] = end_date + + #Adjust incenctives to account for reduced values as adoption increases + yearly_escalation_function = lambda value, end_year: max(value - value * (1.0 / (end_year - start_year)) * (year-start_year), 0) + for field in ['pbi_usd_p_kwh','cbi_usd_p_w','ibi_pct','cbi_usd_p_wh']: + state_incentives[field] = state_incentives.apply(lambda row: yearly_escalation_function(row[field], row['end_date'].year), axis=1) + + # Filter Incentives by the Years in which they are valid + state_incentives = state_incentives.loc[ + pd.isnull(state_incentives['start_date']) | (pd.to_datetime(state_incentives['start_date']).dt.year <= year)] + state_incentives = state_incentives.loc[ + pd.isnull(state_incentives['end_date']) | (pd.to_datetime(state_incentives['end_date']).dt.year >= year)] + + # Combine valid incentives with the cumulative metrics for each state up until the current year + state_capacity_by_year = state_capacity_by_year.loc[state_capacity_by_year['year'] == year] + state_incentives_mg = state_incentives.merge(state_capacity_by_year, + how='left', on=["state_abbr"]) + + # Filter where the states have not exceeded their cumulative installed capacity (by mw or pct generation) or total program budget + #state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['incentive_cap_total_pct']) | (state_incentives_mg['cum_capacity_pct'] < state_incentives_mg['incentive_cap_total_pct'])] + state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['incentive_cap_total_mw']) | (state_incentives_mg['cum_system_mw'] < state_incentives_mg['incentive_cap_total_mw'])] + state_incentives_mg = state_incentives_mg.loc[pd.isnull(state_incentives_mg['budget_total_usd']) | (state_incentives_mg['cum_incentive_spending_usd'] < state_incentives_mg['budget_total_usd'])] + + output =[] + for i in state_incentives_mg.groupby(['state_abbr', 'sector_abbr']): + row = i[1] + state, sector = i[0] + output.append({'state_abbr':state, 'sector_abbr':sector,"state_incentives":row}) + + state_inc_df = pd.DataFrame(columns=['state_abbr', 'sector_abbr', 'state_incentives']) + state_inc_df = pd.concat([state_inc_df, pd.DataFrame.from_records(output)], sort=False) + + dataframe = pd.merge(dataframe, state_inc_df, on=['state_abbr','sector_abbr'], how='left') + + dataframe = dataframe.set_index('agent_id') + return dataframe + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def estimate_initial_market_shares(dataframe, state_starting_capacities_df): + + # record input columns + in_cols = list(dataframe.columns) + + # find the total number of customers in each state (by technology and + # sector) + state_total_developable_customers = dataframe[['state_abbr', 'sector_abbr', 'tech', 'developable_agent_weight']].groupby( + ['state_abbr', 'sector_abbr', 'tech']).sum().reset_index() + state_total_agents = dataframe[['state_abbr', 'sector_abbr', 'tech', 'developable_agent_weight']].groupby( + ['state_abbr', 'sector_abbr', 'tech']).count().reset_index() + # rename the final columns + state_total_developable_customers.columns = state_total_developable_customers.columns.str.replace( + 'developable_agent_weight', 'developable_customers_in_state') + state_total_agents.columns = state_total_agents.columns.str.replace( + 'developable_agent_weight', 'agent_count') + # merge together + state_denominators = pd.merge(state_total_developable_customers, state_total_agents, how='left', on=[ + 'state_abbr', 'sector_abbr', 'tech']) + + # merge back to the main dataframe + dataframe = pd.merge(dataframe, state_denominators, how='left', on=[ + 'state_abbr', 'sector_abbr', 'tech']) + + # merge in the state starting capacities + dataframe = pd.merge(dataframe, state_starting_capacities_df, how='left', + on=['state_abbr', 'sector_abbr']) + + + # determine the portion of initial load and systems that should be allocated to each agent + # (when there are no developable agents in the state, simply apportion evenly to all agents) + dataframe['portion_of_state'] = np.where(dataframe['developable_customers_in_state'] > 0, + dataframe[ + 'developable_agent_weight'] / dataframe['developable_customers_in_state'], + 1. / dataframe['agent_count']) + + # apply the agent's portion to the total to calculate starting capacity and systems + dataframe['adopters_cum_last_year'] = dataframe['portion_of_state'] * dataframe['pv_systems_count'] + dataframe['batt_adopters_cum_last_year'] = dataframe['portion_of_state'] * dataframe['batt_systems_count'] + dataframe['system_kw_cum_last_year'] = dataframe['portion_of_state'] * dataframe['system_mw'] * 1000. + dataframe['batt_kw_cum_last_year'] = dataframe['portion_of_state'] * dataframe['batt_mw'] * 1000.0 + dataframe['batt_kwh_cum_last_year'] = dataframe['portion_of_state'] * dataframe['batt_mwh'] * 1000.0 + + dataframe['market_share_last_year'] = np.where(dataframe['developable_agent_weight'] == 0, 0, + dataframe['adopters_cum_last_year'] / dataframe['developable_agent_weight']) + + dataframe['market_value_last_year'] = dataframe['system_capex_per_kw'] * dataframe['system_kw_cum_last_year'] + + # reproduce these columns as "initial" columns too + dataframe['initial_number_of_adopters'] = dataframe['adopters_cum_last_year'] + dataframe['initial_pv_kw'] = dataframe['system_kw_cum_last_year'] + dataframe['initial_batt_kw'] = dataframe['batt_kw_cum_last_year'] + dataframe['initial_batt_kwh'] = dataframe['batt_kwh_cum_last_year'] + dataframe['initial_market_share'] = dataframe['market_share_last_year'] + dataframe['initial_market_value'] = 0 + + # isolate the return columns + return_cols = ['initial_number_of_adopters','initial_pv_kw','initial_batt_kw','initial_batt_kwh','initial_market_share','initial_market_value', + 'adopters_cum_last_year','system_kw_cum_last_year','batt_kw_cum_last_year','batt_kwh_cum_last_year','market_share_last_year','market_value_last_year'] + + dataframe[return_cols] = dataframe[return_cols].fillna(0) + + out_cols = in_cols + return_cols + + return dataframe[out_cols] + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def apply_market_last_year(dataframe, market_last_year_df): + + dataframe = dataframe.merge(market_last_year_df, on=['agent_id'], how='left') + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def estimate_total_generation(dataframe): + + dataframe['total_gen_twh'] = ((dataframe['number_of_adopters'] - dataframe['initial_number_of_adopters']) + * dataframe['annual_energy_production_kwh'] * 1e-9) + (0.23 * 8760 * dataframe['initial_pv_kw'] * 1e-6) + + return dataframe + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def calc_state_capacity_by_year(con, schema, load_growth, peak_demand_mw, is_first_year, year,solar_agents, last_year_installed_capacity): + + if is_first_year: + # get state starting capacities for solar & storage and sum by state + df = last_year_installed_capacity.groupby('state_abbr')[['system_mw','batt_mw','batt_mwh']].sum().reset_index() + df.rename(columns={'system_mw':'cum_system_mw', 'batt_mw':'cum_batt_mw', 'batt_mwh':'cum_batt_mwh'}, inplace=True) + + # Not all states have starting capacity, don't want to drop any states thus left join on peak_demand + df = peak_demand_mw.merge(df, how='left').fillna(0) + + # rename columns + df.rename(columns={'peak_demand_mw_2014':'peak_demand_mw'}, inplace=True) + + + else: + df = last_year_installed_capacity.copy() + df['cum_system_mw'] = df['system_kw_cum']/1000 + df['cum_batt_mw'] = df['batt_kw_cum']/1000 + df['cum_batt_mwh'] = df['batt_kwh_cum']/1000 + + load_growth_this_year = load_growth.loc[(load_growth['year'] == year) & (load_growth['sector_abbr'] == 'res')] + load_growth_this_year = pd.merge(solar_agents.df[['state_abbr', 'county_id']], load_growth_this_year, how='left', on=['county_id']) + load_growth_this_year = load_growth_this_year.groupby('state_abbr')['load_multiplier'].mean().reset_index() + df = df.merge(load_growth_this_year, on = 'state_abbr') + + df = peak_demand_mw.merge(df, how='left', on='state_abbr').fillna(0) + df['peak_demand_mw'] = df['peak_demand_mw_2014'] * df['load_multiplier'] + + # TODO: drop cum_capacity_pct from table (misnomer) + df['cum_capacity_pct'] = 0 + # TODO: enforce program spending cap + df['cum_incentive_spending_usd'] = 0 + df['year'] = year + + df = df[['state_abbr','cum_system_mw','cum_batt_mw','cum_batt_mwh','cum_capacity_pct','cum_incentive_spending_usd','peak_demand_mw','year']] + + return df + + +#%% +def get_rate_switch_table(con): + + # get rate switch table from database + sql = """SELECT * FROM diffusion_shared.rate_switch_lkup_2020;""" + + rate_switch_table = pd.read_sql(sql, con, coerce_float=False) + rate_switch_table = rate_switch_table.reset_index(drop=True) + + return rate_switch_table + +def apply_rate_switch(rate_switch_table, agent, system_size_kw, tech='solar'): + + rate_switch_table = rate_switch_table.loc[rate_switch_table['tech'] == tech] + rate_switch_table.rename(columns={'rate_id_alias':'tariff_id', 'json':'tariff_dict'}, inplace=True) + rate_switch_table = rate_switch_table[(rate_switch_table['eia_id'] == agent.loc['eia_id']) & + (rate_switch_table['res_com'] == str(agent.loc['sector_abbr']).upper()[0]) & + (rate_switch_table['min_kw_limit'] <= system_size_kw) & + (rate_switch_table['max_kw_limit'] > system_size_kw)] + + rate_switch_table = rate_switch_table.reset_index(drop=True) + + # check if a DG rate is applicable to agent + if (system_size_kw > 0) & (len(rate_switch_table) == 1): + # if valid DG rate available to agent, force NEM on + agent['nem_system_kw_limit'] = 1e6 + # update agent attributes to DG rate + agent['tariff_id'] = rate_switch_table['tariff_id'][0] + agent['tariff_dict'] = rate_switch_table['tariff_dict'][0] + # return any one time charges (e.g., interconnection fees) + one_time_charge = rate_switch_table['one_time_charge'][0] + else: + # don't update agent attributes, return one time charge of $0 + one_time_charge = 0. + + + return agent, one_time_charge + + +#%% +@decorators.fn_timer(logger=logger, tab_level=2, prefix='') +def reassign_agent_tariffs(dataframe, con): + + # define rates to use in replacement of incorrect tariffs + + # map res/com tariffs based on most likely tariff in state + res_tariffs = { + 'AL':17279, # Family Dwelling Service + 'AR':16671, # Optional Residential Time-Of-Use (RT) Single Phase + 'AZ':15704, # Residential Time of Use (Saver Choice) TOU-E + 'CA':15747, # E-1 -Residential Service Baseline Region P + 'CO':17078, # Residential Service (Schedule R) + 'CT':16678, # Rate 1 - Residential Electric Service + 'DC':16806, # Residential - Schedule R + 'DE':11569, # Residential Service + 'FL':16986, # RS-1 Residential Service + 'GA':16649, # SCHEDULE R-22 RESIDENTIAL SERVICE + 'IA':11693, # Optional Residential Service + 'ID':16227, # Schedule 1: Residential Rates + 'IL':16045, # DS-1 Residential Zone 1 + 'IN':15491, # RS - Residential Service + 'KS':8178, # M System Residential Service + 'KY':16566, # Residential Service + 'LA':16352, # Residential and Farm Service - Single Phase (RS-L) + 'MA':15953, # Greater Boston Residential R-1 (A1) + 'MD':14779, # Residential Service (R) + 'ME':15984, # A Residential Standard Offer Service (Bundled) + 'MI':16265, # Residential Service - Secondary (Rate RS) + 'MN':15556, # Residential Service - Overhead Standard (A01) + 'MO':17207, # 1(M) Residential Service Rate + 'MS':16788, # Residential Service Single Phase (RS-38C) + 'MT':5216, # Single Phase + 'NC':16938, # Residential Service (RES-41) Single Phase + 'ND':14016, # Residential Service Rate 10 + 'NE':13817, # Residential Service + 'NH':16605, # Residential Service + 'NJ':16229, # RS - Residential Service + 'NM':8692, # 1A (Residential Service) + 'NV':16701, # D-1 (Residential Service) + 'NY':16902, # SC1- Zone A + 'OH':16892, # RS (Residential Service) + 'OK':15258, # Residential Service (R-1) + 'OR':15847, # Schedule 4 - Residential (Single Phase) + 'PA':17237, # RS (Residential Service) + 'RI':16598, # A-16 (Residential Service) + 'SC':15744, # Residential - RS (SC) + 'SD':1216, # Town and Rural Residential Rate + 'TN':15149, # Residential Electric Service + 'TX':16710, # Residential Service - Time Of Day + 'UT':15847, # Schedule 4 - Residential (Single Phase) + 'VA':17067, # Residential Schedule 1 + 'VT':16544, # Rate 01 Residential Service + 'WA':16305, # 10 (Residential and Farm Primary General Service) + 'WI':15543, # Residential Rg-1 + 'WV':15515, # Residential Service A + 'WY':15847 # Schedule 4 - Residential (Single Phase) + } + + com_tariffs = { + 'AL':15494, # BTA - BUSINESS TIME ADVANTAGE (OPTIONAL) - Primary + 'AR':16674, # Small General Service (SGS) + 'AZ':10742, # LGS-TOU- N - Large General Service Time-of-Use + 'CA':17057, # A-10 Medium General Demand Service (Secondary Voltage) + 'CO':17102, # Commercial Service (Schedule C) + 'CT':16684, # Rate 35 Intermediate General Electric Service + 'DC':15336, # General Service (Schedule GS) + 'DE':1199, # Schedule LC-P Large Commercial Primary + 'FL':13790, # SDTR-1 (Option A) + 'GA':1905, # SCHEDULE TOU-MB-4 TIME OF USE - MULTIPLE BUSINESS + 'IA':11705, # Three Phase Farm + 'ID':14782, # Large General Service (3 Phase)-Schedule 21 + 'IL':1567, # General Service Three Phase standard + 'IN':15492, # CS - Commercial Service + 'KS':14736, # Generation Substitution Service + 'KY':17179, # General Service (Single Phase) + 'LA':17220, # Large General Service (LGS-L) + 'MA':16005, # Western Massachusetts Primary General Service G-2 + 'MD':2659, # Commercial + 'ME':16125, # General Service Rate + 'MI':5355, # Large Power Service (LP4) + 'MN':15566, # General Service (A14) Secondary Voltage + 'MO':17208, # 2(M) Small General Service - Single phase + 'MS':13427, # General Service - Low Voltage Single-Phase (GS-LVS-14) + 'MT':10707, # Three Phase + 'NC':16947, # General Service (GS-41) + 'ND':14035, # Small General Electric Service rate 20 (Demand Metered; Non-Demand) + 'NE':13818, # General Service Single-Phase + 'NH':16620, # GV Commercial and Industrial Service + 'NJ':17095, # AGS Secondary- BGS-RSCP + 'NM':15769, # 2A (Small Power Service) + 'NV':13724, # OGS-2-TOU + 'NY':15940, # SC-9 - General Large High Tension Service [Westchester] + 'OH':16873, # GS (General Service-Secondary) + 'OK':17144, # GS-TOU (General Service Time-Of-Use) + 'OR':15829, # Small Non-Residential Direct Access Service, Single Phase (Rate 532) + 'PA':7066, # Large Power 2 (LP2) + 'RI':16600, # G-02 (General C & I Rate) + 'SC':16207, # 3 (Municipal Power Service) + 'SD':3650, # Small Commercial + 'TN':15154, # Medium General Service (Primary) + 'TX':6001, # Medium Non-Residential LSP POLR + 'UT':3478, # SCHEDULE GS - 3 Phase General Service + 'VA':16557, # Small General Service Schedule 5 + 'VT':16543, # Rate 06: General Service + 'WA':16306, # 40 (Large Demand General Service over 3MW - Primary) + 'WI':6620, # Cg-7 General Service Time-of-Day (Primary) + 'WV':15518, # General Service C + 'WY':3878 # General Service (GS)-Three phase + } + + # map industrial tariffs based on census division + ind_tariffs = { + 'SA':16657, # Georgia Power Co, Schedule TOU-GSD-10 Time Of Use - General Service Demand + 'WSC':15919, # Southwestern Public Service Co (Texas), Large General Service - Inside City Limits 115 KV + 'PAC':15864, # PacifiCorp (Oregon), Schedule 47 - Secondary (Less than 4000 kW) + 'MA':16525, # New York State Elec & Gas Corp, All Regions - SERVICE CLASSIFICATION NO. 7-1 Large General Service TOU - Secondary -ESCO + 'MTN':17101, # Public Service Co of Colorado, Secondary General Service (Schedule SG) + 'ENC':15526, # Wisconsin Power & Light Co, Industrial Power Cp-1 (Secondary) + 'NE':16635, # Delmarva Power, General Service - Primary + 'ESC':15490, # Alabama Power Co, LPM - LIGHT AND POWER SERVICE - MEDIUM + 'WNC':6642 # Northern States Power Co - Wisconsin, Cg-9.1 Large General Time-of-Day Primary Mandatory Customers + } + + dataframe = dataframe.reset_index() + + # separate agents with incorrect and correct rates + bad_rates = dataframe.loc[np.in1d(dataframe['tariff_id'], [4145, 7111, 8498, 10953, 10954, 12003])] + good_rates = dataframe.loc[~np.in1d(dataframe['tariff_id'], [4145, 7111, 8498, 10953, 10954, 12003])] + + # if incorrect rates exist, grab the correct ones from the rates table + if len(bad_rates) > 0: + + # set new tariff_id based on location + bad_rates['tariff_id'] = np.where(bad_rates['sector_abbr']=='res', + bad_rates['state_abbr'].map(res_tariffs), + np.where(bad_rates['sector_abbr']=='com', + bad_rates['state_abbr'].map(com_tariffs), + bad_rates['census_division_abbr'].map(ind_tariffs))) + + # get json objects for new rates and rename columns in preparation for merge + new_rates_json_df = get_electric_rates_json(con, bad_rates['tariff_id'].tolist()) + new_rates_json_df = (new_rates_json_df + .drop(['rate_name','eia_id'], axis='columns') + .rename(columns={'rate_id_alias':'tariff_id','rate_json':'tariff_dict'}) + ) + + # drop bad tariff_dict from agent dataframe and merge correct one + bad_rates = bad_rates.drop(['tariff_dict'], axis='columns') + bad_rates = bad_rates.merge(new_rates_json_df, how='left', on='tariff_id') + + # reconstruct full agent dataframe + dataframe = pd.concat([good_rates, bad_rates], ignore_index=True, sort=False) + dataframe = dataframe.set_index('agent_id') + + return dataframe \ No newline at end of file From b444902068c08d5cbc09b95001d9bc8cf6338f0f Mon Sep 17 00:00:00 2001 From: michaelavs Date: Thu, 21 Nov 2024 13:04:53 -0700 Subject: [PATCH 12/17] Changes from PR 37 --- dgen_os/python/agents.py | 24 ++++++++++++------------ dgen_os/python/dgen_model.py | 13 +++++-------- 2 files changed, 17 insertions(+), 20 deletions(-) diff --git a/dgen_os/python/agents.py b/dgen_os/python/agents.py index 682e9da..cdafdcf 100644 --- a/dgen_os/python/agents.py +++ b/dgen_os/python/agents.py @@ -126,6 +126,7 @@ def chunk_on_row(self, func, cores=1, in_place=True, **kwargs): self.df = results_df self.df.set_index('agent_id', drop=True) self.update_attrs + else: results_df.set_index('agent_id', drop=True) return results_df @@ -139,8 +140,10 @@ def on_frame(self, func, func_args=None, in_place=True, **kwargs): self.df = results_df self.df.set_index('agent_id', inplace=True, drop=True) self.update_attrs + else: results_df.set_index('agent_id', inplace=True, drop=True) + return results_df def run_with_runtime_tests(self, how_to_apply, func, func_args=None, cores=None, **kwargs): @@ -194,10 +197,11 @@ def run_with_runtime_tests(self, how_to_apply, func, func_args=None, cores=None, elif how_to_apply == 'chunk_on_row': results_df = self.apply_chunk_on_row(func, cores=cores, **kwargs) results_df['agent_id'] = results_df['agent_id'].astype(int) - results_df = pd.merge(self.df, results_df, on='agent_id') + results_df = pd.merge(self.df, results_df.drop_duplicates(subset='agent_id'), on='agent_id') + elif how_to_apply == 'on_frame': results_df = self.apply_on_frame(func, func_args=func_args, **kwargs) - + # --- Drop any bad columns added by function --- results_df = results_df.drop(['level_0','index'], axis='columns', errors='ignore') @@ -227,6 +231,7 @@ def run_with_runtime_tests(self, how_to_apply, func, func_args=None, cores=None, #check for NaNs in new columns null_columns = results_df[new_columns].columns[results_df[new_columns].isna().any()].tolist() null_columns = [c for c in null_columns if c not in config.NULL_COLUMN_EXCEPTIONS] + if len(null_columns) > 0: null_agents = [] for i in null_columns: @@ -283,10 +288,8 @@ def apply_on_row(self, func, cores=None, **kwargs): apply_func = partial(func, **kwargs) results_df = self.df.apply(apply_func, axis=1) else: - if 'ix' not in os.name: - EXECUTOR = cf.ThreadPoolExecutor - else: - EXECUTOR = cf.ProcessPoolExecutor + + EXECUTOR = cf.ProcessPoolExecutor futures = [] with EXECUTOR(max_workers=cores) as executor: @@ -323,16 +326,13 @@ def apply_chunk_on_row(self, func, cores=None, **kwargs): Dataframe of agents after application of func """ print('\t\t\t============ APPLY CHUNK ON ROW ============') - + # --- apply function --- if cores is None: apply_func = partial(func, **kwargs) results_df = self.df.apply(apply_func, axis=1) else: - if 'ix' not in os.name: - EXECUTOR = cf.ThreadPoolExecutor - else: - EXECUTOR = cf.ProcessPoolExecutor + EXECUTOR = cf.ProcessPoolExecutor logger.info('Number of Workers inside chunk_on_row is {}'.format(cores)) futures = [] @@ -378,7 +378,7 @@ def apply_on_frame(self, func, func_args, **kwargs): results_df = func(self.df, *func_args, **kwargs) else: results_df = func(self.df, func_args, **kwargs) - + return results_df def to_pickle(self, file_name): diff --git a/dgen_os/python/dgen_model.py b/dgen_os/python/dgen_model.py index f09f865..ef5ed49 100644 --- a/dgen_os/python/dgen_model.py +++ b/dgen_os/python/dgen_model.py @@ -49,7 +49,7 @@ def main(mode = None, resume_year = None, endyear = None, ReEDS_inputs = None): # initialize Model Settings object # (this controls settings that apply to all scenarios to be executed) model_settings = settings.init_model_settings() - + # make output directory os.makedirs(model_settings.out_dir) # create the logger @@ -99,7 +99,6 @@ def main(mode = None, resume_year = None, endyear = None, ReEDS_inputs = None): # get other datasets needed for the model run logger.info('Getting various scenario parameters') - schema = scenario_settings.schema max_market_share = datfunc.get_max_market_share(con, schema) load_growth_scenario = scenario_settings.load_growth.lower() @@ -121,7 +120,8 @@ def main(mode = None, resume_year = None, endyear = None, ReEDS_inputs = None): # ========================================================= solar_agents = iFuncs.import_agent_file(scenario_settings, con, cur, engine, model_settings, agent_file_status, input_name='agent_file') - + + # Get set of columns that define agent's immutable attributes cols_base = list(solar_agents.df.columns) @@ -172,7 +172,7 @@ def main(mode = None, resume_year = None, endyear = None, ReEDS_inputs = None): # is it the first model year? is_first_year = year == model_settings.start_year - + # get and apply load growth solar_agents.on_frame(agent_mutation.elec.apply_load_growth, (load_growth)) @@ -221,10 +221,7 @@ def main(mode = None, resume_year = None, endyear = None, ReEDS_inputs = None): # Apply host-owned financial parameters solar_agents.on_frame(agent_mutation.elec.apply_financial_params, [financing_terms, itc_options, inflation_rate]) - if 'ix' not in os.name: - cores = None - else: - cores = model_settings.local_cores + cores = model_settings.local_cores # Apply state incentives solar_agents.on_frame(agent_mutation.elec.apply_state_incentives, [state_incentives, year, model_settings.start_year, state_capacity_by_year]) From fea805369dc096900f2ba8d32ab8fc8292c9260f Mon Sep 17 00:00:00 2001 From: michaelavs Date: Thu, 21 Nov 2024 13:22:40 -0700 Subject: [PATCH 13/17] updates from PR 38 --- dgen_os/python/input_data_functions.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/dgen_os/python/input_data_functions.py b/dgen_os/python/input_data_functions.py index 885dc74..c6ace1b 100644 --- a/dgen_os/python/input_data_functions.py +++ b/dgen_os/python/input_data_functions.py @@ -1,4 +1,5 @@ import pandas as pd + import numpy as np import os import sqlalchemy @@ -133,8 +134,9 @@ def df_to_psql(df, engine, schema, owner, name, if_exists='replace', append_tran if if_exists == 'append': fields = [i.lower() for i in get_psql_table_fields(engine, schema, name)] for f in list(set(df.columns.values) - set(fields)): - sql = text("ALTER TABLE {}.{} ADD COLUMN {} {}".format(schema, name, f, sql_type[f])) - conn.execute(sql) + with conn.begin(): + sql = text("ALTER TABLE {}.{} ADD COLUMN {} {}".format(schema, name, f, sql_type[f])) + conn.execute(sql) df.to_sql(name, engine, schema=schema, index=False, dtype=d_types, if_exists=if_exists) sql = text('ALTER TABLE {}."{}" OWNER to "{}"'.format(schema, name, owner)) @@ -423,7 +425,6 @@ def import_agent_file(scenario_settings, con, cur, engine, model_settings, agent raise ValueError('Region not present within pre-generated agent file - Edit Inputsheet') solar_agents = Agents(solar_agents_df) - solar_agents.on_frame(agent_mutation.elec.reassign_agent_tariffs, con) else: From 1a64a0cdf3c809e54c27107ddc99ee85f19f7da0 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Thu, 21 Nov 2024 15:01:43 -0700 Subject: [PATCH 14/17] Updates for config file to remove pyarrow use --- dgen_os/python/config.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dgen_os/python/config.py b/dgen_os/python/config.py index 73cf39d..bc6cf40 100644 --- a/dgen_os/python/config.py +++ b/dgen_os/python/config.py @@ -1,7 +1,7 @@ import os import multiprocessing -import pyarrow as pa -import pyarrow.parquet as pq + + #============================================================================== # get postgres connection parameters From ae82ee798b760ea0713557ffafa38af1ed38f8b1 Mon Sep 17 00:00:00 2001 From: Lockshin Date: Mon, 25 Nov 2024 12:34:14 -0900 Subject: [PATCH 15/17] updating deprec schedules to ATB Moderate 2024 --- .../deprec_sch_FY24.csv | 88 +++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 dgen_os/input_data/depreciation_schedules/deprec_sch_FY24.csv diff --git a/dgen_os/input_data/depreciation_schedules/deprec_sch_FY24.csv b/dgen_os/input_data/depreciation_schedules/deprec_sch_FY24.csv new file mode 100644 index 0000000..747beb6 --- /dev/null +++ b/dgen_os/input_data/depreciation_schedules/deprec_sch_FY24.csv @@ -0,0 +1,88 @@ +year,sector_abbr,1,2,3,4,5,6 +2022,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2023,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2024,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2025,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2026,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2027,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2028,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2029,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2030,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 +2031,com,0.941301185,0.886047921,0.834037959,0.785080919,0.7389976,0.695619316 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sqlalchemy - - nrel-pysam=4.2.0 - - pip: - - nrel-pysam-stubs \ No newline at end of file + - nrel-pysam>=5.1.0 diff --git a/dgen_os/python/dg3n.yml b/dgen_os/python/dg3n.yml index 9681156..c34e9ba 100644 --- a/dgen_os/python/dg3n.yml +++ b/dgen_os/python/dg3n.yml @@ -1,8 +1,6 @@ name: dg3n channels: - - defaults - conda-forge - - anaconda - nrel dependencies: - colorama @@ -18,7 +16,6 @@ dependencies: - scipy - spyder - sqlalchemy - - nrel-pysam=4.2.0 - - pip: - - nrel-pysam-stubs + - nrel-pysam + From 5b55acf6375597ae75ef6ae53824475aca38bdc5 Mon Sep 17 00:00:00 2001 From: michaelavs Date: Thu, 5 Dec 2024 10:23:52 -0700 Subject: [PATCH 17/17] Removes imp due to depreciation after python 3.10 and unnecessary in model --- dgen_os/python/data_functions.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/dgen_os/python/data_functions.py b/dgen_os/python/data_functions.py index aa8c5e6..a8114ed 100644 --- a/dgen_os/python/data_functions.py +++ b/dgen_os/python/data_functions.py @@ -20,8 +20,6 @@ import pickle import sys import logging -import imp -imp.reload(logging) import sqlalchemy import json