Merge branch 'master' into input_data_updates

Author: michaelavs

.github/docker_actions/dgen/input_sheet_final.xlsm

@@ -5,7 +5,7 @@ on:
     branches:
       - master
   schedule:
-    - cron: '0 0 * * *' # Daily “At 00:00”
+    - cron: '0 0 1,15 * *' # First and fifteenth of month “At 00:00”
   workflow_dispatch:
 
 concurrency:

.github/workflows/ci.yml

@@ -1,7 +1,12 @@
 name: docker-compose-actions-workflow
 on:
   push:
+    branches:
+      - master
   pull_request:
+  schedule:
+    - cron: '0 0 1,15 * *' # First and fifteenth of month “At 00:00”
+
 jobs:
 
   docker_compose:

.github/workflows/docker_ci.yml

@@ -1,5 +1,5 @@
-| CI           |  [![GitHub Workflow Status][github-ci-badge]][github-ci-link] |
-| :----------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| CI           |  [![GitHub Workflow Status][github-ci-badge]][github-ci-link] [![GitHub Workflow Status][github-docker-badge]][github-docker-link] |
+| :----------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------: |
 | **Docs**     |                                                                    [![Documentation Status][rtd-badge]][rtd-link]                                                                    |
 | **Version**  |                            [![Version][version-badge]][version-link] |
 
@@ -204,6 +204,8 @@ Notes:
 
 [github-ci-badge]: https://img.shields.io/github/actions/workflow/status/NREL/dgen/ci.yml?branch=master&style=for-the-badge&label=CI
 [github-ci-link]: https://github.com/NREL/dgen/actions/workflows/ci.yml
+[github-docker-badge]: https://img.shields.io/github/actions/workflow/status/NREL/dgen/docker_ci.yml?branch=master&style=for-the-badge&label=Docker
+[github-docker-link]:https://github.com/NREL/dgen/actions/workflows/docker_ci.yml
 [rtd-badge]: https://img.shields.io/badge/docs-ready-blue.svg
 [rtd-link]: https://nrel.github.io/dgen/
 [version-link]: https://github.com/NREL/dgen/releases/latest

README.md

@@ -16,4 +16,4 @@ dependencies:
   - scipy
   - spyder
   - sqlalchemy
-  - nrel-pysam>=5.1.0
+  - nrel-pysam=7.1.0

build_envs/environment.yml

@@ -117,7 +117,7 @@ def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_ut
     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'])
+    temp_df = pd.merge(dataframe, net_metering_utility_df.drop_duplicates(), 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'])]
@@ -127,7 +127,7 @@ def apply_export_tariff_params(dataframe, net_metering_state_df, net_metering_ut
 
     # 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'])
+    agents_without_utility_nem = pd.merge(agents_without_utility_nem, net_metering_state_df.drop_duplicates(), 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)
@@ -401,11 +401,9 @@ def apply_financial_params(dataframe, financing_terms, itc_options, inflation_ra
     '''
     dataframe = dataframe.reset_index()
 
-    # Merge financing terms onto agent DataFrame
-    dataframe = dataframe.merge(financing_terms, how='left', on=['year', 'sector_abbr'])
+    dataframe = dataframe.merge(financing_terms.drop_duplicates(), how='left', on=['year', 'sector_abbr'])
 
-    # Merge ITC options on to DataFrame
-    dataframe = dataframe.merge(itc_options[['itc_fraction_of_capex', 'year', 'tech', 'sector_abbr']], 
+    dataframe = dataframe.merge(itc_options[['itc_fraction_of_capex', 'year', 'tech', 'sector_abbr']].drop_duplicates(), 
                                 how='left', on=['year', 'tech', 'sector_abbr'])
 
     # Set inflation rate data to 'inflation_rate' column in agent DataFrame
@@ -441,9 +439,7 @@ def apply_load_growth(dataframe, load_growth_df):
 
     # Create 'county_id' column in agent DataFrame
     dataframe["county_id"] = dataframe.county_id.astype(int)
-    
-    # Merge load growth data onto agent DataFrame
-    dataframe = pd.merge(dataframe, load_growth_df, how='left', on=['year', 'sector_abbr', 'county_id'])
+    dataframe = pd.merge(dataframe, load_growth_df.drop_duplicates(), how='left', on=['year', 'sector_abbr', 'county_id'])
 
     # For residential agents, load growth translates to kwh_per_customer change
     dataframe['load_kwh_per_customer_in_bin'] = np.where(dataframe['sector_abbr']=='res',
@@ -1049,17 +1045,16 @@ def estimate_initial_market_shares(dataframe, state_starting_capacities_df):
         '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=[
+    # merge together
+    state_denominators = pd.merge(state_total_developable_customers, state_total_agents.drop_duplicates(), how='left', on=[
                                   'state_abbr', 'sector_abbr', 'tech'])
 
-    # Merge back to the main dataframe
-    dataframe = pd.merge(dataframe, state_denominators, how='left', on=[
+    # merge back to the main dataframe
+    dataframe = pd.merge(dataframe, state_denominators.drop_duplicates(), how='left', on=[
                          'state_abbr', 'sector_abbr', 'tech'])
 
-    # Merge in the state starting capacities
-    dataframe = pd.merge(dataframe, state_starting_capacities_df, how='left',
+    # merge in the state starting capacities
+    dataframe = pd.merge(dataframe, state_starting_capacities_df.drop_duplicates(), how='left',
                          on=['state_abbr', 'sector_abbr'])
 
 

dgen_os/python/agent_mutation/elec.py

@@ -16,6 +16,6 @@ dependencies:
   - scipy
   - spyder
   - sqlalchemy
-  - nrel-pysam
+  - nrel-pysam=7.1.0
 
 

dgen_os/python/dg3n.yml

@@ -237,8 +237,7 @@ def calc_system_performance(kw, pv, utilityrate, loan, batt, costs, agent, rate_
     # Assign calculated values to the CashLoan module
     loan.SystemOutput.annual_energy_value = annual_energy_value 
     loan.SystemOutput.gen = utilityrate.SystemOutput.gen
-    loan.ThirdPartyOwnership.elec_cost_with_system = utilityrate.Outputs.elec_cost_with_system
-    loan.ThirdPartyOwnership.elec_cost_without_system = utilityrate.Outputs.elec_cost_without_system
+    loan.ChargesByMonth.utility_bill_w_sys = utilityrate.Outputs.utility_bill_w_sys
 
     # Calculate system costs
     direct_costs = (system_costs + batt_costs) * costs['cap_cost_multiplier']
@@ -327,15 +326,16 @@ def calc_system_size_and_performance(agent, rate_switch_table):
 
     # Set the battery type to use based on agent sector
     if agent.loc['sector_abbr'] == 'res':
-        batt = battery.default("GenericBatteryResidential")
+        batt = battery.default("CustomGenerationBatteryResidential")
     else:
-        batt = battery.default("GenericBatteryCommercial")
+        batt = battery.default("CustomGenerationBatteryCommercial")
 
     # Instantiate utilityrate5 model based on agent sector
     if agent.loc['sector_abbr'] == 'res':
-        utilityrate = utility.from_existing(batt, "GenericBatteryResidential")
+        utilityrate = utility.from_existing(batt, "CustomGenerationBatteryResidential")
     else:
-        utilityrate = utility.from_existing(batt, "GenericBatteryCommercial")
+        utilityrate = utility.from_existing(batt, "CustomGenerationBatteryCommercial")
+    tariff_dict = agent.loc['tariff_dict']
 
     ######################################
     ###--------- UTILITYRATE5 ---------###
@@ -424,11 +424,11 @@ def calc_system_size_and_performance(agent, rate_switch_table):
     # 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.from_existing(utilityrate, "GenericBatteryResidential")
+        loan = cashloan.from_existing(utilityrate, "CustomGenerationBatteryResidential")
         loan.FinancialParameters.market = 0
 
     else:
-        loan = cashloan.from_existing(utilityrate, "GenericBatteryCommercial")
+        loan = cashloan.from_existing(utilityrate, "CustomGenerationBatteryCommercial")
         loan.FinancialParameters.market = 1
 
     # Assign values to PySAM CashLoan module
@@ -574,8 +574,8 @@ def calc_system_size_and_performance(agent, rate_switch_table):
     if npv_w_batt >= npv_no_batt:
         system_kw = res_with_batt.x
         annual_energy_production_kwh = batt_annual_energy_kwh
-        first_year_elec_bill_with_system = batt_util_outputs['elec_cost_with_system_year1']
-        first_year_elec_bill_without_system = batt_util_outputs['elec_cost_without_system_year1']
+        first_year_elec_bill_with_system = batt_util_outputs['utility_bill_w_sys_year1']
+        first_year_elec_bill_without_system = batt_util_outputs['utility_bill_wo_sys_year1']
 
         npv = npv_w_batt
 
@@ -602,8 +602,8 @@ def calc_system_size_and_performance(agent, rate_switch_table):
     else:
         system_kw = res_no_batt.x
         annual_energy_production_kwh = no_batt_annual_energy_kwh
-        first_year_elec_bill_with_system = no_batt_util_outputs['elec_cost_with_system_year1']
-        first_year_elec_bill_without_system = no_batt_util_outputs['elec_cost_without_system_year1']
+        first_year_elec_bill_with_system = no_batt_util_outputs['utility_bill_w_sys_year1']
+        first_year_elec_bill_without_system = no_batt_util_outputs['utility_bill_wo_sys_year1']
 
         npv = npv_no_batt
         payback = no_batt_loan_outputs['payback']
@@ -1077,7 +1077,7 @@ def calc_max_market_share(dataframe, max_market_share_df):
     max_market_share_df['payback_period_as_factor'] = (max_market_share_df['payback_period'] * 100).round().astype('int')
 
     # Join the max_market_share table and dataframe in order to select the ultimate mms based on the metric value. 
-    dataframe = pd.merge(dataframe, max_market_share_df[['sector_abbr', 'max_market_share', 'metric', 'payback_period_as_factor', 'business_model']], 
+    dataframe = pd.merge(dataframe, max_market_share_df[['sector_abbr', 'max_market_share', 'metric', 'payback_period_as_factor', 'business_model']].drop_duplicates(), 
         how = 'left', on = ['sector_abbr', 'metric','payback_period_as_factor','business_model'])
 
     # Select for only necessary columns to be returned with dataframe