Chemical Energy Storage Device Inputs
DER Serving Load Overview
-Thermal Battery Energy Profile
- -Thermal Battery Power Profile
diff --git a/omf/models/derUtilityCost.py b/omf/models/derUtilityCost.py index 2ff77e28b..102a13fe2 100644 --- a/omf/models/derUtilityCost.py +++ b/omf/models/derUtilityCost.py @@ -72,10 +72,10 @@ def work(modelDir, inputDict): scenario['ElectricStorage'] = { ##TODO: Add options here, if needed - 'min_kw': float(inputDict['BESS_kw'])+float(inputDict['existing_kw']), - 'max_kw': float(inputDict['BESS_kw'])+float(inputDict['existing_kw']), - 'min_kwh': float(inputDict['BESS_kwh'])+float(inputDict['existing_kwh']), - 'max_kwh': float(inputDict['BESS_kwh'])+float(inputDict['existing_kwh']), + 'min_kw': float(inputDict['BESS_kw']), + 'max_kw': float(inputDict['BESS_kw']), + 'min_kwh': float(inputDict['BESS_kwh']), + 'max_kwh': float(inputDict['BESS_kwh']), 'can_grid_charge': can_grid_charge_bool, 'total_rebate_per_kw': 0, 'macrs_option_years': 0, @@ -142,24 +142,10 @@ def work(modelDir, inputDict): outData['cumulativeCashflow'] = reoptResults['Financial']['offtaker_annual_free_cashflows'] #list(accumulate(reoptResults['Financial']['offtaker_annual_free_cashflows'])) outData['netCashflow'] = reoptResults['Financial']['offtaker_discounted_annual_free_cashflows'] #list(accumulate(reoptResults['Financial']['offtaker_annual_free_cashflows'])) ## or alternatively: offtaker_annual_free_cashflows - ## Temporarily correct financial outputs for any existing BESS - ## (Remove the installation cost of any existing BESS since we are including it in the aggregated BESS: existing utility BESS + proposed consumer BESS) - existing_utility_bess_kw = float(inputDict['existing_kw']) - existing_utility_bess_kwh = float(inputDict['existing_kwh']) - installed_cost_per_kw = float(inputDict['installed_cost_per_kw']) - installed_cost_per_kwh = float(inputDict['installed_cost_per_kwh']) - cost_existing_utility_bess_kw = existing_utility_bess_kw * installed_cost_per_kw - cost_existing_utility_bess_kwh = existing_utility_bess_kwh * installed_cost_per_kwh - totalcost_existing_utility_bess = cost_existing_utility_bess_kw + cost_existing_utility_bess_kwh - outData['NPV'] -= totalcost_existing_utility_bess - outData['cumulativeCashflow'] = [cashflow - totalcost_existing_utility_bess for cashflow in reoptResults['Financial']['offtaker_annual_free_cashflows']] - outData['netCashflow'] = [cashflow - totalcost_existing_utility_bess for cashflow in reoptResults['Financial']['offtaker_annual_free_cashflows']] - ## Calculate adjusted initial investment and simply payback period (SPP) - adjusted_initial_investment = reoptResults['Financial']['initial_capital_costs'] - totalcost_existing_utility_bess + adjusted_initial_investment = reoptResults['Financial']['initial_capital_costs'] outData['SPP'] = adjusted_initial_investment / np.sum(outData['netCashflow']) - ## NOTE: temporarily comment out the two derConsumer runs to run the code quicker """ ## Scale down the utility demand to create an ad-hoc small consumer load (1 kW average) and large consumer load (10 kW average) @@ -396,139 +382,6 @@ def work(modelDir, inputDict): outData['derOverviewData'] = json.dumps(fig.data, cls=plotly.utils.PlotlyJSONEncoder) outData['derOverviewLayout'] = json.dumps(fig.layout, cls=plotly.utils.PlotlyJSONEncoder) - ## Create Thermal Device Temperature plot object ###################################################################################################################################################### - if (inputDict['load_type'] != '0') and (int(inputDict['number_devices'])>0): ## If vbatDispatch is enabled: - - fig = go.Figure() - - ## It seems like the setpoint (interior temp) is fixed for devices except the water heater. - ## TODO: If desired, could code this up to extract the interior temperature from the water heater code. - ## It does not currently seem to output the changing interior temp. - - #if inputDict['load_type'] == '4': ## Water Heater (the only option that evolves interior temperature over time) - # Interior Temperature - #interior_temp = theta.mean(axis=0) - #theta_s_wh #temperature setpoint (interior) - #theta_s_wh +/- deadband - - fig.add_trace(go.Scatter(x=timestamps, - y=temperatures, - yaxis='y2', - mode='lines', - line=dict(color='red',width=1), - name='Average Air Temperature', - showlegend=showlegend - )) - - upper_bound = np.full_like(demand,float(inputDict['setpoint']) + float(inputDict['deadband'])/2) - lower_bound = np.full_like(demand,float(inputDict['setpoint']) - float(inputDict['deadband'])/2) - setpoint = np.full_like(demand, float(inputDict['setpoint'])) ## the setpoint is currently a fixed number - - ## Plot deadband upper bound - fig.add_trace(go.Scatter( - x=timestamps, - y=upper_bound, - yaxis='y2', - line=dict(color='rgba(0,0,0,1)'), ## color black - name='Deadband upper bound', - showlegend=True - )) - - ## Plot deadband lower bound - fig.add_trace(go.Scatter( - x=timestamps, - y=lower_bound, - yaxis='y2', - mode='lines', - line=dict(color='rgba(0,0,0,0.5)'), ## color black but half opacity = gray color - name='Deadband lower bound', - showlegend=True - )) - - ## Plot the setpoint (interior temperature) - fig.add_trace(go.Scatter( - x=timestamps, - y=setpoint, - yaxis='y2', - mode='lines', - line=dict(color='rgba(0, 27, 255, 1)'), ## color blue - name='Setpoint', - showlegend=True - )) - - ## Plot layout - fig.update_layout( - #title='Residential Data', - xaxis=dict(title='Timestamp'), - yaxis=dict(title='Power (kW)'), - yaxis2=dict(title='degrees Celsius', - overlaying='y', - side='right' - ), - legend=dict( - orientation='h', - yanchor='bottom', - y=1.02, - xanchor='right', - x=1 - ) - ) - - #fig.show() - - ## Encode plot data as JSON for showing in the HTML side - outData['thermalDevicePlotData'] = json.dumps(fig.data, cls=plotly.utils.PlotlyJSONEncoder) - outData['thermalDevicePlotLayout'] = json.dumps(fig.layout, cls=plotly.utils.PlotlyJSONEncoder) - - - ## Create Thermal Battery Energy plot object ###################################################################################################################################################### - if (inputDict['load_type'] != '0') and (int(inputDict['number_devices'])>0): ## If vbatDispatch is enabled: - fig = go.Figure() - fig.add_trace(go.Scatter( - x=timestamps, - y=vbatMinEnergyCapacity, - yaxis='y1', - mode='lines', - line=dict(color='green', width=1), - name='Minimum Energy Capacity', - showlegend=True - )) - fig.add_trace(go.Scatter( - x=timestamps, - y=vbatMaxEnergyCapacity, - yaxis='y1', - mode='lines', - line=dict(color='blue', width=1), - name='Maximum Energy Capacity', - showlegend=True - )) - fig.add_trace(go.Scatter( - x=timestamps, - y=vbatEnergy, - yaxis='y1', - mode='lines', - line=dict(color='black', width=1), - name='Actual Energy Capacity', - showlegend=True - )) - - ## Plot layout - fig.update_layout( - xaxis=dict(title='Timestamp'), - yaxis=dict(title='Energy (kWh)'), - legend=dict( - orientation='h', - yanchor='bottom', - y=1.02, - xanchor='right', - x=1 - ) - ) - - ## Encode plot data as JSON for showing in the HTML side - outData['thermalBatEnergyPlot'] = json.dumps(fig.data, cls=plotly.utils.PlotlyJSONEncoder) - outData['thermalBatEnergyPlotLayout'] = json.dumps(fig.layout, cls=plotly.utils.PlotlyJSONEncoder) - ## Create Thermal Battery Power plot object ###################################################################################################################################################### if (inputDict['load_type'] != '0') and (int(inputDict['number_devices'])>0): ## If vbatDispatch is enabled: fig = go.Figure() @@ -654,18 +507,8 @@ def work(modelDir, inputDict): total_kwh_residential_BESS = int(inputDict['numberBESS']) * float(inputDict['BESS_kwh']) rateCompensation = float(inputDict['rateCompensation']) total_residential_BESS_compensation = rateCompensation * np.sum(total_kwh_residential_BESS) - total_kw_BESS = total_kw_residential_BESS + float(inputDict['existing_kw']) - total_kwh_BESS = total_kwh_residential_BESS + float(inputDict['existing_kwh']) BESS = reoptResults['ElectricStorage']['storage_to_load_series_kw'] - monthHours = [(0, 744), (744, 1416), (1416, 2160), (2160, 2880), - (2880, 3624), (3624, 4344), (4344, 5088), (5088, 5832), - (5832, 6552), (6552, 7296), (7296, 8016), (8016, 8760)] - - #total_kwh_residential_BESS_monthly = np.asarray([sum(total_kwh_residential_BESS[s:f]) for s, f in monthHours]) - #total_compensation_residential_BESS_monthly = total_kwh_residential_BESS_monthly * rateCompensation - #print('Total amount paid to member-consumers ($/kWh): ', total_residential_BESS_compensation) - # Model operations typically ends here. # Stdout/stderr. outData['stdout'] = 'Success' @@ -698,8 +541,6 @@ def new(modelDir): ## Chemical Battery Inputs 'numberBESS': '100', ## Number of residential Tesla Powerwall 2 batteries - 'existing_kw': '19000.0', - 'existing_kwh': '56000.0', 'chemBESSgridcharge': 'Yes', 'installed_cost_per_kw': '20.0', #'300.0', ## (Residential: $1,000-$1,500 per kW, Utility: $300-$700 per kW) 'installed_cost_per_kwh': '60.0', #'480.0', ## Cost per kWh reflecting Powerwall’s installed cost (Residential: $400-$900 per kWh, Utility: $200-$400 per kWh)