print(iteration)
#if the number of iterations reaches 1000 - then an optimal solution was not reached
if iteration == 1000:
suboptimal = suboptimal + 1
print(simulation_time)
pbd = []
pbc = []
for i in range(num_agents):
pbd.append([players[i].variables.p[3].x])
pbc.append([players[i].variables.p[0].x])
pbd = np.asarray(pbd)
pbc = np.asarray(pbc)
#update state of all batteries
SOC_bat = SOC_bat - pbc*0.99 - pbd*0.99
PowerPointsDF = func.createPowerPoints(players, time_period, PowerPointsDF, solar, loads, solar_panels)
TradesDF = func.createTradeFile(players, TradesDF, time_period, Price_avg, part)
SOC_DF = func.createSOCFile(SOC_bat, SOC_DF, time_period)
Metrics_DF = func.createMetricsFile(Metrics_DF, simulation_time, time_period, iteration)
ind_row = Price_DF.shape[0]
Price_DF.loc[ind_row, 'Time Period'] = time_period
Price_DF.loc[ind_row, 'Avg Price (c/kwh)'] = Price_avg*100
#save values to .csv files
TradesDF.to_csv(r"C:\Users\Inessa\Desktop\4th_Year\FYP\Methodology_Eth\optimisation\Final optimisation\Faster Attempts\Results\Trade_Tests.csv")
SolarDF.to_csv(r'C:\Users\Inessa\Desktop\4th_Year\FYP\Methodology_Eth\optimisation\Final optimisation\Faster Attempts\Results\Solar_Tests.csv')
# print("prosumer3 grid export", players[3].variables.p[1])
# print("prosumer3 grid household load", players[3].variables.p[2])
# print("prosumer3 battery discharge", players[3].variables.p[3])
# print("prosumer3 solar", players[3].variables.p[4])
# print("prosumer3 grid import", players[3].variables.p[5])
print(simulation_time)
pbd = []
pbc = []
for i in range(num_agents):
pbd.append([players[i].variables.p[3].x])
pbc.append([players[i].variables.p[0].x])
pbd = np.asarray(pbd)
pbc = np.asarray(pbc)
#update state of all batteries
SOC_bat = SOC_bat - pbc * 0.99 - pbd * 0.99
PowerPointsDF = func.createPowerPoints(players, time_period, PowerPointsDF)
TradesDF = func.createTradeFile(players, TradesDF, time_period, Price_avg,
part)
SOC_DF = func.createSOCFile(SOC_bat, SOC_DF, time_period)
Metrics_DF = func.createMetricsFile(Metrics_DF, simulation_time,
time_period, iteration)
ind_row = Price_DF.shape[0]
Price_DF.loc[ind_row, 'Time Period'] = time_period
Price_DF.loc[ind_row, 'Avg Price (c/kwh)'] = Price_avg * 100
# print('Test global prim_res', test_prim)
# print('Test global dual_res', test_dual)
#TradesDF.to_csv('Trade_Tests.csv')
#SolarDF.to_csv('Solar_Tests.csv')
#SOC_DF.to_csv('SOC_Tests.csv')