data = get_parameters() config = config.Config(**data['config']) energy_sources = [ energy_source.EnergySource(**kwargs) for kwargs in data['energy_sources'] ] for ess in energy_sources: ess.tuning_parameter_fit() markets = [market.Market(**kwargs) for kwargs in data['markets']] mpc = mpc_solver.MPCSolver(config=config, markets=markets, energy_sources=energy_sources) # Fake run cc = cyclic_coordinate.CyclicCoordinate(markets, mpc, [10, 10], really_run=False) solutions_fake = cc.Algo5() print("totl: " + str(len(solutions_fake))) cc = cyclic_coordinate.CyclicCoordinate(markets, mpc, [10, 10]) solutions = cc.Algo5() print(solutions[0]) # pe = pareto.ParetoEfficient(solutions) # inefficient_list, efficient_list = pe.pareto_analysis() # tuple(Revenue, value_i(useless), soc_record, soh for each device, power record, prices, percentages) # (216.29629629629628, 2, array([[1. , 1. ], [0.95061731, 1. ]]), # (1.0, 1.0), array([[[ 0., 0.], [24., 0.]],[[ 0., 0.],[ 0., 12.]]]), # [2.2222222222222223, 18.02469135802469, 2.2222222222222223, 18.02469135802469, 2.2222222222222223, 18.02469135802469], # (6.666666666666667, 10.0, 'free')) # assert len(solutions) == 36
energy_sources = [ energy_source.EnergySource(**kwargs) for kwargs in data['energy_sources'] ] costs = (sum([es.cost for es in energy_sources]), sum([es.other_cost for es in energy_sources])) # for ess in energy_sources: # ess.tuning_parameter_fit() markets = [market.Market(**kwargs) for kwargs in data['markets']] mpc = mpc_solver.MPCSolver(config=config, markets=markets, energy_sources=energy_sources) # Fake run cc = cyclic_coordinate.CyclicCoordinate(markets, mpc, costs, data, really_run=False) solutions_fake = cc.Algo5() print("totl: " + str(len(solutions_fake))) sys.stdout.flush() cc = cyclic_coordinate.CyclicCoordinate(markets, mpc, costs, data) solutions = cc.Algo5() # print(solutions) # pe = pareto.ParetoEfficient(solutions) # inefficient_list, efficient_list = pe.pareto_analysis() # tuple(Revenue, value_i(useless), soc_record, soh for each device, power record, prices, percentages) # (216.29629629629628, # 2, # array([[1. , 1. ], [0.95061731, 1. ]]),