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. ]]),