如何在PyPSA模型中为Gurobi求解器设置时间限制并正确处理结果

solverOptions = {
    'LogFile': "gurobiLog",
    'MIPGap': 0.001,
    'BarConvTol': 0.01,
    'TimeLimit': 200, # 200 秒
}

import pypsa
import numpy as np
import pandas as pd
# Pyomo相关的导入在此场景下通常不是必需的，可以移除
# from pyomo.environ import Constraint
# from pyomo.environ import value

# 1. 定义时间范围和频率
start_mt = 1
start_yr = 2022
end_mt = 12
end_yr = 2022
end_day = 31
frequency = 15 # 分钟

snapshots = pd.date_range("{}-{}-01".format(start_yr, start_mt), "{}-{}-{} 23:59".format(end_yr, end_mt, end_day),
                          freq=str(frequency) + "min")
np.random.seed(len(snapshots))

# 2. 创建PyPSA网络
network = pypsa.Network()
network.add("Bus", "Bus")
network.set_snapshots(snapshots)

# 3. 添加负荷
load_profile = np.random.randint(2800, 3300, len(snapshots))
network.add("Load", "Load profile", bus="Bus", p_set=load_profile)

# 4. 定义发电机数据
generator_data = {
    'coal1': {'capacity': 800, 'carrier': 'Coal', 'ramp up': 0.1, 'ramp down': 0.1, 'variable cost': 10, 'co2_emission_factor': 0.95},
    'coal2': {'capacity': 600, 'carrier': 'Coal', 'ramp up': 0.1, 'ramp down': 0.1, 'variable cost': 11, 'co2_emission_factor': 0.95},
    'coal3': {'capacity': 500, 'carrier': 'Coal', 'ramp up': 0.1, 'ramp down': 0.1, 'variable cost': 11, 'co2_emission_factor': 0.95},
    'gas1': {'capacity': 600, 'carrier': 'Gas', 'ramp up': 0.05, 'ramp down': 0.05, 'variable cost': 12, 'co2_emission_factor': 0.45},
    'gas2': {'capacity': 600, 'carrier': 'Gas', 'ramp up': 0.05, 'ramp down': 0.05, 'variable cost': 13, 'co2_emission_factor': 0.45},
    'nuclear1': {'capacity': 300, 'carrier': 'Nuclear', 'ramp up': 0.01, 'ramp down': 0.01, 'variable cost': 4, 'co2_emission_factor': 0.03},
    'nuclear2': {'capacity': 400, 'carrier': 'Nuclear', 'ramp up': 0.01, 'ramp down': 0.01, 'variable cost': 3, 'co2_emission_factor': 0.03},
    'nuclear3': {'capacity': 250, 'carrier': 'Nuclear', 'ramp up': 0.01, 'ramp down': 0.01, 'variable cost': 3, 'co2_emission_factor': 0.03},
    'solar1': {'capacity': 150, 'carrier': 'Solar', 'ramp up': 0.25, 'ramp down': 0.25, 'variable cost': 1, 'co2_emission_factor': 0.0},
    'solar2': {'capacity': 200, 'carrier': 'Solar', 'ramp up': 0.25, 'ramp down': 0.25, 'variable cost': 2, 'co2_emission_factor': 0.0},
    'backup': {'capacity': 1000, 'carrier': 'Import', 'ramp up': 0.25, 'ramp down': 0.25, 'variable cost': 2000, 'co2_emission_factor': 1.0},
}

# 5. 添加发电机和载体
for name, data in generator_data.items():
    network.add("Generator", name,
                bus="Bus",
                carrier=data['carrier'],
                p_nom=data['capacity'],
                marginal_cost=data['variable cost'],
                ramp_limit_up=data['ramp up'],
                ramp_limit_down=data['ramp down'],
                )

network.add("Carrier", "Coal", co2_emissions=0.95)
network.add("Carrier", "Gas", co2_emissions=0.45)
network.add("Carrier", "Nuclear", co2_emissions=0.03)
network.add("Carrier", "Import", co2_emissions=1.0)
network.add("Carrier", "Solar", co2_emissions=0)

# 6. 添加全局约束
network.add(
    "GlobalConstraint",
    "CO2Limit",
    carrier_attribute="co2_emissions",
    sense="<=",
    constant=50000000,
)

# 7. 配置Gurobi求解器选项，包括TimeLimit
solver_name = "gurobi"
solverOptions = {
    'LogFile': "gurobiLog",
    'MIPGap': 0.001,
    'BarConvTol': 0.01,
    'TimeLimit': 20, # 设置时间限制为20秒
}

# 8. 使用network.optimize()进行优化
# 注意：这里使用optimize()代替lopf()
network.optimize(snapshots, solver_name=solver_name, solver_options=solverOptions)

# 9. 导出结果并进行后处理
csv_folder_name = 'model dump'
network.export_to_csv_folder(csv_folder_name)

dispatch = network.generators_t.p
total_gen = dispatch.sum()

co2 = sum([total_gen[gen] * data['co2_emission_factor'] for gen, data in generator_data.items()])
cost = sum([total_gen[gen] * data['variable cost'] for gen, data in generator_data.items()])
print('co2 emission = ', co2)
print('total cost = ', cost)
dispatch['load profile'] = load_profile

dispatch.to_excel('fuel wise dispatch.xlsx')