文章解读与仿真步伐复现思路——EI\CSCD\北大焦点《考虑频率安全及调峰需求 - ToB企服应用市场:ToB评测及商务社交产业平台

# 步骤 1: 导入必要的库
import numpy as np
from scipy.optimize import minimize
from gurobipy import Model, GRB, quicksum
# 步骤 2: 定义第一阶段优化问题 - 调峰优化
def stage_one_optimization(wind_forecast, load_forecast, adjustable_load_params):
# 创建模型
m1 = Model("Stage-One-Optimization")
# 定义决策变量 - 可调节负荷的响应功率
adjustable_load = m1.addVars(wind_forecast.size, name="Adjustable_Load")
# 定义目标函数 - 最大化新能源-负荷混合相似度指标
m1.setObjective(
quicksum((wind_forecast[t] - load_forecast[t] - adjustable_load[t]) ** 2 for t in range(wind_forecast.size)),
GRB.MAXIMIZE
)
# 添加约束条件
# 可调节负荷的响应功率约束
for t in range(wind_forecast.size):
m1.addConstr(-adjustable_load_params['max'] <= adjustable_load[t] <= adjustable_load_params['max'])
# 求解第一阶段优化问题
m1.optimize()
# 获取优化后的可调节负荷响应功率
optimal_adjustable_load = [adjustable_load[t].x for t in range(wind_forecast.size)]
return optimal_adjustable_load
# 步骤 3: 定义第二阶段优化问题 - 频率安全和经济性优化
def stage_two_optimization(wind_forecast, load_forecast, optimal_adjustable_load, params):
# 创建模型
m2 = Model("Stage-Two-Optimization")
# 定义决策变量 - 风电、火电、储能和抽水蓄能的出力计划
wind_output = m2.addVars(wind_forecast.size, name="Wind_Output")
thermal_output = m2.addVars(params['thermal_units'], name="Thermal_Output")
storage_output = m2.addVars(params['storage_units'], name="Storage_Output")
pumped_storage_output = m2.addVars(params['pumped_storage_units'], name="Pumped_Storage_Output")
# 定义目标函数 - 最小化系统运行成本
m2.setObjective(
quicksum(thermal_output[t] * params['thermal_cost'] for t in params['thermal_units']) +
quicksum(storage_output[t] * params['storage_cost'] for t in params['storage_units']),
GRB.MINIMIZE
)
# 添加约束条件
# 功率平衡约束、频率安全约束等
for t in range(wind_forecast.size):
m2.addConstr(
quicksum(wind_output[t]) +
quicksum(thermal_output[t]) +
quicksum(storage_output[t]) +
quicksum(pumped_storage_output[t]) -
load_forecast[t] -
optimal_adjustable_load[t] == 0
)
# 求解第二阶段优化问题
m2.optimize()
# 获取优化结果
optimal_outputs = {
'Wind': [wind_output[t].x for t in range(wind_forecast.size)],
'Thermal': [thermal_output[t].x for t in params['thermal_units']],
'Storage': [storage_output[t].x for t in params['storage_units']],
'Pumped_Storage': [pumped_storage_output[t].x for t in params['pumped_storage_units']]
}
return optimal_outputs
# 步骤 4: 主程序 - 运行仿真
def main_simulation(wind_forecast, load_forecast, adjustable_load_params, params):
# 运行第一阶段优化
optimal_adjustable_load = stage_one_optimization(wind_forecast, load_forecast, adjustable_load_params)
# 运行第二阶段优化
optimal_outputs = stage_two_optimization(wind_forecast, load_forecast, optimal_adjustable_load, params)
# 输出优化结果
print("Optimal Adjustable Load:", optimal_adjustable_load)
print("Optimal Outputs:", optimal_outputs)
# 步骤 5: 初始化参数和数据
# 这里需要根据实际数据初始化风电预测、负荷预测、可调节负荷参数和系统参数
wind_forecast = np.array([...])
load_forecast = np.array([...])
adjustable_load_params = {'max': ...}
params = {
'thermal_units': [...],
'storage_units': [...],
'pumped_storage_units': [...],
'thermal_cost': ...,
'storage_cost': ...
}
# 运行仿真
main_simulation(wind_forecast, load_forecast, adjustable_load_params, params)

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