【英】考虑多能负荷不确定性的区域综合能源系统鲁棒规划(Python代码实现）

以下是使用Python实现考虑多能负荷不确定性的区域综合能源系统鲁棒规划的示例代码：

```python
import numpy as np
from cvxpy import *

# 定义优化问题的参数:
T = 24 # 时间步数
alpha = 1e-4 # 正则化系数
sigma = 0.1 # 噪音方差
gamma = 0.95 # 置信度

# 生成随机负荷和能源价格:
np.random.seed(0)
P_load = np.random.rand(T) * 100 # 负荷
P_solar = np.random.rand(T) * 50 # 太阳能发电量
P_wind = np.random.rand(T) * 30 # 风能发电量
C_electricity = np.random.rand(T) * 0.3 + 0.1 # 电力价格
C_heat = np.random.rand(T) * 0.2 + 0.05 # 热价价格

# 定义决策变量:
P_grid = Variable(T) # 从电网购买的电力
P_battery = Variable(T) # 电池的充放电量
P_heat_boiler = Variable(T) # 燃气锅炉的热输出
P_heat_pump = Variable(T) # 热泵的热输出

# 定义目标函数:
cost = sum(P_grid * C_electricity + P_heat_boiler * C_heat)
+ alpha * (sum_squares(P_grid) + sum_squares(P_battery)
+ sum_squares(P_heat_boiler) + sum_squares(P_heat_pump))

# 定义约束条件:
constraints = [
P_grid >= 0,
P_battery >= -20, # 电池的最小容量
P_battery <= 20, # 电池的最大容量
P_heat_boiler >= 0,
P_heat_pump >= 0,
P_heat_boiler + P_heat_pump == P_load - P_solar - P_wind + P_grid - P_battery,
]

# 定义鲁棒约束条件:
for t in range(T):
constraints += [C_electricity[t] * P_grid[t] + C_heat[t] * P_heat_boiler[t]
+ sigma * norm(np.array([P_grid[t], P_heat_boiler[t], P_heat_pump[t]]), 2) <= gamma * P_load[t]]

# 求解优化问题:
prob = Problem(Minimize(cost), constraints)
prob.solve()

# 输出结果:
print("Total cost: ", prob.value)
print("P_grid: ", P_grid.value)
print("P_battery: ", P_battery.value)
print("P_heat_boiler: ", P_heat_boiler.value)
print("P_heat_pump: ", P_heat_pump.value)
```