修改豪不嚎HHdef optimize_power_flowdemand generators constraints # Create a linear programming problem problem = pulpLpProblemOptimal Power Flow pulpLpMinimize # Define decision variables g

修改后的代码如下：

import pulp

def optimize_power_flow(demand, generators, constraints):
    # Create a linear programming problem
    problem = pulp.LpProblem("Optimal Power Flow", pulp.LpMinimize)
    
    # Define decision variables
    generation = pulp.LpVariable.dicts("Generation", generators, lowBound=0, cat='Continuous')
    
    # Define objective function (minimize cost)
    problem += pulp.lpSum([generation[generator]["cost"] * generation[generator]["capacity"] for generator in generators])
    
    # Define power balance constraint (demand = generation)
    problem += pulp.lpSum([generation[generator]["capacity"] for generator in generators]) == demand
    
    # Add individual generator constraints
    for generator in generators:
        problem += generation[generator]["capacity"] <= generation[generator]["max_capacity"]
        problem += generation[generator]["capacity"] >= generation[generator]["min_capacity"]
    
    # Add system constraints
    for constraint in constraints:
        problem += pulp.lpSum([generation[generator]["capacity"] * constraint[generator] for generator in generators]) <= constraint["limit"]
    
    # Solve the problem
    problem.solve()
    
    # Print the status of the solution
    print("Status:", pulp.LpStatus[problem.status])
    
    # Print the optimal power generation
    for generator in generators:
        print(generator, "Generation:", generation[generator].varValue)

# Example usage
demand = 100  # Power demand
generators = {
    "Generator1": {"capacity": 50, "min_capacity": 0, "max_capacity": 50, "cost": 10},
    "Generator2": {"capacity": 40, "min_capacity": 0, "max_capacity": 40, "cost": 20},
    "Generator3": {"capacity": 30, "min_capacity": 0, "max_capacity": 30, "cost": 30}
}
constraints = [
    {"Generator1": 0.5, "Generator2": 0.3, "Generator3": 0.2, "limit": 60},
    {"Generator1": 0.2, "Generator2": 0.3, "Generator3": 0.5, "limit": 50}
]

optimize_power_flow(demand, generators, constraints)

这段代码使用线性规划来优化电力流动问题，目标是最小化成本。给定电力需求、发电机容量和约束条件，它计算出最优的发电量分配方案，并输出每个发电机的发电量