Python 电力系统优化：最小化成本的电力流动模型

"""\nimport pulp\n\ndef optimize_power_flow(demand, generators, constraints):\n # Create a linear programming problem\n problem = pulp.LpProblem("Optimal Power Flow", pulp.LpMinimize)\n \n # Define decision variables\n generation = pulp.LpVariable.dicts("Generation", generators, lowBound=0, cat='Continuous')\n \n # Define objective function (minimize cost)\n problem += pulp.lpSum([generation[generator]["cost"] * generation[generator]["capacity"] for generator in generators])\n \n # Define power balance constraint (demand = generation)\n problem += pulp.lpSum([generation[generator]["capacity"] for generator in generators]) == demand\n \n # Add individual generator constraints\n for generator in generators:\n problem += generation[generator]["capacity"] <= generation[generator]["max_capacity"]\n problem += generation[generator]["capacity"] >= generation[generator]["min_capacity"]\n \n # Add system constraints\n for constraint in constraints:\n problem += pulp.lpSum([generation[generator]["capacity"] * constraint[generator] for generator in generators]) <= constraint["limit"]\n \n # Solve the problem\n problem.solve()\n \n # Print the status of the solution\n print("Status:", pulp.LpStatus[problem.status])\n \n # Print the optimal power generation\n for generator in generators:\n print(generator, "Generation:", generation[generator].varValue)\n\n# Example usage\ndemand = 100 # Power demand\ngenerators = {\n "Generator1": {"capacity": 50, "min_capacity": 0, "max_capacity": 50, "cost": 10},\n "Generator2": {"capacity": 40, "min_capacity": 0, "max_capacity": 40, "cost": 20},\n "Generator3": {"capacity": 30, "min_capacity": 0, "max_capacity": 30, "cost": 30}\n}\nconstraints = [\n {"Generator1": 0.5, "Generator2": 0.3, "Generator3": 0.2, "limit": 60},\n {"Generator1": 0.2, "Generator2": 0.3, "Generator3": 0.5, "limit": 50}\n]\n\noptimize_power_flow(demand, generators, constraints)\n"""\n