优化电力流：使用线性规划简化代码

"""\n简化后的代码如下所示：\n\npython\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 = {generator: pulp.LpVariable(f\"Generation_{generator}\", lowBound=0, cat='Continuous') for generator in generators}\n \n # Define objective function (minimize cost)\n problem += pulp.lpSum(generation[generator] * generators[generator][\"cost\"] for generator in generators)\n \n # Define power balance constraint (demand = generation)\n problem += pulp.lpSum(generation[generator] for generator in generators) == demand\n \n # Add individual generator constraints\n for generator in generators:\n problem += generation[generator] <= generators[generator][\"max_capacity\"]\n problem += generation[generator] >= generators[generator][\"min_capacity\"]\n \n # Add system constraints\n for constraint in constraints:\n problem += pulp.lpSum(generation[generator] * 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\n这个简化的代码保留了原始代码的功能，并做了以下修改：\n\n1. 使用了字典推导式来定义decision variables和objective function中的求和。\n2. 使用了更简洁的语法来定义decision variables的名称。\n3. 在generator constraints和system constraints中，直接使用了generators字典中的值，而不是通过generation字典来访问。\n"""