Python代码：使用scipy库解决电力系统优化问题

import numpy as np\nfrom scipy.optimize import minimize\n\n# Define the cost function for power generation\ndef cost_function(x):\n # Assuming a linear cost function: C = a * P + b\n a = 0.1 # Cost coefficient\n b = 10 # Cost constant\n return a * x + b\n\n# Define the objective function to be minimized\ndef objective_function(x):\n return np.sum(cost_function(x))\n\n# Define the power demand constraint\ndef demand_constraint(x):\n return np.sum(x) - demand\n\n# Define the power generation limit constraint\ndef limit_constraint(x):\n return max(x) - max_generation\n\n# Define the system constraints\nconstraints = [\n {'type': 'eq', 'fun': demand_constraint},\n {'type': 'ineq', 'fun': limit_constraint}\n]\n\n# Define the initial guess for power generation\nx0 = np.array([0.0])\n\n# Define the power demand and maximum power generation\ndemand = 100.0\nmax_generation = 150.0\n\n# Solve the optimization problem\nsolution = minimize(objective_function, x0, constraints=constraints)\nx_optimal = solution.x\n\n# Print the optimal power generation and total cost\nprint("Optimal power generation:", x_optimal[0])\nprint("Total cost of power generation:", objective_function(x_optimal))