基于一个10机39节点电力系统，生成一段通过调节发电机无功功率输出来调节系统各节点电压偏移量的python代码

在Python中，我们通常使用pandas库来处理数据，并结合matplotlib库进行可视化，对于电力系统的控制，我们可以使用Scipy优化工具包来进行无功功率调整。下面是一个简单的示例，假设我们有一个电力系统模型的DataFrame，其中包含了发电机的数据以及节点电压信息。这里我们将使用线性规划（LP）来调节发电机的无功功率输出：

import pandas as pd
from scipy.optimize import minimize
import matplotlib.pyplot as plt

# 假设df是一个DataFrame，包含发电机电压（PQ）和节点电压偏差(Voltage_Diff)
# df = {'Generator': [...], 'Voltage_Diff': [...]}
# 每行代表一个发电机，列Generator是发电机ID，Voltage_Diff是对应的节点电压偏差

def objective_function(powers, voltage_diffs):
    # Objective function to minimize the sum of squared voltage deviations
    return sum((voltage_diffs - powers['Generator'].values)**2)

def constraint_generator_power(generator, power_limit):
    # Constraint for each generator's real and reactive power limits
    return (generator['Real_Power'] + 1j * generator['Reactive_Power']) - power_limit

# 假设每个发电机有其最大无功功率限制
power_limits = {
    gen_id: (gen['Max_Real_Power'], gen['Max_Reactive_Power'])
    for gen_id, gen in df[df['Type'] == 'Generator'].iterrows()
}

# 定义线性约束
constraints = []
for generator_id, (real_power, reactive_power) in power_limits.items():
    cons = ({'type': 'eq', 'fun': constraint_generator_power, 'args': (df.loc[generator_id], real_power+reactive_power)})
    constraints.append(cons)

# Initial guess for generator powers (set to zero here for demonstration)
initial_guess = {gen_id: [0] for gen_id in df[df['Type'] == 'Generator']['Generator'].unique()}

# Solve the optimization problem
solution = minimize(objective_function, initial_guess, method='SLSQP', bounds=power_limits, constraints=constraints)

# Update DataFrame with optimal powers
df.loc[df['Type'] == 'Generator', 'Optimal_Reactive_Power'] = solution.x

# Plot voltage differences before and after adjustments
plt.figure()
plt.plot(df['Generator'], df['Voltage_Diff'], label='Before')
plt.plot(df['Generator'], df['Voltage_Diff'] - solution.fun, label='After')
plt.xlabel('Generator ID')
plt.ylabel('Voltage Deviation')
plt.legend()

#