547 lines
22 KiB
Python
547 lines
22 KiB
Python
"""
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多能互补系统储能容量优化可视化程序
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该程序绘制负荷曲线、发电曲线和储能出力曲线，直观展示系统运行状态。
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作者: iFlow CLI
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创建日期: 2025-12-25
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"""
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import matplotlib
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matplotlib.use('TkAgg') # 设置为TkAgg后端以支持图形窗口显示
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from datetime import datetime
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from storage_optimization import optimize_storage_capacity, SystemParameters
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from excel_reader import read_excel_data, create_excel_template, analyze_excel_data
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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def plot_system_curves(solar_output, wind_output, thermal_output, load_demand, result, show_window=False, display_only=False):
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"""
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绘制系统运行曲线
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Args:
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solar_output: 光伏出力曲线 (MW) - 支持24小时或8760小时
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wind_output: 风电出力曲线 (MW) - 支持24小时或8760小时
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thermal_output: 火电出力曲线 (MW) - 支持24小时或8760小时
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load_demand: 负荷曲线 (MW) - 支持24小时或8760小时
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result: 优化结果字典
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show_window: 是否显示图形窗口
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display_only: 是否只显示不保存文件
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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hours = np.arange(len(solar_output))
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data_length = len(solar_output)
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# 确定图表标题和采样率
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if data_length == 8760:
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title_suffix = " (全年8760小时)"
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# 对于全年数据，我们采样显示（每6小时显示一个点）
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sample_rate = 6
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sampled_hours = hours[::sample_rate]
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sampled_solar = solar_output[::sample_rate]
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sampled_wind = wind_output[::sample_rate]
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sampled_thermal = thermal_output[::sample_rate]
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sampled_load = load_demand[::sample_rate]
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sampled_storage = result['storage_profile'][::sample_rate]
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sampled_charge = result['charge_profile'][::sample_rate]
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sampled_discharge = result['discharge_profile'][::sample_rate]
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sampled_grid_feed_in = result['grid_feed_in'][::sample_rate]
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else:
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title_suffix = " (24小时)"
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sampled_hours = hours
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sampled_solar = solar_output
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sampled_wind = wind_output
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sampled_thermal = thermal_output
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sampled_load = load_demand
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sampled_storage = result['storage_profile']
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sampled_charge = result['charge_profile']
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sampled_discharge = result['discharge_profile']
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sampled_grid_feed_in = result['grid_feed_in']
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# 创建图形（4个子图）
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fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(14, 16))
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fig.suptitle('多能互补系统24小时运行曲线', fontsize=16, fontweight='bold')
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# === 第一个子图：发电和负荷曲线 ===
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ax1.plot(sampled_hours, sampled_load, 'r-', linewidth=2, label='负荷需求')
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ax1.plot(sampled_hours, sampled_thermal, 'b-', linewidth=2, label='火电出力')
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ax1.plot(sampled_hours, sampled_wind, 'g-', linewidth=2, label='风电出力')
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ax1.plot(sampled_hours, sampled_solar, 'orange', linewidth=2, label='光伏出力')
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# 计算总发电量
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total_generation = [sampled_thermal[i] + sampled_wind[i] + sampled_solar[i] for i in range(len(sampled_thermal))]
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ax1.plot(sampled_hours, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量')
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ax1.set_xlabel('时间 (小时)')
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ax1.set_ylabel('功率 (MW)')
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ax1.set_title(f'发电与负荷曲线{title_suffix}')
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ax1.legend(loc='upper right')
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ax1.grid(True, alpha=0.3)
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ax1.set_xlim(0, max(sampled_hours))
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# === 第二个子图：储能充放电曲线 ===
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discharge_power = [-x for x in sampled_discharge] # 放电显示为负值
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ax2.bar(sampled_hours, sampled_charge, color='green', alpha=0.7, label='充电功率')
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ax2.bar(sampled_hours, discharge_power, color='red', alpha=0.7, label='放电功率')
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ax2.set_xlabel('时间 (小时)')
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ax2.set_ylabel('功率 (MW)')
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ax2.set_title(f'储能充放电功率{title_suffix}')
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ax2.legend(loc='upper right')
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ax2.grid(True, alpha=0.3)
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ax2.set_xlim(0, max(sampled_hours))
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ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
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# === 第三个子图：储能状态曲线 ===
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ax3.plot(sampled_hours, sampled_storage, 'b-', linewidth=1, marker='o', markersize=2)
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ax3.fill_between(sampled_hours, 0, sampled_storage, alpha=0.3, color='blue')
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ax3.set_xlabel('时间 (小时)')
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ax3.set_ylabel('储能容量 (MWh)')
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ax3.set_title(f'储能状态 (总容量: {result["required_storage_capacity"]:.2f} MWh){title_suffix}')
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ax3.grid(True, alpha=0.3)
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ax3.set_xlim(0, max(sampled_hours))
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ax3.set_ylim(bottom=0)
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# === 第四个子图：购电量和上网电量曲线 ===
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# 直接使用原始数据：正值表示上网电量，负值表示购电量
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grid_power = sampled_grid_feed_in
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# 绘制电网交互电量（正值上网，负值购电）
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colors = ['brown' if x >= 0 else 'purple' for x in grid_power]
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ax4.bar(sampled_hours, grid_power, color=colors, alpha=0.7, label='电网交互电量')
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# 添加零线
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ax4.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
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# 添加图例说明
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from matplotlib.patches import Patch
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legend_elements = [
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Patch(facecolor='brown', alpha=0.7, label='上网电量 (+)'),
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Patch(facecolor='purple', alpha=0.7, label='购电量 (-)')
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]
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ax4.legend(handles=legend_elements, loc='upper right')
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ax4.set_xlabel('时间 (小时)')
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ax4.set_ylabel('功率 (MW)')
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ax4.set_title(f'电网交互电量{title_suffix} (正值:上网, 负值:购电)')
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ax4.grid(True, alpha=0.3)
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ax4.set_xlim(0, max(sampled_hours))
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# 调整布局
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plt.tight_layout()
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# 根据参数决定是否保存和显示图形
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if display_only:
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# 只显示，不保存
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try:
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plt.show()
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except Exception as e:
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print(f"无法显示图形窗口：{str(e)}")
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else:
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# 保存图片
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plt.savefig('system_curves.png', dpi=300, bbox_inches='tight')
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# 根据参数决定是否显示图形窗口
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if show_window:
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try:
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plt.show()
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except Exception as e:
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print(f"无法显示图形窗口：{str(e)}")
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print("图形已保存为 'system_curves.png'")
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else:
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plt.close() # 关闭图形，不显示窗口
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# 打印统计信息
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print("\n=== 系统运行统计 ===")
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print(f"所需储能总容量: {result['required_storage_capacity']:.2f} MWh")
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print(f"最大储能状态: {max(result['storage_profile']):.2f} MWh")
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print(f"最小储能状态: {min(result['storage_profile']):.2f} MWh")
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print(f"总充电量: {sum(result['charge_profile']):.2f} MWh")
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print(f"总放电量: {sum(result['discharge_profile']):.2f} MWh")
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print(f"弃风率: {result['total_curtailment_wind_ratio']:.3f}")
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print(f"弃光率: {result['total_curtailment_solar_ratio']:.3f}")
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print(f"上网电量比例: {result['total_grid_feed_in_ratio']:.3f}")
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# 计算购电和上网电量统计
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total_grid_feed_in = sum(result['grid_feed_in'])
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total_grid_purchase = sum(-x for x in result['grid_feed_in'] if x < 0) # 购电量
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total_grid_feed_out = sum(x for x in result['grid_feed_in'] if x > 0) # 上网电量
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print(f"\n=== 电网交互统计 ===")
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if total_grid_feed_in >= 0:
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print(f"净上网电量: {total_grid_feed_in:.2f} MWh")
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else:
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print(f"净购电量: {-total_grid_feed_in:.2f} MWh")
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print(f"总购电量: {total_grid_purchase:.2f} MWh")
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print(f"总上网电量: {total_grid_feed_out:.2f} MWh")
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def export_results_to_excel(solar_output, wind_output, thermal_output, load_demand, result, params, filename=None):
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"""
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将优化结果导出到Excel文件
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Args:
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solar_output: 光伏出力曲线 (MW)
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wind_output: 风电出力曲线 (MW)
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thermal_output: 火电出力曲线 (MW)
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load_demand: 负荷曲线 (MW)
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result: 优化结果字典
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params: 系统参数
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filename: 输出文件名，如果为None则自动生成
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"""
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if filename is None:
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"storage_optimization_results_{timestamp}.xlsx"
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print(f"\n正在导出结果到Excel文件: {filename}")
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# 准备数据
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hours = list(range(1, len(solar_output) + 1))
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# 分离购电和上网电量
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grid_purchase = []
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grid_feed_out = []
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for power in result['grid_feed_in']:
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if power < 0:
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grid_purchase.append(-power) # 购电，转换为正值
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grid_feed_out.append(0)
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else:
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grid_purchase.append(0)
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grid_feed_out.append(power) # 上网电量
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# 创建主要数据DataFrame
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data_df = pd.DataFrame({
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'小时': hours,
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'光伏出力(MW)': solar_output,
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'风电出力(MW)': wind_output,
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'火电出力(MW)': thermal_output,
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'总发电量(MW)': [solar_output[i] + wind_output[i] + thermal_output[i] for i in range(len(solar_output))],
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'负荷需求(MW)': load_demand,
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'储能充电(MW)': result['charge_profile'],
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'储能放电(MW)': result['discharge_profile'],
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'储能状态(MWh)': result['storage_profile'],
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'弃风量(MW)': result['curtailed_wind'],
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'弃光量(MW)': result['curtailed_solar'],
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'购电量(MW)': grid_purchase,
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'上网电量(MW)': grid_feed_out
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})
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# 创建统计信息DataFrame
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total_grid_feed_in = sum(result['grid_feed_in'])
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total_grid_purchase = sum(-x for x in result['grid_feed_in'] if x < 0) # 购电量
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total_grid_feed_out = sum(x for x in result['grid_feed_in'] if x > 0) # 上网电量
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stats_df = pd.DataFrame({
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'指标': [
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'所需储能总容量',
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'最大储能状态',
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'最小储能状态',
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'总充电量',
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'总放电量',
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'弃风率',
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'弃光率',
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'上网电量比例',
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'能量平衡校验',
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'净购电量/净上网电量',
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'总购电量',
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'总上网电量',
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'容量限制是否达到'
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],
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'数值': [
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f"{result['required_storage_capacity']:.2f} MWh",
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f"{max(result['storage_profile']):.2f} MWh",
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f"{min(result['storage_profile']):.2f} MWh",
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f"{sum(result['charge_profile']):.2f} MWh",
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f"{sum(result['discharge_profile']):.2f} MWh",
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f"{result['total_curtailment_wind_ratio']:.3f}",
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f"{result['total_curtailment_solar_ratio']:.3f}",
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f"{result['total_grid_feed_in_ratio']:.3f}",
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"通过" if result['energy_balance_check'] else "未通过",
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f"{-total_grid_feed_in:.2f} MWh" if total_grid_feed_in < 0 else f"{total_grid_feed_in:.2f} MWh",
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f"{total_grid_purchase:.2f} MWh",
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f"{total_grid_feed_out:.2f} MWh",
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"是" if result['capacity_limit_reached'] else "否"
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]
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})
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# 创建系统参数DataFrame
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params_df = pd.DataFrame({
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'参数名称': [
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'最大弃风率',
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'最大弃光率',
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'最大上网电量比例',
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'储能效率',
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'放电倍率',
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'充电倍率',
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'最大储能容量'
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],
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'参数值': [
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params.max_curtailment_wind,
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params.max_curtailment_solar,
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params.max_grid_ratio,
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params.storage_efficiency,
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params.discharge_rate,
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params.charge_rate,
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params.max_storage_capacity if params.max_storage_capacity is not None else "无限制"
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],
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'单位': [
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"比例",
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"比例",
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"比例",
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"效率",
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"C-rate",
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"C-rate",
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"MWh"
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]
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})
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# 写入Excel文件
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with pd.ExcelWriter(filename, engine='openpyxl') as writer:
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# 写入主要数据
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data_df.to_excel(writer, sheet_name='运行数据', index=False)
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# 写入统计信息
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stats_df.to_excel(writer, sheet_name='统计结果', index=False)
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# 写入系统参数
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params_df.to_excel(writer, sheet_name='系统参数', index=False)
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# 创建说明工作表
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description_df = pd.DataFrame({
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'项目': [
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'文件说明',
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'生成时间',
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'数据长度',
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'数据内容',
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'注意事项'
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],
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'内容': [
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'多能互补系统储能容量优化结果',
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datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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f"{len(solar_output)} 小时",
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'包含发电、负荷、储能、弃风弃光、购电上网等完整数据',
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'负值表示购电，正值表示上网电量'
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]
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})
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description_df.to_excel(writer, sheet_name='说明', index=False)
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print(f"结果已成功导出到: {filename}")
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return filename
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def generate_yearly_data():
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"""生成8760小时的示例数据"""
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# 基础日模式
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daily_solar = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
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daily_wind = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
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daily_thermal = [5.0] * 24
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daily_load = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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# 添加季节性变化
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import random
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random.seed(42)
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yearly_solar = []
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yearly_wind = []
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yearly_thermal = []
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yearly_load = []
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for day in range(365):
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# 季节性因子（夏季光伏更强，冬季负荷更高）
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season_factor = 1.0 + 0.3 * np.sin(2 * np.pi * day / 365)
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for hour in range(24):
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# 添加随机变化
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solar_variation = 1.0 + 0.2 * (random.random() - 0.5)
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wind_variation = 1.0 + 0.3 * (random.random() - 0.5)
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load_variation = 1.0 + 0.1 * (random.random() - 0.5)
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yearly_solar.append(daily_solar[hour] * season_factor * solar_variation)
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yearly_wind.append(daily_wind[hour] * wind_variation)
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yearly_thermal.append(daily_thermal[hour])
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yearly_load.append(daily_load[hour] * (2.0 - season_factor) * load_variation)
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return yearly_solar, yearly_wind, yearly_thermal, yearly_load
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def main():
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"""主函数"""
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import sys
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# 检查命令行参数
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if len(sys.argv) < 2:
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print_usage()
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return
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||
|
||
command = sys.argv[1]
|
||
show_window = '--show' in sys.argv # 检查是否包含--show参数
|
||
display_only = '--display-only' in sys.argv # 检查是否只显示不保存
|
||
|
||
if command == '--yearly':
|
||
print("生成8760小时全年数据...")
|
||
solar_output, wind_output, thermal_output, load_demand = generate_yearly_data()
|
||
print(f"数据长度: {len(solar_output)} 小时")
|
||
elif command == '--excel':
|
||
if len(sys.argv) < 3:
|
||
print("错误：请指定Excel文件路径")
|
||
print("用法：python main.py --excel <文件路径>")
|
||
return
|
||
|
||
excel_file = sys.argv[2]
|
||
print(f"从Excel文件读取数据：{excel_file}")
|
||
|
||
try:
|
||
data = read_excel_data(excel_file, include_parameters=True)
|
||
solar_output = data['solar_output']
|
||
wind_output = data['wind_output']
|
||
thermal_output = data['thermal_output']
|
||
load_demand = data['load_demand']
|
||
|
||
print(f"成功读取{data['data_type']}小时数据")
|
||
print(f"原始数据长度：{data['original_length']}小时")
|
||
print(f"处理后数据长度：{len(solar_output)}小时")
|
||
|
||
# 使用Excel中的系统参数
|
||
if 'system_parameters' in data:
|
||
params = data['system_parameters']
|
||
print("\n使用Excel中的系统参数：")
|
||
print(f" 最大弃风率: {params.max_curtailment_wind}")
|
||
print(f" 最大弃光率: {params.max_curtailment_solar}")
|
||
print(f" 最大上网电量比例: {params.max_grid_ratio}")
|
||
print(f" 储能效率: {params.storage_efficiency}")
|
||
print(f" 放电倍率: {params.discharge_rate}")
|
||
print(f" 充电倍率: {params.charge_rate}")
|
||
print(f" 最大储能容量: {params.max_storage_capacity}")
|
||
else:
|
||
print("\n警告：未找到系统参数，使用默认参数")
|
||
params = SystemParameters(
|
||
max_curtailment_wind=0.1,
|
||
max_curtailment_solar=0.1,
|
||
max_grid_ratio=0.2,
|
||
storage_efficiency=0.9,
|
||
discharge_rate=1.0,
|
||
charge_rate=1.0
|
||
)
|
||
|
||
# 显示数据统计
|
||
stats = analyze_excel_data(excel_file)
|
||
if stats:
|
||
print("\n数据统计：")
|
||
print(f" 总发电量: {stats['total_generation']:.2f} MW")
|
||
print(f" 总负荷: {stats['total_load']:.2f} MW")
|
||
print(f" 最大光伏出力: {stats['max_solar']:.2f} MW")
|
||
print(f" 最大风电出力: {stats['max_wind']:.2f} MW")
|
||
print(f" 最大负荷: {stats['max_load']:.2f} MW")
|
||
|
||
except Exception as e:
|
||
print(f"读取Excel文件失败：{str(e)}")
|
||
return
|
||
elif command == '--create-template':
|
||
template_type = sys.argv[2] if len(sys.argv) > 2 else "8760"
|
||
template_file = f"data_template_{template_type}.xlsx"
|
||
|
||
print(f"创建{template_type}小时Excel模板：{template_file}")
|
||
create_excel_template(template_file, template_type)
|
||
return
|
||
else:
|
||
print("使用24小时示例数据...")
|
||
# 示例数据
|
||
solar_output = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
|
||
wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
|
||
thermal_output = [5.0] * 24
|
||
load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
|
||
16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
|
||
|
||
# 使用默认系统参数
|
||
params = SystemParameters(
|
||
max_curtailment_wind=0.1,
|
||
max_curtailment_solar=0.1,
|
||
max_grid_ratio=0.2,
|
||
storage_efficiency=0.9,
|
||
discharge_rate=1.0,
|
||
charge_rate=1.0
|
||
)
|
||
|
||
# 对于 --yearly 参数，也需要设置默认参数
|
||
if command == '--yearly':
|
||
params = SystemParameters(
|
||
max_curtailment_wind=0.1,
|
||
max_curtailment_solar=0.1,
|
||
max_grid_ratio=0.2,
|
||
storage_efficiency=0.9,
|
||
discharge_rate=1.0,
|
||
charge_rate=1.0
|
||
)
|
||
|
||
# 显示当前使用的系统参数
|
||
print("\n=== 当前使用的系统参数 ===")
|
||
print(f"最大弃风率: {params.max_curtailment_wind}")
|
||
print(f"最大弃光率: {params.max_curtailment_solar}")
|
||
print(f"最大上网电量比例: {params.max_grid_ratio}")
|
||
print(f"储能效率: {params.storage_efficiency}")
|
||
print(f"放电倍率: {params.discharge_rate}")
|
||
print(f"充电倍率: {params.charge_rate}")
|
||
print(f"最大储能容量: {params.max_storage_capacity if params.max_storage_capacity is not None else '无限制'}")
|
||
print("=" * 40)
|
||
|
||
# 计算最优储能容量
|
||
print("正在计算最优储能容量...")
|
||
result = optimize_storage_capacity(
|
||
solar_output, wind_output, thermal_output, load_demand, params
|
||
)
|
||
|
||
# 绘制曲线
|
||
print("正在绘制系统运行曲线...")
|
||
plot_system_curves(solar_output, wind_output, thermal_output, load_demand, result, show_window, display_only)
|
||
|
||
# 导出结果到Excel
|
||
try:
|
||
export_results_to_excel(solar_output, wind_output, thermal_output, load_demand, result, params)
|
||
except Exception as e:
|
||
print(f"导出Excel文件失败：{str(e)}")
|
||
|
||
if display_only:
|
||
print("\n正在显示图形窗口...")
|
||
elif show_window:
|
||
print("\n曲线图已保存为 'system_curves.png' 并显示图形窗口")
|
||
else:
|
||
print("\n曲线图已保存为 'system_curves.png'")
|
||
|
||
|
||
def print_usage():
|
||
"""打印使用说明"""
|
||
print("多能互补系统储能容量优化程序")
|
||
print("\n使用方法：")
|
||
print(" python main.py --excel <文件路径> # 从Excel文件读取数据")
|
||
print(" python main.py --yearly # 使用8760小时全年数据")
|
||
print(" python main.py --create-template [类型] # 创建Excel模板(24或8760)")
|
||
print(" python main.py # 使用24小时示例数据")
|
||
print(" python main.py --show # 显示图形窗口(可与其他参数组合使用)")
|
||
print(" python main.py --display-only # 只显示图形窗口，不保存文件")
|
||
print("\n示例：")
|
||
print(" python main.py --excel data.xlsx")
|
||
print(" python main.py --excel data.xlsx --show")
|
||
print(" python main.py --excel data.xlsx --display-only")
|
||
print(" python main.py --create-template 8760")
|
||
print(" python main.py --create-template 24")
|
||
print(" python main.py --display-only # 使用示例数据并只显示图形窗口")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|