271 lines
10 KiB
Python
271 lines
10 KiB
Python
"""
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高级可视化程序 - 多能互补系统储能容量优化
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该程序提供更丰富的可视化功能，包括多种图表类型和交互式选项。
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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.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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from datetime import datetime, timedelta
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from storage_optimization import optimize_storage_capacity, SystemParameters
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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 create_comprehensive_plot(solar_output, wind_output, thermal_output, load_demand, result, params):
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"""
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创建综合可视化图表
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Args:
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solar_output: 24小时光伏出力曲线 (MW)
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wind_output: 24小时风电出力曲线 (MW)
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thermal_output: 24小时火电出力曲线 (MW)
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load_demand: 24小时负荷曲线 (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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"""
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hours = np.arange(24)
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# 创建大型图形
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fig = plt.figure(figsize=(16, 12))
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fig.suptitle('多能互补系统储能容量优化分析', fontsize=18, fontweight='bold')
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# 创建网格布局
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gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)
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# === 主要图表：发电和负荷 ===
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ax1 = fig.add_subplot(gs[0, :])
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# 绘制各发电类型
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ax1.fill_between(hours, 0, thermal_output, alpha=0.7, color='blue', label='火电')
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ax1.fill_between(hours, thermal_output,
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[thermal_output[i] + wind_output[i] for i in range(24)],
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alpha=0.7, color='green', label='风电')
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ax1.fill_between(hours, [thermal_output[i] + wind_output[i] for i in range(24)],
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[thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)],
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alpha=0.7, color='orange', label='光伏')
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# 绘制负荷曲线
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ax1.plot(hours, load_demand, 'r-', linewidth=3, label='负荷需求')
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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('24小时发电与负荷平衡')
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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, 23)
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# === 储能充放电功率 ===
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ax2 = fig.add_subplot(gs[1, 0])
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charge_power = result['charge_profile']
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discharge_power = [-x for x in result['discharge_profile']]
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ax2.bar(hours, charge_power, color='green', alpha=0.7, label='充电')
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ax2.bar(hours, discharge_power, color='red', alpha=0.7, label='放电')
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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('储能充放电功率')
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
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# === 储能状态 ===
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ax3 = fig.add_subplot(gs[1, 1])
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storage_soc = result['storage_profile']
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ax3.plot(hours, storage_soc, 'b-', linewidth=2, marker='o')
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ax3.fill_between(hours, 0, storage_soc, 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 (容量: {result["required_storage_capacity"]:.1f} MWh)')
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ax3.grid(True, alpha=0.3)
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ax3.set_ylim(bottom=0)
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# === 弃风弃光 ===
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ax4 = fig.add_subplot(gs[1, 2])
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curtailed_wind = result['curtailed_wind']
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curtailed_solar = result['curtailed_solar']
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ax4.bar(hours, curtailed_wind, color='lightblue', alpha=0.7, label='弃风')
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ax4.bar(hours, curtailed_solar, color='yellow', alpha=0.7, label='弃光')
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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('弃风弃光功率')
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ax4.legend()
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ax4.grid(True, alpha=0.3)
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# === 能量饼图 ===
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ax5 = fig.add_subplot(gs[2, 0])
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# 计算总能量
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total_gen = sum(thermal_output) + sum(wind_output) + sum(solar_output)
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total_load = sum(load_demand)
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total_curtailed = sum(curtailed_wind) + sum(curtailed_solar)
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total_grid = sum(result['grid_feed_in'])
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# 处理上网电量为负的情况（购电）
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if total_grid >= 0:
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# 有上网电量
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energy_data = [total_load, total_curtailed, total_grid]
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energy_labels = [f'负荷\n({total_load:.1f} MWh)',
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f'弃风弃光\n({total_curtailed:.1f} MWh)',
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f'上网电量\n({total_grid:.1f} MWh)']
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colors = ['red', 'orange', 'green']
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ax5.pie(energy_data, labels=energy_labels, colors=colors, autopct='%1.1f%%', startangle=90)
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ax5.set_title('能量分配')
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else:
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# 从电网购电
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grid_purchase = -total_grid # 转为正值
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energy_data = [total_load, total_curtailed, grid_purchase]
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energy_labels = [f'负荷\n({total_load:.1f} MWh)',
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f'弃风弃光\n({total_curtailed:.1f} MWh)',
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f'购电量\n({grid_purchase:.1f} MWh)']
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colors = ['red', 'orange', 'blue'] # 购电用蓝色
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ax5.pie(energy_data, labels=energy_labels, colors=colors, autopct='%1.1f%%', startangle=90)
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ax5.set_title('能量分配（含购电）')
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# === 发电构成饼图 ===
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ax6 = fig.add_subplot(gs[2, 1])
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gen_data = [sum(thermal_output), sum(wind_output), sum(solar_output)]
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gen_labels = [f'火电\n({gen_data[0]:.1f} MWh)',
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f'风电\n({gen_data[1]:.1f} MWh)',
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f'光伏\n({gen_data[2]:.1f} MWh)']
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gen_colors = ['blue', 'green', 'orange']
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ax6.pie(gen_data, labels=gen_labels, colors=gen_colors, autopct='%1.1f%%', startangle=90)
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ax6.set_title('发电构成')
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# === 关键指标文本 ===
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ax7 = fig.add_subplot(gs[2, 2])
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ax7.axis('off')
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# 显示关键指标
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metrics_text = f"""
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关键指标
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─────────────
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所需储能容量: {result['required_storage_capacity']:.1f} MWh
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储能效率: {params.storage_efficiency:.1%}
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弃风率: {result['total_curtailment_wind_ratio']:.1%}
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弃光率: {result['total_curtailment_solar_ratio']:.1%}
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上网电量比例: {result['total_grid_feed_in_ratio']:.1%}
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能量平衡: {'通过' if result['energy_balance_check'] else '未通过'}
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最大储能状态: {max(storage_soc):.1f} MWh
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最小储能状态: {min(storage_soc):.1f} MWh
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"""
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ax7.text(0.1, 0.5, metrics_text, fontsize=11, verticalalignment='center',
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fontfamily='SimHei', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))
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# 保存图片
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plt.savefig('comprehensive_analysis.png', dpi=300, bbox_inches='tight')
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plt.close()
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print("综合分析图表已保存为 'comprehensive_analysis.png'")
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def create_time_series_plot(solar_output, wind_output, thermal_output, load_demand, result):
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"""
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创建时间序列图表，模拟真实的时间轴
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"""
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# 创建时间轴
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base_time = datetime(2025, 1, 1, 0, 0, 0)
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times = [base_time + timedelta(hours=i) for i in range(24)]
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fig, ax = plt.subplots(figsize=(14, 8))
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# 绘制发电和负荷
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ax.plot(times, load_demand, 'r-', linewidth=3, label='负荷需求')
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ax.plot(times, thermal_output, 'b-', linewidth=2, label='火电出力')
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ax.plot(times, wind_output, 'g-', linewidth=2, label='风电出力')
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ax.plot(times, solar_output, 'orange', linewidth=2, label='光伏出力')
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# 计算总发电量
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total_generation = [thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)]
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ax.plot(times, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量')
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# 设置时间轴格式
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
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ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
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ax.set_xlabel('时间')
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ax.set_ylabel('功率 (MW)')
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ax.set_title('多能互补系统24小时发电曲线 (时间序列)')
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ax.legend(loc='upper right')
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ax.grid(True, alpha=0.3)
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# 旋转时间标签
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
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plt.tight_layout()
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plt.savefig('time_series_curves.png', dpi=300, bbox_inches='tight')
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plt.close()
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print("时间序列图表已保存为 'time_series_curves.png'")
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def main():
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"""主函数"""
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# 示例数据
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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
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wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
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thermal_output = [5.0] * 24
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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,
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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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# 系统参数
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params = SystemParameters(
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max_curtailment_wind=0.1,
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max_curtailment_solar=0.1,
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max_grid_ratio=0.2,
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storage_efficiency=0.9,
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discharge_rate=1.0,
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charge_rate=1.0
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)
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# 计算最优储能容量
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print("正在计算最优储能容量...")
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result = optimize_storage_capacity(
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solar_output, wind_output, thermal_output, load_demand, params
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)
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print("\n=== 优化结果 ===")
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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"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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print(f"能量平衡校验: {'通过' if result['energy_balance_check'] else '未通过'}")
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# 创建各种图表
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print("\n正在生成可视化图表...")
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# 1. 基础曲线图（已在main.py中实现）
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print("1. 基础系统运行曲线图")
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# 2. 综合分析图
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print("2. 综合分析图表")
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create_comprehensive_plot(solar_output, wind_output, thermal_output, load_demand, result, params)
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# 3. 时间序列图
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print("3. 时间序列图表")
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create_time_series_plot(solar_output, wind_output, thermal_output, load_demand, result)
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print("\n=== 所有图表生成完成 ===")
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print("生成的文件:")
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print("- system_curves.png: 基础系统运行曲线")
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print("- comprehensive_analysis.png: 综合分析图表")
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print("- time_series_curves.png: 时间序列图表")
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if __name__ == "__main__":
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main()
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