multi_energy_complementarity/advanced_visualization.py

"""
高级可视化程序 - 多能互补系统储能容量优化

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。

作者: iFlow CLI
创建日期: 2025-12-25
"""

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
from datetime import datetime, timedelta
from storage_optimization import optimize_storage_capacity, SystemParameters

# 设置中文字体
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False


def create_comprehensive_plot(solar_output, wind_output, thermal_output, load_demand, result, params):
    """
    创建综合可视化图表
    
    Args:
        solar_output: 24小时光伏出力曲线 (MW)
        wind_output: 24小时风电出力曲线 (MW)
        thermal_output: 24小时火电出力曲线 (MW)
        load_demand: 24小时负荷曲线 (MW)
        result: 优化结果字典
        params: 系统参数
    """
    hours = np.arange(24)
    
    # 创建大型图形
    fig = plt.figure(figsize=(16, 12))
    fig.suptitle('多能互补系统储能容量优化分析', fontsize=18, fontweight='bold')
    
    # 创建网格布局
    gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)
    
    # === 主要图表：发电和负荷 ===
    ax1 = fig.add_subplot(gs[0, :])
    
    # 绘制各发电类型
    ax1.fill_between(hours, 0, thermal_output, alpha=0.7, color='blue', label='火电')
    ax1.fill_between(hours, thermal_output, 
                     [thermal_output[i] + wind_output[i] for i in range(24)], 
                     alpha=0.7, color='green', label='风电')
    ax1.fill_between(hours, [thermal_output[i] + wind_output[i] for i in range(24)],
                     [thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)],
                     alpha=0.7, color='orange', label='光伏')
    
    # 绘制负荷曲线
    ax1.plot(hours, load_demand, 'r-', linewidth=3, label='负荷需求')
    
    ax1.set_xlabel('时间 (小时)')
    ax1.set_ylabel('功率 (MW)')
    ax1.set_title('24小时发电与负荷平衡')
    ax1.legend(loc='upper right')
    ax1.grid(True, alpha=0.3)
    ax1.set_xlim(0, 23)
    
    # === 储能充放电功率 ===
    ax2 = fig.add_subplot(gs[1, 0])
    charge_power = result['charge_profile']
    discharge_power = [-x for x in result['discharge_profile']]
    
    ax2.bar(hours, charge_power, color='green', alpha=0.7, label='充电')
    ax2.bar(hours, discharge_power, color='red', alpha=0.7, label='放电')
    ax2.set_xlabel('时间 (小时)')
    ax2.set_ylabel('功率 (MW)')
    ax2.set_title('储能充放电功率')
    ax2.legend()
    ax2.grid(True, alpha=0.3)
    ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
    
    # === 储能状态 ===
    ax3 = fig.add_subplot(gs[1, 1])
    storage_soc = result['storage_profile']
    
    ax3.plot(hours, storage_soc, 'b-', linewidth=2, marker='o')
    ax3.fill_between(hours, 0, storage_soc, alpha=0.3, color='blue')
    ax3.set_xlabel('时间 (小时)')
    ax3.set_ylabel('储能容量 (MWh)')
    ax3.set_title(f'储能状态 (容量: {result["required_storage_capacity"]:.1f} MWh)')
    ax3.grid(True, alpha=0.3)
    ax3.set_ylim(bottom=0)
    
    # === 弃风弃光 ===
    ax4 = fig.add_subplot(gs[1, 2])
    curtailed_wind = result['curtailed_wind']
    curtailed_solar = result['curtailed_solar']
    
    ax4.bar(hours, curtailed_wind, color='lightblue', alpha=0.7, label='弃风')
    ax4.bar(hours, curtailed_solar, color='yellow', alpha=0.7, label='弃光')
    ax4.set_xlabel('时间 (小时)')
    ax4.set_ylabel('功率 (MW)')
    ax4.set_title('弃风弃光功率')
    ax4.legend()
    ax4.grid(True, alpha=0.3)
    
    # === 能量饼图 ===
    ax5 = fig.add_subplot(gs[2, 0])
    
    # 计算总能量
    total_gen = sum(thermal_output) + sum(wind_output) + sum(solar_output)
    total_load = sum(load_demand)
    total_curtailed = sum(curtailed_wind) + sum(curtailed_solar)
    total_grid = sum(result['grid_feed_in'])
    
    # 能量分配
    energy_data = [total_load, total_curtailed, total_grid]
    energy_labels = [f'负荷\n({total_load:.1f} MWh)', 
                     f'弃风弃光\n({total_curtailed:.1f} MWh)',
                     f'上网电量\n({total_grid:.1f} MWh)']
    colors = ['red', 'orange', 'green']
    
    ax5.pie(energy_data, labels=energy_labels, colors=colors, autopct='%1.1f%%', startangle=90)
    ax5.set_title('能量分配')
    
    # === 发电构成饼图 ===
    ax6 = fig.add_subplot(gs[2, 1])
    
    gen_data = [sum(thermal_output), sum(wind_output), sum(solar_output)]
    gen_labels = [f'火电\n({gen_data[0]:.1f} MWh)', 
                  f'风电\n({gen_data[1]:.1f} MWh)',
                  f'光伏\n({gen_data[2]:.1f} MWh)']
    gen_colors = ['blue', 'green', 'orange']
    
    ax6.pie(gen_data, labels=gen_labels, colors=gen_colors, autopct='%1.1f%%', startangle=90)
    ax6.set_title('发电构成')
    
    # === 关键指标文本 ===
    ax7 = fig.add_subplot(gs[2, 2])
    ax7.axis('off')
    
    # 显示关键指标
    metrics_text = f"""
    关键指标
    ─────────────
    所需储能容量: {result['required_storage_capacity']:.1f} MWh
    储能效率: {params.storage_efficiency:.1%}
    
    弃风率: {result['total_curtailment_wind_ratio']:.1%}
    弃光率: {result['total_curtailment_solar_ratio']:.1%}
    上网电量比例: {result['total_grid_feed_in_ratio']:.1%}
    
    能量平衡: {'✓' if result['energy_balance_check'] else '✗'}
    
    最大储能状态: {max(storage_soc):.1f} MWh
    最小储能状态: {min(storage_soc):.1f} MWh
    """
    
    ax7.text(0.1, 0.5, metrics_text, fontsize=11, verticalalignment='center',
             fontfamily='SimHei', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))
    
    # 保存图片
    plt.savefig('comprehensive_analysis.png', dpi=300, bbox_inches='tight')
    plt.close()
    
    print("综合分析图表已保存为 'comprehensive_analysis.png'")


def create_time_series_plot(solar_output, wind_output, thermal_output, load_demand, result):
    """
    创建时间序列图表，模拟真实的时间轴
    """
    # 创建时间轴
    base_time = datetime(2025, 1, 1, 0, 0, 0)
    times = [base_time + timedelta(hours=i) for i in range(24)]
    
    fig, ax = plt.subplots(figsize=(14, 8))
    
    # 绘制发电和负荷
    ax.plot(times, load_demand, 'r-', linewidth=3, label='负荷需求')
    ax.plot(times, thermal_output, 'b-', linewidth=2, label='火电出力')
    ax.plot(times, wind_output, 'g-', linewidth=2, label='风电出力')
    ax.plot(times, solar_output, 'orange', linewidth=2, label='光伏出力')
    
    # 计算总发电量
    total_generation = [thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)]
    ax.plot(times, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量')
    
    # 设置时间轴格式
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
    ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
    
    ax.set_xlabel('时间')
    ax.set_ylabel('功率 (MW)')
    ax.set_title('多能互补系统24小时发电曲线 (时间序列)')
    ax.legend(loc='upper right')
    ax.grid(True, alpha=0.3)
    
    # 旋转时间标签
    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
    
    plt.tight_layout()
    plt.savefig('time_series_curves.png', dpi=300, bbox_inches='tight')
    plt.close()
    
    print("时间序列图表已保存为 'time_series_curves.png'")


def main():
    """主函数"""
    # 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]
    
    # 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
    )
    
    # 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
    )
    
    print("\n=== 优化结果 ===")
    print(f"所需储能总容量: {result['required_storage_capacity']:.2f} MWh")
    print(f"弃风率: {result['total_curtailment_wind_ratio']:.3f}")
    print(f"弃光率: {result['total_curtailment_solar_ratio']:.3f}")
    print(f"上网电量比例: {result['total_grid_feed_in_ratio']:.3f}")
    print(f"能量平衡校验: {'通过' if result['energy_balance_check'] else '未通过'}")
    
    # 创建各种图表
    print("\n正在生成可视化图表...")
    
    # 1. 基础曲线图（已在main.py中实现）
    print("1. 基础系统运行曲线图")
    
    # 2. 综合分析图
    print("2. 综合分析图表")
    create_comprehensive_plot(solar_output, wind_output, thermal_output, load_demand, result, params)
    
    # 3. 时间序列图
    print("3. 时间序列图表")
    create_time_series_plot(solar_output, wind_output, thermal_output, load_demand, result)
    
    print("\n=== 所有图表生成完成 ===")
    print("生成的文件:")
    print("- system_curves.png: 基础系统运行曲线")
    print("- comprehensive_analysis.png: 综合分析图表")
    print("- time_series_curves.png: 时间序列图表")


if __name__ == "__main__":
    main()