""" 多能互补系统储能容量优化可视化程序 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。 作者: iFlow CLI 创建日期: 2025-12-25 """ import matplotlib.pyplot as plt import numpy as np 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 plot_system_curves(solar_output, wind_output, thermal_output, load_demand, result): """ 绘制系统运行曲线 Args: solar_output: 光伏出力曲线 (MW) - 支持24小时或8760小时 wind_output: 风电出力曲线 (MW) - 支持24小时或8760小时 thermal_output: 火电出力曲线 (MW) - 支持24小时或8760小时 load_demand: 负荷曲线 (MW) - 支持24小时或8760小时 result: 优化结果字典 """ hours = np.arange(len(solar_output)) data_length = len(solar_output) # 确定图表标题和采样率 if data_length == 8760: title_suffix = " (全年8760小时)" # 对于全年数据，我们采样显示（每6小时显示一个点） sample_rate = 6 sampled_hours = hours[::sample_rate] sampled_solar = solar_output[::sample_rate] sampled_wind = wind_output[::sample_rate] sampled_thermal = thermal_output[::sample_rate] sampled_load = load_demand[::sample_rate] sampled_storage = result['storage_profile'][::sample_rate] sampled_charge = result['charge_profile'][::sample_rate] sampled_discharge = result['discharge_profile'][::sample_rate] else: title_suffix = " (24小时)" sampled_hours = hours sampled_solar = solar_output sampled_wind = wind_output sampled_thermal = thermal_output sampled_load = load_demand sampled_storage = result['storage_profile'] sampled_charge = result['charge_profile'] sampled_discharge = result['discharge_profile'] # 创建图形 fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 12)) fig.suptitle('多能互补系统24小时运行曲线', fontsize=16, fontweight='bold') # === 第一个子图：发电和负荷曲线 === ax1.plot(sampled_hours, sampled_load, 'r-', linewidth=2, label='负荷需求') ax1.plot(sampled_hours, sampled_thermal, 'b-', linewidth=2, label='火电出力') ax1.plot(sampled_hours, sampled_wind, 'g-', linewidth=2, label='风电出力') ax1.plot(sampled_hours, sampled_solar, 'orange', linewidth=2, label='光伏出力') # 计算总发电量 total_generation = [sampled_thermal[i] + sampled_wind[i] + sampled_solar[i] for i in range(len(sampled_thermal))] ax1.plot(sampled_hours, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量') ax1.set_xlabel('时间 (小时)') ax1.set_ylabel('功率 (MW)') ax1.set_title(f'发电与负荷曲线{title_suffix}') ax1.legend(loc='upper right') ax1.grid(True, alpha=0.3) ax1.set_xlim(0, max(sampled_hours)) # === 第二个子图：储能充放电曲线 === discharge_power = [-x for x in sampled_discharge] # 放电显示为负值 ax2.bar(sampled_hours, sampled_charge, color='green', alpha=0.7, label='充电功率') ax2.bar(sampled_hours, discharge_power, color='red', alpha=0.7, label='放电功率') ax2.set_xlabel('时间 (小时)') ax2.set_ylabel('功率 (MW)') ax2.set_title(f'储能充放电功率{title_suffix}') ax2.legend(loc='upper right') ax2.grid(True, alpha=0.3) ax2.set_xlim(0, max(sampled_hours)) ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5) # === 第三个子图：储能状态曲线 === ax3.plot(sampled_hours, sampled_storage, 'b-', linewidth=1, marker='o', markersize=2) ax3.fill_between(sampled_hours, 0, sampled_storage, 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 (总容量: {result["required_storage_capacity"]:.2f} MWh){title_suffix}') ax3.grid(True, alpha=0.3) ax3.set_xlim(0, max(sampled_hours)) ax3.set_ylim(bottom=0) # 调整布局 plt.tight_layout() # 保存图片 plt.savefig('system_curves.png', dpi=300, bbox_inches='tight') plt.close() # 关闭图形，不显示窗口 # 打印统计信息 print("\n=== 系统运行统计 ===") print(f"所需储能总容量: {result['required_storage_capacity']:.2f} MWh") print(f"最大储能状态: {max(result['storage_profile']):.2f} MWh") print(f"最小储能状态: {min(result['storage_profile']):.2f} MWh") print(f"总充电量: {sum(result['charge_profile']):.2f} MWh") print(f"总放电量: {sum(result['discharge_profile']):.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}") def generate_yearly_data(): """生成8760小时的示例数据""" # 基础日模式 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 daily_wind = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4 daily_thermal = [5.0] * 24 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, 16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0] # 添加季节性变化 import random random.seed(42) yearly_solar = [] yearly_wind = [] yearly_thermal = [] yearly_load = [] for day in range(365): # 季节性因子（夏季光伏更强，冬季负荷更高） season_factor = 1.0 + 0.3 * np.sin(2 * np.pi * day / 365) for hour in range(24): # 添加随机变化 solar_variation = 1.0 + 0.2 * (random.random() - 0.5) wind_variation = 1.0 + 0.3 * (random.random() - 0.5) load_variation = 1.0 + 0.1 * (random.random() - 0.5) yearly_solar.append(daily_solar[hour] * season_factor * solar_variation) yearly_wind.append(daily_wind[hour] * wind_variation) yearly_thermal.append(daily_thermal[hour]) yearly_load.append(daily_load[hour] * (2.0 - season_factor) * load_variation) return yearly_solar, yearly_wind, yearly_thermal, yearly_load def main(): """主函数""" import sys # 检查命令行参数 use_yearly_data = len(sys.argv) > 1 and sys.argv[1] == '--yearly' if use_yearly_data: 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") 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] # 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 ) # 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) print("\n曲线图已保存为 'system_curves.png'") if __name__ == "__main__": main()