multi_energy_complementarity/main.py

"""
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
matplotlib.use('TkAgg')  # 设置为TkAgg后端以支持图形窗口显示
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import datetime
from storage_optimization import optimize_storage_capacity, SystemParameters
from excel_reader import read_excel_data, create_excel_template, analyze_excel_data

# 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, show_window=False, display_only=False):
    """
    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
        show_window: 是否显示图形窗口
        display_only: 是否只显示不保存文件
    """
    import matplotlib.pyplot as plt
    import numpy as np

    # 设置中文字体
    plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
    plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
    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]
        sampled_grid_feed_in = result['grid_feed_in'][::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']
        sampled_grid_feed_in = result['grid_feed_in']

    # 创建图形（4个子图）
    fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(14, 16))
    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)

    # === 第四个子图：购电量和上网电量曲线 ===
    # 直接使用原始数据：正值表示上网电量，负值表示购电量
    grid_power = sampled_grid_feed_in

    # 绘制电网交互电量（正值上网，负值购电）
    colors = ['brown' if x >= 0 else 'purple' for x in grid_power]
    ax4.bar(sampled_hours, grid_power, color=colors, alpha=0.7, label='电网交互电量')

    # 添加零线
    ax4.axhline(y=0, color='black', linestyle='-', linewidth=0.5)

    # 添加图例说明
    from matplotlib.patches import Patch
    legend_elements = [
        Patch(facecolor='brown', alpha=0.7, label='上网电量 (+)'),
        Patch(facecolor='purple', alpha=0.7, label='购电量 (-)')
    ]
    ax4.legend(handles=legend_elements, loc='upper right')

    ax4.set_xlabel('时间 (小时)')
    ax4.set_ylabel('功率 (MW)')
    ax4.set_title(f'电网交互电量{title_suffix} (正值:上网, 负值:购电)')
    ax4.grid(True, alpha=0.3)
    ax4.set_xlim(0, max(sampled_hours))

    # 调整布局
    plt.tight_layout()

    # 根据参数决定是否保存和显示图形
    if display_only:
        # 只显示，不保存
        try:
            plt.show()
        except Exception as e:
            print(f"无法显示图形窗口：{str(e)}")
    else:
        # 保存图片
        plt.savefig('system_curves.png', dpi=300, bbox_inches='tight')

        # 根据参数决定是否显示图形窗口
        if show_window:
            try:
                plt.show()
            except Exception as e:
                print(f"无法显示图形窗口：{str(e)}")
                print("图形已保存为 'system_curves.png'")
        else:
            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}")
    
    # 计算总弃风弃光量
    total_curtail_wind = sum(result['curtailed_wind'])
    total_curtail_solar = sum(result['curtailed_solar'])
    print(f"\n=== 弃风弃光统计 ===")
    print(f"总弃风电量: {total_curtail_wind:.2f} MWh")
    print(f"总弃光电量: {total_curtail_solar:.2f} MWh")

    # 计算购电和上网电量统计
    total_grid_feed_in = sum(result['grid_feed_in'])
    total_grid_purchase = sum(-x for x in result['grid_feed_in'] if x < 0)  # 购电量
    total_grid_feed_out = sum(x for x in result['grid_feed_in'] if x > 0)   # 上网电量

    print(f"\n=== 电网交互统计 ===")
    if total_grid_feed_in >= 0:
        print(f"净上网电量: {total_grid_feed_in:.2f} MWh")
    else:
        print(f"净购电量: {-total_grid_feed_in:.2f} MWh")
    print(f"总购电量: {total_grid_purchase:.2f} MWh")
    print(f"总上网电量: {total_grid_feed_out:.2f} MWh")


def export_results_to_excel(solar_output, wind_output, thermal_output, load_demand, result, params, filename=None):
    """
    将多能互补系统储能优化结果导出到Excel文件，包含运行数据、统计结果和系统参数。
    
    Args:
        solar_output (list): 光伏出力曲线 (MW)
        wind_output (list): 风电出力曲线 (MW)
        thermal_output (list): 火电出力曲线 (MW)
        load_demand (list): 负荷需求曲线 (MW)
        result (dict): 包含以下键的优化结果字典:
            - charge_profile: 储能充电功率曲线 (MW)
            - discharge_profile: 储能放电功率曲线 (MW)
            - storage_profile: 储能状态曲线 (MWh)
            - curtailed_wind: 弃风功率曲线 (MW)
            - curtailed_solar: 弃光功率曲线 (MW)
            - grid_feed_in: 电网交互功率曲线 (MW, 负值表示购电)
            - required_storage_capacity: 所需储能总容量 (MWh)
            - total_curtailment_wind_ratio: 总弃风率
            - total_curtailment_solar_ratio: 总弃光率
            - total_grid_feed_in_ratio: 总上网电量比例
            - energy_balance_check: 能量平衡校验结果
            - capacity_limit_reached: 容量限制是否达到
        params (object): 系统参数对象，包含各种技术参数
        filename (str, optional): 输出文件名，如未提供则自动生成
    
    Returns:
        str: 生成的Excel文件路径
    
    生成的Excel文件包含以下工作表:
        - 运行数据: 小时级运行数据
        - 统计结果: 关键性能指标统计
        - 系统参数: 输入参数汇总
        - 说明: 文件使用说明
    """
    """
    将优化结果导出到Excel文件

    Args:
        solar_output: 光伏出力曲线 (MW)
        wind_output: 风电出力曲线 (MW)
        thermal_output: 火电出力曲线 (MW)
        load_demand: 负荷曲线 (MW)
        result: 优化结果字典
        params: 系统参数
        filename: 输出文件名，如果为None则自动生成
    """
    if filename is None:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"storage_optimization_results_{timestamp}.xlsx"

    print(f"\n正在导出结果到Excel文件: {filename}")

    # 准备数据
    hours = list(range(1, len(solar_output) + 1))

    # 分离购电和上网电量
    grid_purchase = []
    grid_feed_out = []

    for power in result['grid_feed_in']:
        if power < 0:
            grid_purchase.append(-power)  # 购电，转换为正值
            grid_feed_out.append(0)
        else:
            grid_purchase.append(0)
            grid_feed_out.append(power)  # 上网电量

    # 创建主要数据DataFrame
    data_df = pd.DataFrame({
        '小时': hours,
        '光伏出力(MW)': solar_output,
        '风电出力(MW)': wind_output,
        '火电出力(MW)': thermal_output,
        '总发电量(MW)': [solar_output[i] + wind_output[i] + thermal_output[i] for i in range(len(solar_output))],
        '负荷需求(MW)': load_demand,
        '储能充电(MW)': result['charge_profile'],
        '储能放电(MW)': result['discharge_profile'],
        '储能状态(MWh)': result['storage_profile'],
        '弃风量(MW)': result['curtailed_wind'],
        '弃光量(MW)': result['curtailed_solar'],
        '购电量(MW)': grid_purchase,
        '上网电量(MW)': grid_feed_out
    })

    # 创建统计信息DataFrame
    total_grid_feed_in = sum(result['grid_feed_in'])
    total_grid_purchase = sum(-x for x in result['grid_feed_in'] if x < 0)  # 购电量
    total_grid_feed_out = sum(x for x in result['grid_feed_in'] if x > 0)   # 上网电量

    stats_df = pd.DataFrame({
        '指标': [
            '所需储能总容量',
            '最大储能状态',
            '最小储能状态',
            '总充电量',
            '总放电量',
            '弃风率',
            '弃光率',
            '上网电量比例',
            '能量平衡校验',
            '净购电量/净上网电量',
            '总购电量',
            '总上网电量',
            '容量限制是否达到'
        ],
        '数值': [
            f"{result['required_storage_capacity']:.2f} MWh",
            f"{max(result['storage_profile']):.2f} MWh",
            f"{min(result['storage_profile']):.2f} MWh",
            f"{sum(result['charge_profile']):.2f} MWh",
            f"{sum(result['discharge_profile']):.2f} MWh",
            f"{result['total_curtailment_wind_ratio']:.3f}",
            f"{result['total_curtailment_solar_ratio']:.3f}",
            f"{result['total_grid_feed_in_ratio']:.3f}",
            "通过" if result['energy_balance_check'] else "未通过",
            f"{-total_grid_feed_in:.2f} MWh" if total_grid_feed_in < 0 else f"{total_grid_feed_in:.2f} MWh",
            f"{total_grid_purchase:.2f} MWh",
            f"{total_grid_feed_out:.2f} MWh",
            "是" if result['capacity_limit_reached'] else "否"
        ]
    })

    # 创建系统参数DataFrame
    params_df = pd.DataFrame({
        '参数名称': [
            '最大弃风率',
            '最大弃光率',
            '最大上网电量比例',
            '储能效率',
            '放电倍率',
            '充电倍率',
            '最大储能容量',
            '额定火电装机容量',
            '额定光伏装机容量',
            '额定风电装机容量',
            '火电可用发电量',
            '光伏可用发电量',
            '风电可用发电量'
        ],
        '参数值': [
            params.max_curtailment_wind,
            params.max_curtailment_solar,
            params.max_grid_ratio,
            params.storage_efficiency,
            params.discharge_rate,
            params.charge_rate,
            params.max_storage_capacity if params.max_storage_capacity is not None else "无限制",
            params.rated_thermal_capacity,
            params.rated_solar_capacity,
            params.rated_wind_capacity,
            params.available_thermal_energy,
            params.available_solar_energy,
            params.available_wind_energy
        ],
        '单位': [
            "比例",
            "比例",
            "比例",
            "效率",
            "C-rate",
            "C-rate",
            "MWh",
            "MW",
            "MW",
            "MW",
            "MWh",
            "MWh",
            "MWh"
        ]
    })

    # 写入Excel文件
    with pd.ExcelWriter(filename, engine='openpyxl') as writer:
        # 写入主要数据
        data_df.to_excel(writer, sheet_name='运行数据', index=False)

        # 写入统计信息
        stats_df.to_excel(writer, sheet_name='统计结果', index=False)

        # 写入系统参数
        params_df.to_excel(writer, sheet_name='系统参数', index=False)

        # 创建说明工作表
        description_df = pd.DataFrame({
            '项目': [
                '文件说明',
                '生成时间',
                '数据长度',
                '数据内容',
                '注意事项'
            ],
            '内容': [
                '多能互补系统储能容量优化结果',
                datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                f"{len(solar_output)} 小时",
                '包含发电、负荷、储能、弃风弃光、购电上网等完整数据',
                '负值表示购电，正值表示上网电量'
            ]
        })
        description_df.to_excel(writer, sheet_name='说明', index=False)

    print(f"结果已成功导出到: {filename}")
    return filename


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
    if len(sys.argv) < 2:
        print_usage()
        return

    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 == '--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}")
                print(f"  额定火电装机容量: {params.rated_thermal_capacity} MW")
                print(f"  额定光伏装机容量: {params.rated_solar_capacity} MW")
                print(f"  额定风电装机容量: {params.rated_wind_capacity} MW")
                print(f"  火电可用发电量: {params.available_thermal_energy} MWh")
                print(f"  光伏可用发电量: {params.available_solar_energy} MWh")
                print(f"  风电可用发电量: {params.available_wind_energy} MWh")
            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,
                    rated_thermal_capacity=100.0,
                    rated_solar_capacity=100.0,
                    rated_wind_capacity=100.0,
                    available_thermal_energy=2400.0,
                    available_solar_energy=600.0,
                    available_wind_energy=1200.0
                )

            # 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,
            rated_thermal_capacity=100.0,
            rated_solar_capacity=100.0,
            rated_wind_capacity=100.0,
            available_thermal_energy=2400.0,
            available_solar_energy=600.0,
            available_wind_energy=1200.0
        )



    # 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(f"额定火电装机容量: {params.rated_thermal_capacity} MW")
    print(f"额定光伏装机容量: {params.rated_solar_capacity} MW")
    print(f"额定风电装机容量: {params.rated_wind_capacity} MW")
    print(f"火电可用发电量: {params.available_thermal_energy} MWh")
    print(f"光伏可用发电量: {params.available_solar_energy} MWh")
    print(f"风电可用发电量: {params.available_wind_energy} MWh")
    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 --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()