用MATLAB代码写一个蒙特卡洛法计算包含风电、火电、光伏的电力系统灵活性的代码

好的，以下是用MATLAB代码实现蒙特卡洛法计算包含风电、火电、光伏的电力系统灵活性的示例代码：

% 系统参数
num_of_scenarios = 10000; % 场景数
num_of_years = 10; % 模拟年数
num_of_hours = 8760 * num_of_years; % 模拟小时数
wind_capacity = 100; % 风电装机容量
solar_capacity = 50; % 光伏装机容量
thermal_capacity = 200; % 火电装机容量
wind_cf = 0.3; % 风电容量因子
solar_cf = 0.2; % 光伏容量因子
thermal_cf = 0.8; % 火电容量因子
wind_forecast_error = 0.1; % 风电预测误差
solar_forecast_error = 0.1; % 光伏预测误差

% 随机场景生成
wind_generation = zeros(num_of_scenarios, num_of_hours);
solar_generation = zeros(num_of_scenarios, num_of_hours);
for i = 1:num_of_scenarios
    % 生成风速场景
    wind_speed = normrnd(8, 2, [1, num_of_hours]);
    wind_speed(wind_speed < 0) = 0;
    % 根据容量因子计算风电发电量
    wind_generation(i, :) = wind_cf * wind_capacity * wind_speed;
    % 生成光照场景
    solar_radiation = normrnd(1000, 200, [1, num_of_hours]);
    solar_radiation(solar_radiation < 0) = 0;
    % 根据容量因子计算光伏发电量
    solar_generation(i, :) = solar_cf * solar_capacity * solar_radiation;
end

% 随机火电发电量生成
thermal_generation = zeros(num_of_scenarios, num_of_hours);
for i = 1:num_of_scenarios
    thermal_generation(i, :) = normrnd(thermal_cf * thermal_capacity, 20, [1, num_of_hours]);
end

% 随机预测误差生成
wind_forecast_error_scenarios = normrnd(0, wind_forecast_error, [num_of_scenarios, num_of_hours]);
solar_forecast_error_scenarios = normrnd(0, solar_forecast_error, [num_of_scenarios, num_of_hours]);

% 计算总发电量
total_generation = wind_generation + solar_generation + thermal_generation;
% 添加预测误差
total_generation_with_error = total_generation + wind_forecast_error_scenarios * wind_capacity + solar_forecast_error_scenarios * solar_capacity;

% 计算灵活性
flexibility = zeros(num_of_scenarios, num_of_hours);
for i = 1:num_of_scenarios
    for j = 1:num_of_hours
        if j == 1
            flexibility(i, j) = total_generation_with_error(i, j+1) - total_generation_with_error(i, j);
        elseif j == num_of_hours
            flexibility(i, j) = total_generation_with_error(i, j) - total_generation_with_error(i, j-1);
        else
            flexibility(i, j) = abs(total_generation_with_error(i, j+1) - total_generation_with_error(i, j-1)) / 2;
        end
    end
end

% 统计结果
average_flexibility = mean(flexibility, 'all');
flexibility_99 = prctile(flexibility, 99, 'all');
flexibility_95 = prctile(flexibility, 95, 'all');

% 输出结果
fprintf('平均灵活性：%f\n', average_flexibility);
fprintf('99%%分位数灵活性：%f\n', flexibility_99);
fprintf('95%%分位数灵活性：%f\n', flexibility_95);

该示例代码中，通过蒙特卡洛方法生成了随机的风速、光照和火电发电量场景，并添加了预测误差。然后，计算出了每小时的总发电量和总发电量的灵活性。最后，统计出了总发电量灵活性的平均值、99%分位数和95%分位数，并输出结果。