Wind generation resources have already been subjected to a multitude of challenges to participate in competitive markets due to their production uncertainty. System flexibility, which is needed for reliable and secure operation of a power system with high penetrations of wind energy, may be provided by gas-fired generating (GFG) units. So, these units are considered as the key supporting assets for wind energy integration. In this paper, a biobjective stochastic self-scheduling strategy is proposed for coordinated operation of GFG units and wind farms owned by a generation company (GenCo), including objectives of profit maximization and financial risk minimization. Hybrid nondominated sorting genetic algorithm-II and mixed integer linear programming techniques are employed to solve the proposed biobjective problem, providing a set of Pareto solutions. Additionally, a fuzzy decision making approach is proposed to choose the most preferred solution among the obtained Pareto solutions based on the level of risk seeking of the GenCo. The uncertainties of forecasting errors of wind power generation and natural gas and electricity market prices, and the contingencies of the GFG units are modeled in the proposed stochastic framework. A Monte Carlo simulation-based validation (MCSV) approach is employed to verify the efficiency of the proposed strategy in different case studies. The results of the MCSV approach throughout 10 000 real world scenarios demonstrate that the scheduling plan procured by the proposed hybrid stochastic strategy is generally more optimal than that of the conventional stochastic and deterministic ones.Wind generation resources have already been subjected to a multitude of challenges to participate in competitive markets due to their production uncertainty. System flexibility, which is needed for reliable and secure operation of a power system with high penetrations of wind energy, may be provided by gas-fired generating (GFG) units. So, these units are considered as the key supporting assets for wind energy integration. In this paper, a biobjective stochastic self-scheduling strategy is proposed for coordinated operation of GFG units and wind farms owned by a generation company (GenCo), including objectives of profit maximization and financial risk minimization. Hybrid nondominated sorting genetic algorithm-II and mixed integer linear programming techniques are employed to solve the proposed biobjective problem, providing a set of Pareto solutions. Additionally, a fuzzy decision making approach is proposed to choose the most preferred solution among the obtained Pareto solutions based on the level of r...

中文翻译：

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考虑风能整合天然气灵活性的发电公司风险约束自我调度

由于其生产的不确定性，风力发电资源在参与竞争性市场方面已经面临诸多挑战。燃气发电 (GFG) 装置可以提供系统灵活性，这是具有高风能渗透率的电力系统可靠和安全运行所必需的。因此，这些机组被视为风能整合的关键支撑资产。在本文中，提出了一种双目标随机自调度策略，用于发电公司 (GenCo) 拥有的 GFG 机组和风电场的协调运行，包括利润最大化和财务风险最小化的目标。采用混合非支配排序遗传算法-II和混合整数线性规划技术来解决所提出的双目标问题，提供一组帕累托解决方案。此外，提出了一种模糊决策方法，以根据 GenCo 的风险寻求水平在获得的 Pareto 解决方案中选择最优选的解决方案。风力发电和天然气和电力市场价格预测误差的不确定性以及GFG 机组的突发事件在所提出的随机框架中进行建模。使用基于蒙特卡罗模拟的验证 (MCSV) 方法来验证所提出策略在不同案例研究中的效率。MCSV 方法在 10 000 个真实世界场景中的结果表明，由所提出的混合随机策略获得的调度计划通常比传统的随机和确定性策略更优。由于其生产的不确定性，风力发电资源在参与竞争性市场方面已经面临诸多挑战。燃气发电 (GFG) 装置可以提供系统灵活性，这是具有高风能渗透率的电力系统可靠和安全运行所必需的。因此，这些机组被视为风能整合的关键支撑资产。在本文中，提出了一种双目标随机自调度策略，用于发电公司 (GenCo) 拥有的 GFG 机组和风电场的协调运行，包括利润最大化和财务风险最小化的目标。采用混合非支配排序遗传算法-II和混合整数线性规划技术来解决所提出的双目标问题，提供一组帕累托解决方案。此外，提出了一种模糊决策方法，以根据 r 的水平在获得的帕累托解决方案中选择最优选的解决方案。