In this paper, a two-stage stochastic day-ahead (DA) scheduling model is proposed incorporating wind power units and compressed air energy storage (CAES) to clear a co-optimized energy and reserve market. The two-stage stochastic programming method is employed to deal with the wind power generation uncertain nature. A linearized AC optimal power flow (LAC-OPF) approach with consideration of network losses, reactive power, and voltage magnitude constraints is utilized in the proposed two-stage stochastic DA scheduling model. Using an engineering insight, a two-level LAC-OPF (TL-LAC-OPF) approach is proposed to (i) reduce the number of binary variables of the LAC-OPF approach which decreases the computational burden, and (ii) obtain LAC-OPF pre-defined parameters adaptively so that the accuracy of LAC-OPF approach is increased as a result of reducing artificial losses. Furthermore, as the CAES efficiency depends on its thermodynamic and operational conditions, the proposed two-stage stochastic DA scheduling model is developed by considering its thermodynamic characteristics to obtain a more realistic market decision at the first place. The proposed model is applied to IEEE 30-bus and 57-bus test systems using GAMS software, and is compared with three traditional approaches, i.e., AC-OPF, DC-OPF, and LAC-OPF. Simulation results demonstrate effectiveness of the proposed methodology.

中文翻译：

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联合能源和储备市场中的随机交流网络约束的CAES和风力发电调度：向更现实的结果迈进

本文提出了一种两阶段随机日前调度模型，该模型结合了风力发电机组和压缩空气储能（CAES），以清除共同优化的能源和储备市场。采用两阶段随机规划方法处理风力发电的不确定性。在拟议的两阶段随机DA调度模型中，采用了考虑网络损耗，无功功率和电压幅度约束的线性化AC最优潮流（LAC-OPF）方法。利用工程见识，提出了一种两级LAC-OPF（TL-LAC-OPF）方法：（i）减少LAC-OPF方法的二进制变量数量，从而减少了计算负担，（ii）自适应地获得LAC-OPF预定义参数，从而由于减少了人为损失而提高了LAC-OPF方法的精度。此外，由于CAES效率取决于其热力学和运行条件，因此，建议的两阶段随机DA调度模型是通过考虑其热力学特性而开发的，从而首先获得了更现实的市场决策。该模型通过GAMS软件应用于IEEE 30总线和57总线测试系统，并与AC-OPF，DC-OPF和LAC-OPF三种传统方法进行了比较。仿真结果证明了所提出方法的有效性。提出的两阶段随机DA调度模型是通过考虑其热力学特性而开发的，从而首先获得了更现实的市场决策。该模型通过GAMS软件应用于IEEE 30总线和57总线测试系统，并与AC-OPF，DC-OPF和LAC-OPF三种传统方法进行了比较。仿真结果证明了所提出方法的有效性。提出的两阶段随机DA调度模型是通过考虑其热力学特性而开发的，从而首先获得了更现实的市场决策。该模型通过GAMS软件应用于IEEE 30总线和57总线测试系统，并与AC-OPF，DC-OPF和LAC-OPF三种传统方法进行了比较。仿真结果证明了所提出方法的有效性。