A Coordinated Bidding Model For Wind Plant and Compressed Air Energy Storage Systems in the Energy and Ancillary Service Markets using a Distributionally Robust Optimization Approach

Clean energy resources, like wind, have a stochastic nature, which involves uncertainties in the power system. Introducing energy storage systems (ESS) to the network can compensate for the uncertainty in wind plant output and allow the plant to participate in ancillary service markets. Advance in compressed air energy storage system (CAES) technologies and their fast response make them suitable for ancillary services. This paper investigates the participation of a combined energy system composed of wind plants and compressed air energy storage system (CAES) in the energy market from a private owner’s viewpoint, including trading in energy markets and bidding for frequency regulation and reserve capacity in ancillary service markets. Since this problem contains various uncertainties associated with market prices, wind generation levels, and regulation signals, distributionally robust optimization (DRO) is used to model the uncertainties and enhance the simultaneous participation of a combined wind-CAES system in day-ahead energy and ancillary service markets. This method combines the advantages of stochastic and robust optimization. In contrast to robust optimization (RO), the method consolidates specific statistical data to reduce conservative results. Simulation results demonstrate the proposed model’s effectiveness in handling uncertainties and provide a framework for investors in this area. In addition, case study analyses are applied to assess the model’s performance and validate the coordination of a wind plant and compressed air energy storage system in participating in a deregulated electricity market. Finally, DRO and RO are compared in modeling the uncertainties of the optimization problem. The optimal outputs demonstrate the effectiveness of DRO in terms of achieving higher realized profits with less conservative results.

清洁能源资源，如风能，具有随机性，这涉及到电力系统中的不确定性。将储能系统（ESS）引入网络中，可以补偿风力发电场输出的不确定性，并使发电场能够参与辅助服务市场。压缩空气储能系统（CAES）技术的进步及其快速响应特性使其适用于辅助服务。本文从私人业主的角度，探讨了由风力发电场和压缩空气储能系统（CAES）组成的综合能源系统在能源市场中的参与情况，包括在能源市场中的交易以及参与辅助服务市场的频率调节和备用容量投标。鉴于该问题包含与市场价格、风力发电水平及调节信号相关的各种不确定性，本文采用分布式鲁棒优化（DRO）来建模不确定性，并增强综合风-CAES系统在日前能源和辅助服务市场中的同时参与。该方法结合了随机优化和鲁棒优化的优势。与鲁棒优化（RO）相比，该方法整合了特定的统计数据以减少保守的结果。仿真结果表明，所提出的模型在处理不确定性方面的有效性，并为该领域的投资者提供了一个框架。此外，通过案例研究分析来评估模型性能，并验证风力发电场与压缩空气储能系统在参与非管制电力市场中的协调性。最后，本文比较了DRO和RO在建模优化问题不确定性方面的差异。最优输出展示了DRO在实现更高实际利润且结果更为保守方面的有效性。