标题： 一种针对固定频率空调需求响应参与的鲁棒优化方法 显示英文标题

标题： A Robust Optimization Approach for Demand Response Participation of Fixed-Frequency Air Conditioners

摘要： 随着可再生能源在新兴电力系统中渗透率的持续增加，系统调峰压力显著加剧。 在这种背景下，需求侧资源，特别是空调负荷，因其巨大的调节潜力和快速的响应能力而受到广泛关注，使其成为提供辅助调峰服务的有前景的候选者。 本研究聚焦于固定频率空调（FFACs），并提出了它们参与需求响应（DR）项目的优化模型和求解方法。 首先，基于马尔可夫假设，开发了一个FFACs的概率响应模型。 其次，通过对此概率模型进行采样，得到了在分散控制下FFAC集群的总功率消耗。 随后，构建了一个鲁棒优化模型，以在DR事件中最大化管理FFAC集群的聚合商的利润，同时考虑聚合响应功率。 该模型明确考虑了温度不确定性，以确保在鲁棒意义上的用户舒适度。 最后，利用所提出模型的结构，将其重新表述为混合整数线性规划（MILP）问题，并使用商业优化求解器进行求解。 仿真结果验证了所提出模型和求解方法的有效性。 显示英文摘要

摘要： With the continuous increase in the penetration of renewable energy in the emerging power systems, the pressure on system peak regulation has been significantly intensified. Against this backdrop, demand side resources particularly air conditioning loads have garnered considerable attention for their substantial regulation potential and fast response capabilities, making them promising candidates for providing auxiliary peak shaving services. This study focuses on fixed frequency air conditioners (FFACs) and proposes an optimization model and solution method for their participation in demand response (DR) programs. First, a probabilistic response model for FFACs is developed based on the Markov assumption. Second, by sampling this probabilistic model, the aggregate power consumption of an FFAC cluster under decentralized control is obtained. Subsequently, a robust optimization model is formulated to maximize the profit of an aggregator managing the FFAC cluster during DR events, taking into account the aggregated response power. The model explicitly considers temperature uncertainty to ensure user comfort in a robust sense. Finally, leveraging the structure of the proposed model, it is reformulated as a mixed-integer linear programming (MILP) problem and solved using a commercial optimization solver. Simulation results validate the effectiveness of the proposed model and solution approach.