Flexible load aggregator optimization scheduling research based on improved seagull algorithm

• Main Authors: Liu, Xiangxiang, Zhu, Zhuohang, He, Zongzhe, Xiao, Hui, Ding, Guili, Wang, Zongyao, Xu, Zhihao, Kang, Bing
• Format: Conference Proceeding
• Language: English
• Published: SPIE 25.09.2023
• ISBN: 9781510668270; 1510668276
• ISSN: 0277-786X
• DOI: 10.1117/12.3004348

Source storage flexible resources such as central air conditioning, electric vehicles, distributed wind power, photovoltaic, and energy storage have considerable regulation potential and are effective alternative resources for grid-side regulation capacity. However, there is no complete theoretical approach for the construction of multi-timescale optimal model and optimization strategy for multi-subject flexible loads. Considering these problems, this paper proposes a multi-timescale model and optimal scheduling strategy for flexible load aggregators based on the quadratic attack of improved seagull algorithm. Firstly, a flexible load hierarchical optimal scheduling architecture is proposed to aggregate flexible load resources; secondly, a generalized aggregation model is established for electric vehicles, temperature-controlled loads and distributed energy storage loads, and in the day-ahead phase, the start-stop schedule of conventional units, optimal charging of electric vehicles and flexible load aggregator operation constraints are considered. In the intra-day phase, the system rotation backup cost and temperature-controlled load constraints are considered to construct an intra-day optimal dispatching model. Then an economically optimal dispatching strategy is proposed to construct a model based on flexible loads such as electric vehicles, temperature-controlled loads, and distributed storage loads, and an improved seagull optimization algorithm based on population optimization and dynamic strategy is proposed to solve the problem. Finally, the correctness and effectiveness of the proposed model algorithm is verified on the improved IEEE33 node system.