Optimal Scheduling of Building Integrated Energy System Based on Improved Sparrow Search Algorithm

作者：Gongfei Zhang, Qiao Yan, Ruiqi Wang, Yi Yan, Qiang Mu, Mingyuan Wang, Zhuliang Shao · 年份：2023 · DOI：10.23919/ccc58697.2023.10239837 · 被引用次数：3 · 研究领域：Energy Load and Power Forecasting、Smart Grid Energy Management、Integrated Energy Systems Optimization

The enhanced coupling of multi-energy supply and the high volatility of renewable energy sources in building integrated energy systems (BIES) make it difficult to solve the optimal strategy for multi-energy supply. In order to solve the above problems, this paper proposes an optimization scheduling method for BIES based on a Logistic chaotic mapping and an adaptive t-distribution improved sparrow search algorithm (Logistic-t-SSA). Firstly, a multi-energy flow model for BIES is established, and an integrated demand response (IDR) model including price-based and regulation-based responses is constructed; Secondly, a long short-term memory network and multi-task learning (LSTM-MTL) model is constructed to predict the future 24-hour load; Then, a model of BIES optimal scheduling is established, and a Logistic-t-SSA algorithm is proposed, which is used to solve the model; Finally, simulation results show that using the Logistic-t-SSA algorithm to solve the optimal operation strategy of BIES, while considering IDR, can effectively improve the economic benefits of the building energy system and reduce the peak-to-valley difference of user loads.