A multi-agent-based microgrid day-ahead optimal operation framework with liquid air energy storage by hybrid IGDT-STA

• DOI: 10.1016/j.est.2024.111318
• Publication Date: 2024-03-22T00:04:21Z
• Authors: Ruiqiu Yao; Hao Xie; Chunsheng Wang; Xiandong Xu; Dajun Du; Liz Varga; Yukun Hu
• Keywords: Microgrid

Liquid air energy storage (LAES) is a promising technology for net-zero transition. Regarding microgrids that utilize LAES, the price of electricity in market can create significant uncertainty within system. To address this issue, information gap decision theory (IGDT) method has proven to be an effective tool resolving uncertainties system operation. The IGDT decision-making designed tackle uncertainty, which significantly enhance abilities situations where scarce. Additionally, state transition algorithm (STA) highly intelligent optimization leverages structural learning. This study proposed novel IGDT-STA hybrid solve optimal operation microgrid with LAES while considering prices. offers two distinct strategies decision-makers who are either risk-averse or risk-taking. These subsequently optimized by STA method. In addition, implemented multi-agent framework flexibility. Through case study, it was found employed good performance compared IGDT-genetic algorithm, stochastic method, and Monte Carlo