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

Abstract

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