Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies

This paper presents a novel decentralized bi-level stochastic optimization approach based on the progressive hedging algorithm for multi-agent systems (MAS) in multi-energy microgrids (MEMGs) to enhance network flexibility. In proposed model, suppliers and consumers of three energy carrier power, heat, hydrogen are considered. system further consists storage such as plug-in electric vehicle aggregators, thermal storage, with application power-to-hydrogen hydrogen-to-power technologies. Furthermore, Latin Hypercube Sampling method has been utilized manage uncertainties. addition, penalty function power exchange pricing model evaluated by electrical marginal price each microgrid determine agreed among MEMGs. The suggested work performs over MAS total profit is maximized 24-h scheduling diverse case studies. Ultimately, approach, converging through seven iterations, indicates an effective performance promising solution MAS-based framework. Besides, optimal MEMGs were converged same topologies. Implementing plays major role increasing improving reliability structure.