A stochastic energy optimization of stand-alone electrical microgrid with storage systems and demand management

Abstract

Standalone electrical microgrids encounter several challenges because of the variability of renewable energy sources and the load demand's uncertainty, which yields inefficiency issues. Tackling these challenges needs advanced optimization strategies that balance energy generation, storage, and demand management. The presented study concentrates on the stochastic energy optimization of a single electrical microgrid by use of renewable energy sources and energy storage structures. A scenario generation method is employed to model uncertainties in load demand and renewable energy generation. Demand-side management is implicated by use of a peak-shaving demand-shifting method, which is complemented by battery and hydrogen storage systems to guarantee reliability through emergency conditions. The optimization algorithm aimed to lessen operational costs and unsupplied demand. Simulations are carried out across three scenarios and two case studies, with and without demand-shifting strategies. The outputs exhibit that the integration of demand-shifting with storage systems notably elevates system function quality and reduces operational costs by up to 4.5% and undelivered energy by 18.6% in peak demand scenarios. Moreover, the suggested demand-shifting approach efficiently decreases annual investment costs, which underscores its economic viability for long-term planning of single microgrids.