An intelligent load priority list–based integrated energy management system in microgrids

In contrast to most contemporary literature on demand-side management (DSM) in microgrids (MG), which often neglects the granularity of the load importance degree prior to the shedding time or at best arbitrarily fixes a certain load priority list (LPL), this paper introduces spatially and temporally varying LPL-based DSM that determines the load category and corresponding amount that should be curtailed. The high penetration of the intrinsically stochastic renewable resources in MGs elevates some reliability problems and supply–demand balance instabilities in MGs, raising the necessity for applying robust and prompt demand response (DR) actions to avoid purchasing expensive energy from the grid, especially during peak hours. The proposed neuro-fuzzy LPL-based DSM is developed and implemented to offer smarter and less severe DR actions that will ensure a minimised operational cost, without shedding critical or essential loads. By applying the methodology on an MG testbed with real and local multi-category demand and hybrid renewable generation data, the results showed the effective optimization of the MG self-generation adequacy through DSM, especially during grid's high-tariff periods.