A novel Three-Stage demand side management framework for stochastic energy scheduling of renewable microgrids. (15th May 2023) - Mobtahej, Mohammadamin; Barzegaran, Mohammadreza; Esapour, Khodakhast
Highlights: This paper integrates DG sources like PV, WT, FC, MT, BESS in the MG. A stochastic model of actual data is used to create 4 various case studies for RES. This paper incorporates utility-driven DSM program in the problem for reducing the operational prices more. The research does not report on the use of quantum-driven computational intelligence for optimizing energy management in MGs. Abstract: By integrating renewable energy resources and communication technology into the structure, microgrids (MG) have become self-sustaining with less relying of fossil-fuel based units. In this regard, it is significant to have a highly effective energy management system (EMS) to handle the high volatility of clean energy resources, the uncertainty of time-varying loads, and the fluctuating nature of the market price. Due to the technical limitations, the MG's optimal performance is currently restricted to maximizing operational prices. A study must be conducted to appraise the effects of demand-side management (DSM) on the overall operational costs and peak reductions when integrated with EMS. This study investigates the effect of a utility-driven mutable load shaping method on non-dispatchable energy resources in the renewable microgrids considering the digital twin of solar and wind units. It proposes a three-stage stochastic EMS frame in order to solve the optimal day-ahead planning and to minimize the operating price of network-coupled MG. An initial stage involves creating four potential cases for solar and wind energy production profiles based on real-time meteorological information with the aim o time t, the active power interchange to or from utility for. f minimizing the uncertainty. Stage two involves configuring the MG system, determining the operating limitations, and incorporating DSM load participation information into the objective function. Stage 3 involves designing an optimal power dispatch configuration for DG units, increasing exports to utilities, and comparing the outcomes of each scenario including or excluding DSM involvement. According to the simulation results obtained for the suggested stochastic model implementing 20% DSM, the suggested approach could sucessfuly achieve a price decrease of almost 43.8%.