Abstract:
This study introduces an optimization-based model featuring weighted and adjustable parame-ters, integrating deterministic and stochastic components to facilitate a seamless transition between control strategies for microgrid management. The impetus for this investigation stems from a discerned void in the existing literature concerning microgrid management, par-ticularly the scarcity of studies addressing non-scheduled loads within various energy market frameworks. In light of this gap, the research aims to evaluate the technical and economic merits of the proposed approach within a microgrid characterized by a load profile exhibiting significant day-of-the-week variations. A Model Predictive Control (MPC)-based control sys-tem serves as the central management entity, utilizing key performance indicators to gauge the efficacy of the model. This control strategy, implemented at the reference signal generator level, is devised to minimize operational expenses and mitigate degradation of the Energy Storage System (ESS). The findings indicate that the deterministic variant of the proposed model yields considerable quantitative benefits for the microgrid, particularly evident in sce-narios with more stable load profiles. Moreover, the deterministic variant facilitates the eluci-dation of a range of values for the weighted parameters of the model, subsequently utilized in the stochastic variant. Conversely, the stochastic variant emerges as particularly advantageous for the microgrid in situations characterized by abrupt load profile changes. Both iterations of the model under investigation demonstrate noteworthy performance compared to established benchmark models.
