Optimal Energy Management in a Smart Microgrid Considering Demand Response and Renewable Energy Uncertainty

Authors

https://doi.org/10.48314/imes.v2i2.44

Abstract

This paper presents a probabilistic planning model to optimize the short-term performance of a smart microgrid, with the primary objective of minimizing total operational costs in the presence of Renewable Energy Sources (RES). To account for the inherent variability of these sources, the prediction errors for wind speed and solar irradiance are modeled using stochastic Probability Density Functions (PDFs). The proposed framework incorporates Demand Response (DR) programs, with voluntary participation from residential, commercial, and industrial consumers, as a key tool to compensate for the uncertainty of renewable power generation. Incentive based payments, offered as packages of price and energy reduction, are utilized to implement the DR programs. The complex optimization problem is solved using a Genetic Algorithm (GA) to find the optimal dispatch schedule. The model is validated on a sample microgrid, and the numerical results clearly demonstrate that a coordinated Demand Side Management (DSM) strategy is highly effective in mitigating the impact of uncertainty from wind and solar generation while significantly lowering overall operational costs.

Keywords:

Smart microgrid, Renewable energy sources, Demand side management, Genetic algorithm, Operational cost minimization

References

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Published

2025-04-12

Issue

Vol. 2 No. 2 (2025): Intelligence Modeling in Electromechanical Systems

Section

Articles

How to Cite

Karimi Tamashi, A., & Sadeghi, S. (2025). Optimal Energy Management in a Smart Microgrid Considering Demand Response and Renewable Energy Uncertainty. Intelligence Modeling in Electromechanical Systems, 2(2), 110-122. https://doi.org/10.48314/imes.v2i2.44

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