Optimization of Operating Cost and Energy Consumption in a Smart Grid

• Published in: IEEE Access
• Main Author: Mahdi B.S.; Sulaiman N.; Shehab M.A.; Shafie S.; Hizam H.; Hassan S.L.B.M.
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182941353&doi=10.1109%2fACCESS.2024.3354065&partnerID=40&md5=b725b535ec01c929703440ad39002583

This paper introduces an optimal bi-objective optimization methodology customized for microgrid systems, encompassing economic, technological, and environmental considerations. The framework portrays the objectives of an intelligent microgrid, aiming to minimize operational costs, CO2 emissions, peak-to-average ratio (PAR), and energy consumption while concurrently enhancing user comfort (UC). A scheduled power allocation strategy is formulated to efficiently cater to the energy needs of residential loads. The stochastic nature of wind and solar resources is characterized by modeling wind speed and solar radiation intensity using a beta probability density function (PDF). The non-dominated sorting genetic algorithm II (NSGA-II) is employed to address optimization challenges. A decision-making process is implemented to select the optimal solution from the non-dominated alternatives. The study presents three scenarios illustrating the optimal operational values for various parameters and energy consumption, providing a comprehensive analysis of the proposed algorithm's efficacy. Leveraging the NSGA-II algorithm, coupled with renewable energy resources and optimal energy storage system scheduling, yielded significant reductions in overall expenses, PAR, CO2 emissions, user discomfort, and energy consumption. MATLAB simulations were conducted to substantiate the efficacy of our proposed approach. The obtained results underscore the effectiveness and productivity of our devised NSGA-II-based approach. Notably, the proposed algorithm demonstrated a substantial reduction in electricity costs by 19.0%, peak-to-average ratio (PAR) by 30.7%, and carbon emissions by 21.7% in scenario-3, as evidenced by a comparative analysis with the unscheduled case. © 2013 IEEE.