Jul 2026· International Journal of Electrical Engineering and Computer Science· pp. 63· 0 citations· 1 references
Abstract
This paper addresses the multi-objective optimization problem for microgrid design, integrating various renewable energy sources and energy storage systems. The proposed framework aims to optimize the sizing of generation plants (solar, onshore wind, offshore wind) and battery storage, while simultaneously minimizing investment costs and managing the probability of power excess or deficit within the microgrid. To handle the inherent variability of wind energy, a symbolic regression method is employed for wind speed forecasting. The optimization task is tackled using two prominent metaheuristic algorithms: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-objective Particle Swarm Optimization (MOPSO) algorithm. The study details the mathematical models for all microgrid components, including their associated investment, maintenance, and replacement costs. A comparative analysis of NSGA-II and MOPSO is presented, evaluating their performance in achieving optimal Pareto fronts regarding investment costs and power balance, highlighting the trade-offs between initial investment and operational efficiency for sustainable microgrid solutions.
The increasing electricity demand associated with agricultural irrigation has created a need for energy-efficient and economically viable renewable energy systems. This paper presents a techno-economic optimization framework for a solar-powered agricultural microgrid intended to supply irrigation pump loads. The propos...
P. Phad, J. Helonde, P. Burade· International journal of com...· 0 citations
This study applies the Quadratic Interpolation Optimization Method (QIOM) to minimize the operating cost of a grid-connected MG over a 24-hour horizon. This optimization-based scheduling framework actively controls the diesel generator (DG) and battery energy storage system (BESS). In contrast, renewable sources such a...
Chi Thach Phan, Thuan Thanh Nguyen, T. Nguyen· International Islamic Univer...· 0 citations
This literature review provides a comprehensive analysis of optimization techniques applied to hybrid renewable energy systems (HRES) across diverse geographical locations and applications. The review examines multiple optimization methodologies including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Mixe...
A. R. Gbopa, I. Adeyemo, G. Adepoju et al.· African Journal of Environme...· 0 citations
The inherent intermittency of renewable energy sources and the mismatch between generation and community load demand pose significant challenges to the reliability of microgrids. To address these issues, this paper proposes a robust multi-objective optimization framework for the capacity sizing of a Hybrid Renewable En...
Pu-Zhuang Liu, Nor Azwan Bin Mohamed Kamari· IOP Conference Series: Earth...· 0 citations
This paper presents a comparative study between hybrid particle swarm optimization (PSO) and genetic algorithm (GA) for energy management in residential microgrids equipped with photovoltaic generation, stationary battery storage, and bidirectional electric vehicles (V2G). The system comprises 40 apartments, 1000 m² so...
B. Amghar, T. Azib, Khelil Sidi Brahim· International Journal of App...· 0 citations
With the global energy prices and demand generally rising, traditional microgrids overly rely on empiricism and manual intervention, only using linear programming to make choices within a limited number of preset schemes. When sudden extreme weather occurs, causing a sharp drop in photovoltaic output, the system can on...
Yu-Heng Cui· MATEC Web of Conferences· 0 citations
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