Rural electrification using renewable energy resources and its environmental impact assessment

Kamal, Md. Mustafa; Mohammad, Arshad; Ashraf, Imtiaz; Fernandez, Eugene

doi:10.1007/s11356-022-22001-3

Cited by 10 publications

(2 citation statements)

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“…Remote areas lacking access to the grid are categorized as off-grid loads. The inherent uncertainty associated with renewable energy sources poses substantial challenges, making reliance on a single renewable energy conversion resource particularly difficult for the electrification of rural loads [2]. Hybrid energy systems (HESs) integrate several power resources, including solar photovoltaic (PV) modules/panels, wind turbine generators (WTs), diesel generators, fuel cells (FCs), and batteries.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, a comparative analysis was conducted in [23] involving Tabu Search (TS), Simulated Annealing (SA), PSO, and HS, emphasizing PSO's superior performance in ASC minimization. Furthermore, hybrid approaches, such as combining GA with PSO [24] and utilizing the CS algorithm alongside GA and PSO [2], demonstrate the potential synergies in optimizing NPC and LPSP. The integration of the Mine Blast Algorithm (MBA) [25] introduces a novel perspective in ASC minimization for PV/WTs/FC systems, showcasing its efficiency and reliability compared to other metaheuristic techniques.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Algorithm with Reinforcement Learning for Microgrid Techno-Economic Optimization

Hussein Farh

2024

Mathematics

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Hybrid energy systems (HESs) are gaining prominence as a practical solution for powering remote and rural areas, overcoming limitations of conventional energy generation methods, and offering a blend of technical and economic benefits. This study focuses on optimizing the sizes of an autonomous microgrid/HES in the Kingdom of Saudi Arabia, incorporating solar photovoltaic energy, wind turbine generators, batteries, and a diesel generator. The innovative reinforcement learning neural network algorithm (RLNNA) is applied to minimize the annualized system cost (ASC) and enhance system reliability, utilizing hourly wind speed, solar irradiance, and load behavior data throughout the year. This study validates RLNNA against five other metaheuristic/soft-computing approaches, demonstrating RLNNA’s superior performance in achieving the lowest ASC at USD 1,219,744. This outperforms SDO and PSO, which yield an ASC of USD 1,222,098.2, and MRFO, resulting in an ASC of USD 1,222,098.4, while maintaining a loss of power supply probability (LPSP) of 0%. RLNNA exhibits faster convergence to the global solution than other algorithms, including PSO, MRFO, and SDO, while MRFO, PSO, and SDO show the ability to converge to the optimal global solution. This study concludes by emphasizing RLNNA’s effectiveness in optimizing HES sizing, contributing valuable insights for off-grid energy systems in remote regions.

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Section: Introductionmentioning

confidence: 99%