Integrated techno-economic-environmental design of off-grid microgrid model for rural power supply in India. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Kushwaha, Pawan Kumar; Bhattacharjee, Chayan
• Is Part Of: Journal of information & optimization sciences. Volume 43:Issue 1(2022)
• Page Start: 37
• Page End: 54
• Publication Date: 2022-01-02
• Subjects: 00xx -- 93xx; Solar photovoltaic -- Methanol generator -- Cost of energy -- Off-grid microgrid -- Carbon emission -- Loss of power supply probability; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2022.2032557
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: The non-availability of the grid and unreliable power supply from utility drastically affects the socio-economic development of the rural areas of India. In this research, a techno- economic-environmental (TEE) design of an off-grid microgrid (OGM) is presented to improve power reliability in a rural village. To address TEE design problem of OGM, a novel objective function is formed and optimization is done by the slime mould algorithm (SMA). The results are compared with the famous PSO algorithm. The considered parameters namely technical (loss of power supply probability (LPSP), a renewable fraction (RF)), economical (cost of energy (COE), total net present cost (TNPC)), and environmental (carbon emission (CE), particulate matter (PM)). Various combinations of solar photovoltaic (PV), wind turbine (WT), diesel generators (DGs), methanol generator (MG), battery, and converter are formed, and compared in TEE aspects, to find the most viable and reliable OGM model. The obtained result shows that the PV-WT-MG-DGs-battery-converter system has superiority and optimal TEE performance over other considered cases with SMA. In comparison to the PSO, SMA shows the best value of TEE parameters, greater capacity to break free from local minima, higher ability to come close to the global optimum, and shorter computational time.