Optimisation of solar/wind/bio-generator/diesel/battery based microgrids for rural areas: A PSO-GWO approach

• Published in: Sustainable cities and society Vol. 67; p. 102723
• Main Authors: Suman, Gourav Kumar, Guerrero, Josep M., Roy, Om Prakash
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2021
• Subjects: Biomass; Hybrid microgrids; Optimisation; PSO-GWO; Rural microgrid; Wind; Rural microgrid; Wind; Hybrid microgrids; Optimisation; PV; Biomass; PSO-GWO
• ISSN: 2210-6707; 2210-6715
• DOI: 10.1016/j.scs.2021.102723

•Feasibility study of solar Solar/Wind/Bio-generator/Diesel/Battery based microgrids for rural areas.•Implementation of Hybrid PSO-GWO for solving optimal sizing problem.•Comparison of well-established optimization tools with the implemented method.•Study implementation on selected rural locations in Indian state of Bihar.•A framework for minimal cost of energy and loss of power supply probability added with appreciable renewable factor. The suitability of microgrids depends upon the balance between the size, cost-efficiency and reliability of the system. The work involves optimal planning of microgrid taking a case of three locations in the Indian state of Bihar. A combination of Solar/Wind/Bio-generator energy generation units along with diesel generation and the battery is implemented for this purpose. The tradeoff between Cost Of Electricity (COE) and Deficiency of Power Supply Probability (DPSP) has been analysed and an objective function is defined as a combination of the two with a bound of Renewable Factor (RF). A hybrid Particle Swarm Optimisation-Grey Wolf Optimiser (PSO-GWO) is implemented for optimisation. The performance of the algorithm is compared with numerous others using 13 classical benchmark test functions and 10 CEC-06 2019 benchmark functions. The obtained microgrids are analysed to supply at an average Cost of Electricity of 0.17($/kWh) meeting at least 93% of the demand. Besides, an average renewable factor of 0.93 makes the system environmental friendly. The work also includes a sensitivity analysis of Cost of Electricity. The results are compared with those using other previously used algorithms in the literature and the effectiveness of the implemented algorithm is established.