Techno-Economic Assessment and Environmental Impact Analysis of Hybrid Storage System Integrated Microgrid

• Published in: Arabian journal for science and engineering (2011) Vol. 49; no. 12; pp. 15917 - 15934
• Main Authors: Ikram, Arafat Ibne, Shafiullah, Md, Islam, Md. Rashidul, Rocky, Md. Kamruzzaman
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2024; Springer Nature B.V
• Subjects: Algorithms; Distributed generation; Electrical loads; Energy distribution; Energy sources; Energy storage; Engineering; Environmental impact; Environmental impact assessment; Fuel cells; Functions (mathematics); Humanities and Social Sciences; Hybrid systems; Impact analysis; Installation costs; Lead acid batteries; Mathematical functions; multidisciplinary; Normal distribution; Particle swarm optimization; Research Article-Electrical Engineering; Residential energy; Science; Wind turbines; Renewable energy sources; Hydrogen energy storage; Economic assessment; Energy storage system; Particle swarm optimization; Environmental impact analysis
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-024-08735-x

Microgrids are designed to utilize renewable energy resources (RER) that are revolutionary choices in reducing the environmental effect while producing electricity. The RER intermittency poses technical and economic challenges for the microgrid systems that can be overcome by utilizing the full potential of hybrid energy storage systems (HESS). A microgrid comprising of a solar photovoltaic panel, wind turbine, lead-acid battery, electrolyzer, fuel cell, and hydrogen (H
2
) tank is considered for techno-economic feasibility and environmental impact assessment on a grid integration scenario. Mathematical functions are utilized to model the components for estimating annual hourly renewable generation and energy storage behavior. The load consumption model for 50 homes is generated using Gaussian distribution to incorporate the uncertainty. Optimal sizing of the microgrid components is determined using the particle swarm optimization (PSO) algorithm to minimize the upfront installation cost and levelized cost of energy (LCOE). Different energy storage penetration scenarios, e.g., 25%, 50%, 75%, and 100% for the microgrid system, are considered where 100% penetration level stands for maintaining the load demand using the available resources without depending on the grid energy supply. The lowest LCOE is found between 0.06 $/kWh and 0.11 $/kWh, and the highest annual GHG is reduced to half compared to the grid emission. GHG is imposed around 62.14 (tCO
2
e/yr) - 73.57 (tCO
2
e/yr) for Madrid and Seville, respectively.