Multi-objective optimization of a hybrid renewable energy system supplying a residential building using NSGA-II and MOPSO algorithms

•Economic, technical, environmental, and social objective functions are considered in the optimization.•The levelized cost of energy in selected solutions is reduced by 51 to 88% by selling excess electricity.•The exploitation of the selected systems significantly reduce CO2 emissions.•The Pareto fr...

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Bibliographic Details
Published in:Energy conversion and management Vol. 294; p. 117515
Main Authors: Cheraghi, Ramin, Hossein Jahangir, Mohammad
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.10.2023
Subjects:
algorithms
batteries
Building energy consumption
carbon dioxide
decision making
energy conversion
environmental assessment
Evolutionary algorithms
fuel cells
generators (equipment)
Genetic algorithm
heat pumps
human development
Multi-objective optimization
natural gas
probability
Renewable energy
renewable energy sources
residential housing
solar collectors
wind turbines
Multi-objective optimization
Evolutionary algorithms
Renewable energy
Building energy consumption
Genetic algorithm
ISSN:0196-8904, 1879-2227
Online Access:Get full text
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Summary:•Economic, technical, environmental, and social objective functions are considered in the optimization.•The levelized cost of energy in selected solutions is reduced by 51 to 88% by selling excess electricity.•The exploitation of the selected systems significantly reduce CO2 emissions.•The Pareto fronts are compared using two standards of diversification and spacing. Multi-objective optimization of a hybrid system is investigated to supply an autonomous residential building. The proposed system consists of photovoltaic panel, wind turbine, ground source heat pump, diesel generator, battery bank, and fuel cell. This study presents an innovative approach in optimization considering all economic, technical, environmental, and social aspects. Objective functions include loss of power supply probability (LPSP), levelized cost of energy (LCOE), CO2 emission, and human development index (HDI) that are optimized simultaneously. Also, the simulation-based approach in NSGA-II and MOPSO algorithms is used to estimate the Pareto front. The Pareto front solutions are the optimum points that help decision-makers choose the best system configuration based on priorities. Due to the importance of renewable energy utilization and reliability, two conditions of renewable fraction (RF) > 70% and LPSP < 0.05 are considered to select the optimal systems. Among the selected systems, the solutions with the highest RF also generated more extra energy. Diesel generators are much less expensive than fuel cells; however, the environmental benefits of the fuel cell make this technology attractive. Therefore, systems that use only the diesel generator as a backup unit have lower LCOE and higher CO2 emissions. LCOE in selected solutions is reduced by 51 to 88% by selling extra power to the grid. The environmental assessment results show that CO2 emissions in selected systems compared to coal-based power plants and natural gas power plants are decreased by 46–100% and 3–100%, respectively. Also, Pareto fronts evaluation shows that the NSGA-II algorithm's solutions covered a more extensive range and scattered more uniformly.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2023.117515