Techno-economic and environmental assessment of a hybrid renewable energy system using multi-objective genetic algorithm: A case study for remote Island in Bangladesh. (15th February 2021)

• Record Type: Journal Article
• Authors: Das, Barun K.; Hassan, Rakibul; Tushar, Mohammad Shahed H.K.; Zaman, Forhad; Hasan, Mahmudul; Das, Pronob
• Is Part Of: Energy conversion and management. Volume 230(2021)
• Publication Date: 2021-02-15
• Subjects: Renewable energy -- Cost of energy -- Life cycle emissions -- Reliability; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.113823
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Hybrid solar, wind, biogas with vanadium redox flow battery-based system is studied. Cost and life cycle emissions are optimised by multi-objective genetic algorithm. Multi-objective provides better environmental benefits than single objective one. Evolutionary algorithm offers cost-effective solutions than the software tool. Electricity cost is comparable with the grid supply at the cost of reliability. Abstract: Renewable hybrid energy systems are well-proven to be capable of supplying reliable power in the remote areas, where grid extension is not viable due to geographical constraints, but not absolutely emissions free. The present study investigates a hybrid energy system that entails photovoltaic module, wind turbine, biogas generator, and vanadium redox flow battery for supplying stable power to a remote Island, Saint Martin, Bangladesh. Two well-known multi-objective optimisation techniques such as non-dominated sorting genetic algorithm II and infeasibility driven evolutionary algorithm are applied to size the hybrid system components based on the cost of energy ($/kWh) and life cycle emissions (kg CO2 -eq/yr) under a certain reliability. In addition, a fuzzy decision-making technique is applied to find the optimal solution. A comparative analysis of using single objective function is compared with the multi-objective one. In addition, results from the non-dominated sorting genetic algorithm II optimisation technique is compared with the widely utilized software hybrid optimisation of multiple energy resources tool and the infeasibility driven evolutionary algorithm. Although the cost of energy is relatively comparable between the objective functions considered, the multi-objective approach provides better environmental benefits than the single objective optimisation system. The analyzed results also indicate that the intelligent techniques are the superior to the hybrid optimisation of multiple energy resources software tool in terms of costs and environmental point of view. Furthermore, the unit electricity cost of the proposed hybrid system configuration is comparable with the grid electricity supply at the loss of power supply probability of over 8% with significantly lower life cycle emissions.