Multi‐objective optimization of hybrid renewable energy system based on biomass and fuel cells. (24th September 2020)

• Record Type: Journal Article
• Authors: Samy, M. M.; Elkhouly, Heba I.; Barakat, S.
• Other Names: Atabani Abdulaziz E. guestEditor.
• Is Part Of: International journal of energy research. Volume 45:Number 6(2021)
• Page Start: 8214
• Page End: 8230
• Publication Date: 2020-09-24
• Subjects: biomass systems -- fuel cells -- hybrid renewable energy systems -- cost of energy (COE) -- loss of power supply probability (LPSP) -- multi‐objective particle swarm optimization (MOPSO) -- statistical analysis; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.5815
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: The main objective of this paper is to determine the optimal sizing of a biomass and fuel cell micro‐grid. The biomass generator will be utilized as the main source of power generation for the study area, while the fuel cell generator will be used as a backup generator to be used if the biomass generator fails to meet the energy requirements of the study area. In this research, excess energy will be used to produce hydrogen which is to be used by the fuel cells to generate energy instead of using batteries. To achieve the goal of this paper, a multi‐objective particle swarm optimization (MOPSO) technique has been proposed to solve the sizing problem for the introduced micro‐grid via an economical perspective which is the cost of energy (COE). The MOPSO algorithm tries to mitigate the COE to the lower values by keeping the loss of power supply probability (LPSP) as minimal as possible. Likewise, statistical analysis has been concluded to study the accuracy of the outcomes of the introduced technique. Three indicators have been offered, which are the process capability indices, the normal probability, and the control chart. The final contribution of the consequences of the two presented objective functions clears that the algorithm process is under statistical control, stable, and very precise. The optimum system from the economic perspective consists of two biomass gensets, 31 fuel cells, 65 electrolyzers, and 186 H2 tanks with an NPC of $ 2 314 842, COE of 0.335 $/kWh at an LPSP of 1.929%.