Performance Analysis of a Renewable Hybrid Energy System with Fuel Cell

Abstract

An increase in the global population and quality of life has led to a rapid increase in energy demands, fossil-based fuels are the major contributors towards meeting the global energy needs. The non-renewable nature of fossil fuels suggests that these would inevitably be exhausted one day if human beings do not make efforts to conserve these resources. In lieu to this, the researchers are bound to explore alternate sources of energy that are capable of meeting the continuous power supply demand, are environmentally friendly, and could contribute to a sustainable society. One of the solutions to the mentioned problem is shifting to renewable sources of energy. Solar energy is one of the most prominent sources of renewable energy. For sustainable power supply various renewable energy sources are connected. The most promising technology is fuel cell as it is a great alternative to battery storage system. It is also least effected by environmental forces. With the help of total life cycle cost (TLCC) the candidate renewable source is selected. In this thesis, various aspects of fuel cell are explored with aspiration to develop most efficient type of modified reversible Proton Exchange Membrane Fuel Cell (PEMFC). Carbon-based porous mediums offer many benefits that favors hydrogen adsorption in it. However, the physical and chemical characterization plays a vital role in decision making on whether the selected medium is suitable for PEMFC. The feasibility of coconut shell derived activated carbon for hydrogen adsorption by ascertaining its physical and chemical characteristics. Due to nonlinear nature of PEMFC, the appropriate modeling and parameter optimization of PEMFCs is also very important. The evolutionary optimization approaches had been utilized in recent past for estimating PEMFCs parameters as exact modeling of the same does not exist in the literature. For the evaluation of PEMFCs performance criteria, a proposed hybrid algorithm is developed i.e., Particle Swarm Optimization Black Widow Optimization (PSOBWO) and Particle Swarm Optimization Rat Search Algorithm (PSORAT). Firstly, the performance of this proposed algorithm is checked by complex benchmark results. After that, this proposed algorithm is applied to extract the parameters of developed fuel cell model. The parameter optimization results are obtained using PSOBWO and PSORAT, and are further compared with those obtained with seven other algorithms. Non-parametric test is performed to justify the outcome. In continuation to above, meta-heuristic hybrid algorithm is applied for parameter extraction of hybrid energy system containing solar and PEMFC is also implemented. This proposed hybrid method rules out the chances of local minima hence, enhancing the precision of the parametric estimation. The outcome errors are compared with the other algorithms and the results are justified using non parametric test. The integration and optimal utilization of different accessible energy resources into a self-contained micro-grid are examined. Using the Hybrid Optimization Model for Electric Renewable (HOMER) software, an integrated hybrid renewable energy system (IHRES) strategy has been suggested and studied. This mainly focused on their levelized cost of energy (LCOE) supply and annualized cost system (ACS). Two combinations of energy sources and storage technologies (battery and fuel cell) for the community load have been considered for the Kutch location in Gujarat. A recently proposed Hybrid algorithm (PSORAT and PSOBWO) is used for optimum component sizing. To validate the effectiveness of the proposed approach, the results obtained are compared with HOMER software. Further, simulation findings are also presented for the further evaluation of the compatibility and system stability.