Design optimization of a hybrid system subject to reliability level and renewable energy penetration

• Published in: Energy (Oxford) Vol. 193; p. 116754
• Main Authors: Ghaffari, Abolfazl, Askarzadeh, Alireza
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.02.2020; Elsevier BV
• Subjects: Adaptive awareness probability; Algorithms; Computer simulation; cost effectiveness; Crow search algorithm; Design; Design optimization; Diesel; Diesel fuels; Diesel generators; Electric power generation; Energy resources; Energy sources; Fuel cells; generators (equipment); Genetic algorithms; Hybrid systems; hydrogen; Particle swarm optimization; Photovoltaic cells; Photovoltaics; power generation; probability; Renewable energy; Renewable energy portion; renewable energy sources; Renewable resources; Robustness; Search algorithms; Sizing; solar collectors; Crow search algorithm; Renewable energy portion; Design optimization; Adaptive awareness probability
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.116754

In recent years, design optimization of hybrid power generation systems which utilize renewable energy resources has received a significant attention. One of the most challenging problems in design of such systems is optimal sizing. To tackle this drawback, this paper presents an efficient and robust optimization approach for size optimization of a hybrid system composed of photovoltaic (PV) panel, diesel generator and fuel cell (FC). The proposed method is a modified version of crow search algorithm (CSA) in which awareness probability is adjusted by an adaptive manner. In the sizing framework, total net present cost (TNPC) is minimized subject to two main constraints: loss of power supply probability (LPSP) and renewable energy portion (REP). Simulation results show that (1) REP has a considerable impact on TNPC of the designed system, (2) optimal combination of PV, diesel generator, FC, electrolyzer and hydrogen tank leads to establishing a reliable and cost-effective hybrid power generation system and (3) the proposed method (CSAadaptive-AP) finds more promising and robust results than original CSA, genetic algorithm (GA) and particle swarm optimization (PSO). •Design optimization of a PV/diesel/FC hybrid energy system is studied.•Loss of power supply probability and renewable energy penetration are regarded as constraints.•A new crow search algorithm with adaptive awareness probability is developed.•Proposed algorithm enhances search power of original crow search algorithm.