Multi-objective optimization of stand-alone hybrid renewable energy system by genetic algorithm

Nowadays, a lot of countries have started reducing share of fossil fuels to be replaced by alternative energies. The role of renewable energy as a proper resource among alternative energies has been growing rapidly in the past few years. Human development, CO2 emission reduction, availability of renewable sources and rising cost of fossil fuels are some of factors that make utilization of renewable energy systems become more important. In Malaysia, there have been some efforts on the utilization of renewable energies. Although usage of renewable sources is the main reason for development, there are a few barriers against the utilization of renewable energy projects. Some of thes obstacles are cost, reliability and performance. Hybrid renewable energy system (HRES) that is a combination of more than two renewable energy sources in one system, has been developed as a possible solution for such problems. HRES as an improved RE system, has been able to make a cost-effective and more reliable unit with better performance as compared to a single renewable resource. Therefore, application of well-optimized HRES is a favorable renewable power solution for decision makers and goverments like Malaysia. In recent years, there have been many works on the optimization of stand-alone HRES,but a few of them have used optimization methods for multi objectives simultaneously. Optimization of more than one objects such as size, cost, control strategy, emissions and etc is a more complex issue. Multi-objective optimization of HRES by Evolutionary Algorithm have become attractive in recent years due to the effectiveness of such algorithms for complicated problems. Among these methods, Genetic Algorithm and Particle Swarm Optimization are known as two most effective methods for HRESs. In this research, multi-objective optimization of stand-alone HRES in Malaysia by Genetic Algorithm is proposed. The optimization process of HRES is explained in three major steps in this thesis. First, a comprehensive literature review and data collection from NASA and Meteorological Department of Malaysia is provided. In the next step,feasibility study shows that solar and wind are suitable sources of renewable energy in Malaysia, but there is a low range of wind speed. Load estimation, tilt angle optimization and multi-objective optimization are on the last sections. HOGA, as a new effective tool for multi-objective optimization by evolutionary algorithm is used in this research. HOGA (Hybrid Optimization by Genetic Algorithms) is developed by Dr.Lopez from Zaragoza university in Spain. The results show that the PV-wind-battery combination is suitable for three study cases in Malaysia and the slope of 0˚ and 5 ˚ is the optimized angle of PV panels for these three study cases. Daily and monthly load for a rural comminity in Malaysia is estimated and sizing, cost and CO2 emission optimization are provided in results. Cost analysis and best solutions for multi-objective oprimization in these villages are explained. Finally, a comparative study between the results and previous research works is provided for validation.

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