Optimization of Distributed Generation Based Hybrid Renewable Energy System for a DC Micro-Grid Using Particle Swarm Optimization
This research work aims to design and optimize a multi-objective function modeled on the variables concerning distributed generators based on photovoltaic, wind and biogas based IC engine hybrid system for community smart DC Micro-grid using particle swarm optimization (PSO). The objectives of the research are to maximize the availability of power and reduction of the cost, which in turn will reduce the size of system with premier achievable availability. The output power of photovoltaic, wind and biogas based IC engine generators would have the highest priority for feeding the direct current bus as per the methodology proposed. It was found that proposed method especially particle swarm optimization had shown the effective cost and availability of photovoltaic, wind and biogas based IC engine generators in terms of accuracy to the practical values and a faster rate of convergence. When compared with other algorithms, particle swarm optimization performs well in reducing the cost of the photovoltaic, wind and biogas based IC engine generators. In addition, it was noted that availability of photovoltaic, wind and biogas based IC engine generators would be easily obtained using PSO algorithm. Future work would pertain to the designing of a similar hybrid Renewable energy system considering Uptime and Ramp rate constraints of biogas based IC engine generators for community smart direct current micro-grid and optimize it using Multi Objective Genetic Algorithm and compare its results with PSO.
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