PSO-Based Multidisciplinary Design of A Hybrid Power Generation System With Statistical Models of Wind Speed and Solar Insolation

With the increasing concerns on air pollution and global warming, the clean green renewable sources of energy are expected to be playing more significant role in the global energy future. Multi-source hybrid power generation systems are representative applications of the renewables' technology. In this investigation, wind turbine generators, photovoltaic panels, and storage batteries are used to build a grid-linked generation system which is optimal in terms of multiple criteria including cost, reliability, and emissions. Multidisciplinary design facilitates the decision maker to make more rational evaluations. A set of tradeoff solutions can be obtained using the multidisciplinary approach, which offers many design alternatives to the decision-maker. A customized particle swarm optimization algorithm is developed to derive these non-dominated solutions. A grid-linked hybrid power system is designed based on the proposed approach. Furthermore, due to the unpredictability of wind speed and solar insolation, autoregressive moving average (ARMA) models are adopted to reflect the stochastic characteristics of wind speed and solar insolation. Sensitivity studies are also carried out to examine the impacts of different weather conditions and economic rates.