Hopfield neural networks approach for design optimization of hybrid power systems with multiple renewable energy sources in a fuzzy environment

Abstract

The global energy sector faces major challenges in providing sufficient energy to the world's ever increasing energy demand. Methods to produce a greener, cost effective and reliable source of alternative energy needs to be explored and exploited. One of those methods is done by integrating (or hybridizing) multiple different alternative energy sources (e.g. wind turbine generators, photovoltaic cell panels and fuel-fired generators, equipped with storage batteries) to form a distributed generation (DG) power system. However, even with DG power systems, cost effectiveness, reliability and pollutant emissions are still major issues that need to be resolved. The model development and optimization of the DG power system was carried out successfully in the previous work using Particle Swarm Optimization (PSO). The goal was to minimize cost, maximize reliability and minimize emissions (multi-objective) subject to the requirements of the power balance and design constraints. In this work, due to the uncertain nature on the weather conditions, the power output from the PV cells, WTG and the storage batteries which are subject to insolation and wind conditions were fuzzified in an effort to create a more realistic model. The optimization (in a fuzzy environment) was then performed by using Hopfield neural network (HNN). The optimized results were then discussed and analyzed.