Neural forecasting and optimal sizing for hybrid renewable energy systems with grid-connected storage system

Abstract

Energy systems with renewable sources are used around the world in order to satisfy both off-grid and on-grid load demands, and are commonly coupled to conventional sources. A good behavior of this kind of systems depends on the renewable sources availability that includes the solar irradiance and the wind speed, as well as the profile variations over the energy demand. Their main objective is to satisfy the load demand while minimizing the use of conventional sources, reducing pollutant emissions and storing the energy excess for deficit conditions. This paper presents modeling, neural forecasting and optimal sizing for hybrid energy systems, which are proposed to minimize both the overall annual cost and the use of conventional sources, which in turn represents reduction of pollutant emissions. In this paper, the use of renewable sources along with load demand variations are predicted by a High Order Neural Network trained with an Extended Kalman Filter, whereas the optimal sizing is calculated by using both a Clonal Selection Algorithm and a Genetic Algorithm. The efficiency of using neural forecasting data is illustrated through a simulation with the results showing the effectiveness of both optimization algorithms for calculating an optimal sizing of the hybrid system, which ultimately represents an optimal cost-effective system.