Development of biomimetic control strategies for the optimal use of renewable sources and energy storage systems

Abstract

In the year 2007, the worldwide energy consumption accumulated to a total of 16.5 billion MWh. While the resources of conventional energy production cause environmental damage, renewable energy sources like solar or wind power offer a solution to substitute for coal or nuclear generated power. Countries like Denmark and Spain have shown that a high penetration of renewable power is possible; however, the production shifts from a demand-driven production to a supply-driven electricity production. This causes the problem that energy could be available while the demand is low or vice versa. Energy storage could be a solution to this challenge. This thesis investigates how an envisioned storage system for a wind park in northern Oregon could be controlled in order to optimize its capacity. Two biomimetic strategies, neural network and fuzzy logic control, were implemented and later optimized by a genetic algorithm to increase the profit from storing the electric energy in the storage unit. Even though the optimization with genetic algorithms leads to improvements in the performance of the neural network and fuzzy logic controller, the results show that biomimetic controllers only perform as good as a simple, unconstrained power split controller. Both controllers are tested with several months of wind and price data.