Model predictive control of integrated distributed energy resources

Renewable energy generators and microgrids are emerging technologies in recent years. The development of renewable energy generators is fostered by the increase in fossil fuel price, and by the growing concern on the environmental issues caused by the use of conventional fuels. Microgrids are considered the future of energy management since they can increase the reliability and energy efficiency of the system, reduce the costs incurred by consumers and companies due to power outage, brownouts and poor power quality, generate revenues by selling electricity back to the grid or to other utilities, reuse the heat produced during electricity generation. The main target of the thesis is to reduce the cost and generate revenues for the microgrid operator by purchasing/selling electricity to the grid in different and comprehensive scenarios. One contribution of this research is to provide a comprehensive framework, based on Model Predictive Control (MPC), to deal with the various possible configurations of the microgrid and of the energy management problem. Various complex issues are considered, such as different charge/discharge constraints on the Battery Energy Storage System (BESS), different purchase/sale tariffs, imbalance charges, etc. The optimization problem can be formulated as a typical quadratic programming (QP) problem, which can be solved with reliable and efficient tools, thus reducing the computational effort of the optimization procedure. Results show the economical advantages that can be gained through the combined usage of renewable energy generators and a BESS in the microgrid.