Real-time economic dispatch of CHP systems with battery energy storage for behind-the-meter applications.

Abstract

The use of combined heat and power (CHP) systems has recently increased due to their high combined 11 efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some 12 challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Sec- 13 ondly, suppose the load drops below a predefined threshold. In that case, the engine will operate at a lower temper- 14 ature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned 15 issues may be solved by combining CHP with a battery energy storage system (BESS); however, the dispatch of 16 CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power 17 export to the grid due to prediction errors. Therefore, this paper proposes a real-time Energy Management System 18 (EMS) using a combination of Long Short-Term Memory (LSTM) neural networks, Mixed Integer Linear Program- 19 ming (MILP), and Receding Horizon (RH) control strategy. The RH control strategy is suggested to reduce the impact 20 of prediction errors and enable real-time implementation of the EMS exploiting actual generation and demand data 21 on the day. Simulation results show that the proposed method can prevent power export to the grid and reduce the 22 operational cost by 8.75% compared to the offline method.