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- ...
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.
