Predictive Control of a Multi -energy Building-integrated Microgrid: A Case Study

Multi-energy microgrids (MGs) are emerging as an efficient way to address the challenge of integrating distributed energy resources (DERs) into buildings. Building-integrated MGs offer flexibility and reliability through system optimization. In this context, the Interreg SUDOE project IMPROVEMENT (In-tegration of Combined Cooling, Heating and Power Microgrids in Zero Energy Public Buildings with High Power Quality and Continuity Requirements) was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of an energy management system (EMS) for the management of thermal resources and users' thermal comfort in public buildings [1]. Optimization-based/free model predictive control (MPC) algorithms are presented and validated using simulations and data collected in a public building equipped with a multi-energy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional-integral-derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.