Predictive Energy Management of a Building-Integrated Microgrid: A Case Study

Romain Mannini, Tejaswinee Darure, Julien Eynard and Stéphane Grieu ()
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Romain Mannini: Processes, Materials and Solar Energy (PROMES-CNRS) Laboratory, University Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Tejaswinee Darure: Processes, Materials and Solar Energy (PROMES-CNRS) Laboratory, University Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Julien Eynard: Processes, Materials and Solar Energy (PROMES-CNRS) Laboratory, University Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Stéphane Grieu: Processes, Materials and Solar Energy (PROMES-CNRS) Laboratory, University Perpignan Via Domitia, Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France

Energies, 2024, vol. 17, issue 6, 1-35

Abstract: The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT 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 a predictive energy management system (PEMS) for the management of thermal resources and users’ thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy 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.

Keywords: model predictive control; public buildings; multienergy microgrid; building-integrated microgrid; thermal resources; users’ thermal comfort (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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