Model Predictive Control for Efficient Management of Energy Resources in Smart Buildings

Francesco Simmini, Tommaso Caldognetto, Mattia Bruschetta, Enrico Mion and Ruggero Carli
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Francesco Simmini: Interdepartmental Centre Giorgio Levi Cases, University of Padova, Via Francesco Marzolo 9, 35131 Padova, Italy
Tommaso Caldognetto: Interdepartmental Centre Giorgio Levi Cases, University of Padova, Via Francesco Marzolo 9, 35131 Padova, Italy
Mattia Bruschetta: Department of Information Engineering, University of Padova, Via Giovanni Gradenigo 6/B, 35131 Padova, Italy
Enrico Mion: Department of Information Engineering, University of Padova, Via Giovanni Gradenigo 6/B, 35131 Padova, Italy
Ruggero Carli: Interdepartmental Centre Giorgio Levi Cases, University of Padova, Via Francesco Marzolo 9, 35131 Padova, Italy

Energies, 2021, vol. 14, issue 18, 1-19

Abstract: Efficient management of energy resources is crucial in smart buildings. In this work, model predictive control (MPC) is used to minimize the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future information about energy prices, absorption and production power profiles, and electric vehicle (EV) usage, such as times of departure and arrival and predicted energy consumption. The predictive control is compared with a rule-based technique, herein referred to as a heuristic approach, that acts in an instant-by-instant fashion without considering any future information. The reported results show that the studied predictive approach allows one to achieve charging profiles that adapt to variable operating conditions, aiming at optimal performances in terms of economic cost minimization in time-varying price scenarios, reduction of rms current stresses, and recharging capability of EV batteries. Specifically, unlike the heuristic method, the MPC approach is proven to be capable of efficiently managing the available energy resources to ensure a full recharge of the EV battery during nighttime while always respecting all system constraints. In addition, the proposed control is shown to be capable of keeping the peak power absorption from the grid constrained within set limits, which is a valuable feature in scenarios with widespread adoption of EVs in order to limit the stress on the electrical system.

Keywords: efficient management; energy resources; heuristic approach; model predictive control; nanogrid; smart buildings (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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