Model predictive control for demand side management in buildings: A survey. (December 2021)

• Record Type: Journal Article
• Authors: Farrokhifar, Meisam; Bahmani, Hamidreza; Faridpak, Behdad; Safari, Amin; Pozo, David; Aiello, Marco
• Is Part Of: Sustainable cities and society. Volume 75(2021)
• Publication Date: 2021-12
• Subjects: Demand side management -- Model predictive control -- Building management system -- Optimization -- Renewable energy sources; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103381
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Practices in demand side management and model predictive control (MPC) are analyzed. Mathematical modeling of MPC and demand side management are demonstrated. A holistic comparison among MPC-based demand management in buildings is represented. MPC provides efficient strategies for energy management in buildings. Different uncertain parameters in the energy management process are handled by MPC. Abstract: Buildings are responsible for a large portion of the world's energy consumption. Any measure that can be taken to optimize the use of energy related to them must be considered. Demand Side Management (DSM) can be used to shave demand peaks and to avoid bootstrapping highly polluting fast ramp-up generators. This though brings a control problem that is complicated by the increasing diffusion of small-scale, renewable energy sources and local storage facilities which are decentralized and, in general, hard to predict reliably. The overall goal of the control strategy is to balance energy, demand/supply, and to minimize costs. This survey focuses on control strategies to support DSM, considering buildings as the load to be managed. Among the various control strategies, model predictive control (MPC) has a predominant role due to its broad applicability and easy portability to many diverse contexts. The method is suitable for any nonlinear, multi-variable, and linear parameter varying system. The survey provides a general, unifying mathematical characterization of the approaches and lays the foundations for comparing and evaluating MPC-based DSM in buildings.