Uncertainty-Aware Model Predictive Control for Residential Buildings Participating in Intraday Markets

IEEE Access, vol. 10, pp. 7834–7851

Abstract

Buildings with generation and storage assets have the opportunity to trade in electricity markets. Residential buildings, however, have load patterns that are more difficult to predict and less flexible – introducing uncertainties to their operation. In addition, local intermittent renewable energy sources further increase these uncertainties. In this regard, an uncertainty-aware model predictive control (UA-MPC) is proposed in this paper. The proposed UA-MPC allows residential building energy management systems to trade in intraday markets without violating the operational constraints of their batteries despite the uncertainties in load consumption and solar irradiation. Moreover, the proposed UA-MPC only needs prediction intervals instead of detailed probability distribution functions to describe the uncertainties. Furthermore, in the proposed UA-MPC, the optimization is formulated as a shortest path problem. Thus, the optimization can be solved in polynomial time, which is desirable in intraday settings. Numerical simulations for two active residential buildings in grid-connected and isolated-neighborhood scenarios have been used to evaluate the proposed UA-MPC. Furthermore, the performance of the proposed UA-MPC was compared with the performance of a deterministic model predictive control (MPC) and a robust MPC. The results show that the proposed UA-MPC eliminates constraint violations that would otherwise occur in using deterministic MPC while providing lower costs than those using robust MPC. The results also show that the proposed UA-MPC can generate bidding curves in a few seconds, demonstrating its applicability in intraday market settings.

Authors 3

1. Wilbert Rey D. Tarnate Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University · University of the Philippines Diliman

   Affiliation as printed

   Electrical and Electronics Engineering Institute, University of the Philippines Diliman, Quezon City, Philippines

   Institute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, Germany

2. Ferdinanda Ponci Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, Germany

3. Antonello Monti Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, Germany