Model Predictive Control based Energy Management System for Home Energy Resiliency

TL;DR

The paper addresses resilience of a single-family home during unplanned outages caused by extreme weather by integrating a PV-battery system with an MPC-based home energy management system (HEMS) that explicitly handles AC startup power and discrete on/off decisions. It contrasts the MPC controller against a Baseline and a Rule-Based controller using three resiliency metrics and real-world data from Hurricane Irma-affected conditions, across multiple PV/battery sizes. The MPC leverages forecast information within a 24-hour planning horizon ($N=144$, $\Delta T_s=10$ min) and uses a multi-term objective to balance thermal comfort, critical-load delivery, and battery readiness, implemented as a MILP solved in GUROBI. Results show that MPC generally outperforms the baselines in energy-constrained scenarios, providing better thermal comfort and greater resilience for critical and other loads, while larger PV/battery sizes uniformly improve performance. The work demonstrates the value of planning-based, forecast-informed control for robust off-grid operation and outlines directions for extending the approach to cold weather, forecast uncertainty, sizing strategies, and community-level distributed control.

Abstract

As the occurrence of extreme weather events is increasing so are the outages caused by them. During such unplanned outages, a house needs to be provided with an energy supply to maintain habitable conditions by maintaining thermal comfort and servicing at least critical loads. An energy system consisting of rooftop photovoltaic (PV) panels along with battery storage is an excellent carbon-free choice to provide energy resiliency to houses against extreme weather-related outages. However, to provide habitable conditions this energy system has to provide not only for the non-air-conditioning (non-AC) load demand but also for the turning on of the AC system which has a considerably higher startup power requirement as compared to its rated power. Hence, an intelligent automated decision-making controller is needed which can manage the trade-off between competing requirements. In this paper, we propose such an intelligent controller based on Model Predictive Control (MPC). We compare its performance with a Baseline controller which is unintelligent, and a Rule-Based controller which has some intelligence, based on three resiliency metrics that we have developed. We perform extensive simulations for numerous scenarios involving different energy system sizes and AC startup power requirements. Every simulation is one week long and is carried out for a single-family detached house located in Florida in the aftermath of Hurricane Irma in 2017. The simulation results show that the MPC controller performs better than the other controllers in the more energy-constrained scenarios (smaller PV-battery size, larger AC startup power requirement) in providing both thermal comfort and servicing non-AC loads in a balanced manner.

Figures (5)

• Figure 1: Schematic of the home energy system with its load description.
• Figure 2: Schematics of MPC and Rule-Based Smart Controllers..
• Figure 3: Schematic of the Baseline Controller.
• Figure 4: Performance comparison of the three controllers based on three resiliency metrics, for all the considered simulation cases $\in A_{I} \times A_{pv} \times A_{bat}$.
• Figure 5: Performance comparison of the three controllers for serving the other load along with the critical load (top row), thermal load (middle row), and PV energy utilization (bottom row) for the simulation case: $\alpha_{I} = 4, \alpha_{pv} = 0.5, \alpha_{bat} = 0.5.$.