The content discusses the development of an intelligent home energy management system (HEMS) based on Model Predictive Control (MPC) to provide energy resiliency to a house during unplanned power outages caused by extreme weather events.
The key highlights are:
The house is equipped with a rooftop solar PV system and a battery storage system to provide an energy-resilient solution during grid outages.
The HEMS needs to manage the trade-off between competing requirements, such as maintaining thermal comfort and servicing critical and non-critical loads, in the face of finite energy supply.
The proposed MPC-based controller is compared with a Baseline controller (representing a commercially installed PV-battery system without intelligent control) and a Rule-Based controller (with some intelligence) based on three resiliency metrics.
Extensive simulations are performed for various scenarios involving different PV-battery system sizes and air conditioning (AC) startup power requirements, using real weather and load demand data from a house in Florida after Hurricane Irma in 2017.
The results show that the MPC controller performs better than the other controllers, especially in more energy-constrained scenarios (smaller PV-battery size, larger AC startup power requirement), in providing both thermal comfort and balanced servicing of critical and non-critical loads.
The study demonstrates that a controller based on intelligent planning and forecast information utilization is more reliable than reactive rule-based controllers in providing home energy resiliency against extreme weather events.
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arxiv.org
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