Increasing Energy Resiliency to Hurricanes with Battery and Rooftop Solar Through Intelligent Control (2102.04406v1)

Abstract: Rooftop solar photovoltaic (PV) panels together with batteries can provide resiliency to blackouts during natural disasters such as hurricanes. Without intelligent and automated decision making that can trade off conflicting requirements, a large PV system and a large battery is needed to provide meaningful resiliency. By utilizing the flexibility of various household demands, an intelligent system can ensure that critical loads are serviced longer than a non-intelligent system. As a result a smaller (and thus lower cost) system can provide the same energy resilience that a much larger system will be needed otherwise. In this paper we propose such an intelligent control system that uses a model predictive control (MPC) architecture. The optimization problem is formulated as a MILP (mixed integer linear program) due to the on/off decisions for the loads. Performance is compared with two rule based controllers, a simple all-or-none controller that mimics what is available now commercially, and a Rule-Based controller that uses the same information that the MPC controller uses. The controllers are tested through simulation on a PV-battery system chosen carefully for a small single family house in Florida. Simulations are conducted for a one week period during hurricane Irma in 2017. Simulations show that the size of the PV+battery system to provide a certain resiliency performance can be halved by the proposed control system.