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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., Comment: 9 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:2003.05570

This paper presents a novel architecture for model predictive control (MPC) based indoor climate control of multi-zone buildings to provide energy efficiency. Unlike prior works we do not assume the availability of a high-resolution multi-zone building model, which is challenging to obtain. Instead, the architecture uses a low-resolution model of the building which is divided into a small number of "meta-zones" that can be easily identified using existing data-driven modeling techniques. The proposed architecture is hierarchical. At the higher level, an MPC controller uses the low-resolution model to make decisions for the air handling unit (AHU) and the meta-zones. Since the meta-zones are fictitious, a lower level controller converts the high-level MPC decisions into commands for the individual zones by solving a projection problem that strikes a trade-off between two potentially conflicting goals: the AHU-level decisions made by the MPC are respected while the climate of the individual zones is maintained within the comfort bounds. The performance of the proposed controller is assessed via simulations in a high-fidelity simulation testbed and compared to that of a rule-based controller that is used in practice. Simulations in multiple weather conditions show the effectiveness of the proposed controller in terms of energy savings, climate control, and computational tractability.

The need for resiliency of electricity supply is increasing due to increasing frequency of natural disasters---such as hurricanes---that disrupt supply from the power grid. Rooftop solar photovoltaic (PV) panels together with batteries can provide resiliency in many scenarios. 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 using forecast of solar generation and household demand, an intelligent decision maker can operate the equipment (battery and critical loads) to ensure that the critical loads are serviced to the maximum duration possible. With the aid of such an intelligent control system, a smaller (and thus lower cost) system can service the primary loads for the same duration that a much larger system will be needed to service otherwise. In this paper we propose such an intelligent control system. A model predictive control (MPC) architecture is used that uses available measurements and forecasts to make optimal decisions for batteries and critical loads in real time. 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 a non-intelligent baseline controller, for a PV-battery system chosen carefully for a single family house in Florida. Simulations are conducted for a one week period during hurricane Irma in 2017. Simulations show that the cost of the PV+battery system to provide a certain resiliency performance, duration the primary load can be serviced successfully, can be halved by the proposed control system., Comment: 8 pages, 9 figures, submitted to The 4th IEEE Conference on Control Technology and Applications (CCTA 2020)

Even though energy efficient climate control of buildings using model predictive control (MPC) has been widely investigated, most MPC formulations ignore humidity and latent heat. The inclusion of moisture makes the problem considerably more challenging, primarily since a cooling and dehumidifying coil model which accounts for both sensible and latent heat transfers is needed. In this work, we propose an MPC controller in which humidity and latent heat are incorporated in a principled manner. We construct low order data-driven models of a cooling and dehumidifying coil that can be used in the MPC formulation. The resulting controller's performance is tested in simulation using a plant that differs significantly from the model used by the optimizer. Additionally, the performance of the proposed controller is compared with that of a naive MPC controller which does not explicitly consider humidity, and also to that of a conventional rule-based controller. Simulations show that the proposed MPC controller outperforms the other two consistently. It is also observed that the naive MPC formulation which does not consider humidity leads to poor humidity control under certain conditions. Such violations in humidity can adversely affect occupant comfort and health.

Flexible loads, especially heating, ventilation, and air-conditioning (HVAC) systems can be used to provide a battery-like service to the power grid by varying their demand up and down over a baseline. Recent work has reported that providing virtual energy storage with HVAC systems lead to a net loss of energy, akin to a low round-trip efficiency (RTE) of a battery. In this work we rigorously analyze the RTE of a virtual battery through a simplified physics-based model. We show that the low RTEs reported in recent experimental and simulation work are an artifact of the experimental/simulation setup. When the HVAC system is repeatedly used as a virtual battery, the asymptotic RTE is 1. Robustness of the result to assumptions made in the analysis is illustrated through a simulation case study., Comment: Added a new simulation case study

In this paper, we present power emulation, a novel design paradigm that utilizes hardware acceleration for the purpose of fast power estimation. Power emulation is based on the observation that the functions necessary for power estimation (power model evaluation, aggregation, etc.) can be implemented as hardware circuits. Therefore, we can enhance any given design with "power estimation hardware", map it to a prototyping platform, and exercise it with any given test stimuli to obtain power consumption estimates. Our empirical studies with industrial designs reveal that power emulation can achieve significant speedups (10X to 500X) over state-of-the-art commercial register-transfer level (RTL) power estimation tools., Comment: Submitted on behalf of EDAA (http://www.edaa.com/)