Jason MacDonald

Quantifying flexibility of residential thermostatically controlled loads for demand response: a data-driven approach

Power systems are undergoing a paradigm shift due to the influx of variable renewable generation to the supply side. The resulting increased uncertainty has system operators looking to new resources, enabled by smart grid technologies, on the demand side to maintain the balance between supply and demand. This study uses a unique data set to estimate and validate models of demand response from residential thermostatically controlled loads (TCLs)---specifically, HVAC units---and quantifies the extent to which a population of TCLs can provide demand response (DR). We use measured temperature setpoints, internal temperatures, compressor cycling ratio and metered energy data collected from over 4200 homes in Texas during the summer of 2012. Using autoregressive moving average (ARMA) models for individual households, we investigate the instantaneous power shed, the duration of the power shed, steady state energy savings and total energy savings. Specifically, we provide insight into the dependency of household DR availability to the temperature setpoint schedule, outdoor air temperature and time of the day.

Experimental Demonstration of Frequency Regulation by Commercial Buildings—Part II: Results and Performance Evaluation

This paper is the second part of a two-part series presenting the results from an experimental demonstration of frequency regulation in a commercial building test facility. In part I, we developed relevant building models and designed a hierarchical controller for reserve scheduling, building climate control, and frequency regulation. In part II, we introduce the communication architecture and experiment settings, and present extensive experimental results under frequency regulation. More specifically, we compute the day-ahead reserve capacity of the test facility under different assumptions and conditions. Furthermore, we demonstrate the ability of model predictive control to satisfy comfort constraints under frequency regulation, and show that fan speed control can track the fast-moving RegD signal of the Pennsylvania, Jersey, and Maryland power market very accurately. In addition, we discuss potential effects of frequency regulation on building operation (e.g., increase in energy consumption, oscillations in supply air temperature, and effect on chiller cycling), and provide suggestions for real-world implementation projects. Our results show that hierarchical control is appropriate for frequency regulation from commercial buildings.

Experimental Demonstration of Frequency Regulation by Commercial Buildings—Part I: Modeling and Hierarchical Control Design

This paper is the first part of a two-part series in which we present results from one of the first worldwide experimental demonstrations of frequency regulation in a commercial building test facility. We demonstrate that commercial buildings can track a frequency regulation signal with high accuracy and minimal occupant discomfort in a realistic environment. In addition, we show that buildings can determine the reserve capacity and baseline power a priori, and identify the optimal tradeoff between frequency regulation and energy efficiency. In part I, we introduce the test facility and develop relevant building models. Furthermore, we design a hierarchical controller for the heating, ventilation, and air conditioning system that consists of three levels: 1) a reserve scheduler; 2) a building climate controller; and 3) a fan speed controller for frequency regulation. We formulate the reserve scheduler as a robust optimization problem and introduce several approximations to reduce its complexity. The building climate controller is comprised of a robust model predictive controller and a Kalman filter. The frequency regulation controller consists of a feedback and a feedforward loop, provides fast responses, and is stable. Part I presents building model identification and controller tuning results. Specifically, we find out that with an appropriate formulation of the model identification problem, a two-state model is accurate enough for use in a reserve scheduler that runs day-ahead. In part II, we report results from the operation of the hierarchical controller under frequency regulation.

Real-Time Charging Strategies for an Electric Vehicle Aggregator to Provide Ancillary Services

Real-time charging strategies, in the context of vehicle to grid technology, are needed to enable the use of electric vehicle fleets batteries to provide ancillary services. In this paper, we develop tools to manage charging and discharging in a fleet to track an automatic generation control signal when aggregated. We propose a real-time controller that considers bidirectional charging efficiency and extend it to study the effect of looking ahead when implementing model predictive control. Simulations show that the controller improves tracking error as compared with benchmark scheduling algorithms, as well as regulation capacity and battery cycling.

Real-time scheduling of electric vehicles for ancillary services

This work is part of a project that aims to demonstrate the concept of Vehicle-to-Grid (V2G) with an operational fleet. A fleet of electric vehicles is operated with the objective of providing regulation services to the grid. The focus of this paper is on the real-time operation of the fleet. Specifically, given an optimal trajectory for the vehicle state of charge, schemes for distributing the regulation power commands among the vehicles are tested. A scheme based on a convex optimization problem is proposed. Several numerical illustrations and simulations show the effectiveness of the scheme respect to common scheduling heuristics in terms of accuracy.