Evangelos Vrettos

A framework for and assessment of demand response and energy storage in power systems

The shift in the electricity industry from regulated monopolies to competitive markets as well as the widespread introduction of fluctuating renewable energy sources bring new challenges to power systems. Some of these challenges can be mitigated by using demand response (DR) and energy storage to provide power system services. The aim of this paper is to provide a unified framework that allows us to assess different types of DR and energy storage resources and determine which resources are best suited to which services. We focus on four resources: batteries, plug-in electric vehicles, commercial buildings, and thermostatically controlled loads. We define generic power system services in order to assess the resources. The contribution of the paper is threefold: (i) the development of a framework for assessing DR and energy storage resources; (ii) a detailed analysis of the four resources in terms of ability for providing power system services, and (iii) a comparison of the resources, including an example case for Switzerland. We find that the ability of resources to provide power system services varies largely and also depends on the implementation scenario. Generally, there is large potential to use DR and energy storage for providing power system services, but there are also challenges to be addressed, for example, adequate compensation, privacy, guaranteeing costumer service, etc.

Combined Load Frequency Control and active distribution network management with Thermostatically Controlled Loads

In this paper, we propose a novel hierarchical control algorithm to enable simultaneous participation of aggregations of Thermostatically Controlled Loads (TCLs) in power system Load Frequency Control (LFC) and active distribution network management in order to increase the integration of Renewable Energy Sources (RES). The algorithm assumes a two-way communication infrastructure and consists of two phases: day-ahead scheduling and real-time operation. In the scheduling phase, the optimal load dispatch is determined by considering demand and RES predictions and solving a robust multi-period AC Optimal Power Flow (AC-OPF). In real-time, a two-step procedure is applied to control the load aggregation to desired set-points that guarantee LFC provision, maximize the absorption of RES power and satisfy Distribution Network (DN) constraints. The effectiveness of the algorithm is illustrated by considering a benchmark Medium Voltage (MV) DN with large shares of photovoltaic (PV) generation and a controllable aggregation of residential Electric Water Heaters (EWHs). The results show that the algorithm properly exploits the demand-side flexibility and guarantees the provision of significant LFC reserves. At the same time, it reduces the curtailed PV energy due to DN stresses and minimizes adverse effects on user comfort.

Load frequency control by aggregations of thermally stratified electric water heaters

In this paper, we present a dynamic model of an electric water heater which describes the thermal stratification inside the water tank. The goal of this modeling is to accurately describe the power consumption behavior of a large water heater population and to assess customer comfort loss (lack of hot water) in the presence of external control actions. We present four rule-based control approaches for aggregate power setpoint tracking and compare them by means of time-domain simulations and numerical comparisons. As a control signal, we use a scaled load frequency control (LFC) time series added to a time-varying baseline of the aggregate water heater load.

Control of thermostatic loads using moving horizon estimation of individual load states

Recent research has shown that thermostatically controlled loads (TCLs) can provide power system services. However, a key challenge is to achieve coordinated control of large populations of resources using existing communication and control infrastructure or with minimal addition of new infrastructure. In this paper, we assume that we only have access to realistic measurements, i.e. data from residential smart meters every 15 minutes and noisy real-time measurements of the aggregate power consumption of TCLs from distribution substations. Our contribution is to develop a moving horizon state estimator (MHSE) to estimate the states of individual stochastic TCLs from these measurements. This is in contrast to previous work that focused on estimating the states of aggregate system models. The proposed MHSE is benchmarked against a simpler model-based prediction. We also propose a scalable closed-loop control structure that uses the MHSE method to provide frequency control with TCL populations. We demonstrate our results via a number of case studies with different TCL aggregations, process and measurement noise characteristics, and controller forcing levels. Our simulations show that the MHSE generally provides accurate state estimates and improves the controller performance.

Centralized and decentralized balance group optimization in electricity markets with demand response

In this paper, the potential of using Demand Response (DR) to minimize balancing energy costs of Balance Groups (BGs) in electricity markets is investigated. Two algorithms are developed based on direct and price-based control concepts, respectively, to control an aggregated pool of office buildings. The direct control algorithm is set up as a centralized Model Predictive Control (MPC) problem yielding an optimal control sequence. This is used as a benchmark for a decentralized price control scheme, which is suboptimal, but still provides a good performance with much lower communication requirements compared to the benchmark. The two approaches are compared using a case study and conclusions regarding their advantages and disadvantages are drawn based on simulation results. The results show that with proper exploitation of the flexibility of office building aggregations significant balancing cost reductions can be achieved with only limited communication which is, in particular, respecting privacy requirements.

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.

Applying Networked Estimation and Control Algorithms to Address Communication Bandwidth Limitations and Latencies in Demand Response

Demand response can provide services to the power network, however, coordination of spatially distributed demand response resources generally requires coping with imperfect communication networks. This work investigates methods to manage communication constraints (e.g., Delays and bandwidth limitations), faced by demand response aggregators who manipulate the on/off modes of residential thermostatically controlled loads (TCLs). We present two model predictive control (MPC) algorithms that exploit a priori knowledge of delay statistics. We also present three Kalman filter-based state estimation methods that handle measurements with heterogeneous delays that are known a posteriori. We simulate the closed loop system to quantify the error while the system tracks simplified power system signals of various frequencies. We find that the MPC algorithm incorporating the full delay distribution, versus only the mean delay, reduces the average tracking error 39%. Also, incorporating individual TCL models, identified on-line, within the state estimator versus only using a TCL aggregation model reduces the average estimation error 19%.

Demand response with moving horizon estimation of individual thermostatic load states from aggregate power measurements

We present an optimization-based state estimation method that allows us to estimate the states of individual thermostatically controlled loads (TCLs), such as air conditioners and space heaters, from measurements of the power consumption of small aggregations of TCLs. The state estimator can be used together with a controller to provide ancillary services to power systems such as frequency control. The main advantage of this method is that it is designed to work with existing communication infrastructure. We assume that aggregate power measurements are available from distribution substations every few seconds, while TCL state measurements are available from smart meters only every 20 minutes. We model TCLs as hybrid systems and propose a moving horizon state estimator (MHSE), which is formulated as a mixed-integer linear program. We demonstrate the performance of the MHSE in two case studies: (a) estimation of TCL states in the absence of external control actions, and (b) a power tracking problem with closed-loop control using broadcast control inputs. To demonstrate the robustness of the method, we conduct a parametric analysis with respect to aggregation size and diversity, process noise characteristics, and control trajectory characteristics. The results show that the method generally provides accurate estimates of TCL states, resulting in improved controller performance in most cases, and is implementable in real-time with reasonable computational power.

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.

Stochastic frequency reserve provision by chance-constrained control of commercial buildings

Robust programs with modulated uncertainty sets are problems in which the uncertainty sets are treated as optimization variables. In many applications, these uncertainty sets can be interpreted as reserve capacities for which rewards are offered. One example is the provision of frequency reserve capacities to the power grid by demand-side resources. This paper studies the case in which the reserves are offered by commercial buildings. We determine the optimal size of the reserve capacity set such that the buildings can follow any reserve demanded within the set, while guaranteeing their comfort constraints. Since the actual reserve demands are uncertain, we interpret the reserve demands as random variables and formulate a chance-constrained program to ensure comfort constraints with high probabilities. By restricting the class of policies to affine decision rules and applying the scenario approach to approximate the chance constraints, we obtain a tractable convex problem. We compare our chance-constrained framework to previous work with robust comfort constraints and demonstrate its efficacy based on a numerical case study.