Improved Battery Models of an Aggregation of Thermostatically Controlled Loads for Frequency Regulation

Recently it has been shown that an aggregation of Thermostatically Controlled Loads (TCLs) can be utilized to provide fast regulating reserve service for power grids and the behavior of the aggregation can be captured by a stochastic battery with dissipation. In this paper, we address two practical issues associated with the proposed battery model. First, we address clustering of a heterogeneous collection and show that by finding the optimal dissipation parameter for a given collection, one can divide these units into few clusters and improve the overall battery model. Second, we analytically characterize the impact of imposing a no-short-cycling requirement on TCLs as constraints on the ramping rate of the regulation signal. We support our theorems by providing simulation results.Comment: to appear in the 2014 American Control Conference - AC

Providing Grid Services With Heat Pumps: A Review

Abstract The integration of variable and intermittent renewable energy generation into the power system is a grand challenge to our efforts to achieve a sustainable future. Flexible demand is one solution to this challenge, where the demand can be controlled to follow energy supply, rather than the conventional way of controlling energy supply to follow demand. Recent research has shown that electric building climate control systems like heat pumps can provide this demand flexibility by effectively storing energy as heat in the thermal mass of the building. While some forms of heat pump demand flexibility have been implemented in the form of peak pricing and utility demand response programs, controlling heat pumps to provide ancillary services like frequency regulation, load following, and reserve have yet to be widely implemented. In this paper, we review the recent advances and remaining challenges in controlling heat pumps to provide these grid services. This analysis includes heat pump and building modeling, control methods both for isolated heat pumps and heat pumps in aggregate, and the potential implications that this concept has on the power system

End-Use Load Profiles for the U.S. Building Stock: Market Needs, Use Cases, and Data Gaps

States and utilities are developing increasingly ambitious energy goals. Part of the solution to meeting these goals is improving electric grid flexibility. This includes shifting electric demand to align with grid needs. Thus, identifying and using building energy efficiency and other distributed energy resources to produce the highest grid value requires highly resolved, accurate and accessible electricity end-use load profiles (EULPs). EULPs quantify how and when energy is used. Currently, few accurate and accessible end-use load profiles are available for utilities, public utility commissions, state energy offices and other stakeholders to use to prioritize investment and value energy efficiency, demand response, distributed generation and energy storage. High-quality EULPs are also critical for determining the time-sensitive value of efficiency and other distributed energy resources, and the widespread adoption of grid-interactive efficient buildings (GEBs).For example, EULPs can be used to accurately forecast energy savings in buildings or identify energy activities that can be shifted to different times of the day. This report serves as the first-year deliverable for a multiyear U.S. Department of Energy-funded project, End-Use Load Profiles for the U.S. Building Stock, that intends to produce a set of highly resolved EULPs of the U.S. residential and commercial building stock. The project team, made up of researchers from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), and Argonne National Laboratory, ultimately will use calibrated physics-based building energy models to create these EULPs

An Occupant-Based Dynamic Simulation Tool for Predicting Residential Power Demand and Quantifying the Impact of Residential Demand Response

With their large impact on the power system and widespread distribution, residential loads provide vast resources that if utilized correctly have the potential to help reduce both electricity cost and demand throughout the day. Previous research in this area has been primarily focused on building more energy efficient homes and improving the efficiencies of appliances and lighting technologies. Far less attention has been given to the ability of residential loads to provide various demand response services. Residential loads with demand response capabilities have the potential to be very useful in both peak shifting and regulation applications, and could be utilized in the future to help maintain power system stability and security. Before this can become a reality, however, the effect residential loads providing demand response services can have on the power system must be understood. One method for determining the overall impact residential demand response can have on the power system is through modeling. In this thesis, the development of a dynamic simulation tool capable of predicting residential power demand on a one-second time scale is discussed. To produce the most accurate results, a bottom-up modeling approach is utilized in which the characteristics of the household, its individual loads, and the behavior of its occupants are modeled. Using this technique, the contribution of each residential load towards the total aggregate demand of the residential sector can be identified. Occupant behavior models are developed using data collected in the American Time Use Survey to create a statistically accurate representation of how occupants interact with major residential loads. These models are simulated using a Markov Chain Monte Carlo method, and predict occupant behavior based on the time of the day and day of the week. To predict residential power demand, dynamic models of the most common residential loads are developed and used in conjunction with these occupant behavior models and environmental input data. Finally, several demand response strategies are applied to this simulation tool to quantify the potential impact residential demand response programs can have on the power system and illustrate the importance of understanding their overall effects

Control and Communication Protocols that Enable Smart Building Microgrids

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand response capabilities of intelligent load network of power-consuming devices such as HVAC components, water heaters, and buildings. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real time markets. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility