Sila Kiliccote

Quantifying Changes in Building Electricity Use, With Application to Demand Response

We present methods for analyzing commercial and industrial facility 15-min-interval electric load data. These methods allow building managers to better understand their facilitys electricity consumption over time and to compare it to other buildings, helping them to “ask the right questions” to discover opportunities for demand response, energy efficiency, electricity waste elimination, and peak load management. We primarily focus on demand response. Methods discussed include graphical representations of electric load data, a regression-based electricity load model that uses a time-of-week indicator variable and a piecewise linear and continuous outdoor air temperature dependence and the definition of various parameters that characterize facility electricity loads and demand response behavior. In the future, these methods could be translated into easy-to-use tools for building managers.

Smart grid technologies and applications for the industrial sector

Abstract Smart grids have become a topic of intensive research, development, and deployment across the world over the last few years. The engagement of consumer sectors—residential, commercial, and industrial—is widely acknowledged as crucial for the projected benefits of smart grids to be realized. Although the industrial sector has traditionally been involved in managing power use with what today would be considered smart grid technologies, these applications have mostly been one-of-a-kind, requiring substantial customization. Our objective in this article is to motivate greater interest in smart grid applications in industry. We provide an overview of smart grids and of electricity use in the industrial sector. Several smart grid technologies are outlined, and automated demand response is discussed in some detail. Case studies from aluminum processing, cement manufacturing, food processing, industrial cooling, and utility plants are reviewed. Future directions in interoperable standards, advances in automated demand response, energy use optimization, and more dynamic markets are discussed.

Open Automated Demand Response Communications Specification (Version 1.0)

The development of the Open Automated Demand Response Communications Specification, also known as OpenADR or Open Auto-DR, began in 2002 following the California electricity crisis. The work has been carried out by the Demand Response Research Center (DRRC), which is managed by Lawrence Berkeley National Laboratory. This specification describes an open standards-based communications data model designed to facilitate sending and receiving demand response price and reliability signals from a utility or Independent System Operator to electric customers. OpenADR is one element of the Smart Grid information and communications technologies that are being developed to improve optimization between electric supply and demand. The intention of the open automated demand response communications data model is to provide interoperable signals to building and industrial control systems that are preprogrammed to take action based on a demand response signal, enabling a demand response event to be fully automated, with no manual intervention. The OpenADR specification is a flexible infrastructure to facilitate common information exchange between the utility or Independent System Operator and end-use participants. The concept of an open specification is intended to allow anyone to implement the signaling systems, the automation server or the automation clients.

Automated Critical Peak Pricing Field Tests: Program Description and Results

Automated Critical Peak Pricing Field Tests: Program Description and Results April 6, 2006 Mary Ann Piette David Watson Naoya Motegi Sila Kiliccote Peng Xu Lawrence Berkeley National Laboratory Sponsored by the Pacific Gas and Electric Company Emerging Technologies Program California Institute for Energy and the Environment LBNL Report Number 59351

Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings

Design and Operation of an Open Interoperable Automated Demand Response Infrastructure for Commercial Buildings Mary Ann Piette, Girish Ghatikar, Sila Kiliccote, David Watson Lawrence Berkeley National Laboratory Ed Koch, Dan Hennage Akuacom

Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing

Controlling electric loads to deliver power system services presents a number of interesting challenges. For example, changes in electricity consumption of Commercial and Industrial (C&I) facilities are usually estimated using counterfactual baseline models, and model uncertainty makes it difficult to precisely quantify control responsiveness. Moreover, C&I facilities exhibit variability in their response. This paper seeks to understand baseline model error and demand-side variability in responses to open-loop control signals (i.e. dynamic prices). Using a regression-based baseline model, we define several Demand Response (DR) parameters, which characterize changes in electricity use on DR days, and then present a method for computing the error associated with DR parameter estimates. In addition to analyzing the magnitude of DR parameter error, we develop a metric to determine how much observed DR parameter variability is attributable to real event-to-event variability versus simply baseline model error. Using data from 38 C&I facilities that participated in an automated DR program in California, we find that DR parameter errors are large. For most facilities, observed DR parameter variability is likely explained by baseline model error, not real DR parameter variability; however, a number of facilities exhibit real DR parameter variability. In some cases, the aggregate population of C&I facilities exhibits real DR parameter variability, resulting in implications for the system operator with respect to both resource planning and system stability.

Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection

Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection Daniel]. Olsen, Nance Matson, Michael D. Sohn, Cody Rose, Junqiao Dudley, Sasank Goli, and Sila Kiliccote Lawrence Berkeley National Laboratory Marissa Hurnmon, David Palchak, Paul Denholm, and Jennie Iorgenson National Renewable Energy Laboratory September 2013

Grid Integration of Aggregated Demand Response, Part 2: Modeling Demand Response in a Production Cost Model

NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Foreword This report is one of a series stemming from the U.S. Department of Energy (DOE) Demand Response and Energy Storage Integration Study. This study is a multinational laboratory effort to assess the potential value of demand response (DR) and energy storage to electricity systems with different penetration levels of variable renewable resources and to improve our understanding of associated markets and institutions. This study was originated, sponsored, and managed jointly by the Office of Energy Efficiency and Renewable Energy and the Office of Electricity Delivery and Energy Reliability. Grid modernization and technological advances are enabling resources, such as DR and energy storage, to support a wider array of electric power system operations. Historically, thermal generators and hydropower in combination with transmission and distribution assets have been adequate to serve customer loads reliably and with sufficient power quality, even as variable renewable generation like wind and solar power become a larger part of the national energy supply. While DR and energy storage can serve as alternatives or complements to traditional power system assets in some applications, their values are not entirely clear. This study seeks to address the extent to which DR and energy storage can provide cost-effective benefits to the grid and to highlight institutions and market rules that facilitate their use. The project was initiated and informed by the results of two DOE workshops; one on energy storage and the other on DR. The workshops were attended by members of the electric power industry, researchers, and policymakers, and the study design and goals reflect their contributions to the collective thinking of the project team. Additional information …

Demand Responsive Lighting: A Scoping Study

LBNL-62226 Demand Responsive Lighting: A Scoping Study Francis Rubinstein Lawrence Berkeley National Laboratory Sila Kiliccote Lawrence Berkeley National Laboratory 1 Cyclotron Rd. Building 90R3111 Berkeley CA 94720 January 3, 2007 This work described in this report was coordinated by the Demand Response Research Center and funded by the California Energy Commission, Public Interest Energy Research Program, under Work for Others Contract No. 500-03-026 and by the U.S. Department of Energy under Contract No. DE-AC02- 05CH11231.

Cyber–Physical Modeling of Distributed Resources for Distribution System Operations

Cosimulation platforms are necessary to study the interactions of complex systems integrated in future smart grids. The Virtual Grid Integration Laboratory (VirGIL) is a modular cosimulation platform designed to study interactions between demand-response (DR) strategies, building comfort, communication networks, and power system operation. This paper presents the coupling of power systems, buildings, communications, and control under a master algorithm. There are two objectives: first, to use a modular architecture for VirGIL, based on the functional mockup interface (FMI), where several different modules can be added, exchanged, and tested; and second, to use a commercial power system simulation platform, familiar to power system operators, such as DIgSILENT PowerFactory. This will help reduce the barriers to the industry for adopting such platforms, investigate and subsequently deploy DR strategies in their daily operation. VirGIL further introduces the integration of the quantized state system (QSS) methods for simulation in this cosimulation platform. Results on how these systems interact using a real network and consumption data are also presented.