Girish Ghatikar

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.

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

The Local Team: Leveraging Distributed Resources to Improve Resilience

In recent years, extreme weather events have severely affected the performance of the electric grid. Very large-scale events (VLSE) with potentially catastrophic impacts on the grid pose more than an inconvenience in todays electricity-driven lifestyle, and the frequency and severity of such events may continue to increase as a consequence of global climate change. This article summarizes the state of the art in leveraging distributed resources to improve resilience of the electric grid. It also highlights the technical questions that need to be addressed through additional research and development if the value of distributed resources is to be maximized.

Analysis of Open Automated Demand Response Deployments in California and Guidelines to Transition to Industry Standards

This report reviews the Open Automated Demand Response (OpenADR) deployments within the territories serviced by California?s investor-owned utilities (IOUs) and the transition from the OpenADR 1.0 specification to the formal standard?OpenADR 2.0. As demand response service providers and customers start adopting OpenADR 2.0, it is necessary to ensure that the existing Automated Demand Response (AutoDR) infrastructure investment continues to be useful and takes advantage of the formal standard and its many benefits. This study focused on OpenADR deployments and systems used by the California IOUs and included a summary of the OpenADR deployment from the U.S. Department of Energy-funded demonstration conducted by the Sacramento Municipal Utility District (SMUD). Lawrence Berkeley National Laboratory collected and analyzed data about OpenADR 1.0 deployments, categorized architectures, developed a data model mapping to understand the technical compatibility of each version, and compared the capabilities and features of the two specifications. The findings, for the first time, provided evidence of the total enabled load shed and average first cost for system enablement in the IOU and SMUD service territories. The OpenADR 2.0a profile specification semantically supports AutoDR system architectures and data propagation with a testing and certification program that promotes interoperability, scaled deployments by multiple vendors, and provides additional features that support future services.

Opportunities, Barriers and Actions for Industrial Demand Response in California

LBNL-XXXXX Opportunities, Barriers and Actions for Industrial Demand Response in California Aimee T. McKane, Mary Ann Piette, David Faulkner, Girish Ghatikar, Anthony Radspieler Jr., Bunmi Adesola, Scott Murtishaw and Sila Kiliccote Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley, California 94720 January 2008 The 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.

Regression models for demand reduction based on cluster analysis of load profiles

This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Companys commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.

Electrical Grid and Supercomputing Centers: An Investigative Analysis of Emerging Opportunities and Challenges

Some of the largest supercomputing centers (SCs) in the United States are developing new relationships with their electricity service providers (ESPs). These relationships, similar to other commercial and industrial partnerships, are driven by a mutual interest to reduce energy costs and improve electrical grid reliability. While SCs are concerned about the quality, cost, environmental impact, and availability of electricity, ESPs are concerned about electrical grid reliability, particularly in terms of energy consumption, peak power demands, and power fluctuations. The power demand for SCs can be 20 MW or more – the theoretical peak power requirements are greater than 45 MW – and recurring intra-hour variability can exceed 8 MW. As a result of this, ESPs may request large SCs to engage in demand response and grid integration.This paper evaluates today’s relationships, potential partnerships, and possible integration between SCs and their ESPs. The paper uses feedback from a questionnaire submitted to supercomputing centers on the Top100 List in the United States to describe opportunities for overcoming the challenges of HPC-grid integration.

Smart Buildings and Demand Response

Advances in communications and control technology, the strengthening of the Internet, and the growing appreciation of the urgency to reduce demand side energy use are motivating the development of improvements in both energy efficiency and demand response (DR) systems in buildings. This paper provides a framework linking continuous energy management and continuous communications for automated demand response (Auto‐DR) in various times scales. We provide a set of concepts for monitoring and controls linked to standards and procedures such as Open Automation Demand Response Communication Standards (OpenADR). Basic building energy science and control issues in this approach begin with key building components, systems, end‐uses and whole building energy performance metrics. The paper presents a framework about when energy is used, levels of services by energy using systems, granularity of control, and speed of telemetry. DR, when defined as a discrete event, requires a different set of building service levels...

Load management of data centers as regulation capacity in Denmark

Replacing the traditional fossil-based electricity generation with clean renewable energy is critical to address carbon emissions and climate change in particular. Denmark has a particularly aggressive strategy for renewable energy generation. By 2020 50% of electricity production is to be wind based and by 2050 the goal is to have an energy production based entirely on renewable energy. Renewable energy such as solar and wind is subject to variations due to changing weather conditions. This requires additional balancing capacity and ancillary services in order to balance the grid for transmission system operators and distribution system operators and balance errors in forecasts made by balance responsible parties. By enabling the demand-side to adapt consumption to match power generation, we can address this in a cost-effective and environmental sound way. In this context, data centers are of special interest as they account for 500 GWh of consumption in Denmark or nearly 2% of the total electricity consumption. This paper performs an analysis on load management capabilities of data centers in Denmark based on the experiences in the U.S. We characterize the load management capabilities of the data centers based on their types, technology, and their application as grid management resources. Further, we identify demand-side entry barriers towards market participation. Our findings suggest that groups of data centers can offer dynamic load flexibility as virtual power plants, and thereby support the evolution of the Danish energy systems towards its 2020 and 2050 goals.

Supercomputing Centers and Electricity Service Providers: A Geographically Distributed Perspective on Demand Management in Europe and the United States

Supercomputing Centers (SCs) have high and variable power demands, which increase the challenges of the Electricity Service Providers (ESPs) with regards to efficient electricity distribution and reliable grid operation. High penetration of renewable energy generation further exacerbates this problem. In order to develop a symbiotic relationship between the SCs and their ESPs and to support effective power management at all levels, it is critical to understand and analyze how the existing relationships were formed and how these are expected to evolve.