Design and Implementation of an Open, Interoperable Automated Demand Response Infrastructure

Design and Implementation of an Open, Interoperable Automated Demand Response Infrastructure Mary Ann Piette, Sila Kiliccote and Girish Ghatikar Lawrence Berkeley National Laboratory Building 90-3111 Berkeley CA 94720 mapiette@lbl.gov, SKiliccote@lbl.gov, GGhatikar@lbl.gov Keywords: Demand response, automation, commercial buildings, peak demand Abstract This paper describes the concept for and lessons from the development and field-testing of an open, interoperable communications infrastructure to support automating demand response (DR). Automating DR allows greater levels of participation and improved reliability and repeatability of the demand response and customer facilities. Automated DR systems have been deployed for critical peak pricing and demand bidding and are being designed for real time pricing. The system is designed to generate, manage, and track DR signals between utilities and Independent System Operators (ISOs) to aggregators and end-use customers and their control systems. 1. INTRODUCTION California utilities have been exploring the use of critical peak pricing (CPP) and other DR pricing and program strategies to help reduce peak day summer time electric loads. Recent experience with DR has shown that customers have limited knowledge of how to operate their facilities to reduce their electricity costs under CPP or in a DR Program [1]. While the lack of knowledge about how to develop and implement DR control strategies is a barrier to participation in DR programs like CPP, another barrier is the lack of automation of DR systems. Most DR activities are manual and require building operations staff to first receive emails, phone calls, and pager signals, and second, to act on these signals to execute DR strategies. The various levels of DR automation can be defined as follows. Manual Demand Response involves a labor- intensive approach such as manually turning off or changing comfort set points at each equipment switch or controller. Semi-Automated Demand Response involves a pre- programmed demand response strategy initiated by a person via centralized control system. Fully-Automated Demand Response does not involve human intervention, but is initiated at a home, building, or facility through receipt of an external communications signal. The receipt of the external signal initiates pre-programmed demand response strategies. The authors refer to this as Auto-DR. One important concept in Auto-DR is that a homeowner or facility manager should be able to “opt out” or “override” a DR event if the event comes at time when the reduction in end-use services is not acceptable. From the customer side, modifications to the site’s electric load shape can be achieved by modifying end-use loads. Examples of demand response strategies include reducing electric loads by dimming or turning off non-critical lights, changing comfort thermostat set points, or turning off non- critical equipment. These demand response activities are triggered by specific actions set by the electricity service provider, such as dynamic pricing or demand bidding. Many electricity customers have suggested that automation will help them institutionalize their demand response. The alternative is manual demand response -- where building staff receives a signal and manually reduces demand. Lawrence Berkeley National Laboratory (LBNL) research has found that many building energy management and controls systems (EMCS) and related lighting and other controls can be pre-programmed to initiate and manage electric demand response. This paper provides an overview of the AutoDR field tests and implementation activities from 2003-2007. A companion paper describes the technology in greater detail. This paper focuses on the automation design history and does not cover the shed strategy or shed measurement details which are covered in previous papers [2,3,4,5]. 2. TECHNOLOGY HISTORY The automated demand response project began in 2002 following California’s electricity market crisis with the goal of addressing three key research questions. First, is it possible using today’s technology to develop a low-cost, fully automated infrastructure to improve DR capability in California? Second, how “ready” are commercial buildings to receive common signals? Third, once a building receives