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Small and medium commercial customers in California make up about 20-25% of electric peak load in California. With the roll out of smart meters to this customer group, which enable granular measurement of electricity consumption to this customer group, the investor-owned utilities plan to offer dynamic prices as default tariffs by the end of 2011. Pacific Gas and Electric Company, which successfully deployed Automated Demand Response (AutoDR) Programs to its large commercial and industrial customers, started investigating the same infrastructures application to the small and medium commercial customers. This project aims to identify available technologies suitable for automating demand response for small-medium commercial buildings; to validate the extent to which that technology does what it claims to be able to do; and determine the extent to which customers find the technology useful for DR purpose. LIST OF TABLES EXECUTIVE SUMMARYDemand response (DR) programs encourage customers to change their electricity use, by reducing their usage during peak periods or shifting usage to off peak periods. DR is used to mitigate grid management problems including generation constraints, transmission constraints and or to reduce costs in utility programs with variable prices. DR programs and tariffs are designed to improve the reliability of the electric grid and reduce the use of electricity during peak times in order to drive down total system costs. This study examines the automated response of small and medium commercial sites to DR events communicated via the Internet using Open Automated Demand Response (OpenADR). OpenADR is an information exchange model that provides utility price, reliability, or other DR event signals to initiate preprogrammed customer demand management strategies. Essentially, OpenADR facilitates automated demand response (AutoDR) through continuous, secure, standardized and open communication signals.Pacific Gas and Electric Company (PG&E) offers AutoDR programs to its large commercial and industrial (C&I) customers. With the future roll out of default dynamic pricing tariffs for small and medium commercial customers, PG&E wanted to assess the applicability of the same infrastructure to this customer group. The tests reported in this paper aim to provide insights about the readiness of available technology to provide DR response capability for small and medium commercial buildings.The goal of this DR emerging technology assessment was to determine how well small and medium commercial buildings could respond to OpenADR signals using available technologies that are able to receive and interpret these signals. Specifically, this study looked at the capability of existing technologies to automatically shed demand to determine the extent to which these technologies, as applied, provided significant DR from non-aggregated small to medium commercial sites. In general, the work reported here was intended to help equipment manufacturers modify or improve their products so that their products c...

Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation.This paper describes the development and testing of a hybrid data-driven and physics modelbased operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system.

Executive SummaryWith the widespread deployment of electronic interval meters, commonly known as smart meters, came the promise of real-time data on electric energy consumption. Recognizing an opportunity to provide consumers access to their near real-time energy consumption data directly from their installed smart meter, we designed a mechanism for capturing those data for consumer use via an open smart energy gateway (OpenSEG). By design, OpenSEG provides a clearly defined boundary for equipment and data ownership.OpenSEG is an open-source data management platform to enable better data management of smart meter data. Effectively, it is an information architecture designed to work with the ZigBee Smart Energy Profile 1.x (SEP 1.x). It was specifically designed to reduce cyber-security risks and provide secure information directly from smart meters to consumers in near real time, using display devices already owned by the consumers. OpenSEG stores 48 hours of recent consumption data in a circular cache using a format consistent with commonly available archived (not real-time) consumption data such as Green Button, which is based on the Energy Services Provider Interface (ESPI) data standard. It consists of a common XML format for energy usage information and a data exchange protocol to facilitate automated data transfer upon utility customer authorization.

In support of DOE's sensors and controls research, the goal of this project is to move toward integrated building to grid systems by building on previous work to develop and demonstrate a set of load characterization measurement and evaluation tools that are envisioned to be part of a suite of applications for transactive efficient buildings, built upon data-driven load characterization and prediction models. This will include the ability to include occupancy data in the models, plus data collection and archival methods to include different types of occupancy data with existing networks and a taxonomy for naming these data within a Volttron 1 agent platform. This research was conducted to: determine desired characteristics of, and technical feasibility of, new sensors that can inexpensively monitor the number of building occupants; explore how existing systems in buildings can be used to estimate the number of occupants as a function of time; and  use energy savings Measurement and Verification (M&V) methods to quantify changes in building energy performance, both with and without the use of occupancy data. Virtual SensingWe have identified more than a dozen potential data sources for virtual occupancy sensing in buildings, and collected sample data on eight of them from LBNL buildings. Specifically, we acquired data from LBNL's telephone system, its Wi-Fi infrastructure, and several sources from the IP network infrastructure. Each source has its own advantages, disadvantages, and peculiarities. A general feature of most sources is that data could be extracted as frequently as desired, and it is almost as easy to analyze results for many buildings as it is to do so for a single one. Since all hardware required is already present in buildings, the implementation cost is close to zero. The technology appears to be highly replicable and scalable. In the primary study buildings, occupancy patterns are readily visible in the data, particularly arrival, departure, and lunchtime. Weekends and holidays are also similarly quite obvious in the data. Measurement and Verification (M&V) AgentCurrent Practice: No occupancy data We used the M&V Agent that was developed for the Transactional Network project that contains a baseline model to a) predict load based on historic building load and weather data, and b) use the load predictions to quantify changes in energy use over time. Therefore, the Agent can be DisclaimerThis document was prepared as an account of work sponsored by the United States Government.

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This document reports on the Grid Modernization Laboratory Consortium effort to identify gaps in standards for the interconnection and interoperability of distributed energy resources (DERs). The project extended over a 3-year period from 2017 to 2019. Under the work plan, the team identified standards and test procedures related to interconnection and interoperability grouped under the broad headings of the primary applicable technology domain: automotive, responsive loads, photovoltaic inverters, inverter-based energy storage, machine-based DERs, and microgrids. The team then conducted a gap analysis by comparing the current standards to the expected future requirements needed for specific grid services. Overall, gaps could be any activity needed to harmonize requirements among standards development organizations, to minimize conflicting requirements among technology domains, or to streamline conformance test procedures.