Improving Facility DRSense - Automated and Scalable Assessment of Demand Response for Green Building Portfolios

CIFE Seed 2011-12 Project

Principal Investigators: Professor Ram Rajagopal, Professor Abbas El Gamal, and Dr. Amit Narayan
Research Staff: Dr. Hae Young Noh, Jeffrey Wong


Our original proposal:
(proposal paper) (proposal presentation slides)

(1) Abstract


Green energy efficient building portfolios need to incorporate significant participation in Demand Response (DR) programs to reduce their operating costs and peak energy consumption. Currently, building owners and managers are unable to evaluate the DR potential of their portfolio, assign the best DR strategies for each individual building and perform a cost/benefit analysis of implementing DR. The main limitation is the very manual one time evaluation in current practice. Instead we propose a DRSense: a tool that combines real-time data, models for building consumption and data mining with advanced statistical methods to derive DR potential. If successful, the open-source web based tool will enable facility operators to continuously assess building performance and optimize the dynamic strategy for DR that achieves the most savings. Given the important role of DR in the future grid, it is essential to develop tools that guide the configuration of DR for a specific building portfolio.

(2) Motivation - Practical scenario and potential impact of the research

Green energy efficient buildings make significant use of renewable technologies and strive for lower operating costs. Costs are higher if more energy is consumed during peak load times. An important technology that is essential for greening a building is Demand Response (DR). DR is a tariff or program established to motivate changes in electric use by end-­‐use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity consumption at times of high market prices or when grid reliability is jeopardized. DR provides an approach to reduce operation costs of a building by shifting or limiting consumption during peak periods. At the same time DR decreases the peak generation requirements of the power system, reducing emissions and increasing grid reliability establishing it as an essential need.

Building owners and managers considering incorporation of DR into their portfolios have to answer a number of important questions:
Today, answering these questions requires an energy audit by a specialized energy management consultant who visits each building and spends considerable amount of time determining the DR strategy and estimating DR potential for a given building. For large commercial buildings where such an assessment can be done once, facility managers have no easy way to determine if a given building is meeting its desired DR goals during a DR event, or where a particular building stands with respect to its ‘peers’ in terms of its DR performance for a given DR event. This type of benchmarking could be useful in improving the participation rates for buildings that are already enrolled in DR programs but are not maximizing the peak energy reduction potential of the building. In fact, on an average only 30-40% of enrolled DR capacity participates in any given DR event. A continuous assessment of demand response can give a more accurate portrait of DR for each building in a portfolio accounting for changes in use patterns and operation decisions, contrasting with a single time assessment and can significantly improve the overall participation in DR events.

Moreover, the time and expense involved in the assessment process precludes most small and medium sized buildings from this type of manual DR assessment. In the absence of an automated, low cost assessment, most customers are not able to determine the benefits and effort involved in signing up for a DR program.

The aim of our research is to develop DRSense: We believe the tool we propose to be necessary to drive a wider adoption and maximize the value of DR.


(3) Proposed approach


   

  


Our findings up to date:

(1) Description of data

The Jerry Yang and Akiko Yamazaki Environment and Energy Building (Y2E2) at Stanford University is a three story building with a subterranean basement. This 166,000 square-foot building is instrumented with 2370 HVAC system measurement points including sensors and control points. Each point collects data every minute using a computer-based data collection system. Among these measurements, we are most interested in electrical power and heating/cooling energy consumption data. The electrical power consumption data include lighting power for each floor, plug loads for east and west wings of each floor, and power consumption for laboratories, server rooms, mechanical equipments, emergency, standby, and a cafe. The heating and cooling energy consumptions are measured in terms of steam and chilled water usage.

(2) Forecasting algorithm and preliminary results

We modeled each power consumption time-series as a time-varying autoregressive process and applied adaptive autoregressive model to it. In order to forecast the power consumption, we explored various model order, prediction horizon, and time window for model fitting. The preliminary results using the lighting power measurements from January to August 2011 show that this model can predict the next 15 minute consumption with less than 5% error, the next 30 minute with less than 10% error, and the next 45 minute with less than 12% error.
    




(3) Future plans

  • Identify power consumption patterns from similar-looking previous measurements
  • Combine different types of information, such as temperature and lighting loads, to improve forecasting
  • Conduct sensitivity analysis to identify important factors for power consumption
  • Find optimal demand response strategy to minimize energy cost



Publications and presentations:

(1) Original proposal

(2) Presentation
  • In progress, TBD
(3) Publication
  • In progress, TBD

(Last updated on Mar 20, 2012)