Modeling, Analysis, and Control of Demand Response Resources

April 27, 2012 - 12:00pm
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While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can play an active role in power systems via Demand Response (DR). Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities).  We present a regression-based baseline model, which allows us to quantify DR performance.  We use this baseline model to understand the performance of C&I facilities participating in an automated dynamic pricing DR program in California.  In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult understand if C&I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We find that, though some facilities exhibit real DR variability, most observed variability results from baseline model error.  In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation. These results have implications for DR program design and deployment.    Emerging DR programs focus on faster timescale DR.  We investigate methods to coordinate aggregations of residential thermostatically controlled loads (TCLs), including air conditioners and refrigerators, to manage frequency and energy imbalances in power systems.  We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. We use Markov Chain models, Kalman filtering, and a look-ahead controller broadcasts. Simulations indicate that it is possible to achieve power tracking RMS errors in the range of 0.26-9.3% of steady state aggregated power consumption, with results depending upon the information available offline and in real-time. We find that, depending upon the performance required, TCLs may not need to provide state information to the central controller in real time or at all. We also estimate the size of the TCL potential resource; potential revenue from participation in markets; and break-even costs associated with deploying DR-enabling technologies. These results lead to a number of policy recommendations that will make it easier to engage residential loads in fast timescale DR. A recording of this seminar is available at:  

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