EAGER: A Dynamical Systems Approach to Modeling and Controlling Price Responsive Demand in Electric Power Systems
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This project envisions a novel framework that will enable power grid operators to close the loop around flexible demand. The researchers will introduce and test a transfer function dynamical systems view of responsive demand, where the response can be to price or other variables that can influence demand response, such as temperature. The project will provide a fresh perspective on modeling the dynamical response of consumer behavior subject to incentives and other influence variables, as well insights into how independent system operators can incorporate demand response fully into consideration. Once successful, this initiative could unlock the collective flexibility untapped in today''s demand response markets. The exploratory research is likely to have a transformative impact on the systematic modeling, analysis, and control of tens of millions of access points in the future grid. It will benefit future grid operators and Energy Management Systems (EMS) in characterizing responsive customers. The team will continue its collaboration with the local grid operator, and will present research findings to Electric Reliability Council of Texas (ERCOT) through site visits. The project will also address quantification of uncertainty associated with aggregating many price-based demand responses. This modeling approach will enable system operators and load serving entities to consider load flexibility in a manner similar to how generators are currently modeled. Thereby it makes possible a holistic closure of loops simultaneously around generation and demand. The intellectual merit of this project is three-fold. First, it offers a unifying dynamical systems approach to modeling both demand as well as supply in the smart grid. While most generators are well characterized by first principles, responsive demand (including demand that is responsive to price) has not been similarly modeled in a manner that reflects the salient feature of electricity consumption. Modeling both genrators and demand response as dynamical systems makes possible a holistic treatment of electricity generation and consumption. Second, this project investigates the aggregated uncertainty associated with responsive demand. This difficult problem will address both the spatial and temporal correlations associated with different demands. Third, a novel optimal contract design for load serving entities will be investigated, to elicit a desirable level of demand response. This early-stage research puts forward an ambitious and potentially highly rewarding plan for modeling and control of responsive demand from a transfer function perspective.
