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A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture

Ehsan Reihani, Mahdi Motalleb, Matsu Thornton and Reza Ghorbani

Applied Energy, 2016, vol. 183, issue C, 445-455

Abstract: With the increasing presence of intermittent renewable energy generation sources, variable control over loads and energy storage devices on the grid become even more important to maintain this balance. Increasing renewable energy penetration depends on both technical and economic factors. Distribution system consumers can contribute to grid stability by controlling residential electrical device power consumed by water heaters and battery storage systems. Coupled with dynamic supply pricing strategies, a comprehensive system for demand response (DR) exist. Proper DR management will allow greater integration of renewable energy sources partially replacing energy demand currently met by the combustion of fossil-fuels. An enticing economic framework providing increased value to consumers compensates them for reduced control of devices placed under a DR aggregator. Much work has already been done to develop more effective ways to implement DR control systems. Utilizing an integrated approach that combines consumer requirements into aggregate pools, and provides a dynamic response to market and grid conditions, we have developed a mathematical model that can quantify control parameters for optimum demand response and decide which resources to switch and when. In this model, optimization is achieved as a function of cost savings vs. customer comfort using mathematical market analysis. Two market modeling approaches—the Cournot and SFE—are presented and compared. A quadratic function is used for presenting the cost function of each DRA (Demand Response Aggregator) which will be used for settling down the DR market. Contribution of each aggregator and the final price are presented. Finally, we have also performed sensitivity analysis on the house cost function’s coefficients for one of the aggregators.

Keywords: Demand response market; Demand response scheduling (search for similar items in EconPapers)
Date: 2016
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