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

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 systematic mathematical market analysis. Two market modeling approaches-the Cournot and SFEare 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. Index Terms-Demand response market, demand response schedulingAn experimental study was performed in [3] to analyze the effects of DLC-induced temperature cycles on university students' thermal sensation and thermal acceptability in lecture theatres as a worst case setting for DLC-induced thermal environments. The results show that operative temperature, vapor pressure, and the rate of temperature change are the three most important predictors during DLC events. DLC has been implemented successfully in several areas including Hawaii, where Hawaiian Electric has a total of 34,000 DLC customers, collectively providing 15 megawatts of controllable peak demand power. The potential of DRs are studied in [4] in Hawaii in balancing supply and demand on an hourly basis. Much research has been completed concerning various energy market creation strategies employing DR resources [5,6,7,8] . In [9] effects of optimal DR resource reserve scheduling, on the system pollution cost were analyzed. Shen et al. [10] provided an overview of how electricity market policy and regulation reforms have allowed DR to become a viable demand-side resource to address the energy and environmental challenges.Incentive-based control is implement...

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“…In addition, different pricing based mechanisms have been proposed to reward the customers who forgoes energy consumption during peak hours. This allows the DR aggregator to purchase DR capabilities from individual customers for load balancing and power quality improvement of the distribution network [3]. Existing research papers in this domain have considered a range of optimization problems including mixed integer linear programming [4], non-linear programming [5], convex [6] and non-convex [7] optimization problems where the objective is to minimize energy consumption or maximize the utility of the customers.…”