New approaches to indirect demand response management in smart grids

Demand response has emerged as a solution to shape the load curve and balance the grid. Particularly, indirect demand response management refers to programs based on the load modification of consumer behavior through price signals or incentive payments. In this dissertation, models are proposed to analyze and integrate DR systems in smart grids. The work is addressed in four parts. First, a rational behavior of a consumer under uncertainty is quantified in Peak Time Rebate programs. Second, a novel demand response contract between a user and an aggregator is proposed to face gaming concerns. This contract is based on the probability of call, which is the chance of a consumer to be selected by the aggregator to serve as demand response resource at a given period. Next, another contract for electric vehicles is presented as a solution in the indirect demand response management. A price-based model is proposed to schedule the charging process. Finally, at the market level, a competition between generators with the presence of demand response is proposed. A smooth inverse demand function is designed for modeling the consumer preferences under incentive-based demand response program.