Stochastic programming model for scheduling demand response aggregators considering uncertain market prices and demands

AuthorsMostafa Vahedipour-Dahraie,Homa Rashidizadeh-Kermani,Miadreza Shafie-khah,João P.S. Catalãoc
JournalInternational Journal of Electrical Power and Energy Systems
Page number528-538
Serial number113
Volume number4
IF3.289
Paper TypeFull Paper
Published At2019
Journal GradeISI
Journal TypeTypographic
Journal CountryIran, Islamic Republic Of
Journal IndexJCR،Scopus

Abstract

This paper proposes a stochastic decision making model for a demand response (DR) aggregator as an interface between the market and customers in a competitive environment. The DR aggregator participates in day-ahead (DA) energy and balancing markets as well as offers selling price to the customers to maximize its expected profit, considering the reaction of customers to the rivals’ offering prices. Moreover, the effect of load reduction due to implementing DR contracts on the decision making process of the DR aggregator is assessed. However, the main focus is on the operation of both shiftable and sheddable loads in price-based DR programs with detail. In order to investigate the behavior of different DR actions from the DR aggregator viewpoint, the restrictions imposed by the preferences of customers to the decisions made by the DR aggregators are modeled via a bi-level stochastic programming approach. The upper level represents the decisions made by the DR aggregator, while the lower level models the customers’ behavior. To deal with various uncertainties, a risk-constrained scenariobased stochastic programming framework is presented where the DR aggregator’s risk aversion is modeled using conditional value at risk (CVaR) method. Finally, a detailed illustrative case study based on the Nordic energy market data is provided and the effects of different DR actions and risk aversion factor on the profit of the aggregator are analyzed.

Paper URL

tags: Bi-level stochastic programming Conditional value-at-risk (CVaR) Demand response (DR) Decision making model