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

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Homa Rashidizadeh-Kermania (Author) Other; Mostafa Vahedipour-Dahraie (Author) Other; Miadreza Shafie-khah (Author) Other; João P. S. Catalão (Author) FEUP

Journal

Title: International Journal of Electrical Power & Energy Systems

Vol. 113

Pages: 528-538

ISSN: 0142-0615

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 43 Citations

Scopus - 48 Citations

INSPEC

Other information

Authenticus ID: P-00Q-NK6

DOI: 10.1016/j.ijepes.2019.05.072

Abstract (EN): 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 scenario-based 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11