A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers. (1st August 2015)

• Record Type: Journal Article
• Authors: Fotouhi Ghazvini, Mohammad Ali; Soares, João; Horta, Nuno; Neves, Rui; Castro, Rui; Vale, Zita
• Is Part Of: Applied energy. Volume 151(2015:Aug. 01)
• Page Start: 102
• Page End: 118
• Publication Date: 2015-08-01
• Subjects: Electricity retail market -- Evolutionary multi-objective optimization -- Retailer -- NSGA-II; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.04.067
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Demand response as effective tool to shield retailers against the financial risks. Fast and elitist evolutionary algorithm to solve the multi-objective problem. Retailers aim to minimize peak demand at serving buses due to capacity charges. Customers change their consumption in response to financial incentives. Abstract: In this paper, we formulate the electricity retailers' short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers' multi-objective problem.