A distributed game-theoretic demand response with multi-class appliance control in smart grid

Latifi, M, Khalili, A, Rastegarnia, A and Sanei, S ORCID logoORCID: https://orcid.org/0000-0002-3437-2801, 2019. A distributed game-theoretic demand response with multi-class appliance control in smart grid. Electric Power Systems Research, 176: 105946. ISSN 0378-7796

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Abstract

We propose an event-triggered game-theoretic strategy for managing the power grids demand side, capable of responding to changes in consumer preferences or the price parameters coming from the wholesale market. The relationship between the retailer and the residential consumers is modeled as one-leader, N-follower Stackelberg game. We provide a detailed characterization of the household appliances to reflect the reality and improve the efficiency of the demand response (DR). Moreover, to consider all the appliances’ essentials, the consumer's objective function is formulated as a mixed integer non-linear program (MINLP), which, unlike conventional procedures, is solved via an integrated method. The proposed method consists of a day-ahead stage, in which the DR problem is solved for the next scheduling horizon, and a real-time stage which runs repeatedly to tackle the change in the parameters and adapt to the new condition. For any change in the grid, the consumers use the estimated optimal parameters (given by the original objective function) and develop another Stackelberg game based solution to maximize the satisfaction level. Given the appliances of multi-class nature, the proposed method is shown to be very tractable for ancillary services and reducing the mismatch between the renewable power generation and the load demand.

Item Type: Journal article
Publication Title: Electric Power Systems Research
Creators: Latifi, M., Khalili, A., Rastegarnia, A. and Sanei, S.
Publisher: Elsevier
Date: November 2019
Volume: 176
ISSN: 0378-7796
Identifiers:
Number
Type
10.1016/j.epsr.2019.105946
DOI
S0378779619302652
Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 25 Jul 2019 08:15
Last Modified: 05 Aug 2021 03:00
URI: https://irep.ntu.ac.uk/id/eprint/37141

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