Genetic Algorithm for Demand Response: A Stackelberg Game Approach

Kadir Amasyali; Mohammed M. Olama; Yang Chen

doi:10.22360/SpringSim.2020.MSSES.002

Genetic Algorithm for Demand Response: A Stackelberg Game Approach

Kadir Amasyali
, Mohammed M. Olama
, Yang Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.

Original language	English
Title of host publication	Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020
Editors	Fernando J. Barros, Xiaolin Hu, Hamdi Kavak, Alberto A. Del Barrio
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781565553705
DOIs	https://doi.org/10.22360/SpringSim.2020.MSSES.002
State	Published - May 2020
Event	2020 Spring Simulation Conference, SpringSim 2020 - Virtual, Fairfax, United States Duration: May 18 2020 → May 21 2020

Publication series

Name	Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020

Conference

Conference	2020 Spring Simulation Conference, SpringSim 2020
Country/Territory	United States
City	Virtual, Fairfax
Period	05/18/20 → 05/21/20

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Stackelberg game
demand response
genetic algorithms
smart grid

Access to Document

10.22360/SpringSim.2020.MSSES.002

Cite this

Amasyali, K., Olama, M. M., & Chen, Y. (2020). Genetic Algorithm for Demand Response: A Stackelberg Game Approach. In F. J. Barros, X. Hu, H. Kavak, & A. A. Del Barrio (Eds.), Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020 Article 9185466 (Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.22360/SpringSim.2020.MSSES.002

Amasyali, Kadir ; Olama, Mohammed M. ; Chen, Yang. / Genetic Algorithm for Demand Response : A Stackelberg Game Approach. Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020. editor / Fernando J. Barros ; Xiaolin Hu ; Hamdi Kavak ; Alberto A. Del Barrio. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020).

@inproceedings{9c96cb672d254a01bced04f1f64be93d,

title = "Genetic Algorithm for Demand Response: A Stackelberg Game Approach",

abstract = "Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.",

keywords = "Stackelberg game, demand response, genetic algorithms, smart grid",

author = "Kadir Amasyali and Olama, \{Mohammed M.\} and Yang Chen",

note = "Publisher Copyright: {\textcopyright} 2020 SCS.; 2020 Spring Simulation Conference, SpringSim 2020 ; Conference date: 18-05-2020 Through 21-05-2020",

year = "2020",

month = may,

doi = "10.22360/SpringSim.2020.MSSES.002",

language = "English",

series = "Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Barros, \{Fernando J.\} and Xiaolin Hu and Hamdi Kavak and \{Del Barrio\}, \{Alberto A.\}",

booktitle = "Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020",

}

Amasyali, K, Olama, MM & Chen, Y 2020, Genetic Algorithm for Demand Response: A Stackelberg Game Approach. in FJ Barros, X Hu, H Kavak & AA Del Barrio (eds), Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020., 9185466, Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020, Institute of Electrical and Electronics Engineers Inc., 2020 Spring Simulation Conference, SpringSim 2020, Virtual, Fairfax, United States, 05/18/20. https://doi.org/10.22360/SpringSim.2020.MSSES.002

Genetic Algorithm for Demand Response: A Stackelberg Game Approach. / Amasyali, Kadir; Olama, Mohammed M.; Chen, Yang.
Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020. ed. / Fernando J. Barros; Xiaolin Hu; Hamdi Kavak; Alberto A. Del Barrio. Institute of Electrical and Electronics Engineers Inc., 2020. 9185466 (Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Genetic Algorithm for Demand Response

T2 - 2020 Spring Simulation Conference, SpringSim 2020

AU - Amasyali, Kadir

AU - Olama, Mohammed M.

AU - Chen, Yang

PY - 2020/5

Y1 - 2020/5

N2 - Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.

AB - Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.

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KW - demand response

KW - genetic algorithms

KW - smart grid

UR - https://www.scopus.com/pages/publications/85092040313

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DO - 10.22360/SpringSim.2020.MSSES.002

M3 - Conference contribution

AN - SCOPUS:85092040313

T3 - Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020

BT - Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020

A2 - Barros, Fernando J.

A2 - Hu, Xiaolin

A2 - Kavak, Hamdi

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 May 2020 through 21 May 2020

ER -

Amasyali K, Olama MM , Chen Y. Genetic Algorithm for Demand Response: A Stackelberg Game Approach. In Barros FJ, Hu X, Kavak H, Del Barrio AA, editors, Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9185466. (Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020). doi: 10.22360/SpringSim.2020.MSSES.002

Genetic Algorithm for Demand Response: A Stackelberg Game Approach

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