Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids

Libo Zhang; Yuly Wu; Weidong Li; Song Yang; Yang Chen; Kaiqi Zhao; Jiamou Liu

doi:10.1007/978-981-96-0026-7_19

Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids

Libo Zhang^*
, Yuly Wu
, Weidong Li
, Song Yang
, Yang Chen
, Kaiqi Zhao
, Jiamou Liu^*

^*Corresponding author for this work

The University of Auckland

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

1 Scopus citations

Abstract

A resilient smart grid is an intelligent power network with high adaptability and recovery capability toward disruptions, climate disasters, or human attacks. Under the guidance of energy justice, a concept of justice in the domain of energy supply, the resource allocation for resilience should not only consider efficiency. In this paper, we consider energy justice issues for a resilient smart grid resource allocation problem. We propose an approach that allocates remedial resources under the guidance of energy justice without losing efficiency. We model the resilient smart grid as a Markov game and adopt the notion of Density-based Correlated Equilibrium as our policy. Experiments conducted across multiple settings validate our algorithm’s efficacy in generating policies under energy justice concerns.

Original language	English
Title of host publication	Knowledge Management and Acquisition for Intelligent Systems - 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Proceedings
Editors	Shiqing Wu, Xing Su, Xiaolong Xu, Byeong Ho Kang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	236-245
Number of pages	10
ISBN (Print)	9789819600250
DOIs	https://doi.org/10.1007/978-981-96-0026-7_19
State	Published - 2025
Externally published	Yes
Event	20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024 - Kyoto, Japan Duration: 18 Nov 2024 → 19 Nov 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15372 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024
Country/Territory	Japan
City	Kyoto
Period	18/11/24 → 19/11/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1007/978-981-96-0026-7_19

Cite this

Zhang, L., Wu, Y., Li, W., Yang, S., Chen, Y., Zhao, K., & Liu, J. (2025). Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids. In S. Wu, X. Su, X. Xu, & B. H. Kang (Eds.), Knowledge Management and Acquisition for Intelligent Systems - 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Proceedings (pp. 236-245). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Vol. 15372 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-0026-7_19

Zhang, Libo ; Wu, Yuly ; Li, Weidong et al. / Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids. Knowledge Management and Acquisition for Intelligent Systems - 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Proceedings. editor / Shiqing Wu ; Xing Su ; Xiaolong Xu ; Byeong Ho Kang. Springer Science and Business Media Deutschland GmbH, 2025. pp. 236-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ).

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abstract = "A resilient smart grid is an intelligent power network with high adaptability and recovery capability toward disruptions, climate disasters, or human attacks. Under the guidance of energy justice, a concept of justice in the domain of energy supply, the resource allocation for resilience should not only consider efficiency. In this paper, we consider energy justice issues for a resilient smart grid resource allocation problem. We propose an approach that allocates remedial resources under the guidance of energy justice without losing efficiency. We model the resilient smart grid as a Markov game and adopt the notion of Density-based Correlated Equilibrium as our policy. Experiments conducted across multiple settings validate our algorithm{\textquoteright}s efficacy in generating policies under energy justice concerns.",

author = "Libo Zhang and Yuly Wu and Weidong Li and Song Yang and Yang Chen and Kaiqi Zhao and Jiamou Liu",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024 ; Conference date: 18-11-2024 Through 19-11-2024",

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Zhang, L, Wu, Y, Li, W, Yang, S, Chen, Y, Zhao, K & Liu, J 2025, Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids. in S Wu, X Su, X Xu & BH Kang (eds), Knowledge Management and Acquisition for Intelligent Systems - 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15372 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 236-245, 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, Kyoto, Japan, 18/11/24. https://doi.org/10.1007/978-981-96-0026-7_19

Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids. / Zhang, Libo; Wu, Yuly; Li, Weidong et al.
Knowledge Management and Acquisition for Intelligent Systems - 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Proceedings. ed. / Shiqing Wu; Xing Su; Xiaolong Xu; Byeong Ho Kang. Springer Science and Business Media Deutschland GmbH, 2025. p. 236-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Vol. 15372 LNAI).

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

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AU - Liu, Jiamou

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AB - A resilient smart grid is an intelligent power network with high adaptability and recovery capability toward disruptions, climate disasters, or human attacks. Under the guidance of energy justice, a concept of justice in the domain of energy supply, the resource allocation for resilience should not only consider efficiency. In this paper, we consider energy justice issues for a resilient smart grid resource allocation problem. We propose an approach that allocates remedial resources under the guidance of energy justice without losing efficiency. We model the resilient smart grid as a Markov game and adopt the notion of Density-based Correlated Equilibrium as our policy. Experiments conducted across multiple settings validate our algorithm’s efficacy in generating policies under energy justice concerns.

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ER -

Zhang L, Wu Y, Li W, Yang S, Chen Y, Zhao K et al. Optimizing Resource Distribution Towards Energy Justice in Resilient Smart Grids. In Wu S, Su X, Xu X, Kang BH, editors, Knowledge Management and Acquisition for Intelligent Systems - 20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 236-245. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ). doi: 10.1007/978-981-96-0026-7_19