DRL-Based Distributed Coordination of ISO and DSOs in Bi-Level Electricity Markets

Luolin Xiong; Anshul Goyal; Kankar Bhattacharya; Yang Tang; Zhaoyang Dong; Feng Qian; Venkata Balaji Thummalacherla

doi:10.1109/TII.2025.3563596

DRL-Based Distributed Coordination of ISO and DSOs in Bi-Level Electricity Markets

Luolin Xiong, Anshul Goyal, Kankar Bhattacharya, Yang Tang^*, Zhaoyang Dong, Feng Qian^*, Venkata Balaji Thummalacherla

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

2 Citations (Scopus)

Abstract

The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand–supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency. © 2005-2012 IEEE.

Original language	English
Pages (from-to)	6422-6432
Number of pages	11
Journal	IEEE Transactions on Industrial Informatics
Volume	21
Issue number	8
Online published	7 May 2025
DOIs	https://doi.org/10.1109/TII.2025.3563596
Publication status	Published - Aug 2025

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62293500 and Grant 62293502, in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, in part by the Fundamental Research Funds for the Central Universities under Grant 222202517006, in part by the State Key Laboratory of Industrial Control Technology, China under Grant ICT2024A22, and in part by the Global STEM Professorship and JC STEM Lab of Future Energy Systems.

Research Keywords

Deep reinforcement learning (DRL)
distributed energy resource
independent system operator (ISO)-distribution system operators (DSO) coordination
retail market
wholesale market

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abstract = "The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand–supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency. {\textcopyright} 2005-2012 IEEE.",

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author = "Luolin Xiong and Anshul Goyal and Kankar Bhattacharya and Yang Tang and Zhaoyang Dong and Feng Qian and Thummalacherla, {Venkata Balaji}",

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T1 - DRL-Based Distributed Coordination of ISO and DSOs in Bi-Level Electricity Markets

AU - Xiong, Luolin

AU - Goyal, Anshul

AU - Bhattacharya, Kankar

AU - Tang, Yang

AU - Dong, Zhaoyang

AU - Qian, Feng

AU - Thummalacherla, Venkata Balaji

PY - 2025/8

Y1 - 2025/8

N2 - The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand–supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency. © 2005-2012 IEEE.

AB - The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand–supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency. © 2005-2012 IEEE.

KW - Deep reinforcement learning (DRL)

KW - distributed energy resource

KW - independent system operator (ISO)-distribution system operators (DSO) coordination

KW - retail market

KW - wholesale market

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M3 - RGC 21 - Publication in refereed journal

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EP - 6432

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

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ER -

DRL-Based Distributed Coordination of ISO and DSOs in Bi-Level Electricity Markets

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