A Projection and Decomposition Approach for Multi-agent Coordinated Scheduling in Power Systems

doi:10.35833/MPCE.2023.000422

Home > Archive>Volume 12, Issue 3, 2024 >991-996. DOI:10.35833/MPCE.2023.000422

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A Projection and Decomposition Approach for Multi-agent Coordinated Scheduling in Power Systems

DOI:

10.35833/MPCE.2023.000422

Author:

Haifeng Qiu ^¹
Haifeng Qiu
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore
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Zhigang Li ^²
Zhigang Li
School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
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Hongjun Gao ^³
Hongjun Gao
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
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Hung Dinh Nguyen ^¹
Hung Dinh Nguyen
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore
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Veerapandiyan Veerasamy ^¹
Veerapandiyan Veerasamy
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore
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Hoay Beng Gooi ^¹
Hoay Beng Gooi
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore
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Affiliation:

1.School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore;2.School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China;3.College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Fund Project:

This work was supported in part by the National Research Foundation (NRF) of Singapore, Intra-CREATE (No. NRF2022-ITS010-0005), Ministry of Education Singapore under its Award AcRF TIER 1 RG60/22, and the NRF of Singapore, Energy Market Authority under its Energy Programme (EP Award EMA-EP004-EKJGC-0003).

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Abstract:

Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty, a generic projection and decomposition (P&D) approach is proposed in this letter. The canonical min-max-min two-stage robust optimization (TSRO) model with coupling constraints is equivalent to a concise robust optimization (RO) model in the version of mixed-integer linear programming (MILP) via feasible region projection. The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm, which ensures the fast convergence to a high-quality solution in the distributed optimization. Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

Key words:Coordinated scheduling ; multi-agent system ; distributed robust optimization ; projection and decomposition

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Received:June 17,2023
Revised:September 01,2023
Adopted:
Online: May 20,2024
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