Receding horizon load restoration for coupled transmission and distribution system considering load-source uncertainty

Jin Zhao, Yao Liu, Hongtao Wang*, Qiuwei Wu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This paper proposes a conditional value-at-risk (CVaR) based two-stage model predictive control (MPC) method for efficient dynamic load restoration decision-making in the coupled transmission and distribution (TS-DS) system with renewable energy. The CVaR values are employed to describe uncertainties of the load and source sides. It benefits on-line load restoration with uncertainties by fast uncertainty management and prediction error correction. In order to improve the computation of the multi-step load restoration optimization in the coupled TS-DS system, a two-stage load restoration model is constructed with the first stage relaxed multi-step optimization and the second stage single-step tracing optimization. By solving linear programming (LP), mixed integer linear programming (MILP) and mixed integer quadratic programming (MIQP) problems, the proposed CVaR based two-stage MPC method achieves on-line receding horizon load restoration of the coupled TS-DS system facing with load-source uncertainty. The effectiveness of the proposed method is validated using the IEEE-118 and IEEE-33 test systems, and a real-world coupled TS-DS system.

Original languageEnglish
Article number105517
JournalInternational Journal of Electrical Power and Energy Systems
Volume116
Number of pages14
ISSN0142-0615
DOIs
Publication statusPublished - 1 Mar 2020

Keywords

  • Model predictive control
  • Power system restoration
  • Transmission and distribution system
  • Uncertainty management

Cite this

@article{41800464159a45e2a4c768a192ebf7d2,
title = "Receding horizon load restoration for coupled transmission and distribution system considering load-source uncertainty",
abstract = "This paper proposes a conditional value-at-risk (CVaR) based two-stage model predictive control (MPC) method for efficient dynamic load restoration decision-making in the coupled transmission and distribution (TS-DS) system with renewable energy. The CVaR values are employed to describe uncertainties of the load and source sides. It benefits on-line load restoration with uncertainties by fast uncertainty management and prediction error correction. In order to improve the computation of the multi-step load restoration optimization in the coupled TS-DS system, a two-stage load restoration model is constructed with the first stage relaxed multi-step optimization and the second stage single-step tracing optimization. By solving linear programming (LP), mixed integer linear programming (MILP) and mixed integer quadratic programming (MIQP) problems, the proposed CVaR based two-stage MPC method achieves on-line receding horizon load restoration of the coupled TS-DS system facing with load-source uncertainty. The effectiveness of the proposed method is validated using the IEEE-118 and IEEE-33 test systems, and a real-world coupled TS-DS system.",
keywords = "Model predictive control, Power system restoration, Transmission and distribution system, Uncertainty management",
author = "Jin Zhao and Yao Liu and Hongtao Wang and Qiuwei Wu",
year = "2020",
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doi = "10.1016/j.ijepes.2019.105517",
language = "English",
volume = "116",
journal = "International Journal of Electrical Power & Energy Systems",
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Receding horizon load restoration for coupled transmission and distribution system considering load-source uncertainty. / Zhao, Jin; Liu, Yao; Wang, Hongtao; Wu, Qiuwei.

In: International Journal of Electrical Power and Energy Systems, Vol. 116, 105517, 01.03.2020.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Receding horizon load restoration for coupled transmission and distribution system considering load-source uncertainty

AU - Zhao, Jin

AU - Liu, Yao

AU - Wang, Hongtao

AU - Wu, Qiuwei

PY - 2020/3/1

Y1 - 2020/3/1

N2 - This paper proposes a conditional value-at-risk (CVaR) based two-stage model predictive control (MPC) method for efficient dynamic load restoration decision-making in the coupled transmission and distribution (TS-DS) system with renewable energy. The CVaR values are employed to describe uncertainties of the load and source sides. It benefits on-line load restoration with uncertainties by fast uncertainty management and prediction error correction. In order to improve the computation of the multi-step load restoration optimization in the coupled TS-DS system, a two-stage load restoration model is constructed with the first stage relaxed multi-step optimization and the second stage single-step tracing optimization. By solving linear programming (LP), mixed integer linear programming (MILP) and mixed integer quadratic programming (MIQP) problems, the proposed CVaR based two-stage MPC method achieves on-line receding horizon load restoration of the coupled TS-DS system facing with load-source uncertainty. The effectiveness of the proposed method is validated using the IEEE-118 and IEEE-33 test systems, and a real-world coupled TS-DS system.

AB - This paper proposes a conditional value-at-risk (CVaR) based two-stage model predictive control (MPC) method for efficient dynamic load restoration decision-making in the coupled transmission and distribution (TS-DS) system with renewable energy. The CVaR values are employed to describe uncertainties of the load and source sides. It benefits on-line load restoration with uncertainties by fast uncertainty management and prediction error correction. In order to improve the computation of the multi-step load restoration optimization in the coupled TS-DS system, a two-stage load restoration model is constructed with the first stage relaxed multi-step optimization and the second stage single-step tracing optimization. By solving linear programming (LP), mixed integer linear programming (MILP) and mixed integer quadratic programming (MIQP) problems, the proposed CVaR based two-stage MPC method achieves on-line receding horizon load restoration of the coupled TS-DS system facing with load-source uncertainty. The effectiveness of the proposed method is validated using the IEEE-118 and IEEE-33 test systems, and a real-world coupled TS-DS system.

KW - Model predictive control

KW - Power system restoration

KW - Transmission and distribution system

KW - Uncertainty management

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DO - 10.1016/j.ijepes.2019.105517

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