Data-driven robust optimization for energy and reserve scheduling considering multi-uncertainty

Zuqing Zheng; Guo Chen; Zixiang Shen; Zhaoyang Dong

doi:10.1016/j.ijepes.2025.110751

Data-driven robust optimization for energy and reserve scheduling considering multi-uncertainty

Zuqing Zheng
, Guo Chen^*
, Zixiang Shen
, Zhaoyang Dong

^*Corresponding author for this work

Department of Electrical Engineering

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

6 Citations (Scopus)

140 Downloads (CityUHK Scholars)

Abstract

The intermittency of renewable energy sources and unscheduled outages threaten power system security, increasing the need for operational flexibility. This paper addresses the energy and reserve scheduling problem under photovoltaic power output uncertainty and N-k transmission line security criteria by proposing a data-driven two-stage robust optimization model. The first stage involves day-ahead scheduling for energy and reserve, while the second stage minimizes re-scheduling cost under the worst-case scenario. The model is decomposed into a master problem and a subproblem, using the column-and-constraint generation method for alternating solutions. A data-driven method constructs an interval-partitioned uncertainty set, effectively capturing the characteristics of photovoltaic power output uncertainty while balancing robustness and economy. The model also integrates generator, energy storage and both price-based and incentive-based demand response strategies to enhance reserve capacity, improving risk mitigation. Simulation results confirm the model’s feasibility and effectiveness for reliable and economical operation.

© 2025 The Authors.

Original language	English
Article number	110751
Journal	International Journal of Electrical Power & Energy Systems
Volume	169
Online published	3 Jun 2025
DOIs	https://doi.org/10.1016/j.ijepes.2025.110751
Publication status	Published - Aug 2025

Funding

This work was supported by the Australian Research Council, Australia under Grants FT190100156 and DP230100801.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Research Keywords

Data-driven method
Energy and reserve scheduling
Two-stage robust optimization
Photovoltaic power uncertainty
N-k security criteria

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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10.1016/j.ijepes.2025.110751

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title = "Data-driven robust optimization for energy and reserve scheduling considering multi-uncertainty",

abstract = "The intermittency of renewable energy sources and unscheduled outages threaten power system security, increasing the need for operational flexibility. This paper addresses the energy and reserve scheduling problem under photovoltaic power output uncertainty and N-k transmission line security criteria by proposing a data-driven two-stage robust optimization model. The first stage involves day-ahead scheduling for energy and reserve, while the second stage minimizes re-scheduling cost under the worst-case scenario. The model is decomposed into a master problem and a subproblem, using the column-and-constraint generation method for alternating solutions. A data-driven method constructs an interval-partitioned uncertainty set, effectively capturing the characteristics of photovoltaic power output uncertainty while balancing robustness and economy. The model also integrates generator, energy storage and both price-based and incentive-based demand response strategies to enhance reserve capacity, improving risk mitigation. Simulation results confirm the model{\textquoteright}s feasibility and effectiveness for reliable and economical operation. {\textcopyright} 2025 The Authors. ",

keywords = "Data-driven method, Energy and reserve scheduling, Two-stage robust optimization, Photovoltaic power uncertainty, N-k security criteria",

author = "Zuqing Zheng and Guo Chen and Zixiang Shen and Zhaoyang Dong",

year = "2025",

month = aug,

doi = "10.1016/j.ijepes.2025.110751",

language = "English",

volume = "169",

journal = "International Journal of Electrical Power \& Energy Systems",

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T1 - Data-driven robust optimization for energy and reserve scheduling considering multi-uncertainty

AU - Zheng, Zuqing

AU - Chen, Guo

AU - Shen, Zixiang

AU - Dong, Zhaoyang

PY - 2025/8

Y1 - 2025/8

N2 - The intermittency of renewable energy sources and unscheduled outages threaten power system security, increasing the need for operational flexibility. This paper addresses the energy and reserve scheduling problem under photovoltaic power output uncertainty and N-k transmission line security criteria by proposing a data-driven two-stage robust optimization model. The first stage involves day-ahead scheduling for energy and reserve, while the second stage minimizes re-scheduling cost under the worst-case scenario. The model is decomposed into a master problem and a subproblem, using the column-and-constraint generation method for alternating solutions. A data-driven method constructs an interval-partitioned uncertainty set, effectively capturing the characteristics of photovoltaic power output uncertainty while balancing robustness and economy. The model also integrates generator, energy storage and both price-based and incentive-based demand response strategies to enhance reserve capacity, improving risk mitigation. Simulation results confirm the model’s feasibility and effectiveness for reliable and economical operation. © 2025 The Authors.

AB - The intermittency of renewable energy sources and unscheduled outages threaten power system security, increasing the need for operational flexibility. This paper addresses the energy and reserve scheduling problem under photovoltaic power output uncertainty and N-k transmission line security criteria by proposing a data-driven two-stage robust optimization model. The first stage involves day-ahead scheduling for energy and reserve, while the second stage minimizes re-scheduling cost under the worst-case scenario. The model is decomposed into a master problem and a subproblem, using the column-and-constraint generation method for alternating solutions. A data-driven method constructs an interval-partitioned uncertainty set, effectively capturing the characteristics of photovoltaic power output uncertainty while balancing robustness and economy. The model also integrates generator, energy storage and both price-based and incentive-based demand response strategies to enhance reserve capacity, improving risk mitigation. Simulation results confirm the model’s feasibility and effectiveness for reliable and economical operation. © 2025 The Authors.

KW - Data-driven method

KW - Energy and reserve scheduling

KW - Two-stage robust optimization

KW - Photovoltaic power uncertainty

KW - N-k security criteria

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001504674900001

U2 - 10.1016/j.ijepes.2025.110751

DO - 10.1016/j.ijepes.2025.110751

M3 - RGC 21 - Publication in refereed journal

SN - 0142-0615

VL - 169

JO - International Journal of Electrical Power & Energy Systems

JF - International Journal of Electrical Power & Energy Systems

M1 - 110751

ER -

Data-driven robust optimization for energy and reserve scheduling considering multi-uncertainty

Abstract

Funding

UN SDGs

Research Keywords

Publisher's Copyright Statement

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