A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties
Developing effective bidding strategies in electricity spot markets is crucial for wind-storage systems (WSS) to improve profits and mitigate risk. During real operations, the bidding strategies of WSS are significantly affected by external uncertainties and internal uncertainties. However, some exi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525001425 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849768969917956096 |
|---|---|
| author | Haowen Xu Minglei Bao Xun Yao Xiaocong Sun Yi Ding Zhenglin Yang |
| author_facet | Haowen Xu Minglei Bao Xun Yao Xiaocong Sun Yi Ding Zhenglin Yang |
| author_sort | Haowen Xu |
| collection | DOAJ |
| description | Developing effective bidding strategies in electricity spot markets is crucial for wind-storage systems (WSS) to improve profits and mitigate risk. During real operations, the bidding strategies of WSS are significantly affected by external uncertainties and internal uncertainties. However, some existing studies only consider a single influencing factor or fail to account for the interaction between the market and participants. To address this problem, a stochastic-robust optimal bidding model (OBM) of WSS considering the combined impacts of external and internal uncertainties is proposed. The proposed model is structured as a bi-level optimization problem to reflect the interaction between WSS bidding and day-ahead market clearing. At the upper level, the bidding strategy of WSS is developed based on the market clearing results by using the stochastic-robust method to consider uncertainties. In the lower level, the clearing process of the day-ahead market is modeled to determine the clearing market price and quantity. To improve computational feasibility and robustness, the modified Column-and-Constraint Generation (C&CG) algorithm is applied to solve the multi-scenario problem efficiently. To validate the effectiveness and practicality of the proposed model, the paper conducts tests on the IEEE 118-bus system and a real-world large-scale system. Taking the IEEE 118-bus system as an example, compared to traditional methods, the proposed approach enables the WSS to achieve a 12% increase in average revenue and a 12.3% improvement in CVaR, indicating higher revenue with lower risk. The computation times of these two test systems are 20 min and 48 min, which can meet the requirements of practical day-ahead market operations. |
| format | Article |
| id | doaj-art-8ff433bd809343fc95f855da90c060ca |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-8ff433bd809343fc95f855da90c060ca2025-08-20T03:03:37ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-06-0116711059110.1016/j.ijepes.2025.110591A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External UncertaintiesHaowen Xu0Minglei Bao1Xun Yao2Xiaocong Sun3Yi Ding4Zhenglin Yang5College of Electrical Engineering, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang, ChinaCollege of Electrical Engineering, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang, China; Corresponding author.State Grid Gansu Electric Power Company, No. 8 Beibinhe East Road, Lanzhou, 730000, Gansu, ChinaCollege of Electrical Engineering, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang, ChinaCollege of Electrical Engineering, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang, ChinaChina Electric Power Research Institute, No. 8 Nanrui Road, Nanjing, 210037, Jiangsu, ChinaDeveloping effective bidding strategies in electricity spot markets is crucial for wind-storage systems (WSS) to improve profits and mitigate risk. During real operations, the bidding strategies of WSS are significantly affected by external uncertainties and internal uncertainties. However, some existing studies only consider a single influencing factor or fail to account for the interaction between the market and participants. To address this problem, a stochastic-robust optimal bidding model (OBM) of WSS considering the combined impacts of external and internal uncertainties is proposed. The proposed model is structured as a bi-level optimization problem to reflect the interaction between WSS bidding and day-ahead market clearing. At the upper level, the bidding strategy of WSS is developed based on the market clearing results by using the stochastic-robust method to consider uncertainties. In the lower level, the clearing process of the day-ahead market is modeled to determine the clearing market price and quantity. To improve computational feasibility and robustness, the modified Column-and-Constraint Generation (C&CG) algorithm is applied to solve the multi-scenario problem efficiently. To validate the effectiveness and practicality of the proposed model, the paper conducts tests on the IEEE 118-bus system and a real-world large-scale system. Taking the IEEE 118-bus system as an example, compared to traditional methods, the proposed approach enables the WSS to achieve a 12% increase in average revenue and a 12.3% improvement in CVaR, indicating higher revenue with lower risk. The computation times of these two test systems are 20 min and 48 min, which can meet the requirements of practical day-ahead market operations.http://www.sciencedirect.com/science/article/pii/S0142061525001425Wind-storage systemStochastic-robust optimizationOptimal bidding modelInternal and external uncertaintiesElectricity market clearing |
| spellingShingle | Haowen Xu Minglei Bao Xun Yao Xiaocong Sun Yi Ding Zhenglin Yang A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties International Journal of Electrical Power & Energy Systems Wind-storage system Stochastic-robust optimization Optimal bidding model Internal and external uncertainties Electricity market clearing |
| title | A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties |
| title_full | A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties |
| title_fullStr | A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties |
| title_full_unstemmed | A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties |
| title_short | A Bi-level Stochastic-Robust Optimal Bidding Model of Wind-Storage System in Spot Markets Considering Internal and External Uncertainties |
| title_sort | bi level stochastic robust optimal bidding model of wind storage system in spot markets considering internal and external uncertainties |
| topic | Wind-storage system Stochastic-robust optimization Optimal bidding model Internal and external uncertainties Electricity market clearing |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525001425 |
| work_keys_str_mv | AT haowenxu abilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT mingleibao abilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT xunyao abilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT xiaocongsun abilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT yiding abilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT zhenglinyang abilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT haowenxu bilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT mingleibao bilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT xunyao bilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT xiaocongsun bilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT yiding bilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties AT zhenglinyang bilevelstochasticrobustoptimalbiddingmodelofwindstoragesysteminspotmarketsconsideringinternalandexternaluncertainties |