Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage*

doi:10.23919/CJEE.2023.000033

中国电气工程学报（英文） ›› 2023, Vol. 9 ›› Issue (3): 39-49.doi: 10.23919/CJEE.2023.000033

收稿日期:2023-08-01 修回日期:2023-08-17 接受日期:2023-08-23 出版日期:2023-09-25 发布日期:2023-09-01

Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage^*

Bo Li¹, Chunjie Qin¹, Ruotao Yu¹, Wei Dai^1,*, Mengjun Shen², Ziming Ma³, Jianxiao Wang⁴

1. School of Electrical Engineering, Guangxi University, Nanning 530004, China;
2. State Grid Sichuan Electric Power Company, Chengdu 610041, China;
3. National Power Dispatching and Control Center, State Grid Corporation of China, Beijing 100031, China;
4. National Engineering Laboratory for Big Data Analysis and Applications (Peking University), Beijing 100871, China

Received:2023-08-01 Revised:2023-08-17 Accepted:2023-08-23 Online:2023-09-25 Published:2023-09-01
Contact: *E-mail: weidai@gxu.edu.cn
About author:Bo Li (Member, IEEE) received his B.E. and Ph.D. degrees in Electrical Engineering from Guangxi University, Nanning, and Chongqing University, Chongqing, China, in 2016 and 2022, respectively. He is currently an Assistant Professor with the School of Electrical Engineering, Guangxi University, Nanning, China. He was a Visiting Scholar in University of California, Berkeley, from 2019 to 2020. His research interests include power system planning and operation, and V2G.
Chunjie Qin received the B.E. degree in Electrical Engineering from North China University of Science and Technology, Hebei, China, in 2022. He is currently working toward the M.S. degree in Electrical Engineering in Guangxi University, Nanning, China. His research interests include frequency constrained unit commitment.
Ruotao Yu is currently working toward the B.S. degree in Electrical Engineering in Guangxi University, Nanning, China. His research interests include charging station planning.
Wei Dai (Member, IEEE) received the Ph.D. degree in Electrical Engineering from Chongqing University, in 2018. He is currently working as an Assistant Professor with Guangxi University. His research interests include multiple energy systems, power systems analysis, renewable energy, and large-scale system problems.
Mengjun Shen (Member, IEEE) received the B.S. degree from Wuhan University, Wuhan, China in 2017. He received the M.S. degree from University of Pittsburgh, US, in 2018. He is currently working in the State Grid Sichuan Electric Power Company, Chengdu, China. His research interests include power system operations, dispatch scheduling.
Ziming Ma (Member, IEEE) received the B.S. and Ph.D. degrees in Electrical Engineering from Tsinghua University, Beijing, China, in 2015 and 2020, respectively. He is currently working in the National Power Dispatching and Control Center, Beijing, China. His research interests include power system operations and electricity markets.
Jianxiao Wang (Member, IEEE) received the B.S. and Ph.D. degrees in Electrical Engineering from Tsinghua University, Beijing, China, in 2014 and 2019, respectively. He was a Visiting Student Researcher with Stanford University, Stanford, CA, USA. He is currently an Assistant Professor with the National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China. His current research interests include smart grid operation and planning, hydrogen and storage technology, transportation and energy systems integration, electricity market, and data analytics.
Supported by:
*Specific Research Project of Guangxi for Research Bases and Talents (2022AC21257).

摘要/Abstract

Abstract: Long-term storage (LTS) can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy curtailment. However, long-term unit commitment (UC) with LTS involves mixed-integer programming with large-scale coupling constraints between consecutive intervals (state-of-charge (SOC) constraint of LTS, ramping rate, and minimum up/down time constraints of thermal units), resulting in a significant computational burden. Herein, an iterative-based fast solution method is proposed to solve the long-term UC with LTS. First, the UC with coupling constraints is split into several sub problems that can be solved in parallel. Second, the solutions of the sub problems are adjusted to obtain a feasible solution that satisfies the coupling constraints. Third, a decoupling method for long-term time-series coupling constraints is proposed to determine the global optimization of the SOC of the LTS. The price-arbitrage model of the LTS determines the SOC boundary of the LTS for each sub problem. Finally, the sub problem with the SOC boundary of the LTS is iteratively solved independently. The proposed method was verified using a modified IEEE 24-bus system. The results showed that the computation time of the unit combination problem can be reduced by 97.8%, with a relative error of 3.62%.

Key words: Constraint splitting, long-term storage, mix-integer programming, unit commitment

. [J]. 中国电气工程学报（英文）, 2023, 9(3): 39-49.

Bo Li, Chunjie Qin, Ruotao Yu, Wei Dai, Mengjun Shen, Ziming Ma, Jianxiao Wang. Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage^*[J]. Chinese Journal of Electrical Engineering, 2023, 9(3): 39-49.

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Fast Solution Method for the Large-scale Unit Commitment Problem with Long-term Storage^*

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