Optimal capacity planning and operation of shared energy storage system for large-scale photovoltaic integrated 5G base stations. (May 2023)
- Record Type:
- Journal Article
- Title:
- Optimal capacity planning and operation of shared energy storage system for large-scale photovoltaic integrated 5G base stations. (May 2023)
- Main Title:
- Optimal capacity planning and operation of shared energy storage system for large-scale photovoltaic integrated 5G base stations
- Authors:
- Zhang, Xiang
Wang, Zhao
Liao, Haijun
Zhou, Zhenyu
Ma, Xiufan
Yin, Xiyang
Wang, Zhongyu
Liu, Yizhao
Lu, Zhixin
Lv, Guoyuan - Abstract:
- Highlights: A dynamic capacity leasing model of shared energy storage system is proposed with consideration of the power supply and load demand characteristics of large-scale 5G base stations. A bi-level optimization framework of capacity planning and operation costs of shared energy storage system and large-scale PV integrated 5G base stations is proposed to realize the decoupling of shared energy storage system capacity planning and operation from 5G base station operation. A bi-level joint optimization problem is formulated to minimize the capacity planning and operation cost of shared energy storage system and the operation cost of large-scale 5G base stations based on the bi-level mixed-integer programming (BiMIP) model. A reformulation and decomposition (R&D)-based shared energy storage system capacity planning and operation joint optimization (SESSION) algorithm is proposed to realize the iterative solution of the bi-level joint optimization problem. Abstract: Shared energy storage (SES) system can provide energy storage capacity leasing services for large-scale PV integrated 5G base stations (BSs), reducing the energy cost of 5G BS and achieving high efficiency utilization of energy storage capacity resources. However, the capacity planning and operation optimization of SES system involves the coordinated operation and cost sharing between SES system and large-scale 5G BSs, which is highly coupled and greatly increases the complexity of joint optimization. In thisHighlights: A dynamic capacity leasing model of shared energy storage system is proposed with consideration of the power supply and load demand characteristics of large-scale 5G base stations. A bi-level optimization framework of capacity planning and operation costs of shared energy storage system and large-scale PV integrated 5G base stations is proposed to realize the decoupling of shared energy storage system capacity planning and operation from 5G base station operation. A bi-level joint optimization problem is formulated to minimize the capacity planning and operation cost of shared energy storage system and the operation cost of large-scale 5G base stations based on the bi-level mixed-integer programming (BiMIP) model. A reformulation and decomposition (R&D)-based shared energy storage system capacity planning and operation joint optimization (SESSION) algorithm is proposed to realize the iterative solution of the bi-level joint optimization problem. Abstract: Shared energy storage (SES) system can provide energy storage capacity leasing services for large-scale PV integrated 5G base stations (BSs), reducing the energy cost of 5G BS and achieving high efficiency utilization of energy storage capacity resources. However, the capacity planning and operation optimization of SES system involves the coordinated operation and cost sharing between SES system and large-scale 5G BSs, which is highly coupled and greatly increases the complexity of joint optimization. In this paper, a joint optimization method of SES system capacity planning and operation for large-scale PV integrated 5G BSs with energy storage planning requirements is proposed. First, a dynamic capacity leasing model of SES system is proposed with consideration of the power supply and load demand characteristics of large-scale 5G BSs. Second, a bi-level joint optimization problem is formulated to minimize the capacity planning and operation cost of SES system and the operation cost of large-scale 5G BSs based on the bi-level mixed-integer programming (BiMIP) model. Then, a reformulation and decomposition (R&D)-based SES system capacity planning and operation joint optimization (SESSION) algorithm for large-scale PV integrated 5G BSs is proposed, which is employed to reformulate the proposed bi-level joint optimization problem into a master problem and two subproblems. The independent solutions of the upper-level problem and the lower-level problem are achieved through the iteration between master problem and subproblems. Finally, the effectiveness and scalability of the proposed algorithm is verified through case studies, which demonstrate that the capacity planning and operation optimization of SES system can effectively achieve mutual benefits between large-scale PV integrated 5G BSs and SES system. … (more)
- Is Part Of:
- International journal of electrical power & energy systems. Volume 147(2023)
- Journal:
- International journal of electrical power & energy systems
- Issue:
- Volume 147(2023)
- Issue Display:
- Volume 147, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 147
- Issue:
- 2023
- Issue Sort Value:
- 2023-0147-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-05
- Subjects:
- 5G base stations -- Photovoltaic -- Shared energy storage -- Bi-level mixed-integer planning -- Capacity planning and operation optimization
Electrical engineering -- Periodicals
Electric power systems -- Periodicals
Électrotechnique -- Périodiques
Réseaux électriques (Énergie) -- Périodiques
Electric power systems
Electrical engineering
Periodicals
621.3 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01420615 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ijepes.2022.108816 ↗
- Languages:
- English
- ISSNs:
- 0142-0615
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.220000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25993.xml