Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment

被引:4
|
作者
Jokar, Hossein [1 ]
Bahmani-Firouzi, Bahman [1 ]
Alhelou, Hassan Haes [2 ]
Siano, Pierluigi [3 ,4 ]
机构
[1] Islamic Azad Univ, Dept Elect Engn, Marvdasht Branch, Marvdasht 7371113119, Iran
[2] Tishreen Univ, Dept Elect Power Engn, Latakia, Syria
[3] Univ Salerno, Dept Management & Innovat Syst, I-84084 Fisciano, Italy
[4] Univ Johannesburg, Dept Elect & Elect Engn Sci, ZA-2006 Johannesburg, South Africa
关键词
Bi-level optimization; demand side management; large scale storage system; unit commit-ment; security-constrained; BILEVEL MODEL; SYSTEM; OPTIMIZATION; MARKETS;
D O I
10.1109/ACCESS.2022.3224458
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a bi-level optimization model including the problem of transmission network market and energy management in the distribution substation is presented. In the proposed bi-level model, the lower level includes the demand-side management (DSM) program and the optimal charge/discharge of large-scale energy storage system (LSESS) at distribution substations to increase grid profits and send decisions to the upper-level transmission market operator. The upper level of the proposed model is a security-constrained unit commitment (SCUC) to minimize production, no-load, startup, shutdown, and active power curtailment costs, and also the unavailability of the generation units. In this paper, to solve the bi-level optimization problem, the Karush-Kuhn-Tucker (KKT) equation modeling method will be used to turn the problem into a single-level problem. One of the advantages of converting a bi-level model to a single-level model compared to the methods of the decomposition algorithms is the lack of use of iterative algorithms, which leads to an increase in problem-solving time. The proposed model is tested on standard distribution substations and transmission networks, which shows that the proposed method is more effective than decomposition algorithms in terms of problem-solving time. The simulation results showed that the proposed method can be more efficient in large optimization problems.
引用
收藏
页码:123723 / 123735
页数:13
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