Caixia Tao1, Qiang Zhuo This email address is being protected from spambots. You need JavaScript enabled to view it.1, Xiuyu Guo1, WeibinWang2, Taiguo Li1, and Xu Bai3
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, 730070 Lanzhou, China 2State Grid Baiyin Electric Power Supply Company, 730900 Baiyin, China 3Chengdu Metro Operation Co., Ltd, 610000 Chengdu, China
Received: April 25, 2022 Accepted: September 18, 2022 Publication Date: October 27, 2022
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Under the low-carbon background, considering the source load storage optimal operation strategy of active distribution network (ADN) is of great significance to the low-carbon, economic and stable operation of ADN. Aiming at the and voltage out of limit problem caused by high proportion distributed energy access, this paper constructs a low-carbon ADN voltage coordination and optimization model considering load demand response and stepped carbon trading, and uses second order cone programming (SOCP) to convex the model. First of all, build the ADN power flow model, model and analyze the voltage coordination devices in the ADN, build the load side comprehensive demand response characteristic model, guide the load side schedulable resources to participate in the ADN voltage coordination and optimization control, and improve the traditional condition of relying only on controllable units. Secondly, the ladder type carbon trading mechanism is introduced to build an ADN voltage coordination control model with low-carbon characteristics, to deeply explore the coordination ability of ADN voltage coordination control between economy and low-carbon, and to ensure that the system voltage is within the allowable range. Finally, CPLEX is used to solve the ADN voltage optimization model, which verifies the effectiveness and feasibility of the proposed model and method.
Keywords: Active distribution grid; second-order cone programming; stepped carbon trading; demand response; voltage regulation
REFERENCES
[1] Y. Fu, J. Liao, and Z. Li, (2017) “Day ahead optimal dispatching and operation of active distribution network considering out of limit risk" Chinese Journal of electrical engineering 37(21): 6328–6338. DOI: 10.13334/j.0258-8013.pcsee.162014.
[2] J. Zhang, H. Zhuang, and J. Liu, (2019) “Two stage voltage coordinated control strategy of active distribution network with distributed energy storage system participating in voltage regulation" Power automation equipment 39(5): 15–21 + 29. DOI: 10.16081/j.issn.1006-6047.2019.05.003.
[3] L. Zhilin and p. Liao, (2021) “Double layer optimization of photovoltaic microgrid group and active distribution network considering demand side response" Journal of power system and automation 33(8): 70–78. DOI: 10.19635/j.cnki.csu-epsa.000692.
[4] x. Li, G. Geng, and J. Yuqi, (2016) “Joint optimization planning of energy storage and demand side response in active distribution network" Power grid technology 40(12): 3803–3810. DOI: 10.13335/j.1000- 3673.pst.2016.12.024.
[5] I. Sharma, K. Bhattacharya, and C. Cañizares, (2015) “Smart distribution system operations with priceresponsive and controllable loads" IEEE Transactions on Smart Grid 6(2): 795–807. DOI: 10.1109/TSG.2014.2372674.
[6] Y. Huang, Z. Lin, X. Liu, L. Yang, Y. Dan, Y. Zhu, Y. Ding, and Q. Wang, (2021) “Bi-level Coordinated Planning of Active Distribution Network Considering Demand Response Resources and Severely Restricted Scenarios" Journal of Modern Power Systems and Clean Energy 9(5): 1088–1100. DOI: 10.35833/MPCE.2020.000335.
[7] M. Farivar and S. Low, (2013) “Branch flow model: Relaxations and convexification-part i" IEEE Transactions on Power Systems 28(3): 2554–2564. DOI: 10.1109/TPWRS.2013.2255317.
[8] M. Farivar and S. Low, (2013) “Branch flow model: Relaxations and convexification-part II" IEEE Transactions on Power Systems 28(3): 2565–2572. DOI: 10.1109/TPWRS.2013.2255318.
[9] R. Liu, K. Wu, and F. Liang, (2022) “Voltage zoning coordinated optimal control of active distribution network with high permeability distributed photovoltaic" Solar energy report 43(2): 189–197. DOI: 10.19912/j.0254-0096.tynxb.2020-0239.
[10] R. Chen, Z. Lu, and Q. Ying, (2021) “Optimal dispatching of distribution network based on multi scene fuzzy set and improved second-order cone method" Power grid technology 45(12): 4621–4629. DOI: 10.13335/j.1000-3673.pst.2021.0618.
[11] D. Huang, M. Wei, L. Qin, H. Zeng, and X. Yang. “Multi-objective collaborative control scheduling optimization considering wind power grid-connected energy storage access”. In: cited By 0. 2021, 580–585. DOI: 10.1109/POWERCON53785.2021.9697466.
[12] Y. Yang, Z. Luo, X. Yuan, X. Lv, H. Liu, Y. Zhen, J. Yang, and J. Wang, (2021) “Bi-Level Multi-Objective Optimal Design of Integrated Energy System under Low-Carbon Background" IEEE Access 9: 53401–53407. DOI: 10.1109/ACCESS.2021.3070654.
[13] J. Chen, K. Ning, Q. Zhang, Q. Xu, W. Chen, and X. Yu. “Low-carbon Optimization Operation of Integrated Energy System Considering Ladder-type Carbon Trading and Carbon Capture”. In: cited By 0. 2022, 1916–1921. DOI: 10.1109/ACPEE53904.2022.9784059.
[14] Q. Xie, H. Cheng, Y. Zhang, and X. Zhang. “Active distribution network planning based on active management”. In: 2014-December. cited By 6. 2014, 1261–1265. DOI: 10.1109/CICED.2014.6991909.
[15] R.Wang, S. Cheng, and Y.Wang, (2022) “Low carbon economy optimal scheduling of regional integrated energy system based on multi-agent master-slave game" Power system protection and control 50(5): 12–21. DOI: 10.19783/j.cnki.pspc.210888.
[16] Y. Zhang, Q. Zhang, H. Wang, and D. Yu. “Hierarchical coordinated voltage correction scheme for active distribution network”. In: cited By 0. 2020, 1779–1783. DOI: 10.1109/EI250167.2020.9347359.
[17] M. Usman, M. Coppo, F. Bignucolo, and R. Turri. “Quadratic Convex Relaxation based centralized OPF for Multi-Phase Active Distribution Networks”. In: cited By 0. 2019. DOI: 10.1109/EEEIC.2019.8783443.
[18] G. Xie. “Research on active reactive power robust optimization of active distribution network considering demand response". (mathesis). Zhengzhou University, 2021. DOI: 10.27466/d.cnki.gzzdu.2021.003122.
[19] Y. Liu, W. Wu, B. Zhang, Z. Li, and Z. Li, (2014) “A mixed integer second-order cone programming based active and reactive power coordinated multi-period optimization for active distribution network" Proceedings of the Chinese Society of Electrical Engineering 34(16): 2575–2583. DOI: 10.13334/j.0258-8013.pcsee.2014.16.007.
[20] T. Jiang, D. Zhang, X. Li, R. Zhang, and G. Li, (2021) “Distributed optimal control of voltage in active distribution network with distributed photovoltaic" Electric Power Automation Equipment 41(9): 102–109 and 125. DOI: 10.16081/j.epae.202108011.
[21] Y. Cai, J. Lin, Y. Song, B. Shu, and K. Zhang, (2015) “Voltage control strategy in active distribution network based on model predictive control" Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 30(23): 42–49.
We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.
