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All issues Volume 580 (2024) E3S Web Conf., 580 (2024) 01006 References
Open Access
Issue
E3S Web Conf.
Volume 580, 2024
2024 2nd International Conference on Clean Energy and Low Carbon Technologies (CELCT 2024)
Article Number 01006
Number of page(s) 7
Section Energy System Modeling and Ecological Resource Management
DOI https://doi.org/10.1051/e3sconf/202458001006
Published online 23 October 2024
  1. National Development and Reform Commission, National Energy Administration. Notice on printing and distributing the administrative measures for access and exit of power selling companies and the administrative measures for orderly release of Distribution network business [EB/OL]. [2023-0128]. http://www.nea.gov.cn/2016-10/11/c_135746081.htm. [Google Scholar]
  2. Wang Junhao, Li Yang, Wu Junhong. Exploring the incremental distribution network pricing mechanism in China—mechanism design based on asymmetric regulation theory[J]. Journal of Shandong University (Philosophy and Social Sciences), 2021(5): 72–83. [Google Scholar]
  3. He Defang, Zeng Jianxun. Study on In-depth Integration of Library Collections Based on Seman tics[J]. Journal of Library Science in China, 2012, 38(04):79–87. [Google Scholar]
  4. Chen Xu, Zhang Yongjun, Huang Xiangmin. Review of reactive power and voltage control method in the background of active distribution network[J]. Automation of Electric Power Systems, 2016, 40(1):143–151. [Google Scholar]
  5. Liu Yanghua, Wu Zhengqiu, Tu Youqing, et al. A survey on distributed generation and its networking technology[J]. Power System Technology, 2008(15):71–76. [Google Scholar]
  6. Deng Wei, Li Xinran, Li Peiqiang, et al. Optimal allocation of intermittent distributed generation considering complementarity in distributed network[J]. Transactions of China Electrotechnical Society, 2013, 28(6): 216–225. [Google Scholar]
  7. ZHANG Boming, Wu Wenchuan, Zheng Taiyi, et al. Design of a multi-time scale coordinated active power dispatching system for accommodating large scale windpower penetration[J]. Automation of Electric Power Systems, 2011, 35(1):1–6. [Google Scholar]
  8. Zhang Na, Lv Quan, Zhang Xian, et al. A multilevel unit commitment coordinated strategy for power system with wind power[J]. Automation of Electric Power Systems, 2013, 37(11):3945. [Google Scholar]
  9. Xiao Hao, Pei wei, Kong li. Multi-time Scale Coordinated Optimal Dispatch of Microgrid Based on Model Predictive Control [J]. Automation of Electric Power Systems, 2016, 40(18):7–14+55. [Google Scholar]
  10. Yan Boyang, Han Xiaoqing, Li Tingjun, Song Tianhao, Wu Yuxiang. Operational Strategy for Active Distribution Networks with Energy Storage Taking into Account Energy-Reserve Joint Markets Transactions [J/ OL]. Automation of Electric Power Systems:1–18[2023-10-10]. [Google Scholar]
  11. Tayab UB, Lu J, Taghizadeh S, et al. Microgrid energy management system for residential microgrid using an ensemble forecasting strategy and grey wolf optimization[J]. Energies, 2021, 14:8489. [CrossRef] [Google Scholar]
  12. Trivedi R, Patra S, Khadem S. A data-driven shortterm PV generation and load forecasting approach for microgrid applications[J/OL]. IEEE Journal of Emerging and Selected Topics in Industrial Electronics:911–919[2022-6-13]. [CrossRef] [Google Scholar]
  13. Wood A J, Wollenberg B F. Power generation, operation, and control[M]. 2th ed. New York: John Wiley and Sons, 1996: 131–166. [Google Scholar]
  14. Kazarlis SA, Bakirtzis AG, Petridis V. A genetic algorithm solution to the unit commitment problem[J]. IEEE Transactions on Power Systems, 1996, 11(1) 83–92. [CrossRef] [Google Scholar]
  15. Li Jinghua, Wei Hua, Xia Xiaoqin. Improved general pattern search filter algorithm for unit commitment problems[J]. Proceedings of the CSEE, 2011, 31(28): 33–41. [Google Scholar]
  16. Xia Qing, Zhong Haiwang, Kang Chongqing. Review and prospects of the security constrained unit commitmenttheory and applications[J]. Proceedings of the CSEE, 2013, 33(16): 94–103. [Google Scholar]
  17. Rajan D, Takriti S. Minimum up/down polytopes of the unit commitment problem with start-up costs[R]. New York: IBM, 2005. [Google Scholar]
  18. Carrion M, Arroyo J M. A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem[J]. IEEE Transactions on Power Systems, 2006, 21(3): 1371–1378. [CrossRef] [Google Scholar]
  19. Frangioni A, Gentile C, Lacalandra F. Tighter approximated MILP formulations for unit commitment problems[J]. IEEE Transactions on Power Systems, 2009, 24(1): 105–113. [CrossRef] [Google Scholar]
  20. Hedman K W, O’Neill R P, Oren SS. Analyzing valid inequalities of the generation unit commitment problem [C]// Proceeding of IEEE/PES Power Systems Conference and Exposition. Seattle, USA, 2009. [Google Scholar]
  21. Costa A, Cｒespo A, Navaｒｒo J, et al. A review on the young history of the wind power shortterm prediction[J]. Ｒ enewable and Sustainable Energy reviews, 2008, 12(6):1725–1744. [CrossRef] [Google Scholar]
  22. Han Fujia, Wang Xiaohui, Qiao Ji, et al. Review on Artificial Intelligence based Load Forecasting Research for the New-type Power System[J/OL]. Proceedings of the CSEE:1–24[2023-10-17]. [Google Scholar]
  23. Xu Man, Qiao Ying, Lu Zongxiang. A comprehensive error evaluation method for shorttime wind power prediction[J]. Automation of Electric Power Systems, 2011, 35(12):20–26. [Google Scholar]
  24. Xiao Bai, Huang Yuru, Jiang Zhuo, et al. The method of spatial load forecasting based on generative adversarial networks for data scarcity scenarios[J]. Proceedings of the CSEE, 2020, 40(24):7990–8001. [Google Scholar]
  25. National Development and Reform Commission, National Energy Administration. Notice on the issuance of the Measures for Power Load Management(2023Edition) [EB/OL]. [2023-0907]/ https://www.gov.cn/govweb//zhengce/zhengceku/202310/content_6907504.htm [Google Scholar]

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