“碳中和”目标下电气互联系统有功-无功协同优化模型

doi:10.16183/j.cnki.jsjtu.2021.233

摘要/Abstract

摘要：

随着碳中和目标的提出,新能源机组逐步成为主力电源,其波动性给系统特别是配电网的控制和优化带来了巨大的挑战.为了解决高比例可再生能源接入配网带来的无功问题,提出一种计及无功优化和多能协同交互的源-网-荷-储集中优化调度模型.该模型以多能系统运行成本最优、网络损耗最小、碳排放量最小为目标,考虑无功补偿、储能调节以及能量转换等手段,以实现电气互联系统的安全低碳经济调度.采用改进二阶锥松弛法对配网模型中非线性等式约束进行凸松弛,采用大M法对离散无功补偿装置的投切容量进行线性化表达,将模型转换为混合整数二阶锥规划问题进行求解.仿真结果表明,所提方法能够有效补偿风机并网点所需无功,协调电、气之间的能量交互,从而提高大规模可再生能源接入后配网的稳定性与弹性,促进可再生能源消纳.

关键词: 有功-无功协同优化, 电气互联系统, 碳中和, 可再生能源, 改进二阶锥松弛

Abstract:

With the objective of carbon neutrality, renewable energy resources gradually become the main power supply, whose variability poses great challenges to the operation and optimization of the system, especially to the power distribution network. In order to solve the problem of reactive power caused by high penetration of renewable energy sources, a centralized optimization model is proposed, which takes reactive power optimization and “generation-network-load-storage” multi-energy integration into account. The model aims at optimizing the operating cost and minimizing network losses and carbon emissions of the system. Reactive power compensation, regulation of energy storage, and energy conversion are considered to achieve safe and low-carbon economic dispatch of the power and gas systems. An improved second-order cone relaxation method is used for the convex relaxation of non-linear equality constraints concerned with the distribution network. The switching capacity produced by discrete reactive power compensators can be exactly linearized by the application of the big M approach. The simulation results demonstrate that the proposed method could effectively compensate the reactive power required by the grid-connection point of wind turbine, coordinate the energy interaction between the power and gas, thus improving the stability and elasticity of the distribution network after integration of large-scale renewable energy sources, which helps promote the consumption of renewable energy.

Key words: coordinated optimization of active power and reactive power, power and gas system, carbon neutrality, renewable energy sources, improved second-order cone relaxation

中图分类号:

TM73

孙欣, 严佳嘉, 谢敬东, 孙波. “碳中和”目标下电气互联系统有功-无功协同优化模型[J]. 上海交通大学学报, 2021, 55(12): 1554-1566.

SUN Xin, YAN Jiajia, XIE Jingdong, SUN Bo. Coordinated Optimization Model of Active Power and Reactive Power in Power and Gas Systems with the Objective of Carbon Neutrality[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1554-1566.

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参考文献 18

[1]	卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9):171-191.
	ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(9):171-191.
[2]	习近平. 《继往开来,开启全球应对气候变化新征程》——在气候雄心峰会上的讲话[EB/OL].(2020-12-12) [2021-06-10]. http://www.gov.cn/gongbao/content/2020/content_5570055.htm.
	XI Jinping. “Building on past achievements and launching a new journey for global climate actions”, a speech at the climate ambition summit[EB/OL].(2020-12-12) [2021-06-10]. http://www.gov.cn/gongbao/content/2020/content_5570055.htm.
[3]	尹积军, 夏清. 能源互联网形态下多元融合高弹性电网的概念设计与探索[J]. 中国电机工程学报, 2021, 41(2):486-497.
	YIN Jijun, XIA Qing. Conceptual design and exploration of multi-factor integrated high-elastic power grid in energy Internet[J]. Proceedings of the CSEE, 2021, 41(2):486-497.
[4]	迟永宁, 梁伟, 张占奎, 等. 大规模海上风电输电与并网关键技术研究综述[J]. 中国电机工程学报, 2016, 36(14):3758-3771.
	CHI Yongning, LIANG Wei, ZHANG Zhankui, et al. An overview on key technologies regarding power transmission and grid integration of large scale offshore wind power[J]. Proceedings of the CSEE, 2016, 36(14):3758-3771.
[5]	罗欢, 陈民铀, 程庭莉. 含风力发电的配电网自适应分时段无功优化[J]. 电网技术, 2014, 38(8):2207-2212.
	LUO Huan, CHEN Minyou, CHENG Tingli. Adaptive time-intervalled reactive power optimization for distribution network containing wind power generation[J]. Power System Technology, 2014, 38(8):2207-2212.
[6]	DING T, LIU S Y, YUAN W, et al. A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks[J]. IEEE Transactions on Sustainable Energy, 2016, 7(1):301-311. doi: 10.1109/TSTE.2015.2494587 URL
[7]	赵平, 赵期期, 艾小猛. 考虑极限场景的主动配电网重构与无功电压调整联合鲁棒优化[DB/OL].(2021-05-13) [2021-06-10]. https://doi.org/10.19595/j.cnki.1000-6753.tces.L90356.
	ZHAO Ping, ZHAO Qiqi, AI Xiaomeng, et al. Network reconfiguration and reactive power voltage regulation coordinated robust optimization for active distribution network considering extreme scenarios[DB/OL].(2021-05-13) [2021-06-10]. https://doi.org/10.19595/j.cnki.1000-6753.tces.L90356.
[8]	WEN Y F, QU X B, LI W Y, et al. Synergistic operation of electricity and natural gas networks via ADMM[J]. IEEE Transactions on Smart Grid, 2018, 9(5):4555-4565. doi: 10.1109/TSG.5165411 URL
[9]	LI Y, LI Z Y, WEN F S, et al. Privacy-preserving optimal dispatch for an integrated power distribution and natural gas system in networked energy hubs[J]. IEEE Transactions on Sustainable Energy, 2019, 10(4):2028-2038. doi: 10.1109/TSTE.5165391 URL
[10]	FARIVAR M, LOW S H. Branch flow model: Relaxations and convexification—Part I[J]. IEEE Transactions on Power Systems, 2013, 28(3):2554-2564. doi: 10.1109/TPWRS.2013.2255317 URL
[11]	FARIVAR M, LOW S H. Branch flow model: Relaxations and convexification—Part II[J]. IEEE Transactions on Power Systems, 2013, 28(3):2565-2572. doi: 10.1109/TPWRS.2013.2255318 URL
[12]	郭尊, 李庚银, 周明, 等. 面向风电消纳的电-气联合系统分散协调鲁棒优化调度模型[J]. 中国电机工程学报, 2020, 40(20):6442-6455.
	GUO Zun, LI Gengyin, ZHOU Ming, et al. A decentralized and robust optimal scheduling model of integrated electricity-gas system for wind power accommodation[J]. Proceedings of the CSEE, 2020, 40(20):6442-6455.
[13]	刘志坚, 刘瑞光, 梁宁, 等. 含电转气的微型能源网日前经济优化调度策略[J]. 电工技术学报, 2020, 35(Sup.2):535-543.
	LIU Zhijian, LIU Ruiguang, LIANG Ning, et al. Day-ahead optimal economic dispatching strategy for micro energy-grid with P2G[J]. Transactions of China Electrotechnical Society, 2020, 35(Sup.2):535-543.
[14]	陈宗法. “双碳”目标下,“十四五”燃气发电如何发展?[J]. 能源, 2021(6):38-41.
	CHEN Zongfa. How will gas develop in the 14th Five-Year Plan under the “double carbon” target?[J]. Energy, 2021(6):38-41.
[15]	LOW S H. Convex relaxation of optimal power flow—Part I: Formulations and equivalence[J]. IEEE Transactions on Control of Network Systems, 2014, 1(1):15-27. doi: 10.1109/TCNS.2014.2309732 URL
[16]	刘斌, 刘锋, 梅生伟, 等. 基于二阶锥优化的含有载调压变压器主动配电网最优潮流[J]. 电力系统自动化, 2015, 39(19):40-47.
	LIU Bin, LIU Feng, MEI Shengwei, et al. Optimal power flow in active distribution networks with on-load tap changer based on second-order cone programming[J]. Automation of Electric Power Systems, 2015, 39(19):40-47.
[17]	贾文皓, 丁涛, 曲明, 等. 适用能源互联网非线性气流方程组的线性优化求解模型[J]. 中国电机工程学报, 2020, 40(24):7938-7949.
	JIA Wenhao, DING Tao, QU Ming, et al. A linear program to nonlinear gas equations in context of energy Internet[J]. Proceedings of the CSEE, 2020, 40(24):7938-7949.
[18]	YUAN Z, HE S, ALIZADEH A, et al. Probabilistic scheduling of power-to-gas storage system in renewable energy hub integrated with demand response program[J]. Journal of Energy Storage, 2020, 29:101393.

可调范围	SVC1出力/ Mvar	SVC2出力/ Mvar	SC出力/ Mvar	OLTC 变比
最小值	-0.2	-0.1	0	0.95
最大值	2	1	2	1.05
调节步长	连续	连续	0.5	0.01

是否计及无功补偿	电压波动水平(标准差)
计及	0.0092
未计及	0.0126

是否考虑 P2G	购电碳排放量/kg	购气碳排放量/kg	总碳排放量/kg
考虑	67239	213524	280763
不考虑	66859	217547	284406

风电渗透率	是否考虑碳排放成本	购电碳排放量/kg	购气碳排放量/kg	总碳排放量/kg
21%	考虑	93810	216427	310237
	不考虑	93990	216344	310334
32%	考虑	83119	216378	299497
	不考虑	83195	216358	299553
53%	考虑	67239	213524	280763
	不考虑	68071	213033	281104