考虑碳交易体系的微电网多阶段鲁棒优化运行

doi:10.3969/j.issn.2097-0706.2024.09.002

综合智慧能源 ›› 2024, Vol. 46 ›› Issue (9): 9-19.doi: 10.3969/j.issn.2097-0706.2024.09.002

考虑碳交易体系的微电网多阶段鲁棒优化运行

彭乐瑶¹(), 马刚¹^,²^,^*(), 陈永华², 颜云松², 赖业宁², 李祝昆², 刘东洋², 唐靖²

1.南京师范大学电气与自动化工程学院，南京 210046
2.国电南瑞科技股份有限公司电网运行风险防御技术与装备全国重点实验室，南京 210097

收稿日期:2024-06-26 修回日期:2024-07-31 出版日期:2024-09-25
通讯作者: *马刚（1984），男，教授，博士，从事新能源发电及入网方面的研究，nnumg@njnu.edu.cn。
作者简介:彭乐瑶（2001），女，硕士生，从事微电网优化调度方面的研究，1519393180@qq.com；
陈永华（1979），男，正高级工程师，从事电力系统安全稳定控制方面的研究；
颜云松（1981），男，正高级工程师，硕士，从事电力系统安全稳定控制方面的研究；
赖业宁（1975），男，正高级工程师，博士，主从事电力系统安全稳定控制方面的研究；
李祝昆（1982），男，正高级工程师，硕士，从事电力系统安全稳定控制方面的研究；
刘东洋（1989），男，工程师，硕士，从事电力系统安全稳定控制方面的研究；
唐靖（1989），男，工程师，从事电力系统安全稳定控制方面的研究。
基金资助:
电网运行风险防御技术与装备全国重点实验室资助项目(SCNR0000KJJS2302147)

Multi-stage robust optimization operation of microgrids considering carbon trading systems

PENG Leyao¹(), MA Gang¹^,²^,^*(), CHEN Yonghua², YAN Yunsong², LAI Yening², LI Zhukun², LIU Dongyang², TANG Jing²

1. School of Electricity and Automation， Nanjing Normal University，Nanjing 210046，China
2. State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks， Nari Technology Company Limited，Nanjing 210097，China

Received:2024-06-26 Revised:2024-07-31 Published:2024-09-25
Supported by:
State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Funded Project(SCNR0000KJJS2302147)

摘要/Abstract

摘要：

随着碳交易市场的成熟，碳配额和国家核证自愿减排量成为微电网优化运行必须考虑的因素。针对优化运行中源荷不确定性所带来的挑战，提出了一种考虑碳交易体系的多阶段鲁棒优化模型。首先，对微电网的源荷不确定集和微电网碳交易体系进行描述，并据此建立了一系列确保微电网鲁棒性和非预期性的可行性约束。针对关于时间耦合的燃气机组和储能装置的约束条件，采用隐式决策规则，引入辅助变量和新的非预期约束，并对模型进行再表述，求解出燃气机组的启停策略及其与储能系统荷电状态的安全输出区间；进而建立日内实时滚动模型，确定了微电网各组成部分的实时功率输出，实现微电网的实时优化调度；最后，导入江苏省某地区实际数据进行算例分析，验证了所提模型的有效性，但在可再生能源已充分优化的前提下，单纯地改变碳配额交易价格对微电网运行影响较小。

关键词: 微电网, 鲁棒优化, 非预期性, 碳交易体系, 储能装置, 碳配额, 国家核证自愿减排量

Abstract:

With the maturity of carbon trading market， carbon emission allowances and certified emission reductions have become factors that must be considered in the microgrid operation optimization. To address the difficulties posed by source-load uncertainties during operation optimization， a multi-stage robust optimization model taking the carbon trading system into account is proposed. Initially， the uncertain sets of microgrid sources and loads and the microgrid carbon trading system are described， and a series of feasibility constraints ensuring the robustness and unpredictability of the microgrid are established accordingly. For the constraints related to the temporal coupling between gas-fired units and energy storage devices， implicit decision rules are employed to introduce auxiliary variables and new unpredictability constraints， and the model is reformulated to determine the start-stop strategy of the gas-fired units and the range of the battery's state of charge. Subsequently， an intra-day real-time rolling model is established to ascertain the real-time output of each component， achieving real-time optimized scheduling of the microgrid. Finally， actual data from a region in Jiangsu Province is used in a case study to analyze and verify the effectiveness of the proposed scheduling model. However， it is noted that under the premise of fully optimizing renewable energy sources， simply changing the price of carbon allowances has a weak impact on the operation of the microgrid.

Key words: microgrid, robust optimization, unpredictability, carbon trading system, energy storage device, carbon emission allowance, certified emission reduction

中图分类号:

TM73

彭乐瑶, 马刚, 陈永华, 颜云松, 赖业宁, 李祝昆, 刘东洋, 唐靖. 考虑碳交易体系的微电网多阶段鲁棒优化运行[J]. 综合智慧能源, 2024, 46(9): 9-19.

PENG Leyao, MA Gang, CHEN Yonghua, YAN Yunsong, LAI Yening, LI Zhukun, LIU Dongyang, TANG Jing. Multi-stage robust optimization operation of microgrids considering carbon trading systems[J]. Integrated Intelligent Energy, 2024, 46(9): 9-19.

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参考文献 29

[1]	檀勤良, 丁毅宏. 考虑碳交易的火电节能调度优化模型及应对模式[J]. 电力自动化设备, 2018, 38(7): 175-181, 188.
	TAN Qinliang, DING Yihong. Optimal energy-saving dispatching model for thermal power considering carbon trading and its coping mode[J]. Electric Power Automation Equipment, 2018, 38(7): 175-181, 188.
[2]	袁坤龙, 张少康, 常冉, 等. 阶梯式碳交易机制下计及电-气-热综合能源系统需求响应优化运行[J]. 电气技术, 2024, 25(1): 8-16.
	YUAN Kunlong, ZHANG Shaokang, CHANG Ran, et al. Optimized operation of demand response of electricity-gas-heat integrated energy system under stepped carbon trading mechanism[J]. Electrical Technology, 2024, 25(1): 8-16.
[3]	姚志华, 余波, 袁法培, 等. 考虑碳排放目标的配电网源网荷储优化调度探究[J]. 电力需求侧管理, 2023, 25(3): 80-86.
	YAO Zhihua, YU Bo, YUAN Fapei, et al. Optimal scheduling of distribution network side source network load storage considering dual-carbon targets in distribution market[J]. Power Demand Side Management, 2023, 25(3): 80-86.
[4]	ZHAO C Y, GUAN Y P. Data-driven risk-averse stochastic optimization with Wasserstein metric[J]. Operations Research Letters, 2018, 46(2):262-267.
[5]	CHENG Z P, JIA D Q, LI Z W, et al. Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization[J]. International Journal of Electrical Power & Energy Systems, 2022, 139: 107957.
[6]	WU L, SHAHIDEHPOUR M, LI T. Stochastic security-constrained unit commitment[J]. IEEE Transactions on Power Systems, 2007, 22(2): 800-811.
[7]	WANG B, ZHANG C, DONG Z Y. Interval optimization based coordination of demand response and battery energy storage system considering SOC management in a microgrid[J]. IEEE Transactions on Sustainable Energy, 2020.
[8]	王鲁浩, 李歧强, 丁然, 等. 可再生能源微网鲁棒多目标优化调度[J]. 电工技术学报, 2017, 32(5): 184-192.
	WANG Luhao, LI Qiqiang, DING Ran, et al. Robust multi-objective optimization scheduling of micro-grids with renewable energy[J]. Transactions of China Electrotechnical Society, 2017, 32(5): 184-192.
[9]	YIN M J, LI K, YU J. A data-driven approach for microgrid distributed generation planning under uncertainties[J]. Applied Energy, 2022, 309: 118429.
[10]	WANG Y B, DONG W, YANG Q. Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets[J]. Applied Energy, 2022, 310: 18528.
[11]	WU Y W, LIM G J, SHI J. Stability-constrained microgrid operation scheduling incorporating frequency control reserve[J]. IEEE Transactions on Smart Grid, 2020, 11(2)1007-1017.
[12]	XU J, YI X K, SUN Y Z, et al. Stochastic optimal scheduling based on scenario analysis for wind farms[J]. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1548-1559.
[13]	刘一欣, 郭力, 王成山. 微电网两阶段鲁棒优化经济调度方法[J]. 中国电机工程学报, 2018, 38(14): 4013-4022,4307.
	LIU Yixin, GUO Li, WANG Chengshan. Economic dispatch of microgrid based on two stage robust optimization[J]. Proceedings of the CSEE, 2018, 38(14): 4013-4022,4307.
[14]	WANG Y W, YANG Y J, TANG L, et al. A Wasserstein based two-stage distributionally robust optimization model for optimal operation of CCHP micro-grid under uncertainties[J]. International Journal of Electrical Power and Energy Systems, 2020, 119 :105941.
[15]	杨畅, 李正烁, 薛屹洵, 等. 基于双步投影算法的多区域互联电热综合能源系统的三层分布式可信分布鲁棒优化调度[J/OL]. 中国电机工程学报: 1-14 (2024-02-01)[2024-03-23]. http://kns.cnki.net/kcms/detail/11.2107.tm.20231201.1038.002.html.
	YANG Chang, LI Zhengshuo, XUE Yi Xun, et al. Trilevel distributed credible distributionally robust optimal of multi-district interconnected electric and heat systems via two-step projection method[J/OL]. Proceedings of the CSEE: 1-14(2024-02-01) [2024-03-23]. http://kns.cnki.net/kcms/detail/11.2107.tm.20231201.1038.002.html.
[16]	许明前, 陈富燕. 基于数据驱动分布式鲁棒的热-电-气综合能源系统日前经济调度优化[J]. 电力系统及其自动化学报, 2021, 33(11): 66-73.
	XU Mingqian, CHEN Fuyan. Day-ahead economic dispatch of heat-electricity-gas integrated energy system based on data-driven distributed robost optimization[J]. Proceedings of the CSU-EPSA, 33(11): 66-73.
[17]	翟桥柱, 周玉洲, 李轩, 等. 非预期性与全场景可行性: 应对负荷与可再生能源不确定性的现状、挑战与未来[J]. 中国电机工程学报, 2020, 40(20): 6418-6433.
	ZHAI Qiaozhu, ZHOU Yuzhou, LI Xuan, et al. Nonanticipativity and all-scenario-feasibility: State of the art, challenges and future in dealing with the uncertain load and renewable energy[J]. Proceedings of the CSEE, 2020, 40(20): 6418-6433.
[18]	潘思佳, 徐潇源, 严正, 等. 基于多仿射决策规则的电-氢耦合系统多阶段鲁棒优化调度[J]. 电力系统自动化, 2023, 47(20): 42-52.
	PAN Sijia, XU Xiaoyuan, YAN Zheng, et al. Multi-stage robust optimal scheduling of electric-hydrogen coupled system based on multiple affine decision rules[J]. Automation of Electric Power Systems, 2023, 47(20): 42-52.
[19]	LI X, ZHAI Q Z. Multi-stage robust transmission constrained unit commitment: A decomposition framework with implicit decision rules[J]. International Journal of Electrical Power & Energy Systems, 2019, 108: 372-381.
[20]	陈颖伦, 夏沛, 张晓星, 等. 考虑CCER机制的碳市场中风-火电装机容量发展趋势[J/OL]. 高电压技术: 1-13. (2024-06-07) [2024-07-27].https://doi.org/10.13336/j.1003-6520.hve.20240174.
	CHEN Yinglun, XIA Pei, ZHANG Xiaoxing, et al. Development trend of wind-thermal installed capacity in carbon market considering CCER mechanism[J/OL]. High Voltage Engineering:1-13(2024-06-07)[2024-07-27].https://doi.org/10.13336/j.1003-6520.hve.20240174.
[21]	冶兆年, 赵长禄, 王永真, 等. 基于纳什议价的共享储能能源互联网络双目标优化[J]. 综合智慧能源, 2022, 44(7): 40-48. doi: 10.3969/j.issn.2097-0706.2022.07.005
	YE Zhaonian, ZHAO Changlu, WANG Yongzhen, et al. Dual-objective optimization of energy networks with shared energy storage based on Nash bargaining[J]. Integrated Intelligent Energy, 2022, 44(7): 40-48. doi: 10.3969/j.issn.2097-0706.2022.07.005
[22]	周成伟, 李鹏, 俞斌, 等. 风光储微电网储能系统容量优化配置[J]. 综合智慧能源, 2022, 44(12): 56-61. doi: 10.3969/j.issn.2097-0706.2022.12.008
	ZHOU Chengwei, LI Peng, YU Bin, et al. Optimal configuration for energy storage system capacity of wind-solar-storage microgrid[J]. Integrated Intelligent Energy, 2022, 44(12): 56-61. doi: 10.3969/j.issn.2097-0706.2022.12.008
[23]	杜刚, 赵冬梅, 刘鑫. 计及风电不确定性优化调度研究综述[J]. 中国电机工程学报, 2023, 43(7): 2608-2627.
	DU Gang, ZHAO Dongmei, LIU Xin. Research review on optimal scheduling considering wind power uncertainty[J]. Proceedings of the CSEE, 2023, 43(7): 2608-2627.
[24]	马跃, 孟润泉, 魏斌, 等. 考虑阶梯式碳交易机制的微电网两阶段鲁棒优化调度[J]. 电力系统保护与控制, 2023, 51(10): 22-33.
	MA Yue, MENG Runquan, WEI Bin, et al. Two-stage robust optimal scheduling of microgrid with a stepped carbon trading mechanism[J]. Power System Protection and Control, 2023, 51(10): 22-33.
[25]	DONG H J, ZHU J Z, LI S L, et al. Short-term residential household reactive power forecasting considering active power demand via deep transformer sequence-to-sequence networks[J]. Applied Energy, 2023, 329:120281.
[26]	向月, 刘俊勇, 魏震波, 等. 考虑可再生能源出力不确定性的微电网能量优化鲁棒模型[J]. 中国电机工程学报, 2014, 34(19): 3063-3072.
	XIANG Yue, LIU Junyong, WEI Zhenbo, et al. Robust model of microgrid energy optimization with uncertain renewable energy sources[J]. Proceedings of the CSEE, 2014, 34(19): 3063-3072.
[27]	CUI Qiong, ZHU Jizhong, SHU Jie, et al. Comprehensive evaluation of electric power prediction models based on D-S evidence theory combined with multiple accuracy indicators[J]. Journal of Modern Power Systems and Clean Energy, 2022, 10(3): 597-605.
[28]	ZHOU Y Z, ZHAI Q Z, WU L. Optimal operation of regional microgrids with renewable and energy storage: solution robustness and nonanticipativity against uncertainties[J]. IEEE Transactions on Smart Grid, 2022, 13(6)4218-4230.
[29]	朱嘉远, 刘洋, 许立雄, 等. 考虑风电消纳的热电联供型微网日前鲁棒经济调度[J]. 电力系统自动化, 2019, 43(4): 40-48.
	ZHU Jiayuan, LIU Yang, XU Lixiong, et al. Robust day-ahead economic dispatch of microgrid with combined heat and power system considering wind power accommodation[J]. Automation of Electric Power Systems, 2019, 43(4): 40-48.

机组	输出功率限制/kW	爬坡限制/ (kW·h^-1)	初始启停状态	初始输出功率/kW
1	[4,25]	4	1	15
2	[2,15]	3	1	10
3	[2,15]	3	0	0
4	[1,5]	1	1	5
5	[1,5]	1	0	0

场景	是否引入碳交易机制	优化方法
1	是	多阶段鲁棒优化法
2	是	基于场景优化法
3	是	确定性优化法

参数	场景1	场景2	场景3
实际碳排放量/kg	405.29	449.95	379.79
碳交易成本/元	-149.16	-133.07	-150.68
风光消纳率/%	98.81	96.27	100.00
调度成本/元	509.29	575.57	469.62

项目	CEA交易价格/（元·kg^-1）
项目	0	0.42	0.50	0.58	0.67	0.76
实际碳排放量/kg	421.6	406.4	392.5	391.1	390.3	389.2
风光总利用量/kW	2 303	2 303	2 303	2 303	2 303	2 303
燃气机组总发电量/kW	921	886	886	865	861	859

考虑碳交易体系的微电网多阶段鲁棒优化运行

Multi-stage robust optimization operation of microgrids considering carbon trading systems

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