计及置信度理论的储能电站多目标鲁棒优化配置方法

doi:10.19799/j.cnki.2095-4239.2024.0691

摘要/Abstract

摘要：

新能源发电的间歇性和不稳定性对电网的稳定运行构成了严峻挑战，而储能技术的应用是解决这些问题的关键。因此，本文提出了一种基于置信度理论的新能源和储能电站多目标优化配置方法，旨在通过合理配置新能源电站和储能系统的容量，提高电网的稳定性和可靠性。首先，本文分析了高比例新能源接入电网带来的不确定性，建立了多目标鲁棒优化模型。接着，基于置信度理论，采用归一化正则约束方法生成多样化的帕累托解集，确保在不同不确定性情况下解的有效性和多样性。最后，通过后验样本分析对每个帕累托解进行长期性能模拟，评估其实际效果。在IEEE输电网上的算例验证结果显示，在较低的置信区间值下，系统总成本较低，但调节能力有限；而在较高的置信区间值下，系统调节能力显著提高，但总成本也相应增加。此外，系统在面对5%的负荷波动时，运行成本降低了15%，供电可靠性提高了10%。

关键词: 储能电站, 置信度理论, 多目标鲁棒优化, 归一化正则约束方法

Abstract:

The intermittency and instability of renewable energy generation pose significant challenges to the stable operation of power grids, and the application of energy storage technology is key to addressing these issues. Therefore, a multi-objective optimization method for the capacity configuration of renewable energy and energy storage stations based on confidence theory is developed in this paper, aiming to enhance grid stability and reliability. First, the uncertainties brought by the high proportion of renewable energy integration into the grid are analyzed and a multi-objective robust optimization model is established. Then, based on confidence theory, a normalized regularization constraint method is developed to generate a diverse Pareto solution set, ensuring the validity and diversity of solutions under different uncertainties. Finally, the long-term performance of each Pareto solution is simulated through posterior sample analysis to evaluate its practical effect. Case studies on the IEEE transmission network show that at lower confidence interval values, the total system cost is lower but the adjustment capability is limited; while at higher confidence interval values, the system's adjustment capability significantly improves, but the total cost also increases. Additionally, when facing a 5% load fluctuation, the system's operating cost is reduced by 15%, and power supply reliability is increased by 10%.

Key words: energy storage stations, confidence theory, multi-objective robust optimization, normalized regular constraints

中图分类号:

TM 72

王凯凯, 梁燕, 高瑾, 郑晓明, 赵海波, 荆永明. 计及置信度理论的储能电站多目标鲁棒优化配置方法[J]. 储能科学与技术, 2024, 13(11): 4017-4029.

Kaikai WANG, Yan LIANG, Jin GAO, Xiaoming ZHENG, Haibo ZHAO, Yongming JING. Muti-objective robust optimization method for energy storage stations considering confidence theory[J]. Energy Storage Science and Technology, 2024, 13(11): 4017-4029.

图/表 14

图1

表1

图2

表2

表3

表4

图3

图4

图5

图6

图7

表5

表6

表7

参考文献 19

1	娄奇鹤, 李彦斌, 赵宇尘, 等. 适应极端事件的配电网弹性规划和投资策略研究综述[J]. 电力建设, 2024, 45(5): 37-47.
	LOU Q H, LI Y B, ZHAO Y C, et al. A review of research on resilience planning and investment strategies for distribution networks adapted to extreme events[J]. Electric Power Construction, 2024, 45(5): 37-47.
2	LIN Y F, LI L, ZHANG J F, et al. A scenario-based stochastic model predictive control approach for microgrid operation at an Australian cotton farm under uncertainties[J]. International Journal of Electrical Power & Energy Systems, 2024, 159: 110025. DOI: 10.1016/j.ijepes.2024.110025.
3	SELCEN AYAZ M, MALEKPOUR M, AZIZIPANAH-ABARGHOOEE R, et al. Probabilistic photovoltaic generation and load demand uncertainties modelling for active distribution networks hosting capacity calculations[J]. International Journal of Electrical Power & Energy Systems, 2024, 159: 110016. DOI: 10.1016/j.ijepes.2024.110016.
4	APRIBOWO C H B, HADI S P, DANANG WIJAYA F, et al. Optimal sizing and placement of battery energy storage system for maximum variable renewable energy penetration considering demand response flexibility: A case in Lombok power system, Indonesia[J]. Energy Conversion and Management: X, 2024, 23: 100620. DOI: 10.1016/j.ecmx.2024.100620.
5	李鹏, 崔荣喜, 卢京祥, 等. 基于数据驱动的源网荷储协同自治方法与理论研究[J]. 供用电, 2024, 41(3): 9-16. DOI: 10.19421/j.cnki.1006-6357.2024.03.002.
	LI P, CUI R X, LU J X, et al. Research on method and theory of source-grid-load-storage collaborative autonomy based on data-driven[J]. Distribution & Utilization, 2024, 41(3): 9-16. DOI: 10.19421/j.cnki.1006-6357.2024.03.002.
6	曹永吉, 张江丰, 王天宇, 等. 基于分布式模型预测控制的自适应二次调频策略[J/OL]. 上海交通大学学报: 1-16[2024-05-31]. https://doi.org/10.16183/j.cnki.jsjtu.2023.352.
	CAO Y J, ZHANG J F, WANG T Y, et al. Adaptive secondary frequency regulation strategy based on distributed model predictive control. Journal of Shanghai Jiao Tong University, 1-16 [2024-05-31]. https://doi.org/10.16183/j.cnki.jsjtu.2023.352.
7	康晓华. 基于改进布谷鸟搜索算法的电力系统多目标运行优化研究[J]. 微型电脑应用, 2024, 40(4): 218-220, 225.
	KANG X H. Research on multi-objective operation optimization of power system based on modified cuckoo search algorithm[J]. Microcomputer Applications, 2024, 40(4): 218-220, 225.
8	孙斌, 王迪. 基于改进NSGA2算法的电力系统经济调度优化[J]. 机电工程技术, 2024, 53(3): 258-263.
	SUN B, WANG D. Economic dispatching optimization of power system based on improved NSGA2 algorithm[J]. Mechanical & Electrical Engineering Technology, 2024, 53(3): 258-263.
9	符杨, 刘智彬, 刘璐洁, 等. 基于主从博弈的海上风电接入系统鲁棒规划[J]. 电力系统自动化, 2023, 47(16): 54-65. DOI: 10.7500/AEPS20221217001.
	FU Y, LIU Z B, LIU L J, et al. Robust planning of offshore wind farm integration system based on Stackelberg game[J]. Automation of Electric Power Systems, 2023, 47(16): 54-65. DOI: 10.7500/AEPS20221217001.
10	李国庆, 董存, 梁志峰, 等. 考虑光伏发电不确定性的跨省两级优化调度模型[J]. 高电压技术, 2021, 47(12): 4420-4432. DOI: 10.13336/j.1003-6520.hve.20201300.
	LI G Q, DONG C, LIANG Z F, et al. Cross-provincial two-level optimal scheduling model considering the uncertainty of photovoltaic power generation[J]. High Voltage Engineering, 2021, 47(12): 4420-4432. DOI: 10.13336/j.1003-6520.hve. 20201300.
11	肖凯. 考虑低碳需求响应的用户侧储能模式分析与优化[J]. 储能科学与技术, 2024, 13(6): 1977-1979. DOI: 10.19799/j.cnki.2095-4239.2024.0490.
	XIAO K. Analysis and optimization of user-side energy storage mode considering low carbon demand response[J]. Energy Storage Science and Technology, 2024, 13(6): 1977-1979. DOI: 10.19799/j.cnki.2095-4239.2024.0490.
12	乔顺庆, 田桂珍, 张建伟, 等储能系统的参数自适应改进VDCM控制策略[J/OL]. 储能科学与技术, 1-13[2024-07-23]. https://doi.org/10.19799/j.cnki.2095-4239.2024.0504.
	QIAO S Q, TIAN G Z, ZHANG J W, et al. Parameter adaptive improved VDCM control strategy for energy storage systems[J/OL]. Energy Storage Science and Technology, 1-13 [2024-07-23]. https://doi.org/10.19799/j.cnki.2095-4239.2024.0504.
13	史昭娣, 王伟胜, 黄越辉, 等. 考虑决策相关随机规划的光热电站容量配置[J]. 中国电机工程学报, 2020, 40(23): 7511-7522. DOI: 10.13334/j.0258-8013.pcsee.191871.
	SHI Z D, WANG W S, HUANG Y H, et al. Capacity optimization of concentrating solar power plant based on decision-dependent stochastic programming[J]. Proceedings of the CSEE, 2020, 40(23): 7511-7522. DOI: 10.13334/j.0258-8013.pcsee.191871.
14	何琳, 黄博, 申亚波, 等. 基于混合智能优化算法的输变电工程全环节关键数据处理方法[J]. 沈阳工业大学学报, 2024, 46(3): 263-269.
	HE L, HUANG B, SHEN Y B, et al. Key data processing method based on hybrid intelligent optimization algorithm for full link of power transmission and transformation project[J]. Journal of Shenyang University of Technology, 2024, 46(3): 263-269.
15	于秋玲, 梁锦照, 陈康平. 基于云边协同的交直流混联电网线路过负荷协调控制方法[J]. 沈阳工业大学学报, 2024, 46(3): 241-247.
	YU Q L, LIANG J Z, CHEN K P. Coordinated control method based on cloud-edge coordination for AC/DC hybrid power grid line overload[J]. Journal of Shenyang University of Technology, 2024, 46(3): 241-247.
16	高伟, 薛杉, 胡秋霞, 等. 多模态深层次高置信度融合跟踪算法[J]. 计算机系统应用, 2024, 33(9): 153-163. DOI: 10.15888/j.cnki.csa.009633.
	GAO W, XUE S, HU Q X, et al. Multi-modal deep-level high-confidence fusion tracking algorithm[J]. Computer Systems and Applications, 2024, 33(9): 153-163. DOI: 10.15888/j.cnki.csa.009633.
17	向加佳, 刘建华, 朱雪松, 等. 基于分布鲁棒优化的光伏电站并网极限容量研究[J]. 电力建设, 2016, 37(6): 151-156. DOI: 10.3969/j.issn.1000-7229.2016.06.021.
	XIANG J J, LIU J H, ZHU X S, et al. Research on capacity limit of grid-connected photovoltaic power station based on distributional robust optimization[J]. Electric Power Construction, 2016, 37(6): 151-156. DOI: 10.3969/j.issn.1000-7229.2016.06.021.
18	杜帅, 贾黎明, 李真娣, 等. "光伏-储能"耦合参与调峰的配电网运行优化配置[J]. 储能科学与技术, 2024, 13(5): 1741-1743. DOI: 10.19799/j.cnki.2095-4239.2024.0364.
	DU S, JIA L M, LI Z D, et al. Optimization configuration of distribution network operation with "photovoltaic energy storage" coupling participation in peak shaving[J]. Energy Storage Science and Technology, 2024, 13(5): 1741-1743. DOI: 10.19799/j.cnki.2095-4239.2024.0364.
19	张楚, 陈栋才, 陈湘萍, 等. 多应用场景下储能最优配置经济性效益分析[J]. 储能科学与技术, 2024, 13(6): 2078-2088. DOI: 10.19799/j.cnki.2095-4239.2024.0055.
	ZHANG C, CHEN D C, CHEN X P, et al. Economic benefit analysis of optimal allocation of energy storage in multiple application scenarios[J]. Energy Storage Science and Technology, 2024, 13(6): 2078-2088. DOI: 10.19799/j.cnki.2095-4239.2024.0055.

置信度函数	适用场景	优点	缺点
三角形函数	能源调度	简单，计算量小	只能近似描述不确定性
正态分布	自然现象	通用，描述准确	对尾部不确定性处理不佳
指数分布	故障建模	适合描述极端事件	对常规事件描述不足
对数正态分布	金融市场	描述范围广	复杂度高，计算量大
矩形函数	均匀分布	易于理解	不适用于大多数实际场景

负荷等级	权重/%	持续时间/h
重	100	1500
中	70	5500
轻	50	2260

BU	DG安装的节点位置与年份	RC _i /元	RTC/元	ETC/元	τ_DP	τ_EP	τ_CR	τ_CC	τ_CO
0.00	第1年{10,13,14,16,27};第3年{30}	502424.01	4061545.12	5123006.26	0.00	0.00	0.00	0.00	0.00
0.25	第1年{10,13,14,16,17,22,28};第2年{30}	547895.45	5219632.32	5036651.18	0.02	0.21	0.41	0.08	0.31
0.50	第1年{10,13,14,16,17,22,28};第2年{30}	601552.15	6202304.59	4602201.27	0.08	0.18	0.51	0.18	0.84
0.75	第1年{10,13,14,16,17,22};第2年{28}	681320.26	7111032.31	4521698.65	0.16	0.310	0.64	0.31	0.98
1.00	第1年{10,13,14,16,17,22};第2年{24}; 第3年{26};第4年{30}	915203.32	8220035.23	4416497.26	0.44	0.55	0.21	0.21	0.21

BU	DG安装的节点位置与年份	RC _i /元	RTC/元	ETC/元	τ_DP	τ_EP	τ_CR	τ_CC	τ_CO
0.00	第1年{18,30,31,32,33};第3年{15}; 第4年{15};第6年{15};第7年{21}	6546842.26	7898569.62	9856269.26	0.00	0.00	0.00	0.00	0.00
0.25	第1年{10,18};第2年{30}	8565302.54	9852654.65	875462.98	0.09	0.01	0.01	0.35	0.35
0.50	第1年{10,13,14,16,17,22};第2年{15}; 第4年{18};第5年{26}	10259445.3	11265987.5	745898.65	0.09	0.18	0.18	0.85	0.51
0.75	第1年{15,18,26,27};第2年{29};第4年{25}	15687845.2	13658596.6	7146513.56	0.15	0.63	0.30	0.97	0.30
1.00	第1年{15,18,25};第3年{24};第4年{18}; 第5年{31}	18596478.5	14598965.6	7059846.79	0.12	0.58	0.58	0.58	0.24

置信度水平	传统优化方法总成本 /万元	改进的鲁棒优化方法总成本/万元
90%	1500	1200
95%	1300	1100
99%	1600	1250