Muti-objective robust optimization method for energy storage stations considering confidence theory

doi:10.19799/j.cnki.2095-4239.2024.0691

Abstract

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

CLC Number:

TM72

Kaikai WANG, Yang 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, doi: 10.19799/j.cnki.2095-4239.2024.0691.

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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4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