Investigation of energy regulation performance based on entropy theory

doi:10.19799/j.cnki.2095-4239.2023.0940

Abstract

Abstract:

Considering the increasing importance of wind and solar energy, the power system increasingly faces prominent energy regulation pressure. Presently, energy storage, as an important supporting factor for building the new power system, faces problems such as high configuration costs and insufficient utilization. Based on Clausius entropy theory, this study constructs a physical quantity, power entropy, to quantify and evaluate the energy-time decoupling ability of an energy system. Taking an energy storage system as an analysis case, this study systematically studies the internal relationship between power entropy and key decision variables of energy storage configuration, such as power, duration, and capacity. It is found that there is a consistent positive correlation between power entropy and a variety of key decision variables, that can measure the comprehensive regulation performance of energy storage; additionally, power entropy also reflects the timing characteristics of energy storage. Based on these characteristics demonstrated by power entropy, this study establishes a power entropy-based optimization analysis method for power system energy storage configuration, and verifies the role of power entropy in energy storage configuration optimization through simulation case analysis. The results show that under different energy storage configurations, the increase in system power entropy is different; additionally, they show that the direction of system optimization is the direction in which the increase in power entropy introduced by energy storage decreases gradually, that is, the direction in which the redundancy of energy storage regulation performance decreases gradually. Thus, the concept of power entropy provides a new path for the optimization of energy storage configuration in future power systems and also for the optimization of multiple energy forms in the integrated energy system.

Key words: energy storage technology, optimal configuration, Clausius entropy, power entropy

CLC Number:

TM 911

Zhenxin SUN, Zhiming ZHANG, Fubo MA, Congjin JIANG, Haoyi DU, Huanjun CHEN, Yukui ZHANG. Investigation of energy regulation performance based on entropy theory[J]. Energy Storage Science and Technology, 2024, 13(5): 1584-1591.

Figures/Tables 7

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

References 14

1	曹一家, 王光增, 曹丽华, 等. 基于潮流熵的复杂电网自组织临界态判断模型[J]. 电力系统自动化, 2011, 35(7): 1-6.
	CAO Y J, WANG G Z, CAO L H, et al. An identification model for self-organized criticality of power grids based on power flow entropy[J]. Automation of Electric Power Systems, 2011, 35(7): 1-6.
2	蒋群. 电力系统可靠性裕度评估[D]. 北京: 华北电力大学, 2011.
	JIANG Q. Evaluation of power system reliability magin[D]. Beijing: North China Electric Power University, 2011.
3	苟竞, 刘俊勇, 刘友波, 等. 基于能量熵测度的电力系统连锁故障风险辨识[J]. 电网技术, 2013, 37(10): 2754-2761.
	GOU J, LIU J Y, LIU Y B, et al. Energy entropy measure based risk identification of power system cascading failures[J]. Power System Technology, 2013, 37(10): 2754-2761.
4	李志民, 李卫星, 王永建. 基于熵理论的最优潮流代理约束算法[J]. 电力系统自动化, 2001, 25(11): 28-31.
	LI Z M, LI W X, WANG Y J. Surrogate constraint algorithm for optimal power flow based on the entropy theory[J]. Automation of Electric Power Systems, 2001, 25(11): 28-31.
5	钟静, 吕飞鹏, 孔德洪, 等. 基于加权潮流转移熵的电网脆弱线路辨识[J]. 电测与仪表, 2016, 53(10): 22-26.
	ZHONG J, LV F P, KONG D H, et al. Vulnerable lines identification of power grid based on weighted transfer entropy of power flow[J]. Electrical Measurement & Instrumentation, 2016, 53(10): 22-26.
6	朱成骐, 孙宏斌, 张伯明. 基于最大信息熵原理的短期负荷预测综合模型[J]. 中国电机工程学报, 2005, 25(19): 1-6.
	ZHU C Q, SUN H B, ZHANG B M. A combined model for short term load forecasting based on maximum entropy principle[J]. Proceedings of the CSEE, 2005, 25(19): 1-6.
7	慈松, 刘前卫, 康重庆, 等. 从"信息-能量" 基本关系看信息能源深度融合[J]. 中国电机工程学报, 2021, 41(7): 2289-2297.
	CI S, LIU Q W, KANG C Q, et al. Fundamental exploration into ICT-energy fusion[J]. Proceedings of the CSEE, 2021, 41(7): 2289-2297.
8	管霖, 卓映君, 周保荣, 等. 复杂波动时间序列的多尺度分解算法及其在可再生能源发电建模应用中的性能评估[J]. 南方电网技术, 2020, 14(6): 11-16, 32.
	GUAN L, ZHUO Y J, ZHOU B R, et al. Multi-scale decomposition algorithms for complicated fluctuant time series and their performance evaluation in renewable energy generation modeling[J]. Southern Power System Technology, 2020, 14(6): 11-16, 32.
9	李浩, 钟声远, 王永真, 等. 基于能量与信息耦合的分布式能源系统配置优化方法[J]. 中国电机工程学报, 2020, 40(17): 5467-5476.
	LI H, ZHONG S Y, WANG Y Z, et al. Optimization method on the distributed energy system based on energy and information coupled[J]. Proceedings of the CSEE, 2020, 40(17): 5467-5476.
10	张儒峰, 姜涛, 李国庆, 等. 基于最大熵原理的电-气综合能源系统概率能量流分析[J]. 中国电机工程学报, 2019, 39(15): 4430-4441.
	ZHANG R F, JIANG T, LI G Q, et al. Maximum entropy based probabilistic energy flow calculation for integrated electricity and natural gas systems[J]. Proceedings of the CSEE, 2019, 39(15): 4430-4441.
11	陈中, 胡吕龙, 丁楠. 基于改进熵的风光储互补并网系统优化运行[J]. 电力系统保护与控制, 2013, 41(21): 86-91.
	CHEN Z, HU L, DING N. Optimized operation of wind-solar-battery hybrid power system based on improved entropy[J]. Power System Protection and Control, 2013, 41(21): 86-91.
12	ZHOU G Y, CHAN C C, ZHANG D, et al. Smart energy evolution road-map based on the correlation between energy and information[J]. Energy Procedia, 2019, 158: 3082-3087.
13	GUO H, XU Y J, CHEN H S, et al. Corresponding-point methodology for physical energy storage system analysis and application to compressed air energy storage system[J]. Energy, 2018, 143: 772-784.
14	李鹤龄. 信息熵、玻尔兹曼熵以及克劳修斯熵之间的关系——兼论玻尔兹曼熵和克劳修斯熵是否等价[J]. 大学物理, 2004, 23(12): 37-40.
	LI H L. The relation between information entropy, Boltzmann entropy and Clausius entropy[J]. College Physics, 2004, 23(12): 37-40.

[1]	Tianchen LI, Jianzheng YIN, Dawei ZHANG, Xiaoheng LIU. Research on renewable energy grid integration strategy based on hydropower station energy storage technology [J]. Energy Storage Science and Technology, 2024, 13(2): 677-679.
[2]	Su YAN, Fangfang ZHONG, Junwei LIU, Mei DING, Chuankun JIA. Key materials and advanced characterization of high-energy-density flow battery [J]. Energy Storage Science and Technology, 2024, 13(1): 143-156.
[3]	Jiajun ZHANG, Xiaoqiong LI, Zhentao ZHANG, Jiahao HAO, Pingyang ZHENG, Ze YU, Junling YANG, Yanan JING, Yunkai YUE. Research progress of compressed carbon dioxide energy storage system [J]. Energy Storage Science and Technology, 2023, 12(6): 1928-1945.
[4]	Tingting QIN, Xuezhi ZHOU, Dingzhang GUO, Yong SHENG, Yujie XU, Zhitao ZUO, Hui LI, Haisheng CHEN. Study on factors influencing rail gravity energy storage system efficiency [J]. Energy Storage Science and Technology, 2023, 12(3): 835-845.
[5]	Meiqian HOU, Qifan NIU, Jie XING, Yinghao SHAN. Optimal configuration of energy storage system in active distribution network with the consideration of reliability [J]. Energy Storage Science and Technology, 2023, 12(2): 504-514.
[6]	Zhiwei ZHAO, Zhi YANG, Zhangquan PENG. Application of time-of-flight secondary ion mass spectrometry in lithium-based rechargeable batteries [J]. Energy Storage Science and Technology, 2022, 11(3): 781-794.
[7]	Liangbo QIAO, Xiaohu ZHANG, Xianzhong SUN, Xiong ZHANG, Yanwei MA. Advances in battery-supercapacitor hybrid energy storage system [J]. Energy Storage Science and Technology, 2022, 11(1): 98-106.
[8]	Ziyan ZHANG, Junyan ZHANG. International competition of key energy storage technologies based on high-quality patents [J]. Energy Storage Science and Technology, 2022, 11(1): 321-334.
[9]	Yun TANG, Fang YUE, Kaimo GUO, Lanchun LI, Wei CHEN. International development trend analysis of next-generation electrochemical energy storage technology [J]. Energy Storage Science and Technology, 2022, 11(1): 89-97.
[10]	Delong ZHANG, Saif MUBAARAK, Siyu JIANG, Longze WANG, Jinxin LIU, Yongcong CHEN, Meicheng LI. Optimal allocation method of energy storage in PV station based on probabilistic power flow [J]. Energy Storage Science and Technology, 2021, 10(6): 2244-2251.
[11]	Zhonghao RAO, Chenzhen LIU, Yutao HUO, Jiateng ZHAO, Changhui LIU. Practice and exploration of teaching for interdisciplinary outstanding and innovative talents training oriented to energy storage technology [J]. Energy Storage Science and Technology, 2021, 10(3): 1206-1212.
[12]	LING Haoshu, HE Jingdong, XU Yujie, WANG Liang, CHEN Haisheng. Status and prospect of thermal energy storage technology for clean heating [J]. Energy Storage Science and Technology, 2020, 9(3): 861-868.
[13]	LIU Qinghua, ZHANG Sai, JIANG Mingzhe, WANG Qiushi, XING Xueqi, YANG Hong, HUANG Feng, LEMMON P John, MIAO Ping. Study on the low-cost flow battery technologies for energy storage [J]. Energy Storage Science and Technology, 2019, 8(S1): 60-64.
[14]	WANG Jianglin, XU Xueliang, DING Qingqing, ZHU Junping, MA Yongquan, ZHAO Lei, LIU Xiaowei. Application and prospect of zinc nickel battery in energy storage technology [J]. Energy Storage Science and Technology, 2019, 8(3): 506-511.
[15]	XIA Xinmao, GUAN Honghao, DING Pengfei, MENG Gaojun. Capacity allocation and optimization strategy of an energy storage system based on an improved quantum genetic algorithm [J]. Energy Storage Science and Technology, 2019, 8(3): 551-558.