Peak Regulation Strategy of Power System Considering the Interaction of Source-Network-Load-Storage Under Different Penetration Rate of PV

doi:10.12204/j.issn.1000-7229.2021.09.008

Abstract

Abstract:

With the rapid growth of solar energy integration, the difference between power system peak and valley load becomes larger due to the PV fluctuating output and reverse peak regulation. Due to the traditional peak-regulating capacity and motivation factor, relying on the existing single peak-regulating resource is difficult to meet the power balance requirements. Therefore, a new peak regulation strategy considering different PV penetration rate is proposed in this paper. Firstly, the multi-dimension space-time characteristics of PV are modeled, and then by establishing the deep interaction based peak-regulating model including source, network, load and storage, the optimal system peak-regulating strategy is obtained, and the dynamic match of different PV integration and system peak-regulating capacity is achieved. The simulation results show that the proposed peak-regulating strategy can meet the system peak-regulating requirements under different PV penetration rate, and can ensure the balance between supply and demand sides and effectively reduce the power loss at the peak load.

Key words: multi-dimensional space-time characteristics, penetration rate, deep interaction of source-net-load-storage, peak regulation capacity of power system

CLC Number:

TM76

YANG Qian, LIU Jichun, JIANG Wanxiao. Peak Regulation Strategy of Power System Considering the Interaction of Source-Network-Load-Storage Under Different Penetration Rate of PV[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(9): 74-84.

Figures/Tables 14

Fig.1 Principle of deeply interactive peak regulation of source-network-load-storage

Fig.2 Grading response requirements

Fig.3 Deep interactive hub of source-net-load-storage

Fig.4 Matching flowchart of photovoltaic penetration rate and peak regulation capacity

Fig.A1 Diagram of the structure of the detailed system's network

Table A1 Power flow of part of the branches

Table 1 Parameters of thermal power units

Fig.5 Local load and PV forecast curves

Table 2 Resource participation under different penetration rate

Table 3 Each resource output and system total peak-regulating cost under different penetration rate

Fig.6 Cost comparison between the traditional method and the optimal strategy of source-network-load-storage

Fig.7 Cost of resource peak regulation under different penetration rate

Fig.8 Loss cost of peak-regulating units under different penetration rate

Fig.9 Peak regulation strategies under different penetration rate

References 21

[1]	戚军, 张晓峰, 张有兵, 等. 考虑阴影影响的光伏阵列仿真算法研究[J]. 中国电机工程学报, 2012, 32(32):131-138.
	QI Jun, ZHANG Xiaofeng, ZHANG Youbing, et al. Study on simulation algorithm of PV array considering shade effect[J]. Proceedings of the CSEE, 2012, 32(32):131-138.
[2]	李乐, 刘天琪. 基于近邻传播聚类和回声状态网络的光伏预测[J]. 电力自动化设备, 2016, 36(7):41-46.
	LI Le, LIU Tianqi. PV power forecasting based on AP-ESN[J]. Electric Power Automation Equipment, 2016, 36(7):41-46.
[3]	张程熠, 唐雅洁, 李永杰, 等. 适用于小样本的神经网络光伏预测方法[J]. 电力自动化设备, 2017, 37(1):101-106.
	ZHANG Chengyi, TANG Yajie, LI Yongjie, et al. Photovoltaic power forecast based on neural network with a small number of samples[J]. Electric Power Automation Equipment, 2017, 37(1):101-106.
[4]	林俐, 田欣雨. 基于火电机组分级深度调峰的电力系统经济调度及效益分析[J]. 电网技术, 2017, 41(7):2255-2263.
	LIN Li, TIAN Xinyu. Analysis of deep peak regulation and its benefit of thermal units in power system with large scale wind power integrated[J]. Power System Technology, 2017, 41(7):2255-2263.
[5]	马丽叶, 王志强, 陆肖宇, 等. 基于机会约束规划的风-火-蓄联合系统优化调度[J]. 电网技术, 2019, 43(9):3311-3320.
	MA Liye, WANG Zhiqiang, LU Xiaoyu, et al. Optimal scheduling of combined wind-thermo-storage system based on chance constrained programming[J]. Power System Technology, 2019, 43(9):3311-3320.
[6]	葛维春, 刘前卫, 刘富家, 等. 高比例清洁能源电网灵活调节方法[J]. 沈阳工业大学学报, 2018, 40(5):481-485.
	GE Weichun, LIU Qianwei, LIU Fujia, et al. Flexible adjustment method for power grid with high-proportion clean energy[J]. Journal of Shenyang University of Technology, 2018, 40(5):481-485.
[7]	张晓辉, 江静, 李茂林, 等. 考虑柔性负荷响应的含风电场电力系统多目标经济调度[J]. 电力系统自动化, 2017, 41(11):61-67.
	ZHANG Xiaohui, JIANG Jing, LI Maolin, et al. Multi-objective economic dispatch of power system with wind farms considering flexible load response[J]. Automation of Electric Power Systems, 2017, 41(11):61-67.
[8]	王志南, 张宇华, 黄珂, 等. 计及多种柔性负荷的虚拟电厂热电联合鲁棒优化调度模型[J]. 电力建设, 2021, 42(7):1-10.
	WANG Zhinan, ZHANG Yuhua, HUANG Ke, et al. Robust optimal scheduling model of virtual power plant combined heat and power considering multiple flexible loads[J]. Electric Power Construction, 2021, 42(7):1-10.
[9]	李淑鑫, 刘文颖, 李亚龙, 等. 荷源联合调峰运行方案的电力节能评估研究[J]. 电力系统保护与控制, 2016, 44(12):7-14.
	LI Shuxin, LIU Wenying, LI Yalong, et al. Evaluation of energy-saving on peak load regulation scheme based on source-load coordination[J]. Power System Protection and Control, 2016, 44(12):7-14.
[10]	NYKAMP S, MOLDERINK A, HURINK J L, et al. Storage operation for peak shaving of distributed PV and wind generation[C]// 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT).IEEE, 2013: 1-6.
[11]	李付强, 王彬, 涂少良, 等. 京津唐电网风力发电并网调峰特性分析[J]. 电网技术, 2009, 33(18):128-132.
	LI Fuqiang, WANG Bin, TU Shaoliang, et al. Analysis on peak load regulation performance of Beijing-Tianjin-Tangshan power grid with wind farms connected[J]. Power System Technology, 2009, 33(18):128-132.
[12]	杨天蒙. 基于状态聚类的含大规模风电电力系统调峰充裕性评估[J]. 陕西电力, 2017, 45(1):22-26.
	YANG Tianmeng. Peak-load regulation adequacy evaluation of power system with high wind power penetration based on states clustering[J]. Shaanxi Electric Power, 2017, 45(1):22-26.
[13]	蒋万枭, 刘继春, 韩晓言, 等. 离网条件下考虑短时间尺度的水光蓄多能互补发电系统备用容量确定方法[J]. 电网技术, 2020, 44(7):2492-2502.
	JIANG Wanxiao, LIU Jichun, HAN Xiaoyan, et al. Reserve optimization for offline multi-energy complementary generation system in short time scale[J]. Power System Technology, 2020, 44(7):2492-2502.
[14]	高伯阳, 吴熙, 王亮, 等. 线间潮流控制器技术现状分析及展望[J]. 浙江电力, 2019, 38(2):7-14.
	GAO Boyang, WU Xi, WANG Liang, et al. Technical status and prospect of interline power flow controller[J]. Zhejiang Electric Power, 2019, 38(2):7-14.
[15]	张晓辉, 江静, 李茂林, 等. 考虑柔性负荷响应的含风电场电力系统多目标经济调度[J]. 电力系统自动化, 2017, 41(11):61-67.
	ZHANG Xiaohui, JIANG Jing, LI Maolin, et al. Multi-objective economic dispatch of power system with wind farms considering flexible load response[J]. Automation of Electric Power Systems, 2017, 41(11):61-67.
[16]	SONG X F, PAN L, ZHANG Z Q, et al. Source-Network-Load coordination planning study based on the big data from electric power metering[C]// 2016 IEEE International Conference on Power and Renewable Energy (ICPRE). IEEE, 2016: 339-344.
[17]	ZOU P, CHEN Q X, XIA Q, et al. Evaluating the contribution of energy storages to support large-scale renewable generation in joint energy and ancillary service markets[J]. IEEE Transactions on Sustainable Energy, 2016, 7(2):808-818. doi: 10.1109/TSTE.2015.2497283 URL
[18]	崔杨, 周慧娟, 仲悟之, 等. 考虑火电调峰主动性与需求响应的含储能电力系统优化调度[J]. 高电压技术, 2021, 47(5):1674-1684.
	CUI Yang, ZHOU Huijuan, ZHONG Wuzhi, et al. Optimal dispatch of power system with energy storage considering deep peak regulation initiative of thermal power and demand response[J]. High Voltage Engineering, 2021, 47(5):1674-1684.
[19]	李海波, 鲁宗相, 乔颖. 源荷储一体化的广义灵活电源双层统筹规划[J]. 电力系统自动化, 2017, 41(21):46-54.
	LI Haibo, LU Zongxiang, QIAO Ying. Bi-level optimal planning of generation-load-storage integrated generalized flexibility resource[J]. Automation of Electric Power Systems, 2017, 41(21):46-54.
[20]	宋艺航, 谭忠富, 李欢欢, 等. 促进风电消纳的发电侧、储能及需求侧联合优化模型[J]. 电网技术, 2014, 38(3):610-615.
	SONG Yihang, TAN Zhongfu, LI Huanhuan, et al. An optimization model combining generation side and energy storage system with demand side to promote accommodation of wind power[J]. Power System Technology, 2014, 38(3):610-615.
[21]	TANT J, GETH F, SIX D, et al. Multiobjective battery storage to improve PV integration in residential distribution grids[J]. IEEE Transactions on Sustainable Energy, 2013, 4(1):182-191. doi: 10.1109/TSTE.2012.2211387 URL