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分布式能源  2020, Vol. 5 Issue (3): 1-8    DOI: 10.16513/j.2096-2185.DE.2005003
  储能新技术及应用 本期目录 | 过刊浏览 |
新能源站侧规模化应用储能的电力系统运行模拟
冉亮1,郭建华2,袁铁江2
1.国网甘肃省电力公司,甘肃 兰州 730000
2.大连理工大学电气工程学院,辽宁 大连 116024
Power System Operation Simulation of Large-Scale Energy Storage on New Energy Station
 RAN Liang1, GUO Jianhua2,YUAN Tiejiang 2
1. State Grid Gansu Power Company, Lanzhou 730000, Gansu Province, China
2. School of Electrical Engineering, Dalian University of Technology, Dalian 116024, Liaoning Province, China
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摘要: 

为了消除新能源的随机性和波动性给电网调度带来的影响﹐在新能源站侧规模化引入储能﹐文章分析了储能系统对含新能源的电力系统运行模拟的影响。结合电力系统运行过程中不同模块功能的特点,提出了含储能的新能源电力系统运行模拟框架,主要包括:在新能源出力模拟方面,利用神经网络对新能源出力进行预测;在经济调度方面,建立含储能的新能源系统经济调度模型﹐其目标函数兼顾了火电排污成本,考虑了备用容量补偿成本及风电场负效率运行惩罚成本;储能成本方面,考虑了初始投资成本和运维成本;在运行分析方面,在超短周期内,利用新能源平均功率变化率以及反调峰概率2个指标对储能的平抑功率波动能力和调峰能力进行评估,进而得到综合经济、环境、抑制波动和调峰能力的运行模拟方案。采用遗传算法以新疆电网为例进行仿真验证,结果表明,新能源站侧规模化应用储能可以有效平抑新能源输出功率的波动和反调峰现象。

关键词: 新能源运行模拟储能系统功率波动调峰    
Abstract

The randomness and volatility of new energy bring difficulties to grid dispatching. Therefore, large-scale energy storage is introduced at the side of new energy stations, and the impact of the energy storage system on the operation simulation of power systems containing new energy is analyzed. This paper combines the characteristics of different modules with different functions during the operation of the power system, and proposes a simulation framework for the operation of new energy power systems with energy storage. It mainly includes: in the simulation of new energy output, using neural networks to predict the output of new energy; in the economy in terms of dispatching, an economic dispatch model for new energy systems with energy storage is established. Its objective function takes into account the cost of thermal power emissions, and considers the cost of reserve capacity compensation and the penalty cost of negative efficiency operation of wind farms. In terms of energy storage costs, the initial investment cost and operation and maintenance cost; in terms of operation analysis, in the ultra-short period, the two indicators of average energy change rate of new energy and peak inversion probability are used to evaluate the energy storage's ability to suppress power fluctuations and peak shaving; Operation simulation program for environment, suppression of fluctuations and peak shaving ability. Using genetic algorithm to take Xinjiang power grid as an example for simulation verification, the results show that the large-scale application of energy storage on the side of new energy stations can effectively suppress the fluctuation and reverse peaking of new energy output power.

Key Wordsnew energy poweroperation simulationenergy storage systempower fluctuationpeak shaving
收稿日期: 2020-05-02
ZTFLH:  TK02  
基金资助:中央高校基本科研业务项目(DUT20RC(5)021)
作者简介: 冉 亮(1983),男,硕士,正高级工程师,研究方向为电力系统及其自动化,28967414@qq.com;|郭建华(1996),男,硕士研究生,研究方向为电力系统及其自动化,gjh877466544@163.com;|袁铁江(1975),男,博士,教授,研究方向为电力系统及其自动化,xjuytj@163.com。

引用本文:

冉亮, 郭建华, 袁铁江. 新能源站侧规模化应用储能的电力系统运行模拟[J]. 分布式能源, 2020, 5(3): 1-8.
RAN Liang , GUO Jianhua, YUAN Tiejiang . Power System Operation Simulation of Large-Scale Energy Storage on New Energy Station[J]. Distributed Energy, 2020, 5(3): 1-8.

链接本文:

http://der.tsinghuajournals.com/CN/10.16513/j.2096-2185.DE.2005003      或      http://der.tsinghuajournals.com/CN/Y2020/V5/I3/1

图1  含储能的新能源系统运行模拟框架
图2  典型日新能源预测功率
图3  典型日系统负荷
图4  典型日未接入储能调度结果
图5  典型日风光火储调度结果
图6  典型日储能充放电功率
表1  系统平抑波动/调峰能力校验结果
图7  新能源渗透率20%、30%、40%时储能充放电功率
图8  功率波动率指标和反调峰概率指标随储能容量的变化情况
图9  功率波动率指标和反调峰概率指标随储能充放电功率限制的变化情况
[1] 江全元,龚裕仲. 储能技术辅助风电并网控制的应用综述[J]. 电网技术,2015, 39(12): 3360-3368.
[1] JIANG Quanyuan, GONG Yuzhong. Review of wind power integration control with energy storage technology[J]. Power System Technology, 2015, 39 (12): 3360-3368.
[2] 何柳,粟时平,罗雪莲,等. 风电并网暂态电能质量扰动的检测与识别[J]. 电力电容器与无功补偿,2020, 41(1): 221-226.
[2] HE Liu, SU Shiping, LUO Xuelian, et al. Detection and identification of transient power quality disturbance of wind power grid-connection[J]. Power Capacitor & Reactive Power Compensation, 2020, 41(1): 221-226.
[3] 李华,王思民,高杰. 一种基于跟踪计划的风光储联合发电系统储能 控制策略研究[J]. 电器与能效管理技术,2019(4): 71-78.
[3] LI Hua, WANG Simin, GAO Jie. Research on energy storage control strategy of wind solar storage combined generation system based on tracking plan[J]. Electrical & Energy Management, 2019(4): 71-78.
[4] 刘燕华,张楠,张旭. 考虑储能运行成本的风光储微网的经济运行[J]. 现代电力,2013, 30(5): 13-18.
[4] LIU Yanhua, ZHANG Nan, ZHANG Xu. Economic operation of Wind-PV-ES hybrid microgrid by considering of energy storage operational cost[J]. Modern Electric Power, 2013, 30(5): 13-18.
[5] 马洲俊,程浩忠,丁昊,等. 含不确定性电源的电力系统柔性生产模拟[J]. 电力系统保护与控制,2013, 41(17): 63-70.
[5] MA Zhoujun, CHENG Haozhong, DING Hao, et al. Flexible production simulation for power system with uncertain energy[J]. Power System Protection and Control, 2013, 41 (17): 63-70.
[6] 杨晓萍,刘浩杰,黄强. 考虑分时电价的风光储联合“削峰”优化调度模型[J]. 太阳能学报,2018, 39(6): 1752-1760.
[6] YANG Xiaoping, LIU Haojie, HUANG Qiang. Optimal dispatching model of wind-sunlight-storage combining with“peak shaving”considering time-of-use electricity price[J]. Acta Energiae Solaris Sinica, 2018, 39 (6): 1752-1760.
[7] 周玮,孙辉,顾宏,等. 计及风险备用约束的含风电场电力系统动态经济调度[J]. 中国电机工程学报,2012, 32(1): 47-55, 19.
[7] ZHOU Wei, SUN Hui, GU Hong, et al. Dynamic economic dispatch of wind integrated power systems based on risk reserve constraints[J]. Proceedings of the CSEE 2012, 32(1): 47-55, 19.
[8] 龚建原,卢继平,章耿勇,等. 含储能系统及风电的电力系统动态经济调度[J]. 重庆师范大学学报(自然科学版), 2013, 30(6): 140-146.
[8] GONG Jianyuan, LU Jiping, ZHANG Gengyong, et al. Environmental and economic dispatch of grid connected large-scale wind farm energy and storage system[J]. Journal of Chongqing Normal University (Natural Science Edition), 2013, 30(6): 140-146.
[9] OREEA V, HASSENA S Z S, FLEMING P J. Generation expansion planning optimisation with renewable energy integration: A review[J]. Renewable and Sustainable Energy Reviews, 2017, 69: 790-803.
[10] 鹿婷,贾继超,彭晓涛. 一种考虑经济调度的风电场储能控制策略[J]. 分布式能源,2019, 4(3): 40-49.
[10] LU Ting, JIA Jichao, PENG Xiaotao. An energy storage control strategy for wind farm considering economic dispatching[J]. Distributed Energy, 2019, 4 (3): 40-49
[11] 袁铁江,车勇,孙谊媊,等. 基于时序仿真和GA的风储系统储能容量优化配比[J]. 高电压技术,2017, 43(7): 2122-2130.
[11] YUAN Tiejiang, CHE Yong, SUN Yili, et al. Optimized proportion of energy storage capacity in wind-storage system based on timing simulation and GA algorithm[J]. High Voltage Engineering, 2017, 43 (7): 2122-2130.
[12] 袁铁江,晁勤,吐尔逊·伊不拉音,等. 大规模风电并网电力系统动态清洁经济优化调度的建模[J]. 中国电机工程学报,2010, 30(31): 7-13.
[12] YUAN Tiejiang, CHAO Qin, Tulson Ibrahim, et al. Optimized economic and environment-friendly dispatching modeling for large-scale wind power integration[J]. Proceedings of the CSEE, 2010, 30 (31): 7-13.
[13] 周双喜,王海超,陈寿孙. 风力发电运行价值分析[J]. 电网技术,2006, 30(14): 98-102.
[13] ZHOU Shuangxi, WANG Haichao, CHEN Shousun. Analysis on operation value of wind power resources[J]. Power System Technology, 2006, 30(14): 98-102.
[14] 马美婷,袁铁江,陈广宇,等. 储能参与风电辅助服务综合经济效益分析[J]. 电网技术,2016, 40(11): 3362-3367
[14] MA Meiting, YUAN Tiejiang, CHEN Guangyu, et al. Analysis on economic benefit of energy storage in auxiliary service of wind power[J]. Power System Technology, 2016, 40(11): 3362-3367.
[15] 巩俊强,邓浩,谢莹华. 储能技术分类及国内大容量蓄电池储能技术比较[J]. 中国科技信息,2012(9): 139-140.
[15] GONG Junqiang, DENG Hao, XIE Yinghua. Energy storage technology classification and comparison of domestic large-capacity battery energy storage technologies[J]. China Science and Technology Information, 2012(9): 139-140.
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