ASSESSMENT OF WIND POWER ACCOMMODATION CAPABILITY IN ELECTRIC HEATING COMBINED SYSTEMS WITH BATTERY ENERGY STORAGE SYSTEMS

Zhu Jianfeng; Zhang Xinsong; Jiang Keke; Chen Pei; Li Daxiang

doi:10.19912/j.0254-0096.tynxb.2021-0487

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (11) : 451-459. DOI: 10.19912/j.0254-0096.tynxb.2021-0487

ASSESSMENT OF WIND POWER ACCOMMODATION CAPABILITY IN ELECTRIC HEATING COMBINED SYSTEMS WITH BATTERY ENERGY STORAGE SYSTEMS

Zhu Jianfeng, Zhang Xinsong, Jiang Keke, Chen Pei, Li Daxiang

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Abstract

To promote wind power accommodation and decrease wind power curtailments, battery energy storage systems （BESS） are incorporated into the electric heating combined systems. A wind power scenario probabilistic model is established based on probability characteristics of the wind power prediction errors to describe random characteristics of the wind power. Next, a wind power accommodation capability evaluation model is formulated for the electric heating combined systems containing BESS. The evaluation model has two optimization objectives with different dimensions： one is to minimize the operation costs, the other is to minimize the curtailed wind energy. There may be conflicts between the objectives. To solve the evaluation model, it is transformed into multiple single-objective optimization problems by means of the improved main objective method. These single-objective optimization problems are solved by using DICOPT solver in GAMS, so that the Pareto solution set of the evaluation model can be obtained. Based on the Pareto solution set, wind power accommodation capability of the electric heating combined systems is analyzed in depth from two aspects of accommodating wind power and costs. Finally, a simulation example is given to verify the effectiveness of the proposed model and algorithm.

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wind power / battery storage / multi-objective optimization / electric heating combined system / assessment of wind power accommodation capability

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Zhu Jianfeng, Zhang Xinsong, Jiang Keke, Chen Pei, Li Daxiang. ASSESSMENT OF WIND POWER ACCOMMODATION CAPABILITY IN ELECTRIC HEATING COMBINED SYSTEMS WITH BATTERY ENERGY STORAGE SYSTEMS[J]. Acta Energiae Solaris Sinica. 2022, 43(11): 451-459 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0487

References

[1] 国家能源局. 我国风电利用水平不断提升[EB/OL]. http://www.nea.gov.cn/2020-10/30/c_139478910.htm, 2020-10-30.
[2] 李军徽, 邢志同, 穆钢, 等. 供热期调峰约束下电网弃风情况分析[J]. 太阳能学报, 2018, 39(3): 596-602.
LI J H, XING Z T, MU G, et al.Analysis on wind power curtailment under restriction of peak load regulation during heating period[J]. Acta energiae solaris sinica, 2018, 39(3): 596-602.
[3] 国家能源局. 2019年风电并网运行情况[EB/OL]. http://www.nea.gov.cn/2020-02/28/c_138827910.htm,2020-02-28.
[4] 蔡霁霖, 徐青山, 袁晓冬, 等. 基于风电消纳时序场景的电池储能系统配置策略[J]. 高电压技术, 2019, 45(3): 993-1001.
CAI J L, XU Q S, YUAN X D, et al.Configuration strategy of large-scale battery storage system orienting wind power consumption based on temporal scenarios[J]. High voltage engineering, 2019, 45(3): 993-1001.
[5] 车勇, 彭超锋, 袁铁江, 等. 计及储能的风电平衡区域电网优化划分方法[J]. 电网技术, 2017, 41(3): 775-781.
CHE Y, PENG C F, YUAN T J, et al.An optimized partitioning method balancing wind power in local power grid considering energy storage system[J]. Power system technology, 2017, 41(3): 775-781.
[6] 任建文, 许英强, 董圣孝. 考虑储能参与的含高比例风电互联电力系统分散式调度模型[J]. 电网技术, 2018, 42(4): 1079-1086.
REN J W, XU Y Q, DONG S X.A Decentralized scheduling model with energy storage participation for interconnected power system with high wind power penetration[J]. Power system technology, 2018, 42(4): 1079-1086.
[7] 严干贵, 刘嘉, 崔杨, 等. 利用储能提高风电调度入网规模的经济性评价[J]. 中国电机工程学报, 2013, 33(22): 45-52.
YAN G G, LIU J, CUI Y, et al.Economic evaluation of improving the wind power scheduling scale by energy storage system[J]. Proceedings of the CSEE, 2013, 33(22): 45-52.
[8] 张新松, 袁越, 曹阳. 考虑损耗成本的电池储能电站建模及优化调度[J]. 电网技术, 2017, 41(5): 1541-1548.
ZHANG X S, YUAN Y, CAO Y.Modeling and scheduling for battery energy storage station with consideration of wearing costs[J]. Power system technology, 2017, 41(5): 1541-1548.
[9] 张粒子, 李丰, 叶红豆, 等. 考虑风电和负荷波动及N-1故障的发电备用优化方法研究[J]. 太阳能学报, 2014, 35(1): 64-73.
ZHANG L Z, LI F, YE H D, et al.Optimal reserve dispatch approach considering wind power and load fluctuations and N-1 fault[J]. Acta energiae solaris sinica, 2014, 35(1): 64-73.
[10] 杨茂, 杨琼琼, 苏欣. 基于风电场等效平均风速的风电功率日前预测研究[J]. 太阳能学报, 2020, 41(2): 85-92.
YANG M, YANG Q Q, SU X.Research of day-ahead wind power forecast based on wind farm equivalent mean wind speed[J]. Acta energiae solaris sinica, 2020, 41(2): 85-92.
[11] 吴晓刚, 孙荣富, 乔颖, 等. 基于风电场功率特性的风电预测误差分布估计[J]. 电网技术, 2017, 41(6): 1801-1807.
WU X G, SUN R F, QIAO Y, et al.Estimation of error distribution for wind power prediction based on power curves of wind farms[J]. Power system technology, 2017, 41(6): 1801-1807.
[12] 庞文涛, 盛德仁, 陈坚红, 等. 含风电系统的多机组协调运行滚动策略[J]. 太阳能学报, 2020, 41(11): 234-240.
PANG W T, SHENG D R, CHEN J H, et al.Rolling strategy for multi unit coordinated operation with wind power system[J]. Acta energiae solaris sinica, 2020, 41(11): 234-240.
[13] 袁越, 吴博文, 李振杰, 等. 基于多场景概率的含大型风电场的输电网柔性规划[J]. 电力自动化设备, 2009, 29(10): 8-12.
YUAN Y, WU B W, LI Z J, et al.Flexible planning of transmission system with large wind farm based on multi-scenario probability[J]. Electric power automation equipment, 2009, 29(10): 8-12.
[14] 张晓辉, 闫柯柯, 卢志刚, 等. 基于场景概率的含风电系统多目标低碳经济调度[J]. 电网技术, 2014, 38(7): 1835-1841.
ZHANG X H, YAN K K, LU Z G, et al.Scenario probability based multi-objective optimized low-carbon economic dispatching for power grid integrated with wind farms[J]. Power system technology, 2014, 38(7): 1835-1841.
[15] 熊强, 陈维荣, 张雪霞, 等. 考虑多风电场相关性的场景概率潮流计算[J]. 电网技术, 2015, 39(8): 2154-2159.
XIONG Q, CHEN W R, ZHANG X X, et al.Scenario probabilistic load flow calculation considering wind farms correlation[J]. Power system technology, 2015, 39(8): 2154-2159.
[16] 吕泉, 李玲, 朱全胜, 等. 三种弃风消纳方案的节煤效果与国民经济性比较[J]. 电力系统自动化, 2015, 39(7): 75-83.
LYU Q, LI L, ZHU Q S, et al.Comparison of coal-saving effect and national economic indices of three feasible curtailed wind power accommodating strategies[J]. Automation of electric power systems, 2015, 39(7): 75-83.
[17] 吕泉, 陈天佑, 王海霞, 等. 含储热的电力系统电热综合调度模型[J]. 电力自动化设备, 2014, 34(5): 79-85.
LYU Q, CHEN T Y, WANG H X, et al.Combined power and heat dispatch model of power system with heat accumulator[J]. Electric power automation equipment, 2014, 34(5): 79-85.
[18] 贺莉, 刘庆怀. 多目标优化理论与连续化方法[M]: 北京: 科学出版社, 2015: 96-106.
HE L, LIU Q H.Multi-objective optimization theory and continuum method[M]. Beijing: Science Press, 2015: 96-106.
[19] 荣爽. 促进供暖期风电消纳的多热源容量规划与协调调度策略[D]. 哈尔滨: 哈尔滨工业大学, 2016.
RONG S.Capacity planning and coordinative dispatching strategy of multi-heating sources to the promotion of wind power consumption during heating season[D]. Harbin: Harbin Institute of Technology, 2016.