基于马尔科夫链的自适应储能需求功率预测模型

doi:10.19595/j.cnki.1000-6753.tces.L90136

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摘要针对储能辅助火电机组二次调频时储能需求功率随机性强的问题,提出一种基于马尔科夫链的自适应储能需求功率预测模型。首先,针对火电机组响应自动发电控制（AGC）指令时功率随机变化性强且难以实时监测的特点,运用马尔科夫随机过程理论来描述储能未来有限时域内的需求功率,并采用后验功率信息实时自适应调整预测模型以适应AGC指令的周期性波动;然后,针对需求功率随机场景繁多的问题,提出一种可变预测时域的场景树生成方法来选择预测场景,该方法能够在树节点数一定的情况下更有效地选择场景;最后,进行了算例分析。结果表明,相比无自适应调整的马尔科夫模型,所提自适应预测模型的预测精度提高了8.28%;采用该文所提场景树方法的预测精度相对于固定场景树结构方法提高了6.67%,较极大似然估计法提高了4.65%。

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	何俊强
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	韦统振

关键词 ：自动发电控制（AGC）, 马尔科夫链, 预测模型, 场景树, 自适应调整

Abstract：Due to the uncertainty of power demand of energy storage system (ESS) when ESS participates in automatic generation control (AGC) with thermal generators, an adaptive ESS power demand prediction model based on Markov chain is proposed. Firstly, according to the uncertainty of the output power of thermal generators in response to the AGC command, the Markov chain is used to model the ESS power demand in prediction horizon, and a posteriori information is used to adapt to the fluctuations of the AGC command. Secondly, to reasonably select random scenarios of power demand, a scenario tree generation approach with variable prediction horizon is presented. The approach can select scenarios more effectively when the number of nodes is fixed. A simulation was implemented to validate the effectiveness of the prediction model. The results show that compared with the Markov model without adaptive adjustment, the presented adaptive prediction model can improve the prediction accuracy by 8.28%. The prediction accuracy of the presented scenario tree approach is improved by 6.67% compared with the fixed scenario tree structure method, and 4.65% higher than the maximum likelihood estimate method.

Key words： Automatic generation control (AGC) Markov chain prediction model scenario tree adaptive adjustment

收稿日期: 2020-06-30

PACS:

TM761

基金资助:中国科学院战略性先导科技专项（A类）（XDA21050302）和中国科学院青年创新促进会项目（2017180）资助

通讯作者: 师长立男,1984年生,工程师,研究方向为电力电子在电力系统中的应用、多类型储能及交直流配电网关键技术。E-mail：shichangli@mail.iee.ac.cn

作者简介: 何俊强男,1987年生,博士研究生,研究方向为储能系统在电力系统中的应用。E-mail：hejunqiang@mail.iee.ac.cn

引用本文:

何俊强, 师长立, 韦统振. 基于马尔科夫链的自适应储能需求功率预测模型[J]. 电工技术学报, 2021, 36(zk2): 563-571. He Junqiang, Shi Changli, Wei Tongzhen. Adaptive Power Demand Prediction Model of Energy Storage Based on Markov Chain. Transactions of China Electrotechnical Society, 2021, 36(zk2): 563-571.

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[1] Latif A, Hussain S S, Das D C.State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/ multi-area traditional and renewable energy based power systems[J]. Applied Energy, 2020, 266(114858).
[2] Ashouri Z A, Toulabi M, Dobakhshari A S.Frequency stability improvement in wind-thermal dominated power grids[J]. IET Generation, Transmission & Distribution, 2020, 14(4): 619-627.
[3] 汤杰, 李欣然, 黄际元, 等. 以净效益最大为目标的储能电池参与二次调频的容量配置方法[J]. 电工技术学报, 2019, 34(5): 963-972.
Tang Jie, Li Xinran, Huang Jiyuan, et al.Capacity allocation of BESS in secondary frequency regulation with the goal of maximum net benefit[J]. Transactions of China Electrotechnical Society, 2019, 34(5): 963-972.
[4] Xie Xiaorong, Guo Yonghong, Wang Bin, et al.Improving AGC performance of coal-fueled thermal generators using multi-MW scale BESS: a practical application[J]. IEEE Transactions on Smart Grid, 2018, 9(3): 1769-1777.
[5] Tan Y, Muttaqi K, Ciufo P.Enhanced frequency regulation using multilevel energy storage in remote area power supply systems[J]. IEEE Transactions on Power System, 2019, 34(1): 163-170.
[6] 马智慧, 李欣然, 谭庄熙, 等. 考虑储能调频死区的一次调频控制方法[J]. 电工技术学报, 2019, 34(10): 2102-2115.
Ma Zhihui, Li Xinran, Tan Zhuangxi, et al.Integrated control of primary frequency regulation considering dead band of energy storage[J]. Transactions of China Electrotechnical Society, 2019, 34(10): 2102-2115.
[7] Akram U, Nadarajah M, Shah R.A review on rapid responsive energy storage technologies for frequency regulation in modern power systems[J]. Renewable & Sustainable Energy Reviews, 2020, 120: 1-18.
[8] 胡泽春, 罗浩成. 大规模可再生能源接入背景下自动发电控制研究现状与展望[J]. 电力系统自动化, 2018, 42(8): 8-21.
Hu Zechun, Luo Haocheng.Research status and prospect of automatic generation control with integration large-scale renewable energy[J]. Automation of Electric Power Systems, 2018, 42(8): 8-21.
[9] 孙冰莹, 刘宗歧, 杨水丽, 等. 补偿度实时优化的储能-火电联合AGC策略[J]. 电网技术, 2018, 42(2): 426-433.
Sun Bingying, Liu Zongqi, Yang Shuili, et al.A real-Time optimization method of compensation degree for storage coordinated with thermal power unit in AGC[J]. Power System Technology, 2018, 42(2): 426-433.
[10] 孙丙香, 李旸熙, 龚敏明, 等. 参与AGC辅助服务的锂离子电池储能系统经济性研究[J]. 电工技术学报, 2020, 35(19): 4048-4061.
Sun Bingxiang, Li Yangxi, Gong Minming, et al.Study on the economy of energy storage system with lithium-ion battery participating in AGC auxiliary service[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4048-4061.
[11] 李欣然, 黄际元, 陈远扬, 等. 基于灵敏度分析的储能电池参与二次调频控制策略[J]. 电工技术学报, 2017, 32(12): 224-233.
Li Xinran, Huang Jiyuan, Chen Yuanyang, et al.Battery energy storage control strategy in secondary frequency regulation considering its action moment and depth[J]. Transactions of China Electrotechnical Society, 2017, 32(12): 224-233.
[12] Donadee J, Wang J.AGC signal modeling for energy storage operations[J]. IEEE Transactions on Power Systems, 2014, 29(5): 2567-2568.
[13] Cheng Y, Tabrizi M, Sahni M, et al.Dynamic available AGC based approach for enhancing utility scale energy storage performance[J]. IEEE Transactions on Smart Grid, 2014, 5(2): 1070-1078.
[14] 赵源筱, 耿光超, 江全元, 等. 考虑功率变化速率的储能辅助单机调频控制策略[J]. 电力自动化设备, 2020, 40(1): 141-147.
Zhao Yuanxiao, Geng Guangchao, Jiang Quanyuan, et al.Frequency control strategy of single-generator supporting by energy storage considering power change rate[J]. Electric Power Automation Equipment, 2020, 40(1): 141-147.
[15] 张圣祺, 袁蓓, 徐青山, 等. 规模化储能参与下的电网二次调频优化控制策略[J]. 电力自动化设备, 2019, 39(5): 82-88.
Zhang Shengqi, Yuan Bei, Xu Qingshan, et al.Optimal control strategy of secondary frequency regulation for power grid with large-scale energy storages[J]. Electric Power Automation Equipment, 2019, 39(5): 82-88.
[16] Wang Ying, Wan Can, Zhou Zhi, et al.Improving deployment availability of energy storage with data-driven AGC signal models[J]. IEEE Transactions on Power Systems, 2018, 33(4): 4207-4217.
[17] Ansari M, Awami A T, Sortomme E, et al.Coordinated bidding of ancillary services for vehicle-to-grid using fuzzy optimization[J]. IEEE Transactions on Smart Grid, 2015, 6(1): 261-270.
[18] 朱晨曦, 张焰, 严正, 等. 采用改进马尔科夫链蒙特卡洛法的风电功率序列建模[J]. 电工技术学报, 2020, 35(3): 577-589.
Zhu Chenxi, Zhang Yan, Yan Zheng, et al.A wind power time series modeling method based on the improved Markov chain Monte Carlo method[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 577-589.
[19] 张帅, 杨晶显, 刘继春, 等. 基于多尺度时序建模与估计的电力负荷数据恢复[J]. 电工技术学报, 2020, 35(13): 2736-2746.
Zhang Shuai, Yang Jingxian, Liu Jichun, et al.Power load recovery based on multi-scale time-series modeling and estimation[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2736-2746.
[20] 董雷, 刘梦夏, 陈乃仕, 等. 基于随机模型预测控制的分布式能源协调优化控制[J]. 电网技术, 2018, 42(10): 3219-3226.
Dong Lei, Liu Mengxia, Chen Naishi, et al.Coordinated optimal control of distributed energy based on stochastic model predictive control[J]. Power System Technology, 2018, 42(10): 3219-3226.
[21] Golchoubian P, Azad N L, Ponnambalam K.Stochastic nonlinear model predictive control of battery-supercapacitor hybrid energy storage systems in electric vehicles[C]//2017 American Control Conference (ACC), Seattle, WA, 2017: 1648-1653.