Welcome to Francis Academic Press

The Frontiers of Society, Science and Technology, 2021, 3(6); doi: 10.25236/FSST.2021.030602.

Considering various optimization model of energy storage in wind power load auxiliary trading game


Wenxu Tian

Corresponding Author:
Wenxu Tian

CHN Energy Longyuan Power Technology &Engineering Co., Ltd.  Haidian Beijing, 100039, China


In winter heating period, the heating mode of "setting electricity by heat" in northeast China causes a large number of wind abandoning, and the wind abandoning absorption rate can be improved by absorbing heat and absorbing wind. The operation of the traditional heat storage system is not linked with the wind farm, and there are still many wind curtailment. In order to track the purchase of wind curtailment in real time, based on the pre-evaluation of short-term power of wind curtailment, a day-before market centralized transaction mode of wind power curtailment reported by wind farms and demand function reported by heat storage enterprises was proposed in the market of auxiliary peak shaving service for wind power storage. By establishing the multi-party game optimal demand function model of heat storage enterprises, the unified clearing price and wind abandoning subscription situation are obtained, and the intra-day settlement method is proposed for intra-day unbalanced electricity quantity.


Electric heat storage, Wind abandoning pre-assessment, Centralized trading, Demand function

Cite This Paper

Wenxu Tian. Considering various optimization model of energy storage in wind power load auxiliary trading game. The Frontiers of Society, Science and Technology (2021) Vol. 3, Issue 6: 4-15. https://doi.org/10.25236/FSST.2021.030602.


[1] http://www.nea.gov.cn/2019-01/28/c_137780779.htm

[2] Wang Xiaohai, Qiao Ying, Lu Zongxiang, et al.A novel method to assess wind energy usage in the heat-supplied season[J].Proceedings of the CSEE, 2015, 35(9):2112-2119.

[3] Wang Caixia, Li Qionghui, Xie Guohui.Evaluation of Wind Power Heating in Facilitating Wind Power Integration Capability during Valley Load Period[J].Electric Power, 2013, 46(12):100-106.

[4] Lü Quan, Chen Tianyou, Wang Haixia, et al.Analysis on peak-load regulation ability of cogeneration unit with heat accumulator[J].Electric Power Automation Equipment, 2014, 38(11):34-41. DOI: 10.7500/AEPS20130724002.

[5] Lü Quan, Li Ling, Zhu Quansheng, 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.DOI: 10.7500/AEPS20140125001.

[6] Lü Quan, Jiang Hao, Chen Tianyou, et al.Wind power accommodation by combined heat and power plant with electric boiler and its national economic evaluation[J].Automation of Electric Power Systems, 2014, 38(1):6-12.DOI: 10.7500/AEPS201206124.

[7] Meibom P, Kiviluoma J, Barth R, et al. Value of electric heat boilers and heat pumps for wind power integration[J]. Wind Energy, 2007, 10(4):321-337.

[8] Papaefthymiou G, Hasche B, Nabe C. Potential of heat pumps for demand side management and wind power integration in the German electricity market[J]. IEEE Transactions on Sustainable Energy, 2012, 3(4):636-642.

[9] Ge Yanfeng, Li Xiaofei, Ge Yangyang, et al.Technical plan for electric heat storage and heating by wind energy curtailment based on joint dispatching of heat and electricity[J].Smart Grid, 2015, 3(10):901-905.

[10] Wang Caixia, Li Qionghui, Xie Guohui.Pricing mechanism and economic analysis of heating supply by wind power [J].Electric Power, 2014, 47(10):156-160.

[11] Lü Quan, Li Ling, Wang Haixia, et al.Peak regulation pricing mechanism between CHP-plant with heat accumulator and wind farm [J]. Electric Power Automation Equipment, 2015, 35(9):118-124.

[12] the National Energy Administration northeast regulator. Northeast power auxiliary service market operation rules (trial) [Z]. Shenyang: northeast National Energy Administration supervision bureau, 2016.

[13] Wang Ruichen, Zhang Maolin, Huang Songbo, et al.Design and Application of Bidding Rules in Yunnan Power Market Based on HHI and Game Theory[J].Yunnan Electric Power, 2017, 45(6):115-119.

[14] Fan Jie, Yang Libing, Li Xiaogang, et al.Simulation analysis on Trans-provincial centralized trade platform based on smart agent model [J].Automation of Electric Power Systems, 2013, 37(9):60-65.

[15] Jing Zhaoxia, Zhu Jisong.Simulation experiment analysis on market rules for monthly centralized bidding [J].Automation of Electric Power Systems, 2013, 41(24):42-48.

[16] Wang Baohong. Yunnan electric power market monthly collective trading through bidding model research [D]. Yunnan, yunnan university of finance and economics, 2017.

[17] China State Grid Corp.Q/GDW 588-2011 Function specification of wind power forecasting[S]. Q/GDW 588-2011, Beijing:China Electric Power Press, 2011.

[18] YE Lin, ZHU Qianwen, ZHAO Yongning. Dynamic Optimal Combination Model Considering Adaptive Exponential for Ultra-short Term Wind Power Prediction [J].Electric Power Automation Equipment, 2015, 39(20):12-18.DOI: 10.7500/AEPS20141128002.

[19] YANG Jie, HUO Zhihong, HE Yongsheng, et al.Ultra-short-term wind power prediction based on wavelet and minimum resource allocation network [J].Power System Protection and Control, 2018,46(9):55-61.

[20] ZHU Qiaomu, LI Hongyi, WANG Ziqi, et al.Short-Term Wind Power Forecasting Based on LSTM [J].Power System Technology, 2017, 41(12):3797-3802.

[21] Cheng Zhonglin, Jiang Quanyuan, Ge Yanfeng.Capacity planning model of phase change thermal storage and profit distribution based on cooperation game [J].Power System Technology, 2017, 41(9):2870-2878.

[22] Ma Li, Fan Menghua, Guo Lei, et al.Latest development trends of international electricity markets and their enlightenment [J].Automation of Electric Power Systems, 2014(13):1-9.DOI: 10.7500/AEPS20140520007.

[23] the liaoning province economic and information commission. Electric heating (heating) in liaoning province electricity trading system and rules [Z]. Liaoning province, liaoning province economic and information commission.

[24] Tasdighi M, Ghasemi H, Rahimi-Kian A. Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling[J]. IEEE Transactions on Smart Grid. 2014, 5(1): 349-357.

[25] Giuntoli M and Poli D.Optimized thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storages[J].IEEE Transactions on Smart Grid.2013, 4(2):942-955.

[26] Fan Pengfei, Zhang Lizi, Xie Guohui.Analysis model for accommodation capability of wind power with adequacy resources involved in system regulation [J].Power System Technology, 2012, 36(5):51-57 .

[27] Jin Hongyang, Sun Hongbin, Guo Qinglai, et al.Multi-day self-scheduling method for combined system of CSP plants and wind power with large-scale thermal energy storage contained [J]. Automation of Electric Power Systems, 2016, 40(11):17-23.DOI: 10.7500/AEPS20150826012.

[28] LI Jialong,, CHEN Yuguo, LIU Sijie, Guo Qinglai, et al.Electricity Market EquilibriumAnalysis Considering Carbon Emission Cost[J].Power System Technology, 2016, 40(5):1558-1563.

[29] Giuntoli M and Poli D.Optimized thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storages[J]. IEEE Transactions on Smart Grid.2013, 4(2):942-955.