A self-scheduling strategy of virtual power plant with electric vehicles considering margin indexes

• Autorzy: Jiao Fengshun , Deng Yongsheng , Li Duo , Wei Bo , Yue Chengyan , Cheng Meng , Zhang Yapeng , Zhang Jiarui

Identyfikatory

DOI

10.24425/aee.2020.134638

• Języki publikacji: EN

Abstrakty

EN

From the perspective of a virtual power plant (VPP) with electric vehicles (EVs), a self-scheduling strategy considering the response time margin (RTM) and state of charge margin (SOCM) is proposed. Firstly, considering the response state of the state of charge (SOC) and charge-discharge state of EVs, a VPP based response capacity determination model of EVs is established. Then, RTM and SOCM indexes are introduced on the basis of the power system scheduling target and the EV users’ traveling demands. The RTM and SOCM indices are calculated and then are used to generate a priority sequence of responsive EVs for the VPP. In the process of the scheduling period and rolling iteration, the scheduling schemes of the EVs in the VPP for multiple time periods are determined. Finally, the VPP self-scheduling strategy is validated by taking an VPP containing three kinds of EV users as an example. Simulation results show that with the proposed strategy, the VPP is able to respond to the scheduling power from the power system, while ensuring the traveling demands of the EV users at the same time.

Słowa kluczowe

EN

electric vehicle (EV); response time margin (RTM); scheduling strategy; state of charge margin (SOCM); virtual power plant (VPP)

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2020
• Tom: Vol. 69, nr 4
• Strony: 907--920
• Opis fizyczny: Bibliogr. 26 poz., rys., wz.

Twórcy

autor

Jiao Fengshun

• China Southern Power Grid Shenzhen Power Supply Bureau Co. LTD Shenzhen, China

autor

Deng Yongsheng

• China Southern Power Grid Shenzhen Power Supply Bureau Co. LTD Shenzhen, China

autor

Li Duo

• China Southern Power Grid Shenzhen Power Supply Bureau Co. LTD Shenzhen, China

autor

Wei Bo

• China Southern Power Grid Shenzhen Power Supply Bureau Co. LTD Shenzhen, China

autor

Yue Chengyan

• ABB Power Grids Investment (China) Limited Beijing, China

autor

Cheng Meng

• ABB Power Grids Investment (China) Limited Beijing, China

autor

Zhang Yapeng

• ABB Power Grids Investment (China) Limited Beijing, China

autor

Zhang Jiarui

• ABB Power Grids Investment (China) Limited Beijing, China

Bibliografia

• [1] Chen C., Guo C., Man Z. et al., Control strategy research on frequency regulation of power system considering Electric vehicles, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (2016).
• [2] Chen Y.K., Lin C.H., Wang W.C., The conversion of biomass into renewable jet fuel, Energy, vol. 201, pp. 1–9 (2020).
• [3] Schuller A., Dietz B., Flath C.M. et al., Charging strategies for battery electric vehicles: Economic benchmark and V2G potential, IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2014–2022 (2014).
• [4] Xia S., Bu S., Luo X. et al., An autonomous real time charging strategy for plug-in electric vehicles to regulate frequency of distribution system with fluctuating wind generation, IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 511–524 (2017).
• [5] Mroczek B., Kołodyńska A., The V2G Process with the Predictive Model, IEEE Access, vol. 8, pp. 86947–86956 (2020).
• [6] Zhang H., Hu Z., Xu Z., Song Y., Evaluation of achievable vehicle-to-grid capacity using aggregate PEV model, IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 784–794 (2017).
• [7] Ko K., Han S., Dan K.S., Performance-Based Settlement of Frequency Regulation for Electric Vehicle Aggregators, IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 866–875 (2018).
• [8] Vagropoulos S.I., Kyriazidis D.K., Bakirtzis A.G., Real-Time Charging Management Framework for Electric Vehicle Aggregators in a Market Environment, IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 948–957 (2016).
• [9] Wang M., Mu Y., Shi Q. et al., Electric vehicle aggregator modeling and control for frequency regulation considering progressive state recovery, IEEE Transactions on Smart Grid, Early Access (2020).
• [10] Cao Y., Tong W. Omprakash K. et al., An EV Charging Management System Concerning Drivers’ Trip Duration and Mobility Uncertainty, IEEE Transactions on Systems Man and Cybernetics Systems, vol. 48, no. 4, pp. 596–607 (2017).
• [11] Zheng J., Wang X., Men K. et al., Aggregation model-based optimization for electric vehicle charging strategy, IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 1058–1066 (2013).
• [12] Wang M., Mu Y., Jai H. et al., Active power regulation for large-scale wind farms through an efficient power plant model of electric vehicles, Applied Energy, vol. 185, pp. 1673–1683 (2017).
• [13] Meng J., Mu Y., Jia H. et al., Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system, Applied Energy, vol. 162, pp. 966–979 (2016).
• [14] Kaur K., Singh M., Kumar N., Multiobjective optimization for frequency support using electric vehicles: An aggregator-based hierarchical control mechanism, IEEE System Journal, vol. 13, no. 1, pp. 771–782 (2019).
• [15] Li G., Huang Y., Bie Z., Reliability Evaluation of Smart Distribution Systems Considering Load Rebound Characteristics, IEEE Transactions on Sustainable Energy, vol. 9, no. 4, pp. 1713–1721 (2018).
• [16] Zhe W., Yue L., Lin C., Electric Vehicle Charging Scheme for a Park-and-Charge System Considering Battery Degradation Costs, IEEE Transactions on Intelligent Vehicles, vol. 3, no. 3, pp. 361–373 (2018).
• [17] Zhang Z., Dong K., Pang X. et al., Research on the EV charging load estimation and mode optimization methods, Archives of Electrical Engineering, vol. 68, no. 4, pp. 831–842 (2019).
• [18] Li C.T., Ahn C., Peng H. et al., Synergistic control of plug-in vehicle charging and wind power scheduling, IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1113–1121 (2013).
• [19] Xu S., Yan Z., Feng D. et al., Decentralized charging control strategy of the electric vehicle aggregator based on augmented Lagrangian method, International Journal of Electrical Power and Energy Systems, vol. 104, pp. 673–679 (2019).
• [20] Jian L., Zheng Y., Shao Z., High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles, Applied Energy, vol. 186, pp. 46–55 (2017).
• [21] Wang M., Mu Y., Jiang T. et al., Load curve smoothing strategy based on unified state model of different demand side resources, Journal of Modern Power Systems and Clean Energy, vol. 6, no. 3, pp. 540–554 (2018).
• [22] Qi Y., Wang D., Jia H. et al., Research on demand response for thermostatically controlled appliances based on normalized temperature extension margin control strategy, Proceedings of the CSEE, vol. 21 (2015).
• [23] Qian K., Zhou C., Allan M. et al., Modeling of load demand due to EV battery charging in distribution systems, IEEE Transactions on Power Systems, vol. 26, no. 2 pp. 802–810 (2011).
• [24] Wang M., Mu Y., Jai H. et al., A preventive control strategy for static voltage stability based on an efficient power plant model of electric vehicles, Journal of Modern Power Systems and Clean Energy, vol. 3, no. 1, pp. 103–113 (2015).
• [25] Zhou C., Qian K., Allan M. et al., Modeling of the cost of EV battery wear due to V2G application in power systems, IEEE Transactions on Power Electronics, vol. 26, no. 4, pp. 1041–1050 (2011).
• [26] Liu C., Wang X., Wu X. et al., Economic scheduling model of microgrid considering the lifetime of batteries, IET Generation, Transmission and Distribution, vol. 11, no. 3, pp. 759–767 (2017).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).