Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240300013-7.doi: 10.11896/jsjkx.240300013

• Interdiscipline & Application • Previous Articles     Next Articles

Real-time Collaborative Pricing Mechanism of Between Vehicle and Power Grid Based on Bi-levelOptimization

WANG Qiong1, LU Yue2, LIU Shun2, LI Qingtao2, LIU Yang2, WANG Hongbiao1, LIU Weiliang3   

  1. 1 State Grid Beijing Electric Power Company,Beijing 100032,China
    2 State Grid Beijing Haidian Power Supply Company,Beijing 100080,China
    3 Department of Automation,North China Electric Power University,Baoding,Hebei 071003,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:WANG Qiong,borin in 1983,Ph.D candidate,engineer.Her main research interests include development of software and hardware for charging stations and power-side infrastructure,alongside the integration of advanced security initiatives in vehicle networking platforms.
    LIU Weiliang,born in 1983,Ph.D,associate professor.His main research interests include modeling,simulation and optimal control of integrated energy system.
  • Supported by:
    State Grid Beijing Electric Power Company Technology Project:Research and Demonstration of V2G/S2G Vehicle Network Interaction and Intelligent Cluster Control Technology for Electric Vehicle Charging and Discharging Stations(520204220008).

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Abstract: Due to the incomplete competition and information on the behavior of electric vehicles,as well as the nonlinearity and uncertainty of power systems,the modeling and solving of real-time pricing problems are highly complex. Existing solutions typically model this as a constrained optimization problem,assuming that the utility function,which is a quantitative representation of the electricity network's economic benefits,is known to the network operators.This overlooks the incomplete information prevalent in actual scenarios.To overcome this limitation,this paper proposes an innovative real-time pricing mechanism for the vehicle-to-grid problem based on a bi-level optimization approach under the condition of unknown utility function parameters.Meanwhile,it considers the power flow equation to reflect the distributed grid's real-time load.This mechanism more accurately reflects the market's real dynamics.In this bi-level model,the upper level represents the optimization problem of the market operators,aiming to maximize their own welfare and minimize the load of the distributed grid.In contrast,the lower level represents the optimization problem of electric vehicles,aiming to maximize their profits or minimize their cost.Through comparative experiment simulations with the fixed pricing and peak-valley pricing methods,the experimental simulation data demonstrates the effectiveness of improving the profit of the grid and vehicles.At the same time,the load of the power grid is reduced.

Key words: Smart grid, Real-time pricing, Bi-level optimization, Optimization algorithm, Powerflow

CLC Number: 

  • TP311
[1]WANG Y,YANG Z F,YU J,et al.Pricing in non-convex electricity markets with flexible trade-off of pricing properties[J].Energy,2023,274:127382.
[2]FOGSTAD M D,KJERSTI B,SIGURD B,et al.Local electricitymarket pricing mechanisms' impact on welfare distribution,privacy and transparency[J].Applied Energy,2023,341:121112.
[3]AMOUNTZIAS C,DAGDEVIREN H,PATOKOS T.Pricingdecisions and market power in the UK electricity market:A VECM approach[J].Energy Policy,2017,108:467-473.
[4]DEY B,BHATTACHARYYA B.Comparison of various elec-tricity market pricing strategies to reduce generation cost of a microgrid system using hybrid WOA-SCA[J].Evolutionary Intelligence,2021,15(3):1587-1604.
[5]SHIRI A,AFSHAR M,RAHIMI-KIAN A,et al.Electricityprice forecasting using Support Vector Machines by considering oil and natural gas price impacts[C]//IEEE International Conference on Smart Energy Grid Engineering.IEEE,2015.
[6]HOGAN W W.Electricity market design:Multi-interval pricing models[J].Harvard University,2002.
[7]SAMADI P,MOHSENIAN-RAD A H,SCHOBER R,et al.Op-timal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid[C]//2010 First IEEE International Confe-rence on Smart Grid Communications(SmartGridComm).IEEE,2010.
[8]SONG X,QU J.An improved real-time pricing algorithm basedon utility maximization for smart grid[C]//Proceeding of the 11th World Congress on Intelligent Control and Automation.2023.
[9]TARASAK P.Optimal real-time pricing under load uncertainty based on utility maximization for smart grid[C]//IEEE International Conference on Smart Grid Communications.IEEE,2011.
[10]AHMADZADEH S,YANG K.Optimal Real Time PricingBased on Income Maximization for Smart Grid[C]//IEEE International Conference on Computer & Information Technology Ubiquitous Computing & Communications Dependable.IEEE,2015.
[11]LI W,BECKER D M.Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling[J].arXiv:2101.05249,2021.
[12]HOGAN W W.Electricity market design:Market models for coordination and pricing[J/OL].Energy Information Administration,2008.https://www.eia.gov/conference/2008/conf_pdfs/Tuesday/Hogan.pdf.https://www.eia.gov/conference/2008/conf_pdfs/Tuesday/Hogan.pdf.
[13]LI X G,YAN M S,XIE X Y.Inducing Mechanism of Three Pricing Methods on Bidding Strategies of Power Generation Companies[J].Automation of Electric Power Systems,2003(5):20-25.
[14]LUO Y H,XING L D,WANG Q,et al.Least Squares Estimation of Parameter in Peak-Valley Time-of-Use Electricity Price User Response Model[J].East China Electric Power,2009,37(1):67-69.
[15]YUAN G X,GAO Y,WANG H J.Real-time Electricity PriceAlgorithm for Smart Grid Based on Utility Classification[J].Journal of University of Shanghai for Science and Technology,2020,42(1):29-35.
[16]HUANG H T,HU X Y,LI X ,et al.Practical Incentive-based Optimal Compensation Pricing Model for Interruptible Loads[J].Power System Technology,2014,38(8):2149-2154.
[17]ZHANG L Z,LI Y A,LI G H,et al.Discussion on the Reactive Power Pricing Method in Power Generation Side Electricity Market[J].Electric Power in China,2002(1):32-36.
[18]LUO Y H,XING L D,WANG Q,et al.Overview of User Response Modeling and Pricing Decision in Peak-Valley Time-of-Use Electricity Price[J].East China Electric Power,2008(6):24-27.
[19]DONG L,TU S Q,LI Y,et al.Dynamic Pricing and EnergyManagement of Multi-Virtual Power Plants Based on Meta-model Optimization Algorithm in Master-Slave Game[J].Power System Technology,2020,44(3):973-983.
[20]ZENG Q Y.Pricing Mechanism of Wholesale Electricity Market with Demand-side Participation[J].Power System Technology,2004(17):6-10.
[21]SHU Z,MEI S,WANG P W.The Game-Theoretic Approach to Pricing in China's Semi-Deregulated Electricity Market[J].Advanced Materials Research,2013,2694:805-806.
[22]JI K,YING L.Network utility maximization with unknown uti-lity functions:A distributed,data-driven bilevel optimization approach[J].arXiv:2301.01801,2023.
[23]JI K,YANG J,LIANG Y.Bilevel optimization:Convergenceanalysis and enhanced design[C]//International Conference on Machine Learning.PMLR,2021:4882-4892.
[24]LIU R,LIU X,ZENG S,et al.Value-function-based sequential minimization for bi-level optimization[J].arXiv:2110.04974,2021.
[25]LIU R,LIU X,YUAN X,et al.A value-function-based interior-point method for non-convex bi-level optimization[C]//International Conference on Machine Learning.PMLR,2021:6882-6892.
[26]CLEMENT-NYNS K,HAESEN E,DRIESEN J.The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J].IEEE Transactions on Power Systems,2009,25(1):371-380.
[27]KEMPTON W,TOMIĆ J.Vehicle-to-grid power implementa-tion:From stabilizing the grid to supporting large-scale renewa-ble energy[J].Journal of Power Sources,2005,144(1):280-294.
[28]LOW S H.Convex relaxation of optimal power flow－Part I:Formulations and equivalence[J].IEEE Transactions on Control of Network Systems,2014,1(1):15-27.
[29]GAN L,TOPCU U,LOW S H.Optimal decentralized protocol for electric vehicle charging[J].IEEE Transactions on Power Systems,2012,28(2):940-951.
[30]FARIVAR M,LOW S H.Branch flow model:Relaxations and convexification－Part I[J].IEEE Transactions on Power Systems,2013,28(3):2554-2564.
[31]WANG D,TURITSYN K,CHERTKOV M.DistFlow ODE:Modeling,analyzing and controlling long distribution feeder[C]//2012 IEEE 51st IEEE Conference on Decision and Control(CDC).IEEE,2012:5613-5618.
[32]CHAKRABARTI A,HALDER S.Power system analysis:ope-ration and control[M].PHI Learning Pvt.Ltd.,2022.
[33]MENG F L,ZENG X J.A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid[J].Soft Computing,2013,17:2365-2380.
[34]YU M,HONG S H.A real-time demand-response algorithm for smart grids:A stackelberg game approach[J].IEEE Transactions on Smart Grid,2015,7(2):879-888.
[35]WANG Y,MA Z M,TAN Y K,et al.Day-ahead power market design and market simulation in Guangdong province[J].Power Demand Side Management,2018,20(1):10-14.
[36]WEI X M,YU K,CHEN X Y,,et al.Analysis of power large user segmentation based on Affinity propagation and K-means algorithm[J].Power Demand Side Management,2018,20(1):15-19,35.
[37]ZHOU L,WU H,JI W L,et al.Vulnerability pre-assessmentmethod for microgrid[J].Power Demand Side Management,2018,20(1):20-24.
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