Balanced charging strategies for electric vehicles on power systems

This paper presents an electric vehicle charging demand management method by modeling the demand dispatch calculation. Despite the demand shift owing to the price signal, the signal is occasionally inaccurate because of the load variability. The electricity rate has features that can shift the electric vehicle charging priorities; the application of the load fluctuation criterion is sufficient for the management plan. We expect that there is a point, wherein, both the electric vehicle users (saving costs) and the system operators (relieving loads) are satisfied with the mutually beneficial arrangements. Our method determines the balanced state in which the loads and costs are considered simultaneously with the proposing criteria. The method allows the discordance between the electrical prices and the system load fluctuations to be managed, while the time-of-use pricing and load deviation indices are accounted for. We focus on the gap corresponding to the load variation and the charging price in a daily scheme. In contrast to the typical valley filling strategies, the aim of this study is to determine and solve the mismatches in the different goals of the costs and loads, if the state is not mutually beneficial. Therefore, to ensure a system operator perspective selectively, we introduce the load weight and ranking method concepts for dispersing the charging loads, lowering the system marginal prices, and investment avoidance because electricity rates cannot describe the load curves accurately. The charging demand calculation is investigated based on the determination of the charging patterns and daily demands using the priority comparison method. The balancing strategy first fills the mutual benefit points with respect to the changing priorities and then, competes to find the balanced points. The significance of the method is that it is based on the unique relationship between two comprehensive competitive strategies. Thus, we determine that valley filling, flat load management, and regulated deviation are insufficient to describe the user and operator behaviors simultaneously.

[1]  Jun Yang,et al.  An improved PSO-based charging strategy of electric vehicles in electrical distribution grid , 2014 .

[2]  Dan Wang,et al.  Power system operation risk analysis considering charging load self-management of plug-in hybrid electric vehicles , 2014 .

[3]  Sang-Keun Moon,et al.  Evaluation of the charging effects of electric vehicles on power systems, taking into account optimal charging scenarios , 2015, 2015 IEEE Power & Energy Society General Meeting.

[4]  Mohammad A. S. Masoum,et al.  Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance , 2016 .

[5]  Willett Kempton,et al.  Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy , 2005 .

[6]  Igor Kuzle,et al.  Value of Flexible Electric Vehicles in Providing Spinning Reserve Services , 2015 .

[7]  Yue Yuan,et al.  Modeling of Load Demand Due to EV Battery Charging in Distribution Systems , 2011, IEEE Transactions on Power Systems.

[8]  Chioke B. Harris,et al.  An empirically-validated methodology to simulate electricity demand for electric vehicle charging , 2014 .

[9]  Arye Nehorai,et al.  An Optimal and Distributed Demand Response Strategy With Electric Vehicles in the Smart Grid , 2014, IEEE Transactions on Smart Grid.

[10]  Phil Blythe,et al.  A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts , 2015 .

[11]  Erotokritos Xydas,et al.  A multi-agent based scheduling algorithm for adaptive electric vehicles charging , 2016 .

[12]  Y. Uriu,et al.  A strategy of load leveling by charging and discharging time control of electric vehicles , 1998 .

[13]  Masoud Esmaili,et al.  Multi-objective optimal charging of plug-in electric vehicles in unbalanced distribution networks , 2015 .

[14]  J. García-Villalobos,et al.  Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks , 2016 .

[15]  Christoph M. Flath,et al.  Impact of electric vehicles on distribution substations: A Swiss case study , 2015 .

[16]  Zhiwei Xu,et al.  Optimal Coordination of Plug-In Electric Vehicles in Power Grids With Cost-Benefit Analysis—Part I: Enabling Techniques , 2013, IEEE Transactions on Power Systems.

[17]  Joao P. S. Catalao,et al.  Smart electric vehicle charging scheduler for overloading prevention of an industry client power distribution transformer , 2016 .

[18]  Filipe Joel Soares,et al.  Integration of Electric Vehicles in the Electric Power System , 2011, Proceedings of the IEEE.

[19]  Mehmet Uzunoglu,et al.  A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account , 2016 .

[20]  Lars Nordström,et al.  A multi-agent system for distribution grid congestion management with electric vehicles , 2015, Eng. Appl. Artif. Intell..

[21]  Wei Shen,et al.  Individual trip chain distributions for passenger cars: Implications for market acceptance of battery electric vehicles and energy consumption by plug-in hybrid electric vehicles , 2016 .

[22]  Nicholas Jenkins,et al.  A data-driven approach for characterising the charging demand of electric vehicles: A UK case study , 2016 .

[23]  Mostafa Sedighizadeh,et al.  Optimal siting and sizing of distribution system operator owned EV parking lots , 2016 .

[24]  Gongguo Tang,et al.  A game-theoretic approach for optimal time-of-use electricity pricing , 2013, IEEE Transactions on Power Systems.

[25]  Michael C. Caramanis,et al.  Management of electric vehicle charging to mitigate renewable generation intermittency and distribution network congestion , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[26]  D. Kirschen,et al.  Quantifying the Effect of Demand Response on Electricity Markets , 2007, IEEE Transactions on Power Systems.

[27]  Inmaculada Zamora,et al.  Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches , 2014 .

[28]  Ghazal Razeghi,et al.  Impacts of plug-in electric vehicles in a balancing area , 2016 .

[29]  Jun Yang,et al.  A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles , 2016 .

[30]  Joakim Widén,et al.  Characterizing probability density distributions for household electricity load profiles from high-resolution electricity use data , 2014 .

[31]  Linni Jian,et al.  Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid , 2015 .

[32]  Snuller Price,et al.  Smart meter, customer choice and profitable time-of-use rate option , 1999 .

[33]  Willett Kempton,et al.  Vehicle-to-grid power fundamentals: Calculating capacity and net revenue , 2005 .

[34]  Henrik Madsen,et al.  Optimal charging of an electric vehicle using a Markov decision process , 2013, 1310.6926.

[35]  Alessandro Di Giorgio,et al.  Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models , 2014 .

[36]  Ramteen Sioshansi,et al.  Using Price-Based Signals to Control Plug-in Electric Vehicle Fleet Charging , 2014, IEEE Transactions on Smart Grid.