Genetic Algorithm for Optimal Charge Scheduling of Electric Vehicle Fleet

Electric Vehicles (EV) are gradually conquering more roads and replacing pollutant conventional vehicles. They seem to be used to store energy in clean and smart grids to mitigate greenhouse gas emissions and eliminate harmful peak loads. This paper establishes a stochastic procedure for modeling and analyzing an electric vehicle (EV) fleet to generate an accurate charging and discharging profiles. For the purpose of managing the EV fleet we use a single-objective optimization, namely, the Genetic Algorithm (GA) to determine the optimal charging/discharging schedule for each EV in the fleet. The proposed optimization allows to make the optimal tradeoff between V2G and G2V operations cost to highly increase benefits from EV batteries by scheduling the charging mode in the low power price periods and discharging mode in the high-power price periods. Moreover, we compare our approach that considers the stochastic nature in the initial state-of-charge (SOC), arriving and departing times to the grid and the characteristic of EV battery packs for each connected EV in workplace and home parking lots, with a naive charging strategy.

[1]  U. Madawala,et al.  A Bidirectional Inductive Power Interface for Electric Vehicles in V2G Systems , 2011, IEEE Transactions on Industrial Electronics.

[2]  Kyung Bin Song,et al.  Impact of Electric Vehicle Penetration-Based Charging Demand on Load Profile , 2013 .

[3]  M. Ouyang,et al.  Beijing passenger car travel survey: implications for alternative fuel vehicle deployment , 2015, Mitigation and Adaptation Strategies for Global Change.

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

[5]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[6]  Fushuan Wen,et al.  Load characteristics of electric vehicles in charging and discharging states and impacts on distribution systems , 2012 .

[7]  A. Sumper,et al.  Deterministic and probabilistic assessment of the impact of the electrical vehicles on the power grid , 2011, 2011 IEEE Power and Energy Society General Meeting.

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

[9]  P Frías,et al.  Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks , 2011, IEEE Transactions on Power Systems.

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

[11]  Armagan Temiz,et al.  Assessment of impacts of Electric Vehicles on LV distribution networks in Turkey , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[12]  Kevin P. Schneider,et al.  The Smart Grid: An Estimation of the Energy and CO2 Benefits , 2010 .

[13]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[14]  Andreas Sumper,et al.  Deterministic and Probabilistic Assessment of the Impact of the Electrical Vehicles on the Power Grid , 2010 .

[15]  J. Driesen,et al.  The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid , 2010, IEEE Transactions on Power Systems.

[16]  Murat Kucukvar,et al.  Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United States , 2015 .

[17]  Wencong Su,et al.  Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources , 2014, IEEE Transactions on Smart Grid.

[18]  Vincenzo Antonucci,et al.  Electric vehicle charging infrastructure planning in a road network , 2017 .

[19]  Chris Davis,et al.  Electric vehicle charging in China's power system: Energy, economic and environmental trade-offs and policy implications , 2016 .

[20]  Joao P. Trovao,et al.  Integration of the Electric Vehicle as a Manageable Load in a Residential Energy Management System , 2015, 2015 IEEE Vehicle Power and Propulsion Conference (VPPC).

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

[22]  Liana Cipcigan,et al.  Probabilistic analysis of electric vehicles charging load impact on residential Distributions Networks , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

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

[24]  Loganathan Umanand,et al.  Multiphase Bidirectional Flyback Converter Topology for Hybrid Electric Vehicles , 2009, IEEE Transactions on Industrial Electronics.

[25]  Yves Roblin,et al.  Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics , 2013 .

[26]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[27]  Andreas Sumper,et al.  Probabilistic Method to Assess the Impact of Charging of Electric Vehicles on Distribution Grids , 2012 .

[28]  Yunfei Mu,et al.  A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles ☆ , 2014 .

[29]  Andreas Sumper,et al.  Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks , 2015 .

[30]  J. Bates,et al.  Spaced Out: Perspectives on parking policy , 2012 .

[31]  Chan-Nan Lu,et al.  Stochastic Analyses of Electric Vehicle Charging Impacts on Distribution Network , 2014, IEEE Transactions on Power Systems.

[32]  Stephen Jia Wang,et al.  Can Electric Vehicles Deliver Energy and Carbon Reductions , 2017 .