Optimal Allocation of Electric Vehicles Parking Lots and Optimal Charging and Discharging Scheduling using Hybrid Metaheuristic Algorithms

The issue of simultaneous planning of electric vehicles and distributed generation resources has received more attention from energy researchers in recent years. Scattered renewable sources do not have a certain amount of production and, according to research, follow possible mathematical functions. Renewable energy sources are modeled on wind and solar production, both of which are moderately generated per hour. In this study, using, the optimal allocation problem of the electric vehicles parking lots and the optimal operation scheduling of the electric vehicles in a smart distribution network are studied as a novel optimization problem. In the proposed problem, the different factors including the technical and the economic issues are considered for achieving a realistic solution. In terms of technical issues, minimizing network losses, and minimizing voltage drop in feeders, as well as supplying all network demand are considered. Also, the total cost of the charging and discharge at the electric vehicles parking lots, and the total cost paid for purchasing power from upstream network are given as economic issues in the proposed problem. Moreover, the price-based DRP is considered due to the implementation of the demand side management program. To obtain the optimal solution, a hybrid metaheuristic algorithms (HMA) has been used. The proposed problem is simulated on the standard IEEE 69-bus. It is solved by the proposed HMA and is compared with another heuristic method. The obtained results confirm the accuracy and efficiency of the proposed problem. The obtained results show increased to an acceptable level, the voltage profile was improved and network losses were reduced. Finally, the results curves and tables show the efficiency of the proposed method.

[1]  Sun Wook Kim,et al.  Development of battery management system for nickel–metal hydride batteries in electric vehicle applications , 2002 .

[2]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[3]  Ebrahim Mortaz,et al.  Microgrid energy scheduling using storage from electric vehicles , 2017 .

[4]  Camino R. Vela,et al.  Genetic fuzzy schedules for charging electric vehicles , 2018, Comput. Ind. Eng..

[5]  M. Marufuzzaman,et al.  Unsupervised learning for deploying smart charging public infrastructure for electric vehicles in sprawling cities , 2020 .

[6]  Heather Contrino 101 - An Overview of the National Household Travel Survey (NHTS), the Nation's Largest Household Travel Survey , 2009 .

[7]  G. Valenti,et al.  A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas , 2016 .

[8]  Meisam Yahyazadeh,et al.  Optimal Placement and Sizing of Distributed Generation Using Wale Optimization Algorithm Considering Voltage Stability and Voltage Profile Improvement, Power Loss and Investment Cost Reducing , 2020 .

[9]  Nicholas A. DiOrio,et al.  Economic Analysis Case Studies of Battery Energy Storage with SAM , 2015 .

[10]  Hongjie Jia,et al.  Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling , 2018 .

[11]  Kiran Jasthi,et al.  Grasshopper optimization algorithm based two stage fuzzy multiobjective approach for optimum sizing and placement of distributed generations, shunt capacitors and electric vehicle charging stations , 2020 .

[12]  Ali Elkamel,et al.  Two-stage stochastic home energy management strategy considering electric vehicle and battery energy storage system: An ANN-based scenario generation methodology , 2020 .

[13]  Li Zhang,et al.  Can the development of electric vehicles reduce the emission of air pollutants and greenhouse gases in developing countries , 2017 .

[14]  Noreen McDonald,et al.  Does the built environment affect when American teens become drivers? Evidence from the 2001 National Household Travel Survey. , 2009, Journal of safety research.

[15]  Majid Jamil,et al.  Optimal sizing and location of SPV (solar photovoltaic) based MLDG (multiple location distributed generator) in distribution system for loss reduction, voltage profile improvement with economical benefits , 2016 .

[16]  Christof Knoeri,et al.  Can electricity pricing leverage electric vehicles and battery storage to integrate high shares of solar photovoltaics? , 2020 .

[17]  Mohammadreza Saraninezhad,et al.  Optimal Placement of Wind Turbines for Reducing Losses and Improving Loadability and Voltage Profile in Distribution Networks by Data Clustering and NSGA-II Algorithm , 2019 .

[18]  Vincenzo Antonucci,et al.  Optimal allocation of electric vehicle charging stations in a highway network: Part 1. Methodology and test application , 2020, Journal of Energy Storage.

[19]  S.M.T. Bathaee,et al.  A self-supporting approach to EV agent participation in smart grid , 2018, International Journal of Electrical Power & Energy Systems.

[20]  I. Lampropoulos,et al.  Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits , 2020 .

[21]  José L. Bernal-Agustín,et al.  Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems , 2019, International Journal of Electrical Power & Energy Systems.

[22]  Olabode Agunbiade,et al.  Prospects of Electric Vehicles in the Automotive Industry in Nigeria , 2020, European Scientific Journal ESJ.

[23]  Mouna Kchaou Boujelben,et al.  A multi-stage stochastic integer programming approach for locating electric vehicle charging stations , 2020, Comput. Oper. Res..

[24]  Timo Laakko,et al.  Data analysis of a monitored building using machine learning and optimization of integrated photovoltaic panel, battery and electric vehicles in a Central European climatic condition , 2020 .

[25]  A. Santos,et al.  Summary of Travel Trends: 2009 National Household Travel Survey , 2011 .

[26]  Makawi Diab Hraiz,et al.  Optimal PV Size and Location to Reduce Active Power Losses While Achieving Very High Penetration Level With Improvement in Voltage Profile Using Modified Jaya Algorithm , 2020, IEEE Journal of Photovoltaics.

[27]  Mohammad Marufuzzaman,et al.  A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid , 2018, Applied Energy.

[28]  A. Mohammadian,et al.  Travel Behavior and the Effects of Household Demographics and Lifestyles , 2009 .

[29]  Eren Özceylan,et al.  Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis , 2018, Energy.

[30]  Ying Fan,et al.  A deployment model of EV charging piles and its impact on EV promotion , 2020 .

[31]  Xu Qingshan,et al.  Optimal EV Charging Control Strategy Based on DC Microgrid , 2016 .

[32]  Walter Mérida,et al.  Optimization of distributed energy resources for electric vehicle charging and fuel cell vehicle refueling , 2020 .

[33]  Anwar Shahzad Siddiqui,et al.  Optimal placement of DG and DSTATCOM for loss reduction and voltage profile improvement , 2017, Alexandria Engineering Journal.

[34]  Steven H. Low,et al.  Adaptive charging network for electric vehicles , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[35]  Fabian Scheller,et al.  From passive to active: Flexibility from electric vehicles in the context of transmission system development , 2020, 2011.05830.

[36]  Rafael Zárate-Miñano,et al.  Influence of the controllability of electric vehicles on generation and storage capacity expansion decisions , 2019 .

[37]  Michael Conlon,et al.  Role of reactive power (STATCOM) in the planning of distribution network with higher EV charging level , 2019, IET Generation, Transmission & Distribution.

[38]  Ali Karaşan,et al.  Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey , 2018, Neural Computing and Applications.