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Interruptible charge scheduling of plug-in electric vehicle to minimize charging cost using heuristic algorithm

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Abstract

In recent times, transportation electrification has been recognized as one of the key solutions to accelerate global GHG emission reductions. As the electric vehicle industry grows faster, plug-in electric vehicles (PEV) are expected to be the most dominant load in the utility sector in less than a decade. Regular charging of the battery energy storage system (BESS) is a mandate for the continued operation of the vehicle, and the PEVs are connected to the utility to charge. Since PEVs are mobility loads, predicting the interconnection of these mobility loads in the utility network for recharging is a major challenge. The intermittent connection of mobility loads to the grid for charging leads to an unpredictable increase in electricity demand and other grid-related issues. Optimal scheduling of PEV charging would conquer the grid-related issues and provide financial benefits to the users. In this paper, an intelligent charge scheduling technique of PEV charging for both residential and commercial charging stations using the heuristic algorithm is proposed and discussed. The primary objective of the algorithm is to achieve the minimization of PEV charging costs by implementing an interrupted charging schedule. The proposed algorithm is tested by conducting exhaustive simulation studies under several conditions for PEVs with different power ratings for residential and commercial charging scenarios. The time-of-use pricing (ToUP) system is adopted as a tariff system in this paper. A detailed comparison of the unscheduled algorithm, the modified placement algorithm (MPA) and proposed heuristic technique-based charge scheduling is carried out through simulation studies. A detailed cost analysis for charging the PEVs with the selected charge scheduling techniques for various conditions is conducted and cost minimization by implementing the proposed charging scheme is validated.

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Abbreviations

BESS:

Battery energy storage system

CPP:

Critical peak pricing

EGR:

Emission gap report

EV:

Electric vehicle

GA:

Genetic algorithm

GHGEs:

Greenhouse gas emission

G2V:

Grid to vehicle

IBR:

Inclined block rate

LRM:

Lagrange relaxation method

MDP:

Markov decision process

MFG:

Mean-field game

MPA:

Modified placement algorithm

PEV:

Plug-in electric vehicles

PSO:

Particle swarm optimization

RTP:

Real-time pricing

ToUP:

Time-of-use pricing

V2G:

Vehicle-to-grid

\(A_{t}\) :

Vehicle entry time

\(D_{t}\) :

Exit time of the vehicle

\({\text{EC}}_{n} (S_{n} )\) :

Estimated cost of charging of the nth vehicle

\(L\) :

Charge duration

\(P_{{{\text{EVn}}}}\) :

Power rating of the BESS of the vehicle in kW

\(S_{n}\) :

Starting time of charging

\(S_{{{\text{gn}}}}\) :

Slot with minimum cost

T :

Utility tariff

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, TKM College of Engineering, Kollam, India

    S. Sheik Mohammed & T. P. Imthias Ahamed

  2. Department of Electrical Engineering, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalyubia, 44971, Egypt

    Shady H. E. Abdel Aleem

  3. Electrical Power and Machines Engineering, The Higher Institute of Engineering at El-Shorouk City, El-Shorouk City, Egypt

    Ahmed I. Omar

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Mohammed, S.S., Ahamed, T.P.I., Aleem, S.H.E.A. et al. Interruptible charge scheduling of plug-in electric vehicle to minimize charging cost using heuristic algorithm. Electr Eng 104, 1425–1440 (2022). https://doi.org/10.1007/s00202-021-01398-z

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