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Discrete Stochastic Control for Energy Management with Photovoltaic Electric Vehicle Charging Station

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU150900" target="_blank" >RIV/00216305:26220/22:PU150900 - isvavai.cz</a>

  • Result on the web

    <a href="https://file.cpss.org.cn/uploads/tpea/v7n22022/10.24295CPSSTPEA.2022.00020.pdf" target="_blank" >https://file.cpss.org.cn/uploads/tpea/v7n22022/10.24295CPSSTPEA.2022.00020.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.24295/CPSSTPEA.2022.00020" target="_blank" >10.24295/CPSSTPEA.2022.00020</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Discrete Stochastic Control for Energy Management with Photovoltaic Electric Vehicle Charging Station

  • Original language description

    This paper develops an intelligent energy management system for optimal operation of grid connected solar powered electric vehicle (EV) charging station at workplace. The optimal operation is achieved by controlling the power flow between the photovoltaic (PV) system, energy storage unit, EV charging station (EVCS) and the grid. The proposed controller is developed considering the PV availability, grid loading and the EV charging load data. This information is modelled using Markov decision process (MDP) to develop a control strategy that eliminates the conventional problem of immediate recharging of energy storage unit after each EV charging by setting a target state of charge (SOC) level. This maximizes the use of PV power for EV charging and minimizes the impact on the grid. To test the operation of the proposed controller, a charging station powered by a 5 kW PV system with 35 kW energy storage unit connected to grid is developed through numerical simulations and experiment. The experiments were carried out for three different conditions under varying irradiance profile and load profile for multiple days. The results estimated the EV load and PV power and optimized the energy storage unit SOC between 0.3-1. Further, the energy management strategy minimized the impact of energy exchange between the grid and charging station by a factor of 2.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database

  • CEP classification

  • OECD FORD branch

    20200 - Electrical engineering, Electronic engineering, Information engineering

Result continuities

  • Project

  • Continuities

    O - Projekt operacniho programu

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    CPSS Transactions on Power Electronics and Applications

  • ISSN

    2475-742X

  • e-ISSN

  • Volume of the periodical

    7

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    CN - CHINA

  • Number of pages

    10

  • Pages from-to

    216-225

  • UT code for WoS article

  • EID of the result in the Scopus database

    2-s2.0-85147845295