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Optimal EV charging algorithm with probabilistic EV demand forecasting

Research Project

Project/Area Number 21K14150
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 21010:Power engineering-related
Research InstitutionUniversity of Tsukuba (2022)
Tokyo University of Science (2021)

Principal Investigator

Kodaira Daisuke  筑波大学, システム情報系, 助教 (20880777)

Project Period (FY) 2021-04-01 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2022: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2021: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Keywords電力需要予測 / EV充放電最適化 / 電気自動車 / 最適化 / 配電系統 / 確率的需要予測 / 蓄電池 / Virtual Power Plant / 計算量低減 / Prediction Interval / Electric Vehicle / machine learning / Probabilistic forecast / 電力系統
Outline of Research at the Start

近年,太陽光発電の普及に伴う電力系統運用への影響が顕在化し,系統の電力需給バランス維持がより難しくなってきている.電気自動車(EV)を用いた電力需給バランスの調整は導入コストが低く,唯一の解決策とも言える手法として広く検討がなされている.しかし,多数のEVの充電制御を行うためには,不確定性の高いEVの充電需要を予測する必要があるという課題がある.また,制御台数が増加するにつれて制御のための計算量が指数関数的に増加する点も課題となる.そこで本研究では,EVの充電需要を確率的に予測することで誤差を考慮できる予測理論を構築し,さらに,充電制御のための計算量を低減する理論の構築を行う.

Outline of Final Research Achievements

There are two main results. The first is the construction of a theoretical framework for predicting the charging demand of a group of electric vehicles (EVs). We developed a machine learning method to generate probabilistic prediction intervals and demonstrated its effectiveness. Additionally, we confirmed that the accuracy of the forecast is critical for optimal charging behavior. The second result is the development of a theory to reduce computational time in optimal charge-discharge algorithms for a large number of EVs. We developed a method that treats multiple EVs as a single large-capacity EV in a virtual sense, enabling the calculation of charge-discharge schedules in a practical computational time. Furthermore, we constructed an optimization algorithm for charge-discharge to maximize profits from selling electricity.

Academic Significance and Societal Importance of the Research Achievements

太陽光発電の普及が電力需給バランスの維持を困難にしている。低コストでこの問題を解決する方法として電気自動車(EV)を用いた制御が注目されているが、多数のEVの制御には不確定性の高い充電需要の予測と計算量の増加という課題がある。本研究では、これらの課題に対応するため、EVの充電需要を確率的に予測し、その誤差を考慮する新たな理論を構築した。また、充電制御の計算量を低減する方法を開発した。これらの理論は、電力系統の調整力を確保するためのEV充電制御技術の社会実装に貢献する。

Report

(3 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Research-status Report
  • Research Products

    (7 results)

All 2022 2021 Other

All Int'l Joint Research (1 results) Journal Article (5 results) (of which Int'l Joint Research: 2 results,  Peer Reviewed: 5 results,  Open Access: 5 results) Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Int'l Joint Research] Kyungpook National University(韓国)

    • Related Report
      2021 Research-status Report
  • [Journal Article] Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function2022

    • Author(s)
      J. Park, J. Choi, H. Jo, D. Kodaira, S. Han, and M. A. Acquah
    • Journal Title

      Energies

      Volume: 15 Issue: 21 Pages: 8071-8071

    • DOI

      10.3390/en15218071

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation2022

    • Author(s)
      H. Yamamoto, J. Kondoh, and D. Kodaira
    • Journal Title

      Energies

      Volume: 15 Issue: 15 Pages: 5337-5337

    • DOI

      10.3390/en15155337

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation2022

    • Author(s)
      Kure T, Tsuchiya HD, Kameda Y, Yamamoto H, Kodaira D, Kondoh J.
    • Journal Title

      Energies

      Volume: 15 Issue: 8 Pages: 2855-2875

    • DOI

      10.3390/en15082855

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations2021

    • Author(s)
      Kodaira D, Tsukazaki K, Kure T, Kondoh J.
    • Journal Title

      Energies

      Volume: 14 Issue: 21 Pages: 7340-7355

    • DOI

      10.3390/en14217340

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access
  • [Journal Article] Adaptive Power Flow Prediction Based on Machine Learning2021

    • Author(s)
      Park J, Kodaira D, Agyeman KA, Jyung T, Han S
    • Journal Title

      Energies

      Volume: 14 Issue: 13 Pages: 3942-3960

    • DOI

      10.3390/en14133842

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Multi-point forecasting of photovoltaic power generation by light gradient boosting machine2022

    • Author(s)
      H. Yamamoto, T. Kure, J. Kondoh, and D. Kodaira
    • Organizer
      Grand Renewable Energy proceedings, 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research

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Published: 2021-04-28   Modified: 2024-01-30  

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