IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v259y2022ics0360544222017558.html
   My bibliography  Save this article

Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks

Author

Listed:
  • Lai, Chun Sing
  • Chen, Dashen
  • Zhang, Jinning
  • Zhang, Xin
  • Xu, Xu
  • Taylor, Gareth A.
  • Lai, Loi Lei

Abstract

Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value.

Suggested Citation

  • Lai, Chun Sing & Chen, Dashen & Zhang, Jinning & Zhang, Xin & Xu, Xu & Taylor, Gareth A. & Lai, Loi Lei, 2022. "Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks," Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:energy:v:259:y:2022:i:c:s0360544222017558
    DOI: 10.1016/j.energy.2022.124852
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222017558
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124852?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Chengzhe & Zhang, Libo & Ou, Zihan & Wang, Qunwei & Zhou, Dequn & Ma, Jiayu, 2022. "Robust model of electric vehicle charging station location considering renewable energy and storage equipment," Energy, Elsevier, vol. 238(PA).
    2. Lai, Chun Sing & Locatelli, Giorgio & Pimm, Andrew & Tao, Yingshan & Li, Xuecong & Lai, Loi Lei, 2019. "A financial model for lithium-ion storage in a photovoltaic and biogas energy system," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Rajani, B. & Kommula, Bapayya Naidu, 2022. "An optimal energy management among the electric vehicle charging stations and electricity distribution system using GPC-RERNN approach," Energy, Elsevier, vol. 245(C).
    4. Luo, Yugong & Feng, Guixuan & Wan, Shuang & Zhang, Shuwei & Li, Victor & Kong, Weiwei, 2020. "Charging scheduling strategy for different electric vehicles with optimization for convenience of drivers, performance of transport system and distribution network," Energy, Elsevier, vol. 194(C).
    5. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    6. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    7. Wu, Xiaomei & Feng, Qijin & Bai, Chenchen & Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei, 2021. "A novel fast-charging stations locational planning model for electric bus transit system," Energy, Elsevier, vol. 224(C).
    8. Lai, Chun Sing & McCulloch, Malcolm D., 2017. "Levelized cost of electricity for solar photovoltaic and electrical energy storage," Applied Energy, Elsevier, vol. 190(C), pages 191-203.
    9. Björn Nykvist & Måns Nilsson, 2015. "Rapidly falling costs of battery packs for electric vehicles," Nature Climate Change, Nature, vol. 5(4), pages 329-332, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paudel, Diwas & Das, Tapas K., 2023. "A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets," Energy, Elsevier, vol. 283(C).
    2. Yin, Rumeng & He, Jiang, 2023. "Design of a photovoltaic electric bike battery-sharing system in public transit stations," Applied Energy, Elsevier, vol. 332(C).
    3. Zhang, Shulei & Jia, Runda & Pan, Hengxin & Cao, Yankai, 2023. "A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid," Applied Energy, Elsevier, vol. 348(C).
    4. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    5. João Fausto L. de Oliveira & Paulo S. G. de Mattos Neto & Hugo Valadares Siqueira & Domingos S. de O. Santos & Aranildo R. Lima & Francisco Madeiro & Douglas A. P. Dantas & Mariana de Morais Cavalcant, 2023. "Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review," Energies, MDPI, vol. 16(18), pages 1-20, September.
    6. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
    7. Ozturk, Zafer & Terkes, Musa & Demirci, Alpaslan, 2024. "Optimal planning of hybrid power systems under economic variables and different climatic regions: A case study of Türkiye," Renewable Energy, Elsevier, vol. 232(C).
    8. Subhojit Dawn & Gummadi Srinivasa Rao & M. L. N. Vital & K. Dhananjay Rao & Faisal Alsaif & Mohammed H. Alsharif, 2023. "Profit Extension of a Wind-Integrated Competitive Power System by Vehicle-to-Grid Integration and UPFC Placement," Energies, MDPI, vol. 16(18), pages 1-24, September.
    9. Dong, Xiao-Jian & Shen, Jia-Ni & Liu, Cheng-Wu & Ma, Zi-Feng & He, Yi-Jun, 2024. "Simultaneous capacity configuration and scheduling optimization of an integrated electrical vehicle charging station with photovoltaic and battery energy storage system," Energy, Elsevier, vol. 289(C).
    10. Pramote Jaruwatanachai & Yod Sukamongkol & Taweesak Samanchuen, 2023. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach," Energies, MDPI, vol. 16(8), pages 1-22, April.
    11. Rasheed, M.B. & Llamazares, Ángel & Ocaña, Manuel & Revenga, Pedro, 2024. "A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow," Energy, Elsevier, vol. 304(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    2. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    3. Lahnaoui, Amin & Stenzel, Peter & Linssen, Jochen, 2018. "Techno-economic analysis of photovoltaic battery system configuration and location☆," Applied Energy, Elsevier, vol. 227(C), pages 497-505.
    4. Tervo, Eric & Agbim, Kenechi & DeAngelis, Freddy & Hernandez, Jeffrey & Kim, Hye Kyung & Odukomaiya, Adewale, 2018. "An economic analysis of residential photovoltaic systems with lithium ion battery storage in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1057-1066.
    5. Han, Xuejiao & Garrison, Jared & Hug, Gabriela, 2022. "Techno-economic analysis of PV-battery systems in Switzerland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    6. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Valuing the option to prototype: A case study with Generation Integrated Energy Storage," Energy, Elsevier, vol. 217(C).
    7. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
    8. Brian Jenkins & David Squires & John Barton & Dani Strickland & K. G. U. Wijayantha & James Carroll & Jonathan Wilson & Matthew Brenton & Murray Thomson, 2022. "Techno-Economic Analysis of Low Carbon Hydrogen Production from Offshore Wind Using Battolyser Technology," Energies, MDPI, vol. 15(16), pages 1-20, August.
    9. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings," Applied Energy, Elsevier, vol. 228(C), pages 2165-2175.
    10. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    11. Young, Sharon & Bruce, Anna & MacGill, Iain, 2019. "Potential impacts of residential PV and battery storage on Australia's electricity networks under different tariffs," Energy Policy, Elsevier, vol. 128(C), pages 616-627.
    12. Angela María Gómez-Restrepo & Juan David González-Ruiz & Sergio Botero Botero, 2024. "Financial Investment Valuation Models for Photovoltaic and Energy Storage Projects: Trends and Challenges," Energies, MDPI, vol. 17(11), pages 1-29, May.
    13. Samuel Matthew G. Dumlao & Keiichi N. Ishihara, 2021. "Dynamic Cost-Optimal Assessment of Complementary Diurnal Electricity Storage Capacity in High PV Penetration Grid," Energies, MDPI, vol. 14(15), pages 1-23, July.
    14. Koecklin, Manuel Tong & Longoria, Genaro & Fitiwi, Desta Z. & DeCarolis, Joseph F. & Curtis, John, 2021. "Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland," Energy Policy, Elsevier, vol. 151(C).
    15. Ettore Bompard & Daniele Grosso & Tao Huang & Francesco Profumo & Xianzhang Lei & Duo Li, 2018. "World Decarbonization through Global Electricity Interconnections," Energies, MDPI, vol. 11(7), pages 1-29, July.
    16. Huang, Qisheng & Xu, Yunjian & Courcoubetis, Costas, 2020. "Stackelberg competition between merchant and regulated storage investment in wholesale electricity markets," Applied Energy, Elsevier, vol. 264(C).
    17. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    18. Carsten Helm & Mathias Mier, 2020. "Steering the Energy Transition in a World of Intermittent Electricity Supply: Optimal Subsidies and Taxes for Renewables Storage," ifo Working Paper Series 330, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    19. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    20. Maria Taljegard & Lisa Göransson & Mikael Odenberger & Filip Johnsson, 2021. "To Represent Electric Vehicles in Electricity Systems Modelling—Aggregated Vehicle Representation vs. Individual Driving Profiles," Energies, MDPI, vol. 14(3), pages 1-25, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:259:y:2022:i:c:s0360544222017558. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.