IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p4728-d608108.html
   My bibliography  Save this article

Power Conversion System Operation to Reduce the Electricity Purchasing Cost of Energy Storage Systems

Author

Listed:
  • Jun-Mo Kim

    (Interdisciplinary Program in Photovoltaic System Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Jeong Lee

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Jin-Wook Kim

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Junsin Yi

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

  • Chung-Yuen Won

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea)

Abstract

A strategy to operate a power conversion system (PCS) to minimize the electricity rate of an energy storage system (ESS) is formulated. The ESS operation method is determined considering the power management system (PMS). The primary functions include peak-cut, peak-shifting, and frequency regulation typically related to electricity rates. Thus, the battery is charged and discharged when the price is low and high, respectively, thereby monetizing the battery. However, the ESS incurs a high cost for the batteries and PCS. Therefore, ESSs that reuse electric vehicle (EV) batteries are being actively developed. Many researchers have attempted to maximize the profit of ESSs by developing algorithms to calculate the optimal ESS capacity by performing a power load analysis of electricity consumers. An ESS selected based on this calculation can be operated through the PMS. This ESS can use the battery state of charge ( SoC ), ranging from 10–90%, to conduct a feasibility analysis using the net present value, which reflects the current electricity rate. This feasibility analysis is performed considering the difference between the initial investment cost of the ESS and the profit obtained from the power generation of the ESS. In South Korea, many policies have been implemented to encourage the installation of ESSs. The ESS promotion policy was implemented until 2020 to reduce the electricity rate, including the contracted capacity of batteries. However, since 2021, this policy has been transformed to reduce the electricity rate based on the daily maximum power generation. Thus, the conventional method of increasing the battery capacity is not suitable, and the profitability should be increased using limited batteries. For ESSs, PCSs composed of single and parallel structures can be used. When installing a large capacity ESS, a PCS using silicon (Si) is adopted to reduce the unit cost of the PCS. The unit price of a silicon carbide (SiC) device has recently decreased significantly. Thus, in this study, a PCS using this SiC device was developed. Moreover, an algorithm was formulated to minimize the electricity rate of the ESS, and the operation of a modular type PCS based on this algorithm was demonstrated.

Suggested Citation

  • Jun-Mo Kim & Jeong Lee & Jin-Wook Kim & Junsin Yi & Chung-Yuen Won, 2021. "Power Conversion System Operation to Reduce the Electricity Purchasing Cost of Energy Storage Systems," Energies, MDPI, vol. 14(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4728-:d:608108
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/4728/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/4728/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Byuk-Keun Jo & Gilsoo Jang, 2019. "An Evaluation of the Effect on the Expansion of Photovoltaic Power Generation According to Renewable Energy Certificates on Energy Storage Systems: A Case Study of the Korean Renewable Energy Market," Sustainability, MDPI, vol. 11(16), pages 1-17, August.
    2. Bo Fu & Chenxi Ouyang & Chaoshun Li & Jinwen Wang & Eid Gul, 2019. "An Improved Mixed Integer Linear Programming Approach Based on Symmetry Diminishing for Unit Commitment of Hybrid Power System," Energies, MDPI, vol. 12(5), pages 1-14, March.
    3. Kutaiba Sabah Nimma & Monaaf D. A. Al-Falahi & Hung Duc Nguyen & S. D. G. Jayasinghe & Thair S. Mahmoud & Michael Negnevitsky, 2018. "Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids," Energies, MDPI, vol. 11(4), pages 1-27, April.
    4. Bridier, Laurent & Hernández-Torres, David & David, Mathieu & Lauret, Phillipe, 2016. "A heuristic approach for optimal sizing of ESS coupled with intermittent renewable sources systems," Renewable Energy, Elsevier, vol. 91(C), pages 155-165.
    5. Leon Fidele Nishimwe H. & Sung-Guk Yoon, 2021. "Combined Optimal Planning and Operation of a Fast EV-Charging Station Integrated with Solar PV and ESS," Energies, MDPI, vol. 14(11), pages 1-18, May.
    6. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F., 2021. "A novel microgrid support management system based on stochastic mixed-integer linear programming," Energy, Elsevier, vol. 223(C).
    Full references (including those not matched with items on IDEAS)

    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. Al-Falahi, Monaaf D.A. & Jayasinghe, Shantha D.G. & Enshaei, Hossein, 2019. "Hybrid algorithm for optimal operation of hybrid energy systems in electric ferries," Energy, Elsevier, vol. 187(C).
    2. Rômulo de Oliveira Azevêdo & Paulo Rotela Junior & Luiz Célio Souza Rocha & Gianfranco Chicco & Giancarlo Aquila & Rogério Santana Peruchi, 2020. "Identification and Analysis of Impact Factors on the Economic Feasibility of Photovoltaic Energy Investments," Sustainability, MDPI, vol. 12(17), pages 1-40, September.
    3. P. M. R. Bento & S. J. P. S. Mariano & M. R. A. Calado & L. A. F. M. Ferreira, 2020. "A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination," Energies, MDPI, vol. 13(24), pages 1-19, December.
    4. Muhammad Umar Afzaal & Intisar Ali Sajjad & Ahmed Bilal Awan & Kashif Nisar Paracha & Muhammad Faisal Nadeem Khan & Abdul Rauf Bhatti & Muhammad Zubair & Waqas ur Rehman & Salman Amin & Shaikh Saaqib , 2020. "Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    5. Manzano, J.M. & Salvador, J.R. & Romaine, J.B. & Alvarado-Barrios, L., 2022. "Economic predictive control for isolated microgrids based on real world demand/renewable energy data and forecast errors," Renewable Energy, Elsevier, vol. 194(C), pages 647-658.
    6. Isaías Gomes & Rui Melicio & Victor M. F. Mendes, 2021. "Assessing the Value of Demand Response in Microgrids," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    7. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).
    8. Damian Hasterok & Rui Castro & Marcin Landrat & Krzysztof Pikoń & Markus Doepfert & Hugo Morais, 2021. "Polish Energy Transition 2040: Energy Mix Optimization Using Grey Wolf Optimizer," Energies, MDPI, vol. 14(2), pages 1-27, January.
    9. Linxin Zhang & Zuobin Ying & Zhile Yang & Yuanjun Guo, 2024. "Dynamic Multi-Energy Optimization for Unit Commitment Integrating PEVs and Renewable Energy: A DO3LSO Algorithm," Mathematics, MDPI, vol. 12(24), pages 1-28, December.
    10. Yajing Gao & Jing Zhu & Huaxin Cheng & Fushen Xue & Qing Xie & Peng Li, 2016. "Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories," Energies, MDPI, vol. 9(7), pages 1-15, July.
    11. Krystyna Kurowska & Hubert Kryszk & Stanisław Bielski, 2022. "Location and Technical Requirements for Photovoltaic Power Stations in Poland," Energies, MDPI, vol. 15(7), pages 1-16, April.
    12. Hernández-Torres, David & Turpin, Christophe & Roboam, Xavier & Sareni, Bruno, 2019. "Techno-economical optimization of wind power production including lithium and/or hydrogen sizing in the context of the day ahead market in island grids," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 162-178.
    13. Pablo Benalcazar & Marcin Malec & Przemysław Kaszyński & Jacek Kamiński & Piotr W. Saługa, 2024. "Electricity Cost Savings in Energy-Intensive Companies: Optimization Framework and Case Study," Energies, MDPI, vol. 17(6), pages 1-16, March.
    14. Silva, Jéssica Alice A. & López, Juan Camilo & Arias, Nataly Bañol & Rider, Marcos J. & da Silva, Luiz C.P., 2021. "An optimal stochastic energy management system for resilient microgrids," Applied Energy, Elsevier, vol. 300(C).
    15. Wilson Pavon & Esteban Inga & Silvio Simani & Maddalena Nonato, 2021. "A Review on Optimal Control for the Smart Grid Electrical Substation Enhancing Transition Stability," Energies, MDPI, vol. 14(24), pages 1-15, December.
    16. Bo Li & Yudong Wang & Jian Li & Shengxian Cao, 2018. "A Fully Distributed Approach for Economic Dispatch Problem of Smart Grid," Energies, MDPI, vol. 11(8), pages 1-21, August.
    17. Lappalainen, Kari & Valkealahti, Seppo, 2022. "Sizing of energy storage systems for ramp rate control of photovoltaic strings," Renewable Energy, Elsevier, vol. 196(C), pages 1366-1375.
    18. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
    19. L. Alvarado-Barrios & A. Rodríguez del Nozal & A. Tapia & J. L. Martínez-Ramos & D. G. Reina, 2019. "An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes," Energies, MDPI, vol. 12(11), pages 1-23, June.
    20. Tsiropoulos, Ioannis & Siskos, Pelopidas & Capros, Pantelis, 2022. "The cost of recharging infrastructure for electric vehicles in the EU in a climate neutrality context: Factors influencing investments in 2030 and 2050," Applied Energy, Elsevier, vol. 322(C).

    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:gam:jeners:v:14:y:2021:i:16:p:4728-:d:608108. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.