IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i9p1526-d409955.html
   My bibliography  Save this article

Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming

Author

Listed:
  • Seung-Ju Lee

    (Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Korea)

  • Yourim Yoon

    (Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Korea)

Abstract

Recently, energy storage systems (ESSs) are becoming more important as renewable and microgrid technologies advance. ESSs can act as a buffer between generation and load and enable commercial and industrial end users to reduce their electricity expenses by controlling the charge/discharge amount. In this paper, to derive efficient charge/discharge schedules of ESSs based on time-of-use pricing with renewable energy, a combination of genetic algorithm and dynamic programming is proposed. The performance of the combined method is improved by adjusting the size of the base units of dynamic programming. We show the effectiveness of the proposed method by simulating experiments with load and generation profiles of various commercial electricity consumers.

Suggested Citation

  • Seung-Ju Lee & Yourim Yoon, 2020. "Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1526-:d:409955
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/9/1526/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/9/1526/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    2. Neufeld, John L., 1987. "Price Discrimination and the Adoption of the Electricity Demand Charge," The Journal of Economic History, Cambridge University Press, vol. 47(3), pages 693-709, September.
    3. Thomas N. Taylor & Peter M. Schwarz, 1986. "A Residential Demand Charge: Evidence from the Duke Power Time-of-Day Pricing Experiment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 135-151.
    4. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    5. Yoon, Yourim & Kim, Yong-Hyuk, 2016. "Effective scheduling of residential energy storage systems under dynamic pricing," Renewable Energy, Elsevier, vol. 87(P2), pages 936-945.
    6. Nottrott, A. & Kleissl, J. & Washom, B., 2013. "Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems," Renewable Energy, Elsevier, vol. 55(C), pages 230-240.
    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. Gülsah Erdogan & Wiem Fekih Hassen, 2023. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations," Energies, MDPI, vol. 16(18), pages 1-29, September.

    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. Brown, David P. & Sappington, David E.M., 2018. "On the role of maximum demand charges in the presence of distributed generation resources," Energy Economics, Elsevier, vol. 69(C), pages 237-249.
    2. Gopinath Subramani & Vigna K. Ramachandaramurthy & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Josep M. Guerrero, 2017. "Grid-Tied Photovoltaic and Battery Storage Systems with Malaysian Electricity Tariff—A Review on Maximum Demand Shaving," Energies, MDPI, vol. 10(11), pages 1-17, November.
    3. Ziyi Zhao, 2023. "Operation Simulation and Economic Analysis of Household Hybrid PV and BESS Systems in the Improved TOU Mode," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    4. Corentin Jankowiak & Aggelos Zacharopoulos & Caterina Brandoni & Patrick Keatley & Paul MacArtain & Neil Hewitt, 2019. "The Role of Domestic Integrated Battery Energy Storage Systems for Electricity Network Performance Enhancement," Energies, MDPI, vol. 12(20), pages 1-27, October.
    5. Hojin Kim & Jaewoo So & Hongseok Kim, 2022. "Carbon-Neutral Cellular Network Operation Based on Deep Reinforcement Learning," Energies, MDPI, vol. 15(12), pages 1-13, June.
    6. Kubik, M.L. & Coker, P.J. & Hunt, C., 2012. "The role of conventional generation in managing variability," Energy Policy, Elsevier, vol. 50(C), pages 253-261.
    7. Kim, Minsoo & Park, Taeseop & Jeong, Jaeik & Kim, Hongseok, 2023. "Stochastic optimization of home energy management system using clustered quantile scenario reduction," Applied Energy, Elsevier, vol. 349(C).
    8. Adrian Grimm & Patrik Schönfeldt & Herena Torio & Peter Klement & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2021. "Deduction of Optimal Control Strategies for a Sector-Coupled District Energy System," Energies, MDPI, vol. 14(21), pages 1-13, November.
    9. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    10. Jungsub Sim & Minsoo Kim & Dongjoo Kim & Hongseok Kim, 2021. "Cloud Energy Storage System Operation with Capacity P2P Transaction," Energies, MDPI, vol. 14(2), pages 1-13, January.
    11. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
    12. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    13. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    14. Georgiou, Giorgos S. & Christodoulides, Paul & Kalogirou, Soteris A., 2019. "Real-time energy convex optimization, via electrical storage, in buildings – A review," Renewable Energy, Elsevier, vol. 139(C), pages 1355-1365.
    15. Liu, Wen & Hu, Weihao & Lund, Henrik & Chen, Zhe, 2013. "Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China," Applied Energy, Elsevier, vol. 104(C), pages 445-456.
    16. Zhou, P. & Jin, R.Y. & Fan, L.W., 2016. "Reliability and economic evaluation of power system with renewables: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 537-547.
    17. Hartmann, Bálint & Divényi, Dániel & Vokony, István, 2018. "Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study," Applied Energy, Elsevier, vol. 222(C), pages 59-66.
    18. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    19. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    20. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.

    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:jmathe:v:8:y:2020:i:9:p:1526-:d:409955. 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.