IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1244-d505136.html
   My bibliography  Save this article

A Stochastic Planning Model for Battery Energy Storage Systems Coupled with Utility-Scale Solar Photovoltaics

Author

Listed:
  • Heejung Park

    (School of Energy Engineering, Kyungpook National University, Daegu 41566, Korea)

Abstract

With recent technology advances and price drop, battery energy storage systems (BESSs) are considered as a promising storage technology in power systems. In this paper, a stochastic BESS planning model is introduced, which determines optimal capacity and durations of BESSs to co-locate utility-scale solar photovoltaic (PV) systems in a high-voltage power system under the uncertainties of renewable resources and electric load. The optimization model minimizing total costs aims to obtain at least 20% electric energy from renewable sources, while satisfying all the physical constraints. Furthermore, two-stage stochastic programming is applied to formulate mathematical optimization problem to find out optimal durations and capacity of BESSs. In scheduling BESSs, chronology needs to be considered to represent temporal changes of BESS states; therefore, a scenario generation method to generate random sample paths with 1-h time step is adopted to explicitly represent uncertainty and temporal changes. The proposed mathematical model is applied to a modified IEEE 300-bus system that comprises 300 electric buses and 411 transmission lines. Optimal BESS durations and capacity are compared when different numbers of scenarios are employed to see the sensitivity to the number of scenarios in the model, and “value of stochastic solution” (VSS) is calculated to verify the impacts of inclusion of stochastic parameters. The results show that the building costs and capacity of BESSs increase when the number of scenarios increases from 10 to 30. By inspecting VSSs, it is observed that an explicit representation of stochastic parameters affects the optimal value, and the impacts become larger when the larger number of scenarios are applied.

Suggested Citation

  • Heejung Park, 2021. "A Stochastic Planning Model for Battery Energy Storage Systems Coupled with Utility-Scale Solar Photovoltaics," Energies, MDPI, vol. 14(5), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1244-:d:505136
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1244/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/5/1244/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Talent, Orlando & Du, Haiping, 2018. "Optimal sizing and energy scheduling of photovoltaic-battery systems under different tariff structures," Renewable Energy, Elsevier, vol. 129(PA), pages 513-526.
    2. Heejung Park, 2020. "Generation Capacity Expansion Planning Considering Hourly Dynamics of Renewable Resources," Energies, MDPI, vol. 13(21), pages 1-15, October.
    3. Hosseinalizadeh, Ramin & Shakouri G, Hamed & Amalnick, Mohsen Sadegh & Taghipour, Peyman, 2016. "Economic sizing of a hybrid (PV–WT–FC) renewable energy system (HRES) for stand-alone usages by an optimization-simulation model: Case study of Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 139-150.
    4. Nayeem Chowdhury & Fabrizio Pilo & Giuditta Pisano, 2020. "Optimal Energy Storage System Positioning and Sizing with Robust Optimization," Energies, MDPI, vol. 13(3), pages 1-20, January.
    5. Xia, Shiwei & Chan, K.W. & Luo, Xiao & Bu, Siqi & Ding, Zhaohao & Zhou, Bin, 2018. "Optimal sizing of energy storage system and its cost-benefit analysis for power grid planning with intermittent wind generation," Renewable Energy, Elsevier, vol. 122(C), pages 472-486.
    6. Marne C. Cario & Barry L. Nelson, 1998. "Numerical Methods for Fitting and Simulating Autoregressive-to-Anything Processes," INFORMS Journal on Computing, INFORMS, vol. 10(1), pages 72-81, February.
    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. Salil Madhav Dubey & Hari Mohan Dubey & Manjaree Pandit & Surender Reddy Salkuti, 2021. "Multiobjective Scheduling of Hybrid Renewable Energy System Using Equilibrium Optimization," Energies, MDPI, vol. 14(19), pages 1-20, October.
    2. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    3. Alexander Micallef & Cyril Spiteri Staines & Alan Cassar, 2022. "Utility-Scale Storage Integration in the Maltese Medium-Voltage Distribution Network," Energies, MDPI, vol. 15(8), pages 1-20, April.
    4. Mengyao Lu & Guitao Xu & Jianjuan Yuan, 2023. "Installation Principle and Calculation Model of the Representative Indoor Temperature-Monitoring Points in Large-Scale Buildings," Energies, MDPI, vol. 16(17), pages 1-19, 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. Hyung Tae Kim & Young Gyu Jin & Yong Tae Yoon, 2019. "An Economic Analysis of Load Leveling with Battery Energy Storage Systems (BESS) in an Electricity Market Environment: The Korean Case," Energies, MDPI, vol. 12(9), pages 1-16, April.
    2. Plain, N. & Hingray, B. & Mathy, S., 2019. "Accounting for low solar resource days to size 100% solar microgrids power systems in Africa," Renewable Energy, Elsevier, vol. 131(C), pages 448-458.
    3. Berna Tektaş & Hasan Hüseyin Turan & Nihat Kasap & Ferhan Çebi & Dursun Delen, 2022. "A Fuzzy Prescriptive Analytics Approach to Power Generation Capacity Planning," Energies, MDPI, vol. 15(9), pages 1-26, April.
    4. Khiareddine, Abla & Ben Salah, Chokri & Rekioua, Djamila & Mimouni, Mohamed Faouzi, 2018. "Sizing methodology for hybrid photovoltaic /wind/ hydrogen/battery integrated to energy management strategy for pumping system," Energy, Elsevier, vol. 153(C), pages 743-762.
    5. Chen, Huifen & Cheng, Yuyen, 2009. "Designing charts for known autocorrelations and unknown marginal distribution," European Journal of Operational Research, Elsevier, vol. 198(2), pages 520-529, October.
    6. Jiao, P.H. & Chen, J.J. & Cai, X. & Wang, L.L. & Zhao, Y.L. & Zhang, X.H. & Chen, W.G., 2021. "Joint active and reactive for allocation of renewable energy and energy storage under uncertain coupling," Applied Energy, Elsevier, vol. 302(C).
    7. Kalema, George & Molenberghs, Geert, 2016. "Generating Correlated and/or Overdispersed Count Data: A SAS Implementation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(c01).
    8. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    9. Ana Rita Silva & Ana Estanqueiro, 2022. "From Wind to Hybrid: A Contribution to the Optimal Design of Utility-Scale Hybrid Power Plants," Energies, MDPI, vol. 15(7), pages 1-19, April.
    10. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    11. Hejn Nielsen, Erland, 2007. "Autocorrelation in queuing network-type production systems--Revisited," International Journal of Production Economics, Elsevier, vol. 110(1-2), pages 138-146, October.
    12. Max Auerswald & Morten Moshagen, 2015. "Generating Correlated, Non-normally Distributed Data Using a Non-linear Structural Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 920-937, December.
    13. Qolipour, Mojtaba & Mostafaeipour, Ali & Tousi, Omid Mohseni, 2017. "Techno-economic feasibility of a photovoltaic-wind power plant construction for electric and hydrogen production: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 113-123.
    14. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    15. Sergey Zhironkin & Fares Abu-Abed & Elena Dotsenko, 2023. "The Development of Renewable Energy in Mineral Resource Clusters—The Case of the Siberian Federal District," Energies, MDPI, vol. 16(9), pages 1-28, April.
    16. Vaziri Rad, Mohammad Amin & Kasaeian, Alibakhsh & Niu, Xiaofeng & Zhang, Kai & Mahian, Omid, 2023. "Excess electricity problem in off-grid hybrid renewable energy systems: A comprehensive review from challenges to prevalent solutions," Renewable Energy, Elsevier, vol. 212(C), pages 538-560.
    17. He, Miao & Zhao, Lei & Powell, Warren B., 2012. "Approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation," European Journal of Operational Research, Elsevier, vol. 222(2), pages 328-340.
    18. D'Agostino, D. & Minelli, F. & D'Urso, M. & Minichiello, F., 2022. "Fixed and tracking PV systems for Net Zero Energy Buildings: Comparison between yearly and monthly energy balance," Renewable Energy, Elsevier, vol. 195(C), pages 809-824.
    19. Handriyanti Diah Puspitarini & Baptiste François & Marco Baratieri & Casey Brown & Mattia Zaramella & Marco Borga, 2020. "Complementarity between Combined Heat and Power Systems, Solar PV and Hydropower at a District Level: Sensitivity to Climate Characteristics along an Alpine Transect," Energies, MDPI, vol. 13(16), pages 1-19, August.
    20. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2022. "Efficiency of resilient three-part tariff pricing schemes in residential power markets," Energy, Elsevier, vol. 239(PD).

    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:5:p:1244-:d:505136. 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.