IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i16p4405-d1727347.html
   My bibliography  Save this article

Optimal Sizing of Residential PV and Battery Systems Under Grid Export Constraints: An Estonian Case Study

Author

Listed:
  • Arko Kesküla

    (STACC OÜ, Narva mnt 18, 51009 Tartu, Estonia)

  • Kirill Grjaznov

    (STACC OÜ, Narva mnt 18, 51009 Tartu, Estonia)

  • Tiit Sepp

    (STACC OÜ, Narva mnt 18, 51009 Tartu, Estonia)

  • Alo Allik

    (Institute of Technology, Estonian University of Life Sciences, 51006 Tartu, Estonia)

Abstract

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency.

Suggested Citation

  • Arko Kesküla & Kirill Grjaznov & Tiit Sepp & Alo Allik, 2025. "Optimal Sizing of Residential PV and Battery Systems Under Grid Export Constraints: An Estonian Case Study," Energies, MDPI, vol. 18(16), pages 1-17, August.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4405-:d:1727347
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/16/4405/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/16/4405/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ciabattoni, Lucio & Grisostomi, Massimo & Ippoliti, Gianluca & Longhi, Sauro, 2014. "Fuzzy logic home energy consumption modeling for residential photovoltaic plant sizing in the new Italian scenario," Energy, Elsevier, vol. 74(C), pages 359-367.
    2. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    3. Cardoso, Gonçalo & Brouhard, Thomas & DeForest, Nicholas & Wang, Dai & Heleno, Miguel & Kotzur, Leander, 2018. "Battery aging in multi-energy microgrid design using mixed integer linear programming," Applied Energy, Elsevier, vol. 231(C), pages 1059-1069.
    4. Khalilpour, Kaveh Rajab & Vassallo, Anthony, 2016. "Technoeconomic parametric analysis of PV-battery systems," Renewable Energy, Elsevier, vol. 97(C), pages 757-768.
    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. Vincenzo Franzitta & Domenico Curto & Davide Rao, 2016. "Energetic Sustainability Using Renewable Energies in the Mediterranean Sea," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
    2. Sadat, Seyyed Ali & Roy, Riya & Pearce, Joshua M., 2025. "Technical, economic and environmental potential of recycled polycarbonate solar photovoltaic frames," Renewable Energy, Elsevier, vol. 242(C).
    3. Bertolini, Marina & D'Alpaos, Chiara & Moretto, Michele, 2018. "Do Smart Grids boost investments in domestic PV plants? Evidence from the Italian electricity market," Energy, Elsevier, vol. 149(C), pages 890-902.
    4. Cédric Clastres & Olivier Rebenaque & Patrick Jochem, 2020. "Provision of Demand Response from the prosumers in multiple markets," Working Papers 2008, Chaire Economie du climat.
    5. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    6. Biondi, Tommaso & Moretto, Michele, 2015. "Solar Grid Parity dynamics in Italy: A real option approach," Energy, Elsevier, vol. 80(C), pages 293-302.
    7. Hernández-Escobedo, Q. & Fernández-García, A. & Manzano-Agugliaro, F., 2017. "Solar resource assessment for rural electrification and industrial development in the Yucatan Peninsula (Mexico)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1550-1561.
    8. Shang, Yuwei & Wu, Wenchuan & Guo, Jianbo & Ma, Zhao & Sheng, Wanxing & Lv, Zhe & Fu, Chenran, 2020. "Stochastic dispatch of energy storage in microgrids: An augmented reinforcement learning approach," Applied Energy, Elsevier, vol. 261(C).
    9. Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang & Rieger, Alexander & Thimmel, Markus, 2018. "One rate does not fit all: An empirical analysis of electricity tariffs for residential microgrids," Applied Energy, Elsevier, vol. 210(C), pages 800-814.
    10. Chen, Cong & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    12. Aghamolaei, Reihaneh & Shamsi, Mohammad Haris & O’Donnell, James, 2020. "Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations," Renewable Energy, Elsevier, vol. 157(C), pages 793-808.
    13. Tang, Qinghu & Guo, Hongye & Zheng, Kedi & Chen, Qixin, 2024. "Forecasting individual bids in real electricity markets through machine learning framework," Applied Energy, Elsevier, vol. 363(C).
    14. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    15. To, Thanh & Heleno, Miguel & Valenzuela, Alan, 2022. "Risk-constrained multi-period investment model for Distributed Energy Resources considering technology costs and regulatory uncertainties," Applied Energy, Elsevier, vol. 319(C).
    16. Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
    17. Haider, Haider Tarish & Muhsen, Dhiaa Halboot & Al-Nidawi, Yaarob Mahjoob & Khatib, Tamer & See, Ong Hang, 2022. "A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids," Energy, Elsevier, vol. 254(PB).
    18. Gallego-Castillo, Cristobal & Heleno, Miguel & Victoria, Marta, 2021. "Self-consumption for energy communities in Spain: A regional analysis under the new legal framework," Energy Policy, Elsevier, vol. 150(C).
    19. Maria Fotopoulou & George J. Tsekouras & Andreas Vlachos & Dimitrios Rakopoulos & Ioanna Myrto Chatzigeorgiou & Fotios D. Kanellos & Vassiliki Kontargyri, 2025. "Day Ahead Operation Cost Optimization for Energy Communities," Energies, MDPI, vol. 18(5), pages 1-20, February.
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:16:p:4405-:d:1727347. 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.