IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225012939.html
   My bibliography  Save this article

Efficient power purchase agreement structures for meeting corporate electricity needs with solar energy

Author

Listed:
  • Simões, Francisco
  • Henriques, Carla
  • Figueiredo, Nuno Carvalho
  • Silva, Patrícia Pereira da

Abstract

Corporate Power Purchase Agreements (CPPA) offer companies an effective way to meet electricity needs with renewable energy, reducing exposure to wholesale market price volatility. However, the risks associated with these contracts vary depending on their structure, specifically the combination of electricity profile and price structure. This study aims to identify the optimal solar CPPA type for companies with a 24/7 consumption profile and no renewable self-production, ensuring the best balance between financial performance and risk exposure. Four electricity profile structures (Pay-as-Produced, Fixed Hourly Profile, Monthly Baseload, and Annual Baseload) were analyzed, along with two price structures (Fixed Price and Variable Price). To evaluate the performance of the eight solar CPPA types considered, a Slacks-Based Measure Data Envelopment Analysis model was applied using three key indicators: Net Present Value, Contract Performance Deviation, and Volume Residual. The findings indicate that among solar CPPA, contracts with a monthly baseload profile and a variable price structure achieve the highest overall performance. In contrast, CPPA with a fixed hourly electricity profile exhibits the lowest performance, regardless of price structure. These insights provide valuable guidance for companies during CPPA negotiations, allowing them to make informed decisions aligned with their business strategy.

Suggested Citation

  • Simões, Francisco & Henriques, Carla & Figueiredo, Nuno Carvalho & Silva, Patrícia Pereira da, 2025. "Efficient power purchase agreement structures for meeting corporate electricity needs with solar energy," Energy, Elsevier, vol. 322(C).
  • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012939
    DOI: 10.1016/j.energy.2025.135651
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225012939
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.135651?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Gabrielli, Paolo & Aboutalebi, Reyhaneh & Sansavini, Giovanni, 2022. "Mitigating financial risk of corporate power purchase agreements via portfolio optimization," Energy Economics, Elsevier, vol. 109(C).
    2. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Dimitriadis, Christos N. & Tsimopoulos, Evangelos G. & Georgiadis, Michael C., 2024. "Co-optimized trading strategy of a renewable energy aggregator in electricity and green certificates markets," Renewable Energy, Elsevier, vol. 236(C).
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    7. K. Tone & M. Tsutsui, 2015. "How to Deal with Non-Convex Frontiers in Data Envelopment Analysis," Journal of Optimization Theory and Applications, Springer, vol. 166(3), pages 1002-1028, September.
    8. Tone, Kaoru & Chang, Tsung-Sheng & Wu, Chen-Hui, 2020. "Handling negative data in slacks-based measure data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 282(3), pages 926-935.
    9. Yang, Haolin & Xu, Siqi & Gao, Weijun & Wang, Yafei & Li, You & Wei, Xindong, 2024. "Mitigating long-term financial risk for large customers via a hybrid procurement strategy considering power purchase agreements," Energy, Elsevier, vol. 295(C).
    10. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    11. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    13. Carla Oliveira Henriques & Maria Elisabete Duarte Neves, 2019. "A multiobjective interval portfolio framework for supporting investor’s preferences under different risk assumptions," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1639-1661, October.
    14. Núñez, Fernando & Canca, David & Arcos-Vargas, Ángel, 2022. "An assessment of European electricity arbitrage using storage systems," Energy, Elsevier, vol. 242(C).
    15. Corton, Maria Luisa & Berg, Sanford V., 2009. "Benchmarking Central American water utilities," Utilities Policy, Elsevier, vol. 17(3-4), pages 267-275, September.
    16. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    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. Carla Henriques & Clara Viseu & António Trigo & Maria Gouveia & Ana Amaro, 2022. "How Efficient Is the Cohesion Policy in Supporting Small and Mid-Sized Enterprises in the Transition to a Low-Carbon Economy?," Sustainability, MDPI, vol. 14(9), pages 1-55, April.
    2. Vicente J. Bolós & Rafael Benítez & Vicente Coll-Serrano, 2023. "Continuous models combining slacks-based measures of efficiency and super-efficiency," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 363-391, June.
    3. Vicente J. Bolos & Rafael Benitez & Vicente Coll-Serrano, 2025. "Conventional and Fuzzy Data Envelopment Analysis with deaR," Papers 2506.03766, arXiv.org.
    4. Hatami-Marbini, Adel & Arabmaldar, Aliasghar, 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application," European Journal of Operational Research, Elsevier, vol. 295(2), pages 604-620.
    5. Branda, Martin, 2013. "Diversification-consistent data envelopment analysis with general deviation measures," European Journal of Operational Research, Elsevier, vol. 226(3), pages 626-635.
    6. Liu, Wenbin & Zhou, Zhongbao & Liu, Debin & Xiao, Helu, 2015. "Estimation of portfolio efficiency via DEA," Omega, Elsevier, vol. 52(C), pages 107-118.
    7. Camanho, Ana Santos & Silva, Maria Conceicao & Piran, Fabio Sartori & Lacerda, Daniel Pacheco, 2024. "A literature review of economic efficiency assessments using Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 315(1), pages 1-18.
    8. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.
    9. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    10. Qunwei Wang & Ye Hang & Jin‐Li Hu & Ching‐Ren Chiu, 2018. "An alternative metafrontier framework for measuring the heterogeneity of technology," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(5), pages 427-445, August.
    11. Han‐Chung Chou & Shiu‐Wan Hung & Wen‐Min Lu, 2022. "Exploring competition efficiency of automobile brands: An extensional data envelopment analysis‐metafrontier framework," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(1), pages 206-218, January.
    12. Mohammad Tavassoli & Mahsa Ghandehari & Masoud Taherinia, 2023. "Rang-adjusted measure: modelling and computational aspects from internal and external perspectives for network DEA," Operational Research, Springer, vol. 23(4), pages 1-34, December.
    13. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    14. Arsen Benga & Glediana Zeneli (Foto) & María Jesús Delgado‑Rodríguez & Sonia Lucas Santos, 2025. "Company efforts and environmental efficiency: evidence from European railways considering market-based emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(5), pages 9977-10012, May.
    15. Tarnaud, Albane Christine & Leleu, Hervé, 2018. "Portfolio analysis with DEA: Prior to choosing a model," Omega, Elsevier, vol. 75(C), pages 57-76.
    16. Chambers, Robert G., 2024. "Numeraire choice, shadow profit, and inefficiency measurement," European Journal of Operational Research, Elsevier, vol. 319(2), pages 658-668.
    17. Imanirad, Raha & Cook, Wade D. & Aviles-Sacoto, Sonia Valeria & Zhu, Joe, 2015. "Partial input to output impacts in DEA: The case of DMU-specific impacts," European Journal of Operational Research, Elsevier, vol. 244(3), pages 837-844.
    18. Amineh Ghazi & Farhad Hosseinzadeh Lotfi & Masoud Sanei, 2020. "Hybrid efficiency measurement and target setting based on identifying defining hyperplanes of the PPS with negative data," Operational Research, Springer, vol. 20(2), pages 1055-1092, June.
    19. Maria Silva Portela & Pedro Borges & Emmanuel Thanassoulis, 2003. "Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies," Journal of Productivity Analysis, Springer, vol. 19(2), pages 251-269, April.
    20. Yung-ho Chiu & Chin-wei Huang & Chung-te Ting, 2012. "A non-radial measure of different systems for Taiwanese tourist hotels’ efficiency assessment," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 45-63, March.

    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:eee:energy:v:322:y:2025:i:c:s0360544225012939. 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.