IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v358y2024ics0306261923019530.html
   My bibliography  Save this article

Power purchase agreements for plus energy neighbourhoods: Financial risk mitigation through predictive modelling and bargaining theory

Author

Listed:
  • Kandpal, Bakul
  • Backe, Stian
  • Crespo del Granado, Pedro

Abstract

This paper introduces a continuous 24/7 power purchase agreement (PPA) designed for contracting photovoltaic (PV) generation within sustainable plus energy neighbourhoods (SPENs) or local energy communities, aiming to ensure a stable economic revenue stream for community stakeholders. The PPA involves the sale of solar PV generation, auctioned at a fixed strike price, to an external off-taker. Employing statistical prediction tools such as long short-term memory and auto-regressive modelling, the proposed framework allows hour-to-hour power delivery commitments between seller and buyer, accurately estimating the agreed-upon volume of renewable energy to be exchanged. A fixed PPA price is negotiated utilizing Nash Bargaining Theory, aiming to optimize revenue for the SPEN while minimizing procurement costs for the buyer, thus achieving an economic equilibrium that mitigates the long-term price risk prevalent in wholesale energy markets. Additionally, the proposed methodology includes utilization of a battery energy storage system (BESS) to store excess power or address supply–demand contractual disparities during periods of low PV generation. Simulation results obtained under varying climatic conditions and energy market dynamics across different countries, demonstrate that the proposed PPA framework, by combining risk assessment strategies and statistical learning methods, can effectively reduce associated financial risks while maximizing payoff for the community.

Suggested Citation

  • Kandpal, Bakul & Backe, Stian & Crespo del Granado, Pedro, 2024. "Power purchase agreements for plus energy neighbourhoods: Financial risk mitigation through predictive modelling and bargaining theory," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261923019530
    DOI: 10.1016/j.apenergy.2023.122589
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923019530
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122589?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 search 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. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    3. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2020. "Robust design optimization and stochastic performance analysis of a grid-connected photovoltaic system with battery storage and hydrogen storage," Energy, Elsevier, vol. 213(C).
    4. Nazari, AmirAli & Keypour, Reza & Amjady, Nima, 2021. "Joint investment of community energy storage systems in distribution networks using modified Nash bargaining theory," Applied Energy, Elsevier, vol. 301(C).
    5. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Yu, Nanpeng & Tesfatsion, Leigh & Liu, Chen-Ching, 2012. "Financial Bilateral Contract Negotiation in Wholesale Electricity Markets Using Nash Bargaining Theory," ISU General Staff Papers 201201010800001470, Iowa State University, Department of Economics.
    7. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    8. Balasubramanian, S. & Balachandra, P., 2021. "Effectiveness of demand response in achieving supply-demand matching in a renewables dominated electricity system: A modelling approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. Chowdhury, Jahedul Islam & Balta-Ozkan, Nazmiye & Goglio, Pietro & Hu, Yukun & Varga, Liz & McCabe, Leah, 2020. "Techno-environmental analysis of battery storage for grid level energy services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Li, Hong Xian & Zhang, Yitao & Li, Yan & Huang, Jiaxin & Costin, Glenn & Zhang, Peng, 2021. "Exploring payback-year based feed-in tariff mechanisms in Australia," Energy Policy, Elsevier, vol. 150(C).
    11. Mendicino, Luca & Menniti, Daniele & Pinnarelli, Anna & Sorrentino, Nicola, 2019. "Corporate power purchase agreement: Formulation of the related levelized cost of energy and its application to a real life case study," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    12. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    13. Bruck, Maira & Sandborn, Peter, 2021. "Pricing bundled renewable energy credits using a modified LCOE for power purchase agreements," Renewable Energy, Elsevier, vol. 170(C), pages 224-235.
    14. Dong, Changgui & Zhou, Runmin & Li, Jiaying, 2021. "Rushing for subsidies: The impact of feed-in tariffs on solar photovoltaic capacity development in China," Applied Energy, Elsevier, vol. 281(C).
    15. Wei, Chun & Shen, Zhuzheng & Xiao, Dongliang & Wang, Licheng & Bai, Xiaoqing & Chen, Haoyong, 2021. "An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining," Applied Energy, Elsevier, vol. 295(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. 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. Szymon Słotwiński, 2022. "The Significance of the “Power Purchase Agreement” for the Development of Local Energy Markets in the Theoretical Perspective of Polish Legal Conditions," Energies, MDPI, vol. 15(18), pages 1-14, September.
    3. Marios Stanitsas & Konstantinos Kirytopoulos, 2023. "Sustainable Energy Strategies for Power Purchase Agreements (PPAs)," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    4. Wang, Jing & Kang, Lixia & Huang, Xiankun & Liu, Yongzhong, 2021. "An analysis framework for quantitative evaluation of parametric uncertainty in a cooperated energy storage system with multiple energy carriers," Energy, Elsevier, vol. 226(C).
    5. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    6. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    7. Wang, Xuejie & Li, Bingkang & Wang, Yuwei & Lu, Hao & Zhao, Huiru & Xue, Wanlei, 2022. "A bargaining game-based profit allocation method for the wind-hydrogen-storage combined system," Applied Energy, Elsevier, vol. 310(C).
    8. Dumitrescu, Raluca & Lüth, Alexandra & Weibezahn, Jens & Groh, Sebastian, 2022. "Prosumer Empowerment Through Community Power Purchase Agreements:A Market Design for Swarm Grids," Working Papers 19-2021, Copenhagen Business School, Department of Economics.
    9. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
    10. Medine Colak & Mehmet Yesilbudak & Ramazan Bayindir, 2020. "Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information," Energies, MDPI, vol. 13(4), pages 1-19, February.
    11. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    12. Ahn, Hyeunguk, 2024. "A framework for developing data-driven correction factors for solar PV systems," Energy, Elsevier, vol. 290(C).
    13. Athanasios I. Salamanis & Georgia Xanthopoulou & Napoleon Bezas & Christos Timplalexis & Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Dimosthenis Ioannidis & Dionysios Kehagias & , 2020. "Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting," Energies, MDPI, vol. 13(22), pages 1-31, November.
    14. Sambasivam, Balasubramanian & Xu, Yuan, 2023. "Reducing solar PV curtailment through demand-side management and economic dispatch in Karnataka, India," Energy Policy, Elsevier, vol. 172(C).
    15. Bangjun, Wang & Feng, Zhaolei & Feng, Ji & Yu, Pan & Cui, Linyu, 2022. "Decision making on investments in photovoltaic power generation projects based on renewable portfolio standard: Perspective of real option," Renewable Energy, Elsevier, vol. 189(C), pages 1033-1045.
    16. 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.
    17. Marko Hočevar & Lovrenc Novak & Primož Drešar & Gašper Rak, 2022. "The Status Quo and Future of Hydropower in Slovenia," Energies, MDPI, vol. 15(19), pages 1-13, September.
    18. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    19. Behrang Shirizadeh, Quentin Perrier, and Philippe Quirion, 2022. "How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    20. Kwag, Kyuhyeong & Shin, Hansol & Oh, Hyobin & Yun, Sangmin & Kim, Tae Hyun & Hwang, Pyeong-Ik & Kim, Wook, 2023. "Bilevel programming approach for the quantitative analysis of renewable portfolio standards considering the electricity market," Energy, Elsevier, vol. 263(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:eee:appene:v:358:y:2024:i:c:s0306261923019530. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.