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Enhancing bargaining power for energy communities in renewable power purchase agreements using Gaussian learning and fixed price bargaining

Abstract

Power purchase agreements aim to secure long-term contracts between sellers and buyers, particularly in renewable energy transactions. However, successful negotiations for a fixed long-term price and energy volume while ensuring maximum utility for stakeholders remains a significant challenge. This paper introduces a comprehensive model for negotiating 24/7 power purchase agreements, focusing on hourly pricing to address deficits and surpluses throughout each day of the contract timeline. The model incorporates demand flexibility through battery storage, settling on the strike price using Nash Bargaining theory and optimal management of energy consumption relative to market price fluctuations. A soft margin support vector machine classification model determines the buyer’s maximum acceptable price. Moreover, Gaussian process classification is employed to calculate a probabilistic, risk-adjusted strike price, enabling a data-driven approach to power purchase negotiations. The proposed model’s performance is demonstrated through a detailed case study of Norway, illustrating how demand flexibility can significantly lower long-term power purchase agreement contract prices. The analysis of yearly price trends indicates that incorporating flexibility resources in long-term energy contracts may lead to a reduction in strike prices by around 25%. Moreover, such flexibility enhances demand-generation matching, thereby increasing renewable energy transactions within such agreements.

Keywords:
Power purchase agreements; Energy communities; Nash Bargaining theory; Robust optimization; Support vector machines; Demand response
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Category

Academic article

Client

  • Research Council of Norway (RCN) / 257660

Language

English

Author(s)

  • Bakul Kandpal
  • Stian Backe
  • Pedro Andres Crespo del Granado

Affiliation

  • Norwegian University of Science and Technology
  • SINTEF Energy Research / Energisystemer

Year

2024

Published in

Energy

ISSN

0360-5442

Publisher

Elsevier

Volume

309

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