Mitigating long-term financial risk for large customers via a hybrid procurement strategy considering power purchase agreements

Haolin Yang, Siqi Xu, Weijun Gao, Yafei Wang, You Li and Xindong Wei

Energy, 2024, vol. 295, issue C

Abstract: In facing urgent climate issues, large electricity customers committed to the RE100 initiative, aiming to transition entirely to renewable energy sources (RES). However, they encounter significant challenges in managing the unpredictability of RES generation and the volatility of market prices. This study unveils a groundbreaking hybrid procurement model that integrates Power Purchase Agreements (PPAs) with Battery Energy Storage Systems (BESS) to mitigate these financial risks through a novel method. Employing a sophisticated Mixed Integer Linear Programming (MILP) model alongside an innovative deep learning forecast for long-term PPAs planning, we present a unique solution that significantly boosts financial returns and enhances risk mitigation for large electricity customers. Validated with real-world data across three distinct customer profiles, our model demonstrates a notable increase in expected Net Present Value (NPV) by up to 13.58% compared to traditional strategies and improved earnings stability under adverse market conditions. Our proposed study not only charts a path toward more effective long-term RES procurement strategies but also provides large electricity customers with a strategic framework to skillfully navigate the complexities of the electricity market in alignment with their sustainability commitments.

Keywords: Renewable energy sources; Power purchase agreements; Stochastic programming; Risk-averse optimal operational strategy; Deep learning networks (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:295:y:2024:i:c:s0360544224008107

DOI: 10.1016/j.energy.2024.131038

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