Leveraging Quantum Computing For Recourse-Based Energy Management Under PV Generation Uncertainty

• URL: http://arxiv.org/abs/2509.23133v1
• Date: Sat, 27 Sep 2025 05:34:23 GMT
• Title: Leveraging Quantum Computing For Recourse-Based Energy Management Under PV Generation Uncertainty
• Authors: Daniel Müssig, Mustafa Musab, Markus Wappler, Jörg Lässig,
• Abstract summary: The integration of distributed energy resources, particularly photovoltaic (PV) systems and electric vehicles (EVs) introduces significant uncertainty into modern energy systems.<n>This paper explores a novel approach to address these challenges by formulating a complexity and optimization problem that models the uncertain nature of PV power generation and the flexibility of bi-directional EV charging.<n>We implement and solve the PV model using quantum algorithms, demonstrating the potential of quantum-enhanced optimization for high-dimensional and uncertainty-driven energy management problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The integration of distributed energy resources, particularly photovoltaic (PV) systems and electric vehicles (EVs), introduces significant uncertainty and complexity into modern energy systems. This paper explores a novel approach to address these challenges by formulating a stochastic optimization problem that models the uncertain nature of PV power generation and the flexibility of bi-directional EV charging. The problem is structured as a two-stage stochastic program with recourse, enabling the system to make optimal day-ahead decisions while incorporating corrective actions in real time based on actual PV output and EV availability. Leveraging the capabilities of quantum computing, we implement and solve the stochastic model using quantum algorithms, demonstrating the potential of quantum-enhanced optimization for high-dimensional and uncertainty-driven energy management problems. Our results indicate that quantum computing can provide efficient and scalable solutions for complex recourse problems in smart grid applications, particularly when integrating variable renewable generation and flexible demand resources.

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