Efficient reformulations of ReLU deep neural networks for surrogate modelling in power system optimisation

• URL: http://arxiv.org/abs/2601.14673v1
• Date: Wed, 21 Jan 2026 05:40:27 GMT
• Title: Efficient reformulations of ReLU deep neural networks for surrogate modelling in power system optimisation
• Authors: Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A. R. Liisberg, Julian Lesmos-Vinasco,
• Abstract summary: Decarbonisation of distributed power systems is driving an increasing reliance on energy resources.<n> complex and nonlinear interactions are difficult to capture in optimisation.<n>This paper proposes a reformulation for a class of convexified ReLUs (DNNLPs)<n>The proposed reformulation is benchmarked against state-of-the-art alternatives.
• Score: 0.9612977347324178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ongoing decarbonisation of power systems is driving an increasing reliance on distributed energy resources, which introduces complex and nonlinear interactions that are difficult to capture in conventional optimisation models. As a result, machine learning based surrogate modelling has emerged as a promising approach, but integrating machine learning models such as ReLU deep neural networks (DNNs) directly into optimisation often results in nonconvex and computationally intractable formulations. This paper proposes a linear programming (LP) reformulation for a class of convexified ReLU DNNs with non-negative weight matrices beyond the first layer, enabling a tight and tractable embedding of learned surrogate models in optimisation. We evaluate the method using a case study on learning the prosumer's responsiveness within an aggregator bidding problem in the Danish tertiary capacity market. The proposed reformulation is benchmarked against state-of-the-art alternatives, including piecewise linearisation (PWL), MIP-based embedding, and other LP relaxations. Across multiple neural network architectures and market scenarios, the convexified ReLU DNN achieves solution quality comparable to PWL and MIP-based reformulations while significantly improving computational performance and preserving model fidelity, unlike penalty-based reformulations. The results demonstrate that convexified ReLU DNNs offer a scalable and reliable methodology for integrating learned surrogate models in optimisation, with applicability to a wide range of emerging power system applications.

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