Neural Network-enabled Domain-consistent Robust Optimisation for Global CO$_2$ Reduction Potential of Gas Power Plants
This addresses the challenge of scalable decarbonization for global climate action by improving gas power plant efficiency, though it is incremental in applying existing methods to a specific domain.
The paper tackles the problem of domain-inconsistent solutions when using neural network models with optimization solvers by introducing a neural network-driven robust optimization framework that integrates data-driven domain constraints, achieving a 0.76 percentage point mean improvement in energy efficiency for a gas power plant and estimating a potential annual global CO2 reduction of 26 Mt.
We introduce a neural network-driven robust optimisation framework that integrates data-driven domain as a constraint into the nonlinear programming technique, addressing the overlooked issue of domain-inconsistent solutions arising from the interaction of parametrised neural network models with optimisation solvers. Applied to a 1180 MW capacity combined cycle gas power plant, our framework delivers domain-consistent robust optimal solutions that achieve a verified 0.76 percentage point mean improvement in energy efficiency. For the first time, scaling this efficiency gain to the global fleet of gas power plants, we estimate an annual 26 Mt reduction potential in CO$_2$ (with 10.6 Mt in Asia, 9.0 Mt in the Americas, and 4.5 Mt in Europe). These results underscore the synergetic role of machine learning in delivering near-term, scalable decarbonisation pathways for global climate action.