Data-driven Bi-level Optimization of Thermal Power Systems with embedded Artificial Neural Networks

2026-02-14

AI Summary

This study addresses the computational intractability of conventional simultaneous optimization in industrial thermal power generation systems, which arises from strong variable coupling and hierarchical optimization structures. To overcome this challenge, the authors propose a data-driven bilevel optimization framework that uniquely integrates artificial neural networks (ANNs) with Karush–Kuhn–Tucker (KKT) conditions, analytically embedding the lower-level problem into the upper level to yield an efficiently solvable single-level formulation. The approach further enables robust bound construction under operational uncertainties. Validated on a 660 MW coal-fired unit and a 395 MW gas turbine system, the method achieves solution times of only 0.22–0.88 seconds while producing output power and heat rate results in close agreement with benchmark solutions, thereby significantly enhancing computational efficiency and practical applicability for energy-efficiency optimization in the context of Industry 5.0.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Industrial thermal power systems have coupled performance variables with hierarchical order of importance, making their simultaneous optimization computationally challenging or infeasible. This barrier limits the integrated and computationally scaleable operation optimization of industrial thermal power systems. To address this issue for large-scale engineering systems, we present a fully machine learning-powered bi-level optimization framework for data-driven optimization of industrial thermal power systems. The objective functions of upper and lower levels are approximated by artificial neural network (ANN) models and the lower-level problem is analytically embedded through Karush-Kuhn-Tucker (KKT) optimality conditions. The reformulated single level optimization framework integrating ANN models and KKT constraints (ANN-KKT) is validated on benchmark problems and on real-world power generation operation of 660 MW coal power plant and 395 MW gas turbine system. The results reveal a comparable solutions obtained from the proposed ANN-KKT framework to the bi-level solutions of the benchmark problems. Marginal computational time requirement (0.22 to 0.88 s) to compute optimal solutions yields 583 MW (coal) and 402 MW (gas turbine) of power output at optimal turbine heat rate of 7337 kJ/kWh and 7542 kJ/kWh, respectively. In addition, the method expands to delineate a feasible and robust operating envelope that accounts for uncertainty in operating variables while maximizing thermal efficiency in various scenarios. These results demonstrate that ANN-KKT offers a scalable and computationally efficient route for hierarchical, data-driven optimization of industrial thermal power systems, achieving energy-efficient operations of large-scale engineering systems and contributing to industry 5.0.

Problem

• bi-level optimization
• thermal power systems
• computational scalability
• hierarchical optimization
• coupled performance variables

Innovation

• bi-level optimization
• artificial neural networks
• KKT conditions
• thermal power systems
• data-driven optimization