A Review of Configuration Optimization for Thermal Energy Systems

The optimization of thermal energy system configurations is an ongoing research area with significant potential for improving system efficiency. Configuration optimization plays a crucial role in generating advanced thermodynamic cycles and achieving end-to-end design without manual intervention. However, the inclusion of discrete variables in configuration optimization, along with continuous design variables, presents challenges in problem formulation. In this paper, we provide a comprehensive review of configuration optimization in thermal energy systems, covering various aspects from problem formulation to algorithm selection and technical implementation. Current research in configuration optimization involves screening candidates based on different levels of knowledge, leading to the classification of methods into task-oriented, super-structure, and heuristic search approaches. Regarding optimization algorithms, we discuss integer programming, mixed-integer nonlinear algorithms, and heuristic algorithms such as genetic algorithms and simulated annealing, highlighting their suitability for different optimization problem formulations. We analyze the application of optimization techniques in power generation, heating, ventilation, air conditioning (HVAC), and combined systems. Notably, limited attention has been given to HVAC configuration design due to complex fluid property considerations. Additionally, the integration of multi-objective optimization, including cost and CO2 emission minimization, remains relatively scarce. We compare various solvers and discuss algorithm stability. Overall, this review serves as a valuable reference and guide for configuration optimization in thermal energy system design, offering insights into current research trends and identifying areas for future exploration.