An efficient yet accurate optimization algorithm for thermal systems integrating heat current method and generalized Benders decomposition

Thermal system optimization is critical for energy conservation but challenging due to highly complicated systems and nonlinear governing equations. Herein, an efficient yet accurate optimization algorithm based on generalized Benders decomposition (GBD) is developed to address this challenge. It decomposes the entire optimization into a subproblem and a master problem. The subproblem performs the system simulation by developing a hybrid simulation method, which uses GBD and fixed-point iteration sequentially for acceleration. The master problem is constructed based on the gradients of the objective with respect to decision variables, where the solution of every iteration gives an approximation of the optimal solution. The two generated problems are iteratively solved in an alternating manner to converge towards the optimal solution. Test results of a supercritical carbon dioxide recompression system indicate that the hybrid simulation method expands the convergence region to similar to 167 % which is nearly 4 times larger than that of the fixed-point iteration, and consumes about 10 % calculation time compared with sequential modular method. The optimization algorithm reproduces the results obtained from the genetic algorithm but reduces the calculation time for an order-of-magnitude and has higher stability. The high efficiency and accuracy suggest the proposed algorithm as a powerful system optimization tool.