Two-stage superstructure model for optimization of distributed energy systems (DES) part I: Model development and verification

The optimization of distributed energy systems (DES) is challenging because of the diversity of the types of energies involved and the complexity of the structure. Mathematically, the optimization of DES is a mixed-integer non-linear programming problem (MINLP). The optimal tradeoff between precision and computational efficiency, to find the global optimal solution, is a core issue that needs to be solved. This present work proposes a two-stage superstructure model which is solved by the random walk algorithm with compulsive evolution (RWCE), to better approximate the global optimal solution of the MINLP. The paper is divided into two parts; the first focuses on the modeling methodology and model solving strategy. Moreover, to confirm the applicability and effectiveness of the recommended method, DESs for three case studies, i.e., business park, residential building, and hotel, were optimized from system planning point of view. On comparison with literatures, it was found that the proposed method had positive effects on further improving the economy of the system at different scales and configurations. The resulting decrease in the total annual cost of the three systems was 12%, 36%, and 2%, respectively. Further research on system operation optimization will be published as the second part of this paper. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a two-stage superstructure model to solve the optimization problem of distributed energy systems (DES) using the random walk algorithm with compulsive evolution (RWCE). The recommended method is found to improve the economy of DES at different scales and configurations, based on optimization studies of case studies including business park, residential building, and hotel.