Genetic algorithm-based operation strategy optimization and multi-criteria evaluation of distributed energy system for commercial buildings

Abstract

Distributed energy systems (DESs) are supposed to help alleviate energy crisis and environmental issues. However, the effects of operation strategy on their design and performance have not been fully understood yet. Based on the genetic algorithm, this research aims to conduct the design optimization and performance analysis of the DES in shopping malls, hotels, and office buildings under seven operation strategies, that is, following thermal/electric energy load (FTL and FEL), following hybrid load with no/maximal surplus energy, and with no thermal startup threshold (FHL-ns, FHL-ms, and FHL-nst), and following monthly/seasonal electric–thermal load ratio (FMLR and FSLR). Results show that: (1) The optimal capacities of power generation unit are 623-1 782 kW, while the optimal electric cooling ratios are 0.5-0.9. (2) The optimal primary energy saving ratio, exergy difference, equivalent CO2 and PM2.5 emission reduction ratios, total cost saving ratio, and operation and maintenance cost saving ratio are respectively up to 0.298, 0.114, 0.495, 0.912, -0.166, and 0.221. (3) The optimal strategy for the above commercial buildings is respectively FMLR, FMLR, and FHL-nst, and the comprehensive performance is negatively related to the coal-to-gas consumption ratio. Overall, results can offer a reference for selecting the optimal strategy for DESs in commercial buildings.