Multi-objective design optimization of multiple energy systems in net/nearly zero energy buildings under uncertainty correlations

Menglong Lu, Yongjun Sun and Zhenjun Ma

Applied Energy, 2024, vol. 370, issue C, No S0306261924010031

Abstract: Net/nearly zero energy buildings (NZEBs) are being recognized as a promising solution to improve environmental friendliness and reduce energy consumption in the building sector. Traditionally, the energy systems in NZEBs were sized under deterministic conditions. However, such an approach may introduce a failure in achieving the expected system performance due to the presence of multiple uncertainties. Despite considering uncertain parameters in some reported studies, the correlations among uncertainties were often neglected. To maximize the benefits of using NZEBs, an uncertainty-based optimization method is proposed in this study to size energy systems. The correlations among uncertainties are tackled using the copula theory. Additionally, a novel scenario reduction technique to address the correlated uncertainties is introduced to decrease the computational cost. The NSGA-II, coupled with TOPSIS and Shannon entropy, is utilized to formulate and solve the multi-objective optimization problem. The results from simulation analysis validated the robustness and reliability of the designed systems under uncertainties, with the average values of annual thermal discomfort, self-consumption ratio, self-sufficiency ratio, and net interaction level of 8.6 h, 73.0%, 75.5%, and 38.0%, respectively. This approach could be extended and adapted to facilitate the optimal design of multiple energy systems in NZEBs.

Keywords: Uncertainty correlations; NZEB; Renewable energy system; Monte Carlo simulation; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2024.123620

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