Genetic Algorithm and Mont Carlo Method for Global Sensitivity Analysis of Key Parameters Identification of Net Zero Energy Buildings Towards Power Grid Interaction Optimization

Utilizing renewable energy to meet the energy demand, net-zero energy building (NZEB) is considered a promising solution to the worsening energy and environmental problems. Due to the intermittent and unstable characteristics of renewable energy (e.g. solar energy), NZEB needs to frequently exchange energy with the power grid. Such frequent energy interactions can impose negative impacts on the grid in terms of power balance and voltage stability. Existing studies demonstrated that there exist many influential parameters to NZEB grid interaction. However, the impacts of influential parameters have not been systematically compared and the key parameters with critical impacts are still unknown. Without knowing the key parameters, researchers may mistakenly optimize non-critical parameters, thereby leading to limited performance improvements; or they have to take parameters more than necessary into consideration, thereby causing unnecessarily high computation loads. Therefore, this study proposes a novel method to identify the key parameters affecting NZEB grid interactions. In the method, global sensitivity analysis is adopted to quantitatively compare the impacts of 24 influential parameters in three major performance aspects including over/under voltage, grid dependence and energy loss. Meanwhile, Monte-Carlo method is used to simulate the parameter uncertainties. The identified key parameters have been verified through comparing their performance improvements and computation loads. Providing an effective way to identify key parameters out of numerous ones, the study results can substantially reduce the unnecessary considerations of non-critical parameters in design optimizations. Also, the identified key parameters can be used for improving NZEB grid interaction with limited computing power requirement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.