A GA-based NZEB-cluster planning and design optimization method for mitigating grid overvoltage risk

Abstract

Net-zero energy buildings (NZEBs) are considered as a promising method to mitigating the energy problems. Due to the intermittent characteristics of renewable energy (e.g., solar energy), NZEBs need to frequently exchange energy with the grid, which imposes severe negative impacts on the grid especially the overvoltage risk. Both planning and design are essential for reducing NZEB connected grid overvoltage, but most existing studies isolated the efforts from planning to design, thereby failing to achieve the best cumulative result. More importantly, existing studies oversimplified overvoltage quantification by using aggregated power interactions to represent overvoltage risk, which cannot consider the complex voltage influences among grid nodes. Due to the isolated efforts and the quantification oversimplification, existing studies can hardly achieve overvoltage risk minimization. Therefore, this study proposes a novel GA (genetic algorithm)-based method in which the key planning and design parameters are optimized sequentially for mitigating the overvoltage risk. Meanwhile, distribution network model has been adopted to precisely quantify the grid overvoltage. The study results show that the proposed method is highly effective in reducing NZEB cluster connected grid overvoltage risk. The proposed method can be used in practice for improving NZEB cluster planning and system design as grid interaction is considered.