The use of renewable energy to meet the energy demand, net-zero energy building (NZEB) is considered a promising solution to the worsening energy and environmental problems. Owing to the intermittent 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 especially the overvoltage risk. Through NZEB system design and control optimization, the building side can reduce the grid overvoltage risk. However, existing studies rarely achieved an efficient grid overvoltage risk minimization owing to the following reasons.

First, existing studies demonstrated that many influencing parameters exist for the NZEB-grid interaction. But the impacts of the influencing parameters have not been systematically investigated, 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 computational loads. Second, both NZEB planning and system design optimizations are essential in reducing the connected grid overvoltage, but most existing studies isolated the efforts from planning to design, thereby failing to achieve the best cumulative results. Moreover, existing studies oversimplified overvoltage quantification by directly using the total energy interactions to represent the grid overvoltage, which can easily cause unsatisfactory/poor accuracy in evaluating the impacts of NZEB-grid interactions on a real power network. Owing to isolated efforts and quantification oversimplification, existing design optimizations cannot achieve overvoltage risk minimization. Third, the associated control optimization has also been proven effective in reducing the grid overvoltage risk. Existing coordinated controls have achieved a reduction of power exchange at building cluster level, which is the main cause of the grid overvoltage. But similar to NZEB system design optimizations, existing coordinated controls oversimplified grid overvoltage quantification by directly using the total power exchanges to represent grid overvoltage, which eventually prevented such controls from achieving overvoltage risk minimization.

Therefore, this thesis proposes a systematic method for grid overvoltage risk minimization by combining the efforts from key parameter identification, NZEB planning and system design optimization, and NZEB system control optimization. Regarding key parameter identification, a novel method to identify the key parameters influencing NZEB grid interactions is proposed. In this method, global sensitivity analysis is adopted to quantitatively compare the impacts of 24 influencing parameters in three major performance aspects: over/under voltage, grid dependence and energy loss. Meanwhile, the Monte Carlo method is used to simulate the parameter uncertainties. The identified key parameters have been verified by comparing their performance improvements and computational 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. In addition, the identified key parameters can be used to improve the NZEB-grid interaction with limited computing power requirements.

Regarding NZEB planning and system design optimization, a genetic algorithm (GA)-based NZEB-cluster planning and design optimization method to minimize the overvoltage risk is proposed by sequentially optimizing the key influencing parameters, that is, NZEB location, PV ratio, and battery allocation. Meanwhile, a grid distribution network model, which can precisely consider the complex voltage influences among grid nodes, is adopted to quantify the grid overvoltage. Regarding NZEB system control optimization, a novel coordinated control method is proposed in which a power distribution network model has been adopted for more accurate overvoltage quantification. Meanwhile, the battery operations of individual NZEBs are iteratively coordinated using a sequential optimization approach to achieve the global optimum with substantially reduced computational complexity. For verification, the proposed coordinated control has been systematically compared with an uncoordinated control and a conventional coordinated control in grid overvoltage minimization.

The study results show that the proposed NZEB design and control method is highly effective in reducing the NZEB cluster-connected grid overvoltage risk. The proposed method can be used in practice to improve the design and control of NZEB as grid interaction is considered.