Distribution Network Transformer Layout Planning Model Considering the Impact of Distributed Power Sources and Electric Vehicles

Under the background of the "dual - carbon" strategy, the rapid penetration of Distributed Generation (DG) and Electric Vehicles (EVs) has had a profound impact on the operation of distribution networks. Traditional distribution transformer layout planning methods find it difficult to cope with the uncertainty challenges on both the source and load sides. This paper constructs a multi - objective optimization model for the distribution transformer layout in the distribution network that integrates the synergistic effects of DG and EVs. The model uses the Beta distribution and Weibull distribution to describe the randomness of photovoltaic and wind power outputs respectively, and combines the Monte Carlo method to simulate the spatiotemporal distribution of EV charging loads, thus quantifying the source - load uncertainty. Under the operational constraints such as voltage, current, and capacity, the optimization objectives are to minimize the three whole-life cycle costs, namely distribution transformer investment, network loss, and outage loss. The case results based on the IEEE 33 - bus system show that, compared with traditional and single-side source modeling schemes, the model in this paper can reduce the total capacity of distribution transformers by 16.7% and the whole-life cycle comprehensive cost by 21.5%, while controlling voltage deviation within 5%, effectively enhancing voltage stability and system operational robustness. This research provides theoretical support and practical reference for the intelligent planning of distribution networks in the scenario of high-penetration DG and EV access.