Low-voltage distribution networks are the last link in the chain of electricity transmission to final customers. They have a life expectancy of at least 30 years, but in practice they often operate for even longer. The increasing electrification of transport and heating, and the integration of renewable energy sources into the grid, are creating additional loads for which these networks were not originally designed. As a result, technical problems arise, which distribution companies usually solve by planning reinforcements to the network.
The master thesis deals with the automation of low-voltage distribution network planning processes by developing algorithms that enable faster and more reliable decision making. The developed approach includes the automatic integration of new customers and the selection of appropriate conductor cross-sections on the entire feeder.
The first part of the thesis presents in detail the methodology currently used in Slovenia. To better assess its effectiveness, it is compared with approaches in Germany and Italy. A comparison between calculated and measured stress drops is also made, which reveals the limitations of the existing methodology, as it is still based on static loads. In contrast, foreign practices already include dynamic data and smart grid components.
The second part of the thesis focuses on the implementation of the Slovenian methodology in the Python programming environment using the Pandapower library. On this basis, algorithms are developed for automated integration of new customers, selection of appropriate conductor cross-sections and determination of the connection capacity of individual customers.
The results confirm that automated approaches significantly simplify technically demanding planning processes and allow the network to adapt more quickly to the actual needs of users. The MSc thesis thus provides an important foundation for the further development of advanced algorithms in the field of distribution network planning.
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