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Open data in distribution network asset management

Otto Räisänen. Open data in distribution network asset management. 3.11.2023 (Väitöskirja).

Climate change and the energy transition are causing major changes to the distribution network environment. The electrification of society raises expectations for clean, reliable, and affordable electricity supply. Meeting these increased expectations in the changing environment requires DSOs to incorporate large amounts of external data into their analysis procedures to properly assess the impacts of different trends, such as electrification of transportation and an increase in distributed generation. The amount of data available to DSOs has grown significantly through simultaneous advances in internal data collection, such as smart meters, and an increase in external data sources. A large amount of data with potential use cases in distribution asset management is nowadays available as open data. Furthermore, significant efforts are made by different organizations to increase the accessibility and quality of open data through standardization and creation of data portals. However, thus far, DSOs have not taken advantage of the full potential of open data.

In this doctoral dissertation, the use of open data in asset management is studied by an extensive review of open data sources and types and studies using open data in cases related to distribution asset management. The potential of open data is investigated in more detail through three case studies. In the first case study, a novel methodology for long-term energy demand forecasting based on open data is proposed consisting of a Laspeyeres decomposition analysis combined with time series analysis and forecasting. The methodology is shown to produce adequate results; however, the results could be improved with additional data. In the second case study, a geospatial analysis method is used to estimate the generation potential of large-scale PV farms built in peat production areas. Furthermore, the potential for P2X generation is assessed. The results show that peat farms are typically located close to existing electricity networks and P2X end use and transmission infrastructure, making them suitable locations for large-scale PV systems. The third case study focuses on developing a novel crown-snow load outage risk estimation method for overhead lines. The model uses a combination of aerial inspection data gathered by a DSO and a variety of open data variables, such as tree height, species, and diameter, and an advanced forest crown snow-load risk map product originally produced for forestry purposes as explanatory variables. The interactions between the variables and the model are studied with explainable AI methods. The model shows a good performance and is able to produce accurate risk estimates for up to 50 m resolution.

The outage risk model developed in this dissertation could be used by DSOs for more targeted supply security investments and improved operational outage management, leading to significant cost savings. The energy demand forecasting and methodologies for estimation of large-scale solar PV generation potential enhance the DSOs’ understanding of load development in their network areas and can be used as a basis of long-term strategic planning. This dissertation shows that there are a wide variety of open data with potential to bring value to all aspects of distribution asset management.