[UC23] Analysis of Grid Segments & Elements
It is vital to have the ability to systematically create and apply analytical models to distribution grid segments and individual elements.
The Utility Problem
With new trends and challenges in the energy sector, utilities need to be thoughtful about every step they take. This is not true only on a macro level but also on a micro level. Decisions on questions such as “Where should we deploy smart meters/IoT sensors next?”, “Which grid area needs inspection first?”, “What part of the grid needs OPEX to improve the power quality?” or “Which parts of the grid are experiencing congestion?” are essential to answer. The decisions on simple questions like these have a significant impact on the effectiveness of grid operations. Proper and systematic prioritization is crucial for many analysts addressing this topic.
For utilities, it is important that decision-making keeps pace with the trends and uses innovative data analytics techniques. One of the innovative data analytics techniques in the electrical distribution utility space for prioritization is the creation and application of weighted analysis models for grid segments or elements.
The building and application of weighted models are in contrast to single-factor analytics. Single-factor analyses are often conducted through the lens of a single indicator – factor. This approach is acceptable but only allows for a combination of multiple factors. Furthermore, different factors indicate different effects. Thus it is essential to include them in the analysis.
For these reasons, it is vital to have the ability to systematically create and apply analytical models to distribution grid segments and individual elements. The results of such analysis allow prioritization for the following best action in the grid location where the action has the most significant impact.
Another critical element is the correct visualization of this data. By displaying the data in tables and on a map, a more efficient evaluation can be achieved.
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