# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 1 Introduction  
Power distribution systems are made up of components for control, protection, transformation, and regulation, which govern the delivery of power to customers. The complexity of such systems is due to the multiplicity of interconnected elements. Although many actions of these elements are automated, they are geographically spread and are not easily accessible. Therefore, they cannot be fully monitored and controlled. Many of the components are considered \"small\" and of low value compared to the elements of a transmission system. This makes the task of overseeing, analyzing, and maintaining them even more tedious. The growing interest in smart grids and distributed energy resources further complicates the processes of controlling and managing the distribution grid.  
Conventionally, electric power utilities keep the information about their assets, including grids, in relational databases spread across multiple organizational units. For example, we consider a request to access data related to certain components, their types and parameters, details of their physical location and connections, and information about linked customers and their sites. To satisfy such a request, a series of actions must be performed. They are related to accessing several databases and performing multiple operations on data structures that contain information on components of interest.  
Recent developments in graph-based databases create an opportunity to store data in a different format. They allow for representing information as a network of nodes and relationships between them. The main benefits of graph databases include:  
* Ease of integrating different types of data (numerical and symbolic) of different origin;
* Ability to absorb new, continuously incoming data;
* Efficiency in accessing a variety of data and performing queries, resolving multiple relations between individual elements of the database;
* Simplicity in developing and executing algorithms--working on graphs--to generate new information that is not available or very difficult to access when data are spread across multiple databases.  
This paper shows how the information about the power grid's components can be stored as a knowledge graph (KG) in a graph database [1]. We describe the vocabulary for denoting the system's components and the relations between them needed for constructing a graph. The vocabulary addresses two facets: integrating information of different natures (topology versus maintenance, outage data) and extracting, in a simple way (compared to relational databases), information about various aspects of the grid. Subsequently, we introduce GridKG, a graph representing a fragment of a distribution grid. Several examples showing how such a graph can be used to analyze the grid are included, focusing on the effects of outages. The main contributions of this paper are as follows:  
* Development of a KG that integrates all types of data and information about a distribution system: its topology, metadata on its components and customers, and details regarding maintenance tasks performed on the components, as well as events that take place in the system (Sections 4 and 5);
* Construction of a vocabulary--concepts and relations--for building KGs representing power distribution systems (Section 4);
* Development of queries illustrating a process of utilizing the developed graph: finding electrical paths (Section 5.1) and switching elements (Section 5.2), and occurrence of system events (Section 5.3);
* An approach for evaluating the effect of outages on customers at various geographical locations based on Monte Carlo simulations (Section 6).