IoT Asset Management Using Graph Database Modeling
Power grid systems are considered an integral part of modern society. Their robustness and efficiency not only impact our daily lives, but also influence economy, politics, and the global environment. Power grids continually evolve over time to accommodate and promote societal developments; currently, transmission grids are going through a transition from relying on traditional centralized utility-based power generation to integrating distributed energy resources (DER) such as photovoltaic (PV) systems, distributed energy storage (DES) such as battery systems, and demand response (DR). With the additional push for the integration of smart Internet-of-Things (IoT) devices, the smart grid is continually evolving into an increasingly interconnected cyber-physical system (CPS) that requires a paradigm shift in both its planning and operation. The use of graph databases as a sophisticated tool for modeling the connection between system components– transformers, distribution lines, DER, DES, IoT devices, grid operator, electricity consumers, etc.–can aid in managing the rapidly evolving smart grid.
To address the demand of managing the integration of connected devices and enabling new business models from the heavily interconnected systems, current architectural reference models were considered and components of each synthesized into a software stack for smart application development. This work lends its implementation approach to the utility of graph theory in modeling complex systems, and implements a graph database for managing and maintaining connected components that emphasize each component’s virtual and physical connectivity, its technical functionalities, and its state. The graph database microservice is then integrated with a highly available web framework and communication broker service in a multi-layered software framework to integrate Internet-of-Things devices and make services available over the web. The framework’s–and its respective components’–efficacy has been demonstrated in a simplified use case for modeling, connecting, and controlling interconnected homes in a modern smart grid and abstracting transactional device data for new business models, such as demand response ancillary services.
Layered Software Design Approach
Neo4j graph components
Neo4j cypher queries are buildt on relationships
Transactional States of the grid can be queried at ease
Queries built on relationships enable new energy informatic analyses, such as the one above for demand response
MQTT topics are flexible and ease pub/sub communication
Web interface for sending mqtt commands
Smart Grid subgraph showing relationships between various actors.
node-red provides great tools for rapid iot development and device simulation
Node-red flow of a gateway for controling a home energy management system.