Visualizing and Managing Smart Grids with Geospatial Big Data: The SEMS Approach
Geospatial big data plays a pivotal role in the context of smart grids, revolutionizing the way modern electrical grids are monitored, managed, and optimized. Smart grids integrate advanced sensing, communication, and control technologies to enhance the efficiency, reliability, and sustainability of electricity distribution. While locational information of the smart meters is pivotal, the consumption patterns combined with other information like consumer type, land use and local weather conditions can really enhance the assessment of energy requirements and usage thus leading to a Spatial Energy Management System (SEMS). This will significantly enrich location-aware decision-making, real-time monitoring, and predictive analysis, thus improving energy resource optimisation and achieving the goal of SDG-7. The SEMS web application represents a critical advancement, facilitating dynamic visualization of temporal clusters, across energy sources and their evolution over time. By overlaying clusters across different time periods, utility personnel gain insights into customer categorizations vs actual utilization, enabling understanding of demand fluctuations, outages and faults, fault localization within the electric network and demand-generation assessment of renewable energy.
The primary objective is the development of a dynamic web-based SEMS application capable of visualizing temporal changes and consumption patterns, while also providing alerts to facilitate proactive management.
The initial phase of the methodology focuses on the Jeedimetla region in Hyderabad, leveraging real-world data encompassing 6,000 households categorized into residential, commercial, and industrial segments. Quantum Geographic Information System (QGIS) software is employed to establish the electric network. To store the spatial and temporal consumption data, the data storage infrastructure employs PostgreSQL, enhanced with the PostGIS extension. This combination is selected due to its robust capabilities in accommodating and managing complex datasets. Additionally, a comprehensive and refined data model has been established to serve as the framework for storing Advanced Metering Infrastructure (AMI) data. Spatial data retrieved from the PostgreSQL database is exposed through a Web GIS Server (GeoServer) as a Web Feature Service, facilitating its integration into applications. This spatial information is utilized within the OpenLayers Software Development Kit (SDK) to visually render data within a JavaScript-based application environment. Non-spatial data is accessed through Application Programming Interfaces (APIs) integrated within a Node server, operating as REST endpoints. Concurrently, the React JavaScript library is employed to present this non-spatial information to end-users in an interactive format.
The SEMS Geospatial Visualization Engine comprises three key components. The first component features two primary views: the Basic View, which presents hourly usage consumption data, and the Combined View, integrating map and graph interfaces. This combined view allows users to select specific time periods, customer locations, transformers, or feeders, with the graph view displaying corresponding hourly consumption data. Both views facilitate the visualization of consumption patterns for customer classes or types, which are pre-defined in the system.
The second component of the SEMS Visualization Engine aggregates data at the daily level, classifying users into low, medium, and high consumption categories. These classifications are visually represented on the map view, providing insights into consumption trends across the study area.
The third component integrates weather data sourced from an open-source Weather API with customer energy consumption data. Weather information is stored in the PostgreSQL database, and the combined view enables the graphical representation of weather data changes alongside energy consumption data. This integration enhances understanding of how weather fluctuations impact customer energy usage patterns.
By leveraging advanced sensing, communication, and control technologies, three components of SEMS Geospatial Visualization engine framework provides a comprehensive platform for understanding and visualizing energy consumption and demand. The spatial view aids in developing spatial clusters, which assist electric utility personnel in comprehending consumption patterns and energy demand requirements for specific areas, facilitating efficient management of power shortage scenarios.
The SEMS visualization engine supports various utility use cases, including energy loss identification, detection of energy overuse, and dynamic reclassification of users based on current consumption data. As a prospective avenue for future research, integrating this Geospatial Visualization Engine framework with machine learning-based prediction models holds promise for forecasting energy consumption and dynamically classifying users. Such advancements are anticipated to further empower utility personnel in decision-making, real-time monitoring, and predictive analytics within smart grid environments.