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Database Server Stack

To process the time series data of an Energy Community decent.ec utilizes a high–availability cluster of InfluxDB database servers and its utilities. The following functions of our MicroGrid SCADA (Supervisory Control and Data Acquisition) system are critical for ensuring the efficient operation, monitoring, and control of energy infrastructure.

1. Data Collection and Storage​

  • Real-Time Data Logging: The SCADA database continuously collects and logs data from various field devices, sensors, and energy meters across the energy network. This data includes parameters such as voltage, current, power flow, temperature, and more.
  • Historical Data Storage: The database stores historical data, enabling long-term analysis and trend observation. This is essential for predictive maintenance, load forecasting, and optimizing energy usage patterns.

2. Data Retrieval and Querying​

  • Efficient Data Retrieval: The database must be optimized for quick data retrieval, especially when accessing real-time or historical data for monitoring, reporting, or decision-making.
  • Query Support: It supports specialized time-series queries to fetch and analyze data efficiently and visualize results on the integrated dashboards. This allows operators to extract specific data points and trends relevant to the system's operation.

3. Data Aggregation and Processing​

  • Data Aggregation: The system aggregates data from multiple assets and locations, providing an overview of the energy network’s performance. Aggregated data is used for generating reports, dashboards, and real-time visualizations.
  • Data Processing and Transformation: The Telegraf component preprocesses and normalizes data (e.g., converting units, scaling values) to ensure consistency and accuracy for downstream analytics and visualization tools.

4. Event and Alarm Management​

  • Event Logging: The database logs all events, including alarms, warnings, and operational changes. This helps in tracking and auditing the system's performance and response actions.
  • Alarm Management: It stores and processes alarm conditions, enabling the system to trigger alerts or automated actions when certain thresholds are exceeded (e.g., high temperature or voltage).

5. Data Integrity and Redundancy​

  • Data Validation: Ensures that data collected from field devices is accurate and consistent, implementing error-checking protocols to validate incoming data.
  • Redundancy and Backup: The high–availability database features redundancy and backup mechanisms to prevent data loss and ensure continuous operation, even during system failures.

6. Data Analysis and Reporting​

  • Real-Time Analysis: The database supports real-time analytics functions, allowing operators to monitor the energy network’s performance, detect anomalies, and respond quickly to changing conditions.
  • Historical Reporting: the system generates historical reports for performance analysis, regulatory compliance, and optimization purposes. This involves querying historical data and summarizing it in dashboards or detailed reports.

7. Integration with Other Systems​

  • Interfacing with External Databases: The database cluster integrates with the IOT data retrieval systems and the IOT Dashboard to form an energy management system (EMS), and optional external predictive maintenance platforms.
  • API Support: The database stack provides an API for external applications to access data, perform remote control actions, or integrate with external data analytics platforms for deeper insights.

Elements of the Stack​

The TICK Stack is a suite of open-source tools designed by InfluxData for monitoring, storing, visualizing, and alerting on time-series data. The acronym TICK stands for Telegraf, InfluxDB, Chronograf, and Kapacitor, which are the four main components of the stack:

  1. Telegraf: A plugin-driven server agent that collects metrics from various sources, including IoT devices, databases, and APIs, and sends them to different destinations such as InfluxDB.
  2. InfluxDB: A high-performance, time-series database optimized for handling large amounts of time-stamped data efficiently. It supports real-time querying and data retention policies, making it suitable for IoT, DevOps, and other monitoring scenarios.
  3. Chronograf: The visualization tool of the stack that allows users to build dashboards for monitoring and managing the data collected by Telegraf and stored in InfluxDB. It also includes features for setting up alerting rules and creating interactive data visualizations.
  4. Kapacitor: A data processing engine that analyzes both real-time (stream) and historical (batch) data from InfluxDB. It allows users to create alerting, anomaly detection, and other automation rules using its scripting language called TICKscript.

a visual representation of how the TICK Stack components interact and how data flows through the system

The TICK Stack follows a push model for data collection, unlike other monitoring solutions like Prometheus, which use a pull model. It is widely adopted for its flexibility, open-source nature, and integration capabilities, providing a complete end-to-end solution for time-series monitoring and management. This stack is particularly useful for developers, system administrators, and organizations looking to implement efficient monitoring systems without the high costs associated with commercial tools.

By leveraging the TICK Stack, organizations can create a scalable monitoring system that collects, stores, visualizes, and alerts on data in a unified manner, making it a robust choice for handling IoT, infrastructure, and application monitoring use cases.