The need for efficient energy management systems has become crucial in the current context, characterized by rising energy costs and the need to reduce environmental impact. In this scenario, the Ei-TWIN project aims to develop an innovative platform based on Digital Twin technology applied to the energy sector.
This article will explore the potential of this system as an artificial intelligence solution for energy management, taking the Ei-TWIN project as an example and its practical applications in two distinct contexts: an industrial plant of a pharmaceutical SME and an energy community.
Why an energy management system is essential
The global energy crisis, with rising gas and electricity prices, highlights the need for more efficient resource management. Italy, like many other European countries, heavily depends on gas imports, making energy efficiency a national priority.
In this context, the Ei-TWIN energy management system project aims to develop a platform for applying a Digital Twin in the energy sector. A Digital Twin is a virtual representation of an object or system, linked to it throughout its lifecycle. This technology enables performance monitoring, consumption forecasting, and energy use optimization, helping to reduce waste and improve overall efficiency.
The Ei-TWIN Project: an innovative approach to energy management systems
The Ei-TWIN project consists of 12 development objectives, divided between industrial research and experimental development, aiming to create a software platform for applying an Energy Digital Twin in two use-case scenarios:
- An industrial plant of a pharmaceutical SME: This use case focuses on optimizing energy efficiency in a production environment by analyzing consumption and processes to identify areas for improvement.
- An energy community: In this scenario, the goal is to optimally manage energy flows between buildings, maximizing self-consumption and reducing exchanges with the electricity grid.
The project aims to overcome the limitations of traditional approaches by integrating Big Data processing and AI techniques to provide advanced functionalities such as process optimization and predictive maintenance.
Key components of the Ei-TWIN Platform
The Ei-TWIN platform is composed of several layers, each with a specific function:
- Field Layer: Responsible for collecting data from IoT sensors and building management systems (BMS), using protocols such as Modbus and BACNet.
- Ingestion Layer: Acquires data from the field layer and sends it to the Data Lake, performing an initial format validation.
- Data Lake: The core of the platform, where raw data is stored and structured for analysis. This “schema-on-read” architecture allows flexible data querying. Data is acquired in real-time using Apache Kafka and stored in Elasticsearch.
- Analysis Layer: Utilizes AI and Machine Learning techniques to analyze data, provide forecasts, optimize processes, and detect anomalies.
- Presentation Layer: Provides dashboards and reporting tools to visualize analysis results and monitor the system’s status.
The Ei-TWIN data model
The Ei-TWIN project employs a flexible and comprehensive data model that includes data from various internal and external sources:
- Internal sources: Data from machinery (on/off status, usage time), energy data (active/reactive energy, active power, voltage, current), and production data (production phases, quantities produced).
- External sources: Meteorological data (temperature, humidity, solar radiation) that influence energy consumption.
The data model is structured in two main formats:
- JSON Model: Used for time-series data, with a hierarchical structure including timestamps, telemetry, diagnostics, and metadata.
- Relational Model: Used for structured information such as production batches and process phases, organized into tables with relationships.
Technologies and tools used
The Ei-TWIN project extensively leverages the technologies and architecture of the PlugAIn platform for Big Data processing. The Data Lake is based on a reduced version of the PlugAIn platform, as shown in the following diagram.
Key technologies include:
- Kubernetes: Used to create the private cloud hosting the Ei-TWIN Data Lake.
- Apache Kafka: Used for message exchange between various components and data ingestion.
- Elasticsearch: Used for storing sensor data.
- Spring Boot:: Used for developing connectors to data sources.
Use cases: practical applications of the digital twin
The Ei-TWIN energy management system project involves implementing the platform in two specific use cases to test and validate its functionalities:
- Industrial Plant: The goal is to optimize energy efficiency in a pharmaceutical company by analyzing machine and production process consumption. The Digital Twin will help identify the most energy-intensive phases, forecast consumption, and optimize energy usage.
- Energy Community: In this use case, the platform will manage energy flows between buildings in an energy community, maximizing self-consumption and reducing exchanges with the electricity grid. The Digital Twin will monitor consumption, predict renewable energy production, and optimize distribution among users.
Advantages and benefits of the Ei-TWIN Project
The Ei-TWIN energy management system project aims to provide several advantages and benefits:
- Energy Efficiency: Reduction of energy consumption and waste through process optimization and predictive models.
- Sustainability: Lower environmental impact by reducing greenhouse gas emissions.
- Cost Optimization: Lower energy costs due to more efficient resource use.
- Predictive Maintenance: Anticipating failures and malfunctions for more effective and timely maintenance.
- Flexibility: Adapting energy management to different operational conditions and specific user needs.
- Interoperability: Integration with various information sources and management systems.
Conclusion
In the field of energy management systems, the Ei-TWIN project is a concrete example of how Digital Twin technology and Artificial Intelligence can be applied to improve energy efficiency and promote sustainability.
The platform developed in the project offers an innovative and flexible energy management solution in various contexts, helping to reduce consumption, optimize costs, and enhance business competitiveness.
The adoption of an energy management system like the one proposed by Ei-TWIN is essential to addressing present challenges and building a more sustainable future.
Authors: Massimiliano Ruffolo and Simone Vizza
