This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement
The RENergetic system aims to provide visualizations of the data coming from energy island systems and corresponding forecasts for the different types of users, such as visitors, residents, and managers. The data and forecasts are also used by optimization algorithms and demand response. These methods aim to achieve the energy island goals, for instance increasing the renewable energy share and maximizing energy autarky.
That means that the system should be capable of ingesting various types of data coming from different sources in the energy island. The data model developed in RENergetic is capable of storing structural data about energy island, its users and other assets’ relations, as well as storing time series data, such as sensor measurements.
Users can interact with the system through the RENergetic interactive platform, a web application provided by RENergetic system. Alternatively, an Application Programming Interface (API) enables the integration of the RENergetic system into already existing applications in the energy island. Furthermore, the API can be used to communicate with the energy island systems to inject its data or to transmit control signal to installed equipment.
Different types of users can use the RENergetic platform. Visitors of the energy island can receive information about the status of an energy island, e.g., utilization of renewables, using their personal devices, displays installed in building halls, or other physical installations. More information with greater detail is available to residents and associates of the energy island. This includes recommendations for changing the parameters of appliances for participation in heat demand response programs as well as dashboards with historic and forecasted energy data. Energy island managers receive additional information about the different energy vectors including, for example, the forecasted energy consumption and the recommended settings for energy sources.
A microservice architecture is proposed to implement the main blocks of the RENergetic system. The overall functionality of the RENergetic system is built through the interaction of microservices. For example, visualization of a forecast requires the forecasting, data storage and interactive platform microservices to interact. The API and Access Management microservice plays a special role because it is also responsible for orchestrating the operation of all other services. Furthermore, it provides an API for communicating with external third-party systems.
Figure – Microservice Architecture of RENergetic ICT System
Such modular architecture enables flexible configuration of the system. This is important because not all energy islands have the necessary data or systems required for the operation of all services. In case some functionality is not needed or cannot be realised, the corresponding service can be excluded from an installation. A service-oriented architecture also simplifies extension of the system in the future. For instance, if optimization in an additional domain is required, a new service for that can be developed and easily integrated into the existing RENergetic system.
In order to reach sustainability targets, the RENergetic system suggest operation schedules of various assets from different energy sectors. The optimization process in RENergetic is divided into two levels. The global multi-vector optimizer fixes the energy flow at inter-connectors between domains, e.g., charging stations, heat pumps or combined heat and power plants. While domain-specific optimizers try to optimize the energy usage within their domain, respecting the interconnector decision by the multi-vector optimizer. For instance, which electric vehicle will charge at which time, what heat demand response signals are sent to whom, or where to place ancillary services in the power grid domain.
The Forecasting microservice identifies patterns in the historical data and provide temporal forward projections about one or series of measurements in the time series database, for instance energy consumption and supply.
Demand response microservices aim to involve users in energy optimization. For instance, Electric Vehicle Demand Response (EV DR) can operate in two modes. In manual EV DR, users receive notification on the best moments to charge their car. In the automated EV DR, the RENergetic system remotely controls the charging process to achieve energy island goals considering constraints from the user, for instance charging deadline and minimum state of charge.
More details about each microservice in the RENergetic architecture, the development process and the deployment of the system can be found in: