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let’s dev Blog | Smart Prognosis of Energy with Allocation of Resources
by Karl
18. Februar 2021

Smart Prognosis of Energy with Allocation of Resources

The following article reports on the research activities completed last year by let's dev as part of the research project "Smart Prognosis of Energy with Allocation of Resources" (SPEAR). The international project was implemented with a consortium consisting of partners from Germany, Portugal, Sweden, Spain and Turkey. The duration was three years. One focus of the project was on the energy optimization of production processes, production lines and buildings.

The objective of the SPEAR was to develop an optimization platform that improves industrial production processes with regard to energy-related aspects. One focus of the project was the energy optimization of production processes, production lines and industrial buildings. The developed platform can optimize the energy consumption of existing and new plants. The results are applicable in the context of a virtual commissioning as well as to running production systems. The optimization platform can be used as a local application or as a cloud-based service and relies on optimization algorithms that provide a forecast of the expected energy consumption over a defined period of time.

As part of the project, digital twins of individual components were extended by a corresponding energy model, allowing complex (mechanical) systems to be constructed. A wizard was developed to simplify and speed up the creation process. The models themselves were neutrally defined and built on the Functional Mock-up Interface (FMI) in combination with the AutomationML (AML) data format. This combination allows to describe dynamic behavior as well as physical data and meta information of components, plants and lines.

Overview of work packages

By accurately calculating energy consumption, industrial customers can save on energy costs. Energy-intensive processes, for example, can be shifted to time windows where sufficient and more cost-effective energy is available. This plays a significant role, for example, in the use of renewable energies such as wind power or solar energy. In addition to improving the use of renewable energy by matching production schedules to availability periods, utilities can more easily design, manage, and balance the power grid. This reduces overall energy demand. At the same time, industrial customers are supported in reducing their effective emissions - which is becoming increasingly important due to legal and regulatory aspects.

let's dev was in charge of WP5 "Implementation and Interfaces of the Optimization Platform" and was responsible for the execution and coordination of all activities regarding the SPEAR Optimization Platform: from the analysis of the requirements to the realization of the demonstrator.

The user-centered design approach was followed from the beginning and the target group was described with personas. The insights gained were iteratively integrated into the demonstrator. In addition to the user-centered design, the technical specification and implementation of the cloud application was also a major part of our work. Thus, different communication protocols and interfaces were realized to meet the needs of partners and use cases. Apache Kafka was a central technology of the optimization platform.

Energy Forecast

A major innovation of SPEAR is the mirroring of the energy consumption of the production plant by simulating extended behavioral models on low-cost hardware. This hardware is also used for embedded hardware-in-the-loop (HIL) simulations that run in parallel with the real plant, so that the digital twin of a plant is extended by a digital shadow. This allows highly flexible and cost-effective approaches to be explored, implemented, and production system planning and execution optimized through accurate simulation of production processes. This supports efforts such as Industry 4.0 or Smart Industry.

In relation to the focused production facilities, the German SPEAR consortium reflected the entire value chain including machine and simulation experts, as well as experts for modeling energy consumers and suppliers from renewable energies. Both large and medium-sized manufacturing companies as well as academic institutions were involved. The balanced competencies enabled the project's concepts to be implemented and standardization efforts to be advanced.

Bundesministerium für Bildung und Forschung
ITEA

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