Company Research & Development

The innovation project DAEDALOS optimizes the connection between the design of power systems, pre-operational planning, and real-time monitoring to ensure the stability of future power systems, including hybrid AC/DC and HVDC networks.

The main goal of DAEDALOS is the development and testing of innovative tools in two realistic demonstrator environments that ensure grid stability and reliability in hybrid AC/DC systems and HVDC power grids.

The goal of the project is the development of a quantum key distribution system (QKD) based on the novel material lithium niobate on insulator (LNOI). The research institutions aim to realize a chip-based and device-independent QKD system, known as MDI-QKD. MDI-QKD represents a special form of quantum key distribution in which multiple users transmit quantum signals to a central node.

By verifying the correlation of measurement results, the confidentiality of the key exchange is ensured, while simultaneously excluding a range of known attack vectors on the detection system. 

A specific application case that PSI is investigating with its partners in this project is the use in secure communication for gas control systems, such as PSIcontrol.

ENSURE is researching innovative solutions to make the power grid future-proof. In doing so, ENSURE relies on a modular approach with components for future network structures and also considers sector coupling. The core task of the research is to develop and test technologies that ensure supply security in light of upcoming changes. The demands on tomorrow's energy supply are high: it must be reliable, economical, and sustainable, while also being broadly accepted by society.

PSI focused on AI-based technologies for future asset management in this project, which also includes consideration of redispatch measures as part of maintenance and repair activities. PSI is also engaged in concepts for the complex data exchange between network levels and operators.

The LI-SA project addresses the challenge of system management in interconnected networks, particularly in transmission networks, which have become increasingly complex in recent years. The growing loss of rotating masses from conventional power plants and the increase in converter-based generation facilities require new concepts. However, maintaining grid security remains of utmost importance. In this context, the online execution of dynamic grid security assessments (Online-DSA) plays a crucial role for grid operators to evaluate grid stability during ongoing network operations.

PSI focuses on testing hybrid SCADA network monitoring and EMT simulation systems, as well as assistance systems with DSA functionalities for decision-making in low-inertia systems.

In the PROGRESS project, curative measures for relief in high and extra-high voltage networks are being researched and tested. These measures adjust actuators in the network to specifically influence voltages and currents after faults, making the utilization of the network more efficient and reducing preventive congestion management measures. The implementation requires a comprehensive analysis and expansion of the system architecture, both on the side of the control systems and the secondary technology, as well as the integration with operational processes and coordination between transmission and distribution network operators.

The research need arises from the requirements for a safe and efficient use of curative measures. The research project addresses two focal points, which will be piloted in the context of field tests. On one hand, temporary thermal congestion currents are calculated, which enable the higher utilization of conductor cables under given weather conditions for a specific duration. Furthermore, the determination of curative measures at the interface of transmission and distribution networks is implemented both in the control system and in the field. Here, the coordination between transmission system operators and distribution system operators as well as the optimized determination of curative measures are taken into account. This information is then transmitted to the field technology, so that in case of need, an automated triggering of the curative measure can take place.

The electrification of the mobility and heating sectors, as well as the increasing feed-in from photovoltaic systems, poses a challenge for distribution network operators, as the distribution networks are not designed for the changing power flows.

Therefore, AI technology, focusing on network automation, assessment of network data (qualitative labeling), and active network management, is required to ensure a high quality of supply in future scenarios and to purposefully design the expansion of the network with optimal strategies. 
Network condition monitoring at low and medium voltage levels, as well as the integration of controllable systems, is necessary to utilize loads and feed-ins in a grid-friendly manner.

The digitization of the electrical distribution network using an Energy Internet of Things platform (eIoT) and active network management through Smart Grid Microservices is a necessary step that PSI realizes here with the help of PSIqualicision AI, PSIngo, and PSIconnect.

In the DISEGO project, research and industry collaborate interdisciplinary to develop a future-proof technology and implement it in practice.

The research project Beautiful aims to develop control room simulators, training concepts, and ergonomic assistance systems to support the decision-making of control room personnel.

Safe operation of energy system infrastructures such as electricity and gas networks relies on reliable technologies and the experiences of personnel in the control rooms. However, due to increasing complexity in operations, employees are facing greater challenges. Similar developments can be observed in many industries where complex systems are monitored.

The evaluation is based on the energy supply in regional distribution networks and relies on work science studies on cognitive ergonomics. The insights gained are to be transformed into transferable concepts to improve the workload of control room personnel.

PSI's goal is to research innovative operating concepts and assistance systems for different stress situations. A process simulator is to depict authentic supply and transport situations as well as exceptional stress situations and cyberattacks to enable entirely new user experiences.

The overall project IKIGas aims to develop innovative, self-learning tools for the analysis, forecasting, and decision support for gas networks. With the help of industrial artificial intelligence, anomalies are to be detected more quickly and suitable countermeasures identified. This strengthens the resilience of the gas network and improves supply security.

Selected application scenarios for the use of artificial intelligence in civil security research are validated using a demonstrator with real data. This includes the parallel, iterative determination of decision recommendations to initiate countermeasures in a timely manner and to recognize or avoid crises early.

Indicators for potential crises are to be identified regardless of their causes. It is particularly challenging when smaller problems lead to a critical state. In such cases, there is often an insufficient historical data base to train an AI model. Therefore, the use of adversarial learning is being researched to increase decision-making certainty to avoid critical situations.

The Construct-X project will establish a federated data space for the construction sector with its partners to provide data for applications in the cloud-edge continuum. The aim is decentralized, latency-free, and secure processing as well as sovereign provision of the data. The focus area "Supply Chain / Logistics Management" of PSI concentrates on resilience in this context.

The goal is to consider the identification of foreseeable and acute risks in the supply chain when selecting value creation partners as well as in the design and control of logistics chains, and to integrate this into a comprehensive supply chain management in the form of a digital supply chain twin.

In this project, the practicality of available vehicles and charging infrastructure for various logistics tasks is being tested in a one-year field trial with around 320 electric trucks. The goal of the project is to implement the charging infrastructure (LIS) in commercial areas in such a way that local companies can share a cost-effective and demand-oriented charging option for electric trucks, increasing the utilization of the charging infrastructure and reducing costs for companies. 

PSI is implementing a cloud-based connector system to link the data streams in order to bring smart charging solutions into practice.

The baggage customs control systems at airports detect and process cases of luggage that contains smuggled goods, i.e., items whose import is illegal or whose import has not been declared for customs clearance. Depending on the risk assessment of a particular arrival, the baggage customs control staff may decide to route all luggage from the arriving flight through non-invasive screening devices (X-ray/CT) to identify suspicious luggage that is then deemed suitable for manual inspection.

Traffic in Germany significantly contributes to CO2 emissions, especially in rural areas, where people in suburban neighborhoods emit more CO2 through their mobility behavior than in urban regions. Moreover, in rural areas, the delivery and transportation of goods are often associated with longer travel distances than in metropolitan regions. Additionally, online commerce is strongly represented in the countryside, with its global logistics causing more emissions than regional economic cycles.

The GreenTwin project develops sustainable logistics for the last mile through an AI-supported platform that links CO2 emissions with logistics services. A digital twin calculates the ecological balance of regional deliveries in real-time based on housing types, supply, infrastructure, leisure, and mobility offerings, as well as demographic composition. Based on this, simulations are developed and methods of Artificial Intelligence are researched to make last-mile logistics more CO2-efficient and to enhance the quality of life, work, and stay. The goal is to reduce dependence on large internet trading platforms and to bundle regional products in a CO2-saving manner.

PSI contributes its expertise in logistics, particularly in route planning, to a consortium of renowned research partners.

In the research project BaSys4Transfer, efforts are being made to eliminate barriers to the digitization of production for small and medium-sized enterprises. The project builds on two previous BaSys funding projects. In this third project, the middleware is to be prepared for widespread use.

The motivation of the project is to promote the standardized use of digital twins based on management shells in software systems for manufacturing control such as ERP, MES, and SCADA. Objectives include modeling manufacturing processes in MES systems using management shells, as well as the transition from traditional work plan models to process-oriented manufacturing control using BPMN. Additionally, the integration and connection of legacy systems, as well as connectivity with Industry 4.0 systems, such as MDE and plant parameterization, are being pursued.