The energy company E.ON has begun supporting our QMPC application project to solve planning problems in space operations and is adding an important use case of its own: the efficient integration of electric vehicles into future energy grids. In this way, the collaboration between QMPC and E.ON not only contributes to more efficient mission planning with the help of quantum computers, but also to the development of resilient and efficient power grids.
The aim of QMPC is to solve scheduling problems in space mission planning: How can personnel and material be optimally deployed when constraints must also be taken into account during planning? Such scheduling problems are notoriously difficult to solve. Formulated mathematically, they are NP-complete and, above a certain size, can no longer be optimally solved with conventional computers in a reasonable amount of time.
Within the framework of QMPC, E.ON will work on a Decentralised Energy System Scheduling Problem (DESS), a timetable problem variant for better planning of vehicle-to-grid systems for electric mobility. As in space mission planning, this involves solving optimisation problems with constraints. When is the best time to charge a car battery if one also has to consider the minimum state of charge of the battery at the end of the planning period, the total amount of electricity sold in a given time window and logical constraints on car batteries?
Given the high degree of scaling in the vehicle-to-grid concept, classical computers quickly reach their limits when faced with such optimisation problems. The models that must contain the mesh of states, conditions and logical links are simply too complex for classical methods.
Highly relevant use cases in space and on Earth
Quantum computers are a possible solution: they could investigate larger models and are also potentially suitable for the real-time optimisation of power grids. However, the capacity of today’s systems is far from sufficient for this. QMPC, helmed by team leader Sven Prüfer from DLR Space Operations and Astronaut Training, is therefore trying to implement quantum algorithms capable of solving larger problems. This is done, for example, through hybrid approaches, improved problem encoding and circuit optimisation. Generic algorithms can also be optimised for specific problems.
With the Vehicle-to-Grid approach, E.ON is now expanding the space focus of QMPC to include a highly relevant use case with many planning problems on a large scale — or in short: an exciting challenge for the energy supply and mobility of the future.