Applications, interfaces and data formats for quantum computing algorithms in energy system modelling
We take advantage of quantum computing to optimize and accelerate the planning and operation of future energy systems by solving for larger and more complex systems.
A variety of challenges in the context of the energy transition can be addressed through energy system modelling (ESM). For operational and investment decisions, electricity, gas and heat supply are simulated or optimized in high temporal and spatial resolution. Solving large-scale optimization problems (OP) with classical hardware and solver software is increasingly reaching its limits in ESM. For example, a fully resolved OP of Germany’s high-voltage grid (sector coupling included) can no longer be solved with conventional means. For this reason, the development of quantum algorithms for OP in ESM represents a great opportunity.
In order to mitigate the effects of global warming, many countries have set specific targets to reduce greenhouse gas (GHG) emissions, most of which are generated by burning fossil fuels for energy production. To achieve climate-neutral energy, precise energy system planning is needed to find ways to phase out fossil fuels. This is very challenging as it requires a system-wide transformation: from centralized fossil fuel power plants to decentralized renewable energy generators, taking into account sector coupling (electricity, gas, heat and transport), which needs to be reliably implemented globally and locally. The above challenges often translate into the need to generate and solve large, complex models. Quantum computing has already shown its potential to significantly reduce computation times for certain types of problems. Attraqt’em aims to foster the further development of this approach.
IIn this project, we aim to use DLR QCI quantum computer to improve Mixed-Integer Linear Problem (MILP) optimization in the context of ESM. This has the potential to vastly expand the size of the problem that can be solved once effective quantum computing solutions are available for this purpose. An interface is being developed between a defined set of ESM optimization problems that can be expressed as MILP and hybrid problems that could benefit from the combination of classical and quantum computing. Nevertheless, we have already identified three candidate MILP types that could be promising for this endeavor: Unit Commitment, Investment Planning, and Resilience Analysis problems. These involve the coordination of large numbers of generators to meet demand, the expansion of the power system, and the ability of the grid to resume normal operations after an interruption.
Planned Calls for Proposal
Energy Operations | Energy System Planning | Energy System Optimization | Optimization Calculus | Quantum Algorithm | Complexity Reduction Methods | Definition of Use Cases.