The transformation of the current energy system to a sustainable structure characterized by renewable energies is a central social challenge of the 21st century. To achieve this, the inherent volatility of renewable energy sources requires a move away from conventional, hierarchically structured top-down energy networks towards flexible, cross-sectoral and intelligent energy systems. Therefore, in the course of the energy transition, so-called micro- and smart grids (MSG) represent an important solution component to ensure a clean, efficient and cost-effective energy supply in the future. MSG is the concept of a local grid consisting of energy sources (e.g. wind power), energy storage (e.g. battery) and energy consumers of different sectors (e.g. electricity, heat, mobility). The local integration of regenerative energies by means of MSGs, e.g. within industrial companies or residential areas, relieves the energy networks and, thus, reduces the need for cost- and resource-intensive network expansion.
The central hurdle to the establishment of MSGs is safe operation. MSGs are highly heterogeneous, complex and have a significant stochastic component, which is caused by the uncertainty of consumer behavior and renewable power plants. Classical methods of control engineering are not considered to be effective here. In contrast, reinforcement learning (RL) is a data-driven operating concept from the field of machine learning (ML), which has been able to celebrate promising success with similarly complex and stochastic problems (e.g. stock exchange trading). Nevertheless, research into MSG-specific ML strategies is still fraught with a high risk, since the security and availability of energy networks must meet the highest requirements: even a single wrong decision can lead to a complete system failure (black-out). In the absence of mathematically provable guarantees, the use of adaptive, data-driven methods of ML, whose behavior is fundamentally unpredictable, is extremely challenging in this context.