  The ongoing energy transition drives the development of decentralised
renewable energy sources, which are heterogeneous and weather-dependent,
complicating their integration into energy systems. This study tackles this
issue by introducing a novel reinforcement learning (RL) framework tailored for
the co-optimisation of design and control in energy systems. Traditionally, the
integration of renewable sources in the energy sector has relied on complex
mathematical modelling and sequential processes. By leveraging RL's model-free
capabilities, the framework eliminates the need for explicit system modelling.
By optimising both control and design policies jointly, the framework enhances
the integration of renewable sources and improves system efficiency. This
contribution paves the way for advanced RL applications in energy management,
leading to more efficient and effective use of renewable energy sources.
