The emerging smart infrastructure in energy networks represents a major paradigm shift in resource allocation management with the aim to extend the centralised supply management model, towards a decentralised supply-and-demand management that is expected to enable more efficient, reliable and environment-friendly utilisation of primary energy resources. Together with this vision, there are new and complex tasks to manage, in order to ensure safe, cost-reducing and reliable energy network operations. This includes the integration of various renewable energy systems, like the photovoltaic or the wind energy, which are able to reduce the greenhouse gas emissions but that are working under greater uncertainty; as well as the interaction of transport and storage systems for energy that are envisioned through techniques like ‘Power to Gas’ and fuel cells, which are using the electrical and the gas transportation network. Further tasks can be found in the fact that the market participants (e.g. simply households) are becoming more autonomous and intelligent through technologies like smart metering, which requires a coordinated demand side management for millions of producers, consumers or, if this applies, prosumers by means negotiations and agreements. Information and communication technologies are key enablers of the envisioned efficiencies, both on the demand and the supply sides of the smart energy networks, where the agent-paradigm provides an excellent first modelling approach for the distributed characteristic in energy supply systems. On the demand side they aim at supporting end-users in optimising their individual energy consumption, e.g. through the deployment of smart meters providing real-time usage and cost of the energy and the use of demand-response appliances that can be controlled according to the user preferences, energy cost and carbon footprint. On the supply side they aim at optimising the network load and reliability of the energy provision, e.g. through active monitoring and prediction of the energy usage patterns, and proactive control and management of the reliable energy delivery over the networks. It is also envisaged that they will be able to influence the demand through the dynamic adjustments of the energy price in order to influence the end user behaviour and energy usage patterns throughout and across the energy networks for electricity, gas and heat. Although a significant effort and investment have been already allocated into the development of smart grids, there are still significant research challenges to be addressed before the promised efficiencies can be realised. This includes distributed, collaborative, autonomous and intelligent software solutions for simulation, monitoring, control and optimization of smart energy networks and interactions between them.