The Paris Climate Conference in 2015 has provided one guide to restrict climate variation “well below two degrees Celsius”, which has triggered studies for clean alternative fuels for energy systems. To this end, many countries and territories in the world, such as China, the European Union, Japan and the United Kingdom, have declared their zero-emission targets and plans. The increasing pressure for energy and environment protection has led to research for advanced energy sectors, such as smart grids, intelligent transportation networks, heat and natural gas networks, and so on.

Multiple energy carriers (MECs), focusing on the deep integration of advanced multi-energy and information technologies, are regarded as a popular form of resilient energy utilization for future development. On the basis of intelligent perception and cyber communication, MECs realize the deep integration and real-time interaction of calculation, communication, control and artificial intelligence. Furthermore, the MEC will generate and utilize abundant data to achieve optimal energy distribution. Zero carbon of energy systems requires innovation in various aspects, such as policy, markets, planning and operation, which all remain to be further explored.

This Special Issue solicits original research on zero carbon energy systems, as well as reviews of state-of-the-art academic research, industrial practical and international experience. This Special Issue aims to bring together researchers from diverse fields and specializations, such as communications engineering, computer science, electrical and electronics engineering, transportation engineering, mathematics and specialists in the areas related to MEC technology.

Potential topics include but are not limited to the following:

• Policy and regulations on zero-carbon energy systems
• Carbon and energy markets facilitating zero-carbon energy systems
• Applications of advanced control and modern learning techniques in the optimization of MECs, i.e., smart grids, intelligent transportation networks, heat and natural gas networks, etc.
• Advanced control and modern learning-based approaches for the forecasting of renewable energy sources, loads and electricity prices
• Advanced control and modern learning methods for state evaluation and fault detection of MEC equipment
• Applications of advanced control and modern learning techniques in the MEC market to accommodate the increasing penetration levels of renewable energy sources
• Applications of machine learning for predictive maintenance and asset management
• Simulation and analysis of zero-carbon energy systems
• Interaction between zero-carbon energy systems with other sectors such as water, transportation, agriculture and industry
• Planning and design of zero-carbon energy systems