The energy transition requires massive reductions in greenhouse gas emissions, which means rapid and substantial improvements in energy efficiency and developments of renewable energy technologies. For high fractions of renewable energies, the whole energy system must be more flexible in all parts of the system, i.e. generation, demand, transmission; instead of supply following demand, the opposite is required. The system also requires more energy storage, control, and interconnection between regions and countries. Energy system integration (ESI) involves integrating previously distinct sectors such as power and heat (sector coupling), to provide more flexibility. The challenge here is to trade-off some competing objectives in to obtain a higher overall system efficiency.

All of this leads to increased data requirements relating to all parts of the system, e.g. generation, transmission/distribution and demand. This data is employed to monitor and optimise the systems involved, at multiple temporal and spatial scales. Any intelligent monitoring system (and future improvements thereof) relies on data integration and standardisation, development of robust data sharing protocols (e.g. via Data Trusts), and the exploitation of knowledge via machine learning approaches. Patterns in the data, either time-series or otherwise, can be paramount to enabling effective decision making when it comes to system integration and interoperability. Energy transition relies on multiple objectives being met (both quantitative and qualitative in nature), and interventions must address challenges of data availability, protection, storage/management and security, amongst others.

Objectives

  • Increase renewable/low carbon fractions of energy supply / integrate these efficiently into the energy system
  • Improve the flexibility of the energy system by better integration
  • Map energy system integration possibilities (energy vectors/storages etc.) to different temporal and spatial dimensions
  • Assess the requirements for ESI in energy systems with increasing renewable/low carbon fractions
  • Exploit increasing energy system data to derive insights about energy system status at multiple spatial and temporal timescales
  • Provide markets and incentives for system actors to exploit/utilize technical flexibility potential
  • Exploit the use of intelligent systems not only for monitoring purposes but also for extracting knowledge from data and how they can be integrated in day-to-day operation
  • Integrate diverse data sources into multimodal systems for system optimisation, asset management and reliability