Abstract/Summary

Industry is exposed to the consequences of Government energy policy. This project aims to improve the understanding of the energy system modelling tools used for the purpose of policy development. This is in order to help industry, including SSE, the industry sponsor, better understand future policy direction, and inform their future strategic planning. This project used a multi-disciplinary approach to investigate this problem. A review was undertaken of the modelling tools used in academia and by Government, interviews were conducted to understand perceptions of the use of modelling, and finally representative versions of the core model types were developed to better understand the insights which they can provide to future system challenges. A confusing landscape of model types and terminologies exist, many of which are used by Government. This research has identified a core set of models which are widely used in academic literature and are seen to be influential in the UK policy making agenda. The model types display differences in their representation of time resolution, the level of general unit detail and the operational strategies which they can consider. This project has constructed simplified versions of each model type, either using open source tools, or developing code from first principles. Further adaptations were made to model the technologies present in the case study energy system of Shetland. Each model type was analysed to determine what insight it can provide to the Industry questions which were defined at the outset of the project. Two attributes were identified which are important when modelling the impact of flexible technologies. These are: i. The ability to reflect chronology and have visibility across time steps is important for any storage technology in order to ensure operational constraints are considered and to enable optimal charging profiles to be calculated. ii. An understanding of the real demand for the technology is essential to represent its potential for flexibility. In the case study undertaken this was the separation of heat and power demand. Industry, with support from Government, needs to recognise the need for increased data on real demand for heat, as well as other demands to improve the modelling capability to represent the value of DSR technologies. Stakeholder perceptions of these models were examined, in addition to a technical assessment of their ability to adapt and provide insight to future policy challenges. The work has demonstrated the value of simpler models. It recommends that Government increase their use of simpler models, to enable increased collaboration with stakeholders and improved confidence in Government modelling activities.