The challenge which IDLES is addressing requires a broad, multidisciplinary approach. A highly collaborative programme of work has been developed which incorporates six, interconnected, projects spanning across whole energy system analysis.
The programme aims to deliver a set of joined models that together can support long-term strategic planning and operation of an integrated energy system, providing evidence and tools to the academic, industrial and policy-making communities.
At the core of our approach is Project 1, developing new multi-vector modelling at high temporal and spatial resolutions. Projects 2 to 6 will provide inputs and key learnings from their topic areas to Project 1 and will also play a key role in validating scenarios generated by Project 1 models. Further details on each Project can be found below.
Project 1: High temporal and spatial resolution for multi-carrier and cross-sectorial energy system modelling
- Project Lead: Professor Nilay Shah
- Investigator Team: Professor Goran Strbac, Dr Adam Hawkes, Dr Antonio Pantaleo, Dr Marko Aunedi
This project is developing the central modelling framework for the whole programme. The fundamental structure of the model is based on the Resource-Technology Network (RTN) representation and it determines the optimal network structure (e.g. location, size, technology mix, interactions between technologies, end users and infrastructures) and its operation, considering simultaneously the short-term dynamics and a long-term planning horizon. In addition to the core framework development we are also focussing on local vs national objective interactions - exploring how integration at local level changes asset investments at national level - and how best to incorporate security and resilience into the models.
Project 2: Characterisation of new technologies for whole-system modelling
- Project Lead: Professor Christos Markides
- Investigator Team: Dr Adam Hawkes, Dr Antonio Pantaleo, Dr Billy Wu, Professor Tim Green
- Researchers: Dr Paul Sapin, Matthias Mersch, Dr Antonis Sergis
In Project 2, our work characterises energy generating, conversion and storage technologies so that their inclusion in a whole-system model can reveal their value (e.g. in providing flexibility and adaptability) for the energy system. Our ambition is not only to assess the performance and cost of readily available systems but also to reveal the untapped potential of existing technologies and unlock that of emerging, disruptive solutions. To do so, we’re building a collection of comprehensive thermodynamic and costing models for energy technologies, providing rich, reliable and accurate technology models to the central whole-system modellers.
Project 3: Data-driven modelling and decentralised control
- Project Lead: Dr Aruna Sivakumar, Dr Mirabelle Muuls
- Investigator Team: Dr Seth Flaxman, Professor Nick Jennings
- Researchers: Dr Jacek Pawlak, Dr Ahmadreza Faghih Imani, Dr. Zeynep Gurguc, Dr Rosa Sanchis-Guarner
In Project 3 we’re tackling the challenges associated with energy demand modelling, aiming to better understand the increasing complexity of demand in order to predict and shape it. This will provide insights into the degree to which the demand side of the energy system can be actively engaged in whole system management and control. We’re developing a new conceptual framework using an activity-based approach (as commonly applied in the domains of transport and urban planning); building on this framework to develop energy agent behaviour models; collecting and analysing behavioural data; developing machine learning methods and short term demand prediction methods.
Project 4: Resilience and risk management of smart whole-energy systems
- Project Lead: Professor Goran Strbac
- Investigator Team: Professor Nilay Shah, Dr Marko Aunedi
- Researchers: Dr Xi Zhang
With the security of supply in the integrated energy systems gradually moving from redundancy in assets to intelligent control, i.e., from preventive to corrective control, it becomes necessary to adequately quantify the risks associated with the paradigm shift towards non-traditional technologies and solutions such as DSR, storage for tackling electrical network problems and new smart network technologies. Within Project 4 we investigate topics such as; developing and enhancing models to perform a comprehensive risk assessment of smart grid solutions; developing tools for establishing the option value of smart technologies in network planning when facing uncertainties in future development; developing new approach for efficient design and operation of multi-energy microgrids.
Project 5: Incremental versus strategic development of future energy systems
- Project Lead: Dr Wolfram Wiesemann
- Investigator Team: Professor Goran Strbac, Dr Marko Aunedi
- Researchers: Dr Stefano Moret, Dr Spyros Giannelos, Dr Paola Falugi
In Project 5 we focus on enhancing existing, and developing new, long-term energy system models that capture the benefits of long-term strategic decision-making over short term planning. We are addressing the risk and ambiguity present in long-term energy planning, and the challenges associated with the large scale of the optimisation problems (due to the long time horizons and interconnected nature of the energy systems). Insights obtained from our models will help identify strategically important technologies and solutions as cost-effective options for energy system decarbonisation, thus guiding investors and policy makers by quantifying the benefits of investing in certain infrastructure assets ahead of their full utilisation.
Project 6: Market design: alignment of investors’, customers’ and society objectives
- Project Lead: Dr Iain Staffell
- Investigator Team: Professor Richard Green, Dr Robert Gross, Professor Goran Strbac
- Research Associates: Dr Malte Jansen, Dr Dimitrios Papadaskalopoulos
The core models from Project 1 propose future energy systems that are optimised for carbon, cost and security. In Project 6 we test the economic logic of these candidate systems, whether current regulations, policies and markets will naturally steer us towards the identified optimal systems. Or if not, can they be redesigned to do so with minimal intervention and distortion? Topics addressed include developing new types of energy market models in which accurate prices guide decisions, firms maximise profits and consumer behaviour is meaningfully incorporated, and acquiring data on cross-vector active consumers, such as the characteristics of how demand can be shifted.