Periodic Reporting for period 1 - SENTINEL (SUSTAINABLE ENERGY TRANSITIONS LABORATORY)
Période du rapport: 2019-06-01 au 2020-11-30
SENTINEL recognises the necessity of accelerating the energy transition, ultimately leading to complete elimination of energy sector greenhouse gas emissions. Accelerating this transition requires us to develop a new set of energy modelling tools, able to represent and analyse the drivers and barriers to complete decarbonisation, including decentralisation, large-scale expansion of fluctuating renewable power leading to a vastly increased need for system-side flexibility, sector-coupling including the electrification of mobility and heating, and the impacts of different market designs on the behaviour of energy sector actors. It is also critical to evaluate potential ecological and wider environmental impacts of a scaling up of zero-carbon energy technologies.
The business of modelling complete decarbonization at a high level of detail is new, reflecting how quickly political goals have shifted. While many energy policies are backed by computational models, we do not know exactly how and when policymakers use models, and to what extent policymakers influence modelling performed, in the context of such a fundamental system shift. Hence a starting point for our efforts is to empirically investigate the two-fold processual interaction between computational energy modelling and energy policymaking. We then feed this in to the improvement of a suit of models, so that the information they can provide can better match their users' needs.
These models examine a wide range of separate issues covering the full suite of societal changes associated a shift in energy supply and use. They will be integrated, in an open source manner, on a single user-friendly online platform. Moreover, other modelling teams, even those not associated with the SENTINEL project, will be able to add and integrate their own models to the platform. the first test of the usefulness of that platform will take place in the context of three separate case studies, examining energy policy questions at the national, regional, and European scales.
We draw implications for development and use of models for and in policymaking: Models should be improved to be applied as ‘sustainable energy transition laboratories’, not delivering exact numbers, but to be used for exploring questions and policies. In this regard, they can be applied to catalyse the political and societal debate on what are the pros and cons of different possible energy futures. Open-source models and an open modelling platform can foster model understanding, trust and use, as well as deliver comparable and credible results for European and national policymaking. Importantly, all stakeholders from the energy sphere should have equal access to such tools, even if they are not modelling experts, as it increases model legitimacy and impact in policymaking.
We examine how in separate areas (environmental and social aspects, energy demand, energy supply, economic impacts and market design) energy models have served policy-makers in the past, and how they are likely to in future. Our goal is to move models from solving yesterday's problems to solving tomorrow's. We apply our models to our set of case studies, to address the specific problems that policy and decision-makers face in delivering Europe's energy transition across various geographic settings and scales. Once we apply the models to the problems, this will help us evaluate how well the framework meets their needs and enable us to refine it further.
We will disseminate our results and promote the Sentinel Platform to a diverse target audience, including policy-analysts; model developers; and research scientists. In addition we will organise and participate in a set of conferences and events, via which we help to build a community of model users and developers to carry this work forward. We have been working closely with our partner project OpenENTRANCE to date and taking an integral role in delivery of the EMP-E 2020 conference, with a view to planning EMP-E 2021 (intended online).
In SENTINEL, we apply these insights to improve and link our models. We do so not around problems chosen by researchers, but rather in the course of case studies at a range of geographic scales. Linking models designed as stand alone entities is a challenge and we have a full time coder working with the separate modelling teams in order to develop protocols needed to soft-link the results. In a second phase of linkages, we will allow for the propagation of uncertainties across the linked models. The entire packages will then be available on a single platform. Our goal is to make models more useful to a wide set of stakeholders than they have been in the past, in a manner that is open, robust, and transparent.