Reasons behind this project

With decarbonation a must, oil shall let electricity and hydrogen become the two future energy carriers. If not stemming from hydrocarbons, hydrogen is intended to be generated by electrolysis, thanks to RES generated power surpluses. Hydrogen and electricity can be alternatives in end-uses or as intermediaries.

The emergence of such an integrated system will involve, integrate and/or transform all other present energy carriers (e.g. methane) and/or networks at local, regional and global level. Hydrogen is however actually only the keystone in an already ongoing sector coupling (electricity, gases, heat, industry, transportation etc.).

Sector coupling is expected to help increase the share of intermittent sources and the energy system overall efficiency. However, such coupling is highly complex: extended and interlinked time and geographical scales, systemic effects, multiplicity of actors and value chain stage, investment risks and capital intensity, regulation and technology uncertainty etc. How to plan for sector coupling giving such complexity?

Planning studies (e.g. expansion, adequacy) often rely on so-called Energy System Optimisation Models based on mathematical programming formalisms. They usually rely on perfect market assumptions, at yearly and European scales. They ensure technical feasibility to a certain extent by accounting for energy balances dynamically and technical constraints. Here, complexity implies trade-offs between accuracy and tractability (and interpretability). In practice, related tools, knowledge and data are spread among the actors, making the planning task even more difficult.

One way of tackling the abovementioned challenges is “couple” modelling tools. Examples from the literature, especially on expansion planning, include uni or bidirectional coupling strategies, the later bringing challenges on the nature of the feedback loop.

Objectives:

The MuESSLi (Multi-Energy System Smart Linking) project aims to bring model coupling to another level by first building proxies of models before coupling them. “Smart-linking” will allow for solving bigger problems, while ensuring cooperation between actors with a certain level data privacy.  The project now gathers five PhDs in three universities (DTU, TU Delft, UP Comillas) and three industrial partners (RTE, TTE and GRT Gaz) with the following actions:

• Invent smart-linking methods, demonstrating it with power/gas/hydrogen for operation simulation and investment scenario assessment (starting with a simplified network modelling).
• Expand smart-linking to cross-sector investment optimisation if appropriate, or at least investigate and highlight key-issues.
• Expand smart-linking to other sectors, starting with heat.
• Expand smart-linking to allow for networks detailed modelling and constraints consideration.

+ beyond research works:

• Architecture a new fully open-source platform to model, optimise and better plan the future multi-energy system and relying on existing tools and models;
• Foster a living opensource project and community of users and developers to populate it and enrich it, taking advantage, when time comes, of new mathematical tools, increased computing ability, etc.

The 5 PhD topics respectively focus on:

• The building of proxies of sector-specific operational models using sensitivity analysis methods and physic-informed machine learning; and bi-directional linking of the sector-specific proxies with the original model of the sector of interest.
• The building of proxies of sector-specific operational models using multi-reduction methods and dynamic machine learning; integration of the sector specific proxies within the model of the sector of interest.
• Acceleration of the operational assessment of energy systems and building of proxies for operational models through a modular and agnostic approach based on efficient sampling and multi-fidelity modelling.
• Unperfect market modelling through multi-agent (multi-sector) equilibrium problems with equilibrium constraints; and building of proxies of the unperfect modelling to be integrated to the conventional operational energy system models.
• Bi-directional coupling between multi-sector expansion and balancing models. The latter representing manual and automatic reserves and forecast uncertainties to better account for flexibility needs.

Credits: pictures are from Jason Blackeye, Tommy Krombacher, Shaun Dakin sur Unsplash