ExpectedOutcome:
Projects are expected to contribute to all the following outcomes:
- Provide regional, national and European public authorities and network operators, with customisable open source models of the components of the energy system, as well as tools to assemble these component models into a model of the energy system integrating the infrastructure related to all energy carriers in a given geographical area, with static and dynamic modelling capabilities.
- Provide regional, national and European public authorities and network operators, with an open source tool to allow them to better plan and optimise the development of renewable and low emission energy sources and the enhancement of infrastructure (including storage) to meet the future energy needs in a geographical area, while minimising the total investment and operation cost, hence satisfying the future final uses of energy (sometimes used as a feedstock) by consumers, at lowest cost and with better quality of service.
Scope:
Advanced modelling tools to perform regional / cross-border and cross-energy vector system planning and optimisation on a long time horizon, where cross-sectoral disruptive innovations in industry, mobility and building sector can be included
Building on existing open source models or on the opening of currently proprietary models, as far as they are available, the project should develop and validate open source models of the components of the energy system and provide tools to integrate these component models into a system model to satisfy the (future) needs in a geographical area, thereby providing a planning tool for cost and emissions optimisation of the enhanced energy system at pan-European level. The aim is to better plan and optimise the expansion of the energy generation and transmission and storage systems to meet the (future) energy needs aggregated at a granularity level finer or equal to the NUTS2 level; the distribution layer to individual energy users is not to be considered.
The open source modelling tool should be composed of the most relevant of the following modules:
The multi-physics component models is expected to model the cost (CAPEX, fixed and variable OPEX, economic lifetime) and technical performances (including GHG emissions) of the components, they should be parametrised to take into account the local climate and socio-economic characteristics of the geographical area where they will be located, as well as the time-dimension, such as the season and time of day. The component models should be capable of dynamic modelling with appropriate time steps (e.g. quarterly or hourly power profile of sources for intraday balance assessment; weekly or monthly profile for seasonal balance). The component models need to be described with standard modelling languages and be modular, so that each one can be updated without impact on the others and can be assembled with other models. They should cover most of the components in the following list, at the very least one component in each of the 9 categories below:
- Renewable energy sources: energy production units of several typical sizes, covering technologies, such as for example photovoltaic, concentrated solar power, solar thermal, geothermal, onshore wind, offshore wind, hydroelectric, tidal, wave, biogas, biomass … Modelling of their cost, GHG emissions, typical (average) production performance of the sources, taking into account (where applicable): the season (month of the year), time (hour of the day), geo-location (at NUTS-2 level), and other parameters that can affect cost/performance. Where applicable, the statistic variability of their performance should be given and power profiles should be generated, for running dynamic simulations when the component modules will be integrated into system modules. A large-scale source should have its own model, small-scale sources (such for example wind turbines or household PV) should be aggregated (e.g. households PV aggregated at the level of a city).
- Non-renewable primary energy sources (natural gas, coal, oil, uranium …): extraction, import; modelling of the cost, capacity, GHG emissions and geolocation.
- Non-renewable energy conversion: refineries producing fuels or hydrogen; modelling the cost and performance (including GHG emissions) of the conversion from the primary energy carrier to secondary energy carriers, including CCS where applicable.
- Non-renewable electricity production (coal, natural gas, oil, nuclear …): cost and performance of existing or new power plants, including CCS where applicable; modelling the transformation from the primary energy carrier to electricity (including GHG emissions).
- Renewable energy conversion: production of hydrogen and other renewable or low-emissions gaseous or liquid fuels; modelling of their cost and performances (power, efficiency …); modelling the conversion from the primary energy carrier to secondary energy carrier and by-products (O2, CO2, including GHG emissions …).
- Energy storage models: stationary batteries (large scale and house), electric vehicle batteries, hydropower storage, thermal storage, methane storage, hydrogen storage … ; modelling of their cost and performances: power, efficiency, capacity, life expectancy, state-of-charge (for dynamic modelling), life cycle GHG emissions.
- Transport pipelines (including recompression stations): cost (per km) and performance (capacity, efficiency, GHG emissions) of existing and new natural gas, hydrogen, CO2 pipelines, district heating/cooling pipelines or of upgrading pipelines to admixtures of renewable gasses or to pure hydrogen or to CO2; as well as other infrastructure (e.g. LNG terminals) or logistics (e.g. transport by ship).
- Transmission power lines: cost (per km) and performance (capacity, efficiency), of existing or new power lines, or for upgrading existing power lines to higher voltage/capacity.
- Energy consumers: modelling of the energy use profile of typical consumers (industry, buildings, households, local heat networks, mobility and transport) for the different types of energy carriers, taking into account (where applicable): the season (month of the year, and associated average temperature), time (hour of the day), geo-location (at NUTS-2 level), and other parameters that can affect their energy use. Where applicable, the statistic variability of their performance should be given and power profiles should be generated, for running dynamic simulations when the component modules will be integrated into system modules. Where applicable, their capability to shift their consumption in time (demand response) and to store energy should be modelled, including the cost of this flexibility service. A large-scale user should have its own model; small-scale users, such for example household or eVehicle and (bi-directional) charging or refuelling stations, should be aggregated at the level of a city or NUTS2 region.New methods to take into accounts new types of assets connected to the grids (Electric Vehicles (EV), microgrids, storage, small scale production, non-synchronous generators, etc.) and considering the cost-effective coupling with other energy networks.
System modelling, planning and optimisation tool:
- A system modelling tool should be developed to integrate the models of the components located in a geographical area into a system model. The models will use available data on the future needs of industry and other end-user sectors. The system modelling tool should allow both static and dynamic simulations, to assess the intraday, weekly and seasonal balances and associated grids stability. The modelling tool has to be modular and open to ensure coupling with other models, for example models including the exchange of resources and materials (enabling industrial-urban symbiosis and circularity), as well as socio-economic and market models.
- Based on the system model, an optimisation and medium-long term grid planning tool should be developed to optimise the development pathways for renewable energy and other low emissions sources, storage and the enhancement of infrastructure, to meet the future energy needs in a geographical area, while minimising the total investment and operation cost, hence satisfying the future final uses of energy vectors of consumers, at lowest cost and climate impact.
- Visualisation tools should be developed to support the system modelling, the optimisation process and their results, notably in the format of dynamic energy heat maps. The compatibility of the results format with the JRC visualisation tools should be ensured.
Validation of the models and tools:
- Methodologies and procedures should be developed for the certification of the components models and, if possible, system models.
- The component models, system modelling and optimisation tools need to be validated by using them in support of the planning of the energy transition of two real-life geographical areas: one macro-region (e.g. several small or large countries) and one large (possibly cross-border) industrial cluster. The validations should cover the range of models and tools developed, and should therefore include in particular the dynamic modelling of relevant energy sources (intermittent and dispatchable), different existing or new energy networks, conversion between different energy vectors, energy storage and energy consumers capable of demand response.
The component models and the system modelling/optimisation tools need to be a properly documented and open source development allowing the EC, the Member States and Associated Countries and other public authorities or private organisations to use the tools for their planning needs, or to develop additional add on modules. The models and tools, as well as the relevant documentation and user guides, should be published under an appropriate open license and made available to the modelling community on the Energy Modelling Platform for Europe[1]. The results of the project have to be disseminated, notably at the EMP-E annual conference. Upon completion of the projects presently supporting the EMP-E platform and conference, the selected project should take over supporting the platform and organising the annual conference.
The research should entail interviews with relevant grid operators and public administrations in all EU Member States/ Associated Countries, so as to collect their views on how the tools could best meet their needs. At least 2 interviews per MS/AC should be foreseen.
The development of the models, simulation, optimisation and visualisation tools will be closely coordinated with Commission services (including the Joint Research Centre).
The selected projects are expected to contribute to relevant BRIDGE[2] activities.
Cross-cutting Priorities:
Artificial IntelligenceDigital Agenda
[1]http://www.energymodellingplatform.eu/
[2]https://www.h2020-bridge.eu/
