Project Summary
The project will develop an integrated hierarchical network of established models
for simulating the operation of decarbonised future GB energy system scenarios
with highly interconnected gas and power networks. The realistic modelling of
power-to-gas and storage operators' behaviour will be emphasised. The integrated
models will be demonstrated on a simulation platform as real-time digital twins for
future system scenarios.
Considerable novelty will lie in the combination of modelling scale and granularity;
representation of many autonomous decentralised agents making sub-optimal
decisions; and the optimal resolution of dilemmas arising from the finite energy
budgets constraining primarily weather-driven low to zero carbon scenarios.
Innovation Justification
Multi-vector energy systems have been the subject of considerable study in recent
years and are assessed in models to support investment plans, policy, and
strategy related to P2G systems. However, these interconnected systems are
complicated, with many interactions across regions and between times of day and
times of year. Existing models will typically simplify many aspects to make models
user-friendly but, in the long-run this might make their insight and recommendation
less robust. Therefore, the core innovative aspect of this project is the
development and demonstration of a realistic P2G modelling suite drawing on
techniques from a wide range of analytic disciplines which can balance scalability,
computational efficiency, and physical realism, and the application to the specific
GB context. The insight this project will provide for long-duration hydrogen storage
and P2G systems make it a natural fit for this SIF theme.
We are confident that none of the existing SIF and NIA projects reduce the novelty
of our proposal, but many offer learnings, with over 50 relevant ongoing or recent
projects identified.
Specific areas include:
(i) energy system management under finite total energy resource
(ii) energy systems meteorology, an evolving branch of applied meteorology
specific to energy systems;
(iii) physically realistic and granular co-optimisation of highly coupled gaselectricity networks; and
(iv) modelling of decentralised energy systems
Gaps in whole-system modelling capability will be further investigated in the
Discovery phase, to be carried into Alpha and Beta.
Prior to discovery, individual elements of the modelling suite have relatively high
TRLs (between 4 and 9, depending on the element), but the TRL, IRL and CRL of
a full suite of integrated hierarchical models are only 1. These will increase to 2
after the Discovery phase, with an aim to reach 7 by the end of Beta.
The scope and effort of this project is consistent with the need to model the whole
energy system -- to our knowledge, no previous work has considered the
interaction of the gas and electricity systems at the scale we intend to, while
capturing all the realistic and complex factors that might affect energy constraints.
Relatively low readiness levels, collaboration between many stakeholders, and the
specialist skills required mean that this solution is not ready or appropriate to be
deployed through business-as-usual activity.
We have not yet disregarded any approaches that could inform the solution, and
all options will be considered in more detail during Discovery.
Impacts and Benefits
Financial - future reductions in the cost of operating the network
When used in decision-making, the project's modelling suite should allow energy
system operators to achieve lowest cost options for operating integrated energy
systems, electrolysing, storing, and burning hydrogen at times which are optimal
across the whole system, helping to minimise renewable curtailment in theprocess. Operation of the energy system cost over £3bn in 2021 and 2022, and
these costs will rise in the future, meaning benefits of £10ms per year are easy to
imagine. The tools demonstrated in this project will help to keep these costs as
low as possible.
Financial - cost savings per annum for users of network services
Reducing operating costs will ultimately lead to annual cost savings for energy
customers. The tools would aid efficient investment planning for whole energy
systems. One recent report estimated this infrastructure might cost up to £1 bn by
2035, and it seems likely that these costs would increase through the 2040s and
2050s. These tools will also ensure resilience of energy supply, meaning
customers get greater value for money from the network.
Environmental - carbon reduction -- indirect CO2 savings per annum
The modelling suite could lead to an indirect carbon benefit from capturing more
curtailed renewable energy through hydrogen, avoiding the need for other
generation sources. While the energy system decarbonises, this might require
carbon intense generation sources, with potentially 1,000 of kT CO2 (e) saved per
year in the 2030s. This could have indirect cost savings associated with any costs
imposed on carbon emissions
Revenues - improved access to revenues for users of network services
The recent Royal Society report on large-scale energy storage highlighted that
new market mechanisms may need to be defined to incentivise long-duration
storage, including storage volume capacity markets. This project could inform how
such markets are designed and operated. It could also help developers of
electrolysers and hydrogen storage to understand the value they can add to the
system, possible revenue streams and business models.
New to market - products and processes
This solution is the first-of-its-kind integrating the concepts mentioned in Question
3. This system could be deployed across the globe on other integrated energy
networks and there is opportunity for the partners to export this capability to other
markets.
Metrics for measuring these benefits will be assessed in WP6 and may include net
present values, energy-not-served, and carbon intensities.
