Search

Zero‑carbon electricity is a pre-requisite for decarbonisation of the wider economy and many scenarios envisage rapid expansion of renewables. IEA estimates a need to double the annual rate of investment to 2030. A rapid transition to low carbon electricity creates a risk to investors because projects being financed now will generate their revenue in a zero‑carbon electricity system for which there is no track record of price formation. Although the cost of renewable power has fallen greatly, future revenues from renewables are still affected by market price risks that are often outside the control of generators. If risks are difficult to quantify, they may be mis-priced, leading to inefficient premiums being added to the cost of capital, which could increase the overall cost of the transition. In this context many countries around the world provide support for low carbon energy, such as the UK Contracts for Difference. In this paper we investigate exposure to price risk caused by uncertainty over the mix of technologies used to achieve decarbonisation, which we term 'transition risk', and how this varies under different technology mixes, and for different policy regimes. We show that exposing investors to transition risk could increase the cost of delivering the renewables needed for a zero‑carbon electricity system by around 25% or £7bn/year compared to policy options that reduce exposure to wholesale market price risk. The paper concludes that as long as transition risks remain high, de-risking policy mechanisms can help to minimise the overall cost of achieving net-zero. • We characterise the current stage of the low-carbon transition as a 'build phase', requiring large volumes of investment • Uncertainty over the final physical state of the decarbonised system causes significant financial risk to investors • 'Contracts for difference' (CfDs) help to manage this 'transition risk' and reduce the cost of capital for projects • Exposing projects fully to transition risk could increase costs by a third, and raise the cost of transition by £7bn per year [ABSTRACT FROM AUTHOR]

The map shows the centroids (geometric centres) of the local gas distribution zones (dotted circles), the offshore pipeline network connecting the potential hydrogen storage sites to the UK gas terminals. The scaling of the circles is logarithmic base ten, hence the energy storage need of LDZ is generally one or two orders of magnitude smaller than the storage capacity of individual storage sites. The LDZ are indicated in italic next to their matching centroid. Their storage need in TWh is also indicated. SC: Scotland, NO: Northern England, NE: North East England, NW: North West, EM: East Midlands, WM: West Midlands, WN: Wales North, WS: Wales South, SW: South West, SO: Southern England, SE: South East, NT: North Thames, EA: East Anglia. • A new method comparing geological storage capacity to storage need is presented. • Geological hydrogen storage capacity exceeds UK heating seasonal storage needs. • Offshore UK gas fields can potentially store 2661.9 terawatt-hour of hydrogen. • Hydrogen storage is unlikely to compete with other subsurface uses. • Stores used with hydrogen, instead of natural gas, can store up to 75% less energy. Hydrogen is considered as a low-carbon substitute for natural gas in the otherwise difficult to decarbonise domestic heating sector. This study presents for the first time, a globally applicable source to sink methodology and analysis that matches geological storage capacity with energy demand. As a case study, it is applied to the domestic heating system in the UK, with a focus on maintaining the existing gas distribution network. To balance the significant annual cyclicity in energy demand for heating, hydrogen could be stored in gas fields offshore and transported via offshore pipelines to the existing gas terminals into the gas network. The hydrogen energy storage demand in the UK is estimated to be ~77.9 terawatt-hour (TWh), which is approximately 25% of the total energy from natural gas used for domestic heating. The total estimated storage capacity of the gas fields included in this study is 2661.9 TWh. The study reveals that only a few offshore gas fields are required to store enough energy as hydrogen to balance the entire seasonal demand for UK domestic heating. It also demonstrates that as so few fields are required, hydrogen storage will not compete for the subsurface space required for other low-carbon subsurface applications, such as carbon storage or compressed air energy storage. [ABSTRACT FROM AUTHOR]

• Representative pathways can be sought to characterise a larger pathway ensemble. • Clustering performance is sensitive to the distribution of pathways. • K-means is the most robust approach for pathway grouping. • Hierarchical clustering might perform better if pathway distribution is irregular. • Sectoral standardisation can reveal the trade-offs between metrics in each sector. Long-term decarbonisation pathways to achieve ambitious low-carbon targets involve a range of uncertainties. Different energy system modelling approaches can be used to systematically evaluate the influence of the uncertainties, but this often leads to an unmanageable number of pathways. Summarising the large ensemble through a more limited number of representative pathways, to inform stakeholders, can be challenging. This study thus explores how to identify representative decarbonisation pathways using clustering algorithms, which can assist in grouping similar data points in uncategorised datasets, such as pathway ensembles. However, the suitability of clustering algorithms for pathway characterisation has not been investigated to date. Hence, k-means, hierarchical clustering, Gaussian mixture model, spectral clustering, and density-based clustering are adopted for comparisons. An illustrative pathway ensemble for the United Kingdom is applied to evaluate their performance based on cluster validity indices. Three metric transformations, including power, standardisation and sectoral standardisation, are also applied to create three additional sets of pathways for testing. The k-means algorithm is found to outperform others consistently, although hierarchical clustering might also be applicable if the distribution of pathway proximity is uneven. The results also highlight the utility of the approach in revealing distinctive trade-offs between technologies among the identified representative pathways. For instance, the electrification of heating can be replaced by district heating in the residential sector. The described, novel approach can be applied to characterise other sets of pathways, with greater technological details generated by any energy system models, to reveal insights for long-term decarbonisation. [ABSTRACT FROM AUTHOR]