Jan 2016
Gas Distribution
Gas to the Future
Jan 2016
Jan 2017
Northern Gas Networks
Gregory Dodds - 07966887355
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Network Innovation Allowance
Gas Distribution Networks

This proof of research project will employ the use of prescriptive analytics to support proactive strategy development and scenario analysis for the UK gas distribution sector.

The prescriptive analytics that will be employed in this project has the potential to go beyond current analytical capabilities in the UK gas distribution sector. Traditional analytics approaches are categorised as descriptive and diagnostic. These provide rich analysis of historic data but do not directly address future decisions and challenges. More recently, predictive analytics has provided forward looking forecasts. However, predictive analytics do not provide any optimisation capability to determine optimal future decisions. Prescriptive analytics enhances (not replaces), other analytical capabilities by integrating the knowledge gained from descriptive/diagnostic analytics, the forward looking trends of predictive analytics to provide actionable, forward looking insights. This enables decision makers to better take advantage of future opportunities or mitigate future risks.

To this end, a prescriptive analytics model will be developed to quantify, and clearly communicate the costs and benefits of investing in infrastructure assets to accept alternative sources of gas, so that we can meet the challenge of the Energy Trilemma, and maintain the role of the gas sector in the UK energy mix. The model will be based on Northern Gas Networks’ assets and data but could be replicated across other GDNs.

The scope of modelling for this project will be limited to future shale and hydrogen gas scenarios. However, the final interface will illustrate how the model could be adapted to incorporate alternative sources of gas. These two scenarios have been chosen to limit the complexity of the model and to ensure that the project is not abortive.

The base case position is the existing RIIO GD1 business plan and will include a level of asset replacement to meet safety and demand constraints consistent with what is currently delivered (i.e. bases case assumes nothing changes). All scenario impacts will be measured against the ‘business as usual’ base case.

The objective of this project is to employ prescriptive analytics, to quantify the following for a range of shale and hydrogen gas scenarios:

The total Investment required (Opex, Capex and Repex) to facilitate future scenarios

Net change in GHG emissions (using the traded cost of carbon) to understand the benefit achieved in moving towards a low carbon economy.  i.e. net change per scenario measured against the base case position

Volume of shale, hydrogen and natural gas imported and transported

The Resilience of Shale gas demand (redundancy of demand)

The Number and type of key technologies required (e.g. Steam Methane Reformers)

The Requirement for regulatory change.

A key objective is to ensure the objectives outlined above are communicated in an easily understandable and believable way.  As such, the project will also provide a web-based graphical user interface that can demonstrate the different investment options for different levels of benefit (GHG emissions) for different ‘futures’ for shale and hydrogen gas.

Further, the model will quantify the overall uncertainty for each of the above listed variables across all scenarios, using error bars showing, for example, the 10th and 90th percentiles.

The project will be judged a success if:

The prescriptive analytics model represents and effectively communicates the different investment options and the associated benefit for a range of different ‘futures’ for shale and hydrogen through a graphic user interface.

The model provides information that is believable and can be understood and accepted at the correct ‘order of magnitude’ by NGN experts and wider industry. This information will include:

o Total Investment required (Opex, Capex and Repex) to facilitate future scenarios

o Net change in GHG emissions

o Volume of shale, hydrogen and natural gas imported and transported

o Resilience of shale gas demand

o Number and type of key technologies required (e.g. Steam Methane Reformers)

o Requirement for regulatory change

The model provides a simple user interface for manipulating scenarios

The model clearly demonstrates the art of the possible in terms of prescriptive analytics, scenario versus investment modelling, and the trade-off of cost, benefit and risk.

The model provides confidence that the prescriptive analytics model is an effective communication tool and that the technology can contribute to informing our approach to meeting the Energy Trilemma