Data For Sustainable Energy (D4SE) Pathways Grant : Business analysis and marker research

e2caa585-4025-439a-9485-a0bceecb14b0

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NERC
NERC
NERC
NERC
NERC

£59,215

Feb. 10, 2017

April 9, 2017

Renewable energy project can be complex to deliver. The factors that affect the successes of a renewable project are not just technical but also political. For example, shallow geothermal heat projects are affected by technical parameters like slope aspect, geology and permeability but also constraints intended to make project both safe and acceptable to the local population, such as proximity to major infrastructure or within protected area likes Sites of Special Scientific Interest; Areas of Outstanding Natural Beauty; National Parks etc.
Data4SustainableEnergy (D4SE) is intended to help solve this problem by compiling all of these information sources together and assesses the suitability of a site for many different types of Renewable Energy Technology. The system provides advice as to which is the best technology to pursue on that site and provides a single score which can be understood by a user without specialist geological knowledge.
The weighting of the various factors that control both the resources and the constraints are assessed using a technical approach called Multi-Criteria Decision Analysis advised by experts who advised on installation of renewable energy technologies. These outputs are presented using a webGIS to end users.
However there are many potential imaginable end-users for this system
- Landowners with single sites to develop aiming to maximise revenue and minimise installation expenditure
- Landowners with multiple, site seeking to choose the best technology for the best site
- Renewable energy industry consultants and installers to help them advise clients and maximise revenue
- Local authorities seeking to control land use to greatest benefit.

Each of these users has somewhat different requirements in terms of the data that they need and precisely how it is presented. We also need to understand:
- how each group might use the information that is provided to aid that business,
- the scale of the total market and thus the opportunity that is present and
- the potential charges that BGS could reasonably charge for access to this information

Market research will help us understand the parameters and then quantify the best approach for developing this idea in the future.

Potential Impact:
Renewable energy technologies present a massive opportunities for the UK to reduce carbon emissions and ultimately to significant to decarbonise our economy.

UK PLC will be the most significant beneficiaries of this project as it will simplify the processes for development of the renewable energy technologies and thus decrease the cost for developing carbon-free / low carbon energy with multiple potential benefits:
- thus benefiting the environment and the welfare of UK citizens
- increasing energy security by reducing dependence on imported hydrocarbons and
- oncreasing resilience by moving energy generation away from major river valleys which are their current predominant location.

D4SE should ensure that the development of renewable energy projects are speeded up and less likely to be derailed during the planning processes. It will also lower cost in terms of making sure that inappropriate technology projects are not pursued.

Other more direct key beneficiaries will be the renewable energy industry as D4SE will facilitate costs savings in this industry though more targeted advice and saving on site investigations by eliminating non-relevant technologies. Large landowners will benefit by providing targeted advice as to the best renewable energy technology to use on that site to give the best return on investment. Local authorities and environmental protection organisations will be able to assess potential for renewable energy around an area.

Market research is necessary to understand the optimal format for delivering RET information to initial beneficiaries by optimising the exact format of this and also allowing the weighting of the various factors to be optimised.