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www.cranfield.ac.uk

New computational methods and data for energy transition

Dr Nazmiye Balta-Ozkan

6/03/2018, EERA Workshop, ENEA, Italy

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Increasing share of renewables

‘It’s all renewing so why are we still being so tight with what we’re using and so paranoid about it?’

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Number of unknown unknowns increasing

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25000

30000

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

20/06/2017

MW

Nuclear Coal

Pumped Hydro Hydro

Interconnectors Other

Wind CCGT

Solar Demand seen by National Grid

'True' Demand (incl. solar)

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New methods & data to answer “new” questions

Individual Aggregate

Stat

ion

ary

Mo

bile

Quality of data

What is happening

now?

Methods to capture & analyse data

Spatial resolution

Temporal resolution What is likely

to happen next?

What actions to take now?

Key challenges

Questions to answer

Data-rich environment

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• “Sentiment” derived from Twitter posts improves stock market forecasts

• Social media data contains information on feelings, movement and behaviour

• Other areas: Sales predictions, elections, health industry

• Can we use it for energy?

‘Twitter mood predicts the stock market’

Source: Bollen et al. (2011)

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New data sources for energy research

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Social media data

• Contains information on behaviour, movement and preferences

• Posts and geo-tags

• Mohammadi & Taylor (2017) - correlation between people’s movements and building energy use

• Other potential uses

• “I think that’s got great potential.”

• “I’m sceptical.”

• “There’s a natural language problem”

• Inconsistent and unreliable?

1. New data sources

2% geo-tagged

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Transport data

• Traffic sensors, Oyster cards

• When people will arrive at home

• When and where people will charge EVs

• Kosmides et al., 2016 – optimising EV routes for energy efficiency

New data sources (2)

• “That can be very important.”

• “I can see a lot of use for that especially if it’s coming from a reliable and consistent source, for example, from an Oyster card”

• “It’s not necessarily about direct energy use… It’s more, where should we be investing in infrastructure”

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Privacy and security concerns:

• Smart meter data reveals detailed information about a consumer’s life

• Combining sources could make individuals identifiable

• Need to develop secure data-handling protocols

Ownership vs access:

• Whoever “generated” data should own it

• Who else should have access to it?

Roles of different parties:

• Relationships between suppliers and DNOs

• DNOs “want a button they can press”

• Ofgem “don’t engage, well outside their comfort zone”

2. Risks and barriers

Shower

DinnerBreakfast

Out of the house

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Machine learning

• Common in academia and small businesses

• Unexplored in DNOs

• “I think this is one area that has big potential.”

• “I’m not a very big fan of non-transparent methods.”

Organisational change:

• “People are going to need a bit more technical knowledge.”

• “Everybody from the guy in the control room to the field engineer… The data trend is up.”

3. New computational methods

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• Bottom-up modelling across different scales

• Energy transitions – how to detect a new social trend?

• Machine learning to predict demand and supplies

• Early sign to detect extreme events

• New paradigms – peer2peer trading, ‘energy as a service’

• Real time interventions across different scales

• General Data Protection Regulation

4. Some emerging issues