1. 5th May 2016
Internet of Energy & Big Data
Warwick Forster – Technical Manager – Energy Storage
warwick.forster@vectorenergy.com.au

2. Who is Vector
▪ Vector Limited is an electricity and gas distribution
business in New Zealand
▪ Over 600,000 customers in NZ
▪ Has employed residential solar & storage in NZ
▪ Metering and energy storage businesses in Australia &
Pacific Islands
▪ Expanding beyond traditional “poles & wires” business

3. Internet of Energy & Big Data
▪ Competition in energy markets which were deregulated
in the 1990’s under the Hilmer reforms split competing
retailers and generation from former state owned
electricity utilities, whilst networks were maintained as
natural monopolies.
▪ With the advent of storage and distributed generation,
this has challenged the traditional concepts of
transmission & distribution networks sourcing power
from remote generators to customers. This has meant
that the notion of natural monopolies for networks is
now potentially less relevant with microgrids.

4. Internet of Energy & Big Data
▪ Connectivity and amount of data available has grown
exponentially globally in society since the 1990’s.
▪ In the energy context this has facilitated:
– Competitive energy markets where power stations are
dispatched every 5 minutes
– Ancillary services markets for frequency etc.
– Smart metering
– Innovative energy offerings/solutions to customers
– Full retail competition bringing competition to all
consumers

5. Internet of Energy & Big Data
▪ Energy consumers now have much greater choice
around retailers, contracts, PV, and storage.
▪ Whilst choice is usually a benefit for consumers, too
much choice can cause consumers to make adverse
selections due to complexity.
▪ Very difficult for consumers to value alternative
proposals without significant knowledge of the
economics of proposals.
▪ PV and/or storage investments whilst creating some
certainty also effectively put back some of the risk of
“generation” investments which must be considered
against the alternate uncertainty of conventional
contracting with a retailer.

6. Internet of Energy & Big Data
▪ Key considerations to optimise financial outcome for
consumer can be as complex as these listed here:
Solar
Production
Consumer
demand
Energy Market
Prices/
Arbitrage
Frequency
Control
Markets
Demand
Charge
reduction/
peak lopping
Network
Support

7. Internet of Energy & Big Data
0
500
1000
1500
2000
2500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
Customer Demand Reduction Goal
Wed 07 Jan 2015 00:00 Load Duration New Demand max Discharge

8. Internet of Energy & Big Data
-300
-200
-100
0
100
200
300
-1500
-1000
-500
0
500
1000
1500
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
kW
Price$/MWh
Charge cycle Arbitrage Goal
Charge/discharge 7/01/2015

9. Internet of Energy & Big Data - Conclusion
▪ Energy consumers now face potentially complex
decisions in real time as well as at time of investment
decision.
▪ Some economic goals can be in conflict with others
making optimisation difficult. i.e. energy arbitrage vs
demand reduction.
▪ Not really practical to make these decisions manually.
▪ Question of optimisation will also need to be valued i.e.
how do the savings compare against the initial
investment? Difficult at this early stage to value given
limited history. Will be easier to assess historically with
availability of data and real world installations. Not
unlike initial questions on PV and storage investment.