I was honored to be the keynote speaker for the EnergyTech Spain conference, where I had the opportunity to explain my vision on the Future of the Electricity: Distributed Resources, Decentralization, Digitalization, and development of new Business Models that will result in a more flexible, dynamic and resilient system enabled as a Platform connecting multiple players in a multidirectional way.

1. Jose M. Seara - 2018
The Future of Electricity
/ DERs
/ Digitalization
/ Artificial Intelligence
The Future of Electricity
/ DERs
/ Electrification
/ Digitalization
/ Artificial Intelligence
Jose M. Seara
1

2. Jose M. Seara - 2018
Grid Edge Technologies
/ The next rising Star
2

3. Grid Edge Technologies
/ The next rising Star
Jose M. Seara - 2018
Grid edge
technologies
Tipping point?
Source: World Economic Forum & New York Times
PercentageofUShouseholdsreached
100
75
50
25
1900 1915 1930 1945 1960 1975 1990 2005 2020 2030 2040
1900-20 tech / 30-40 years
Telephone, stove, electricity,
automobile
1920-40 tech / 25-30 years
Radio, fridge, clothes washer
1940-60 tech / 30-40 years
Clothes dryer, AC,
dishwasher
1960-80 tech / 20 years
Color TV, microwave, VCR
Post 1980 tech / 15-20 years
Computer, cellphone, internet
Grid edge technologies / ?? Years
Decentralized generation, storage,
smart meters, electric vehicles
3

4. Jose M. Seara - 2018
Transformation
of the Electricity System
4

5. Transformation
of the Electricity System
Jose M. Seara - 2018
/ One-way flows
/ Shared infrastructure
/ Centralized network
/ Centralized management
//Limited flexibility
/ Simple and Regulated markets
/ One-to-One network
Centralized Generation
/ Limited two-way flows
/ Both isolated and shared
infrastructures
/ Integration into centralized
network
/ Decentralized management
/ Some flexibility
/ Some complexity and
mostly Regulated markets
/ One-to-One network
Distributed Energy
Resources (DERs)
/ Two-way flows
/ Both isolated and shared
infrastructures
/ Many users connected to
multiple networks
/ Mix of Centralized and
Decentralized management
/ Flexible, Dynamic, Resilient
/ Complex market structures
/ Many-to-Many network
Energy Cloud
Source: Navigant5

6. Jose M. Seara - 2018
Factors fueling Disruption
/ The Solar energy revolution……
6

7. Factors fueling Disruption
/ The solar energy revolution……
Jose M. Seara - 2018
/ US high: 0.034
/ Alberta, Canada: 0.014
/ US low: 0.023
Solar Tariffs are Falling Globally Due to Proliferation of Auctions
/ Spain: 0.046
0.0780
0.0699
0.0651
0.0602
0.0566
0.0496
0.0479
0.0378 0.0367
0.0291
0.0242
0.0178 0.0177
Brazil
(Feb 16)
Turkey
(Mar 17)
France
(Jul 17)
Zambia
(Jun 16)
Germany
(Jun 17)
El
Salvador
(Jan 17)
Peru
(Feb 16)
India
(May 17)
USA
Palo Alto
(Feb 16)
UAE
(Sep 16)
Saudi
Arabia
(Oct 17)
Mexico
(Nov 17)
Chile
(Aug 16)
S/kWh
Wholesale market
2016 price
7

8. Jose M. Seara - 2018
Factors fueling Disruption
/ ……and its implications
8

9. Factors fueling Disruption
/ …… and its implications
Jose M. Seara - 2018
California ISO: Typical Spring Day
12am 3am 6am 9am 12pm 3pm 6pm 9pm
Hour
10.000
14.000
12.000
16.000
18.000
20.000
22.000
24.000
26.000
28.000
Megawatts
2013
2018
2019
2014
2015
2016
2017
2020
2020 ramp need
13,000 MW in three hours
over generation risk
2012
Source: California ISO CommPR/20169

10. Jose M. Seara - 2018
Factors fueling Disruption
/ Innovative Business Models
10

11. Factors fueling Disruption
/ Innovative Business Models
Jose M. Seara - 2018
The role of the electricity grid is
evolving beyond supplying
electricity to passive Consumers
and becoming a platform that
connects and coordinates energy
transactions between multiple
producers and active Prosumers
Energy Market
Data Flows
Energy
Flows
Power PlantDistribution Transmission
Prosumer
Prosumer
Prosumer
Smart
Virtual
Grid
Prosumer
Consumer
11

12. Jose M. Seara - 2018
Converging Trends
/ Accelerating transformation
12

13. Converging Trends
/ Accelerating transformation
Jose M. Seara - 2018
ELECTRIFICATIONDIGITALIZATION
DECENTRALIZATION
/ EV + AV
/ Smart Charging
/ Vehicle to Grid - V2G
IoT /
Smart metering /
Smart sensors /
Smart Grids /
Analytics /
Advanced technologies /
/ Back-up generation
/ Demand Response
/ Microgrids
Distributed Generation /
Distributed Storage /
Cogeneration /
Source: World Economic Forum13

14. Jose M. Seara - 2018
Converging Trends
Accelerating Transformation
/ Decentralization
14

15. Converging Trends Accelerating Transformation
/ Decentralization
Jose M. Seara - 2018
Denmark’s progress over the past two decades
Centralized System of the mid 1980’s Decentralized System of Today
Small CHP (Combined Heat & Power)
Large CHP (Combined Heat & Power)
Wind
15

16. Jose M. Seara - 2018
Converging Trends
Accelerating Transformation
/ Electrification
16

17. Jose M. Seara - 2018
Converging Trends Accelerating Transformation
/ Electrification
Sub-Sahara Africa
Southeast Asia
India
Other developing Asia
Other
MillionPeople 2000 20162010
400
800
1200
1600
2000
Source: International Energy Agency. Energy Access Outlook
2017 (Other includes Middle East, North Africa and LATAM
GEOGRAPHIC
Electrification of Developing World
17

18. Jose M. Seara - 2018 Source: International Energy Agency. Global EV Outlook 2017
Converging Trends Accelerating Transformation
/ Electrification
MillionCars
2016 2025 2040
100
200
300
Electric car fleet
China
India
European Union
United States
Other Countries
0.1 million EV sales in 2017. 15-25% of by 2025BMW
30 thousand annual EV sales by 2017GM
4.52 million annual EV sales by 2020Chinese OEMs
0.1 million annual EV sales by 2020Daimler
13 new EV models by 2020Ford
Two-thirds of the EV 2030 salesHonda
1.5 million cumulative sales of EV by 2020Renault-Nissan
0.5 million annual EV sales by 2018. 1 million annual by 2020Tesla
2-3 million annual electric car sales by 2025Volkswagen
1 million cumulative electric car sales by 2025Volvo
AnnouncementOEM
SECTORIAL
Electrification of Transport
18

19. Jose M. Seara - 2018
Converging Trends
Accelerating Transformation
/ Digitalization
19

20. Converging Trends Accelerating Transformation
/ Digitalization
Jose M. Seara - 2018 Source: International Energy Agency20

21. Converging Trends Accelerating Transformation
/ Digitalization
Jose M. Seara - 2018
1987
2 TB
1012 bytes
1997
60 PB
1015 bytes
2007
54 EB
1018 bytes
2017
1.1 ZB
1021 bytes
Internet Traffic 1987 - 2017
Source: IEA Digitalization & Energy 2017, based on Cisco21

22. Converging Trends Accelerating Transformation
/ Digitalization
Jose M. Seara - 2018 Source: NaturEner USA
Network Remote Control
22

23. Jose M. Seara - 2018
Converging Trends
Accelerating Transformation
/ Artificial Intelligence
23

24. Converging Trends Accelerating Transformation
/ Artificial Intelligence
Jose M. Seara - 2018
Artificial intelligence is any
technique that enables computers
to mimic human intelligence
Machine Learning includes
statistical techniques that enable
machines to improve at tasks with
experience
Deep Learning permit software to
train itself to perform tasks.
Machines learn on their own from
spotting patterns and anomalies in
large data sets
Source: ARK Investment Management LLC
Wolfram Alpha
IBM
Deep Blue
Cyc
Video
Game AI
IBM Watson Pandora
Radio
Facebook
Newsfeed
Spotify
Discover Weekly
Email
Spam Filter
Netflix
Recommendation
Engine
Google
Page Rank
AlphaGo
Skype
Translator
Google
Photos
Gmail
Smart Reply
Google
RankBrain
Tesla
Auto Pilot
Baidu
Voice Search
Facebook Photo
Tagging
Artificial Intelligence
Machine Learning
Deep Learning
24

25. Artificial Intelligence.
/ Energy Applications.
Jose M. Seara - 2018
/ Short-term energy forecasting,
combined with radar, satellites
and weather stations
/ Integration of Renewable
Energy resources
/ Preventive maintenance
/ Wind turbine operations:
Sensor data to gauge wind
speed and direction
/ Solar operations:
Sensor data to gauge sunlight
intensity
/ Energy storage, useful lifetime.
Renewables
/ Advanced monitoring and diagnosis
/ Optimization, Self-optimization
capabilities. Self-Healing
/ Automatic grid topology configuration
/ Adaptive protection
/ Advance forecasting support
/ Asset Management
/ Operation gas turbines to
minimize NOx
/ Preventive Maintenance
/ Microgrids, Smart Grids
/ Managing EV in the Smart-Grid
/ Energy Efficient EV Routing
/ Battery Efficiency Maximization
/ Congestion Management
/ Integration EV with the Grid. V2G
Infrastructure
/ Demand-Response/energy
management
support
/ Energy efficiency
/ DSEMS for residential, commercial,
industrial customers
/ User behavior to optimize
consumption
/ DR reward/penalty mechanisms, as
real-time, day-ahead, time-of-use, or
critical peak pricing
Demand-Response
25

26. Jose M. Seara - 2018
Start-ups and Grid
Companies Deploying
AI in Energy
26

27. Start-ups and Grid Companies Deploying
AI in Energy
Jose M. Seara - 2018
AppOrchid
Deploying deep learning and a Natural
Language Processing based interface
for the Danish TSO energiser.dk to
understand grid behavior under
variable wind conditions.
Alpiq
GridSense uses AI to gauge, learn and
anticipate user behavior. It uses this
information to ensure optimized
energy consumption within a building.
Siemens
Siemens has developed an AI system
that continuously optimizes the
operation and control of combustion in
gas turbines.
Xcel
Detailed weather reports, mining and
combining data from local satellite
reports, weather stations and nearby
wind farms. Algorithms are trained to
identify patterns and make predictions.
Arria
Creating a virtual senior engineer in a
control center, diagnosing issues and
writing work orders, press releases on
outages and personalized customer
usage reports.
Hazama Ando
Hazama Ando Corp has developed a
new smart energy system using an AI
(artificial intelligence)- based energy
management.
Smart Cloud
NERC, NUSERDA, NYISO and several
ISOs working with SmartCloud’s AI
driven solutions capture and apply
knowledge in real time to improve
management of complex situations.
Verdigris
Founded in 2011 in California. It offers a
cloud based software platform that
claims to leverage AI to help large
commercial buildings to optimize
energy consumption.
DeepMind
Google’s DeepMind is in talks with
National Grid to apply AI to energy use.
Already Google was able to reduce its
total data center power consumption
by 15% with AI.
NexTracker
Will use software developed by
BrightBox, to increase energy
production on solar farms, thereby
enabling faster operations and easier
maintenance.
Upside
Upside has developed an Advanced
Algorithmic Platform(AAP), that
manages demand response of different
devices to be run in parallel.
PowerScout
It uses AI to model potential savings on
utility costs for smart homes using
algorithm to match clients to
potential hardware installation
providers in an online marketplace
format to ensure competitive rates
GE
GE is using AI to enhance wind turbine
efficiency in Japan, raising power
output by around 5% and lower costs
by 20%.
Open Energi
Exploring how AI and machine learning
can be leveraged to orchestrate of
demand-side flexibility - from industrial
equipment, cogeneration and battery
storage systems.
Verdigris
Verdigris applies AI to building
management to site not electrical
signals and identify the type of
equipment in the building.
Terminus7
Delivers practical AI solutions to solve
today’s Business challenges
Source: Indigo Advisory & Jose M. Seara27

28. Jose M. Seara - 2018
The 4th Industrial Revolution
28

29. The 4th Industrial Revolution
Jose M. Seara - 2018
“We stand on the brink of a technological revolution that will fundamentally alter
the way we live, work, and relate to one another. In its scale, scope, and complexity,
the transformation will be unlike anything humankind has experienced before”.
Klaus Schwab. Founder and Executive Chairman - World Economic Forum
Source: World Economic Forum29

30. Jose M. Seara - 2018
Case Study
/ Advance Energy City
San Leandro, CA, US
30

31. Case Study
/ Advance Energy City
San Leandro, CA, US
Jose M. Seara - 2018
Enel & Olidata
Arrive to San Leandro
2014
Olidata & Eco & Urbana
Form Olidata Smart Cities
March 2015
SolarWeek in San Leandro
March 2015
San Leandro Tech Campus
Microgrid Design Starts
May 2015
Olidata $2MM Seed
Funding
August 2015
San Leandro 100% Clean
Energy goal is set
September 2015
Enel, Sol System, PDE
Partnerships
October 2015
California Energy
Commission awarded
ZipPower Grant 1.5MM & 8MM
March 2016
San Francisco Office opens
April 2016
OSIsoft becomes equity
Investor in ZipPower
July 2016
Software and business
Model Definition
2016-2018
Peer-to-peer Network Live
& Token is Launched
July 2019-2020
Source: City of San Leandro31

32. Jose M. Seara - 2018
Case Study
/ Virtual Network in Germany
32

33. Case Study
/ Virtual Network in Germany
Jose M. Seara - 2018
Powerlines connecting the
windy north to the industrial
south become congested,
meaning electricity cannot
be transported.
2
Suppliers who bought wind
power supply customers in
the south may not receive
that power, putting industry
and house holds at risk of
failed supply
3
Heavy winds on the
North Sea and Baltic
coast increase
power production
from North German
wind farms
1
Grid operators have to
throttle production from
conventional power
plants and/or wind farms
in the north in order to
stabilize the grid.
4
Grid operators tell power
stations in the south to
ramp up production to
make up for wind power
that cannot reach the
south.
5
Case Study:
Grid operator Tennet and
solar battery maker Sonnen
will use blockchain
technology to build a virtual
network of decentralized
storage systems to stabilize
the grid
Source: Sonnen33

34. Jose M. Seara - 2018
Future
/ The Microgrid Dream House
34

35. Future
/ The Microgrid Dream House
Jose M. Seara - 2018
Today’s green technology
can turn your home into a
mini power plant:
/ Use what you can
/ Sell the rest to the Grid
/ Take power from the Grid
when need it
EV: Electric Car
By next year, this power system could
include a plug-in hybrid vehicle. The car
battery can serve as additional backup
storage for the home and the grid.
Smart Meter
A commercial time-of-day electric meter
shows the varying price of electricity, so
the owner can use less and sell more
during peak hours.
DERs: Batteries
Battery storage allows more
flexibility to go on or off grid.
Demand Response
Timer Switch
A switch attached to a timer
directs the power from the
solar panels to batteries or
onto the grid. During peak
hours of noon to 2.p.m, when
power is most expensive, the
house runs off the batteries.
DERs: Solar Panels
A 400-watt, 3x5 foot
panel made sells on
Amazon for $3.000.
The average home
would need at least 10
to meet its energy
needs.
DERs: Microwind Turbine
The Skystream model from
Southwest Windpower
delivers 2.4 kW- up to 90%
of the average home’s use.
Efficiency: Insulation
A typical home can cut
25% to 50% of energy
use through simple
weather-proofing and
from energy-efficient
appliances.
Source: Jack Uldrich35

36. Jose M. Seara - 2018
The exciting Future of Electricity
Jose M. Seara
LinkedIn: https://www.linkedin.com/in/jmsseara/
Twitter: @jmsseara