EDP InovaçãoWho we are, what we do

31st October 2017

Estoril

Our World is Changing,

and we want it to be an

opportunity rather than a

threat

If you don't have a strategy, you're part of

someone else's strategy.

Alvin Toffler, writer and futurist

We believe in “Open Innovation”

EDP INNOVATION SUPPORTS INNOVATION

FROM IDEA STAGE UNTIL INVESTMENT

EDP INNOVATION TIMELINE

2008

2009

2010

2012

2013

2014

2016

2017

2014

2007

EDP INNOVATION STATISTICS

2011 2012

2015 20162012

LED Street & Industrial Lighting

DIRECT INVESTMENTS

Big Data/Complex Events Processing Efficient Water Heat Exchanger

2011

Floating Offshore Platform O&M Wind Farms Design Electric Plugs

2014

2014 2016

EDP Generation - EDP Renewables

CONVERTIBLE SPONSORSHIPS & DEBT

EDP Commercial

EDP Commercial

2014

EDP Renewables

CONVERTED CONVERTED

CONVERTIBLE SPONSORSHIPS & DEBT

EDP Generation

EDP Commercial

20172017

EDP Generation

2017

EDP Generation

2017

OUR PARTNERS ALONG THE WAY

INVESTING IN KNOWLEDGE

THROUGH STARTUPS

CLIENT-FOCUSED

SOLUTIONS

SMARTER GRIDS CLEANER ENERGY

DATA LEAP

ENERGY STORAGE

EDP INNOVATION’S

PRIORITIES

CLIENT-FOCUSED

SOLUTIONS

SMARTER GRIDS CLEANER ENERGY

DATA LEAP

ENERGY STORAGE

EDP INNOVATION’S

PRIORITIES

«Leap (verb):To jump from a surface; to jump over

something; to move quickly.»Source: Merriam-Webster online dictionary

ICTs and data science are advancing at unprecedented pace. At EDP, we are

firmly committed in embracing digital transformation, taking a leap in our

capability to use digital and data to create competitive advantage.

We will look back on this time and look at data as a natural resource that

powered the 21st century, the same way vapor, electricity and oil powered the

Industrial Revolution.

Ginni Rometty, IBM CEOSource: IBM

… but it were the GAFAs who did it …

They started in a garage

Belgium

GDP (2016)

$465 Bn

GAFAs had in 2016 an agregated revenue of $470Bn, equivalent to Belgium

and more than twice the Portuguese GDP

This is a Global Game

1997

Google: Keeping hardware and software costs to a minimum was

always a concern. Hardware is designed in-house, and it is a lot about

Open Source (SW & HW)

1999 2000 presently

EDP

Being able to scale and to keep up with latest technology

requires Open Source

“Our mission is to build technology for others to be able to build technologyupon.”

Accelerate digital transformation by promoting adoption of state-of-the art full-stack development tools and technologies to enable continuous delivery

▪ Identify state-of-the-art software development tools supporting microservices application architectures

▪ Support corporate IT in the definition of a blueprint for continuous delivery

Data Leap’s activities are focused on four strategic development vectors, with clear objectives defined for each

Develop projects involving advanced data analytics, implementing machine learning algorithms that may generate competitive advantage for EDP.

Ramp-up knowledge acquisition in the Internet of Things (IoT) and Machine to Machine (M2M) systems

Create technical capability and build-up competences to process “big data”, identifying business areas with the highest potential benefit from the technology and promoting adoption by leading pilot projects.

▪ Identify opportunities for application of Machine Learning / Advanced Analytics within EDP’s Business Units

▪ Implement pilot projects and measure benefits

▪ Identify mature solutions for adoption in the IoT area, using pilot business applications to assess technologies

▪ Support corporate IT in the definition of an enterprise-wide strategy for IoT

▪ Support corporate IT in defining and implementing EDP Group’s Big Data corporate architecture

▪ Identify and test innovative Big Data tools and techniques

Mission Short-term objectives

Big Data

Machine Learning & Advanced Analytics

Internet of Things /

M2M

1

2

3

Full-stackDevelopment & Continuous

Delivery

4

DSI

Regarding building in-house Big Data capabilities, EDP’s journey started in 2013 replicating a big data scenario from an EDF paper, and has since materialized in a corporate data lake implementation

2013 2014 2015 2016 2017

Source,Setup

Configure,Test, Operate

Data aggregation experiment

PREDIS project support

R&D supportCorporate big data

architecturePurpose

# Nodes

# Cores (CPU)

# Storage (TB)

1346 6

528184 464

29.27.5 459

21

42

17

Big Data Experiment

“Low-Cost” Big Data Cluster

“Enterprise”Big Data Cluster

Big Data Appliance

Big

Dat

aIn

tern

et o

f Th

ings

Ad

van

ced

An

alyt

ics

Co

nti

nu

ou

s D

eliv

ery

Investment (€) ~80k~300 ~719k~50

1. Integration: Ingest data from historian database (OSI-PI) into Big Data platform

2. Preprocessing: Calculate effective duration / number of cycles for each load case

3. FAST: Model each load case (analytics task) and run load cases (140 cases × 4 minutes, parallelize with big data)

4. Total Damage: Post-process load data with FAST-generated load cases, resulting in the total estimated damage

5. Package: Integrate all system components in a package, empowering users to compute damage for any wind farm

The Turbine Lifetime Assessment initiative, developed with EDPR, aims to predict the actual life time of each wind turbine by computing fatigue damage based on actual conditions registered in the field

• Computing fatigue damage based on actual conditions registered in the field enables data-driven decisions on asset replacement and decommissioning, instead of relying on manufacturer standard guidelines.

• Existing tools make this process a practical impossibility because of data volume and processing times (for example, the Boquerón test site, with 75 turbines, has 11.5M O&M events and 27.6M wind data points for 7 years data with 10-minute granularity)

Business Scenario

Objectives

Fonte da Mesa wind farm outcome

• EDPI has now 45 nodes (Low Cost Big DataCluster) ready for testing, significantly improving computational times and enabling new analysis;

• 12 sectors from Fonte Da Mesa were tested (each sector ≈ 350 tests);

• As of Jan 2017, EDPR has run more than 4200 tests on EDPI’s Cluster;

• Preliminary results demonstrate the possibility of extending turbine lifetime over 20 years, the blade being the most critical component.

Big

Dat

aIn

tern

et o

f Th

ings

Ad

van

ced

An

alyt

ics

Co

nti

nu

ou

s D

eliv

ery

7h30’Windows PC

(4 cores)

Performance benchmark, 1 sector (350 tests)

1hBig Data Cluster

(30 cores)

87% optimization

With EDP’s Security Operations Center (SOC), a project is ongoing to apply machine learning to cibersecurity events, helping in identification and corrective analysis of potential threats

USER EDP

SIEM

PC 1 PC 2 PC 3

Server A Server A Server B

ALERTA

DSI

SIEM

BitSight

ALERTA

Lista de

Ameaças

DSI

USER EDP

PC

BotnetCurrent state: Bitsight provides EDP with a list of known botnets, which is loaded in SIEM; When an EDP PC communicates with a botnet, an alert is triggered.

Use case 1: Botnets Use case 2: multiple connections

Current state: When the same user establishes communication with multiple source IP addresses, an alert is triggered.

Big

Dat

aIn

tern

et o

f Th

ings

Ad

van

ced

An

alyt

ics

Co

nti

nu

ou

s D

eliv

ery

Project goal: Proactively identify unknown botnets active on EDP’s network, through pattern recognition on security log data.

Project goal: Identify multiple connection with single user in real time, using 2-minute sliding windows.

Use case 3: SW vulnerabilities Use case 4: Suspicious comms Use case 5: Torrent streams Use case 6: Data theftNew New New New

Current state: Little visibility over vulnerability patching in software applications.

Current state: No capacity in SOC to analyze all communications flagged by firewall as suspicious.

Current state: Not all suspicious P2P streaming flows are being blocked.

Current state: No monitoring of potentially damaging data flows outside usual user patterns.

Project goal: Automatically identify unpatched applications.

Project goal: Classify and cluster suspicious comms events.

Project goal: Identify P2P streaming torrents for analysis.

Project goal: Identify suspicious UL/DL activity for analysis.

With Labelec, a new analytics initiative was recently kicked-off to optimize object identification in massive numeric and photographic data collected in line inspections, reducing human intervention

▪ During aerial line inspections, 3 types of data are collected: images, thermographic data and laser (numeric 3-D point clouds). The inspection report highlights possible issues, for example relating security distances from vegetation to power lines.

▪ There is some level of automation in the processing of source data, however manual labor is still a significant part of the process.

Business scenario

▪ Reduction of manual labor by implementing automatic mechanisms to perform data classification.

▪ Improvement of operator performance and reduction of operational errors due to manual intervention, by building automatic defect and anomaly detection algorithms applied to photographic images.

Project objectives

Big

Dat

aIn

tern

et o

f Th

ings

Ad

van

ced

An

alyt

ics

Co

nti

nu

ou

s D

eliv

ery

Point classifiedas vegetation

Point classified aspart of a line

Point classifiedas terrain Sample image

Implementation of a data processing model using machine learning with classification and clustering algorithms. (Q2 2017)

Implementation of a process for automatic detection of defects by applying cognitive models to image data. (TBD – external partner?)

Phase 1 Phase 2

A predictive model was developed for EDPP, aiming to detect abnormal vibration patterns at the Lares plant and enable preventive action, avoiding damage to the machinery and the subsequent downtime and related costs

• Lares CCGT has a history of downtime caused by incidents related to abnormal vibrations;

• The unplanned downtime has severe impact on the plant’s profitability;

• The plant’s machinery includes sensors collecting vibration data and multiple other measurements such as temperature in real time;

• Can abnormal vibrations be predicted to avoid unplanned downtime?

• Phase 1: Create a predictive data analytics model for abnormal vibration patterns;

• Phase 2: Implement an operationalization mechanism to visualize vibration patterns in real time and improve the plant’s operations management.

Business Scenario

Objectives

DONE

PROPOSAL DELIVERED

Outcome

• The model is able to find abnormal operating regimes before real incidents which led to unplanned downtime.

• The model found abnormal operating regimes in Oct 2016 for which a validation will be performed on the upcoming visual inspection.

Vibrations rising more than normal

• Dashboards with signal (vibrations) trends were developed and made available to EDPP, to follow potential abnormal operating events.

Big

Dat

aIn

tern

et o

f Th

ings

Ad

van

ced

An

alyt

ics

Co

nti

nu

ou

s D

eliv

ery

Also with EDPD, the MAPDIS pilot is being implemented for predictive maintenance on substation circuit breakers, aiming to optimize maintenance operations by anticipating equipment failures

▪ The pilot will address ~9.000 circuit breakers (high and medium voltage) with power cut devices, spread out through ~400 substations;

▪ Data from 3 operational systems will be used: SAP (maintenance orders and plans); SIT (technical information on network assets) and SCADA-BI (operational data, including failed commands). External data, such as geo information on each substation, will also be considered;

▪ A multidisciplinary team from EDPI, EDPD, SAS and Corporate IT (DSI) is involved in the project.

Business Scenario

▪ Predict the probability of failure of the next automatic command sent to a substation circuit breaker, based on a machine learning model trained using historical data from the last 3 years;

▪ Build a BI dashboard that shows the predicted probability of failure for each circuit breaker, optimizing the maintenance process by focusing on critical assets first;

▪ Boost the development of internal skills and competences in advanced analytics, sharing knowledge and best practices.

Objectives

Inte

rnet

of

Thin

gsA

dva

nce

d A

nal

ytic

sC

on

tin

uo

us

Del

iver

y

Collect and preprocess data; Develop, train and evaluate model using machine learning algorithms; Develop BI dashboard.(Q3 2017)

Technological impact assessment by DSI; Handover to EDPD JUMP project including solution documentation, predictive models and SAS code. (Q4 2017)

Phase 1: MAPDIS pilot Phase 2: JUMP handover

Big

Dat

a

9.000

400

5,3%

Circuit breakers

Substations

Failed Commands

With DSI we are looking at new ways to develop and put applications into operation.DevOps and Continuous Deliveries are new disciplines we are investing in.

On the Internet of Things topic, EDPI is performing a benchmark comparison of commercial IoT platforms, using re:dy as base implementation scenario for the assessment

1. Identify cloud platforms to be used by EDP in IoTapplications and new products/services

2. Benchmark key features: Scalability, ease of implementation, standard features and customizable functions

3. Compare costs using a common base model (with re:dy as a use case for comparison)

4. Assess potential strategies to build data analytics scenarios from platform-enabled data

Goals

The objective is to share all conclusions with DSI, contributing to define a corporate strategy for IoT

AWS

▪ Very mature PaaS offering;▪ Direct involvement with AWS, plus additional

support from local partner (Magic Beans) having relevant resources and experience;

▪ EDPI assessment:• IoT: AWS IoT re:dy integration done successfully;

low effort, good support and plenty of documentation available; lean SDK in both C and Java programming languages.

• Several other AWS PaaS components used to date: RDS, VPC, DynamoDB, API Gateway, Lambda e SQS, reinforce positive experience.

ORACLE

▪ PaaS offering not yet presented;▪ A demo was made of a SaaS-based IoT application

for data and device management; however, this is only one of the pieces of the IoT puzzle.

Done or ongoing Future work

SAP

▪ SAP’s cloud platform being enriched with IoTplatform through acquisition of PLAT.ONE;

▪ Hands-on trial from May 17 (pending confirmation).

IBM

▪ Workshop with IBM done May 10-11.

▪ Very mature PaaS offering;▪ Direct involvement from Microsoft;▪ EDPI assessment: IoT integration with re:dy

ongoing; steep learning curve requires significant effort, not much online documentation, heavy SDK.

MICROSOFT

Big

Dat

aIn

tern

et o

f Th

ings

Ad

van

ced

An

alyt

ics

Co

nti

nu

ou

s D

eliv

ery

Our future focus will be cutting edge technologies such asBlockchain, Chat Bots and we will continue betting on Hackathons

CLIENT-FOCUSED

SOLUTIONS

SMARTER GRIDS CLEANER ENERGY

DATA LEAP

ENERGY STORAGE

EDP INNOVATION’S

PRIORITIES

Forget the concept of Core Business

“We want Google to be

the third half of your

brain.”

Sergey Brin

In 2015 Sergey Brin has stated … All GAFAs (Google, Apple,

Facebook, Amazon) are betting on Artificial Intelligence

Some of our current focus areas: innovative solutions for E-mobility, digital customer engagement, smart home applications, new business models leveraged on digital channels

Innovative solutions for e-mobility

Digital customer engagement

Smart home applications

New business models on digital channels

Feasible

Usable

Valuable

Delightful

Minimum Viable Product (MVP) Minimum Loveable Product (MLP)

Goal

With customer centricity in mind, the aim is to focus on developing not only minimum viable products (MVPs), but minimum loveable products (MLPs)

CLIENT-FOCUSED

SOLUTIONS

SMARTER GRIDS CLEANER ENERGY

DATA LEAP

ENERGY STORAGE

EDP INNOVATION’S

PRIORITIES

EDP Inovação

The energy generation sector is changing at a swift pace and we need to anticipate the sector trends

In: Climate Change News, Jul-16

In: Wind Power Offshore, Sep-16

In: Recharge News, Nov-16

In: PV Maganize, Apr-16

In: Sun & Wind Energy, May-16

Solar Sold in Chile at Lowest Ever, Half Price of CoalIn: Bloomberg, Aug-16

EDP Inovação 42

Cleaner Energy workgroup is focused in 3 strategic pillars aligned with the trends of the sector

New energies

Improve efficiency and create knowledge

BigData&Analytics in asset management (1)

Strategic PillarsC

lean

er

Ene

rgy

Wo

rkG

rou

p

1

2

3

DATA LEAP(in coordination with workgroup)

Strict collaboration with Data Leap workgroup to develop internal know-how in Big Data and Advanced Analytics to generate a positive impact in asset management

Identify opportunities to increase energy yield extraction and reduction of O&M cost of energy assets. Increase knowledge about energy generation assets to extract more value

Identify and create opportunities to maintain EDP in the leadership of renewable energy generation with a diversified and efficient portfolio

EDP Inovação 43

The EDP group focus is on increasing knowledge in operation of solar farms. In parallel, new technologies are being tested, with potential to further reduce LCOE costs.

Project focused in increasingthe knowledge about solar PVpanels but also its O&M(inlcuiding cleaning,degradation and shadowing)and its impact in the businesscase of solar farms

Solar PV – SunLab I/II Solar CPV – CPVLab

Test and demonstrate new PVsolar technologies that canimprove efficiency of solarfarms and change radically thebusiness model.

Solar Glass-Glass and Bifacial

Test concentrated solar PVtechnologies with potential inthe medium term to acquireknowledge on performanceand O&M of this technology.

EDPP is testing a Floating PVplant, with potential for placeswhere the available land isscarce, sharing costs of gridconnection and potential forincreased efficiency.

Floating Solar PV

EDP Inovação 44

Floating offshore wind is strategic technology to open new markets for EDP. The WindFloat is on a quick pace towards commercialization.

▪ The WF1, with a 2 MWwind turbine completed 5years of operation withhigh availability.▪ The prototype wassuccessful decommissionedin July 16, demonstratingthe simplicity of theoperation.

WindFloat 1

▪ The WindFloat Atlanticis a pre-commercialwindfarm using 3 x 8.4MWwind turbines, of the coastof Viana do Castelo. It isgoing to be the first projectfinanced floating offshorewind farm, proving itsbankability.

WindFloat Atlantic

▪ The LEFGL project wasawarded by the Frenchgovernment.▪ The project consist in4x6 MW installed in theMediterranean, lead byEngie.

LEFGL

2011 - 2016 2019 COD 2020-21 COD

EDP Inovação45

In parallel, new innovations are being integrated in asset management for onshore wind, along with collaboration with several startups along the wind value chain.

FAST / Turbine Lifetime

DELFOS

▪ FAST, an aeroelastic modelingtool is currently being used toestimate the remaining lifetime ofEDPR onshore assets.▪ In parallel, Data Leap groupemployed big data tools to postprocess the operational data ofthe EDPR’s turbines.

▪ DELFOS, EDP Open Innovationwinner, has join EDP Starter.▪ Their methodology to useanalytics is under discussion withEDPR to better forecast windturbine failures and reduce O&Mcosts.

With the Data Leap Group With the EDP Starter

CLIENT-FOCUSED

SOLUTIONS

SMARTER GRIDS CLEANER ENERGY

DATA LEAP

ENERGY STORAGE

EDP INNOVATION’S

PRIORITIES

Origin: German Reseach Center for Artificial Intelligence

Cyber-physical systems (CPS) are engineered systemsthat are built from, and depend upon, the seamless integration of computational algorithms and physicalcomponents.

Origin: Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring; Salman Ali et al.

Advanced Analytics and Big Data and AI are an importante part of Industry 4.0

Smartgrids is a part of the solution. It should be possible to leapfrog present paradigm.

Power systems are facing three trends: decarbonisation, decentralization, digitalisaton.

• Transactive enegy model• Distributed generation• Two-way flows• Distributed generation• Distributed storage• Distributed Control• Responsive loads• Loads/ Local Generation IoT

enabled

• Central generation• One-way flows• Central control• Dummy loads

Old Grid Energy 4.0 Grid, or the Energy Web

Sensors Controls

Cyberspace / Cloud

drawing origin: solutions.3M.com

EDP Inovação 50

The main projects of the area that are being developed in Portugal, Spain and Brasil try to create solutions for the existing and expected challenges that the DSO will face, and internalizing knowledge inside EDP.

Predis Sinapse

Short term Load and generation forecast in “real time” to improve Energy balance and grid operations using Big Data Technologies.

Increase the visibility over the distribution grid using external information and automating the detection of low voltage outages.

…×n

BT Zero

Secondary transformer with integratedmetering in order to decrease non technicallosses.

We are also actively looking for Startups in EDP Ventures and EDP Starter ecossistems that can provide solutions for the expected challenges in the near future.

EDP Inovação

With EDPD, PREDIS is producing disaggregated load forecasts at PT-level based on historical load profiles, seasonality and weather data

▪ Predictive model runs daily, considers temperature forecast, historic electricity loads, working days vs. weekends and national holidays, yearly and daily seasonality;

▪ Load forecasts are produced for every substation and distribution transformer with a 15-minute granularity, for a 3-day forecast range (limited by reliability of temperature forecasts);

▪ Current Mean Absolute Percentage Error (MAPE metric) is 12.9% for power transformers and 9.8% for substations;

▪ Parallel execution of model in big data cluster takes 2h33’, vs. 9d05h12’ if calculations were performed sequentially.

Outcome achieved so far

▪ Incorporate customer-owned power transformers (PTCs) data in predictive model;

▪ Incorporate dynamic grid topology in the predictive model (predictive model variations for each possible grid configuration), aiming at real-time energy balance;

▪ Incorporate regional holidays and events as input variable and add extra weather features (e.g. humidity);

▪ Incorporate renewable energy sources in PREDIS (cooperation with EDPR for wind generation forecast model and with Portuguese universities for PV forecast model).

▪ Extend forecast window to one month;

Future improvements planned

39.075 753

12,8%

Power transformers Substations

Mean error98,8%

Run time reduction

3 days 15 minuteForecast range Forecast granularity

Inte

rnet

of

Thin

gsA

dva

nce

d A

nal

ytic

sC

on

tin

uo

us

Del

iver

yB

ig D

ata

SINAPSE is a great example of using IOT technology to “do more with less”

EDP TELCO

1) Telcos inform EDP in real time when network elements have lost power.

2) EDP feeds back if it was a DSO power fault and when service has been restored.

CLIENT-FOCUSED

SOLUTIONS

SMARTER GRIDS CLEANER ENERGY

DATA LEAP

ENERGY STORAGE

EDP INNOVATION’S

PRIORITIES

In the future PV with storage may become the most competitive solution

Origin: Boston Consulting Group

55

Flexibility / Storage services

There will be new challenges & opportunities

Seasonal / Bulk storage

Capacity / flexibility markets

Energy management

Energy management

RES integration

Autonomous networksSelf-consumptionLV/MV Grid services

VPP

Ancillaryservices

Arbitrage

EDP Inovação

But we see that many different technologies are still making way

Technologies

Applications

Source: GIA – Grupo interplataformas de Almacenamiento

EDP Inovação 57

So our aim is gathering know-how in different technologies / applications

EDP Inovação 58

Also leveraging on current projects and EV roll-out for flexibility management. Objective to be ready to tackle new business models

StoreData V2G

Levering on live energy storage projects. Gather demo project data in central database. Develop analytics to analyze performance of projects

Vehicle-to-grid allows for bidirectional power flow, enabling new functionalities for Electric Vehicles

2nd life batteries

Re-utilization of EV batteries for stationary purpose. Analysis of performance and costs for repurpose.

Challenges:

Storage technologies: EV: Flexibility:Technologies EV charging / discharging Aggregation / VPPBMS / Integration Re-utilization New BM

Business Units

EDPInovação

Database

Analytics

End of 1st life

Repurpose for 2nd life

Residential and griduses

Recycling Recycling

EDP Inovação 59

V2G

BMS

+

-DC AC

CAN bus

Second

Life

Batteries

ENERGY MGMT

AUTOMATED DEMAND OPTIMIZATION

M&V, PROJECT TRACKING

OPTIMAL ENERGY TRADING

DEMAND RESPONSE & ANCILLARY SERVICES

PREDICTIVE MAINTENANCE

Bringing inteligence into the system

EDP Inovação

We believe in “Open Innovation”