Big Energy Data. Daniele Miorandi Executive VP R&D U-Hopper.

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1 Big Energy Data Daniele Miorandi Executive VP R&D U-Hopper

2 Energy + Big Data =?

3 Is big data new in energy?

4 Nothing new under the sun? 200 PB Utilities & system operators have been collecting information on the grid for decades The three Vs? volume velocity variety

5 The tech pull Computing infrastructure is a commodity Big data tools and techniques are becoming a commodity available as open source available as a service (in case you are lazy )

6 The tech pull (cont d) Measuring at the endpoints: smart meters Measuring in the grid: PMUs ++

7 The net result Measuring more, measuring more often Ability to process more data more easily

8 Data for what? Optimization of infrastructure and related operations New value-added services for customers

9 In the meantime, in the grid..

10 Disrupting the grid From centralized generation to distributed one (consumers > prosumers) From controllable generation to intermittent one (renewables) From one-way power flows to two-way ones From uncontrollable loads to elastic ones (demand-side management) From state-owned monopoly to open markets to energy storage (well, almost)

11 Driver: RES integration Generation mix in ENTSO-E member TSOs countries in Fossil fuels ( lignite and hard coal, gas, oil, mixed fuels, peat ) Renewable generation ( wind, solar, biomass, geothermal, hydro ) TWh Thermal nuclear Hydraulic generation 79,71 Non-identifiable generation 9.66 source: ENTSO-E (2015) 26.3 % 2.4 % 30.4 % 0.3 % 40.5 % ENTSO-E renewable generation 1 Renew able net generation of which hydro of which wind of which biomass of which solar year TWh n. a n. a TWh Share of energy produced of each member TSOs country 2014 in % 2 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % AT BA BE BG CH CY CZ DE DK EE ES FI FR GB 3 GR HR HU IE IS IT LT LU LV ME MK NI 4 NL NO PL PT RO RS SE SI SK Renewable generation Hydraulic Thermal nuclear Non-identifiable generation Fossil fuels

13 Is it true?

14 $8.5B 2014, estimate by Navigant Research

15 $17.1B in 2024 CAGR ~ 7.2%

16 Why should energy companies invest in Big Data?

17 Net Present Value source: Navigant Research

18 Where is the value?

19 Where is value generated? source: Navigant Research

20 Benefits breakout source: Navigant Research

21 Big Energy Data Success Stories Examples & Business Champions

22 Market cap: $535M Opower Inc is a cloud-based software provider to the utility industry. The Company's software analyzes energy data and presents personalized insights to consumers in order to motivate reductions in energy consumption.

23 Market cap: $107M EnerNOC Inc provides energy management software and related solutions for commercial and industrial customers, as well as for utility and grid operators.

24 Valuation: $95M Tendril Networks, Inc. provides cloud-based energy services management (ESM) solutions. The company offers Energy Services Management Platform that helps energy service providers to meet energy efficiency goals across various channels

26 UA TR SI SE RU RU RS RO PT PL NO NL MK ME MD LV LU LT IT HR GR GB FR FI ES EE DK DE CZ CH BY BG BE BA AT MA DZ TN AL IS IE SK HU NI UA_W Values in GWh 10,000 5,000 2,500 1, Physical energy flows ENTSO-E members Synchronous with ENTSO-E region source: ENTSO-E (2015)

27 Issue with Europe Fragmentation At the market level At the regulatory level (!) At the standard level (but work in progress)

28 Threats & Opportunities Data governance Regulate role of service provider? Data from smart meters? Security & privacy The grid as a critical infrastructure > protect it from cyberattacks Privacy of prosumers in the presence of smart meters

29 Conclusions Big data has a great potential to make the energy sector more efficient and more effective Potential for delivering high benefits to customers (energy companies & prosumers) Technology is there, yet innovation is slow In EU, fragmented market & regulatory framework

30 Backup

31 Cost/Benefit Analysis source: Navigant Research

32 Big Data: More Examples Energy management solutions (B2B) Behavioural energy efficiency Predictive maintenance Analytics services RESs prediction Market dynamics.

ERMInE Database. Presentation by Nils Flatabø SINTEF Energy Research. ERMInE Workshop 2 - Northern Europe Oslo, 1. November 2006

ERMInE Database. Presentation by Nils Flatabø SINTEF Energy Research. ERMInE Workshop 2 - Northern Europe Oslo, 1. November 2006 ERMInE Database Presentation by Nils Flatabø SINTEF Energy Research ERMInE Workshop 2 - Northern Europe Oslo, 1. November 26 Overview Content of the Ermine Database Electronic Questionnaire RTD&D Data