Big Data Europe 2nd Workshop in Enegry, Brussels 4/10/2016

1. Data Management and Analytics in Smart
Grids, Comprising Distributed Generation
Efacec Alberto Jorge Bernardo abernardo@efacec.com
BDE 2nd Workshop for Energy, Brussels04/10/2016

2. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.2
2-oct.-16www.big-data-europe.eu
 Societal challenges
o Economy de-carbonization
Increasing social responsibility and sustainability
 Greenhouse gases emissions reduction
o Society reliance on electric energy
o New policy and regulatory aspects impacting all sectors

3. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.3
28-sept.-16www.big-data-europe.eu
 Smart Grids enabling drivers
o The societal challenges awareness, by key decision makers
o Information and Communications Technologies – ICT

4. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.4
2-oct.-16www.big-data-europe.eu
 The Smart Grid concept
o User-centric – flexible demand, microgeneration, markets
o Grids refurbishing – automation, QoS, asset management
o Security of supply – grid resilience and flexibility
o Interoperability – cross border, transit congestion, markets
o Distributed generation and renewable energy sources
o New trends – electric vehicles, storage, demand response

5. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.5
 Ongoing developments of Smart Grids
o Deployment of Advanced Metering Infrastructures
o Massive deployment of DG/RES
o Increasing deployment of Electric Vehicles
o Incursion on massive distribution grid monitoring & control
o Integration of different technical and management systems
o Integration of different stakeholders’ systems
28-sept.-16www.big-data-europe.eu

6. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.6
 Current challenges of Smart Grids
o Cost-effectiveness of solutions
o Combined outcome serving multiple stakeholders
Technical – grid control, decision making, efficiency
 Business – cost savings, penalty mitigation, profits increase
 Forecasting – scheduling, planning, investments, markets
o Huge amount of historical and real time data
4-oct.-16www.big-data-europe.eu

7. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.7
 Electric / Smart Grids domain related data
o Electrical measurements from grid monitoring devices
o Fault alarms, quality of electric signal, quality of service
from Remote Terminal Units, Protection Relays and
Qualimeters
o Energy, electrical measurements & QoS from Smart Meters
o Synchrophasors data from Phasor Measurement Units
3-oct.-16www.big-data-europe.eu

8. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.8
 Electric / Smart Grids domain related data
o Condition-based asset management data
o Load and generation data, comprising dispersed assets
2-oct.-16www.big-data-europe.eu

9. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.9
 Data from other sources
o Geographical information system (GIS) data
o Global positioning system (GPS) time-reference data
o Weather and lightning data
o Seismic data
o Animal migration data
o Electricity market data
3-oct.-16www.big-data-europe.eu

10. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.10
 Big Data means Volume, Variety and Velocity
o Need for cross-checking and correlate a high volume
of multiple data sources, which show a huge speed of
generation and disappearance…
o … and the grid topology is never static!
2-oct.-16www.big-data-europe.eu

11. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.11
 Smart Grid applications enabled by data analytics
o Boosting Advanced Smart Metering towards enabling LV
grid fault detection, quality of supply measurement,
electric signal (power) quality, as well as technical and
commercial losses calculation
o Improving Grid Operation by using more precise
forecasting models (generation, loads) at different levels
of the grid, aiming at hosting further renewable capacity
3-oct.-16www.big-data-europe.eu

12. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.12
 Smart Grid applications enabled by data analytics
o Facilitating new and evolving Markets which rely on the
efficient treatment of the detailed data available from
smart metering, e.g. for demand response mechanisms
o Improving Outage Management, by combining call taking
and smart metering data, by considering the role of
renewable generation and electric vehicles, as well as by
addressing them for intentional islanding (backup & V2B)
3-oct.-16www.big-data-europe.eu

13. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.13
 Smart Grid applications enabled by data analytics
o Improving Asset Management, by combining condition
monitoring data of used assets with grid operation
data, aiming at boosting predictive maintenance and at
assisting on decision making processes
3-oct.-16www.big-data-europe.eu

14. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.14
 Big challenges when addressing Big Data
o Even though the current progress of data mining
technologies provides opportunities for big data
analysis serving the power system, the lack of a utility
unified data model is a bottleneck for an efficient data
integration and applications deployment
3-oct.-16www.big-data-europe.eu

15. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.15
 Conclusions
o The electric sector (especially the Smart Grid) is eager
for the outcome of strategic Big Data developments
o Any solutions should be
 Comprehensive – 3Vs
 Adaptive and dynamic – flexible
Distributed and multi-layered – business-shaped
3-oct.-16www.big-data-europe.eu

16. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.16
 Next Steps
o Efacec will gladly work with BDE
Assessing possible areas of interest towards mutual teamwork
 Assessing the use of BDE platform, combined with Efacec’s own
processes and technologies
3-oct.-16www.big-data-europe.eu

17. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.17
 Next Steps
o Efacec will gladly work with BDE
Addressing target stakeholders and defining use cases, for
 Technical driven applications – e.g. improved grid resilience and
state awareness for operational decisions
 Market driven applications – e.g. improved forecasting for market
related decisions
 Coping with the Smart Grid paradigm, leveraging a higher
penetration of RES/DG and EV, towards economy decarbonisation
3-oct.-16www.big-data-europe.eu

18. BigDataEurope 2nd Workshop in Energy 4/10/2016
“Data management and analytics in smart grids, comprising DG” p.18
Thank you!
3-oct.-16www.big-data-europe.eu
Efacec Alberto Jorge Bernardo abernardo@efacec.com