Bde sc3 2nd_workshop_2016_10_04_p03_efacec

Data Management and Analytics in Smart

Grids, Comprising Distributed Generation

Efacec Alberto Jorge Bernardo [email protected]

BDE 2nd Workshop for Energy, Brussels04/10/2016

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



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




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



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



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




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




Electric / Smart Grids domain related data

o Condition-based asset management data

o Load and generation data, comprising dispersed assets




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




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!




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 capacity3-oct.-16www.big-data-europe.eu




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




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




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




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




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




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




Thank you!


Efacec Alberto Jorge Bernardo [email protected]

Bde sc3 2nd_workshop_2016_10_04_p03_efacec

Technology