Outline
1. Smart grids : What ? Where ? What for ?
2. A road map for Smart Grids functionalities and issues 3. Some experiments done at EDF R&D
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Smart Grids : What ?
Where ? What for ?
Smart-Grids projects everywhere in the world ...
Key: red=electricity, green=gas, blue=water and triangle=trial or pilot where circle=project
EDF R&D : Créer de la valeur et préparer l’avenir 5
... And in France
Commission de Régulation de l’Énergie (CRE) : www.smartgrids-cre.frERDF :
linky.erdfdistribution.fr
A law on smart-metering has been signed by the government by the end of August 2010. It implies :
- On 01/01/2012 all new meters have to be smart
- On 31/12/2014 , 50% of installed meters have to be smart - On 31/12/2016 , 95 % of installed meters have to be smart.
Smart Grids : what ? And what for ?
Environmental, economical, social and policy drivers lead to a
deep change of the energy sector :
Climate change, environmental concerns
Increased pressure of operational and financial efficiency Increasing awareness of consumers, role of citizens
Technological pressure (IT, smart devices)
Source – Wikipedia
A smart grid delivers electricity from suppliers to consumers using digital technology with two-way communications to control appliances at consumers' homes to save energy,
reduce cost and increase reliability and transparency. It overlays the electrical grid with an information and net metering system, and includes smart meters. Such a modernized
eletricity network is being promoted by many governments as a way of addressing energy independence, global warming and emergency resilience issues.
A road map for Smart
Grids functionalities
and issues
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A road map for Smart Grids functionalities
Today Future
AMI / smart metering
Integrated communication
Decision support
A road map for Smart Grids functionalities
Today Future
AMI / smart metering
Integrated communication
Decision support
Individual load curves (hourly), available in a batch mode (the day after)
Real time alarms
SCADA, supervisory functions
real time pricing, demand response
self healing
full integration of renewables and distributed generation
increase customer participation
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A road map for Smart Grids functionalities and
issues
Today Future
AMI / smart metering
Integrated communication
Decision support
Entreprise service bus, SOA
MDMS
Standards : CIM, Zigbee
distributed intelligence (routers, database, CEP)
real time processing for
metering data and network data
closed-loop system of systems transactive hierarchichal control privacy
Deployment
Smart Grid Data Management challenges
Data Management challenges : need for scalabality ?
Storage of large volume of time-series
Historians, large scale relational data-bases, distributed NoSQL approaches
Trade off between distributed intelligence and centralised processing
Systems of systems, transactive hierachichal control
Real-time processing that might be included into distributed architecture
CEP tools
Real time BI (low latency)
Distributed data-mining
Answer to scalabality, but also privacy
Adaptive modelling, on-line machine learning
The necessary functions remain the same, the key issue is manage the complexity to support the necessary business capabilities at any scale as well as manage the separation of responsabilities to avoid “dueling” control systems (SDG&E)
The necessary functions remain the same, the key issue is manage the complexity to support the necessary business capabilities at any scale as well as manage the separation of responsabilities to avoid “dueling” control systems (SDG&E)SDG&E
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Some experiments done
at EDF R&D
On-going research project : needs for storing
large volume of time series
Data :
Individual – up to 30 millions- and aggregated load curves (10 minutes intervals, at least 2 years lenght)
Meteorological data (hourly measures of temperatures, georeferenced) Contractual information
Needs :
Curve selection
Computation of aggregated curves
Missing data processing, synchronisation, forecasting modeling ... ...
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On-going research project : needs for storing
large volume of time series (2)
Solutions considered :
Data historians
Relational data-bases
NoSQL approaches (Hadoop HBASE with PigLatin and Hive scripts ...)
Local and cloud storage
Real-time computation of aggregated curves
Computation of aggregated load curves :
Grouped by logical criteria : i.e. customer segment Grouped by topological criteria : i.e. network node
Issues
“Real-time” processing (30 millions of curves, 700000 feeders) Poor data quality : missing data, delays ...
Bandwidth constraints
Elements of answers :
Broadcast multipath architecture On the fly estimation
Real-time computation of aggregated curves
(2)
Use of summation sketches
The method is duplication insensitive
Implementation within the StreamBase CEP ; on the fly processing of 300000 curves (duplicated 6 times)
Evolution of demand forecasting approaches
Today :
Short term (national) forecasts have a very good accuracy (MAPE : 1,5%)
The current methods should work on stationary signals, and forecast the
demand for well defined portfolio
Tomorrow :
Variable portfolio, non stationary signals (customers should leave and join the
company, difficult periods to forecast, uncertainties, integration of renewables
and new uses like electric vehicles)
Massive individual data will be available :
Streaming data : how to take them into account for short term (1 day ahead) or very short term (from 1 hour to 24 hours ahead) forecasts ?
Adaptivity
Individual data : how could forecast approachs benefit from their use ?
Evolution of demand forecasting approaches (2)
Results :
Work on adaptive GAM (Generalized Additive Models) for short term
forecasting
Example :
Residus de prévision 1 /9 /2 0 0 7 1 7 /9 /2 0 0 7 4 /1 0 /2 0 0 7 2 1 /1 0 /2 0 0 7 1 6 /1 1 /2 0 0 7 4 /1 2 /2 0 0 7 2 0 /1 2 /2 0 0 7 2 0 /1 /2 0 0 8 6 /2 /2 0 0 8 2 2 /2 /2 0 0 8 1 0 /3 /2 0 0 8 2 8 /3 /2 0 0 8 1 6 /4 /2 0 0 8 1 8 /5 /2 0 0 8 3 /6 /2 0 0 8 2 0 /6 /2 0 0 8 7 /7 /2 0 0 8 2 6 /7 /2 0 0 8 1 2 /8 /2 0 0 8 3 1 /8 /2 0 0 8 -2 0 0 0 0 1 0 0 0 3 0 0 0 GAM GAM en ligneRésidus de prévision sur un an
(fenêtre de taille 100, modélisation du niveau moyen et de l’effet retard en ligne)
Smart Grid data management challenges ?
Smart grids projects (or large experiments) do exist all around the
world
Deployment, IT and social projects
Roadmap : long term vision will drive a very strong evolution of the energy
sector
Needs for scalability :
Storage of large volumes of time series
Centralised or distributed approaches
Real-time processing (scalable and distributed CEP)
Large-scale data-mining :
Distributed data-mining could give answers to scalabality and privacy On-line models
Special thanks to
Alexis Bondu
Xavier Brossat
Yousra Chabchoub
Leeley Daio Pires Dos Santos
Alzennyr Gomes Da Silva
Yannig Goude
Benoît Grossin
Georges Hébrail
Bruno Jacquin
Sylvie Mallet
Amandine Pierrot
Rererences
MDMS : rapport du GTM Research
www.gtmresearch.com/report/the-emergence-of-meter-data-management-mdm
Smarter Energy @ IBM Research, Brian Gaucher, Manager Smarter Energy,
IBM T.J. Watson Research Center
Analytics and transactive control design for the Pacific Northwest Smart
Grid Demonstration Project, P. Huang, J. Kalagnanam, R. Natarajan (IBM
Research Watson), D. Hammerstrom and R. Melton, Battle Memorial Institue,
Pacific
Northwest
Division,
Richland.
(
http://www.ieee-smartgridcomm.org/techprogram.html)
hadoop.apache.org
Agrégation robuste de données en temps réel : application aux compteurs
électriques communicants, Y. Chabchoub, B. Grossin, soumis à EGC 2011
Short term electricty load forecasting with adaptive GAM models, Y. Goude,
A. Pierrot, ISF 2010
A range of methods for electrical consumption forecasting, X. Brossat, ISF
2010.
MDMS : Metering Data Management Systems
MDMS : software platform acquiring metering data from numerous sources and
providing them, after
integration, synchronisation and cleansing, to differents target ‘users’.
