“collaborative automation: water network and the virtual market of energy”, an example of...
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“Collaborative automation: water network and the virtual market of energy”,an example of Operational Efficiency improvement through Analytics
Stockholm, ITF Conference, 6th February 2014Analytics for solution team, V. Boutin
Customers are looking for integrated solutions that make their lives easier while optimizing costs. Innovation is essential to satisfying those requirements.
The convergence of automation, information, and communication technology has created dramatic new opportunities for advancing energy efficiency.
Innovation is about combining these opportunities with smart services to deliver high-value yet easy-to-deploy solutions.
Pascal Brosset, SVP Innovation, Schneider Electric
Schneider Electric at a glance
24 billion € sales in 2012 41% of sales in new economies 140 000+ people in 100+ countries 4-5% of sales devoted to R&D
Analytics 3.0
Digitization and Analytics bring new opportunities to improve Operational Efficiency
In the new era, big data will power consumer products and services.
by Thomas H. Davenport
X 2Increase of the volume of data every two years
1 BillionCollective volume of data points being generated by Smart meters in the US every day
17 b$Estimated total revenue for big data by 2015 (IDC)
Beyond basic KPIsOpportunity to extract value out of collected data
CloudBig data storage and analysis across various information inputs
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What are Analytics ?
…….…What if trends continue?.........................
..………What action is needed?.....................................
………..……..What will happen next?.............................
……………………………What best can happen?............................
…..Why is this happening?......................
……………..How many? How often? Where?.............................................
……………What happened? ……………....………………………………………….
StatisticalAnalysis
Forecasting
PredictiveModelling
Optimization
Valu
e f
or
Cust
om
ers
Degree of Intelligence
…………..What is the cause of the problem? …………………….
NotificationAlerts
QueryDrilldown
Ad Hoc Reports
Standard Reports
7 Analytic features for Operational Efficiency
to create new information such as prevision, patterns, early detection of problems
to take better actions regarding organization, planning and control
to provide rationale for building an optimized design and development strategy for the future
Data correlation & prediction
Performance evaluation & benchmarking
Condition monitoring, diagnostic, maintenance
Context dependent control
Resources & activities planning and scheduling
Decision support through simulation
Data Disagreggation & information discovery
Few concrete examples
Virtual or smart sensorsGet advanced information (such as fermentation for beer micro-filtration, or milk powder hulidity…) by collecting and mixing several correlated data items
Early detection of abnormalitiesExtract early signals that would detect abnormal behaviours and possibly link to performance degradations
Demand response for water distributionDetermine the best srategy for pumping, while ensuring that the water demand will be entirely met, and leveraging variable energy prices (modulation market)
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Technologies to make it happen
Analytics technologies
Analytics to OPTIMIZE
Analytics to SIMULATE
Analytics to MODELPhysical models
Dynamic system
modeling
Pattern learning
Pattern discovery
An
aly
tic
s t
o I
NT
ER
AC
T
Visual analytics
Better control, supervision, operation management, design and continuous improvement
Data from
Low cost Self powered CommunicatingEasy to install
Pervasive sensors
Energy sensor
Comfort sensor
Infrastructure for data collection and integration with heterogeneous applications and legacy systems
Enable collaborative automation by networked embedded devices
An example in more details:
Collaborative automation between water networks and virtual energy market
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Water is easier to store than electricity and water utilities can turn it into cash
Energy cost is a challenge for water distribution companies
Water networks offer good opportunities for virtual energy market
Technical enablers are necessary Decision making tool ensuring that the water
demand will be entirely fulfilled, evaluating the economic equation, and providing the best strategy to maximize benefits
Control system
A typical use case example
Automatic calculation of modulation capabilities for 24 coming hoursBased on: Previsional pumping plan Water demand and operational constraints Energy prices dynamic context
What-if scenarios and decisionFor each potential modulation, the water network manager can: Preview the pumping scheduling, tanks
storage and pressure levels Select the modulation offers to be sent to
aggregator
Transaction with aggegator
When the energy demand resource will be required, the updated pumping plan will be sent to operation system
Technical point of view
Main technical bricksOn the water network side Water hydraulic simulation (Aquis simulation) Water demand forecast Modulation capabilities calculation (Artelys optimization)Coming from aggregator Transaction module Energy prices
Arrowhead technology for bricks interoperability
Results and Take away
Water demonstration was based on a simulated environment Extracted from the distribution network of Birkerod
(small town in Denmark)
10 to 15% cost savings expectations for the demo case Hypothesis: intraday capacity market contract For other cases, benefits will greatly depend on water
network characteristics and energy market
More generally, some key success factors for new features based on analytics: Technical infrastructures for easy data sharing Services for interoperability between heterogeneous
bricks Good interfaces, understanding and interaction with
people And an evidence not to forget: the final added value!
