“Collaborative automation: water network
and the virtual market of energy”,
an example of Operational Efficiency
improvement through Analytics
Stockholm, ITF Conference, 6th February 2014
Analytics for solution team, V. Boutin
Schneider Electric at a glance
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
 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
X2
Increase of the volume of data every two years
1 Billion
Collective volume of data points being generated by
Smart meters in the US every day
Digitization and Analytics
bring new opportunities to
improve Operational
Efficiency
17 b$
Estimated total revenue for big data by 2015 (IDC)
Beyond basic KPIs
Opportunity to extract value out of collected data
Cloud
Big data storage and analysis across various
information inputs
Analytics 3.0
In the new era, big data will power
consumer products and services.
by Thomas H. Davenport
What are Analytics ?
Value for Customers
……………………………What best can happen?............................
Optimization
Predictive
………..……..What will happen next?.............................
Modelling
…….…What if trends continue?.........................
Forecasting
Statistical
…..Why is this happening?......................
Analysis
Notification ..………What
Alerts
Query …………..What
Drilldown
Ad Hoc
……………..How
Reports
Standard
……………What
Reports
action is needed?.....................................
is the cause of the problem? …………………….
many? How often? Where?.............................................
happened? ……………....………………………………………….
Degree of Intelligence
7 Analytic features for
Operational Efficiency
to create new information
such as prevision, patterns, early detection of
problems
Data
correlation &
prediction
Context
dependent
control
to take better actions
regarding organization, planning and control
to provide rationale for
building an optimized design and development
strategy for the future
Decision
support
through
simulation
Data
Disagreggation
& information
discovery
Resources &
activities
planning and
scheduling
Performance
evaluation &
benchmarking
Condition
monitoring,
diagnostic,
maintenance
Virtual or smart sensors
Get advanced information (such as
fermentation for beer micro-filtration, or
milk powder hulidity…) by collecting
and mixing several correlated data
items
Early detection of
abnormalities
Few concrete examples
Extract early signals that would detect
abnormal behaviours and possibly link to
performance degradations
Demand response for
water distribution
Determine the best srategy for
pumping, while ensuring that the
water demand will be entirely met,
and leveraging variable energy
prices (modulation market)
Technologies to make it happen
Better control, supervision, operation management,
design and continuous improvement
Analytics to INTERACT
Analytics to OPTIMIZE
Analytics to SIMULATE
Analytics technologies
Physical
models
Analytics to MODEL
Dynamic
system
modeling
Pattern
learning
Data from
Visual
analytics
Pattern
discovery
Low cost
Self powered
Communicating
Easy to install
Pervasive sensors
Energy sensor
Comfort sensor
Enable collaborative
automation by networked
embedded devices
Infrastructure for data
collection and integration
with heterogeneous
applications and legacy
systems
An example in more details:
Collaborative automation
between water networks and
virtual energy market
4
Energy cost is a challenge for water
distribution companies
Water networks offer good
opportunities for virtual energy
market
Water is easier to store than
electricity and water utilities
can turn it into cash
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 hours
Based on:
 Previsional pumping plan
 Water demand and operational constraints
 Energy prices dynamic context
What-if scenarios and decision
For 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
Main technical bricks
On the water network side
 Water hydraulic simulation (Aquis simulation)
 Water demand forecast
 Modulation capabilities calculation (Artelys optimization)
Coming from aggregator
 Transaction module
 Energy prices
Technical point of view
Arrowhead technology for bricks
interoperability
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
Results and Take away
 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!
To contact us
Veronique.boutin@schneider-electric.com
Alexandre.marie@artelys.com
Denis.genon-catalot@lcis.grenoble-inptf.fr
Thank you for your attention