“Collaborative automation: water network and the virtual market of energy”, an example of Operational E fficiency i

1. “Collaborative automation: water network and the virtual market of energy”, an example of OperationalEfficiencyimprovementthroughAnalytics Stockholm, ITF Conference, 6th February 2014 Analytics for solution team, V. Boutin

2. 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

3. X 2 Increase of the volume of data every two years • Digitization and Analytics bring new opportunities to improve Operational Efficiency 1 Billion Collective 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 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

4. What are Analytics ? Optimization ……………………………What best can happen?............................ PredictiveModelling ………..……..What will happen next?............................. Forecasting …….…What if trends continue?......................... StatisticalAnalysis …..Why is this happening?...................... Value for Customers ..………What action is needed?..................................... NotificationAlerts Query Drilldown …………..What is the cause of the problem? ……………………. Ad Hoc Reports ……………..How many? How often? Where?............................................. Standard Reports ……………What happened? ……………....…………………………………………. Degree of Intelligence

5. 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 Decision support through simulation Performance evaluation & benchmarking Data Disagreggation & information discovery Condition monitoring, diagnostic, maintenance Resources & activities planning and scheduling Context dependent control

6. Virtual or smart sensors Getadvanced information (such as fermentation for beermicro-filtration, or milkpowderhulidity…) by collecting and mixingseveralcorrelated data items • Few concrete examples Earlydetection of abnormalities Extractearlysignalsthatwoulddetectabnormalbehaviours and possiblylink to performance degradations Demandresponse for water distribution Determine the best srategy for pumping, whileensuringthat the water demandwillbeentirely met, and leveraging variable energyprices (modulation market)

7. Technologies to makeithappen

8. Better control, supervision, operation management, design and continuousimprovement • Analytics technologies Analytics to OPTIMIZE Analytics to INTERACT Analytics to SIMULATE Physicalmodels Analytics to MODEL Visual analytics Pattern learning Pattern discovery Dynamic system modeling Data from

9. Lowcost • Self powered • Communicating • Easy to install • Pervasive sensors Energysensor Comfortsensor

10. Infrastructure for data collection and integration with heterogeneous applications and legacy systems Enable collaborative automation by networked embedded devices

11. An example in more details: Collaborative automation between water networks and virtualenergymarket

12. 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

13. A typical use case example • Automatic calculation of modulation capabilities for 24 coming hours • Basedon: • Previsionalpumping plan • Water demand and operationalconstraints • 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 When the energy demand resource will be required, the updated pumping plan will be sent to operation system Transaction with aggegator

14. Main technical bricks • On the water network side • Water hydraulic simulation (Aquis simulation) • Water demandforecast • Modulation capabilitiescalculation (Artelysoptimization) • Comingfromaggregator • Transaction module • Energyprices • Arrowheadtechnologyfor bricks interoperability • Technical point of view

15. 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, benefitswillgreatlydepend on water network characteristics and energymarket • More generally, somekeysuccessfactors for new featuresbased on analytics: • Technical infrastructures for easy data sharing • Services for interoperabilitybetweenheterogeneous bricks • Good interfaces, understanding and interaction with people • And an evidence not to forget: the final added value! • Results and Takeaway

16. To contact us Veronique.boutin@schneider-electric.com Alexandre.marie@artelys.com Denis.genon-catalot@lcis.grenoble-inptf.fr • Thankyou for your attention