Enel Group at a glance

Transcription

1 Enel Group at a glance 2013 Group key numbers 2013 Operating numbers Presence in 40 countries Customers served 61 million Employees 71, TWh electricity generated 404 TWh electricity distributed 295 TWh electricity sold 2013 Key economics Growth 2013 vs Revenues: 80,5 bn +7,6% in EBITDA 2013 EBITDA: 17,0 bn -7.2% in Net Financial Debt CAPEX: 5,3 bn +1% in Customer increase Business continuity Ongoing positive free cash flow Efficiency Deleveraging, portfolio optimization and Group reorganization Business development Growing in emerging markets and new technologies, leveraging existing Platforms and customer value Stock exchange Enel is listed on the Milan stock exchange (~1.2 mln shareholders). 14 other Group companies are listed on the stock exchanges of Italy, Spain, Russia, Argentina, Brasil, Chile and Peru. Consolidated Results for 2013 (12 th March) 1

2 Emerging Big Data Themes COST EFFECTIVENESS HOW TO MAXIMIZE THE ROA FOR PRODUCTION PLANTS INFORMED DECISION MAKING HOW TO EFFECTIVELY SUPPORT ANY DECISION WITH DATA AND ANALYTICS PRODUCTS AND SERVICES IMPROVEMENTS HOW TO REINVENT OPERATIONS LEVERAGING ON THESE NEW TECHNOLOGIES NEW SPECIFIC INITIATIVES FOR ENERGY UTILITIES Ø MARKETING & SALES PREDICTIVE CUSTOMER BEHAVIOR AND INDIVIDUAL CONSUMER INTELLIGENCE (HOW CONSUMERS USE ENERGY) Ø DISTRIBUTION GRID OPERATIONS (REAL TIME ENERGY MANAGEMENT DECISIONS) AND NON TECHNICAL LOSSES Ø GENERATION PREDICTIVE MAINTENANCE (HOW TO OPTIMIZE MAINTENANCE COSTS) 2

3 Generation Predictive Maintenance History of a successful Big Data approach About Generation, we were skeptical to adopt predictive analytics based on our data The equipment chosen for the PoC was the feed water pump for a carbon plant. We collected structured and unstructured data After two months, we realized that data quality was not good enough to build and train the models, so we selected another plant IBM and our technical people shared knowledge about data, processes and failures and at the end of July the model was able to predict some failure up to two weeks in advance In 2013 Enel started a large innovation program identifying three different areas for testing predictive maintenance: Renewable, Distribution & Generation After a preliminary evaluation, on April 2104 we selected IBM to run a PoC with the goal of identifying some day/week in advance the performance degradation and failure events On September we provided IBM with new data coming from a different pump in order to test the models reusability. Just after one day, we obtained good results, demonstrating the validity of the approach 3

4 Generation Predictive Maintenance The cycle Ø Predict degradation of pump early enough to commit power and auxiliary related equipments Ø Plan recommendation maintenance Target field for Analysis 3 Conduct Root Cause Analysis Predictor Fields for Analysis More then one model (machine learning) Generate Predictive & Statistical Models 2 4 Display Alerts and Recommended Actions Turbine Rotor Abnormal Vibration Notification Main pump Abnormal Vibration Notification Strong correlation with monthly average anomaly count Turbine rotor Abnormal Vibration Notification 1 5 Collect & Integrate Data Act upon Insights + Work Orders Work Maintenance Card Data from sensors 4

5 Generation Predictive Maintenance Results & Benefits Failure prediction up to 20 days in advance for fluid loss in Main and Booster Pump Failure Probability Computation of the fluid loss in Booster pump Cost saved due to avoidance of a load deviation caused by a big/small fail Cost saved due to warehousing optimization Reduce Capital Costs Value Cost saved due to maintenance labor optimization Cost saved due to equipment life increase Reduce Operating Costs Cost saved due to faults number reduction Cost saved due to fuel consumption reduction Increase Profit 5

6 Lesson Learned and Remarks Selling Innovation and Experimentation is not an easy Task: it is necessary to consume significant resources to achieve the buy-in from business owners. Now the business line is fully convinced of the value of Big Data adoption. Data integration from heterogeneous sources increases value: the level of accruable benefits depends on data variety and quality. Leverage unstructured internal data and define a suitable approach to cope with data quality challenges. Big Data Skills (Architects, Data Scientists) are keys: it is necessary to do training in order to build specific skills and in parallel fill the gaps with niche players from your organization Big Data solutions are not packaged / plug-and-play: you need to work hard and develop your own solution 6

7 IBM Global Business Services Energy and Utilities Global Center of Competence E&U Predictive Analytics to Improve Power Plant Efficiency, Flexibility & Reliability - A Reality or a Myth? Biren Gandhi Renewable Power Generation Solutions Leader Biren.Gandhi@de.ibm.com 7 7 Copyright IBM Corporation 2014

8 Paradigm shifts enabled by the big data technology, in the way insights are generated and decisions are made. Leverage more of the data being captured Reduce effort required to leverage data Data leads the way and sometimes correlations are good enough Leverage Data as it is Captured 8 8 Copyright IBM Corporation 2014

9 Big data programs binds different data sources to provide action oriented role based outcomes Operators & Maintenance Field team Central team Operational instructions Plant / site team Data Analyst Data Scientist / Statisticians Diagnostics & equipment experts Operations & Maintenance Management Equipment's Master data Operational tags Historian Operational & Analytical Data Work Orders Notifications Failure Classifications CMMS Calculate pump efficiency degradations and other KPIs Separate startup/ shutdown transient periods from steady state operation intervals Relate failures within SAP to DCS alarms and Pi data Time intervals Failures Classifications Measurements KPIs Time Series analysis Physicsbased Anomaly detection Others Early warning Early predictors warning Early predictors warning predictors Early warning Early predictors warning Failure predictors Predictive Models Scoring Current Data Operational and Maintenance management dashboard, reports and adhoc analysis Optimization & Rules engine Historical Data Pre-Processing (Data mining, text analytics) Advanced Analysis Model / output Training Recommendation Decision 9 9 Copyright IBM Corporation 2014

10 IBM Insights Foundation for Energy: A cloud based platform coupled with analytics, data and domain expertise. Enterprise Products/Solutions & Workloads API RUN BUILD HOST API Software as a Service API Economy Platform as a Service Cloud Operating Environment Infrastructure as a Service Software Defined Environment External Ecosystem Marketplace Content Packs GBS Asset Apps. Data Services API API API Analytics Applications Strategic Client Apps. 3 rd Party IBM Aligned Apps. Analytics Platform Services Modelling Services Analytics Services Rendering Services Solutions 3 rd Party non- Aligned Apps. Business Services Storage Compute Network App Users Developers Operations 10 Copyright IBM Corporation 2014

11 Asset library is leveraged in client situation, it is updated continuously and is the basis for the IBM apps. Common Asset Decisions Replace or upgrade equipment Revise network topology Apply new technologies Revise maintenance strategies Improve equipment monitoring Increase Automa:on, IT, Enterprise Integra:on Improve Design standards Improve O&M prac:ces At the Asset Level Overload Increase Ra:ng Upgrade Monitor Condi:on Extend Life Replace At the System Level System Topology Opera:ons Maintenance Programs Protec:on/Automa:on Conges:on Mgmt Design Standards Risk Analy:cs Spa:al Risk Analy:cs Failure Cause & PaPern Analy:cs Analy:cs Risk Predic:on Risk Analy:cs for Cri:cal energy Infrastructure Asset Health & Reliability Analysis Asset Health Assessment Reliability Modeling Asset Treatment Iden:fica:on Automated & Real Time Outage Planning Real Time Asset Monitoring Sensor Sensor Analy:cs Failure es:ma:on Capex & Opex Op:miza:on Spa:al Temporal & Emergency Spa:o - Scheduling scheduling Predic:ve Maintenance Planning Planning Next Best Ac:on Iden:fica:on Capital Planning Distribu:on Grid Monitoring Grid Improvement Op:misa:on 111 Copyright IBM Corporation 2014

12 12 Copyright IBM Corporation 2014

13 13 Copyright IBM Corporation 2014