Analytics for Smart Grids - DONG Energy Case Study on Optimization of Business Processes and Integration of IT Platforms 1
Signe Bramming Andersen DONG Energy, Senior Manager Head of Asset & Energy Management sigba@dongenergy.dk Jesper Vinther Christensen Director & Founder, Similix Lead Architect, DONG Energy Smart Grid Programme jesper@similix.dk 2
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DONG Energy Results 2015 4
Mission: Clean, Independent & Cost Efficient Energy Henrik Poulsen, CEO, DONG Energy Sustainability Report 2015 5
Goal 6
Distribution of Electricity 8,4 TWh distributed in 2015 7 From 2015 Annual Report
What is Grid Analytics about? Business wide understanding of the asset portfolio Optimizing long term investment planning Optimizing maintenance cost Safety Your data is some of your most important assets 8
Smart Energy is about implementing a data and process centric strategy. Defining a transparent and communicated master data management strategy, delegating the responsibility of data ownership for each data component. Having aligned business processes across business units, integrating the Planning, Maintenance and Operation of the Critical Infrastructures into unified processes Implementing a seamless integration between software platforms ensuring process support and maintaining high data quality Maintenance Operation Planning 9
DONG Energy Smart Grid Projects MDM/AMI & Smart Meter Roll-out New Esri Utility Network* Low Voltage ADMS** Integrated SCADA Platform + HV ADMS** Outage Management System Merge of DMS & OMS GridHub/HAS (Smart Grid Analytics) Advanced Distribution Management System (MV) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 * Proof of Concept Esri Alpha Program for new Utility Network ** Future possible projects - Not decided
Smart Grid Architecture From an IT-perspective Processes Information Software That support the Planning, Maintenance and Operation of Electric Power Systems Infrastructure 11
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation Work Planning SCADA Engineering Designer Express ONS Letter SAP ISU GIS OMS Outage Report SAP PM DMS ELFAS SPC GridHUB NE PLAN Note: Most processes are different on high, medium and low voltage levels. 12
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation Work Planning SCADA Engineering Designer Express ONS Letter SAP ISU GIS OMS Outage Report SAP PM DMS ELFAS SPC GridHUB NE PLAN Note: Most processes are different on high, medium and low voltage levels. 13
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation ONS Letter Work Planning SCADA Engineering SAP ISU New Utility Network OMS Outage Report SAP PM DMS ELFAS SPC GridHUB NE PLAN Note: Most processes are different on high, medium and low voltage levels. 14
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation ONS Letter Work Planning SCADA Engineering SAP ISU New Utility Network ADMS Outage Report SAP PM ELFAS SPC GridHUB NE PLAN Note: Most processes are different on high, medium and low voltage levels. 15
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation ONS Letter Work Planning SCADA Engineering SAP ISU New Utility Network ADMS Outage Report SAP PM ELFAS GridHUB NE PLAN Note: Most processes are different on high, medium and low voltage levels. 16
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation ONS Letter Work Planning SCADA Engineering SAP ISU New Utility Network ADMS Outage Report SAP PM ELFAS GridHUB Note: Most processes are different on high, medium and low voltage levels. 17
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation ONS Letter Work Planning SCADA Engineering SAP ISU New Utility Network ADMS Outage Report SAP PM ELFAS MDM GridHUB Note: Most processes are different on high, medium and low voltage levels. 18
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation ONS Letter Work Planning SCADA Engineering SAP ISU New Utility Network ADMS Outage Report SAP PM ELFAS Spatial Data Warehouse MDM GridHUB Note: Most processes are different on high, medium and low voltage levels. 19
Processes, Systems and Integrations Projects Scheduled Maintenance Incidents Customer Connection Dansk Energi Long Term Grid Planning Near real time load estimation SMS Outage Reporting Customer Service Information Equipment Value Estimation Work Planning Condition Based Maintenance ONS Letter SCADA Engineering SAP ISU New Utility Network ADMS Outage Report SAP PM ELFAS Spatial Data Warehouse MDM GridHUB Note: Most processes are different on high, medium and low voltage levels. 20
The ADMS Project Scope
Data is needed!! Grid Asset Data (Inside the Fence) Grid Asset Data (Outside the Fence Base Maps & Ortho Photos Schematic Representation of Network Current Network Switching State SCADA Points Costumer & Consumption Data HV Cables Load Profiles
Optimizing the Grid in Real Time By combining Grid data, Asset information, Customer Consumption, Load Profiles, and real time SCADA measurements the ADMS calculation engine is able to calculation the energy flow in the entire grid in near real time. 23
Dataflow Esri ArcGIS Supports Design and Maintenance processes Leading system for the Static Network and owns the Normal Network State Visualization & Cartographic representation Model-based Integration based on CIM IEC 61970 + 68 DATA ACQUISITION Schneider ADMS Operational perspective Leading System for the Dynamic State of the Network and owns the Current Network State Fault & Alarm handling Study & Playback scenarios DATA ENRICHMENT & ANALYTICS ETL Engine record snapshots of the DATA dynamic DISTRIBUTION network state Schneider HAS/GridHub on MS SQL Risk, Contingency and Investment planning Time Series Analysis Snapshot of dynamic model 50 Billions Records added per year 24
CIM Adaptor (Export Process) Enterprise Service Bus CIM Adaptor (Import Process) Maintaining the Network Model in DMS ArcGIS Platform Schneider DMS Platform Electric Network Network Model & Asset Data 1 Network Model Repository SAP Platform 3 4 5 Dynamic Network Model Historian Master Data Asset Data 2 Measurements SCADA
Case: New Transport Hub in Høje Taastrup 26
Network patch introducing new substations to the network 27
Project Life Cycle Extending and Maintaining the grid Project manager Plans the work to be carried out Delivers plans to GIS documentation department Delivers finial documentation when the job is carried out. GIS Project Assistant Documents the plans for the project in GIS. Creates patch exports for DMS. Post changes in GIS when project is in production in DMS. Documents final implementation once information is received from project manger. Plan DMS Model Manager Evaluates patches and approve them for later energization or reject them and return them to GIS for corrections. Draws schematic layout in DMS. Ensures that new data sent to DMS is correct. Handles full feeders containing final documentation of the network. Document Grid Operator Controls the switching state of the network Communicates with the field crew during commissioning or decommissioning of equipment. Energize patches in DMS which updates the electric model and triggers feedback to GIS. Prepare Commission Field Crew Follows the orders of the grid operator. Carries out the work planned by the project manager. Delivers information for final documentation of the finished project to the project manager
Outage Notification Services Outage Notification & Reporting Call Center Customer Calls Systems for gathering and managing meter readings and events. If meters supports last gasp. MDM AMI Outage Notification Outage Notification Systems for handling 0.4 and 10kV planned and unplanned incidents. Input from SCADA, Call Center and eventual AMI. OMS DMS SCADA Outage Incidents Systems for managing outage information on web-site, Social Medias, and data exchange with management, customers, and authorities. Reports Web Site SMS s & Letters Exchange formats 29 29
SAP PI Handling Planned Outages 1 Planned Outage Services OMS Platform OMS Outage Notification Database Outage Notification Services 2 3 4 Send SMS 1 Send Letter 2 3 OMS will send messages about planned work to SAP-PI. Based on the messages from OMS, the Outage Notification Services determine what kind of letters that should be send to the customers. SMS s are send using service endpoints on SAP- PI and a 3 rd party SMS Service Provider. SMS s can either schedule or cancel an outage. 4 If we are unable to send a SMS, a letter are send using service endpoints on SAP-PI. Letters can either schedule or cancel an outage. 30
SAP PI Integration Architecture Sending of SMS 1 Unplanned and ongoing work adaptor OMS Platform OMS Outage Notification Database Outage Notification Services 2 1 3 Send SMS OMS will send messages about unplanned and ongoing work to SAP-PI. 2 3 Based on the messages from OMS, the Outage Notification Services determine what kind of SMS's that should be send to the customers. SMS's are send when a unplanned outage is confirmed, ETR is changed, or closed. 31
In summery we are able to Send SMS s three days before a planned outages or if we are not able to send SMS s to a customer sending a postal letter 7 days before the outage Send a reminding SMS 24 hours before the outage Send SMS s to customers affected by an incident Send SMS s if estimated time to restoration is extended Send SMS s when power has been restored 32
ICCP The Moving Parts Access to DMS Clients from Office Network Services for the Control Room Integrations and Data Exchange Data Replication
The GridHub Datamodel 34
Which data do we have? Snapshot of complete MV grid with topology and measurements every 10 th minute. Customer load with synthesized load curves. Load flow every 10. min with calculated loads and voltages for all components Short circuit for breakers and switches every 24 hours. Contingency (N-1) every 4 hours. 35
Querying & Visualization of time series 36
Grid Analysis Minimum Voltage 37
What is the minimum voltage during a year in the secondary substations? Abnormal configuration Normal configuration 38
Example of usage: Utilization Profile 39
High Quality Data The cornerstone of a Smart Grid Smart Grid Ambition Data Quality is relative to the usage. Working with Smart Grid this fact becomes very evident The business case of the Smart Grid strongly depends on the organization's ability to produce and maintain high quality data The real option for automating the grid requires accurate data Smart Grid Ambition The data model must capture a rich and precise electric representation of the grid Completeness and classification correctness must be close to 100% (if not 100%) Data is shared among multiple systems for multiple purposes Data Requirement Data Requirement 40
Implementing the GIS-ADMS integration Form a Master Data Management Strategy - Get an overview of what data is needed in which system and in which process - Delegate the responsibility of storing each needed data element to a particular system - Ensure that all data elements are maintained as close to the business process changing the configurations Work with business processes - Understanding the actual processes - Select the ones that will be supported - Draw the primary data flows Enrich the data - Identify the critical data for supporting processes in ADMS - Validate the current state on these data: Completeness, accuracy, classification, topology? - Plan the data cleansing processes, and how the data is validated in source and target systems Make a masterplan - Establishing the needed organization - Defining the key milestones - Align Expectations 41
Future Directions Further integration of maintenance processes across technology platforms Extend the use of GIS to analyze, explorer and visualize data Streamline the current integration and dataflow Support new business process for asset management and Smart Maintenance Un/installBreaker() SAP PM Local Central Un/installBreaker() (Orchestration) Un/installBreaker() ArcGIS Un/installBreaker() ADMS Switching Plan 1) Breaker1 Done 2) Ground Cable1 Done ConfirmOperation() ADMS 3) Breaker2 Released 4) Ground Cable2 LOCK 42
Future GIS will expand to be a customer system Capacity extension by flexibility Modelling flexibility Predicting electrical vehicles, heat pumps, solar panels 43
MAINTENANCE STRATEGIES Time-based maintenance (TBM): TBM is performed at regular and scheduled intervals, loosely based on the service history of a component and/or the experience of service personnel. This maintenance policy can be expensive and may not minimize the annualized cost of equipment. Condition-based maintenance (CBM): CBM periodically evaluates the state of equipment deterioration expressed quantitatively as a score or failure risk, and maintains equipment when the condition falls below acceptable thresholds. Additionally, CBM approaches rank assets within a given asset group with respect to each other, thereby enabling a prioritization of investments. Reliability-centered maintenance (RCM): RCM considers both the probability of equipment failure and the system impact should a failure occur. RCM approaches rely on frameworks for estimating network reliability indices based on the failure rates of the different components. Most commonly, such frameworks operate at the level of individual feeders and can be divided into analytical and simulation approaches. 44
The Smart Grid Journey B u s i n e s s Control room silo Security = no integration Data redundancy IT/OT integration Cyber security IT master data shared with OT Predictive analysis OT dynamic data shared wit IT apps Big data Real time analytics Analytics based operation Augmented reality? T a s k s Process control zone Common Information Model, Process data zone, security patching Cloud, Incolumnstore, performance tuning Machine learning C o m p e t e n c e s Low IT department involvement Infrastructure architecture IT infrastructure, CIM, EBS Integration architecture Information modelling MS Xvelocity, Hadoop, Azure Cloud architecture Data science Algorithms, data lake NextGen software design Mathematical modelling 45
Lessons learned Key to success: Strategic partnerships with selected vendors System Architecture Data Quality Organizational Change Management Together enabling the Smart Energy business processes across the utility value chain - transforming the business into a data driven utility. Bridging traditional IT and organizational silos Focusing on data modelling and data ownership Establishing cross department trust and understanding of business processes 46
Signe Bramming Andersen, Senior Manager, Head of Asset & Energy Management, Group IT, DONG Energy. Responsible for implementing IT-platforms for supporting DONG Energy s Smart Energy Programmes including ADMS, Wind Farm Management, Power Hub (VPP) Signe has worked with DONG Energy since 1999 and holds a Master in Economics & Business Administration. Contact: sigba@dongenergy.dk Jesper Vinther Christensen, founder and Owner of SIMILIX, a consultancy company offering independent advisory consultancy on IT and Organizational Transformations. Since 2011 Jesper has been the Lead Architect of the DONG Energy Smart Grid Programme. Jesper holds a Ph.D. in GeoScience & Computing Science and has 20 years of experience with ITprojects, especially with System Integration, Enterprise Architecture and Geographic Information Systems. Contact: jesper@similix.dk