Grid Model Data Management in the New Distribution World Randy Rhodes Technical Executive Oracle OpenWorld October 24, 2018

Managing Grid Model Data for Distribution The Emerging Reality Why This is Hard What We’re Doing About It What’s In It for You?

The Emerging Reality: Complex, Low-Carbon Networks What Is Today Networked Transmission Radial Distribution to Load Bulk Generation in Wholesale Market Networked Transmission Markets What Will Be Radial Distribution New Grid-Connected Equipment PV Storage EV New Expectations Regulatory Customer Let’s take a quick look at changes coming at us. In the good old days, the electric grid had a network transmission, grid with one-way power flows to customers at the end of the distribution system. And wholesale markets interacted with transmission-connected generation. But the world is changing. CLICK We have new distribution grid –connected equipment that makes the feeder a two-way energy highway. We have new entities participating in markets using assets not owned by the utility. CLICK There are shifting expectations both on the part of regulators and consumers. And all sorts of new technologies provide both opportunities and challenges. It’s clearly not your grandfather’s distribution grid any more. New Technologies Sensors Intelligent Relays Tablets AR New Players DER Aggregators

The Impact New Studies Evaluating Non-Wires Alternatives and Distributed Energy Resources Hosting Capacity Analysis T &D Reliability Planning Planning Models, methods, and tools to support asset and resource planning functions to ensure the safe, reliable, and efficient modern system. Planning Models, methods, and tools to support asset and resource planning functions to ensure the safe, reliable, and efficient modern system. Non-Wires Solution Evaluation Protection Design Expansion Planning DER & Load Forecasting Interconnection Evaluation In reaction to those changes, (at least in the US), utilities are deploying all sorts of new and enhance functionalities in both the planning and operations domains (including volt var optimization)

The Impacts New Complex Operational Scenarios and Analyses Short-term DER & Load Forecasting DER Monitoring/ Control Situational Awareness Operations Monitoring, controls, automation technologies and tools to optimize and ensure the safe, secure, and reliable operation of the modern system. Operations Monitoring, controls, automation technologies and tools to optimize and ensure the safe, secure, and reliable operation of the modern system. Volt-VAr Control Outage Management & Scheduling Adaptive Settings Training Simulator Load Shedding In reaction to those changes, (at least in the US), utilities are deploying all sorts of new and enhance functionalities in both the planning and operations domains (including volt var optimization) FLISR Demand Response

The Impacts Everyone Needs a Network Model What will they DO? Future planners and engineers will execute more power flow-based simulations At the ISO At the Transmission Control Center At the Distribution Control Center At the substation At the grid edge What do they NEED? Better model coordination across T and D applications Better continuity between planning and operations More and more network model data These figures represent Present and Future coordination requirements across T&D. The internal coordination interfaces are in grey, whereas interactions across the transmission and distribution interface are colored. The magnitude of the shift between current practices and future coordination schemes is illustrated by the addition of the new colored links between distribution and transmission utilities. Furthermore, green colored lines indicate that the direction of the dependency is from the transmission side on the distribution side. The purple links indicate the opposite dependency. Growth in Functional Interaction Between Transmission and Distribution Functions (U.S. DOE Advanced Grid Research Project)

Why This is Hard Network Model Data Is Complex What is Network Model Data? “Data representing a simplified view of the electrical grid, including equipment, its electrical behavior and its connectivity, as well as its operating state at a moment in time, that is sufficient to describe a starting point for network analysis.” Distribution grid model data is just tough, compared to transmission: More detailed More frequent update cycles More source systems More target systems More data quality issues The goal: internally consistent, ‘electrically logical’ network models And network model data is particularly difficult to manage because: It’s big – 100s of thousands of pieces of equipment whose behavior we need to model in the distribution world It’s made up of multiple types of data that change with different triggers and which are ‘owned’ by different entities It is typically, in the distribution world, sourced from a system (the GIS) which has data quality issues and which often serves a major purposes unrelated to network model data management On top of all of those issues, has to be assembled into electrically logical collections of data – cases that accurately define the grid at a moment in time, so that it can serve as a valid starting point for a power flow So the nature of the data itself makes its management challenging

Why This is Hard Grid Model Data Is Challenging Different consumers need different expressions of the network Different parts of the system Different types of data Different levels of detail Different system states Different points in time Add to that the requirements of the consuming applications, which look like this: We have only one grid, but different applications want cases with: <see slide with animation> The situation truly is challenging

Why This is Hard The Distribution Application Portfolio is Messy Targets and cases And distribution utilities (at least in the US) typically don’t do a very organized job today of managing network model data. If you look at the applications that are needing every more accurate, up-to-date network model data CLICK And the cases in which that data is provided to them

Why This is Hard The Distribution Application Portfolio is Messy Targets and cases Sources We see that the data in those cases comes from a wide variety of sources across the distribution enterprise

Why This is Hard The Distribution Application Portfolio is Messy Targets and cases Sources Physical network model data Physical network model data – data about equipment and connectivity – comes from a variety of sources

Why This is Hard The Distribution Application Portfolio is Messy Targets and cases Sources Physical network model data Case assumptions As does case assumption (or operating state) data

Why This is Hard The Distribution Application Portfolio is Messy Targets and cases Sources Physical network model data Case assumptions Engineering design And in the distribution domain, we often see another type of data shared – engineering design data – from which the equipment and connectivity data can be derived

Why This is Hard The Distribution Application Portfolio is Messy Targets and cases Sources Physical network model data Case assumptions Engineering design Multiple data flows Wide variety of tools Many deployment patterns No overarching data management architecture When you add in the ways in which the data flows and combine it with the many different application deployment patterns that exist at different utilities, it’s pretty clear that both at the utility level and at the industry level, we really have no overarching data management architecture.

What We’re Doing About It EPRI GMDM Project Distribution GIS and Grid Model Data Management (GMDM) Project Launched in late 2017 as a 30-month initiative Participating utilities: Ameren, Arizona Public Service, ConEd, Duke, ESB (Ireland), FirstEnergy, Great River Energy, Pacific Gas & Electric, Salt River Project More arriving soon Deliverables: On-site workshops and as-is assessments at selected utilities GIS strategies for better data quality Data architecture supporting Network Model Management The project launched last fall We currently have 9 member utilities And are in the middle of our initial work, which is doing what we call ‘deep-dives’ at a number of utilities. The deep-dives are intended to keep the work of the project firmly grounded in the real world.

What We’re Doing About it EPRI GMDM Project GMDM Project Goals Define industry architecture for Distribution grid model data management Promote industry understanding of grid model data management and vendor product support Provide participating utilities with actionable strategies for improving GIS data and grid model data derived from it Advance the data exchange standards to fully support distribution grid models So we are proposing a multi-year, multi-utility collaborative project which will Define an architecture for Distribution grid model data management Promote industry understanding of and vendor product support for effective Distribution grid model management Provide participating utilities with actionable strategies for improving their GIS data accuracy, their mobile solutions and their grid model data management Advance the CIM data exchange standard to effectively support grid data management inside the Distribution utility

What We’re Doing About It EPRI GMDM Project Architecture Development Planning Business Process Operations Business Process Business Process Business Process Business Process Business Process Business Process Business Process Business Process Business Process Business Process Business Process Business Process Business Object Business Object Business Object

What We’re Doing About It EPRI GMDM Project The 5 BIG KEYS to Network Model Management (NMM) 1. Power Grid Model Defines how the network is built. 95% of all model data Not the responsibility of an individual study engineer. Shared by all studies. 3. Time The physical model has a time dimension. Past, present network Defined by construction process. Validated by state estimation. Plans (projects): Defined as result of planning process. Studies are based on points in time. The entire picture evolves as new decisions are made. 2. Steady State Hypothesis Defines specific steady-state condition for the network. Varies with each kind of study. Study engineers are responsible for the data. Sources for data vary. For example… Load forecasting Market outcomes Similar studies 4. Distributed Model Authority Grid ownership is split among many different entities. Analytical models are assembled from their contributions. 5. Object Identification Local naming conventions and requirements conflict.

What We’re Doing About It EPRI GMDM Project Concepts and constructs from the IEC CIM ensure success ASSEMBLIES (cases and models) constructed from MODEL PARTS that fit together in a FRAMEWORK and PROJECTS that project future changes Model Parts Assemblies Projects

What’s In It for You EPRI GMDM Project Benefits NMM for Distribution Operations Planning Customer World-class team (EPRI, utilities, and key contractors) Using methods tested at CAISO, ERCOT, ENTSO-E, and large transmission operators in the U.S. Design Work and Asset Management Building on past projects and published research Network Model Manager and Repository: A Guide to Exploring the Potential of Centralized Network Model Management for the Interested Utility (3002000609) Network Model Manager Technical Market Requirements: The Transmission Perspective (3002003053) Example Request for Proposal for Transmission Network Model Manager Tool (3002009552) Using the CIM for Network Analysis Data Management (3002002587) Case Study on Network Model Management Solution Design (3002009610) Leading Practices for Network Model Management (publishing Q4 2018, 3002014082)

EPRI Distribution Grid Model Data Management Project For more information Visit our project information site: http://integratedgrid.com/distribution-gis-and-grid-model-data-management/ Contact: Pat Brown pbrown@epri.com Randy Rhodes rrhodes@epri.com Technical Advisor (West): Christine Hertzog, chertzog@epri.com Technical Advisor (East): Chris Kotting, ckotting@epri.com