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

2. 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?

3. 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

4. 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)

5. 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

6. 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)

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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.

15. 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.

16. 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

17. 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

18. 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.

19. 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

20. 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 ( )
Network Model Manager Technical Market Requirements: The Transmission Perspective ( )
Example Request for Proposal for Transmission Network Model Manager Tool ( )
Using the CIM for Network Analysis Data Management ( )
Case Study on Network Model Management Solution Design ( )
Leading Practices for Network Model Management (publishing Q4 2018, )

21. EPRI Distribution Grid Model Data Management Project
For more information
Visit our project information site:
Contact:
Pat Brown
Randy Rhodes
Technical Advisor (West):
Christine Hertzog,
Technical Advisor (East):
Chris Kotting,