Presentation from the EPRI-Sandia Symposium on Secure and Resilient Microgrids: Overview of Microgrid Research, Development, and Resiliency Analysis, presented by Rob Hovsapian, Idaho National Laboratory, Baltimore, MD, August 29-31, 2016.

1. www.inl.gov
Overview of Microgrid Research,
Development, and Resiliency
Analysis
Rob Hovsapian, Ph.D.
Manager, Power and Energy Systems
Idaho National Laboratory
EPRI-Sandia Symposium on Secure and
Resilient Microgrids
August 29th , 2016

2. Core Capabilities of Power & Energy
Systems Department
• Facilities for accurate real-world
model development for power
system dynamic analysis
• High fidelity test environment to
test models based on real-world
data in real-time for de-risking
device integration.
• 10-20 nanosecond scale
simulation for power electronic
dynamics
• Control hardware in the loop and
rapid prototying of controllers.
• Advanced control technologies
and decision making strategies
Differentiating Capabilities
• Front-end controller
development
• Multi-agent protection
systems and
reconfiguration schemes
• Multi-agent adaptive
control
• Aggregators
• PMUs
• Relays & protection devices
• Inverters
Real-Time Digital Simulation of Power Systems
Control Systems and Advanced Protection
Devices and Systems Integration
• µs-scale simulation of grid /
microgrid events
• Co-simulation of
transmission-distribution-
microgrid communication in
power systems
simultaneously
• Calibrate protection
hardware settings in real-
time prior to field deployment.
• Fuel Cells
• LT and HT
Electrolyzers
• Microgrids
• Computational Science
• Energy and Storage Technologies
Related INL Core Competencies
• Power & Energy Systems
• Advanced Control Systems
Collaboration with Academia & Industry
Using unique laboratory infrastructure to create a holistic ecosystem for developing, testing, and deploying power system technologies
• Electric Vehicles and Fuel Cell
Electric Vehicles
• Pumped Storage Hydro
• Supercapacitors
• Batteries
Energy Storage
WSU CSU
FSU HSU
Real-time Grid
Scenario
Analysis
Advanced
Controls
Ancillary
Services
Grid Stability
Resilient Microgrid

3. Energy
Systems
IntegrationEnergy
Conversion
First Principles
Research
EV
Holistic Systems Engineering Approach for
solving next generation energy challenges
INL Power & Energy Systems focuses on investigating power-grid problems using real-time models, develop advanced controls and
strategies to mitigate the identified problems, and de-risk integration of variety devices to the microgrid / power grid.
PV Battery
Super
Capacitor
Wind
Turbine
Pumped Storage
Hybrid
Power Grid
• Models based on real-world
data in real-time
• Physics-based modeling
• Novel protection schemes
and algorithm
Energy conversion
& storage
• Thermal
• Mechanical
• Electrical
• Chemical
• Nuclear
Grid Integration of
• Electrical Vehicles
• Supercapacitors
• Flywheels
• Pumped Storage Hydro
• Batteries & Electrolyzers
Pumped-storage Hydro
for Integrating Multiple
Run-of-the-river
Concentrated
Solar Power
Safe and Efficient
Integration of Grid
Devices to Existing
Power Grid
IMPACTS & TAKEAWAYS
Physics model-based approach towards solving
power grid problems in real-time help mimic real-
world conditions with high accuracy.
Research on integrating industrial hydrogen
production to enable better demand response
and grid stability by integration of electrolyzers
Electrical-Mechanical-Thermal cosimulation
capability involving Pump-storage hydro,
Concentrated Solar Power integrated with power
grid.
Real-time testbed enables Transmission,
Distribution and Communication co-simulation for
investigating cybersecurity vulnerabilities
Electrolyzer
integration for
demand
response and
grid ancillary
services

4. EMTP / RTDS
Simulator
INL Energy Systems Laboratory’s
Demonstration Complex and Test Bed
• For the renewable technologies
– Modeling, simulation, and
hardware-in-the-loop capabilities for
demonstrations and dynamic analysis
• Energy farms / microgrids
• Integration power & energy systems
• Control and integration strategies
• Coupling with energy storage
4
Fuel Cell

5. Microgrid Management System (μGMS)!
5
A μG is a modified
power distribution
network that can be a
part of the grid or
independently
generate, distribute,
and regulate the flow
of electricity to meet
consumer demands.
It can operate either
grid connected or
islanded and, if
required, can switch
between the two.
μGMS is a specially-designed software tool
that interacts with utility signals & coordinates
communication between μG components in
order to meet microgrid objectives.
Creative Commons graphics courtesy Siemens

6. Microgrid & μGMS Objectives

7. INL Current Utility Microgrid Projects
 Funded by California
Energy Commission’s
Electric Program
Investment Charge
 PON-14-301
 Program Goal:
Demonstration of Low
Carbon-Based Microgrids
for Critical Facilities
 Partners – INL, Siemens,
Tesla (Utility scale Storage)
Humboldt University, PG&E

8. California Energy Commissioner – Project
Future & Existing Energy Infrastructure

9. One-Line Diagram of 12 kV Line Joining Service
Transformers at the Casino, Hotel and Admin
Office Bldg
Future Renewable generation
sources:
 Solar PV Plant 0.25 MW
 Battery 0.2 MW
Existing Load and Generation:
•Estimated peak load is approx 0.7 MW
•Estimated average load is approx 0.5 MW
•Diesel generator for base generation 1 MW
•Fuel cell + biomass 0.175MW

10. CEC- Project Architecture and Functionality
Testing via CHIL
Microgrid Modes of
Operation:
1. Gridconnected
2. Black start transition
3. Off-grid operation
4. Resynchronization toPG&E
network
PG&E Power System Network
INL
Blue Lake Rancheria , CA
Siemens
MGMS
Modbus/DNP3.0 connection

11. Microgrid Research, Development and System
Design

12. Integrated CHIL & HIL Microgrid Test
Environment
I/OBus
CERTS Microgrid
CommunicationLayer
IECProtocols(IEC61850)
Real Time Digital Simulator
(RTDS)
Controller-Hardware-In-the-Loop
(CHIL)
Hardware-In-the-Loop
(HIL)

13. Standard Resilience Terms
• Resilience
Withstand attacks, Recover from attacks, Adapt to changing
conditions, Prevent future attacks proactively.
• Resilience Quantification
Codifying the methods and approaches of studying, operating
and designing resilient microgrid.
• Resilience Metric
A “number” that eases comparison, optimization to implement
most resilient configuration.
• Resilience Framework
Generalization of approaches & metric so that all distribution
systems can be assessed using this technology

14. Difference between Resilience & Reliability Metrics
14
Reliability metrics: measure of “implosions”
• Power system disruptions due to operational limitation of
utility, machinery damage, momentary outages.
• Does not consider events which are not fault of utilities
(like, superstorms)
• Computed over long time durations
Resilience Metrics: measure of “explosions”
• There are several natural and man-made threats constantly
being made to circumvent ordinary protection systems and
disrupt power system operation.
• Considers external events that disrupt power system
operation
• Can be computed for near-term, real-time (operational), or
over long time durations (planning)

15. DER Cyber-vulnerability Analysis Testbed (DER-CAT)
RTDS
Geographically Distributed Simulation for Larger Power Systems
TCP/IP
RTDS at Remote
Sites
at INL
Dynamic Power System Model
Co-Simulation Environment with Hardware-in-the-Loop
RTDS
Ethernet
Power
Hardware
Control
Hardware
Allows cyber-vulnerability testing
Ethernet
Dynamic Power System Model
Simulation Environment
DER Controller DER Monitoring
NS-3 Simulator

16. Test Scenario 1: DER Interconnection
Distribution System Modeling
Integration of DER to the Utility System
Study the additional communication
requirements due to DER integration
Use DER-RAT to compute cyber-
physical resiliency of the network
Developed and modeled on
DER-CAT
Compare base case with cost-benefit
analysis of the test condition

17. Test Scenario 2: Slow Oscillation Attacks
• Slow Oscillations between two
interconnected power systems are hard to
detect, or easy to ignore.
• Repeated slow oscillation can be used to
create unprecedented harmonics in the
system leading to blackouts
Two- Area Interconnected Power
System Modeling in DER-CAT
Integration of DER to the Power System
Simulate <1 Hz oscillations between the
two areas of the system through
interconnected DER manipulation
Use DER-RAT to compute cyber-
physical resiliency of the network
Simulate conditions leading to unstable
power swings
DER Integration DER Integration
< 1 Hz oscillations

18. Test Scenario 3: Bad Data Injection
• Malicious Data can be injected at HV, MV, or
LV of the power system.
• Corruption of PMU Data concentrator can
lead to wide-spread control failure of the
power system
Use DER-CAT to create coupled
transmission and distribution networks
Integration of DER to the Dist. System
Manipulate data obtained through RTDS
measurements (or HIL PMU), and DER
generation variables in real-time
Use DER-RAT to compute cyber-
physical resiliency of the network
Run Bad-data detection algorithm
RAT

19. Test Scenario 4: Demand Response Hack
DR Signal
• Increase in DR signal and TOU pricing
interactions with customers
• Vulnerabilities in communication with
customer
Use DER-CAT to create coupled
transmission and distribution networks
Integration of DER to the Dist. System
Manipulate DER Generation & load
consumption behavior of consumers to
create less than conducive grid loading
conditions
Use DER-RAT to compute cyber-
physical resiliency of the network
Study Power System dynamics against
unwarranted consumer action

20. Test Scenario 5: Critical Load Restoration Despite
Denial of Service (DoS) Attack
Use DER-CAT to create coupled
transmission and distribution networks
Perpetrate DoS attack to a critical load
Load and Frequency Control of Power
System despite Attack
Use DER-RAT to compute cyber-
physical resiliency of the network
• This study will focus on the dynamic
performance of a power system during
Denial-of-Service (DoS) attacks on (i) critical
loads, and (ii) load frequency control (LFC)
of smart grids.

21. – Microgrids (islanded configuration) have significant
dynamic and transient swings due to low inertia
– Real-time simulators (EMTP) allow an accurate
modeling and assessment of such challenges
– Real-time simulators allow microgrid models to
interface
• MGMS as Controller-Hardware-In-the-Loop (CHIL)
• Power devices as Power-Hardware-In-the-Loop (PHIL)
– A unique way of controller rapid prototyping,
functionality, interoperability, & interconnection testing
of MGMS
– A systematic resilience framework that can analyze
and quantify threats is critical
21
Observations and Way Forward

22. Thank you
rob.hovsapian@inl.gov
850-339-9432