2.4_overview of microgrid research, development, and resiliency analysis_hovsapian_epri/snl...
TRANSCRIPT
ww
w.in
l.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
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 AnalysisAdvanced ControlsAncillary ServicesGrid StabilityResilient Microgrid
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
SuperCapacitor
WindTurbine
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 & TAKEAWAYSPhysics 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 electrolyzersElectrical-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
EMTP / RTDSSimulator
INL Energy Systems Laboratory’sDemonstration 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
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
Microgrid & μGMS Objectives
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
California Energy Commissioner – ProjectFuture & Existing Energy Infrastructure
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
CEC- Project Architecture and Functionality Testing via CHIL
Microgrid Modes of Operation:
1.Grid connected 2.Black start
transition3.Off-grid
operation4.Resynchronizati
on to PG&E network
PG&E Power System Network
INL
Blue Lake Rancheria , CA
Siemens MGMS
Modbus/DNP3.0 connection
Microgrid Research, Development and System Design
Integrated CHIL & HIL Microgrid Test Environment
I/O B
us
CERTS Microgrid
Com
mun
icat
ion
Laye
rIE
C P
roto
cols
(IEC
618
50)
Real Time Digital Simulator(RTDS)
Controller-Hardware-In-the-Loop(CHIL)
Hardware-In-the-Loop(HIL)
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 MetricA “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
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)
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
ControlHardware
Allows cyber-vulnerability testing
Ethernet
Dynamic Power System Model
Simulation EnvironmentDER Controller DER Monitoring
NS-3 Simulator
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
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
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 algorithmRAT
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
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.
– 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
