Download Power Grid Research at PNNL

Power Grid Research at Pacific Northwest
National Laboratory
Moe Khaleel
Laboratory Fellow and Director
Computational Sciences and Mathematics Division
September 10, 2009
HPC User Forum
1
Overview
Introduction
Problems on the electrical grid
Need for HPC on the electrical grid
Operations
Integration of renewables
Cyber security
Need for HPC at vision level
State estimation
Multithreaded platforms for contingency analysis
Looking forward
Transform the way the U.S. generates, transmits,
distributes and uses electricity
Current U.S. electricity infrastructure is inadequate for national energy priorities
in the 21st century. Three main areas need to be addressed:
Capacity
Grid management (wide area, real-time)
Vulnerability, resiliency, reliability
The future state of the grid must be able to:
Substantially increase the integration of renewables
Reduce carbon emissions
Provide flexibility to enable electrification of transportation and reduce dependence on oil
imports (substitute electricity for oil)
Respond to increased demand
Reality: The current grid infrastructure/operation is limited
Currently manage/engage grid at sub-optimal (service territory) level; can’t efficiently
move electrons across large enough spaces
No ability to see performance across grid (lacks transparency)
Inability to integrate renewables; need storage, ability to offset, transmission across
service territories where generated, load/dispatch renewables
System communication has been one-directional: supply to demand
Fragmented authority, control, market, function, regulation
Need to build new functionality and infrastructure into the grid
Future grid must be transformed while maintaining reliability
and affordability (serving public good)
3
Transform the way the U.S. generates, transmits,
distributes and uses electricity
Capacity:
Transmission infrastructure cannot meet future load growth and large-scale renewable
connectivity to grid
Utilities not incentivized to build physical infrastructure
Difficult to site & permit new transmission infrastructure
Renewable resource physically isolated from high grid transmission infrastructure
Grid Management:
Unable to manage grid at national, interconnect scale
Large scale models that allow examination and optimization of future national grid
do not exist
Integrated wide area models (variable renewable generation, energy storage,
distributed generation, demand management) that describe real-time power flow
and predict reliability do not exist
Ability to see and understand the grid at interconnections scale are limited; wide area
grid performance is not accessible, transparent so can’t optimize supply and demand
across limited service areas
Transparent real-time monitoring and operation currently not in place
Large-scale wind generation introduces significant variability
Large scale electric energy storage capability is limited – pumped hydro, flywheels,
electro-chemical systems connected to and supporting the power grid
Vulnerability and Resiliency
Susceptible to cyber, other threats – can we prevent, respond to threats?
Resilient to catastrophic events – can we rapidly recover?
4
Power System Elements
Problems on the electrical grid
Inadequacy of current control center functions
Slow, not able to keep up with the change of the grid
Static, no dynamic information for real-time operations
Computational Issues with today’s grid operations
Real-time grid view: static
Not able to capture grid dynamics
Low computational efficiency: not keep up with system
changing
No use of high-performance computing architectures
Problems on the electrical grid
The need for HPC on the electrical grid
Major HPC Architectures
Shared-memory architecture for extensive data sharing and
un-uniform data access
Distributed-memory architecture for less data sharing and
uniform data access
Hybrid architecture - re-configurable architecture: e.g. FPGA
+ shared-memory
Performance of Parallel Algorithms/Programs
Speedup/Scalability
Reliability
The need for HPC on the electrical grid
Parallel Computing is essential
Only explicitly parallelized algorithms can take advantage of
multi-core parallel computers
Parallel Computing is “an art”
Parallelization approaches are problem-dependent
Computing implementation needs to consider “good match”
of computing architecture and the problems
Today’s Electrical Grid Operations Paradigm
Normal operations
slow
static
• Asset underutilization
• Limited market opportunities
• Lead to emergency operations
Emergency operations
• Blackouts and cascading failures
Operator
SCADA
~ seconds
State
Estimation
~ minutes
Off-line
Transient/Voltage
Stability Analysis
seasonal
Contingency
Analysis
~ minutes
Market
Operation
Ratings & Limits
Violations
~ hours
Constrained
solutions
Trends Impacting Control System Security
Open Protocols
Open industry standard protocols are replacing vendorspecific proprietary communication protocols
General Purpose Computing Equipment and Software
Standardized computational platforms increasingly used to
support control system applications
Interconnected to Other Systems
Connections with enterprise networks
to obtain productivity improvements
and information sharing
Reliance on External Communications
Increasing use of public telecommunication
systems, the Internet, and wireless for control
system communications
Increased Capability of Field Equipment
“Smart” sensors and controls with enhanced
capability and functionality
The Emerging Cyber Threat
Industry has long history of planning for and coping with natural
disasters and other reliability events
Through industry standard operating procedures, there is much effort
expended to reduce likelihood of cascading outages leading to
widespread blackouts
Historically, cyber security focused on countering unstructured
adversaries
e.g., individuals, untargeted malicious software, human error
Very little protection against structured adversaries intent on
exploiting vulnerabilities to maximize consequences
e.g., terrorist groups, organized crime, nation states
Insider threat remains very challenging, can be used as part of structured
threat vector
New possibilities for widespread sustained outages resulting from
cyber attack are now being contemplated
But industry still not ready to cope with this threat
The need for HPC: State Estimation
Power system State Estimation (PSE)
Given: power grid topological information, telemetry on line
flows, bus injections or bus voltages
Compute: a reliable estimate of the system state (bus
voltages), validate model structure and parameter values
Calculated using Weighted Least-Squares (WLS) method
WLS: minimize
Where r = z - h(x), and r is the residual vector, x is the
system state, z is a vector of measured quantities, h is a
vector function, wi is the weight for residual ri and W is as
diagonal matrix.
This is a non-linear problem, which is solved using the
Newton-Raphson iterative procedure
The need for HPC: State Estimation
PSE
Every iteration of the method requires solving a large set of sparse
linear equations
Sparse matrices are derived from the topology of the power grid
being analyzed
The number of non-zeros per row varies greatly and the matrix is
badly conditioned
The set of linear equations can be solved using direct solvers such
as sparse LU factorization or iterative solvers such as sparse
Conjugate Gradient (CG)
PSE is a critical element of the software used by power
grid control centers
Under real-time constraints (< 10 seconds)
Commercial PSE solvers are not commonly parallel
Power System State Estimation
August 14, 2003
Blackout
Situational Awareness?
August 13, 2003
Normal
Do we know what really happened?
Could it be prevented?
Source: NOAA/DMSP
Model Validation Need and Challenges
Reality
Model
Recorded system dynamics vs. simulation results:
California and Oregon Intertie (COI) real power flow during
the August 10, 1996 event
The need for HPC: Multithreaded Platforms for
Contingency Analysis
Growing class of scientific applications is becoming memory-bound
Many scientific applications exhibit irregular memory access patterns
CPU and memory technology trends indicate that the situation will
not improve anytime soon
Multithreaded architectures offer an appealing alternative for irregular
applications
Processors tolerate memory access latencies by switching execution
context between multiple hardware threads
Examples of such architectures are the Cray MTA-2 and XMT systems
and the Sun Niagara
Latency tolerance mechanisms should improve the performance of
irregular, data-intensive applications with abundant fine-grained
parallelism
Role of Contingency Analysis
From “N-1” to “N-x”
To improve situational awareness
From Balancing Authorities to a
Wide Area
Example: 35 BAs in west
Further require “N-x” CA
To better understand cascading
failures
N-x Contingency Analysis
Result in a large number of cases. “N5”  1020 cases for the west =~ 1020
seconds + lots of data
Needs: better contingency selection
and post-processing
Looking forward: PNNL’s vision of advanced
contingency analysis
Industry Need:
Multi-failure (N-x) massive contingency analysis
Issue:
Massive number of cases
Massive amount of data
Need:
Smart case selection
Operator-oriented data processing
10Z cases
Major
Tasks:
10Y cases 10X cases
Contingency
Analysis
Data
Prediction
Our Solution:
past
Graph
Theory
Expected
Outcomes:
Contingency
Index Sorting
Visual Analytics
& Graph Trending
Interactive Graphing
An advanced tool for contingency analysis to support the operation of
the nation’s critical infrastructures, e.g. power grids
Analysis capabilities through generic implementation of algorithms on
Cray XMT ThreadStorm processors, applicable to other problems
now
Time
future
Tool Suites for
Advanced Power System Simulation
SCADA
Measurements
Phasor
Measurements
Parallel state
estimation
Dynamic state
estimation
Parallel contingency
analysis
On-line voltage
stability
Look-ahead dynamic
simulation
Dyn. contingency
analysis
Market Monitoring
Visualization
 National Level: monitoring, planning, design
 Utility Level
 Applicable to current grid operations  Next-generation grid operation tools
Looking forward: PNNL’s long-term vision
for electrical grid operations
Normal operations
fast
dynamic
• Optimized asset utilization
• Enabled regular & ancillary markets
• Predict emergencies
Emergency operations
• Prevent/mitigate failures
SCADA &
Phasor
~ (sub)seconds
Dynamic
State
Estimation
Operator
Dynamic CA, Violations
On-line TSA/VSA
~ (sub)seconds
~ (sub)seconds
Dynamic
Ratings & Limits
Real-Time
Constrained
Optimized Market solutions
Operation
< 1 hour
Electricity Infrastructure Operational Center
at PNNL
Use HPC to analyze sensor data for real-time grid
monitoring, prediction, and operation: data-driven
models, stochastic simulations, visual analytic tools,
secure sensor network infrastructure, data
provenance
22
QUESTIONS?