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Bringing the Smart Grid to RAPS
CSIRO ENERGY
Dr Saad Sayeef| Research Scientist RAPS 2016, Melbourne
March 2016
• Australia’s national science agency
• Established in 1926
• Over 5000 staff
• Annual budget ~A$1.2B
• 184 companies based on CSIRO IP
• 3500 patents granted or pending
• Currently working on projects in 68 countries
• Our Mission: • We deliver great science and innovative solutions
for industry, society and the environment
Introduction to CSIRO
Our business units – simplifying the model
9 Towards National Research Flagships
+ National Research Facilities
and Collections
FOOD, HEALTH & LIFE SCIENCE
INDUSTRIES
ENVIRONMENT
MANUFACTURING, MATERIALS &
MINERALS
ENERGY
INFORMATION & COMMUNICATIONS
Load
1
2
3
4
RAPS Systems Stronger Constraints, Effects and Motivations
Is it so far away?
Plug and Play Software
Local Controller
Local Controller
Local Controller
Local Controller
System Simulator
System Optimiser
System Planner
Manual Data Input
Original network and systems configuration
Performance Store
Solar Systems Explicit Storage
Systems Loads & Implicit
Storage
Spinning Reserve & Distributed Generation
System State
Recorder
System Controller
System State
Forecaster
Environmental Sensors
Plug and Play Solar
Plug and Play System Design
Plug and Play Software Integrated tools for operation, planning and review of a hybrid energy
deployment
– System Simulator
– Estimates the state of the underlying electricity network
– System Optimiser
– provides high-level control signals for on-site storage, discretionary loads, spinning reserve and other inverter-based systems
– System Planner
– Identifies optimal energy options according to planning objectives (capital expenditure, maintenance costs, environmental performance, energy export revenue and system reliability)
– Performance Store
– catalogues the software control signals and resultant system responses. This provides a mechanism for fault detection and diagnosis should a system error occur
Skycam solar forecasting
(Very) short-term solar forecasting
10-30 min forecast horizon, 10s update
Inexpensive whole-sky cameras (~$1000)
Low cost hardware means: Widespread deployment more practical, but
Image processing more challenging
Cloud Classification – Unprocessed Image
Red-Blue Ratio (RBR) Classifier
- Misclassifies near sun - Misses thin and near-horizon
clouds
+ Good performance for overcast & dark clouds
Random Forest Classifier
Misses dark & non-textured cloud areas
+ Sensitive to cloud edges, thin & distant clouds
+ Good near-sun performance
Combined RBR + Random Forest Classifiers • Bright red = models agree
• Darker red = only one model detects
cloud
+ The two models are complementary
New Model: Final Combined Result
Cloud Presence Detection Demo • Sudden cloud formation event
• Left chart: measured DNI, GHI, PV Power
• Red histogram bars show cloud % in concentric rings around sun
• Sharp increase, ~7 min before gives ample warning of shading event
Cloud Motion Vector Forecasting Demo • Clear morning, intermittent afternoon, approaching cloud front
• Detected 25 minutes in advance of shade event
• Left chart: measured DNI, GHI, Diffuse, PV Power, and forecast Cloud Pixel
Fraction
Different clouds = Different challenges
Diesel savings vs. solar forecast accuracy
De-risking, validation and development
CSIRO Minigrid Laboratory Large Commercial
Commercial
Residential
Thank You Dr Saad Sayeef Research Scientist Grids and Energy Efficiency Systems CSIRO Energy [email protected]
CSIRO ENERGY
Why? Solar Forecasting Timeframes & Applications Ground-based imagery (skycam) forecasting is useful for a range of applications
Fast update rate and high spatial resolution
Can forecast cloud movement accurately 1-30 min ahead
Short-term forecasting is
suitable for many
applications