TEAM CACHE MONEY:SOLAR INSOLATION FORECASTINGPRELIMINARY DESIGN REVIEW
B. DiRenzo, L. Hager, A. Fruge,M. Dickerson, C. Duclos, N. Frank, T. Furlong
OUTLINE Objectives Background System Overview Primary Use Case High Level Functional Decomposition Risks and Contingencies Division of Labor Budget Milestones
B. DiRenzo
OBJECTIVES Create an inexpensive, real-time, and
accurate solar insolation forecasting map. Targeted for use by power companies to
efficiently stabilize the power grid with solar generated energy.
Make large scale use of PV arrays more feasible and reliable.
B. DiRenzo
BACKGROUND Up to 40% of power can be supplied by solar
energy (eg Hawaii). Cloud cover creates major drop-off in energy
production. Leads to grid being unstable. Similar methods exist for wind energy. Unreliability limits use of on-grid PV arrays.
B. DiRenzo
POWER OUTPUT (W) FROM A PV ARRAY ON A CLOUDY DAY VS. A SUNNY DAY
*PV data provided by Professor Gasiewski6:00 8:24 10:48 13:12 15:36 18:000
1000
2000
3000
4000
5000
6000
7000
8000
Cloudy Day
Sunny Day
L. Hager
SYSTEM OVERVIEW Remote smart-phone sensors
Transmits photos of cloud coverage On-grid PV array power sensors
Transmits real-time power measurements Localized server
Parses data and computes forecast using cloud motion vectors in real-time
Generates insolation forecast map with error bars
L. Hager
PRIMARY USE CASE Power Engineer seeks to use the final GUI
application to make smart decisions about how the power company will generate power in the near future.
Engineer may also want to look back on past predictions to compare with actual solar statistics.
T. Furlong
HIGH LEVEL DESIGN
T. Furlong
FUNCTIONAL DECOMPOSITION LEVEL 0
T. Furlong
FUNCTIONAL DECOMPOSITION: LEVEL 1
T. Furlong
LEVEL 2 SUB-SYSTEM: REMOTE SENSOR
Camera
To Server via 3G
Battery Bank
Android Timing
Application
Charge Controller
A. Fruge
LEVEL 2 SUB-SYSTEM: ON-GRID PV SENSOR
A. Fruge
LEVEL 2 SUB-SYSTEM: SERVER
Database:Saves forecast map and inputs
appropriate forecast data to
map creator
Network:Receives data from
sensors and inputs to
appropriate location
GUI:Receives user
input and displays
appropriate forecast map
Receives Data
Image Processor:Determines cloud motion vectors and sends to forecaster
Forecaster:Creates forecast
map every minute, using data received and updates
database
Inputs cloud images
Inputs power measurements
Inputs motion vectors
Inputs forecast data
Inputs forecasting map
User inputs, then GUIdisplays to user
Cloud images
Residential power measurements
Map Creator:Receives
forecasting data and outputs
forecasting map to GUI
Inputs forecast dataInputs requested map dataC. Duclos
C. Duclos
• Due to lack of sunlight, Remote Sensor may lose power.– Battery is chosen to be large enough to power the sensor
for up to 4 days with no sunlight.
• Due to lack of network coverage, data from Remote Sensor may not be transmitted in real time or at all.– Program will be able to compensate for an incomplete
data set through the error calculations.
RISKS AND CONTINGENCIES
M. Dickerson
• Camera lens may have obstructions preventing pictures from obtaining accurate cloud data.– Software will be able to tell the difference between
obstructions and clouds.– Protective casing will mitigate the amount of debris that
will be able to cover the lens.
• Direct sunlight may cause CCD array to be burned, and therefore lose image quality or create “blind spots” on images.– Protective lens filter will ensure minimal damage to the
CCD array.
RISKS AND CONTINGENCIES CONTINUED
M. Dickerson
DIVISION OF LABORJob Owner(s)
Remote smartphone sensor
B. DiRenzo, A. Fruge
On-Grid PV Array L. Hager, N. FrankLocalized Server C.Duclos, T. FurlongPower Systems M. Dickerson
Chief Financial Officer L. Hager
N. Frank
BUDGET
Subtotal 4900N. Frank
N. Frank
FIRST SEMESTER MILESTONES
N. Frank
SECOND SEMESTER MILESTONES
N. Frank
THE END
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