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