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Team Cache Money: Solar Insolation Forecasting Preliminary 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
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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 Gasiewski 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 A. Fruge
Level 2 Sub-System: Server User inputs, then GUIdisplays to user Image Processor: Determines cloud motion vectors and sends to forecaster GUI: Receives user input and displays appropriate forecast map Inputs cloud images Receives Data Network: Receives data from sensors and inputs to appropriate location Cloud images Inputs motion vectors Inputs forecasting map Forecaster: Creates forecast map every minute, using data received and updates database Residential power measurements Map Creator: Receives forecasting data and outputs forecasting map to GUI Inputs power measurements Inputs forecast data Database: Saves forecast map and inputs appropriate forecast data to map creator Inputs forecast data Inputs requested map data C. Duclos
Risks and Contingencies • 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. M. Dickerson
Risks and Contingencies Continued • 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. M. Dickerson
Division of Labor N. Frank
Budget N. Frank
First Semester Milestones N. Frank
Second Semester Milestones N. Frank