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Solar powered internet methods
|By Michigan Tech's Open Sustainability Technology Lab.
Wanted: Students to make a distributed future with solar-powered open-source 3-D printing.
The potential of successful PV mesh networks is based on five variables: system design and sizing, solar energy availability, economic analysis, and integrating geospatial information to optimize deployment locations. An example system was designed and built to assess the economic feasibility and overall system design. Utilizing PVSyst (refs) and ArcGIS (refs) technology, energy availability and demand was calculated for the system. Further analysis was done on the economic potential of the system compared to comparable technologies using previously published works (refs) and geographical referencing.
Locations of the study were chosen for their differences in solar energy availability and costs associated with Internet infrastructure implementation. Additionally, public or educational access to GIS data needed to be available. Therefore, the three locations chosen were in North America and compared townships in California, United States; Kentucky, United States; and Ontario, Canada. Each area has unique demographics, solar and infrastructure availability.
The following design methodology was created, employed and demonstrated in an example system. The system includes three units to demonstrate the mesh network and its functionality with one unit connected directly to a broadband source and the other two functioning as relays. It was decided that the system use ultracapacitors where possible to demonstrate their potential in such an application and the feasibility of implementing such a system. The sizing of the electronic components was determined using PVSyst. The associated converters and controllers were selected to optimize efficiency while minimizing cost and necessary components.
Mechanical components including the structural elements, mounting hardware, and fasteners were selected based upon the following criteria: cost, weight, strength, component life, transportability, ease of use, and maintenance. The structural support system was designed to hold the solar array at the optimum angle for the latitude at which the system will be utilized. Using the design methodology, several example systems were built for testing.
Include here exactly how this was done - e.g. screen shots explanations. For the mechanical design show how this was done. For the components - give a list and a weblinks to all parts.
Sizing of system parts
Equipment list inclues: PV panel, ultracapacitor bank, router, converters, batteries, hardware - this will be based on either PVSyst or GRASS/rsun in ArcGIS.
A micro-controller was chosen to control the converters and was selected to be XXX. Using PVSyst, it was determined that the system required a 13Ah capacity battery and a 190 Wp capacity solar panel. This would provide enough power and store sufficient energy for nights and low light situations. An ultracapacitor bank of 10 3000F is being used as the storage. The size of Ultra cap bank is 16.84 Ah and each Ultra capacitor is 2.25 Ah. Through simple calculations we find the total Ultra capacitors required is around 8 Taking an optimistic approximation of 10 Ultracapacitors will provide enough room for unprecedented events.
Going with an autonomy of 1 day and a maximum Loss of Load of 5% we can arrive at the size of the system as follows:
Ultra cap size = Total Wattage * number of hours *allowed autonomyDepth of Discharge*Efficiency*Operating voltage
The router chosen is a Cisco Linksys WRT54-- 6W. The Cisco router has specifications of: DOD=0.98, Efficiency=0.95, and an operating voltage of 9V.
The design of the mechanical components focused on simplicity and functionality. Material properties for each mechanical component were assessed to ensure all components could withstand the loading conditions including wind loads, rain loads, and snow loads (Messenger 2004). Each mechanical component was then sized using a factor of safety of 1.5. The frame is constructed from steel angle iron. A waterproof housing was employed to protect all necessary electronic components with size selected accordingly. This was done using a simple Rubber Maid container and waterproofing it with silicone glue on the seams. Wheels where incorporated into the frame for ease of transportation. A rack to hold the ultracapcitors in the waterproof housing was made using lightweight, inexpensive styrofoam.
The results of sizing from PVSyst obtained are as 13Ah capacity battery, 92Wp capacity solar panel, Generalizing the design, a 10 ultracapacitor bank of 3000F each is being selected as the storage, and a 190Wp solar panel is used. Material properties for each mechanical component were assessed to ensure all components could withstand the loading conditions including wind loads, rain loads, and snow loads (PV Systems Engineering 2006). Each mechanical component was then sized using a factor of safety of 1.5.
Is this common? How do you justify such a pad Says who? Say where this came from
The implementation of the hardware is going to be made using DC/DC converters as shown in the schematic shown below
Describe what is going on below
The firing of the MOSFET’s is done using a Micro controller based circuit, it has been proposed to use the Arduino Uno to implement this circuit. The Perturb and Observe algorithm being considered to be used to implement the MPPT control .The advantage of this method is , it is easy to implement and robust in structure which increases the efficiency of the system.
The present cost of bringing broadband Internet access to rural areas was compared to the implementation of PV mesh networks. A Comparison of router configuration was also done in order to optimize energy demand, with system configuration and antenna spacing. Finally, a comparison was made between the use of standalone PV systems and connecting areas to electric grid in distance from current availability. In order to perform the economic analysis the cost of stacking standalone systems to a rural area from a WLAN hub , a comparison to the cost of extending electric and broadband infrastructure was made. The total cost of the system was calculated to include solar panels, inverter, batter bank, router, mounting, installation, and travel to rural areas (McLaughlin et al. 2010). Additionally, connection costs and WLAN hub was also included but varies with distance from land lines.
Lots more details needed here -- think equations for the comparison.
Geographical potential using GIS
In this section we will use Geographical Information Systems (GIS) to identify optimal areas for the PV mesh network deployment. Information on obtaining a copy of ArcGIS can be found at . In order to assess variables influencing PV system implementation, a geographical representation of deployment potential is useful in identifying optimal locations. Local level spatial disparities are necessary to locate places lacking infrastructure and technology for broadband access (Grubesic and Murray 2001).
Due to the potential of emerging technology and because of its relatively high solar irradiance availability, California was used as an initial case study. Among the geo-processing capabilities of GIS perhaps the most useful is the overlay function. GIS has shown its usefulness in rural and urban planning, sustainable development, and identifying site specific locations. To do this we will mainly look at population density and current broadband access.
First, base layers are needed for creating a quality map. Many free shape files can be found online. For our map we used California Basemap  and a general basemap for the United States and Pacific Ocean . After unzipping the files, open ArcMap and ‘add data’. Select the shapefile you would like to use. If one of the files has labels, for example state names, you can double click on the file and select the ‘Display’ tab and select the field that contains names next to the ‘Field’ drop down window. The window should look like this.
Next we need to download and add to the map county boundaries, which were found here:  These files are in the same geographical format as census data which will be helpful in the next step. After uploading the map will look like this.
Now we need to find census data in order to map population density. These files can be found at the U.S. Census Bureau’s FactFinder page.  Download the data that is the most appropriate for your application. Download the data in txt format in order to transfer to excel. This will allow us to link the data with our shapefiles in ArcMap. Open the file in excel. If you downloaded population data, calculate the population density with this equation.
Population Density=Population/(Land Area*2,589,988)
Now the file can be linked with a shapefile in our map. To do this. Click on the file you downloaded from census.gov and select ‘Join and Relate’ and then ‘Join’. This will pop up this window to select the file and table to connect. Note: The files can only be linked if they share at least one attribute.
After identifying the symbology to be displayed and number of categories, you should get a map that looks something like this.
Next, lets try to find some utilities data for California. Specifically, we are interested in broadband providers. Luckily the California Public Utilities Commission has already compiled much of this data and keeps ongoing and up to date files of various service providers. Maps with broadband service are found here:  After adding this layer, the combined maps will look like this.
An "underserved" area is an area where broadband is available, but no wireline or wireless facilities-based provider offers service at advertised speeds of at least 6 mbps download and 1.5 mbps upload. “Served” areas exceed these advertised speeds.
Refining the data to look at certain demographics or population densities is easy with “Select by Attributes”. This allows you to define specific parameters in a table or shapefile and export it as a new database or map. In this case we wanted to isolate the higher population density areas.
In the next map we will combine the high population density file (in purple) with the “underserved” areas to see if there are areas with relatively high population density but no broadband access. This can be done with the “Select by Location” function in GIS.
Overlaying this file results in two optimal locations, roughly 2x3 miles, with high population density and no access to broadband that would make ideal locations for mesh network pilot projects. The two areas are difficult to see in the below map.
But by zooming in we can see the two areas.
Make sure that the area you choose is not water by downloading a river and lake shapefile.
Proper ground validation is important with GIS to ensure that the area does indeed provide the attributes you were looking for.
Potential next steps include using high resolution satellite imagery for object recognition analysis. By identifying individual houses a more accurate map can be made of high density housing clusters. Additionally this will allow for optimal routing algorithms to be applied for technology dissemination.
The example system was tested using the following methodology. The system, comprised of three stand alone units, was placed outdoors with each branch unit roughly 500 meters from the main unit. The range and capacity of the mesh system was then measured. Finally, current and voltage readings for each unit were collected.
Since the units were placed on rooftops, it was assumed that shading played a limited role and that systems were placed in an optimized location above shaded areas. Therefore, calculations regarding orientation and sizing due to shading parameters were ignored.
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