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*''MPPT algorithm''- Through the results of the Simulation, it is explained how the Perturb & Observe [http://en.wikipedia.org/wiki/Maximum_power_point_tracking MPPT] Algorithm works.
*''MPPT algorithm''- Through the results of the Simulation, it is explained how the Perturb & Observe [http://en.wikipedia.org/wiki/Maximum_power_point_tracking MPPT] Algorithm works.


===Renewable energy potential on brownfield sites: A case study of Michigan===
[http://www.sciencedirect.com/science/article/pii/S0301421510005513 Renewable energy potential on brownfield sites: A case study of Michigan]<ref> S. Adelaja, J. Shaw, W. Beyea, and J. D. Charles McKeown, “Renewable energy potential on brownfield sites: A case study of Michigan,” Energy Policy, vol. 38, no. 11, pp. 7021–7030, Nov. 2010.</ref>
<br />
Abstract: The highlight of the journal paper is to show the potential a [http://en.wikipedia.org/wiki/Brownfield_land Brownfield land] has in being replaced as a potential land for Solar Farms. Since this paper is a case study of Michigan and we had chosen one of the non-food crops to be in Michigan, this paper can provide us certain interesting & relevant details like:
*''Solar Potential map of Michigan''- This map, prepared by the Michigan State University, shows the areas of great-low solar potential in Michigan. Hence, if we were to design a PV farm in Michigan replacing the non-food crop, this map can prove to be useful as it also displays the [http://en.wikipedia.org/wiki/Irradiance Irradiance] value in W/m^2.





Revision as of 20:47, 27 January 2015

Introduction

My name is Ram Krishnan and my topic is the "Study of the effect of replacing non-food crops with PV farms to the farmers". As for the non-food crops I have chosen Cotton, Tobacco & Alcohol and will be analysing how replacing these crops with PV farms may prove to be a boon or a bane to the farmers.

Objective

In the first stage of the Literature Review I have assembled the basic data of these non-food crops like the production of crops per acre at a specific location, the $/acre per year earned etc. The data which we required, as aforementioned, is the number of acres used by the farmer to plant the crops, the yield/ acre he gets and the income he gets from selling these crops. This will help us draw a comparison sheet when we put this data against the number of acres which CAN be used by the farmer for PV setup and the remuneration he can get from the electricity generated. It will help us get an idea if this idea will prove fruitful to the farmers.

So, the Literature Review will comprise of 2 stages - The First stage is assembling all the Agricultural Data and the Second stage is assembling all the PV farms data.

Non-food Crops Agriculture Statistics

a)Cotton Production Statistical Data in Georgia, USA

Cotton Production Statistics in Georgia, USA.[1]
Abstract : The link provides extensive details about the cotton production in the state of Georgia, USA published by the Georgia Cotton Commission. The data which is of much interest to us in this project would be:

  • Yield (average pounds per acre)
  • Crop Value (both lint & seed).
  • Total amount of acres planted
  • Total amount of acres harvested

These details would be very useful to us when we compare the remuneration provided by PV farms vs the efficiency cotton crop produces. We can choose a specific farm in of the counties mentioned on the link. We will calculate the amount of acres used by PV farms and the income it provides VS the amount of acres used by the crop and the $/acre it provides.


b)Cotton Production Statistical Data in Missouri USA

Cotton Production Statistics in Missouri, USA[2]
[3] Abstract: The link provides an insight into the crop production & efficiency in the state of Missouri USA. This link can prove to be an excellent source as we can use the following data for our project:

  • Harvested Acres
  • Yield (pounds per acre)
  • Average annual price over the last 5 years.


Tobacco Production Statistical Data in North Carolina, USA

Tobacco Production Statistical Data in North Carolina, USA[4]
Abstract: We have chosen North Carolina because for the year 2013, it had the highest production of Tobacco. Tobacco has always been a very important contributor to North Carolina's economy. Once again the link provides abundant relevant data like :

  • Yield (Pounds per harvested acres)
  • Harvested Acres
  • Value of Production (Dollars/Acre).

We can choose the data used for Burley Tobacco crops as these crops are used to make cigarettes. The link also provides us data like the number of farms allocated to the various counties in the state. This will help us use GIS over a particular farm very easily.

Alcohol Production Statistical Data in Michigan, USA

Alcohol Production Statistical Data in Michigan, USA[5]
Abstract: The paper, published by the researchers of Michigan State University, gives a detailed description of the crop Barley in the state of Michigan, USA. Barley is primarily used in the production of Beer and is extensively grown in the Northern Part of North America. Though the paper goes into detail the way a Barley crop is planted, grown and harvested, we are primarily interested in the data provided about the

  • Michigan Barley production ranging from 1914-2013 in Bushels/acre
  • Economics: $ received per Bushel.
  • Break up of the expenses incurred by the farmer per acre during the process of growing Barley.
  • Average Yield for the year 2013

One very unique characteristic about the paper, which may prove to be very helpful for my topic is that the paper provides an in depth break up of the expenses incurred by the farmer per acre during the process of growing Barley. This data can prove useful when we compare it to the expenses incurred while installing PV systems.

PV Farm Technical Specs


Objective - The main objective of this section of Lit Review is to assemble as much technical data on PV farms as possible. Technical data ideally should comprise of maximum information on the layout of the farm. Some of the information maybe:

  • Capacity of PV Solar farm
  • Type of Arrays and Orientation
  • Type of Modules used
  • Types of Racks used & their spacing
  • Output Efficiency
  • Revenue Earned
  • Cost of Setup of PV plant

During the course of the project, when we will be simulating a Solar PV setup, the above mentioned details will come handy. Since our primary objective is to show that the efficiency and revenue generated by a PV plant will be more/less than what a agricultural crop can generate, this approach may prove to be beneficial.

PV Setup in Georgia Power Headquarters, A Southern Company.

PV Setup in Georgia Power, a Southern Company[6]
Abstract: The state of Georgia, USA is actively trying to harness the potential of Solar Energy. Since we have chosen one of the non-food crops (cotton) to be in Georgia, it would be ideal to collect information about various types of PV setups in Georgia. One such PV setup has been established by Georgia Power's Corporate Headquarters in the year 2009. Their setup basically consists of rooftop PV arrays with the objective of producing electricity for the building. Their project, funded by Southern Company, can provide relevant details for our project as one of their objectives is to provide cost & performance data to help their customers. Through their interactive website that provides real-time web data we are interested in the following information:

  • Total capacity of the PV setup - The website describes the kW capacity expected from the setup. This information is useful for us as we can then infer how much kW of power can be produced with their kind of PV array setup.
  • Solar Production - The company publishes the amount of Solar Power they produce on a daily basis. Although this feature is currently not yet active and is "coming soon" , this feature can provide valuable data.
  • Weather - Probably one of the most valuable features the company provides, this feature gives us information about the amount of Irradiance (W/m^2), Module temperature, Ambient Temperature Wind Speed and Wind Direction. This information combined with the Solar Production data, can help us analyse the amount of Solar Energy produced under a certain set of conditions.
  • Comparisons- Although this feature is currently under construction, the data obtained from this can help us analyse the Energy data produced on a daily basis and compare it against the other data too.

Limitations faced-

  • Owing to the confidentiality of the project, it may prove difficult to obtain the exact technical specifications of the Arrays and the Inverters used. Obtaining other kinds of Technical Data may also prove to be difficult as the company may not be ready to divulge the information.

Solar Farm in Colorado

Multicriteria GIS modeling of wind and solar farms in Colorado[7]
Abstract: The basic objective of this paper was to analyze the suitable land area to tap solar (and in this case wind also) potential in Colorado, USA. The paper publishes its results based on using various Geographic Information System (GIS) modelling techniques to find out which land cover was most suitable for a Solar Farm.
Since our project involves simulation & design of Solar PV farms using the information provided by GIS, this paper can provide an useful insight on how to model a efficient PV farm based on information obtained from the GIS.

The following information from this paper is useful for us in our project

  • Methods to Obtain relevant data- The article describes what are the exact parameters required to design a solar farm. This is can prove to be helpful when designing & simulating our PV farm.
  • Land cover characteristics for solar- This section of the article proves to be an excellent source of valuable information as it defines what type of Land Cover has the maximum solar potential. For example, the article describes that the Inter mountain basins landscape have maximum Solar Potential. Hence while designing our PV farm, in order to maximize efficiency, we can keep these factors in mind.
  • Using GIS model to locate land masses with maximum Solar potential- A section of the article describes how to use the GIS model scores in identifying land masses which have a very high potential for tapping Solar Energy. We can refer to this method in our project.

Parallel DC-AC Conversion System Based on Separate Solar Farms with MPPT Control

Parallel DC-AC Conversion System Based on Separate Solar Farms with MPPT Control[8]
Abstract: This article focuses primarily on the simulation part of a PV farm, which can provide us useful information on how to go about building and simulating a PV plant with the help of MATLAB SIMULINK.

The information from this article that is relevant to us is:

  • Energy conversion Model- The paper describes the exact model employed by them to get the Current, Voltage and Power curves of each solar farm. This model, which primarily behaves like a distribution system too, can come in handy when we simulate our PV model.
  • PV panel model- The article also describes the type of PV panel used and the o/p capacity it possesses. They also briefly discuss the characteristics of the panel, like voltage & current parameters etc.
  • Inverter characteristics- MATLAB wave form outputs of the Inverter are also published to help analyze the AC output for the said given DC input. This can be useful in studying the efficiency of the system.
  • Formulae for calculating Photovoltaic Cell Current & Voltage- The paper employs a simple formula to calculate Ipv which is basically the current at the o/p of a Photovoltaic cell. This formula can be employed by us in our research.
  • MPPT algorithm- Through the results of the Simulation, it is explained how the Perturb & Observe MPPT Algorithm works.

Renewable energy potential on brownfield sites: A case study of Michigan

Renewable energy potential on brownfield sites: A case study of Michigan[9]
Abstract: The highlight of the journal paper is to show the potential a Brownfield land has in being replaced as a potential land for Solar Farms. Since this paper is a case study of Michigan and we had chosen one of the non-food crops to be in Michigan, this paper can provide us certain interesting & relevant details like:

  • Solar Potential map of Michigan- This map, prepared by the Michigan State University, shows the areas of great-low solar potential in Michigan. Hence, if we were to design a PV farm in Michigan replacing the non-food crop, this map can prove to be useful as it also displays the Irradiance value in W/m^2.


Reference </ref>

References

  1. Georgia Cotton Commission: Georgia Agricultural Statistics as of May 2013. http://www.georgiacottoncommission.org/index.cfm?show=10&mid=5
  2. University of Missouri Extension: http://extension.missouri.edu/scott/crop-budgets.aspx
  3. United States Department of Agriculture: http://www.nass.usda.gov/Quick_Stats/
  4. North Carolina Department of Agriculture & Consumer Services: http://www.ncagr.gov/markets/commodit/horticul/tobacco/
  5. Malting Barley Production in Michigan" by Ashley McFarland, Christian Kapp, Dr. Russell Freed, Jim Isleib & Scott Graham: http://agbioresearch.msu.edu/uploads/396/36753/Research_Files/Malting_Barley_Production_in_Michigan_-_GMI035.pdf
  6. Georgia Power Headquarters Solar Project - Southern Company: http://buildingdashboard.com/clients/southernco/
  7. J. R. Janke, “Multicriteria GIS modeling of wind and solar farms in Colorado,” Renewable Energy, vol. 35, no. 10, pp. 2228–2234, Oct. 2010.
  8. I. Colak, E. Kabalci, and G. Bal, “Parallel DC-AC conversion system based on separate solar farms with MPPT control,” in 2011 IEEE 8th International Conference on Power Electronics and ECCE Asia (ICPE ECCE), 2011, pp. 1469–1475.
  9. S. Adelaja, J. Shaw, W. Beyea, and J. D. Charles McKeown, “Renewable energy potential on brownfield sites: A case study of Michigan,” Energy Policy, vol. 38, no. 11, pp. 7021–7030, Nov. 2010.
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