Tyler Archer December 4, 2012 NRS509 August GIS & Wind Turbine Siting

Transcription

1 Tyler Archer December 4, 2012 NRS509 August GIS & Wind Turbine Siting In a world with growing energy needs, maybe countries and power companies are looking to expand into alternative and renewable energies. One rapidly growing renewable is wind energy. Whether land based or offshore, wind turbines have been popping up nearly everywhere. GIS plays a major role in siting and positioning wind turbines and farms so they can produce a maximum amount of power without hindering (or greatly hindering) other land or ocean uses. Before breaking down GIS s involvement in wind siting, the following is a very brief outline of the current state of the wind energy sector. In the United States, several western states such as Texas, Iowa and California are considered the leaders in the land based wind farms. Many other states, including Rhode Island, have dozens of single turbines spread throughout the state. Currently, there are no offshore wind turbines or farms in America, however several have been proposed off the coasts of Cape Cod, Block Island, New Jersey and Delaware, among others. The American Wind Energy Association (AWEA) has reported that nearly 5000MW of wind power have been installed so far in 2012 and the U.S. wind industry now totals 51,630 MW of cumulative wind capacity (and more than 40,000 turbines) through the end of September The state of wind power in Europe is the exact opposite. Due to centuries of continued land use and development, there are not many large tracts of free land available. Therefore, many European countries have looked to the sea for their wind energy needs. Denmark, Germany and the United Kingdom are Europe s wind leaders in terms of production and number of turbines with other countries like Spain guiding the manufacturing. According to a recent European Wind Energy Association (EWEA) report, in the first six months of 2012, Europe installed offshore wind turbines, with a combined capacity totaling MW. Overall, 13 wind farms were under construction. 2 The EWEA has set a goal of generating 150GW annually by 2030, meeting 14% of EU electricity demand. 3 In order to meet this high demand, large amounts of ocean space will be reserved and this is where GIS comes in. For land use planning, developers use traditional zoning guidelines. For ocean space planning, coastal managers use marine spatial planning techniques. In either case, GIS plays a key role in organizing the various uses vying for space in our crowded world. When it comes to planning and siting wind turbines and farms, the process involves mapping several overlaying feature classes to find suitable space that does not infringe on existing uses. For this project, seven studies were reviewed and all used a variation of layer overlays to complete their models. A study from San Francisco outlines what should be included in a wind siting GIS model nicely: (1) Physical features such as wind resources, obstacles and terrain, 1 AWEA, Industry Stats, 2 EWEA, The European Offshore Wind Industry Key Trends and Stats, 1 st Half 2012, July EWEA, Policy Issues,

2 (2) Environmental factors including land use, vegetation and sensitive areas, (3) Human Impact factors including proximity to development and recreation areas 4 So if the first step is identifying what data sets are required, the second is actually going and getting them. Many of the studies reviewed used publicly available data and did not collect their own. However, a study from Spain gathered their data in a different way. 5 They issued surveys to a number of stakeholder groups (these groups generally represented interests in the layers listed above) and asked to identify key or important areas on maps they would like see protected from wind development. After gathering all the maps, the study team worked with the groups to see how much they would be willing to compromise on their claims. Finally, this data was combined with physical data such as wind speed and elevation to find feasible locations. Although it worked in the study, in the real world environment, this is probably the most difficult way to site wind farms in that its very difficult to keep all the stakeholder groups happy and involving fully in the siting process could lead to extended debates, conflicts and delays. Some studies used very extensive datasets and included a few dozen different layers. Others, like the San Francisco study, worked very well with about ten or so. Regardless of the number of datasets, most of the studies weighted their various aspects and calculated a site feasibility index. Certain desirable features were weighted highly or positively such open space, proximity to roads or readily available grid connection. Other features, such as proximity to residential areas, steep landscape grade or locations of protected areas, were weighted negatively. When it comes to offshore siting, features such as shipping lanes, fishing grounds, dumping sites, and MPAs should all be included in the analysis. The results were added together and mapped using a graduated scale. One study, from Denmark, employed a slightly different weighting technique. 6 It used what it called the fuzzy set theory. Fuzzy set theory allows for gradual inclusion or exclusion of certain factors rather than hard single digit scoring. Under typical weighting practices, features are given a 0,1,2,3,etc. weight based on its favorability. This leaves some wiggle room, but not much. The fuzzy set theory practiced in the Danish study is analogous to kriging and other interpolation methods in that it builds gradual connections between points. When used in wind farm siting, it allows developers to form compromises among sites and needs of stakeholders. The final result is a map like the following, where the desirable areas are highlighted. This type of rules based approach is simple to complete and easy to use or change as new data becomes available. 4 Rodman, L.C. and R.K. Meentemeyer., A Geographic Analysis of Wind Turbine Placement in Northern California. Energy Policy 34: Ramirez-Rosado, I.J., E. Garcia-Garrido, L.A. Fernandez-Jimenez, P. Zorzano-Santamaria, and V. Miranda Promotion of New Wind Farms Based on a Decision Support System. Renewable Energy 33: Hansen, H. S., GIS-based Multi-Criteria Analysis of Wind Farm Development. ScanGIS Conference Proceedings

3 7 7 Baban, S.M.J., and T. Parry Developing and Applying a GIS-Assisted Approach to Locating Wind Farms in the UK. Renewable Energy 24:

4 Tyler Archer December 4, 2012 NRS509 August GIS & Wind Turbine Siting Annotated Bibliography Baban, S.M.J., and T. Parry Developing and Applying a GIS Assisted Approach to Locating Wind Farms in the UK. Renewable Energy 24: This project was frequently cited in most of the other articles; however, it was a little different. Data collection was conducted through a questionnaire, which was mailed to members of the private and public sectors interested in wind turbine construction. It was used to identify criteria, policies and factors that would be used to identify suitable areas. Many of the criteria ended up being very similar to the other articles and included features such as buffers around roads, homes, woodlands; the slope of the land; type of land (agricultural or not); etc. Another interesting aspect of this project was that it developed two maps one with all the criteria given equal weight and another by assigning them weights and this resulted in some major differences between the two. This reflects the importance of weighting variables properly. Hansen, H. S., GIS based Multi Criteria Analysis of Wind Farm Development. ScanGIS Conference Proceedings ScanGIS stands for Scandinavian Research Conference on Geographical Information Science. This paper came about thanks to increased implementation of the Kyoto Protocol in the EU and an interest in wind energy expansion in the Baltic Region. The purpose was to develop a multicriteria method for siting wind farms in the region. The paper uses both classic Boolean logic and what it calls fuzzy set theory, which allows for gradual inclusion or exclusion of certain factors. This article does a nice job breaking down the differences between the two schools of thought and uses charts to depict how they can be used together. Ramirez Rosado, I.J., E. Garcia Garrido, L.A. Fernandez Jimenez, P. Zorzano Santamaria, C. Monteiro, and V. Miranda Promotion of New Wind Farms Based on a Decision Support System. Renewable Energy 33: This paper focuses on wind turbine siting in La Rioja, a Spanish district about 2/3s the way from Madrid to Bilbao. Instead of creating a list with a number of features to build its model, this project reaches out to various stakeholder groups, asks them to identify regions they value on maps and tries to accommodate their collective needs. The second step is to create tolerance maps, meaning they try to figure out how much each stakeholder is willing to compromise on their original claims. Finally, the information is combined with wind speed and landscape data to find feasible site locations. This is probably the most difficult way to site wind farms in that its very difficult to keep all the stakeholder groups happy and involving fully in the siting process could lead to extended debates, conflicts and delays.

5 Rodman, L.C. and R.K. Meentemeyer., A Geographic Analysis of Wind Turbine Placement in Northern California. Energy Policy 34: This project uses a rules based modeling approach to find acceptable sites for wind farms. The authors developed their model without using current sites and, interestingly, their model accurately depicts areas where large scale wind farms have been developed or proposed in nine counties surrounding San Francisco Bay. Their model breaks down into three parts physical, environmental and human impact. Each part is weighted to create an overall index number, which is then mapped for the region. While the results showed limited availability for large scale farms, small scale farms have greater potential in the region. Sliz Szkliniarz, B., and Joakim Vogt GIS Based Approach for the Evaluation of Wind Energy Potential. Renewable and Sustainable Energy Reviews 15: The objective of this paper is to develop an approach to support the decision making process connected with the site selection for wind energy projects using GIS and specifically developed a case study for a province in Poland. Not only does it include industry standards like landscapes and proximities to certain areas or features, but it also used wind speeds to calculate load hours and cost of production, the ultimate driver in wind energy development. In fact, this study determined two districts within this Polish province with the high economic viability for wind power are underdeveloped and are under producing. Vagiona, D.G., and N.M. Karanikolas A Multi Criteria Approach to Evaluate Offshore Wind Farms Siting in Greece. Global NEST Journal 14.2: This paper uses a similar multi criteria approach found in the other articles but shifts to offshore siting and therefore, the list of criteria change. Now included are water depth, distance to protected areas, shipping routes, fishing grounds, etc. Because of the added layers, this project makes it clear that offshore development includes many more stakeholders than the typical land based siting process. van Haaren, R., and V. Fthenakis GIS Based Wind Farm Site Selection Using Spatial Multi Criteria Analysis for New York State. Renewable and Sustainable Energy Review 15: This project uses a three part cost revenue optimization to site wind turbines. (1) Exclude infeasible areas based on land use or geographic constraints. (2) Identify best sites based on potential revenue, cost of construction, access to the grid and land clearing. (3) Assess ecological impacts. This technique found that most of New York State has moderate feasibility levels with pockets of high value along the Canadian border.

6