A geographic analysis of wind turbine placement in Northern California

Rodman, Laura C.; Meentemeyer, Ross K.

doi:10.1016/j.enpol.2005.03.004

Cited by 145 publications

(78 citation statements)

References 2 publications

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“…Some studies used Boolean logic to exclude lands based on "hard constraints" where development is legally prohibited (e.g., parks) or operationally infeasible (e.g., greater than a threshold distance from roads and transmission lines) [4]. Other studies combined values of energy potential with those of technical and environmental factors to derive an overall suitability score [5,6,8,9]. Known biological constraints such as designated critical habitat for endangered species can be incorporated in this approach.…”

Section: Introductionmentioning

confidence: 99%

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Siting solar energy development to minimize biological impacts

2013

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a b s t r a c tAfter solar and other renewable energy developers select generally suitable sites for exploration, they frequently encounter conflict over biodiversity conservation values that were not factored into the initial suitability rating methods. This paper presents a spatial multicriteria analysis method for modeling risk of conflict with biological resources and applies the model in the California deserts where such conflicts are rapidly rising. The premise of the model is that the least conflict will occur on sites that are the most ecologically degraded with low conservation value and that would engender low off-site impacts when connecting to existing transmission infrastructure. Model results suggest sufficient compatible land exists in flat, non-urban areas to meet state solar energy targets of 8.7 GW of installed capacity in the California deserts for 2040. The model is a promising tool to fill the gap between site suitability analysis for renewable energy and regional biodiversity conservation planning to identify areas where rapid impact assessment and permitting will generate the least regrets.

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Section: Introductionmentioning

confidence: 99%

“…Its application for renewable energy is more recent, however [4e6]. Geographic information systems (GIS) have been used to model spatial patterns of suitability and constraints for development of solar [6e8], wind [6,9,10], and wave or tidal [11,12] resources. Two basic approaches have been used.…”

Section: Introductionmentioning

confidence: 99%

Siting solar energy development to minimize biological impacts

2013

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“…Van Hoesen and Letendre identified the viewshed as a critical factor to quantify the visual impact of wind turbines in the State of Vermont, U.S. [12]. Rodman and Meentemeyer utilized a rules-based GIS modeling approach to assess the suitability of wind farms in Northern California, U.S., in which factors were grouped into three categories (i.e., physical, environmental and socio-economic) [7]. A group of technical, economic and policy factors were incorporated into a GIS tool to define the locations for wind farm sites in Iowa, U.S. [13].…”

Section: Nebraska Wind Energy Potentialmentioning

confidence: 99%

“…Thus, finding suitable locations for wind farm installation is critical for the economic success of wind farm operation. In the past few decades, Geographic Information Systems (GIS) have been widely used to assist in searching for suitable sites for wind farms [7,9,10]. GIS provides the functionalities of integrating a large spectrum of geospatial information into the decision-making of wind energy development.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the state of Nebraska has considerable potential for further wind energy development. One of the major technical challenges to wind energy development is to locate the areas with high suitability for placing wind farms [7]. Wind farms generally require large initial investments; for example, a wind farm built in Ainsworth, Nebraska in 2005 cost around 81 million U.S. dollars [8].…”

Section: Introductionmentioning

confidence: 99%

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A Geospatial Approach for Prioritizing Wind Farm Development in Northeast Nebraska, USA

Miller

2014

IJGI

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Being cleaner and climate friendly, wind energy has been increasingly utilized to meet the ever-growing global energy demands. In the State of Nebraska, USA, a wide gap exists between wind resource and actual energy production, and it is imperative to expand the wind energy development. Because of the formidable costs associated with wind energy development, the locations for new wind turbines need to be carefully selected to provide the greatest benefit for a given investment. Geographic Information Systems (GIS) have been widely used to identify the suitable wind farm locations. In this study, a GIS-based multi-criteria approach was developed to identify the areas that are best suited to wind energy development in Northeast Nebraska, USA. Seven criteria were adopted in this method, including distance to roads, closeness to transmission lines, population density, wind potential, land use, distance to cities, slope and exclusionary areas. The suitability of wind farm development was modeled by a weighted overlay of geospatial layers corresponding to these criteria. The results indicate that the model is capable of identifying locations highly suited for wind farm development. The approach could help identify suitable wind farm locations in other areas with a similar geographic background.

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Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa, USA

Petrov

Wessling

2014

Wind Energy

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Because of the current shift away from fossil fuels and toward renewable energy sources, it is necessary to plan for the installation of new infrastructure to meet the demand for clean energy. Traditional methods for determining wind turbine site suitability suffer from the selection of arbitrary criteria and model parameters by experts, which may lead to a degree of uncertainty in the models produced. An alternative empirically based methodology for building a wind turbine siting model for the state of Iowa is presented in the study. We employ 'ecological niche' principles traditionally utilized to model species allocation to develop a new multicriteria, spatially explicit framework for wind turbine placement. Using information on suitability conditions at existing turbine locations, we incorporate seven variables (wind speed, elevation, slope, land cover, distance of infrastructure and settlements, and population density) into two machine-learning algorithms [maximum entropy method (Maxent) and Genetic Algorithm for Rule Set Prediction (GARP)] to model suitable areas for installation of wind turbines. The performance of this method is tested at the statewide level and a six-county region in western Iowa. Maxent and GARP identified areas in the Northwest and North Central regions of Iowa as the optimum location for new wind turbines. Information on variable contributions from Maxent illuminates the relative importance of environmental variables and its scale-dependent nature. It also allows validating existing assumptions about the relationship between variables and wind turbine suitability. The resultant models demonstrate high levels of accuracy and suggest that the presented approach is a possible methodology for developing wind turbine siting applications.

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A geographic analysis of wind turbine placement in Northern California

Cited by 145 publications

References 2 publications

Siting solar energy development to minimize biological impacts

Siting solar energy development to minimize biological impacts

A Geospatial Approach for Prioritizing Wind Farm Development in Northeast Nebraska, USA

Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa, USA

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