InVEST Offshore Wind Energy Model

The InVEST Offshore Wind Energy model measures the electricity generation potential of wind over ocean and large lake surfaces. For a chosen region, the model estimates expected wind power and harvested energy, and calculates the levelized cost of energy and the net present value of constructing and operating a wind energy facility.

wind powerenergyoceanlarge lake surfaces

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Contributor(s)

Initial contribute: 2019-07-14

Authorship

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Stanford University
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Classification(s)

Application-focused categoriesNatural-perspectiveOcean regions
Application-focused categoriesNatural-perspectiveAtmospheric regions

Detailed Description

English {{currentDetailLanguage}} English

Quoted from: https://storage.googleapis.com/releases.naturalcapitalproject.org/invest-userguide/latest/wind_energy.html

Summary

Offshore wind energy is gaining interest worldwide, with 5,400 megawatts (MW) installed as of January 2013 and a growth rate around 25% per year (GWEC, 2013). Consistently higher offshore winds and proximity to coastal load centers serve as two of the major reasons wind energy developers are looking offshore. The goal of the InVEST offshore wind energy model is to provide spatial maps of energy resource availability, energy generation potential, and (optionally) energy generation value to allow users to evaluate siting decisions, use tradeoffs, and an array of other marine spatial planning questions. The model was developed to allow maximum flexibility for the user, in that it can be run with default data and parameters, but it can just as easily be updated with new turbine and foundation information, grid connection information, and parameter values that fit the user’s context. Model outputs include wind power potential, energy generation, offset carbon emissions, net present value, and levelized cost of energy, all given at the farm level.

Peer-reviewed references for this model are http://dx.doi.org/10.1016/j.aquaculture.2014.10.035 for the financial portion of the model and http://dx.doi.org/10.1016/j.marpol.2015.09.024 for the physical portion.

Introduction

This wind energy model provides an easily replicable interface to assess the viability of wind energy in your region under different farm design scenarios. The outputs are raster maps, whose point values represent the aggregate value of a farm centered at that point. This allows for detailed analysis of siting choices at a fine scale, though it comes at the cost of assuming that conditions are sufficiently symmetric around the center point so that the center point represents the median conditions of all turbines in the farm. Since the user can select the number of turbines for the farm, and the raster maps do not give an indication of farm size, the model also outputs a representative polyline polygon at a randomly selected wind data point that indicates the size of the farm.

To run the model, you are asked to supply information into the graphical user interface. This includes information about wind energy conditions, the type of turbine, number of turbines, the area of interest, etc. To make the model easier to run, it includes default data in .csv tables on two common offshore wind turbines: 3.6 MW and 5.0 MW. We also include two wind speed datasets: a global dataset and a dataset covering the Northwest Atlantic. Finally, it includes a table of less commonly changed default values used to parameterize various parts of the model, called the “Global Wind Energy Parameters” file. These .csv files are required inputs, and may be modified if alternate values are desired by directly editing the files using a text editor or Microsoft Excel. When modifying these files, it is recommended that the user make a copy of the default .csv file so as not to lose the original default values.

模型元数据

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Natural Capital Project (2019). InVEST Offshore Wind Energy Model, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/97a29e11-0218-441b-b2a1-a1273a936464
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History

Last modifier
zhangshuo
Last modify time
2021-01-11
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Contributor(s)

Initial contribute : 2019-07-14

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Authorship

:  
Stanford University
:  
View
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History

Last modifier
zhangshuo
Last modify time
2021-01-11
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