WindView is an open-source situational awareness and decision support platform that provides grid operators with knowledge of the state and performance of their power system. Its emphasis is on wind energy and enabling the reliable and efficient integration of larger amounts of wind energy. This three-year project involves close collaboration with Western Area Power Authority's Electric Power Training Center to develop a production-level version of WindView, including feedback and training sessions, for the Western Area Power Authority network. The focus will be on advanced visualization to display pertinent information, extracted through computational techniques, from wind power forecasts for high-wind-penetration systems. WindView will be publicly available for industry and academic researchers and will incorporate forecasting tools designed by NREL and Argonne National Laboratory.
Power Predictor Wind And Solar Energy Measurement Tool
Download: https://shoxet.com/2vB6W0
The Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework is a dynamic and flexible tool that models the impacts of clean energy (including wind energy) supply chains during the transition from a linear to a circular economy.
NREL's demand-side grid (dsgrid) model harnesses decades of sector-specific (including wind) energy modeling expertise to create comprehensive electricity load data sets at high temporal, geographic, sectoral, and end-use resolution to understand current and future U.S. electricity load for power systems analyses.
FLORIS provides a computationally inexpensive, controls-oriented modeling tool of the steady-state wake characteristics in a wind farm. This open-source software framework models turbine interactions in planned and existing wind power plants, and can be used to design and analyze wind farm control strategies and wind farm layout optimizations.
A standardized tool for producing operational analyses of wind power plants, OpenOA identifies and analyzes the drivers of wind farm performance. The first open-source software tool of its kind, OpenOA helps wind industry professionals make more accurate predictions and more informed decisions in their work, thereby reducing investment risk.
The REopt web tool allows users to evaluate the economic viability of distributed wind energy and other renewable energy sources, identify system sizes and dispatch strategies to minimize energy costs, and estimate how long a system can sustain critical load during a grid outage.
The Renewable Energy Potential (reV) model model is a first-of-its-kind detailed spatiotemporal modeling assessment tool that empowers users to calculate renewable energy capacity, generation, and cost based on geospatial intersection with grid infrastructure and land-use characteristics. Available as open source since February 2020, the reV model currently supports wind turbine technologies.
SOWFA (Simulator fOr Wind Farm Applications) is a set of computational fluid dynamics solvers, boundary conditions, and turbine models. SOWFA employs computational fluid dynamics to enable users to investigate wind turbine and wind power plant performance under a full range of atmospheric conditions and terrain. With this tool, researchers and wind power plant designers can examine and minimize the impact of turbine wakes on overall plant performance. Read the SOWFA fact sheet.
The 2021-2022 Enacted State Budget established a process for the New York State Department of Taxation and Finance to develop a standard appraisal methodology for solar and wind energy systems with a nameplate capacity equal to or greater than one megawatt.
Solar and wind power development is increasing exponentially in the United States. However, these energy sources may affect wildlife, either directly from collisions with the turbine blades or photovoltaic arrays or indirectly from loss of habitat and migration routes. An important component to understanding the effects of these renewable energy projects on wildlife is accurate and precise estimates of fatality.
Current protocols for estimating bird and bat fatality at wind-power facilities is to search designated areas below turbines to find carcasses. Carcasses may be scavenged or difficult to detect, so statistical tools and search protocols are available to estimate actual fatality. However, current estimators only adjust the observed number of carcasses found on search plots for scavenger removal, search efficiency and time between searches, not for variation in plot size among studies nor portion of the plot actually searched. Simple counts of carcasses found at wind farms also do not reflect actual fatalities because some are removed by scavengers, or are overlooked by or fall within areas inaccessible to searchers. Because the density of carcasses generally declines with distance from the turbine, the location and configuration of inaccessible areas can greatly affect the proportion of carcasses that might be missed.
Solar energy development can present several different sources of mortality to wildlife, especially birds. Estimates of the total birds killed at an entire facility must consider each of the various sources of fatality in order to be accurate. Monitoring and estimation tools similar to those available for wind energy facilities are in short supply.
Biological Statistician Manuela Huso, along with her colleage Dan Dalthorp, lead the USGS in developing more accurate fatality detection tools and monitoring protocols to assist both the solar and wind energy industry and the agencies that regulate their development and operations.
USGS scientists developed statistical tools to help wildlife managers and wind energy operators infer whether incidental wildlife take levels are consistent with, higher than, or lower than permitted levels, over both short-term and long-term time scales. The tools examine the accuracy of the triggers that signal wind energy companies to undertake actions to remain within allowed take rates.
Early power plants produced electricity primarily from coal, steam or hydroelectric energy. Today, Texas still generates electricity from some of these traditional sources but increasingly relies on natural gas as well as renewable resources, primarily wind.
This would allow three times as much energy to be produced by wind power in Europe compared to today, not only because there are more farms, but because those farms can take advantage of better wind conditions.
In another research paper also published today in Energy, the pair modelled the hourly output of solar panels across Europe. They found that even though Britain is not the sunniest country, on the best summer days solar power now produces more energy than nuclear power. However, the pattern of this solar output through the year substantially changes how the rest of the power system will have to operate.
Wind and solar energies have a strong dependence on weather conditions, and these can be difficult to integrate into national power systems that requires consistency. If there is excess power generated by all energy sources, then some supplies have to be turned off.
Dr Staffell said he spent two years crunching the data for his own research and thought that creating this tool would make it quicker for others to answer important questions: Modelling wind and solar power is very difficult because they depend on complex weather systems. Getting data, building a model and checking that it works well takes a lot of time and effort.
The CPUC's Self-Generation Incentive Program (SGIP) provides incentives to support existing, new, and emerging distributed energy resources. SGIP provides rebates for qualifying distributed energy systems installed on the customer's side of the utility meter. Qualifying technologies include wind turbines, waste heat to power technologies, pressure reduction turbines, internal combustion engines, microturbines, gas turbines, fuel cells, and advanced energy storage systems. 2ff7e9595c
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