mjaniec + res + wind_forecasting   8

IBM Systems Magazine 201403 - Improved Weather Forecasting Could Boost the Efficiency of Renewable Energy Sources
Q: What data sources are used to power HyRef?
A: To have a far more precise and accurate weather forecast, you must have good input data, so we’re technically data scavengers. We use whatever data we can get our hands on, including real-time access to weather stations, some of which are operated by a diverse set of government agencies. But some of us have tried to be fairly clever about using data that isn’t ordinarily employed for driving weather forecasts. For example, we’re leveraging data from NASA spacecraft. They give us a lot of detailed information about the earth’s surface—such as temperatures, land use, soil types, terrain details—because characteristics of the surface greatly influence the weather.

There are also company-driven private networks that we partner with for data. Those networks exist to provide current conditions, but we’re using them as input into the model to improve forecasts.

For the HyRef deployment with wind farms in China [part of the world’s largest energy initiative combining wind and solar, which is sponsored by the State Grid Jibei Electricity Power Company Limited, a subsidiary of the State Grid Corporation of China] we introduced a very important innovation. In wind farms, particularly newer wind farms, the turbines themselves will have instrumentation. Some of the instrumentation are engineering sensors telling you what different parts of these complex machines are doing, but they also have meteorological sensors, which aren’t necessarily for forecasting weather. They’re there to monitor the performance of the turbines. One of the reasons these can’t ordinarily be used for forecasting is because these large devices have big, moving components—which are noisy—and any measurements are going to be highly contaminated. And then there’s the issue that the sensors are behind the blades, so the wind is coming in, turning the blades and extracting energy, and you’re not measuring the wind. You’re measuring what’s left of the wind, which isn’t going to tell you about the weather that’s coming into the farm.

So the innovation is to take the collection of data from these sensors in real time plus the number of turbines in a wind farm and then look at how they’re varying in time and in geography in order to remove the noise and the contamination to extract a signal about the incoming wind that drives the turbines. This provides better input into the weather model to potentially improve the forecast of the power.

Q: How quickly are forecast models made available?
A: There’s a computational component associated with this, and it requires a supercomputer to produce forecasts fast enough for them to be useful. If we’re trying to forecast a day ahead of time, we want to produce a forecast in an hour or less.
IBM  HyREF  RES  wind_farms  wind_forecasting  China  SGCC 
may 2014 by mjaniec
TechReview 20130813 - Better Weather Analysis Could Lead to Cheaper Renewables
If a plant’s operators could more accurately forecast the output of renewable power sources, they’d have less reason to rely on energy storage, which is typically needed now to provide a smooth flow of power into the transmission grid. “In the industry, storage is seen as the next disruptive technology,” says Michael Valocchi, vice president in IBM’s energy and utilities consulting business. “(But) if I can really predict in this manner, it’s not that I don’t need storage, but it makes storage less important.”

Utilities often rely on specialized companies to produce wind and solar forecasts based on weather models and other meteorological data, including anemometers on wind turbines. But wind measurements taken from turbines are often unreliable because energy has already been extracted from the incoming wind, and because vibrations affect readings, says IBM researcher Lloyd Treinish, the chief scientist of IBM’s weather modeling system. For its project in China, IBM analyzed data from all the turbines to come up with a more accurate representation of actual wind speed and direction, he says.

IBM also built a meteorological model specific to this site in northern China and installed video cameras to track the movements of clouds to inform solar forecasts. The entire data set is fed into a supercomputer to generate the forecasts.
RES  IBM  wind_farms  wind_forecasting  China  HyREF  energy_storage 
may 2014 by mjaniec
NCAR 20130507 - NCAR powers up renewable energy forecasts
The National Center for Atmospheric Research (NCAR), building on a pioneering wind energy forecasting system that saved millions of dollars for Xcel Energy ratepayers in eight states, has entered into a new agreement with the utility for even more sophisticated weather forecasts.

In the next two years, NCAR scientists and engineers will develop custom forecasting systems to predict sudden changes in wind, shut down turbines ahead of potentially damaging icing events, and even predict the amount of energy generated by private solar panels. The systems will be used by Xcel Energy control centers in Denver; Minneapolis; and Amarillo, Texas.

NCAR has entered into a two-year agreement with Xcel Energy to focus on the following areas:

* Forecasting “ramp” events. A new system under development at NCAR can provide utility managers with advance notice of a major change in wind energy over a few hours due to a passing front or another atmospheric event. The system, known as VDRAS (Variational Doppler Radar Analysis System) relies on techniques that combine observations from radars and other tools with computer simulations to create more accurate forecasts for particular wind farms.

* Predicting ice and extreme temperatures. To keep aircraft safe from potentially lethal icing conditions while aloft, NCAR has created state-of-the-art ice forecasting systems that use computer models and specialized algorithms. Applying similar technology, researchers at NCAR and Pennsylvania State University will develop a 48-hour forecasting system at designated wind farms to predict the impacts of freezing rain and fog on wind turbines, which cannot operate when coated in ice. The team also will forecast extreme low and high temperatures, which can cause wind farms to temporarily shut down.

* Generating solar forecasts. Xcel Energy customers who have their own solar panels draw far less energy from the grid while the sun is out, and can even sell excess energy back to the utility. To help Xcel Energy better anticipate when their customers are getting power from their own panels, NCAR will create a solar energy forecasting system, using a combination of computer models and specialized cloud observing tools.
RES  wind_farms  wind_forecasting  NCAR  Xcel_Energy  USA 
may 2014 by mjaniec
REW 20130801 - Wind Forecasting with Super-Duper Computer
Iberdrola and the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS), with the collaboration of the National Centre for Renewable Energies (CENER), aim to create an innovative information technology model with the research & development project.

Both the development of the project and its subsequent application will take place in the facilities of BSC, using software run by MareNostrum, one of the top 30 fastest supercomputers in the world.

MareNostrum has 48,896 Intel processors and a calculation capacity of 94.21 Teraflops - a measure of computing speed equal to one trillion floating-point operations per second.

By more accurately forecasting yields, the Sedar Project (High Resolution Wind Simulation) is expected to pave the way for the construction of wind farms with greater guarantees on investment, its developers say.
RES  Iberdrola  wind_farms  wind_forecasting  MareNostrum  Spain  Sedar_Project 
may 2014 by mjaniec
IBM 20130812 - IBM Drives the Future of Renewable Energy with New Wind and Solar Forecasting System
IBM announced an advanced power and weather modeling technology that will help utilities increase the reliability of renewable energy resources. The solution combines weather prediction and analytics to accurately forecast the availability of wind power and solar energy.

The solution, named "Hybrid Renewable Energy Forecasting" (HyRef) uses weather modeling capabilities, advanced cloud imaging technology and sky-facing cameras to track cloud movements, while sensors on the turbines monitor wind speed, temperature and direction. When combined with analytics technology, the data-assimilation based solution can produce accurate local weather forecasts within a wind farm as far as one month in advance, or in 15-minute increments.

By utilizing local weather forecasts, HyRef can predict the performance of each individual wind turbine and estimate the amount of generated renewable energy.

State Grid Jibei Electricity Power Company Limited (SG-JBEPC), a subsidiary company of the State Grid Corporation of China (SGCC), is using HyRef to integrate renewable energy into the grid. This initiative led by SG-JBEPC is phase one of the Zhangbei 670MW demonstration project, the world's largest renewable energy initiative that combines wind and solar power, energy storage and transmission.

The Hybrid Renewable Energy Forecaster represents advancements in weather modeling technology, stemming from other innovations such as Deep Thunder. Developed by IBM, Deep Thunder provides high-resolution, micro-forecasts for weather in a region - ranging from a metropolitan area up to an entire state - with calculations as fine as every square kilometer.

IBM  RES  wind_forecasting  HyREF  China  SGCC  Zhangbei  Deep_Thunder  wind_farms 
may 2014 by mjaniec
CleanTechnica 20140313 - Teaching An Old Wind Turbine New Tricks
Specialists working at Siemens Corporate Technology working with technicians from Technische Universität Berlin and IdaLab GmbH in the ALICE project (Autonomous Learning in Complex Environments) have developed self-optimisation software for wind turbines which will enable turbines to produce one percent more electricity annually under moderate wind conditions.

The software will “teach” turbines how to automatically optimise their operation in response to weather conditions, using sensor data to make changes to their settings based on wind speed and other factors.

Not only will this reduce wear and tear on the turbines, but it will help exploit the existing weather conditions to produce electricity.
RES  Germany  wind_farms  wind_forecasting  Siemens 
march 2014 by mjaniec
Bloomberg 20140307 - India Reconsiders Wind Forecasting on Inaccurate Results
An Indian rule requiring wind farms to predict output or face fines has been temporarily suspended as the regulator reconsiders the best way to ensure stability of the grid, which suffered the world’s biggest outage in 2012.

“The mechanism has been put on hold,” said Sunil Jain, chief executive officer at Hero Future Energies Pvt. and president of the Wind Independent Power Producers Association.

The Central Electricity Regulatory Commission last year said it would penalize wind farms that failed to predict their day-ahead generation within a 30 percent band. Developers including Tata Power (TPWR) Co. and Goldman Sachs Group Inc.’s ReNew Wind Power Pvt. protested the directive, saying it was impossible to comply with and that fines would wipe out profits in an industry that has drawn about $10 billion of investment since 2011.

“Not a single project has been able to produce data within the margins,” Jain said in an interview in New Delhi this week. “It defeats the purpose. It’s too inaccurate.”

The industry is asking for the rules to be modified so that a centralized, state-level load dispatcher can compile more accurate, region-wide predictions, which is how scheduling is done in Europe, Jain said.
RES  India  wind_forecasting  wind_farms  Tata_group  ReNew_Wind_Power  Goldman_Sachs 
march 2014 by mjaniec
PR 20140205 - EDP Renewables Adds GE's Wind PowerUp* Technology to Increase Output for GE Wind Fleet
EDP Renewables (EDPR) will use GE's Wind PowerUp*, a GE Predictivity solution, to increase the power output of 402 GE 1.5-77 wind turbines located at five U.S. wind farms.

"GE's PowerUp software will allow us to improve the power curve and increase the annual energy production of these 402 wind turbines," said Brian Hayes, executive vice president, EDP Renewables.

The wind farms that will be installing PowerUp are Blue Canyon V wind farm in Oklahoma, the Meadow Lake III wind farm in Indiana and the Top Crop I, Top Crop II and Railsplitter wind farms in Illinois. When PowerUp is activated, a GE software program performs a complete before and after wind farm power performance analysis, validating the performance improvement. By adjusting performance dials, including speed, torque, pitch, aerodynamics and turbine controls, PowerUp is able to improve the power output of each unit and the overall wind farm.

PowerUp is part of GE's brilliant wind platform, which harnesses the power of the Industrial Internet to analyze tens of thousands of data points on a wind farm every second, driving higher power output and creating new revenue streams for customers. It is an ecomagination qualified product.
RES  EDPR  wind_farms  PowerUp  GE  power_curve  Brian_Hayes  wind_forecasting 
february 2014 by mjaniec

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