The Need for Wind Power Analytics
Author: L. Russell Records
Renewable energy sources like wind power and solar energy, combined with regulations that govern their consumption, are turning the traditional power generation and distribution model around 180 degrees. In this new utility model, successful, integrated utility companies will require the real-time ability to manage the power grid and its generation, transmission and distribution assets.
Executives and senior managers at utility companies must become experts at running integrated power companies, with the ability to gather and integrate data from their own facilities as well as consumer-produced power. Doing so means taking advantage of advances in big data applications and analytics to deal with the complexities they now face.
Renewable Energy Sources are Growing
Among many sources of alternative energy, wind power is becoming an increasingly important source in many countries. Wind turbines today generate from 100 kilowatts for residences or "microgrids" up to the newest technology at over 7 megawatts (MW) each. One megawatt can power 250 to 300 homes. The largest wind turbine, the Vestas 164, produces 7.5 MW and has a rotor diameter of 160 meters.1
The use of wind power is growing rapidly in most developed countries. The three largest wind power producers today are the United States (27.7 percent), China (16.7 percent) and Germany (10.6 percent). The United States today has more than 45,000 wind turbines, providing enough electricity to power 14.7 million homes — roughly equivalent to the number of homes in Colorado, Iowa, Maryland, Michigan, Nevada and Ohio combined.2
Renewable Energy Generation and Management - Changing Business Model
Power companies face multiple challenges to their basic business model as renewable energy sources like wind and solar are integrated into the power grid.
Legacy power grids were designed with a few centralized power generation facilities that led out to the service areas where line capacities became lower and lower, following the form of a tree. Primary high-voltage lines form the trunk from which branches of descending size and capacity branch out. While this offers efficient distribution from source to consumer, renewable energy produces new origination points, putting energy back into the grid from the "wrong" end.
In Germany, customers generate more than half of the renewable power available. When customers are producing at high levels, German utility provider RWE is forced to cut back its own generation from oil, gas and coal. When this coincides with a period of high demand such as a hot, sunny day, the need for power generated by quick-start plants is diminished. Peak power is one of the utility's most profitable sources of revenue.
When you add the growing customer demand for "off-grid" renewable power being generated by customers, the grid topology and control systems become even more problematic. The industry needs to invest $1.5 – $2 billion to replace aging and obsolete power transmission and distribution systems. To afford this, they need new paying customers. What they're getting instead are additional power providers who want to be paid for their power or constraint fees.
How Wind Power Analytics can Help
These changes will require utility companies to start thinking of themselves as managers of the portfolio of energy sources. Utilities that leverage emerging technologies such as machine-to-machine learning, sensor data analytics, mobility and cloud technologies, gain the ability to manage assets as a whole, balancing the variable output of wind power with the steady supply assured by traditional generation sources.
Wind power analytics fall into three categories: weather and wind predictions, turbine and overall wind farm performance, and predictive maintenance. Data for computing wind analytics is readily available from most turbine vendors as well as integrated information from other business systems and external sources,
With data like this available from many different sources, utilities can answer a broad range of questions that allow them to accommodate the variable nature of wind power and manage their use of other generation sources. One of the most important outcomes is being able to better predict the need for expensive maintenance.
To realize the benefits of wind power analytics requires a company to carefully consider the business objectives it wants to meet. The company must consider what types of data sources are required, where those sources are located and what type of algorithms will help provide the right analytics.
This paper will examine the role wind power analytics (WPA) can play in helping utility companies operate a mix of conventional and renewable power generation assets optimally, and provide services that their customers need and want.
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