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Analytics for renewable energy storage integration: Predicting the unpredictable

Big Data Technology Writer

As energy companies comply with regulatory requirements to use less fossil fuel, batteries may represent the missing link between renewable energy generation and utilization. According to Scientific American, the intermittent nature of renewable sources like wind and solar makes storage a primary area of focus. When integrated into a grid, stored energy can smooth disparities between consumer demand and availability, offering an availability advantage to utilities.

Experts predict that the proliferation of sophisticated lithium-ion and flow battery storage will make renewable resources more cost-effective. ComputerWorld notes that it may even obviate any need for fusion-generated power or other futuristic resources by eventually allowing solar power to reach generation costs of two to four cents per kilowatt hour.

While the industry is still several years from a point where renewable energy and storage options are cheaper than fossil fuel, companies are currently presented with enormous opportunities. By leveraging analytics in battery implementation, organizations can maximize cost-savings and efficiency in a minimal amount of time.

Managing supply and demand with renewable energy analytics

https://kapost-files-prod.s3.amazonaws.com/uploads/direct/1449598909-13-3777/analytics-for-renew_blog.jpgLast year, renewable sources generated an estimated 9.1 percent of the world's power supply, according to The Guardian. However, for U.S.-based utility and energy organizations to realize the remarkable cost savings of energy storage, they'll need to adopt a strong analytics base. To effectively derive value from storage analytics, organizations must analyze their resources in real time to optimize generation of renewable energy.

Vestas, the Denmark-based wind turbine manufacturer and serving organization, has implemented an approach to streaming analytics that demonstrates "business case certainty" to prospective customers. While it is notoriously difficult to predict wind energy production for maximizing site selection, Vestas analyzes petabytes of data from instrumented, interconnected and intelligent sources. This has yielded business gains such as:

  • 97 percent faster forecasting response time
  • Improved turbine placement accuracy
  • Lower customer cost per KwH
  • 40 percent lower IT energy footprint

In addition to leveraging real-time analysis, organizations should start looking forward with the help of predictive analytics. By using Internet of Things-driven technology to detect and predict changes in renewable energy availability based on current and historical weather patterns, it will become possible to maximize productivity of renewable energy. Dong Energy writes "if we act now rather than later, the energy transformation towards cleaner energy will be less expensive."

Storage analytics for customer experience management and demand response

For energy and utilities companies, storage presents new possibilities for industrial customer experience management. Property management organization Glenwood Management in New York, where there is an increasingly deregulated market, uses day-ahead energy pricing information from vendors to determine how they'll allocate their storage resources throughout the day. According to Demand Energy, Glenwood's two megawatt-hours of available storage represent more than 15 percent of their daily required load.

Energy and utility organizations with the ability to predict renewable availability in advance of shortages can plan to draw from storage resources or notify industrial and residential customers of opportunities to rely on stored energy. This will usher in a new level of efficacy to existing and new U.S. Department of Energy demand response programs, giving utilities the ability to purchase energy from private storage when necessary.

Updating the concept of demand response programs to incorporate renewable power storage will require adoption of Internet of Things-driven, real-time analytics and the ability to leverage IoT technologies to reconcile predicted shifts in availability and demand.

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