As traditional energy models are disrupted by innovation and technologies, analytics is changing the renewable industry.
FREMONT, CA: Renewable power is becoming a prevalent source of electricity, accounting for 70 percent of renewable energy production and providing 16.6 percent of global electricity demand. Unlike conventional energy sources like nuclear and coal power plants, the provision of renewable energy can be ramped up and down very rapidly to adapt to dynamic market conditions. The World Economic Forum says that there’s more than $2.4 trillion in value from the transformation of the power grid over the next ten years, largely driven by technologies like smart pumps, electric vehicles, network technologies, and IoT devices, to name a few. As communities and various industries are striving to lessen their dependence on carbon energy sources, the energy market will continue to transform.
Distributed Energy Resources (DERs) have proven to be an effective remedy to help meet these hurdles, but selecting the right solution to scale for customers can be highly challenging. Data transparency and security are key as customers navigate the ever-changing energy landscape. This data can only be unearthed with tools that can simplify the challenges surrounding DERs and their inter-connectivity. Successful integration of DER technology with current data sources can have a transformative effect on a business, enabling customers to visualize a holistic overview of their environmental, technological, and financial objectives, all while reducing the requirement for fossil-based energy resources. Once best practices have been established that enable utilities to securely connect with grid edge devices, firms can start to truly reap the advantages of the new future of electricity.
Data related to asset performance is evolving significantly. One of the major drivers of this has been incorporating sensors leveraged to measure the operating efficiency of an asset. Once data has been recorded for a significant number of assets, it can be generalized into a model and utilized for forecasts. One form of large datasets, which could be vital in this case, is weather data. This is being assembled with asset sensors, satellite imaging, and independent weather stations. It offers time-series data on progressing weather patterns, which can then be leveraged to measure the effect of weather on asset performance.