From a global standpoint, clean, renewable energy is going mainstream. Today, it no-longer operates on the margins of national energy mixes.
Citing a few examples, the current proportion of energy for the likes of smaller energy markets, such as Iceland, and Portugal use majority renewable energy for their populations. However, at 0.5, and 10 million people, respectively – they appear as mere simulation grounds for pilot projects.
However, to the contrary, much credit goes to Germany, for enabling a considerable emphasis on renewables for its energy mix. The nation has set a 2050 target of 100% renewable energy for meeting future demands.
Solar energy in-particular is experiencing a boom period, with the installation of photovoltaic panels in 2017 accounting for more energy production than new Fossil and Nuclear fuels combined.
Intro to EnergyTech
But for renewable energy to thrive, it must optimise power production. Fortunately, advancements in computing power have made this a new reality.
EnergyTech is the use of Industry 4.0-centric technologies to boost and support the operational capacity of power facilities. Some practical examples include; simplifying operations, improving maintenance, as well as enabling proactive, and systematic implementations.
Easing the entry of renewables into the mainstream energy market is an absolute priority for suppliers. This is offset by keeping facilities at optimum performance.
Initial costs of establishing renewable energy power facilities are considerable. However, the costs of solar kilowatt generation are decreasing. What must improve are systems and maintenance schedules of these energy facilities.
EnergyTech is of course not limited to renewables. As the likes of Microsoft, Google, and Amazon also service hydrocarbon industries with EnergyTech capabilities.
The major baseline technologies used within EnergyTech are familiar members of the Industry 4.0 family. For clarity, I have listed these, alongside their contexts here:
EnergyTech In Action
I’ve made some insight into the best examples of EnergyTech in the field. Many are in development stages; however, as you will discover, these technologies are ready for deployment.
IoT Predicts Local Weather
Global: Big Data Analytics, Predictive Algorithms
IBM Watson utilises big data and predictive models, to forecast weather patterns in micro-regions, specific to such narrow areas as plots of farmland, or small cities.
Internet-of-Things, or IoT, sensors positioned inside wind turbines provides actionable real-time data.
Such ability is advantageous to traditional meteorological forecasts and offers wind farm operators considerable foresight into prevailing conditions, enabling an accurate power supply calculation.
Optimising National Energy Grids & Maintenance Systems
Germany: Artificial Intelligence, Predictive Algorithms
A leader among advanced economies in boasting a diverse energy mix, Germany already makes substantial use of wind and solar energy.
Considering this diverse mix, day-specific demand spikes, such as Christmas, can surpass maximum output. Conversely, weather volatility directly curtails wind turbines’ and solar collectors’ ability to produce electrical power.
In these instances, AI responds in-kind, automatically tapping into fossil fuel power stations, thus supplementing the grids’ power deficit, thereby preventing lapses in the general power supply.
United Kingdom, Germany: Big Data Analytics, Predictive Algorithms
ROMEO, an industry-based consortium, supported by the European Union, aims to develop an understanding of, and improve the lifecycle of wind turbines and wind farms.
On-board IoT sensors inside turbines detect abnormal machine behaviours, environmental factors, and the overall state of wind farms. The offshore location of these wind farms in; East Anglia and Teeside, and Wikinger, along the North and Baltic seas, render reactive maintenance resource-ineffective.
The project will form the benchmark from which predictive algorithms will enable proactive maintenance plans for wind farms in some of the most logistically-isolate locations.
Sustaining Geothermal Power Facilities
Japan: Machine Learning, Predictive Algorithms , Artificial Intelligence
Toshiba Energy Systems & Solutions Corporation spearheads a research program aiming to reduce the rate of problem occurrences by 20%. Through implementations of sensors and AI, the program successfully reduces breakdowns and improves production capacities of geothermal power plants by up to 10%.
This model notably applies to a critical preventative measure, involving the application of chemical solutions, designed to maintain an optimal turbine capacity. Adverse effects of otherwise incorrect, or untimely applications of these chemical solutions have the potential to shut down the entire facility.
Historic operational data from generators is compiled and evaluated, enabling future maintenance plans, vastly improving what is termed as currently less cost-effective to Solar and Wind energy.
The Way of the Future
A resonating attribute of EnergyTech is the ability to make large-scale, calculated predictions. By tapping into the IoT, and Big Data, power companies now possess the ability to shift from reactive to proactive maintenance – a comparatively scalable, and cost-effective tactic.
Such are methods which serve to provide solutions for this sunrise industry, enabling better prospects, and cost-effective operations from the ground-up.
Implementation requires software know-how in industrial-scale applications. So software developers are the vital asset in acquiring this level of technology. Energy providers will undoubtedly take notice of this.