Australia’s energy landscape is rapidly changing. In fact, we now have more solar per capita than anywhere else in the world. With the uptake of solar, battery storage and other distributed energy assets increasing there is a need to do things differently. Virtual Power Plants are one of the key technologies in the new energy paradigm.
Intelligent virtual power plants such as Karit’s are transforming renewable energy forecasting, management and optimisation with machine learning. In this article, we’ll define machine learning and renewable energy forecasting, show you a few examples of how we use it in our Karit Virtual Power Plant platform, and explain how your business can benefit.
More than 3 million rooftop solar systems are now installed on homes and businesses across Australia. According to GlobalData, solar installations are expected to grow four-fold by 2030 and reach a capacity of 80.22GW.
This influx of solar, along with batteries and other distributed energy assets, has meant that we need to take a fresh approach to how energy is managed. The old system that was built for one-way energy flows and centralised generation is no longer sustainable, can’t cope with two-way energy flows and won’t meet changing societal expectations. Technological advancements in cloud-based software, internet of things (IoT) applications, artificial intelligence and virtual power plants are enabling this change.
When you connect to a virtual power plant you can boost the effectiveness of your solar and other distributed energy assets. Virtual power plants that use artificial intelligence come into their own as they deliver the greatest benefits for communities, businesses, and energy retailers alike.
Essentially, a virtual power plant (VPP) is a network of decentralised and distributed energy assets that are managed and optimised by a central control platform. It means that energy assets, like solar, batteries and electric vehicles, can be orchestrated across different locations and work together like a power plant.
The global VPP market is growing significantly year on year which is great news for the rapid decarbonisation of our planet. However, it’s important to note that not all VPPs are created equal. Some VPPs simply use an IoT communications device to control inverters and batteries to manage peak demand. That’s it. On the other end of the spectrum, more sophisticated VPPs use software informed by machine learning to carry out forecasts and efficiently manage and optimise the VPP in real-time.
What is Machine Learning?
A subset of artificial intelligence (AI), machine learning allows a computer system or machine to learn from past data so that they deliver accurate results.
So, how does a VPP use machine learning? To illustrate the point, let’s take a look inside the control room, the brain as you will, of our Karit Virtual Power Plant.
Our Karit VPP is a cloud based command and control platform that connects with specifically designed energy management devices, we call Karit Cakes. These devices collect energy and asset data and enable commands to be sent between the distributed energy assets and the control centre platform.
The VPP’s control centre platform is integrated with real-time weather and energy market data. This provides a framework for the intelligent management of energy flows and VPP performance.
Our Karit VPP uses sophisticated data modelling and machine learning to interpret grid, demand, market, weather and asset performance data to drive automated responses to changes in the operating environment of the customer.
So what is the benefit of machine learning?
When VPPs use machine learning in the renewable energy forecasting process it leads to more accurate, efficient and timely predictions and responsiveness. This ultimately helps you to achieve your goals: whether you’re looking to reduce costs, manage demand, make money and/or demonstrate your environmental responsibility.
Before we look at how VPPs use renewable energy forecasting, let’s take a moment to explain the concept.
What is Renewable Energy Forecasting?
Renewable energy forecasting is a calculation or prediction of the amount of renewable energy that will be generated and consumed in the foreseeable future. This forecast is based on weather and performance monitors, market predictions and other predetermined factors.
The great thing about machine learning is that it means that forecasting can be completed in a matter of seconds rather than days. It also gives VPPs the ability to respond in real-time to electricity supply and demand needs as well as being able to optimise energy use, assets and storage.
Renewable energy forecasts are used to help distributed energy asset owners to make informed decisions about energy planning, scheduling, trading and optimisation.
How do Virtual Power Plants Use AI in Forecasting?
With recent advancements in machine learning, software systems can learn patterns from historical data and produce accurate forecasting numbers for the future.
At Karit, we have leading data scientists and analysts delving deep into the insights, building, testing and refining the forecasting models.
