Using data science techniques to implement a virtual power plant platform and demonstrate that the overall cost of energy can be reduced
Opportunity
The leading Australian energy provider sought to be an early mover in the market and implement a virtual power plant platform.
The organisation was looking for a solution that would allow them to determine the optimal dispatch regime for Distributed Energy Resources (DER) (i.e. renewable energy units or systems that are commonly located at houses or businesses to provide them with power, including rooftop solar panels; battery storage; etc.).
The organisation had a vision for a cloud-based platform that would be able to control heavy electrical load in both residential and commercial premises, as well as storage devices including batteries and electric vehicles.
The key outcome the energy provider was working towards was to demonstrate that the overall cost of energy can be reduced by implementing central artificial intelligence control over the portfolio of DER devices.
Solution
Designed and built a best in class scalable and cost-efficient virtual power plant platform using cloud-based technologies. As part of this:
Designed the platform in line with the organisations existing security standards and with the ability to ingest large volumes of data in near real-time from multiple sources, such as customer and device information, network tariffs, feed-in tariffs, and historic demand data
Designed the platform to be device agnostic, meaning any smart device, either energy producing or consuming or both, can be controlled and optimised to maximise value
Developed a forecasting engine to produce device level price forecasts as well as demand forecasts for each individual home or business
Developed a constraints engine where device details and operating preferences are translated to meet customer requirements
Developed a central optimisation engine which has the ability to produce schedules for 3 million devices within a 5 minute window, optimised to maximise the value in the system
Industry:
Energy, Utilities and Resources
Essential seven focus
Outcomes
Effective management of a consistent energy supply to customers and reduction in power surge risks through monitoring and prediction of usage volumes
Optimisation of supply chain efficiencies and a reduction in costs for the business through the use of internal and external data to inform the forecasting engine
Tangible impact on the ease of customer decision making with the ability to predict millions of customer schedules in minutes
