The adoption of distributed energy resources and the digital transformation underway in the energy sector raise new challenges for the industry. These include defining new financial models, addressing security concerns and managing data transfer costs while ensuring a seamless and engaging consumer experience.
carbonTRACK’s intelligent energy platform was created with the decentralised energy ecosystem in mind, with many of the following features available right from its 2013 launch.
The carbonTRACK platform consists of hardware, network infrastructure, control systems, analytical engines and a powerful machine learning and predictive engine. These include:
Analytical-Q is an advanced machine leaning engine with multi-facet deployment capability. It leverages the distributed computing power of the carbonTRACK fleet and platform services and provides a deterministic model for system decision making.
Analytical-Q combines this collective and individual intelligence to create a bespoke Energy Map for each carbonTRACK user.
2. Energy Map
A multi-dimensional map based on the energy ecosystem within which a carbonTRACK system is installed. It provides operational and environmental information to the carbonTRACK control system, such as Time Of Use (TOU) rates, Weather, Market energy data, load characteristics and grid-level demand portfolio details, in a compressed 24 byte format.
3. carbonTRACK Operating System (CTOS)
CTOS is as sub-operating system capable of hosting and managing several applications in parallel, allowing them to share the same hardware resources.
CTOS is made up of a trio of Sub System applications called CTCore, CTHelper and CTSystem. Together they provide failsafe, redundancy and self-healing capability. CTOS provides a greater level of application performance security by monitoring applications under its care and shutting down any apps that are not performing as expected.
These features enable carbonTRACK to open the Application Hub to partners who may want to extend the carbonTRACK platform services feature sets for their own use. They can now develop their own apps to run on the carbonTRACK Platform safely and securely.
4. Automated load and source optimisation AutoLSO
carbonTRACK’s AutoLSO functionality automatically optimises loads and storage devices in the following categories:
5. Distributed Security Model (DSM)
carbonTRACK’s DSM is being developed to further enhance the Platform’s security features. It uses multiple nodes within the carbonTRACK network - located across multiple geographical locations - to provide encryption and authentication tokens for data transportation. Every carbonTRACK system operating on CTOS will act as a node in this model. As the number of systems in the network increase, the security keys will get stronger providing an auto-scaling feature.
6. Minimum Data Transfer (SecureMDT)
carbonTRACK’s patented SecureMDT protocol is a highly compressed two-way communication protocol designed to enable the transfer of low volumes of data with low latency. It can be deployed across a wide range of hardware and operating systems with a high level of security. SecureMDT is not limited to the physical network link layer. It can be used on 3G, 4G, 4G-LTE CAT M1 and Ethernet/Wi-Fi interfaces. The main advantage of this protocol is the way it transfers data using a high compression ratio. By way of an example, 20,000 carbonTRACK Smart Gateways operating with SecureMDT will use less than 2GB of data in a month.
The success of a decentralised energy model requires coordinated response capability where energy supply and demand are seamlessly managed in micro-grids, embedded networks and virtual power plants.
The above carbonTRACK technology elements create a co-ordinated response control hub - arranging and controlling loads in a hierarchical order as determined by an energy consumer /generator or an energy aggregator/supply manager.
carbonTRACK’s platform services can deliver a utility scale message to all devices and sub-nodes attached to carbonTRACK devices. The platform can also schedule events based on Energy Map trigger points and provides forward planning models for redundancy.