With WIS4Flex DSOs are able to better dimension the flexibility potential of their LV clients that belong to a given substation and thus have a higher visibility of what may be made available through flexibility market mechanisms to help achieve a more optimized and cost-efficient grid management.
Applying AI-powered analytics such as Non-Intrusive Load Monitoring (NILM) algorithms, neural networks and other approaches and relying only on the energy consumption profiles of existing clients, as collected from the DSOs, WIS4Flex identifies and quantifies existing electric loads and is capable of quantifying and forecasting the flexibility potential that can be used in flexibility markets for ancillary grid services.
Load disaggregation from DSO collected smart metering data to identify energy consumed by the main appliances.
Consumer Segmentation based on the Demand Response potential for each client, identification of cross selling opportunities and more
Solar PV and EVs
Data Analytics for the identification of solar PV solutions and Electrical Vehicles in the Low Voltage grid
Identification, quantification and forecast of aggregated (per substation) or individual flexibility potential
Relying only on DSO collected smart metering data, FSPs and Aggregators are able to forecast and plan flexibility availability to be used to provide grid services, while HEMS are able to enrich the level of information they provide their clientes with load disaggregation information.
Energy retailers and Flexibility aggregators will be able to easily identify and segment clients with specific loads (such as Solar PV installations, EVs, water heaters, etc.) to engage them in Demand Response programs or adopt cross-selling strategies as a way to generate new revenue streams.
Easily integrate WIS4Flex with your solutions through a dedicated API or the Interconnect semantic interoperability framework (based on SAREF ontologies).
AI-based load disaggregation to identify the most common appliances from DSO smart metering data
Quantification of aggregate flexibility potential
Clustering approaches for consumer segmentation