Member - michel clemence

Odit-e propose machine learning algorithms able to process metering data to automatically digitize utilities’ networks.

Type Startup or self-employed
Founder 2017
Company Size 11
Member Type
Innovator
Founders Michel Clemence
Headquarters 38240 Meylan, France
Social network
michel clemence

About

Distribution networks collect productions, serve consumers, and now have to deal with flexibilities, but their behavior is still obscur, leading to oversizings and increased costs. Managing these flexible networks requires a whole new level of transparency and understanding of their behavior. However, the complexity of distribution networks, especially Low Voltage ones, coupled with the lack of reliable information, make their digitization a real challenge. Our unique selling point is to create a digital twin of the network with no other information than the measurements provided by smart meters. No network map, no electrical information, no manual effort to model the network. This has been made possible by combining a deep expertise in electrical networks with a broad knowledge of machine learning algorithms. Our digital twin enable digital simulations and real time visualizations for utilities, and therefore flexible management of their networks, without requiring any effort.
Headquarters 38240 Meylan, France

SDG’s of application

The Sustainable Development Goals are a call to action to end poverty, protect the planet and ensure peace and prosperity everywhere.
SDG 6
Clean Water and
Sanitation
SDG 6 icon
SDG 7
Affordable and
Clean Energy
SDG 7 icon
SDG 9
Industry, Innovation
and Infrastructure
SDG 9 icon
SDG 11
Sustainable Cities
and Communities
SDG 11 icon
SDG 12
Responsible Consumption
and production
SDG 12 icon

Offers from michel clemence

Other products

Low Voltage networks’ behaviors are largely unknown,therefore distribution network operators have to take big margins when planning their evolution. Odit-e has developed a way to process metering data in order to automatically build the network’s digital twin, from which network components can be extracted. Network simulation tools can therefore be automatically fed with appropriate network models, without requiring any manual effort: it then becomes possible for any utility to use such modern efficient tools This network identification has two main outputs: The network topology giving the connection scheme of final customers (transformer, LV feeder, phase, sequencing, trees…). This algorithm has already been validated Network characteristics (such as cable impedances or neutral grounding) can be extracted from the digital twin and sent to network simulation tools. This algorithm is still a prototype

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Available inEastern Africa, Middle Africa, Western Africa, Eastern Europe, Categories of applicationAffordable and clean energy, Sustainable cities and communitiesSector of applicationEnergy distribution & management, Building and shelters Status Concept

Low Voltage networks’ behavior are largely unknown, so that distribution network operators have to take big margins when planning their evolution. The growing insertion of renewables, coupled with the need for flexibility, is pushing for a change. Distribution network planning need to evolve towards digitization. Odit-e has found a way to process metering data in order to automatically build the network’s digital twin, opening the door to digital simulations. It becomes possible to precisely estimate the impact of a new production, or electric vehicle charging station, to avoid unnecessary reinforcements and size the proper flexibilities. Network capacity maps can also be computed. By bridging physical and virtual worlds, Odit-e enable data driven decision making for flexible distribution networks.

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Available inMiddle Africa, Western Africa, Categories of applicationAffordable and clean energy, Sustainable cities and communitiesSector of applicationClean energy production, Energy distribution & management, Communities infrastructures, Building and shelters Status Tested implementation

Low Voltage networks have an extremely low transparency level. The current smart meters deployment is a huge opportunity, unfortunately they do not send information in real time. Up to day, real-time knowledge of low voltage networks stays out of reach, preventing any real-time management of flexibilities or predictive maintenance. Odit-e has developed an innovative way of providing real-time visualization for Low Voltage networks, by reconstructing its electrical “most probable” state from all the available information (primary substation, weather and calendar data). This statistical state estimation, made possible by combining electrical knowledge with artificial intelligence, drastically increase the transparency level for Low Voltage networks.

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Available inEastern Africa, Middle Africa, Western Africa, Categories of applicationAffordable and clean energy, Sustainable cities and communitiesSector of applicationEnergy distribution & management, Urban and inter-urban mobility, Communities infrastructures, Building and shelters Status Tested implementation