Nexans – Nexans invests in Sensewaves, a French start-up revolutionizing power grid with AI

EMR Analysis

More information on Nexans: See the full profile on EMR Executive Services

More information on Christopher Guérin (Chief Executive Officer, Nexans + President, Europacable): See the full profile on EMR Executive Services

More information on Jean-Christophe Juillard (Deputy Chief Executive Officer, Chief Financial Officer and Chief Information Officer, Nexans): See the full profile on EMR Executive Services

 

More information on Elyette Roux (Executive Vice President, PWR-Grid & Accessories, Nexans + Non Executive Board Member, Reka Cables by Nexans): See the full profile on EMR Executive Services

More information on Jérôme Fournier (Corporate Vice President, Innovation, Services & Growth, Nexans): See the full profile on EMR Executive Services

 

 

 

More information on Sensewaves (Minority stake by Nexans): https://www.sensewaves.io/ + Operating flexible, reliable and safe electricity grids, now and in the future.

Based in France, our team of highly qualified and passionate individuals share a common guiding philosophy: a commitment to helping Energy & Utilities companies operate cleaner, more resilient and safer grids both now and in the future.

AI-powered analytics for Energy and Industry. A new generation of analytics intelligence specifically designed to rapidly respond to today’s evolving Energy landscape. We make connected assets smarter by transforming raw sensor data into meaningful information with Adaptix, our adaptive Time Series Analytics platform. 

Through our Adaptix.Grid software, we help utilities bring visibility and situational awareness into their power network in just a matter of weeks, allowing for significant time savings and better decision making through advanced grid management operations.

More information on Fivos Maniatakos (Co-founder and Chief Executive Officer, Sensewaves): https://www.sensewaves.io/about-us/ + https://www.linkedin.com/in/fivos-maniatakos-a1818213/ 

More information on Adaptix.Grid by Sensewaves (Minority stake by Nexans): https://www.sensewaves.io/adaptix-grid/ + Adaptix.Grid by Sensewaves is the first platform for the dynamic optimization of the electricity grid. It provides the necessary analytics and analysis tools to integrate the legacy with the new infrastructure, ensuring agility and flexibility of operations as well as grid sustainability both in the short and long-term.

Adaptix.Grid integrates Artificial Intelligence and advanced algorithmic engineering to revolutionize the operations of the grid operator. The software takes advantage of the existing data residing in different silos within the utility and cross-references them to their maximum potential.

 

 

 

More information on American Electric Power (AEP): https://www.aep.com/ + Our team at American Electric Power is committed to improving our customers’ lives with reliable, affordable power. We are investing $54 billion from 2025 through 2029 to enhance service for customers and support the growing energy needs of our communities. 

Our nearly 16,000 employees operate and maintain the nation’s largest electric transmission system with 40,000 line miles, along with more than 225,000 miles of distribution lines to deliver energy to 5.6 million customers in 11 states. 

AEP also is one of the nation’s largest electricity producers with approximately 29,000 megawatts of diverse generating capacity. We are focused on safety and operational excellence, creating value for our stakeholders and bringing opportunity to our service territory through economic development and community engagement. 

Our family of companies includes AEP Ohio, AEP Texas, Appalachian Power (in Virginia and West Virginia), AEP Appalachian Power (in Tennessee), Indiana Michigan Power, Kentucky Power, Public Service Company of Oklahoma, and Southwestern Electric Power Company (in Arkansas, Louisiana, east Texas and the Texas Panhandle). AEP also owns AEP Energy, which provides innovative competitive energy solutions nationwide. AEP is headquartered in Columbus, Ohio.

• 2024 Revenues: $19.7 billion

More information on William J. Fehrman (Chairman, President & Chief Executive Officer, AEP): https://www.aep.com/about/leadership/ 

More information on Edward Hunt (Head of Strategic Initiatives, AEP): https://www.linkedin.com/in/edward-hunt-cfa-286638162/ 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Grid, Microgrids, DERs and DERM’s:
  • Grid / Power Grid:
    • The power grid is a network for delivering electricity to consumers. The power grid includes generator stations, transmission lines and towers, and individual consumer distribution lines.
      • The grid constantly balances the supply and demand for the energy that powers everything from industry to household appliances.
      • Electric grids perform three major functions: power generation, transmission, and distribution.
  • Microgrid:
    • Small-scale power grid that can operate independently or collaboratively with other small power grids. The practice of using microgrids is known as distributed, dispersed, decentralized, district or embedded energy production.
  • Smart Grid:
    • Any electrical grid + IT at all levels.
  • Micro Grid:
    • Group of interconnected loads and DERs (Distributed Energy Resources) within a clearly defined electrical and geographical boundaries witch acts as a single controllable entity with respect to the main grid.
  • Distributed Energy Resources (DERs): 
    • Small-scale electricity supply (typically in the range of 3 kW to 50 MW) or demand resources that are interconnected to the electric grid. They are power generation resources and are usually located close to load centers, and can be used individually or in aggregate to provide value to the grid.
      • Common examples of DERs include rooftop solar PV units, natural gas turbines, microturbines, wind turbines, biomass generators, fuel cells, tri-generation units, battery storage, electric vehicles (EV) and EV chargers, and demand response applications.
  • Distributed Energy Resources Management Systems (DERMS):
    • Platforms which helps mostly distribution system operators (DSO) manage their grids that are mainly based on distributed energy resources (DER).
      • DERMS are used by utilities and other energy companies to aggregate a large energy load for participation in the demand response market. DERMS can be defined in many ways, depending on the use case and underlying energy asset.

 

 

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
  • Agentic AI:
    • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.

 

 

• Transmission and Distribution (T&D):
  • Vital components of the electricity supply chain, ensuring that electricity reaches consumers safely and reliably.
    • Transmission:
      • Transmission is the first vital component of the electricity supply chain.
      • High-voltage electricity: Electricity is generated at power plants at relatively low voltages. To transport it efficiently over long distances, the voltage is increased significantly using transformers.
      • Long distances: High-voltage transmission lines, often carried on tall towers, transport bulk electricity from power plants to substations located closer to cities and towns.
      • Minimizing losses: Transmitting electricity at high voltage reduces energy loss during transportation.
    • Distribution
      • Distribution is the final stage of the electricity supply chain, bringing power to the end user.
      • Lower voltage electricity: At substations, the high-voltage electricity is reduced to lower, safer levels suitable for homes and businesses.
      • Local delivery: Distribution lines, typically the ones you see along streets, carry electricity from substations to individual customers.
      • Final stage: Distribution is the final step in the electricity delivery process, bringing power directly to homes and businesses for everyday use.

 

 

• Power Utility – Utilities:
  • Also known as an electric utility or power company, is a company or entity responsible for generating, transmitting, and distributing electricity to consumers. They often operate in regulated markets and are major providers of energy in most countries.

 

 

• Transmission System Operator (TSO): 
  • A Transmission System Operator (TSO) is an entity entrusted with transporting energy in the form of natural gas or electrical power on a national or regional level, using fixed infrastructure. The term is defined by the European Commission.
• Distribution System Operators (DSO) & Distribution Network Operator (DNO):
  • Distribution System Operators (DSOs) are entities responsible for distributing and managing energy from the generation sources to the final consumers. Digitalization is the key to securing the DSO model, which requires investments in automation, smart meters, real-time systems, big data and data analytics.
  • A DNO (Distribution Network Operators) already performs much of the tasks that a DSO does, but there are differences. A conventional distribution network is not an active but a reactive or passive network. Passive distribution networks are designed to accept bulk power from the transmission system and distribute it, down the network, to consumers.

 

 

• Extra Low-Voltage (ELV):
  • Extra-Low Voltage (ELV) is defined as a voltage of 50V or less (AC RMS), or 120V or less (ripple-free DC).
• Low-Voltage (LV):
  • The International Electrotechnical Commission (IEC) defines Low Voltage (LV) for supply systems as voltage in the range 50–1000 V AC or 120–1500 V DC.
• Medium-Voltage (MV):
  • Medium Voltage (MV) is a voltage class that typically falls between low voltage and high voltage, with a common range being from 1 kV to 35 kV. In some contexts, this range can extend higher, up to 69 kV.
• High-Voltage (HV):
  • The International Electrotechnical Commission define high voltage as above 1000 V for alternating current, and at least 1500 V for direct current.
• Super High-Voltage or Extra High-Voltage (EHV): 
  • Super High-Voltage or Extra High-Voltage (EHV) is the voltage class used for long-distance bulk power transmission. The range for EHV systems is typically from 230 kV to 800 kV.
• Ultra High-Voltage (UHV): 
  • Ultra High-Voltage (UHV) is the highest voltage class used in electrical transmission, defined as a voltage of 1000 kV or greater.

 

 

• Power Quality:
  • The term power quality may be defined as a wide variety of electromagnetic phenomena that characterize the voltage and current at a given time and location in the power system.
  • Power quality refers to the characteristics of the electrical supply that ensure reliable and efficient operation of electrical equipment. It encompasses factors like voltage, frequency, and waveform, and deviations from these standards can lead to equipment malfunction, damage, or even loss of data. Essentially, it’s about how well the power supply matches the needs of the equipment.