Trialog/EVIDEN guidance on using Digital Twin Reference Architecture and contributions to CEI Architecture considerations Antonio Kung (Trialog) and Lara Lopez (EVIDEN) both attended the plenary meeting of ISO/IEC JTC1/SC41 in...
Domain:
Urban
Partners:
Polygones, CLEMAP, HES-SO, Val D’Anniviers
Goal:
The aim is to showcase how innovative Smart IoT solutions, already deployed within networks of prosumers, can be integrated into the O-CEI platform to enhance user acceptance and engagement.
The aim is to develop new Smart Energy Services (SES) based on an enhanced solution.
Democratization of energy demand forecasting for e-vehicle charging SES
This scenario focuses on residential buildings from Meyrin, which have local grids and charging stations for electric vehicles (EVs). It enables EV owners and software vendors to optimize vehicle charging through grid insights, integrating unidirectional power flow (V1G) as well as more extended Vehicle-to-X (V2X) for energy network enhancement, with O-CEI monitoring, Smart Contracts, and new business models.
Advanced graph data processing and LLM for energy prediction
O-CEI Data Fabric will be employed as an information graph containing energy data and results from previous scenario, and will integrate energy data, user inputs, and AI models to forecast energy consumption and production, using edge computing, LLM models, and serverless functions for efficient data processing and collaboration.
Self-adaptive energy and computation continuum in distributed tourism area
Edge equipment in Val D’Anniviers will use O-CEI orchestration, cybersecurity, and Machine Learning Operations for context-aware energy forecasting, improving efficiency in villages and ski stations through decentralized analytics and continuous updates.
In all scenarios, social acceptance by design will be driven by a combination of qualitative surveys, conjoint analysis, and discrete choice experiments (DCE) optimized on edge devices.
Domain:
Telecom
Partners:
GAP, Nova, CERTH, Ares2T
Goal:
The goal of this pilot is to leverage a private 5G network and cybersecurity measures to optimize the EV charging ecosystem, improving resource balancing, reducing costs, and enabling data-driven services
Pilot 7 presents two complementary scenarios focused on 5G-driven EV charging.
Edge optimization of power flow to EV needs
This scenario transforms chargers into “intelligent” bidirectional elements, optimizing grid power flow through O-CEI microservices, data management, and 5G’s low-latency, ensuring efficient load balancing and reduced environmental impact.
Increasing trustworthiness and security in EV charging data exchange
The O-CEI blueprint will incorporate security measures, data sovereignty, and auditing to ensure trustworthy data flow from sensors and EVs, supporting energy balance decisions with continuous monitoring and response to cyber threats.
Domain:
Agrifood
Partners:
Garaia, Innovalia, Zettrack, Agrimac
Goal:
This pilot uses O-CEI’s monitoring, data integration, and decentralized computing to optimize energy management of electric tractors in kiwi farming, reducing emissions, energy use, and addressing travel and battery cost challenges.
P6 introduces three scenarios aimed at enhancing the energy efficiency of agricultural operations of electric tractors.
Real-time energy consumption aid for agricultural decision
The O-CEI platform will provide farmers with energy optimization suggestions, managing fleet, climate, and location data. It will propose action plans for efficient area coverage and extended charging downtime, using real-time services.
Remote route optimization and task planning of robot tractors
The O-CEI AI-block will optimize agricultural tasks in kiwi plantations, using state-of-the-art models for efficient operations and route planning, with real-time data storage and semi-autonomic management.
Autonomous tractor re-charging strategy
The tractors in this pilot will optimize battery charging through edge analytics and AI predictions, either autonomously or via a farmer-controlled app, minimizing battery use, extending lifespan, and reducing costs for improved sustainability.
Domain:
Agrifood
Partners:
Green Supply Chain, Embio Diagnostics, Charalambides Christis
Goal:
The pilot uses O-CEI technologies for IoT-based monitoring and energy optimization, aiming to reduce the energy footprint per kilogram of dairy product in Cyprus, promoting sustainable farming despite challenging climates.
Livestock farming and the dairy industry are vital to Cyprus’s economy but face significant challenges from rising energy costs and the unstable agro-environmental conditions affected by climate change.
IoT-Assisted Livestock Management based on Edge Intelligence and automation
The pilot optimizes energy use in stables while reducing animal heat stress, using IoT sensors and cameras for behavior classification, edge computing for HVAC control, and forecasting for efficient milk production.
Controlled sharing of data from various stables to the dairy production factory
O-CEI mechanisms and marketplace will securely share trusted stable data on milk quality, feed mix, and energy footprint, using AI to forecast parameters, optimize energy consumption, and improve production practices with secure data management.
Intelligent selection of energy mix for dairy production
This scenario optimizes cheese production’s energy sources (grid, solar, biofuel) based on demand, availability, and price, using O-CEI models and Decision Support System (DSS) to reduce costs, environmental impact, and optimize operations.
Calculation of energy footprint of dairy (halloumi) products
This scenario aggregates energy consumption data across the production chain, using O-CEI orchestration and IoT-edge-cloud elements to optimize energy use, enhance trustworthiness, and audit halloumi production’s sustainability.
Domain:
Logistics
Partners:
CMA CGM, Prodevelop, Awake.ai, Enemalta, Schneider
Goal:
This pilot aims to optimize energy usage in Malta’s port operations by aligning demand with capacity, using edge-optimized control systems for real-time adjustments based on vessel schedules, container handling equipment (CHE) usage, and the island grid data.
The Malta pilot focuses on three interconnected scenarios that leverage O-CEI technologies for monitoring, prioritization, and AI-driven predictions. These scenarios demonstrate how O-CEI technologies can handle simultaneously energy management across vessels, port terminals, and residential settings to achieve a sustainable and efficient energy ecosystem.
Vessel Energy Management
The O-CEI platform will integrate energy flows from berthed vessels, using decentralized monitoring, IoT, and cybersecurity utilities to minimize blackouts and optimize energy control through orchestration and real-time dashboards.
Terminal Energy Management
This scenario integrates terminal asset energy data with O-CEI orchestration, coordinating peak power demands from Container Handling Equipment (CHE), and reefers to avoid unnecessary peaks, with individual control systems.
Home Consumers
This scenario replaces home electricity meters with an O-CEI orchestrated control system, managing device priorities for critical services and appliances through secure data governance and pub/sub-models of the project.
Domain:
Mobility
Partners:
Austrian Postal Service, Energie Steiermark, AVL
Goal:
This pilot optimizes electric fleet operations by integrating intelligent charging, V2G, and renewable energy utilization, reducing emissions, energy consumption, costs, and enhancing grid stability.
The Austrian Postal Service (POST) operates 5000 electric trucks that connect to EV charging stations owned by POST or Energie Steiermark. Both the vehicles and charging stations can share data through O-CEI software services, helping to speed up the adoption of smart, sustainable energy and mobility strategies.
P3 introduces three innovative use cases designed to optimize energy consumption, enhance grid stability, and reduce emissions through the integration of O-CEI technologies. These use cases highlight how advanced energy strategies and O-CEI technologies can transform electric fleet operations and grid management, driving sustainability and efficiency.
Infrastructure analysis to optimize Storage Battery Systems
For this use case, we analyse the power infrastructure (PV panels, batteries, charging infrastructure), the control and communication infrastructure, and the various communication protocols. This knowledge will then be used to optimize the location and operation of stationary storage battery systems.
Vehicle-to-Grid (V2G) – Vehicles as Mobile Energy Storage Units
Building on the first scenario, this use case optimizes EVs as mobile storage units, utilizing O-CEI for intelligent charging, discharging, and fleet route planning, enhancing fleet resilience and battery autonomy. Among others, we will assess the economic balance of the state of health degradation of the EV battery vs. revenue generation by providing energy to the grid at market price.
Smart and Innovative Energy Management Systems
O-CEI will evaluate mobile storage systems, such as trucks, supporting grid peak shaving and reactive power management. A virtual power plant integrates energy delivery, billing, and regulatory data for interventions.
Domain:
Electricity Grid
Partners:
Continental, FENIX2.0, ERTICO
Goal:
P2 scales Continental’s innovations by integrating a Software Defined Vehicle (SDV), transforming it into an active hub for computation, data exchange, and context-aware connectivity.
P2 introduces four SDV scenarios, each focusing on Vehicle-as-a-Service (VaS) to address key areas of urban mobility through O-CEI-enabled technologies. These scenarios demonstrate the transformative role of SDVs as active infrastructure elements, revolutionizing safety, sustainability, energy integration, and grid optimization in urban mobility.
VaS for Vehicle Safety
This scenario enhances vehicle safety with AI-driven system monitoring, OTA updates, advanced cybersecurity, and real-time technologies for the reliable, secure, and dynamic operation of embedded systems (e.g., engine management, braking, steering, infotainment)
VaS for Sustainability
This scenario leverages AI utilities from the O-CEI marketplace to optimize EV powertrain efficiency, minimize carbon footprints, and ensure driver engagement with sustainable, real-time feedback and seamless integration.
VaS for Fleet V2G Integration
It uses AI to optimize EV charging and energy return via Vehicle-to-Grid (V2G) capabilities, with edge and cloud software supporting fleet-wide data analysis and operations.
VaS for Grid Optimization
This scenario uses V2G, smart grids, and AI to optimize energy use, schedule charging during off-peak hours, utilize dynamic pricing, and grid balancing for sustainable vehicle interactions.
Domain:
Energy grid
Partners:
UCC, EDF, MDU, Powerledger, Smart MPower, Eko Kvarner
Goal:
The pilot aims to transition towards a greener, more efficient, and resilient energy system, paving the way for a sustainable and robust energy future.
Community-Driven Renewable Energy Integration
The O-CEI project empowers prosumers/flexumers to trade energy, optimize usage, and store excess via AI-driven tools, boosting grid stability and renewable energy use.
Smart Thermal Load Management
Sensor data and smart controls dynamically manage heating for energy efficiency using edge computing.
Wide Smart Grid Optimization
Seasonal demand patterns are optimized with renewable integration and decentralized learning models.
EV and Heat Pump Synchronization
Synchronizing renewable energy for household appliances and EVs ensures comfort and charging availability.
Residential Energy Management
IoT gateways and secure O-CEI utilities empower users to monitor energy use and promote grid flexibility.
Advanced Load Scheduling
Proactive scheduling for appliances like electric water heaters and space heaters, will enhance grid capacity and user comfort using edge computing and robust connectivity.
Each of these scenarios highlights the role of O-CEI in creating innovative, sustainable, and resilient energy solutions tailored to specific community needs and environments.
