The Energy Digitalisation (EDG) group at UBITECH focuses on using digital technologies and mathematical/AI models to make energy systems smarter, more efficient, and more sustainable. Our mission is to accelerate the transition to a cleaner and more reliable energy future by combining data driven frameworks, such as AI and machine learning with digital tools like IoT, blockchain technology and digital twins. By enhancing grid management, enabling real-time monitoring, and providing intelligent decision support for diverse energy stakeholders, we reduce environmental impact and improve operational resilience. Our work also empowers citizens to become active participants in the energy transition. Working closely with industry, academia, and research partners and public authorities, EDG group drives innovation and contributes to policies development that supports digital transformation in the energy sector.
Key Research Areas:
- Energy Efficiency & Demand Side Management: We develop advanced forecasting techniques and dynamic pricing models to optimize energy consumption, reduce peak loads, and encourage sustainable habits among end-users and prosumers. By leveraging IoT sensors, real-time feedback, and mathematical optimization, we enable stakeholders to adapt usage patterns and minimize overall energy costs.
- Forecasting & Optimization Models: We leverage in-depth mathematical expertise to develop accurate energy forecasting and optimization frameworks. Our models maximize self-consumption for prosumers and profit margins for aggregators through sophisticated algorithms, predictive analytics, and dynamic pricing mechanisms—enabling data-driven decision-making for all market participants.
- Smart Grids & Grid Optimization: Our research focuses on advanced grid monitoring, intelligent control, and resilience strategies for next-generation energy systems. Through AI- driven analytics and edge computing, we enhance grid reliability and resilience and facilitate seamless integration of renewables.
- Renewable Energy Integration: We investigate methods for harmoniously incorporating solar, wind, and other renewables into existing energy infrastructures. By addressing challenges like intermittency, storage, and dispatch optimization, our work contributes to a cleaner, climate-friendly energy mix that benefits both providers and consumers.
- Privacy Preservation & Cybersecurity: We design secure frameworks and protocols that safeguard user data and operational information across distributed energy networks. Our privacy-preserving solutions maintain trust, ensure regulatory compliance, and protect critical infrastructure from evolving cyber threats.
- Digital Marketplaces & Energy Trading: Our team explores decentralized platforms and smart contracts to enable transparent, cost-effective energy trading across diverse stakeholders. By applying blockchain technologies, automated negotiation mechanisms, and advanced market analytics, we foster secure, peer-to-peer exchanges and novel market opportunities.
Technological Domains
The Energy Digitalisation (EDG) Group focuses on harnessing advanced digital technologies, data-driven analytics, and mathematical modeling to accelerate the shift to sustainable, resilient, and decentralized energy ecosystems. Our key technological domains include:
- Digital Twins: Digital twins serve as virtual counterparts of physical energy systems, enabling real-time monitoring, predictive maintenance, and scenario simulation. Within the EDG group, we employ simulation frameworks (e.g., MATLAB/Simulink, Gridlab-D, Pandapower) and data orchestrators (e.g., Dagster) to collect data and replicate operational conditions. This allows us to test new algorithms, validate system performance, and optimize resource allocation before making real-world interventions. Our focus on digital twins supports data-driven decision-making that enhances grid reliability, and helps stakeholders manage energy assets more effectively.
- Hardware in the Loop Simulations: By integrating real hardware components (such as inverters, controllers, or sensors) into simulated environments, HIL simulations bridge the gap between lab testing and actual field deployment. We utilize real-time simulation platforms (e.g., OPAL-RT) to assess control strategies, power electronics, and communication protocols under realistic conditions. This approach is crucial for evaluating algorithms and use cases prior to large-scale rollouts.
- GenAI: The EDG group works on large language models (LLMs), vector databases, and next-generation chatbots. By pairing advanced neural architectures (e.g., Transformers) with retrieval-augmented generation (i.e., storing and searching embeddings in vector databases), we can create context-aware assistants that empower citizens to make informed decisions about energy usage and trading. In parallel, we explore specialized GenAI solutions that can bridge real-time data streams with digital twins to enhance situational awareness and decision support for system operators.
- Blockchain: Blockchain technologies ensure transparency, security, and trust within decentralized energy marketplaces. We explore smart contract platforms like Ethereum and Hyperledger Fabric to automate energy trading, billing, and incentive mechanisms. By integrating blockchain with IoT devices and data analytics, we facilitate peer-to-peer transactions, transparent settlement processes, and tamper-proof records of energy exchanges—paving the way for new market opportunities and active citizen participation.
- Big Data Management: The EDG group implements scalable, object-based storage services (e.g., based on MinIO) and robust identity access management services (e.g., based on Keycloak) to ingest, store, and process ever-growing energy datasets. Through MinIO’s S3-compatible architecture, we handle high-volume sensor readings, time-series data, and market transactions in a fault-tolerant and cost-efficient manner. We couple this with Keycloak’s centralized authentication, authorization, and user management, ensuring secure data sharing and fine-grained access control across diverse energy stakeholders. By integrating big data frameworks (e.g., Apache Spark, Apache Kafka) on top of these storage and identity layers, we enable streaming analytics, advanced data governance, and near-real-time decision support.
- Data Space implementation: We participate in the development of interoperable, secure, and GDPR-compliant data spaces that facilitate data sharing among energy stakeholders. By adopting international standards (e.g., IDSA) and semantic ontologies for energy data, we ensure seamless information exchange across platforms. These data spaces underpin collaborative initiatives, enabling real-time coordination between grid operators, prosumers, and service providers—accelerating innovation while preserving data sovereignty and privacy.
Specialized Expertise
The Energy Digitalisation (EDG) Group specializes in harnessing data-driven analytics, mathematical modeling, and emerging technologies to reshape modern energy systems. Our key areas of expertise include:
- Advanced Forecasting & Optimization: Employing sophisticated optimization and AI solutions such as mixed-integer linear programming, Long-Short Term Memory (LSTM) architecture, etc. We develop models for load/generation forecasting, self-consumption maximization and profit maximization. These frameworks enable near-real-time decisions that support dynamic pricing, aggregator profit maximization, and self-consumption strategies. Validated across multiple EU-funded projects, our solutions have demonstrated reductions in peak demand and better utilization of the flexibility that comes from Distributed Energy Resources (DERs), such as PVs and batteries.
- Secure Data Management & Privacy Preservation: Beyond big data technologies, our privacy-by-design approach integrates object-based storage (e.g., MinIO) and centralized identity/access controls (e.g., Keycloak) to protect vast, heterogeneous datasets. We implement GDPR-compliant methods, anonymization techniques, and fine-grained authorization to safeguard operational data. Our methodologies have been successfully deployed in both decentralized data space initiatives and centralized platforms, ensuring regulatory adherence and secure stakeholder collaboration.
- AI-Driven Control & Decision Support: We leverage GenAI and digital twin technologies to create explainable AI assistants for control room operators. These advanced tools unify deep learning (e.g., large language models) with physics-based simulations, enabling proactive contingency planning and enhanced decision-making. Field trials in Horizon Europe projects have showcased faster response times, improved grid stability, and improved system operator user experience.
Description
SYNERGIES brings forward a reference Energy Data Space Implementation that will attempt to unleash the data-driven innovation and sharing potential across the energy data value chain by leveraging on data and intelligence coming from diverse energy actors (prioritizing on consumers and introducing them as data owners/ providers) and coupled sectors (buildings, mobility) and effectively making them reachable and widely accessible. In turn, SYNERGIES will facilitate the transition from siloed data management approaches to collaborative ones which promote the creation of a data and intelligence ecosystem around energy (and other types of) data and enable the realization of data (intelligence)-driven innovative energy services that (i) value the flexibility capacity of consumers in optimizing energy networks’ operation, maximizing RES integration and self-consumption at different levels of the system (community, building), (ii) evidently support network operators in optimally monitoring, operating, maintaining and planning their assets and coordinating between each other (TSO-DSO collaboration) for enhancing system resilience, (iii) create an inclusive pathway towards the energy transition, through consumer empowerment, awareness and informed involvement in flexibility market transactions, (iv) step on real data streams and intelligence to deliver personalized and automated features to increase prosumer acceptance and remove intrusiveness, (v) facilitate the establishment of sustainable LECs by enhancing their role with Aggregator and BSPfunctions, and (vi) establish solid groundsfor the creation of a new economy around energy data produced and shared across a complex value chain, in a secure, trustful, fair and acceptable manner.Key Contributions
Within SYNERGIES, UBITECH team drives the development of the Local Flexibility Pooling and Sharing App for Intra-Community Flexibility Transactions, enabling local flexibility sources/ prosumers to engage in flexibility transactions with aggregators/ Local Energy Community Operators and monetize their flexibility through balancing and ancillary services to network operators. Moreover, UBITECH undertakes the implementation of the backbone infrastructures of the SYNERGIES Energy Data Space, namely a) the Data Mesh Coordination Platform that resides in the cloud and is responsible for all data blocks and services across the different SYNERGIES Data Fabric Environments; and (b) the SYNERGIES Data Fabric Environments (Centralized Cloud, Federated Private Cloud, Federated Private Server and Edge environments) allowing the different stakeholders to integrate, host, analyse and serve/share their data assets in a secure and effortless manner.
Description
ENFLATE aims at developing and demonstrating in six demonstration campaigns across five countries, a collaborative platform of tools enabling consumer-driven business models for energy services, valorising their multi-vector flexibility potential, integrating them with other non-energy services (cross-industry services), like health and mobility ones. In particular, ENFLATE will carry out pilots in demo sites in five dispersed European countries adapting to the local energy requirements, and will (i) enhance system flexibility by promoting the synergy among various energy vectors and additional cross-industry services (e.g., health, mobility), (ii) accelerate decarbonization strategies, through local energy management and promotion of local DERs, (iii) improve assets energy usage levels and cost-efficiency, (iv) test new market designs and business models for grid services that enable new revenue streams for consumers while (v) engaging all actors and consumers in the energy value chain coupled also with non-energy ones (e.g., quality of healthcare, comfort, mobility, emergency service for elderly people).Key Contributions
Within ENFLATE, UBITECH undertakes the overall technical coordination and is responsible for describing the interactions and requirements for the various components of the ENFLATE ICT platform and data flows, including the interconnection with external, existing EU platforms. UBITECH leads the development of the ENFLATE Data Management Layer, including the identification and implementation of the necessary measures for the resolution of the several data handling, privacy and security issues. Finally, UBITECH is responsible for the design and deployment of the overall ENFLATE architecture.
Description
SINNOGENES project aims to develop the Storage INNOvations (SINNO) energy toolkit, a complete framework of methodologies, tools and technologies that will enable the grid integration of innovative storage solutions beyond the state-of-the art, while demonstrating sustainability, technical performance, lifetime, non-dependency on location geographical particularities and cost. It will develop successful energy storage business cases and systems and deploy them in innovative and 'green' energy systems at different scales and timeframes. SINNOGENES will target the effective integration of innovative energy storage systems and value chains at the interface of renewable energies and specific demand sectors, while ensuring the compatibility of systems and standards of distributed energy storage for participation in flexibility markets. Six pilot projects will take place in Portugal, Spain, Germany, Greece and Switzerland while a detailed scalability and replicability analysis will prove the wide impact of SINNOGENES project innovations at pan European level.Key Contributions
Within SINNOGENES, UBITECH undertakes the overall technical coordination and develops the necessary functionalities that will ensure that access to data and access to SINNOGENES provided services are controlled at all different levels, data transfers are encrypted, and processed data are properly anonymized. UBITECH team drives the implementation of the SINNOGENES middleware, an implementation of a federated Energy Data Space.
Description
HumAIne will research, develop, validate and promote a novel operating system for Human-AI collaboration, which will enable the development of advanced decision making applications in dynamic, unstructured environments in different industrial sectors. The HumAIne OS will empower AI solution integrators to implement Human-AI collaboration systems that outperform AI systems and humans when working in isolation. HumAIne’s developments will be integrated into a single OS platform, which will coordinate four interwind components offering Active Learning (AL), Neuro-Symbolic Learning, Swarm Learning (SL) as well eXplainable AI (XAI) capabilities. These advanced AI paradigms are ideal for exploiting true Human-AI collaboration since, in each of them, the worker is the key actor with complete control and understanding of the performed operations. AL enables the development of effective Human-in-the-Loop systems that involve humans when AI faces increased uncertainty. Neuro-Symbolic Learning combines DL with semantics and rules to complete highly complex tasks with high accuracy while requiring considerably less training data than current AI models. Advanced XAI models will be made available, providing explanations of models’ predictions while considering the global context instead of just analysing the feature importance of a single AI model. HumAIne’s XAI will provide guidance to humans to enable the timely optimisation of AL and SL models where human participants provide feedback dynamically as well as fine-tuning of Neuro-Symbolic models. The platform will handle various types of structured and unstructured data, including inputs from humans that will be semantically correlated through ontologies, knowledge graphs, and semantic interoperability.Key Contributions
Within HumAIne, UBITECH leads the development of technologies for secure data collection and management, ensuring reliable, privacy-preserving access to pilot and AI datasets through sovereign identities, role-based access control, and single sign-on mechanisms integrated with the storage of the HumAIne platform. In parallel, UBITECH drives the implementation and validation of the Smart Energy Pilot, leveraging HumAIne’s collaborative AI capabilities, including Active Learning, Explainable AI, and Human-Machine Interfaces, to support human-centric decision-making in power grid operators. These developments are evaluated through HumAIne’s Benchmarking Suite to ensure transparency, improved decision-making, and trustworthiness.
Description
ODEON is conceived under the main principle that the envisaged Digital and Green Transition can only be realized through the creation of an inclusive ecosystem of stakeholders characterized by the integration of a mesh of Data, Intelligence, Service and Market flows, jointly enabling the resilient operation of the energy system under increased RES integration and effective orchestration of flexibility offered by the wealth if distributed energy, building and mobility flexible assets residing at the edges of the system, across prosumers’ premises and LEC boundaries. ODEON introduces a sound, reliable, scalable and openly accessible federated technological framework (i.e. ODEON Cloud-Edge Data and Intelligence Service Platform and corresponding Federated Energy Data Spaces. AI Containers, Smart Data/AIOps orchestrators) for the delivery of a wealth of services addressing the complete life-cycle of Data/AIOps and their smart spawn in federated environments and infrastructures across the continuum. It will integrate highly reliable and secure federated data management, processing, sharing and intelligence services, enabling the energy value chain actors and 3rd parties to engage in data/intelligence sharing, towards the delivery of innovative data driven and intelligence-powered energy services in accordance to the objectives set by the DoEAP. ODEON results will be extensively validated in 5 large-scale demonstration sites in Greece, Spain, France, Denmark and Ireland involving all required value chain actors, diverse assets, heterogeneous grid and market contexts, and multi-variate climatic and socio-economic characteristics to support its successful replication and market uptake.Key Contributions
Within ODEON, UBITECH drives the elaboration on the project's foundations, resulting in the documentation of the project's concept, the definition of the ODEON Minimum Viable Product (MVP), the documentation of the requirements stemming from the end-users as actors and perspective customers of ODEON, and the design of the architecture and define the specifications for the technology development and integration activities of the project.
Description
The EU-funded DIGITISE project leverages digital technologies and data to accelerate the clean energy transition. It aims to enhance digital literacy among consumers and prosumers, empowering them to engage in digital energy activities and markets. By combining expertise in energy systems, flexibility services, energy markets and human engagement in digital ecosystems, DIGITISE integrates advanced technologies – such as innovative cross-sector services, digital twins, DLT-enabled marketplaces, AI analytics, big data management, and interoperability and security – into a comprehensive consumer empowerment framework. This framework will be validated in a living lab and four large-scale demonstration sites in Croatia, Greece, Ireland and Spain, involving consumers, prosumers, retailers, aggregators and other stakeholders.Key Contributions
Within DIGITISE, UBITECH undertakes the overall administrative project management and consortium coordination, and is responsible for the implementation of the DIGITISE Digital Twin Solutions and Tools for Consumers Engagement in Marketplace Environments. In particular, UBITECH will deliver i) the open-source DIGITISE Household Digital Twin implementation that will effectively contribute to optimization of data-driven services and enhancement of consumers digital energy literacy; (ii) the DIGITISE Flexibility Marketplace for open and inclusive participation and fair compensation of consumers and prosumers.
Description
TwinEU will leverage a unique set of competences coming from grid and market operators, technology providers and research centres to create a concept of Pan European digital twin based on the federation of local twins so to enable a reliable, resilient, and safe operation of the infrastructure while facilitating new business models that will accelerate the deployment of renewable energy sources in Europe. TwinEU will define a set of appropriate interfaces and API to relevant technological building blocks and components to fully support a variety of different DT applications, which may eventually be also used by third party. Alignment with ongoing effort in BRIDGE (DERA v3.0) and with SGAM model and with EU-level IDSA and GAIA-X Data Space Architectures will be carried out in a bidirectional way. TwinEU will leverage on H2020 OneNet and HE ENERSHARE to design and implement a federated DT ecosystem and underlying infrastructure, which will consists of a choreography of closed loop DTs instances, each of them provided with the capability to 1) synchronize with real assets by bidirectional data exchange, 2) self learn dynamically the real context and accordingly take autonomous decisions based on online AI edge and reduced-order physical models and self-evolve the model itself to better represent the real asset, 3) define the appropriate scale, granularity and space resolution, 4) makes use of Model-to-Data (M2D)-driven Federated Learning to enable a decentralized edge-level model learning for effective privacy- and confidentiality management trading off latency versus computational resources.Key Contributions
In TwinEU, UBITECH realizes the Big Data management dimension of the TwinEU for batch and real-time data ingestion, management and curation while designing and delivering the respective data collection services bundle. Also, UBITECH ENERGY will connect the AI technologies of TwinEU addressingdata driven challenges (i.e., demand/generation forecasting, behavioural analytics, elasticity profiling and forecasting) and will enable service, data, and models discoverability for the orchestration of data space ecosystem instantiated for the interoperation of the local DTs and the pan-European view - evolving the IDSA compliant Federated Catalogue into the implementation of Services Workbench that will act as the mediator for the utilization of open services.
Description
UBITECH's EDG research group has been awarded a cascading grant from FFplus for its innovative project, mAIEnergy. This groundbreaking initiative leverages Generative AI and High-Performance Computing (HPC) to empower citizens in their active participation in the energy transition. mAIEnergy aims to provide a digital assistant designed to enhance energy literacy and digital empowerment among citizens, playing a critical role in the energy transition. The core mission of mAIEnergy is to support digital empowerment and energy literacy of citizens in order to make energy concepts more accessible and engaging for diverse audiences. The proposed digital assistant will leverage personalization and localization to enhance understanding of renewable energy, efficiency, markets, and smart grid technologies, while guiding citizens on incentives for adopting renewables and comparing energy providers that align with sustainability goals. It will also help compare different energy providers and plans, highlighting those that align with sustainability goals. mAIEnergy will create a Generative AI-based co-pilot that integrates a wealth of multi-modality energy data to guide citizens through the complexities of the energy transition. By using cutting-edge technologies such as AI, Generative AI, and HPC, the project aims to enhance citizens’ understanding and engagement with energy data, driving them towards more informed, sustainable energy decisions. Thus, mAIEnergy aims to leverage AI-driven solutions to bridge the gap between complex energy systems and individuals, providing personalized insights and guidance to promote sustainable energy use and informed decision-making. This will pave the way for broader public participation in a greener, more inclusive energy future. Led by EDG group, consortium consists of partners from Greece (UBITECH) and Austria (University of Innsbruck and FEN Research).Key Contributions
In mAIEnergy, UBITECH undertakes the overall administrative project management and consortium coordination, and is responsible for the development of a digital assistant—the mAIEnergy Co-pilot—designed to empower citizens with enhanced digital energy literacy. UBITECH is responsible for establishing the underlying infrastructure using open-source technologies such as Milvus, OpenSearch, and Neo4j to build interconnected vector databases, which serve as the foundation for a Hybrid Retrieval-Augmented Generation (HybridRAG) information retrieval system. Building on this infrastructure, UBITECH deploys and adapts open-source Large Language Model (LLM) implementations, fine-tuning them with domain-specific knowledge embedded in the vector databases.
Description
The frESCO project aims to engage with ESCOs and aggregators and enable the deployment of innovative business models, on the basis of novel energy and integrated energy service bundles that properly combine and remunerate local flexibility for optimizing local energy performance both in the form of energy efficiency (energy savings) and demand side management (demand response). Such new service and business models will bring under common Pay for Performance Contracts (extended form of current EPCs) two currently differentiated service offerings to enable the realization of next-generation smart energy service packages. The frESCO service packages will combine moderate retrofitting services (smart equipment for data collection and remote/ automated control) with energy efficiency, distributed energy resources (generation and storage) with self-consumption optimization schemes and smart home automation with the provision of balancing and ancillary services to the grid (under the form of demand response), to engage residential consumers in energy efficiency/ grid optimization activity through integrated and tailor-made packages addressing different customer groups, their unique needs and preferences, without overlooking important aspects of the modern citizens demand for comfort and well-being (human-centric energy optimization).Key Contributions
Within frESCO, UBITECH drived the architectural design and the specifications of the frESCO solution, as well as the development of the multi-service package toolkit for ESCOs and aggregators that enabled them to provide the bundled energy services to residential consumers with enough flexibility so as to adapt their offer to their customers’ requirements. In particular, UBITECH developed the Energy Management Analytics and Self-Consumption Optimization Tool for ESCOs, targeted on one hand the extraction of insights with regards to energy management aspects to ESCOs and on the other hand on the provision of optimal plans to ESCOs for planning their self-consumption characteristics, as well as the Advanced Flexibility Analytics and Optimal VPP configuration tool for Consumer-Centric Demand Response Optimization, concerning the optimisation of fitting as much as possible energy demand and response in smart grid settings.
Description
The PHOENIX project changes the role of buildings from unorganised energy consumers to active agents orchestrating and optimising their energy consumption, production and storage, with the goal of increasing energy performance, maximising occupants’ benefit, and facilitating grid operation. The project designs a portfolio of ICT solutions covering all aspects from hardware and software upgrades needed in legacy equipment and optimal deployment of sensors, to data analytics and services for both building users and energy utilities. PHOENIX takes advantage of artificial intelligence technologies, as well as edge/cloud computing methods, to provide the highest level of smartness to existing buildings. The PHOENIX tools offer the possibility of establishing a new framework that enables the optimisation of the energy use and infrastructure exploitation, while at the same time facilitates the creation of new SMEs and Start-Up ideas to exploit new revenue streams and business opportunities.Key Contributions
Within PHOENIX, UBITECH focused on the implementation of data analytics tools that enabled the development of user-centric services to building’s occupants to generate on-the-fly automatized decisions for comfort preservation and wellbeing, utilizing the data context from metering and sensing within buildings for improving situation awareness, as well as the information from smart devices such as: occupancy, CO2 levels, humidity, temperature, lighting, energy consumption, type of energy intensive devices, local micro-generation availability, potential forecasted information (e.g. weather), both from historic and real-time data pools. Moreover, UBITECH drived the implementation of Cost-effective, User-Friendly Services for Building Users and Occupants, incorporating Comfort, Convenience & Wellbeing related services and Predictive Maintenance, Automatic SRI Calculation & EPC Evaluation Services in a unified, interactive dashboard.
Description
BEYOND delivers a Big Data Management Platform with an advanced AI analytics toolkit that enables the execution of a wealth of descriptive-predictive-prescriptive analytics out of a blend of real-life building data focusing on Personal Analytics (consumer behaviour, comfort and flexibility profiling), Industrial Analytics (Energy Performance, Predictive Maintenance, Forecasting & Flexibility analytics), along with Edge Analytics towards intelligent real-time automated control of building assets.Key Contributions
In BEYOND, UBITECH undertakes the overall administrative project management and consortium coordination, and is responsible for developing the Building Portfolio Management Optimization tool. It offers a holistic view and respective insights over highly populated building portfolios/ customers of energy retailers towards: (i) examining advanced billing concepts (e.g. dynamic electricity pricing) by segmenting, clustering and analysing consumption behaviours, inferring the elasticity of specific clusters against varying electricity pricing levels and deploying highly effective energy pricing campaigns, towards optimizing the performance of their portfolio and hedging against non-anticipated imbalances; (ii) monitoring their compliance to Energy Efficiency obligations imposed by the European Commission and adopted by the Member States and designing appropriate portfolio management/ energy efficiency strategies and campaigns to achieve the anticipated targets; and (iii) analysing spatio-temporal patterns of their portfolio, identifying trends and outliers and receiving valuable knowledge for the design and delivery of added value services per individual customer or clusters of them to satisfy their needs for energy cost reduction through targeted added-value energy service bundles (e.g. retrofitting or renovation, personalized energy efficiency guidance, energy performance certification, DR).
Description
The OneNet project addresses the growing needs of TSO‘s and DSO’s to have real-time insight into the operation of their networks to work in a closely coordinated way, while unlocking and enabling new flexibility markets in a fair and open way. Goal is to enable a cost effective, seamless and secure bidirectional power flow to and from network customers as active players while supporting grid operators in their system responsibilities. In particular, OneNet provides a seamless near real time integration of all the actors in the electricity network across countries with a view to create the conditions for a synergistic operation that optimizes the overall energy management while creating an open and fair market structure. This synergistic process is enabled by open IT architectures that guarantee continental level interoperability. This new open architecture will provide new market mechanisms encouraging new business models which will be developed to support both large population areas and small- and medium-sized DSOs and TSOs, in Europe. OneNet will develop a unique concept of scalable interoperable data management able to unlock flexibility at European level creating fair conditions for all the stakeholders. OneNet will have an agnostic approach to define solutions that are not only open today but also open to future development.Key Contributions
Within OneNet, UBITECH led the design and implementation of the Cyber-security and data privacy architectural layer. More specifically, UBITECH undertook the development of the (i) Network Traffic & Endpoint Infrastructure Monitoring tool, responsible for continuous monitoring of the source traffic/logs/events that come through the OneNet Connector, to assist on the cyber-security preservation aspects of the OneNet solution. Malicious network activity and system vulnerabilities are identified so that data access policies to the OneNet system can be updated or enhanced. (ii) Data Analysis, Rating & Classification tool, responsible for network traffic classification or clustering based on the machine learning algorithm used (supervised or unsupervised). The algorithm can extract useful features around the data traffic such as basic features (source/destination IP address, source/destination host port, frame length), time-based features (number of frames received in a specific time interval), connection-based features (number of packets flowing from source to destination and vice versa) or even classify under normal/abnormal traffic.Electricity Transmission System Digital Twin
The Transmission System Digital Twin creates a simulation-based virtual replica of transmission networks. It accurately models the physical and operational characteristics of the grid, including buses, lines, generators, and loads, enabling realistic power flow and N-1 security analyses. Integrating real-time and historical data, the Digital Twin allows system operators to simulate “what-if” scenarios, such as equipment outages, in a risk-free environment. It supports calculation of voltages, line currents, and detection of threshold violations, enhancing proactive planning, operator training, and real-time operational decision-making.
Supporting Technologies:
PyPSA-Eur is used for the extraction of detailed topological representation of national networks, buses (indicative of substations and generators) and transmission lines. Pandapower is the selected tool providing simulation capabilities. The frontend is a custom implementation in Angular using graph and digital map libraries. Near real-time and historical data injection is achieved through integration with external systems like ENTSO-E Transparency Platform, providing open data by European TSOs, the IPTO open APIs providing details specific to the Greek system and OpenMeteo providing weather data. More information available here
Household Digital Twin
The Household Digital Twin steps on the real-time data streams and historical data from the consumer side like metering, sub-metering, IoT, generation, storage, EVs and more, and effectively fuses simulation functions (physical models) with derivative data models (AI models) to facilitate (i) the monitoring and assessment in real-time of the performance of consumer assets (energy assets, buildings and their systems, EV charging points etc..), (ii) the definition optimal context-aware and human-centric control strategies over flexible assets and devices and (iii) the further optimization of building performance by continuously assessing the effectiveness of applied strategies and supporting the re-design of updated and more effective ones over selected devices.
Supporting Technologies:
Arras, formerly known as GridLab-D, is an open-source tool included in the Linux Foundation Energy projects. Arras is appropriately extended to build a household digital twin interacting with the simulated distribution grid and actively participating in local markets. The developed Household Digital twin is integrated with a data space implementation to retrieve static and real-time data streams together with data analytics and AI model results.
Generative AI-based co-pilot supporting citizens in energy transition
The main aim of mAiEnergy, a generative AI-based co-pilot, is to support digital empowerment and energy literacy of citizens by making energy concepts more accessible and engaging for diverse audiences. By integrating a wealth of multi-modality energy data publicly available online and leveraging Generative AI technology and High-Performance Computing (HPC), mAiEnergy, enhances understanding of topics like renewable energy sources, energy efficiency, energy and flexibility markets, and smart grid technologies, achieving a high societal and environmental impact. Through personalization and localization, it also assists citizens in navigating the energy transition by providing information on available incentives, grants, programs and more.
Supporting Technologies:
Three existing open-source vector databases are integrated to effectively handle all data modalities. (i) Milvus specializes in sustainability and energy-related data. (ii) OpenSearch is a versatile database that includes textual, document, GIS, and vector search capabilities. (iii) Neo4j is a graph database that facilitates advanced data analytics and querying through its graph-based structure. A Hybrid Retrieval-Augmented Generation (HybridRAG) framework is developed, to create an information retrieval system able to query the above-mentioned databases constraining our generative AI to the targeted enterprise content sourced from vectorized documents and images, and other data formats using the embedding models for that content. Three open-source LLM models are used to create the LLM supporting mAIEnergy, encompassing a number of strengths and covering the needs of mAIEnergy: (i) Command-r, an LLM with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. (ii) Mistral/Mixtral designed to handle complex NLP tasks such as text generation, summarization, and conversational AI. (iii) LLaVA, Large Language and Vision Assistant is an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.
HIL Experimentation Testbed
The Hardware-in-the-Loop (HIL) Testbed is a platform for executing power system simulations aiming the development, testing, and validation of complex real-time embedded systems in smart grid environments. It provides high-fidelity Digital Twinning capabilities for both real-time and accelerated applications, enabling comprehensive support for planning and operational activities. This testbed allows the connection of control and protection devices to real-time (RT) simulations of power systems through low voltage/current signals and industrial communication protocols. This setup captures the dynamic interactions among components and enables closed-loop testing to study device functionality in realistic operational conditions. Overall, the HIL Testbed is a critical tool for ensuring the reliability, safety, and efficiency of smart grid technologies through advanced simulation and validation techniques
Supporting Technologies:
An OPAL-RT HIL FPGA-based RT-simulator is the core of the HIL testbed, while MATLAB/Simulink is used for the implementation of the physical models of power systems.
Decentralized Flexibility Marketplace
The decentralized flexibility marketplace brings forward a consumer-centric market design to facilitate the active participation of consumers in energy activities and the realization of significant benefits. Through this market design the DLT-enabled Flexibility Marketplace provides every single consumer with the right to engage in multiple market transactions and exchange flexibility sources trustfully and fairly. This environment allows distributed flexibility sources/consumers to engage in smart flexibility contracts and flexibility transactions with aggregators (and other actors i.e. retailers/Local Energy Communities (LECs) addressing this role) and monetize their flexibility while streamlining the processes related to contract set-up, activation, measurement, settlement and remuneration of flexibility according to the value it can obtain in upward flexibility markets and the quantified value of lost utility for consumers.
Supporting Technologies:
Ethereum is the selected blockchain implementation to support the contracting, settlement & remuneration mechanisms. Smart contracts are written in Solidity, while the frontend implementation is based on Angular. The marketplace is integrated with the Synergies energy data space for data retrieval and results sharing among market participants and the relevant stakeholders.
Federated & Interoperable Energy Data Space
The Federated & Interoperable Energy Data Space is a decentralized system, enabling secure and efficient sharing of energy and relevant sector (like transport) related data among diverse stakeholders, such as energy providers, grid operators, consumers, and regulators. In this implementation, data remains with its original owner, ensuring data sovereignty and privacy, while allowing controlled access. The interoperability aspect ensures that different platforms and systems can seamlessly exchange, understand, and use the data through common standards and protocols. This approach supports collaboration, drives innovation, and enhances the flexibility and sustainability of the energy ecosystem. Our data space implementation leverages the eIDAS (Electronic Identification, Authentication, and Trust Services) framework for identity verification using sovereign electronic identities supported by Connecting European Facilities.
Supporting Technologies:
The Connector and the Dynamic Attribute Provisioning System (DAPS) implementations are extensions of the opensource IDS building blocks and are provided as opensource. Additional centralized and decentralized services related to transfer orchestration handling local storage and file transfer, data anonymization and token-based authentication/authorization mechanisms are dockerized services implemented in java, node js and python. Keycloak implements Identity Access Management and through its integration with eIDAS it provides access to the data space leveraging sovereign electronic identities at EU level.
Secure Data Collection & Management System
A robust digital infrastructure designed to efficiently gather, store, process, and protect data from diverse and distributed sources, such as IoT devices, sensors, smart meters, and user inputs. This system ensures end-to-end data integrity and confidentiality through advanced attribute-based access control, and secure communication protocols, safeguarding sensitive information against unauthorized access and cyber threats. It provides real-time data ingestion, validation, and advanced querying/filtering functionalities, facilitating seamless integration with analytics platforms and decision-making tools, while ensuring transparent data handling practices. By providing a scalable, reliable, and secure environment, it enables the development of advanced decision-making applications in dynamic, unstructured environments in different industrial sectors like energy, health, smart cities, manufacturing, banking and finance.
Supporting Technologies:
MinIO is used as an object storage solution, suitable for storing different file formats, handling access control through policies enabled for fine grained control. Keycloak implements Identity Access Management. Integrated with an eIDAS node, through an authentication back-end component, keyclock provides access to the storage system leveraging sovereign electronic identities. A custom API has been developed, in Python using FastAPI framework, to support different functionalities to interact with MinIO, such as querying/filtering through custom metadata attached to the objects.
Virtual Power Plant (VPP) and Portfolio Management Application
A comprehensive digital platform that enables energy retailers, aggregators and operators to manage, optimize, and gain insights into large-scale, decentralized energy systems and building portfolios. It aggregates distributed energy resources—such as solar panels, wind turbines, batteries, electric vehicles, and flexible loads—and controls them as a single virtual entity to optimize energy generation, consumption, and storage in real time. By leveraging advanced algorithms, real-time data analytics, and artificial intelligence, the application manages flexibility sources by organizing them into clusters with similar behavior, simplifying operations while maximizing efficiency. Beyond operational control, it provides powerful portfolio management capabilities, including performance tracking, regulatory compliance monitoring, and strategic decision support. By analyzing consumption behaviors, identifying elasticity to dynamic pricing, and uncovering spatio-temporal patterns, the platform empowers retailers to deploy targeted energy efficiency strategies, meet European energy regulations, and offer personalized services such as dynamic pricing campaigns, or demand response programs—ultimately optimizing business performance and delivering added value to end customers.
Supporting Technologies:
The application uses an open-source scalable and extendable dockerized microservices architecture, with open REST APIs. It consists of the Dashboard providing a GUI to the end-user implemented in Angular framework; the load balancer (NGNIX web server) that serves as the intermediate component between the frontend and the backend, responsible for redirecting the client requests to the respective microservices; the core backend microservice implemented in Spring framework holding core functionalities such as user registration and authorization, events and logs management, as well as notifications and recommendations management; the authentication microservice that offers the user management service and allows only authenticated user requests to reach the backend services implemented through Keycloak; the analytics microservice, implemented through the Django framework and the local storage based on Elasticsearch.
Group Leader
Dr Magda Foti (Head of Group)
Expertise: Smart Grids, Energy Markets, Digital Twins, Decentralized Systems and Data Analytics
Short Bio
Magda, Head of Energy Digitalisation (EDG) Research Group, with a PhD from the University of Thessaly, specializes in the digitization of energy systems, focusing on decentralized energy markets and demand response through machine learning and game theory. Her research integrates power systems with advanced technologies like blockchains and optimization tools, contributing significantly to the field with publications and conference presentations. Magda’s professional journey includes roles in academia, the European Commission, and ICT companies, where she has deepened her expertise in smart grids and energy transition.
Key Team Members
Ms. Katerina Drivakou (Energy Systems Researcher)
Expertise: Power Systems, Energy Policy & Economics, Energy Efficiency
Short Bio
Katerina Drivakou is an Energy Systems Researcher at UBITECH. She works in various EU Horizon 2020 and Horizon Europe projects carrying out research on the topics of flexibility markets, demand response, energy efficiency and smart grids, while being responsible for the projects’ technical implementation. Katerina has also work experience in the energy analytics domain, having worked as an Energy Analyst, supporting the development of energy monitoring tools, promoting electromobility and consulting on the energy efficiency potential of commercial and industrial buildings. She holds an integrated master’s degree in Electrical and Computer Engineering with a major in Electric Power Systems and she is currently pursuing an MSc in Energy: Strategy, Law & Economics.
[LinkedIn]
Mr. Costas Mylonas (Senior R&D Architect)
Expertise: Digital Twins, Smart Grids, AI and Generative AI
Short Bio
Costas was born in Athens, Greece. He received the Diploma degree in electrical and computer engineering from the University of Patras, Greece, in 2016, and the M.Sc. degree in energy science and technology from the Swiss Federal Institute of Technology, Zürich, Switzerland, in 2020. Since 2022, he has been a Research and Development Software Engineer with UBITECH. During the M.Sc. degree, he was with the ABB Corporate Research Center, Switzerland.
[LinkedIn]
Ms. Eleftheria Petrianou (R&D Engineer)
Expertise: Energy Flexibility, Simulations and Machine Learning
Short Bio
Eleftheria was born in Thessaloniki, Greece. In early 2021, she received her Bachelor's Degree with an Integrated Master's from the department of Electrical and Computer Engineering of University of Thessaly in Volos, Greece. Her main goal since the realization of her diploma thesis has been to successfully apply recent technologies in data science, machine learning and AI in the energy sector. From December of 2020 she works as a Data Engineer and Researcher in UBITECH, engaged in research projects focused on the digitalization of the energy domain.
[LinkedIn]
Ms. Esen Kunt (Senior Delivery & Fundraising Manager)
Expertise: R&D Project Management
Short Bio
Esen has extensive expertise in managing complex R&D programs in more than 15 years, with a strong background in Energy and AI. She possesses an MSc degree in New Media Research and Design from the esteemed University of Twente, the Netherlands. Her professional journey in the technology sector commenced in 2010 at Cyntelix Corporation BV, a distinguished multinational Dutch R&D software Small-Medium Enterprise (SME) that specializes in pioneering Information and Communication Technologies (ICT) solutions and fostering international project collaborations. Since then, she gained valuable experience working with various technology companies primarily SMEs and university technology transfer offices in the Netherlands, Turkiye, Greece and Belgium. She excels in grant proposal development, technical concept design, and overseeing multinational projects the relevant public grant organization such as the European Commission, EUREKA, Innovate UK, etc. Currently, she is a Technical Project Manager at the EDG Energy Digitalization Research Group.
[LinkedIn]
Recent Highlights
Collaboration & Partnerships
Academia & Research:
- Research Center for Energy Resources and Consumption - CIRCE (Spain)
- ETRA INVESTIGACION Y DESARROLLO SA - Grupo Etra (Spain)
- TXT E-SOLUTIONS SPA - TXT (Italy)
- TEKNOLOGIAN TUTKIMUSKESKUS VTT OY - VTT (Finland)
- University College Dublin - UCD (Ireland)
- Danmarks Tekniske Universitet - DTU (Denmark)
- Institute of Communication and Computer Systems - ICCS (Greece)
- University of Piraeus Research Center UPRC (Greece)
- Fraunhofer-Gesellschaft FIT, CSP and FOKUS (Germany)
- University of Murcia (Spain)
Industry & Public Bodies:
- HEDNO (Greece)
- IPTO (Greece)
- Motor Oil (Greece)
- Metlen Εnergy & Metals (Greece)
- MIWenergía (Spain)
- Cuerva (Spain)
- INZENJERING ZA ENERGETIKUI TRANSPORT DD – KONCAR (Croatia)
- Uludağ Elektrik Dağıtım A.Ş. - UEDAS (Turkey)
- Troya Çevre Derneği (Turkey)
- Collective Energy community (Greece)
- Suite5 (Cyprus)
- RINA Consulting (Italy)
- ENEA (Italy)
For collaboration opportunities and other inquiries, contact us at [email protected]
