Gathering 22 partners from the EU, Norway, Switzerland and Ukraine, iDesignRES aims to provide public authorities and network operators with open-source toolboxes allowing to plan and to optimise the uptake of low and zero emission energy sources at regional, national and European scales. With these open-source tools, iDesignRES provides latest multi-physics component energy models, comprehensive energy system modelling (in particular assembling of component models), long-term multi-carrier grid planning and optimisation of investment and operation. iDesignRES embraces a new degree of openness, transparency and accessibility to use energy system models and their components. It delivers a harmonized data structure compatible with all modelling approaches (i.e., standardized in a common modelling language). iDesignRES relies on an already built database compliant with these standardized data formats. Based on this modelling and data infrastructure, iDesignRES will develop a unique and innovative cloud-hub platform to run any energy system model online. This will facilitate research collaboration (easier software testing and version control), and increase computational performance (open licenses, cloud services and supercomputing) for average users. To demonstrate and validate the tools, iDesignRES has five real-life cases: two macro region cases (North Sea area and South East Europe), two industrial clusters (Basque and Lombardy regions), and one expansion case in Ukraine. The models and scenario results are combined in the final iDesignRES visualisation tools aimed to inform policy makers and grid operators. The outcome is that the public and relevant authorities can design a renewable energy system entirely online with easily accessible suite of tools, capable to: model at high spatial-temporal resolutions (sub-hourly NUTS level2 regions), to represent detail energy system physics, and to assemble models with a high degree of modularity.

The smart energy ecosystem constitutes the next technological leap of the conventional electrical grid, providing multiple benefits such as increased reliability, better service quality and efficient utilization of the existing infrastructures. However, despite the fact that it brings beneficial environmental, economic and social changes, it also generates significant security and privacy challenges, as it includes a combination of heterogeneous, co-existing smart and legacy technologies. Based on this reality, the SDN-microSENSE project intends to provide a set of secure, privacy-enabled and resilient to cyberattacks tools, thus ensuring the normal operation of EPES as well as the integrity and the confidentiality of communications. In particular, adopting an SDN-based technology, SDN-microSENSE will develop a three-layer security architecture, by deploying and implementing risk assessment processes, self-healing capabilities, large-scale distributed detection and prevention mechanisms, as well as an overlay privacy protection framework. Firstly, the risk assessment framework will identify the risk level of each component of EPES, identifying the possible threats and vulnerabilities. Accordingly, in the context of self-healing, islanding schemes and energy management processes will be deployed, isolating the critical parts of the network in the case of emergency. Furthermore, collaborative intrusion detection tools will be capable of detecting and preventing possible threats and anomalies timely. Finally, the overlay privacy protection framework will focus on the privacy issues, including homomorphic encryption and anonymity processes

Artificial intelligence (AI) has lately proved to be a coin with two sides. On the one hand, it can be leveraged as a powerful defensive mechanism to improve system preparedness and response against cyber incidents and attacks, and on the other hand, it can be a formidable weapon attackers can use to damage, compromise or manipulate systems. AI4CYBER ambitions to provide an Ecosystem Framework of next-generation trustworthy cybersecurity services that leverage AI and Big Data technologies to support system developers and operators in effectively managing robustness, resilience, and dynamic response against advanced and AI-powered cyberattacks. The project will deliver a new breed of AI-driven software robustness and security testing services that significantly facilitates the testing experts work, through smarter flaw identification and code fixing automation. Moreover, the project will provide cybersecurity services for comprehension, detection and analysis of AI-powered attacks to prepare the critical systems to be resilient against them. Incident response support by AI4CYBER will offload security operators from complex and tedious tasks offering them mechanisms to optimize the orchestration of the most appropriate combination of security protections, and continuously learn from system status and defences’ efficiency. The AI4CYBER framework will ensure fundamental rights and values-based AI technology in its services, through the integration of demonstrable explainability, fairness and technology robustness (security) capabilities in the AI4CYBER components. The ecosystem will be validated in three scenarios: i) Detection and Mitigation of AI-powered Attacks against the Energy Sector, ii) Robustness and autonomous adaptation of Banking applications to face AI-powered attacks and iii) Resilient hospital services against advanced and AI-powered cyber-physical attacks.