Abstract: Hydrogen aroused a great interest in the last decades as a promising energy vector for the energy transition as well as renewable energy storage. This article investigates the technical feasibility of a hydrogen-powered fleet, in the context of a microgrid with renewable generation. The hydrogen demand is satisfied through the electrolyser powered by local RES and periodical external refueling of a local hydrogen storage system. The model of the microgrid is implemented in the open-source GNU Octave environment and is exploited to size the local storage capacity. Different types of vehicles were considered and hydrogen pressure levels were distinguished among the system components. The model was applied to the waste collection fleet and the headquarters of ASM S.p.A., a utility located in the center of Italy, chosen as a case study. The dataset of 143 vehicles was used to model the hydrogen-powered fleet. Three scenarios were evaluated: hydrogen production using i) Reverse Power Flow, ii) PV production and iii) electricity drown from the grid. The storage capacity depends on the scenarios and in the case study it ranges from about 35 to 47 m3, in comparison with 13.6 m3 of current diesel refueling system, assuming the same frequency of external refueling.

Abstract: Artificial Intelligence (AI) is expected to radically reshape the energy sector value chain, by improving business processes performance, while increasing environmental sustainability and propagating high social value among citizens. Additionally, a vast amount of energy data is available, coming from several sources including smart grid sensors, simulators and open data sources. However, the energy sector is characterised by uncertain business cases, fragmented regulations, immaturity of standards and lack of high-end ICT workforce of EPES stakeholders. Additionally, the lack of interoperability across data stream providers, of energy data ownership and sharing, and of a holistic, safe and cooperative AI perspective amongst the EPES community actively hinder the effective integration of AI services in the sector. This publication presents the I-NERGY modular framework for supporting AIon-Demand in the energy sector by capitalising on state-of-the-art AI, including resources of the AI4EU platform, as well as IoT, data analytics, adaptive learning and digital twin technologies. The solution, developed within the EU funded I-NERGY project, will enable AIbased cross-sector multistakeholder analytics tools for integrated and optimised smart energy systems management, based on interoperable data exchange. The project will also manage Open Calls for the delivery of innovative AI-driven energy services through the provisioning of financial support to third parties.

Abstract: Artificial Intelligence (AI) is expected to radically reshape the energy sector value chain, by Abstract: Artificial Intelligence (AI) holds the premise to transform the energy sector and the underlying value chain; scarcity of AI expertise in the energy community, fuzzy and unclear regulations on access to data, standards’ immaturity and uncertain business cases are hampering though the full exploitation of its potential. In this context, the goal of this paper is to present the I-NERGY project, an Innovation Action that targets to promote AI in the energy sector by reinforcing the AI-on-demand (AI4EU) platform service offering and ecosystem. To this end, the paper introduces the I-NERGY project concept, the domain challenges it addresses and the target audience towards which it is addressed, exposes the project technical solution and pilot use cases that respectively incarnate, and exemplify and validate it and emphasizes its open call mechanism for providing financing support to third SMEs for energy use cases and AI services proliferation.