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.

Abstract: Modern power systems introduce new dynamics, which may require the appropriate selection of the modeling method for each type of dynamic simulation. In this paper, electromagnetic transient (EMT), Root Mean Square (RMS), and Shifted Frequency Analysis (SFA) modeling are compared in the scope of the dynamic simulation of transient stability analysis. The comparison is carried out by analyzing the accuracy of the simulation results as a function of the simulation step size. The evaluation is conducted for classic and low inertia systems for the accuracy of rotor angle transients and the critical clearing time, being the variables of interest in transient stability analysis. The real-time open source simulator DPsim is employed. The main advantage presented by DPsim, is the possibility to run the same simulation scenario with the same solver, but in three different modeling domains. This powerful feature enables a systematic comparison between the modeling methods. With respect to transient stability analysis, the results of the comparative analysis support the usage of SFA, both for classic and low inertia systems. SFA incorporate more dynamics than RMS simulation into the models, while at the same time allowing larger step sizes than EMT simulation.

Abstract: In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances—such as the COVID-19 pandemic—can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures—namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)—with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese national 15-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models; (ii) to become aware of the serious consequences of crisis events on model performance; (iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context.

Abstract: Internet of things (IoT) along with big data technologies can accrue significant added value in several domains and improve people’s everyday life. One of the domains that can be benefitted the most by the aforementioned technologies is Smart Buildings. This is because, several aspects of people’s everyday lives can be improved through IoT services, such as energy consumption, health, heating, building security and more. IoT services can be divided to near real-time, and static based on the time that they require in order to return results. Significant amount of research papers has been dedicated to the second for services such as energy forecasting, while for near real-time services there are not so many publications, while, most of the existing ones focusing mostly on obtaining meaningful results. In this publication we propose a conceptual architecture for building a near real-time Anomaly Detection service for smart buildings using the Fog Computing paradigm, to achieve scalability and low latency. Moreover, we provide a technical glance of the proposed solution, suggesting specific technologies for each functionality as well as restrictions for each technology. It is worth mentioning that the proposed approach can be easily adapted for other near real-time services with little modifications.