Power systems are increasingly affected by various sources of uncertainty at all levels. The investigation of their effects thus becomes a critical challenge for their design and operation. Sensitivity Analysis (SA) can be instrumental for understanding the origins of system uncertainty, hence allowing for a robust and informed decision-making process under uncertainty. The SA value as a support tool for model-based inference is acknowledged; however, its potential is not fully realized yet within the power system community. This is due to an improper use of long-established SA practices, which sometimes prevent an in-depth model sensitivity investigation, as well as to partial communication between the SA community and the final users, ultimately hindering non-specialists’ awareness of the existence of effective strategies to tackle their own research questions. This paper aims at bridging the gap between SA and power systems via a threefold contribution: (i) a bibliometric study of the state-of-the-art SA to identify common practices in the power system modeling community; (ii) a getting started overview of the most widespread SA methods to support the SA user in the selection of the fittest SA method for a given power system application; (iii) a user-oriented general workflow to illustrate the implementation of SA best practices via a simple technical example.

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. Meeting and optimizing the consumption of water for irrigation also means making more resources available for drinking water and industrial use, and maintaining an optimal state of the environment. In this study we show the effectiveness of the combined use of numerical weather predictions and hydrological modelling to forecast soil moisture and crop water requirement in order to optimize irrigation scheduling. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations from space and unconventional information from the cyberspace through crowdsourcing.

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Interoperability testing is widely recognized as a key to achieve seamless interoperability of smart grid applications, given the complex nature of modern power systems. In this work, the interoperability testing methodology proposed by the European Commission Joint Research Centre is applied to a specific use case in the context of smart grids. The selected use case examines a flexibility activation mechanism in a power grid system and includes DSO SCADA, Remote Terminal Unit and flexibility source, interacting to support a voltage regulation service. The adopted test bed consists of a real-time power grid simulator, a communication network emulator and use case actors' models in a hardware-in-the-loop setup. The breakdown of the interoperability testing problem is accomplished by mapping the use case to the SGAM layers, specifying the Basic Application Profiles together with the Basic Application Interoperability Profiles (BAIOPs) and defining the design of experiments to carry out during the laboratory testing. Furthermore, the concepts of inter-and intra-BAIOP testing are formalized to reflect complementary interests of smart grid stakeholders. Experimental results prove the applicability of the methodology for testing the interoperability of large-scale and complex smart grid systems and reveal interesting features and possible pitfalls which should be considered when investigating the parameters responsible for the disruption of a system interoperability.Energies 2020, 13, 1648 2 of 25 as the main challenge for the deployment of the SGs, in which technologies and companies from very diverse domains converge: electricity technologies at large, grid measurement, protection and control, Distributed Energy Resource (DER) management, industrial automation and power electronics, Information and Communication Technologies (ICTs) at large, building and home automation, smart metering. This diversity of domains gives place to the overlapping of many standards and different standardization approaches.There is quite an extensive research work in IOP literature focusing on different SG domains with respect to their relevant standards. In this regard, and in response to the European M/490 mandate, the "CEN-CENELEC Smart Grid Set of Standards" document [3] provides a comprehensive list of standards for supporting and fostering the deployment of SG systems in Europe facilitating interoperable solutions. In particular, it provides any SG stakeholder with a selection guide in order to set out the most appropriate (existing and upcoming) standards to consider, depending on the specific SG system and the Smart Grid Architecture Model (SGAM) layer of interest.Paper [4] explores the modelling structure of IEC 61850 standard for microgrid protection systems and highlights the IOP issues that might arise from the ambiguity and flexibility in IEC 61850 and proposes a framework for microgrid protection which can include an IOP testing to check interactions between different devices. However, it does not elaborate how the IOP testin...

This paper presents the development of an energy management system (EMS) for a renewable energy community (REC) with the load-generation balancing objective. In this regard, rule-based and optimization mechanisms are proposed for the REC management in line with the scope of a field trial and considering the scarcity of the measurement and historical data. This typical data scarcity along with the intermittent behaviour of renewable energy resources introduce an unavoidable level of uncertainty-not being adequately addressed in the EMS literature-that might ultimately affect the proper REC management. Hence, a comprehensive performance analysis of the proposed EMS has been conducted via global sensitivity analysis (GSA). Particularly, variance-based sensitivity analysis has been employed to investigate how the variability of a set of selected indicators of the REC performance is apportioned to the different sources of uncertainty specifically related to the forecast and flexibility availability. Results show that the EMS performance is consistent with the EMS objective. The application of GSA reveals though interesting findings that contradict antecedent misconceptions about how different uncertainty sources affect the EMS performance. Although being related to the specific REC under study, the present work specializes GSA method in novel ways that pave the path for its reusability in the context of other EMS applications with different boundary conditions. By highlighting the necessity of GSA and showcasing its suitability to study the EMS performance under an uncertainty framework, the present work offers a precious tool to support system operators in their decision making process.