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PublicationCentralized Model Predictive Control for Transient Frequency Control in Islanded Inverter-Based Microgrids( 2023)Grid-forming inverters can establish frequency and voltage regulation in autonomous microgrids by themselves without relying on the external grid and as such are significant enablers for the integration of inverter-interfaced renewable energy sources. The most prominent grid-forming strategies, the droop-based techniques, cannot provide an error-free frequency regulation after a step-wise disturbance and thus are completed by the secondary integral action. In this paper, we propose a model predictive control for fast frequency response in an AC, purely inverter-based microgrid operating in islanded mode. The controller acts as a secondary control unit and adjusts the control actions of the underlying droop controllers leading to an error-free frequency transient control. The controller has been designed leveraging the concept of frequency divider - a simple analytical formulation that estimates the frequency at every system bus. The proposed control is successful in providing a fast frequency response, i.e., it assures a system-wide error-free frequency regulation under different operating circumstances including load changes and varying X/R-ratios. Numerical simulations are carried out in the WSCC 9-Bus and IEEE 39-Bus systems to verify the performance of the proposed method during transients and a comparison with respect to the more common secondary frequency control is given.
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PublicationTransparency in Medical Arti¯cial Intelligence Systems( 2023)Many of the artificial intelligence (AI) systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often useful and valuable as the decision itself. This paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.
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PublicationPersistent Identification for Conferences( 2022-04-05)
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PublicationTemporal Performance Analysis for Block-Structured Process Models in Cortado( 2022)
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PublicationA Service Oriented Architecture for the Digitalization and Automation of Distribution Grids( 2022)Modern distribution grids are complex systems that need advanced management for their secure and reliable operation. The Information and Communication Technology domain today offers unprecedented opportunities for the smart design of tools in support of grid operators. This paper presents a new philosophy for the digitalization and automation of distribution grids, based on a modular architecture of microservices implemented via container technology. This architecture enables a service-oriented deployment of the intelligence needed in the Distribution Management Systems, moving beyond the traditional view of monolithic software installations in the control rooms. The proposed architecture unlocks a broad set of possibilities, including cloud-based implementations, extension of legacy systems and fast integration of machine learning-based analytic tools. Moreover, it potentially opens a completely new market of turnkey services for distribution grid management, thus avoiding large upfront investments for grid operators. This paper presents the main concepts and benefits of the proposed philosophy, together with an example of field implementation based on open source components carried out in the context of the European project SOGNO.
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PublicationIoT Middleware Platforms for Smart Energy Systems( 2022)Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms' performances.
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PublicationHow Can Interactive Process Discovery Address Data Quality Issues in Real Business Settings? Evidence from a Case Study in Healthcare( 2022)The focus of this paper is on how data quality can affect business process discovery in real complex environments, which is a major factor determining the success in any data-driven Business Process Management project. Many real-life event logs, especially healthcare ones, can suffer from several data quality issues, some of which cannot be solved by pre-processing or data cleaning techniques, leading to inaccurate results. We take an innovative Process Mining (PM) approach, termed Interactive Process Discovery (IPD), which combines domain knowledge with available data. This approach can overcome the limitations of noisy and incomplete event logs by putting “humans in the loop”, leading to improved business process modelling. This is particularly valuable in healthcare, where physicians have a tacit domain knowledge not available in the event log, and, thus, difficult to elicit. We conducted a two-step approach based on a controlled experiment and a case study in an Italian hospital. At each step, we compared IPD with traditional PM techniques to assess the extent to which domain knowledge helps to improve the accuracy of process models. The case study tests the effectiveness of IPD to uncover knowledge-intensive processes extracted from noisy real-life event logs. The evaluation has been carried out by exploiting a real dataset of an Italian hospital, involving the medical staff. IPD can produce an accurate process model that is fully compliant with the clinical guidelines by addressing data quality issues. Accurate and reliable process models can support healthcare organizations in detecting process-related issues and in taking decisions related to capacity planning and process re-design.
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PublicationOn using contextual correlation to detect multi-stage cyber attacks in smart grids( 2022)While the digitization of the distribution grids brings numerous benefits to grid operations, it also increases the risks imposed by serious cyber security threats such as coordinated, timed attacks. Addressing this new threat landscape requires an advanced security approach beyond established preventive IT security measures such as encryption, network segmentation, or access control. Here, detective capabilities and reactive countermeasures as part of incident response strategies promise to complement nicely the security-by-design approach by providing cyber security situational awareness. However, manually evaluating extensive cyber intelligence within a reasonable timeframe requires an unmanageable effort to process a large amount of cross-domain information. An automated procedure is needed to systematically process and correlate the various cyber intelligence to correctly assess the situation to reduce the manuel effort and support security operations. In this paper, we present an approach that leverages cyber intelligence from multiple sources to detect multi-stage cyber attacks that threaten the smart grid. We investigate the detection quality of the presented correlation approach and discuss the results to highlight the challenges in automated methods for contextual assessment and understanding of the cyber security situation.
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PublicationEnhanced Transformation of BPMN Models with Cancellation Features( 2022)
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PublicationImpact of Cyber-attacks on EV Charging Coordination: The Case of Single Point of Failure( 2022)Expanding adoption of electric vehicles (EVs) and broad deployment of charging stations push the limits of distribution grid infrastructure and increase the importance of effective charging coordination. Smart EV chargers with several functionalities and charging coordination solutions that can manage the charging sessions of hundreds of EVs are becoming common, with the increasing risk of triggering significant operational problems in case of cyberattacks. The information exchange between the charging coordinator, distribution network operator, and users is essential in the scheduling of a large number of charging sessions, relying on customer preferences, without violating operational grid constraints. Both the user mobile apps used for charging session reservations and DSO-charging coordinator interfaces are vulnerable to cyberattacks which may cause considerable technical and economic consequences. An important concern is the potential impacts of attacks when a single node or communication link is compromised. This study investigates the impacts of false data injection (FDI) and hijacking attacks on EV charging coordination in case of a single point of failure. Hijacking of one user's mobile app and FDI attack on the DSO-charging coordinator interface are investigated by simulating a 24-hour scenario with 12 chargers, 34 realistic charging sessions, and an EV charging coordination approach based on each session's tolerance to delays. The study highlighted considerable negative impacts that could be encountered in case of a single point of failure in EV charging coordination.
