Tommaso Bragatto

This paper examines the challenges of integrating a Decision Support System for Flexibility Exploitation (DSSFE) into distribution networks. The study systematically addresses uncertainties arising from heterogeneous monitoring infrastructures and from the knowledge of the network parameters, such as line impedances. The impact on a DSSFE application of sensor accuracy (i.e., high/medium accuracy and/or pseudo-measurements), different placement of the sensors in the nodes of the network and the uncertainty in the knowledge of the network parameters is extensively evaluated by means of a stochastic approach. Key figures of merit, such as false positives, false negatives, and residual congestions, are defined to quantify DSSFE performance. The analysis offers insights into infrastructure design trade-offs and on the achievable performance of DSSFE applications.

This study investigates the impact of monitoring infrastructure characteristics (specifically sensor penetration and measurement accuracy) on the effectiveness of voltage regulation and congestion management within distribution networks. As distribution system operators transition toward active management, the integration of Distributed renewable Generation (DG) and demand response introduces significant physical and cyber-physical uncertainties. To address these challenges, a smart grid service framework has been employed to optimize flexibility resources from aggregated users and DG inverters through a genetic algorithm. The framework was tested on the IEEE European Low Voltage Test Feeder across various scenarios defined by distributed monitoring systems’ penetration and their measurement accuracy. Results show that sensor penetration has a dominant impact: increasing monitoring coverage from 0% to 100% raises the percentage of cases with fewer than one residual congestion from 46.2% to 91.9% (sensors with an accuracy class of 2%), reaching 97.9% with an accuracy class of 0.5%, while voltage violations are eliminated under full monitoring. These findings suggest that widespread sensor deployment, with a suitable measurement accuracy, is a fundamental prerequisite for reliable and efficient smart grid operation.

Hybrid AC/DC microgrids (HMGs) are pivotal for integrating renewable resources, yet their stability and resilience are fundamentally constrained by the power electronic converters that interface them. This paper provides a critical review and synthesis of the co-dependent advancements in HMG converter topologies, control strategies, and fault management. Through a systematic analysis of the state of the art, this review examines the evolution from classical control to intelligent, software-defined converter functions. The analysis reveals a fundamental bifurcation in design philosophy between low-voltage (LV) and medium-voltage (MV) systems, driven by a trade-off between power density Gallium Nitride (GaN) and systemic reliability silicon carbide (SiC). Furthermore, it highlights the rise of virtualization, namely virtual Inertia control (VIC) and adaptive virtual impedance control (AVIDC), as a dominant paradigm to compensate for the physical limitations of low-inertia, resistive grids. Finally, this review identifies a critical, synergistic dependency in fault management, where ultra-fast solid-state circuit breakers (SSCBs) guarantee the survivability of vulnerable voltage source converters (VSCs), which in turn enables software-based resilience via fault ride-through (FRT). This synthesis concludes that the converter has become the intelligent nexus of the HMG and identifies the primary barriers to widespread adoption as the computational, economic, and standardization gaps in this new cyber–physical domain.

The growing adoption of electric vehicles (EVs) is driving the demand for public charging infrastructure, including ultrafast charging stations (UFCS) with power ratings exceeding 100 kW. However, the rapid expansion of public charging infrastructure necessitates appropriate network reinforcements and strategic development plans from grid operators. In this context, this paper provides an analysis of the impact of UFCS on existing sub-transmission and distribution networks. The study employs a methodology to assess hosting capacity and grid reinforcements in relation to UFCS, validating the results against stochastic variations in UFCS power demand. The research evaluates the effects of widespread UFCS deployment on both an existing distribution network (DN) and a sub-transmission network (SN), examining three distinct electric mobility development scenarios. The findings reveal that public charging infrastructure could significantly increase the load on distribution networks, necessitating the identified grid reinforcements to ensure secure grid operation. In the case of the SN, the results indicate that the current hosting capacity is sufficient to accommodate the medium-term development of public charging infrastructure.

Efficient waste management remains critical to achieving sustainable urban development, addressing challenges related to resource conservation, environmental preservation, and carbon emissions reduction. This review synthesizes advancements in waste management technologies, focusing on three transformative areas: optimization techniques, the integration of electric vehicles (EVs), and the adoption of smart technologies. Optimization methodologies, such as vehicle routing problems (VRPs) and dynamic scheduling, have demonstrated significant improvements in operational efficiency and emissions reduction. The integration of EVs has emerged as a sustainable alternative to traditional diesel fleets, reducing greenhouse gas emissions while addressing infrastructure and economic challenges. Additionally, the application of smart technologies, including Internet of Things (IoT), artificial intelligence (AI), and the Geographic Information System (GIS), has revolutionized waste monitoring and decision-making, enhancing the alignment of waste systems with circular economy principles. Despite these advancements, barriers such as high costs, technological complexities, and geographic disparities persist, necessitating scalable, inclusive solutions. This review highlights the need for interdisciplinary research, policy standardization, and global collaboration to overcome these challenges. The findings provide actionable insights for policymakers, municipalities, and businesses, enabling data-driven decision-making, optimized waste collection, and enhanced sustainability strategies in modern waste management systems.