In in vivo postsurgery monitoring, the use of wireless biodegradable implantable sensors has gained and is gaining a lot of interest, particularly in cases of monitoring for a short period of time. The employment of biodegradable materials allows the circumvention of secondary surgery for device removal. Additionally, the use of wireless communication for data elaboration avoids the need for transcutaneous wires. As such, it is possible to prevent possible inflammation and infections associated with long-term implants which are not wireless. It is expected that microfabricated biodegradable sensors will have a strong impact in acute or transient biomedical applications. However, the design of such high-performing electronic systems, both fully biodegradable and wireless, is very complex, particularly at small scales. The associated technologies are still in their infancy and should be more deeply and extensively investigated in animal models and, successively, in humans, before being clinically implemented. In this context, the present review aims to provide a complete overview of wireless biodegradable implantable sensors, covering the vital signs to be monitored, the wireless technologies involved, and the biodegradable materials used for the production of the devices, as well as designed devices and their applications. In particular, both their advantages and drawbacks are highlighted, and the key challenges faced, mainly associated with fabrication techniques, and control over degradation kinetics and biocompatibility of the device, are reported and discussed.

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A key issue in Low Voltage (LV) distribution systems is to identify strategies for the optimal management and control in the presence of Distributed Energy Resources (DERs). To reduce the number of variables to be monitored and controlled, virtual levels of aggregation, called Virtual Microgrids (VMs), are introduced and identified by using new models of the distribution system. To this aim, this paper, revisiting and improving the approach outlined in a conference paper, presents a sensitivity-based model of an LV distribution system, supplied by an Medium/Low Voltage (MV/LV) substation and composed by several feeders, which is suitable for the optimal management and control of the grid and for VM definition. The main features of the proposed method are: it evaluates the sensitivity coefficients in a closed form; it provides an overview of the sensitivity of the network to the variations of each DER connected to the grid; and it presents a limited computational burden. A comparison of the proposed method with both the exact load flow solutions and a perturb-and-observe method is discussed in a case study. Finally, the method is used to evaluate the impact of the DERs on the nodal voltages of the network.

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This paper presents the preliminary results of our research activity aimed at forecasting the number of voltage sags in distribution networks. The final goal of the research is to develop proper algorithms that the network operators could use to forecast how many voltage sags will occur at a given site. The availability of four years of measurements at Italian Medium Voltage (MV) networks allowed the statistical analyses of the sample voltage sags without performing model-based simulations of the electric systems in short-circuit conditions. The challenge we faced was to overcome the barrier of the extremely long measurement times that are considered mandatory to obtain a forecast with adequate confidence. The method we have presented uses the random variable time to next event to characterize the statistics of the voltage sags instead of the variable number of sags, which usually is expressed on an annual basis. The choice of this variable allows the use of a large data set, even if only a few years of measurements are available. The statistical characterization of the measured voltage sags by the variable time to next event requires preliminary data-conditioning steps, since the voltage sags that are measured can be divided in two main categories, i.e., rare voltage sags and clusters of voltage sags. Only the rare voltage sags meet the conditions of a Poisson process, and they can be used to forecast the performance that can be expected in the future. However, the clusters do not have the characteristics of memoryless events because they are sequential, time-dependent phenomena the occurrences of which are due to exogenic factors, such as rain, lightning strikes, wind, and other adverse weather conditions. In this paper, we show that filtering the clusters out from all the measured sags is crucial for making successful forecast. In addition, we show that a filter, equal for all of the nodes of the system, represents the origin of the most important critical aspects in the successive steps of the forecasting method. In the paper, we also provide a means of tracking the main problems that are encountered. The initial results encouraged the future development of new efficient techniques of filtering on a site-by-site basis to eliminate the clusters.