To this day, tasks like planning and managing warehouses remain complex. Automated storage and retrieval systems have enhanced warehousing efficiency, yet designing them optimally is still challenging, despite their importance for the efficient operation of the warehouse. This paper aims to present a novel framework for automating the optimal design of vertical lift modules (VLMs), focusing on tray types, quantities, and item-tray sector assignments, based on a specified inventory list. The approach accounts for VLM's physical, manufacturing, and ergonomic constraints to ensure a manufacturable system design. To manage computational complexity, the size of the mixed-integer problem is reduced through an exact clustering of items, sectors, and layouts. The proposed framework is demonstrated through numerical simulations using real data from an Italian VLM producer.

We consider a class of Nash games with nonconvex coupling constraints where we leverage the theory of tangent cones to define a novel notion of local equilibrium: Clarke’s local generalized Nash equilibrium (CL-GNE). Our first technical contribution is to show the stability of these equilibria on a specific local subset of the original feasible set. As a second contribution, we show that the proposed notion of local equilibrium can be equivalently formulated as the solution of a quasi-variational inequality, remarkably, with equal Lagrange multipliers. Next, we define conditions for the existence and uniqueness of the CL-GNE. To compute such an equilibrium, we propose two discrete-time distributed dynamics, or fixed-point iterations. Our third technical contribution is to  prove convergence under (strongly) monotone assumptions on the pseudo-gradient mapping of the game. Finally, we apply our theoretical results to a competitive version of the optimal power flow control problem. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people’s mobility. Postprint accepted for publication in the proceedings of the 2020 AEIT International Annual Conference (AEIT). How to cite: P. Scarabaggio, M. La Scala, R. Carli and M. Dotoli, ”Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy,” 2020 AEIT International Annual Conference (AEIT), 2020. DOI: https://doi.org/10.23919/AEIT50178.2020.9241136  © 2020 AEIT 978-8-8872-3747-4. Personal use of this material is permitted. Permission must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Power distribution grids are commonly controlled through centralized approaches, such as the optimal power flow. However, the current pervasive deployment of distributed renewable energy sources and the increasing growth of active players, providing ancillary services to the grid, have made these centralized frameworks no longer appropriate. In this context, we propose a novel noncooperative control mechanism for optimally regulating the operation of power distribution networks equipped with traditional loads, distributed generation, and active users. The latter, also known as prosumers, contribute to the grid optimization process by leveraging their flexible demand, dispatchable generation capability, and/or energy storage potential. Active users participate in a noncooperative liberalized market designed to increase the penetration of renewable generation and improve the predictability of power injection from the high voltage grid. The novelty of our game-theoretical approach consists in incorporating economic factors as well as physical constraints and grid stability aspects. Lastly, by integrating the proposed framework into a rolling-horizon approach, we show its effectiveness and resiliency through numerical experiments. Postprint accepted for publication in IEEE Transactions on Control of Network Systems How to cite:  P. Scarabaggio, R. Carli and M. Dotoli, (2022)  “Noncooperative Equilibrium Seeking in Distributed Energy Systems Under AC Power Flow Nonlinear Constraints,” in IEEE Transactions on Control of Network Systems. DOI: https://doi.org/10.1109/TCNS.2022.3181527 © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Recently, the decreasing cost of storage technologies and the emergence of economy-driven mechanisms for energy exchange are contributing to the spread of energy communities. In this context, this paper aims at defining innovative transactive control frameworks for energy communities equipped with independent service-oriented energy storage systems. The addressed control problem consists in optimally scheduling the energy activities of a group of prosumers, characterized by their own demand and renewable generation, and a group of energy storage service providers, able to store the prosumers’ energy surplus and, subsequently, release it upon a fee payment. We propose two novel resolution algorithms based on a game theoretical control formulation, a coordinated and an uncoordinated one, which can be alternatively used depending on the underlying communication architecture of the grid. The two proposed approaches are validated through numerical simulations on realistic scenarios. Results show that the use of a particular framework does not alter fairness, at least at the community level, i.e., no participant in the groups of prosumers or providers can strongly benefit from changing its strategy while compromising othersâ\euro™ welfare. Lastly, the approaches are compared with a centralized control method showing better computational results. This preprint has been accepted for publication in Control Engineering Practice, Elsevier. How to cite: Nicola Mignoni, Paolo Scarabaggio, Raffaele Carli, Mariagrazia Dotoli, “Control frameworks for transactive energy storage services in energy communities”, Control Engineering Practice, Volume 130, 2023, 105364, ISSN 0967-0661, https://doi.org/10.1016/j.conengprac.2022.105364. © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.

The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Specifically, this paper proposes an accurate SIRUCQHE epidemiological model, with eight compartments (namely, Susceptible, Infected, Removed, Unsusceptible, Contagious, Quarantined, Hospitalized, and Extinct). Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses, thus considering also the possibility that some individuals may receive only one injection if the resulting expected fatalities are low. To show the effectiveness of the resulting strategies, we first calibrate the model on the Israeli scenario using real data to get reliable predictions on the pandemic dynamics. Lastly, we estimate the impact of the vaccine administration on the virus dynamics and, in particular, based on validated model, we assess the impact of the first dose of the Pfitzer’s vaccine confirming the results of clinical tests. Extended version of the paper published in Proceedings of the IEEE 16th International Conference on Automation Science and Engineering (CASE) How to cite: Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., & Dotoli, M. “Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration.” In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) (pp. 990-995). IEEE. DOI: https://doi.org/10.1109/CASE49439.2021.9551418  © 2021 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This paper proposes a stochastic non-linear model predictive controller to support policy-makers in determining robust optimal non-pharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socio-economic categories) to minimize the socio-economic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified non-pharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries’ characteristics and different levels of the spatial scale. Postprint accepted for pubblication in IEEE Transactions on Automation Science and Engineering (T-ASE) How to cite: P. Scarabaggio, R. Carli, G. Cavone, N. Epicoco and M. Dotoli, (2021) “Non-Pharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread,” in IEEE Transactions on Automation Science and Engineering. DOI: http://doi.org/10.1109/TASE.2021.3111338 © 2021 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach. Postprint accepted for publication in IEEE Transactions on Control Systems Technology How to cite: P. Scarabaggio, S. Grammatico, R. Carli and M. Dotoli, (2021) “Distributed Demand Side Management With Stochastic Wind Power Forecasting, ”IEEE Transactions on Control Systems Technology, 2022. DOI: http://doi.org/10.1109/TCST.2021.3056751 © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.

The ability of social networks to disseminate information across individuals and groups is based on the social influence that people have on each other. In this context, the so-called influence maximization problem consists in identifying the most influential nodes of a given social network. This problem has practical relevance in a wide range of applications and despite the underlying computational complexity, several solution techniques have been presented in the related literature. Nonetheless, bringing together technical feasibility and satisfactory accuracy is still a challenging open research issue. In this work, we address the influence maximization problem with two complementary contributions. First, we analyze two well-known influence diffusion models, namely the independent cascade and the linear threshold models, and provide a new methodology for addressing the problem of computing the influence spread in a given network. Subsequently, we propose a novel approach to select an initial set of nodes that optimizes the influence spread in large-scale scenarios. We apply these techniques to several large-scale experiments in real-world scenarios achieving results comparable with the best-performing state-of-the-art algorithms in a shorter computational time. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.