To respond to the global need for sustainable energy solutions and the imperative to combat climate change, Renewable Energy Communities (REC) have emerged as a promising solution to achieve energy transition goals. O...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
To respond to the global need for sustainable energy solutions and the imperative to combat climate change, Renewable Energy Communities (REC) have emerged as a promising solution to achieve energy transition goals. Of course, some optimization tools need to be developed to face the challenges related to their operational management and maximize their potential. In this context, this paper proposes a bilevel optimization approach for the optimal management of a REC, focusing on maximizing shared energy and economic benefits. The high-level models the problem of the Energy Community Manager (ECM), who aims at maximizing shared energy rewarded with proportional incentives; instead, the low-level problem focuses on each Energy Community Participant (ECP) aiming to minimize individual costs. To solve this problem Karush-Kuhn-Tucker (KKT) conditions are exploited to convert low-level problems into constraints for the high-level problem. The proposed approach is first applied to a case study involving six ECPs, then a scalability analysis is performed considering 20 ECPs to simulate a more realistic scenario. According to the results, each ECP would obtain an income per year for sharing energy which could be significant especially for those suffering from energy poverty.
In this paper, an optimization approach is introduced, aimed at managing truck arrivals throughout the day at a port terminal with the goal of reducing congestion in various terminal areas. A prediction model is utili...
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Global crises, such as the Russia–Ukraine war and the COVID-19 pandemic, have caused disruptions to the agri-food supply chains. Resilience strategies can be used to address these disruptions. Therefore, this study a...
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Droplet-based bioprinting has shown remarkable potential in tissue engineering and regenerative ***,it requires bioinks with low viscosities,which makes it challenging to create complex 3D structures and spatially pat...
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Droplet-based bioprinting has shown remarkable potential in tissue engineering and regenerative ***,it requires bioinks with low viscosities,which makes it challenging to create complex 3D structures and spatially pattern them with different *** study introduces a novel approach to bioprinting sophisti-cated volumetric objects by merging droplet-based bioprinting and cryobioprinting *** leveraging the benefits of cryopreservation,we fabricated,for thefirst time,intricate,self-supporting cell-free or cell-laden structures with single or multiple materials in a simple droplet-based bioprinting process that is facilitated by depositing the droplets onto a cryoplate followed by crosslinking during *** feasibility of this approach is demonstrated by bioprinting several cell types,with cell viability increasing to 80%–90%after up to 2 or 3 weeks of ***,the applicational capabilities of this approach are showcased by bio-printing an endothelialized breast cancer *** results indicate that merging droplet and cryogenic bioprinting complements current droplet-based bioprinting techniques and opens new avenues for the fabrication of volumetric objects with enhanced complexity and functionality,presenting exciting potential for biomedical applications.
The reduction of pollutant emissions due to the truck operations in a maritime terminal is the objective of this work. The proposed approach is firstly based on a suitable prediction method developed by the authors an...
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The reduction of pollutant emissions due to the truck operations in a maritime terminal is the objective of this work. The proposed approach is firstly based on a suitable prediction method developed by the authors and aimed at forecasting the number of trucks reaching the terminal to bring export containers in each day of a specified time interval. Once that the curve of truck arrivals is predicted, an emission model is adopted to evaluate the corresponding pollutant emissions. As a result, it is possible to verify in advance whether at certain days the overall emissions overcome a critical threshold. If this happens, it is needed to redistribute the truck arrivals in order to maintain emissions as close as possible to the threshold. This is optimally done by stating and solving an optimization problem whose solutions are tested in the case study of export flows in the PSA Genova Pra’ (PSA GP) terminal.
The presented work falls in the field of modelling the presence of multiple clusters of connected autonomous vehicles (CAVs), i.e., groups of CAVs in traffic flow that, if properly controlled, can positively influence...
The presented work falls in the field of modelling the presence of multiple clusters of connected autonomous vehicles (CAVs), i.e., groups of CAVs in traffic flow that, if properly controlled, can positively influence traffic behavior by acting as actuators of specific control strategies. An extended version of the well-known Cell Transmission Model (CTM) is used as a traffic model, where the cells of a highway stretch are divided into two types: cells with clusters of CAVs and cells without. The classical CTM is used to model the second type of cells, while for the first type we model the presence of a cluster as a moving bottleneck that divides the cell into different parts. The proposed control-oriented traffic model has been tested on a case study based on real traffic data.
The objective of this work is to predict the emissions generated by trucks upon their arrival at a port terminal. This prediction is based on a forecast model that predicts truck arrivals, serving as a key input for t...
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The objective of this work is to predict the emissions generated by trucks upon their arrival at a port terminal. This prediction is based on a forecast model that predicts truck arrivals, serving as a key input for the proposed methodology. By using the curve of truck arrivals within specific time intervals, an emission model is adopted to estimate the corresponding pollutant emissions. Then, a redistribution algorithm is designed to optimize the scheduling of truck arrivals, effectively mitigating the occurrence of possible peaks in emissions. The algorithm, which takes into account constraints about the truck operations inside the terminal, operates by redistributing the arrival patterns of trucks in a smooth way in order to also consider the truck operators’ reluctance to change the existing schedule. The case study of export flows in the PSA Genova Pra’ (PSA GP) terminal is addressed in the paper.
While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decen...
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