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.

Photovoltaic (PV) technology is one of the most popular means of renewable generation, whose applications range from commercial and residential buildings to industrial facilities and grid infrastructures. The problem of determining a suitable layout for the PV arrays, on a given deployment region, is generally non-trivial and has a crucial importance in the planning phase of solar plants design and development. In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays’ layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window. Subsequently, we formalize the mathematical optimization problem, whose constraints’ set is characterized by non-convexities. In order to propose a computationally tractable approach, we provide a tight parametrized convex relaxation. The resulting optimization resolution procedure is tested numerically, using realistic data, and benchmarked against the traditional global resolution approach, showing that the proposed methodology yields near-optimal solutions in lower computational time. Note to Practitioners: The paper is motivated by the need for efficient algorithmic procedures which can yield near-optimal solutions to the PV arrays layout problem. Due to the strong non-convexity of even simple instances, the existing methods heavily rely on global or stochastic solvers, which are computationally demanding, both in terms of resources and run-time. Our approach acts as a baseline, from which practitioners can derive more elaborate instances, by suitably modifying both the objective function and/or the constraints. In fact, we focus on the minimum set of necessary geometrical (e.g., arrays position model), astronomical (e.g., irradiance variation), and operational (e.g., power requirements) constraints which make the overall problem hard. The Appendices provide a guideline for suitably choosing the optimization parameters. All data and simulation code are available on a public repository. This preprint has been accepted for publication in IEEE Transactions on Automation Science and Engineering. How to cite: N. Mignoni, R. Carli and M. Dotoli, "Layout Optimization for Photovoltaic Panels in Solar Power Plants via a MINLP Approach", in IEEE Transactions on Automation Science and Engineering.

In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs), used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve the energy allocation process. Acting as selfish agents, prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method, showing sufficient conditions for guaranteeing convergence. The proposed model predictive control approach is validated through numerical simulations under realistic scenarios. This preprint has been accepted for publication in IEEE Transactions on Control Systems Technology. How to cite: N. Mignoni, R. Carli and M. Dotoli, “Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities,” in IEEE Transactions on Control Systems Technology.

Cuckoo is a popular card game, which originated in France during the 15th century and then spread throughout Europe, where it is currently well-known under distinct names and with different variants. Cuckoo is an imperfect information game-of-chance, which makes the research regarding its optimal strategies determination interesting. The rules are simple: each player receives a covered card from the dealer; starting from the player at the dealer’s left, each player looks at its own card and decides whether to exchange it with the player to their left, or keep it; the dealer plays at last and, if it decides to exchange card, it draws a random one from the remaining deck; the player(s) with the lowest valued card lose(s) the round. We formulate the gameplay mathematically and provide an analysis of the optimal decision policies. Different card decks can be used for this game, e.g., the standard 52-card deck or the Italian 40-card deck. We generalize the decision model for an arbitrary number decks’ cards, suites, and players. Lastly, through numerical simulations, we compare the determined optimal decision strategy against different benchmarks, showing that the strategy outperforms the random and naive policies and approaches the performance of the ideal oracle. This preprint has been accepted for publication in IEEE Transactions on Games. How to cite: N. Mignoni, R. Carli and M. Dotoli, “Optimal Decision Strategies for the Generalized Cuckoo Card Game,” in IEEE Transactions on Games. © 2023 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.