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In recent years, as renewable energy has been increasingly deployed in pursuit of a decarbonized society, photovoltaics (PV) have become particularly widespread in the residential sector owing to their ease of installation. However, because PV output fluctuates with solar irradiance, it is difficult to ensure a stable power supply with PV alone. It is therefore effective to configure a distributed energy system (DES) that combines PV with energy storage equipment such as battery (BT) and heat pump water heater (HPWH). In addition, electricity sharing among households has attracted attention as a means of maximizing the utilization of PV-generated electricity. Furthermore, comprehensive control of the operation of these systems is expected to improve the electricity self-sufficiency of individual households and the district as a whole, thereby reducing environmental impact. In this study, we consider a district model composed of multiple dwellings equipped with a DES integrating PV, HPWH, and BT, and introduce electricity sharing by numerical simulation while optimizing the operation schedules of the system. The target region is assumed to be Japan, and two optimization objectives are defined (1) minimization of primary energy consumption and (2) minimization of running cost and the corresponding results are compared. System optimization is performed using a mixed-integer linear programming (MILP) formulation. As a result, in scenario (1), the introduction of electricity sharing reduces weekly primary energy consumption by approximately 5%. In scenario (2), it is shown that the control outcomes, including how PV-generated electricity is utilized, change significantly depending on given conditions such as the selling price of exported electricity. These findings clarify both the potential of DES and electricity sharing to reduce environmental impact and the effectiveness and limitations of the proposed optimization method.

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

This study explores Bangladesh’s present energy condition, renewable energy (RE) possibilities and designs an optimal 100% RE-based off-grid power system for St. Martin’s Island, Bangladesh. The optimal size of a hybrid renewable microgrid based on photovoltaic (PV) cells, a battery energy storage system (BESS), fuel cells (FC), and an electrolysis plant (EP) is proposed. Advanced direct load control (ADLC) and rooftop PV meet the energy demand at the lowest cost, and profits are maximized by selling chemical products produced by seawater electrolysis. Four cases are explored with the mixed-integer linear programming (MILP) optimization technique using MATLAB® software to demonstrate the efficacy of the suggested power system. The system cost in case 1 is lower than in the other cases, but there is no chance of profiting. Cases 2, 3, and 4 have greater installation costs, which may be repaid in 8.17, 7.72, and 8.01 years, respectively, by the profits. Though the revenue in case 3 is 6.23% higher than in case 2 and and 3.85% higher than in case 4, case 4 is considered the most reliable power system, as it can meet the energy demand at the lowest cost while increasing profits and not putting a burden on customers.

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

Optical wireless communication (OWC) is a promising technology that can provide high data rates while supporting multiple users. The optical wireless (OW) physical layer has been researched extensively, however, less work was devoted to multiple access and how the OW front end is connected to the network. In this paper, an OWC system which employs a wavelength division multiple access (WDMA) scheme is studied, for the purpose of supporting multiple users. In addition, a cloud/fog architecture is proposed for the first time for OWC to provide processing capabilities. The cloud/fog-integrated architecture uses visible indoor light to create high data rate connections with potential mobile nodes. These OW nodes are further clustered and used as fog mini servers to provide processing services through the OW channel for other users. Additional fog-processing units are located in the room, the building, the campus and at the metro level. Further processing capabilities are provided by remote cloud sites. Two mixed-integer linear programming (MILP) models were proposed to numerically study networking and processing in OW systems. The first MILP model was developed and used to optimize resource allocation in the indoor OWC systems, in particular, the allocation of access points (APs) and wavelengths to users, while the second MILP model was developed to optimize the placement of processing tasks in the different fog and cloud nodes available. The optimization of tasks placement in the cloud/fog-integrated architecture was analysed using the MILP models. Multiple scenarios were considered where the mobile node locations were varied in the room and the amount of processing and data rate requested by each OW node was varied. The results help to identify the optimum colour and AP to use for communication for a given mobile node location and OWC system configuration, the optimum location to place processing and the impact of the network architecture. This article is part of the theme issue ‘Optical wireless communication’.

In this paper, a closed-loop supply chain network design problem with multiple recovery options is studied. First, the deterministic problem is formulated as a Mixed Integer Linear Program (MILP). A sensitivity analysis is carried out in order to investigate the impact of variations of the main input parameters such as customer return rates, revenues, costs as well as the proportions of returns assigned to each recovery option, on the network structure and the company profit. Then, a stochastic version of the model is developed to account for the high uncertainties faced by companies. A scenario-based approach is used to model the uncertainties of return rates, revenues, costs and the quality of returns. The computational results show that the solution of the stochastic problem is stable over different replications and that the benefit from using stochastic modeling increases when the penalty over non collected returns increases.

This paper addresses an extension of the flexible job shop scheduling problem by considering that jobs need to be moved around the shop-floor by a set of vehicles. Thus, this problem involves assigning each production operation to one of the alternative machines, finding the sequence of operations for each machine, assigning each transport task to one of the vehicles, and finding the sequence of transport tasks for each vehicle, simultaneously. Transportation is usually neglected in the literature and when considered, an unlimited number of vehicles is, typically, assumed. Here, we propose the first mixed integer linear programming model for this problem and show its efficiency at solving small-sized instances to optimality. In addition, and due to the NP-hard nature of the problem, we propose a local search based heuristic that the computational experiments show to be effective, efficient, and robust.

This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.

Industries require, nowadays, to be more adaptable to unforeseen real-time events as well as to the rapid evolution of their market (e.g. multiplication of customers, increasingly personalized and unpredictable demand, etc.). To meet these challenges, manufacturers need new solutions to update their production plan when a change in the production system or its environment occurs. In this context, our research work deals with a dynamic scheduling problem of a real Hybrid Flow Shop considering the specific constraints of a perfume manufacturing company. This paper proposes a Digital Twin-driven dynamic scheduling approach based on the combination of both optimization and simulation. For the optimization, we have developed a mixed integer linear programming (MILP) scheduling model taking into account the main specific scheduling requirements of our case study. Regarding the simulation approach, a 3D shop floor model has been developed including the additional stochastic aspects and constraints which are difficult or impossible to model with a MILP approach. These two models are connected with the real shop floor to create a digital twin (DT). The developed DT allows the re-scheduling of production according to internal and external events. Finally, validation scenarios on a perfume case study have been designed and implemented in order to demonstrate the feasibility and the relevance of the proposed digital twin-driven dynamic scheduling approach.

•Optimal inventory profile and flow decisions for the COVID-19 Vaccine Supply Chain.•Optimization of daily vaccination plans in the clinics.•Development of a novel mixed-integer linear programming model.•A decomposition algorithm to successfully address large-scale problems.•Reactive planning of vaccinations through a rolling-horizon technique. This work presents a novel framework to simultaneously address the optimal planning of COVID-19 vaccine supply chains and the optimal planning of daily vaccinations in the available vaccination centres. A new mixed integer linear programming (MILP) model is developed to generate optimal decisions regarding the transferred quantities between locations, the inventory profiles of central hubs and vaccination centres and the daily vaccination plans in the vaccination centres of the supply chain network. Specific COVID-19 characteristics, such as special cold storage technologies, limited shelf-life of mRNA vaccines in refrigerated conditions and demanding vaccination targets under extreme time pressure, are aptly modelled. The goal of the model is the minimization of total costs, including storage and transportation costs, costs related to fleet and staff requirements, as well as, indirect costs imposed by wasted doses. A two-step decomposition strategy based on a divide-and-conquer and an aggregation approach is proposed for the solution of large-scale problems. The applicability and efficiency of the proposed optimization-based framework is illustrated on a study case that simulates the Greek nationwide vaccination program. Finally, a rolling horizon technique is employed to reactively deal with possible disturbances in the vaccination plans. The proposed mathematical framework facilitates the decision-making process in COVID-19 vaccine supply chains into minimizing the underlying costs and the number of doses lost. As a result, the efficiency of the distribution network is improved, thus assisting the mass vaccination campaigns against COVID-19.