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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.

Nonlinear characteristics of a battery energy storage system (BESS) may cause errors in the stored energy between the operation plan and the actual operation. These errors may hinder the reliability of the power system especially in environments such as microgrids with limited power generation resources and high uncertainty. This study proposes a method to alleviate the occurrence of such errors in the charging/discharging scheduling process of the BESS by piecewise linearizing its nonlinear characteristics. Specifically, the stored energy in a BESS that changes nonlinearly according to the size of the charging/discharging power was modeled using the special ordered set of the type 2 (SOS2) method. The proposed model and the typical BESS-operation models with constant power conditioning system (PCS) input/output power efficiency were applied to the unit commitment (UC) problem in a microgrid environment, and the results were compared with the actual operation results. The proposed model operated similarly to the actual operation compared to the typical model, reducing the error in charging/discharging energy. Consequently, the proposed model was made cost-effective by reducing the cost of error correction and reduced the risk of deviating from operating range of the BESS. This study demonstrates that the proposed method can efficiently solve the operational problems caused by the nonlinear characteristics of BESS.

Purpose: The actual market characteristic oriented toward customers' requirements compels decision-makers to foresee customization abilities. Mass customization represents a valuable approach to combine customizable offers with mass production processes. From a supply chain standpoint, this paper attempts to develop an integrated procurement, production and distribution modeling to describe the generated framework structure formulation within tactical decision planning level. Design/methodology/approach: The paper provides a mixed integer linear programming model of a three echelon supply chain illustrated from the automotive industry with (a) customers: Original Equipment Manufacturers (OEMs) identified as leaders and (b) first-tier supplier: wiring harnesses manufacturer (c) second-tier supplier: raw material supplier, identified as followers. The model formulation is depicted through dyadic relationships between stakeholders considering the specific operation enablers of the environment such as make to order, modular approach in addition to the corresponding inventory management policy. Findings: The integrated model is solved by an exact method which illustrates the feasibility of the formulation in addition to the observance of the applied constraints. A sensitivity analysis is performed to highlight the interdependency across some key parameters to provide managerial insights within the studied framework while keeping the optimal solvability of the model. Research limitations/implications: The limitation of this study is the computational experiment study. An extensive experiment with a real-word case will outline the optimal solvability status of the exact method and the necessity for a performance benchmark through the approximate solving approaches. Originality/value: The present research aims to contribute as first studies toward mathematical modeling for supply chain decision planning endeavor operating within mass customization business model.

The economic and environmental benefits brought by electric vehicles (EVs) cannot be fully delivered unless these vehicles are fully or partially charged by renewable energy sources (RES) such as photovoltaic system (PVS). Nevertheless, the EV charging management problem of a parking station integrated with RES is challenging due to the uncertain nature of local RES generation. This paper aims to address these difficulties by deploying an energy storage system (ESS) in parking stations and exploiting the charging and discharging scheduling of EVs to achieve better utilization of intermittent PVS for EV charging. A real-time charging optimization scheme is also formulated, using mixed-integer linear programming (MILP) to coordinate the charging or discharging power of EVs along with the power dispatches of power grid and ESS based on the vehicles’ charging or discharging priorities and electricity price preferences. Extensive simulations show that the proposed approach not only maximizes the satisfaction of EV owners in terms of fulfilling all charging and discharging requests, but also minimizes the overall operational cost of the parking station by prioritizing the utilization of energy from PVS, ESS, and scheduling of every EV’s charging and discharging.

In this paper, the mixed integer linear programming (MILP) for solving unit commitment (UC) problems in a hybrid power system containing thermal, hydro, and wind power have been studied. To promote its efficiency, an improved MILP approach has been proposed, while the symmetric problem in MILP formulas has been solved by reforming hierarchical constraints. Experiments on different scales have been conducted to demonstrate the effectiveness of the proposed approach. The results indicate a dramatic efficiency promotion compared to other popular MILP approaches in large scale power systems. Additionally, the proposed approach has been applied in UC problems of the hybrid power system. Two indexes, fluctuation degree and output degree, have been proposed to investigate the performance of renewable energy sources (RES). Several experiments are also implemented and the results show that the integration of pumped hydroelectric energy storage (PHES) can decrease the output of thermal units, as well as balance wind power fluctuation according to the load demand.

In this paper, we consider the job shop scheduling problem (JSS) with non-anticipatory, per-machine, sequence-dependent setup times (SDST). The contributions of this paper are twofold. First, we propose a formulation in the form of a mixed-integer linear programming (MILP) model to modelize the aforementioned problem. Second, we play a pioneering effort for the effective adaptation of a novel metaheuristic known as electromagnetism-like algorithm (EMA) to solve the foregoing problem under the minimization of makespan. Afterwards, we evaluate the performance of the proposed MILP model, the EMA, and other effective metaheuristic algorithms from the literature on two different sets of benchmarks: small-sized and large-sized instances. The rationale behind applying the MILP model and the other algorithms at the small-sized instances is to compare the solutions obtained by the metaheuristic algorithms and the optimal solutions obtained by the MILP model (optimality gap analysis). Subsequently, to demonstrate the competitiveness of the EMA against some effective algorithms in the literature, we conduct an experimental design based on Taillard's benchmark, which is considered as large-sized instances. The purpose of conducting this very experiment is to show whether the acceptable performance of the EMA is transferrable to large-sized instances. The computational evaluations simply manifest the superiority of our proposed algorithm vs the other high-performing algorithms over both small and large instances.

During the reflow process, solder joints are formed on the boards with the placed components, so the temperature settings in the reflow oven chamber are vital to the quality of the PCB. Inappropriate profiles cause various defects such as cracks, bridging, and delamination. Solder paste manufacturers have generally provided the ideal thermal profile (i.e., target profile), and PCB manufacturers have attempted to meet the given profile by fine-tuning the oven’s recipe. The conventional method tunes the recipe to gather thermal data with a thermal measurement device. It adjusts the profile, which relies on the trial-and-error method which takes much time and effort. This paper proposes (1) a recipe initialization method for determining the initial recipe for collecting training data, (2) a stage-based (ramp, soak, and reflow) input data segmentation method for data preprocessing, (3) a backpropagation neural network, (BPNN) model for predicting the required zone temperature to reduce the gap between the actual processing profile and the target profile, (4) a mixed-integer linear programming (MILP) algorithm for generating the optimal recipe to minimize the temperature settings, and (5) a cross-validation with the oven of different model and design. This paper aims to enable non-contact prediction of required air temperature from one experiment. The MILP optimization model utilized the constraints of the upper and lower bounds obtained from the prediction result. The model has been cross-validated with different initial recipes and different target profiles. As a result, within 10 min of starting the experiment, the generated optimal recipe improved the fitness to the targeted profile by 4.2%, which resulted in 99% and, meanwhile, lowered the energy cost by 23%.

An MILP-based distributed energy management for the coordination of networked microgrids is proposed in this paper. Multiple microgrids and the utility grid are coordinated through iteratively adjusted price signals. Based on the price signals received, the microgrid controllers (MCs) and distribution management system (DMS) update their schedules separately. Then, the price signals are updated according to the generation–load mismatch and distributed to MCs and DMS for the next iteration. The iteration continues until the generation–load mismatch is small enough, i.e., the generation and load are balanced under agreed price signals. Through the proposed distributed energy management, various microgrids and the utility grid with different economic, resilient, emission and socio-economic objectives are coordinated with generation–load balance guaranteed and the microgrid customers’ privacy preserved. In particular, a piecewise linearization technique is employed to approximate the augmented Lagrange term in the alternating direction method of multipliers (ADMM) algorithm. Thus, the subproblems are transformed into mixed integer linear programming (MILP) problems and efficiently solved by open-source MILP solvers, which would accelerate the adoption and deployment of microgrids and promote clean energy. The proposed MILP-based distributed energy management is demonstrated through various case studies on a networked microgrids test system with three microgrids.

This study proposes a mixed-integer linear programming (MILP) framework for the operation of bidirectional electric vehicle charging stations (EVCS) in public parking facilities, incorporating gamification mechanisms to enable flexible smart charging. Based on real EV charging session datasets, the model exploits user-provided extended connection time (idle time) as a source of operational flexibility in Level-2 charging infrastructures. By allowing vehicles to remain connected beyond the completion of charging, the optimisation model increases its scheduling capacity, enabling load shifting towards low-tariff periods and enhancing coordinated vehicle-to-vehicle (V2V) energy exchanges. The flexibility programme is incorporated into the optimisation framework through dedicated cost components that represent user participation and extended connection time. In this way, user-provided idle time directly influences the scheduling decisions of the model. A Monte Carlo analysis was conducted to evaluate the robustness of the proposed approach, confirming stable performance across different occupancy scenarios. Results demonstrate that sustained user flexibility significantly improves operator revenues, as providing additional connection time allows the model to allocate a greater amount of flexible time and enables a higher number of coordinated V2V operations.

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction.