Search Results (155)

Electric vehicle (EV) charging stations play a pivotal role in the widespread adoption and integration of electric vehicles into mainstream transportation systems. While the effects of climate change and greenhouse gases are increasing worldwide, the transition to electric vehicles is of high importance in terms of both ecological and sustainability. EV charging stations serve as the backbone of this transition, providing essential infrastructure to support the charging needs of EV owners and facilitate the transition to electric vehicles. In this study, a MINLP mathematical model is developed for the multi-objective optimization of EVCS. For implementation, Istanbul’s European side and a large-scale synthetic case are addressed considering both current demand and estimations for low, medium, and high EV numbers by the Energy Market Regulatory Authority (EMRA) for 2030 and 2035. The primary aim is to minimize station numbers, capacity, waiting time, and station idle time while meeting the demand. During the solvation of the mathematical model, both present demand and future EV usage forecasts are taken into consideration. This involves simulating different scenarios using EMRA’s 2030 and 2035 estimates and determining the optimal locations and capacities for charging stations for each demand level. Efficiencies in different scenarios were evaluated and the created mathematical model provides to optimize EV charging stations in multiple ways, there will be savings in total cost and labor force. The findings of the study will provide a valuable guide to the EV charging station infrastructure planning of the highways, regions, and urban areas to be selected in possible studies. The multi-directional optimization model addressed in this study will support decision-makers and industry experts in making informed decisions towards the sustainable and efficient development of EV charging infrastructure. Full article

The rapid clustering of Chemical Industrial Parks (CIPs) has escalated the risk of cascading disasters (e.g., toxic leaks and explosions), underscoring the need for resilient emergency logistics systems. However, traditional two-stage optimization models often yield suboptimal outcomes due to decoupled facility location and routing decisions. To address this issue, we propose a unified mixed-integer nonlinear programming (MINLP) model that integrates site selection and routing decisions in a single framework. The model accounts for multi-source supply allocation, enforces minimum safety distance constraints, and incorporates heterogeneous economic factors (e.g., regional land costs) to ensure risk-aware, cost-efficient planning. Two deployment scenarios are considered: (1) incremental augmentation of an existing emergency network and (2) full network reconstruction after a systemic failure. Simulations on a regional CIP cluster (2400 × 2400 km) were conducted to validate the model. The integrated approach reduced facility and operational costs by 9.77% (USD 13.68 million saved) in the incremental scenario and achieved a 15.10% (USD 21.13 million saved) total cost reduction over decoupled planning in the reconstruction scenario while maintaining an 8 km minimum safety distance. This integrated approach can enhance cost-effectiveness and strengthen the resilience of high-risk industrial emergency response networks. Overall, the proposed modeling framework, which integrates spatial constraints, time-sensitive supply mechanisms, and disruption risk considerations, is not only tailored for hazardous chemical zones but also exhibits strong potential for adaptation to a variety of high-risk scenarios, such as natural disasters, industrial accidents, or critical infrastructure failures. Full article

In industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi-sensor transmissions introduce significant interference and latency, impairing the KF’s ability to continuously obtain reliable observations. Meanwhile, existing remote state estimation systems typically rely on oversimplified wireless communication models, unable to adequately handle the dynamics and interference in realistic network scenarios. To address these limitations, this paper formulates a novel dynamic wireless resource allocation problem as a mixed-integer nonlinear programming (MINLP) model. By jointly optimizing sensor grouping and power allocation—considering sensor available power and outage probability constraints—the proposed scheme minimizes both estimation outage and transmission delay. Simulation results demonstrate that, compared to conventional approaches, our method significantly improves transmission reliability and KF estimation performance, thus providing robust technical support for remote state estimation in next-generation industrial wireless networks. Full article

A deterministic Mixed-Integer Nonlinear Programming (MINLP) model for the Integrated Generation and Transmission Expansion Planning (IGTEP) problem is presented. The proposed framework is distinguished by its foundation on the complete AC power flow formulation, which is solved to global optimality using BARON, a deterministic MINLP solver, which ensures the identification of truly optimal expansion strategies, overcoming the limitations of heuristic approaches that may converge to local optima. This approach is employed to establish a definitive, high-fidelity economic and technical benchmark, addressing the limitations of commonly used DC approximations and metaheuristic methods that often fail to capture the nonlinearities and interdependencies inherent in power system planning. The co-optimization model is formulated to simultaneously minimize the total annualized costs, which include investment in new generation and transmission assets, the operating costs of the entire generator fleet, and the cost of unsupplied energy. The model’s effectiveness is demonstrated on the IEEE 14-bus system under various dynamic load growth scenarios and planning horizons. A key finding is the model’s ability to identify the most economic expansion pathway; for shorter horizons, the optimal solution prioritizes strategic transmission reinforcements to unlock existing generation capacity, thereby deferring capital-intensive generation investments. However, over longer horizons with higher demand growth, the model correctly identifies the necessity for combined investments in both significant new generation capacity and further network expansion. These results underscore the value of an integrated, AC-based approach, demonstrating its capacity to reveal non-intuitive, economically superior expansion strategies that would be missed by decoupled or simplified models. The framework thus provides a crucial, high-fidelity benchmark for the validation of more scalable planning tools. Full article

Traffic crashes and congestion generate high social and economic costs, yet traditional traffic monitoring methods, such as police patrols, fixed cameras, and helicopters, are costly, labor-intensive, and limited in spatial coverage. This paper presents a novel Drone Routing and Scheduling with Flexible Multiple Visits (DRSFMV) framework, an optimization model for planning drone-based highway monitoring under realistic operational constraints, including battery limits, variable monitoring durations, recharging at a depot, and target-specific inter-visit time limits. A mixed-integer nonlinear programming (MINLP) model and a linearized version (MILP) are presented to solve the problem. Due to the NP-hard nature of the underlying problem structure, a heuristic solver, Hexaly, is also used. A case study using real traffic census data from three Southern California counties tests the models across various network sizes and configurations. The MILP solves small and medium instances efficiently, and Hexaly produces high-quality solutions for large-scale networks. Results show clear trade-offs between drone availability and time-slot flexibility, and demonstrate that stricter revisit constraints raise operational cost. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

The optimal integration of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks is important to reduce their operating costs, improve their voltage profiles, and enhance their power quality. To this effect, this paper proposes a mixed-integer convex (MI-Convex) optimization model for the optimal siting and sizing of PV systems and D-STATCOMs, with the aim of minimizing investment and operating costs in electrical distribution networks. The proposed model transforms the traditional mixed-integer nonlinear programming (MINLP) formulation into a convex model through second-order conic relaxation of the nodal voltage product. This model ensures global optimality and computational efficiency, which is not achieved using traditional heuristic-based approaches. The proposed model is validated on IEEE 33- and 69-bus test systems, showing a significant reduction in operating costs in both feeders compared to traditional heuristic-based approaches such as the vortex search algorithm (VSA), the sine-cosine algorithm (SCA), and the sech-tanh optimization algorithm (STOA). According to the results, the MI-convex model achieves cost savings of up to 38.95% in both grids, outperforming the VSA, SCA, and STOA. Full article

Power systems’ increased running costs and overuse of fossil fuels have resulted in continuing energy scarcity and momentous energy gap challenges worldwide. Renewable energy sources can meet exponential energy growth, lower reliance on fossil fuels, and mitigate global warming. An MINLP optimization method to solve the RES-hybrid system economic dispatch of electric vehicle charging stations is proposed in this paper. This technique bridges the gap between theoretical models and real-world implementation by balancing technical optimization with practical deployment constraints, making a timely and meaningful contribution. These contributions extend the practical application of MINLP in modern grid operations by aligning optimization outputs with the stochastic character of renewable energy, which is still a gap in the existing literature. The proposed economic dispatch simulation results over 24 h at an hourly resolution show that all generation units contributed proportionately to meeting EVCS demand: solar PV (51.29%), ESS (13.5%), grid (29.92%), and wind generator (8.29%). The RES-hybrid energy management systems at charging stations are designed to make the best use of solar PV power during the EVCS charging cycle. The supply–demand load profile problem dynamic in EVCS are designed to reduce reliance on grid electricity supplies while increasing renewable energy usage and reducing carbon impact. Full article

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article

This article addresses the optimal network expansion problem in rural distribution systems using a mixed-integer nonlinear programming (MINLP) model that simultaneously performs route selection and conductor sizing in radial distribution systems. The proposed methodology was validated on 9- and 25-node test systems, comparing the results against approaches based on the minimum spanning tree (MST) formulation and metaheuristic approaches (the sine-cosine and tabu search algorithms). The MINLP model significantly reduced the total costs. For the nine-node system, the total cost decreased from USD 131,819.33 (MST-TSA) to USD 77,129.34 (MINLP), saving USD 54,689.99 (41.48%). Similarly, the costs of energy losses dropped from USD 111,746.73 to USD 63,764.12, a reduction of USD 47,982.61 (42.94%). In the 25-node system, the total costs fell by over 65% from USD 371,516.59 to USD 128,974.72, while the costs of energy losses decreased by USD 210,057.16 (61.06%). Despite requiring a higher initial investment in conductors, the MINLP model led to substantial long-term savings due to reduced operating costs. Unlike previous methods which separate network topology design and conductor sizing, our proposal integrates both aspects, ensuring globally optimal solutions. The results demonstrate its scalability and effectiveness for long-term distribution planning in complex power networks. The experimental implementation was carried out in Julia (v1.10.2) using JuMP (v1.21.1) and BONMIN. Full article

Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization under concurrent batch processing scenarios. To address these challenges, we propose a non-uniform deployment inference framework (NUDIF), which achieves high-throughput distributed inference service by adapting to heterogeneous resources and balancing inter-stage processing capabilities. Formulated as a mixed-integer nonlinear programming (MINLP) problem, NUDIF is responsible for planning the number of instances for each sub-model and determining the specific devices for deploying these instances, while considering computational capacity, memory constraints, and communication latency. This optimization minimizes inter-stage processing discrepancies and maximizes resource utilization. Experimental evaluations demonstrate that NUDIF enhances system throughput by an average of 9.95% compared to traditional single-pipeline optimization methods under various scales of cluster device configurations. Full article

Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study investigates the operational characteristics of methanol dual-fuel liners and develops a mixed-integer nonlinear programming (MINLP) model aimed at minimizing operating costs. Furthermore, an improved genetic algorithm (GA) integrated with the Nonlinear Programming Branch-and-Bound (NLP-BB) method is proposed to solve the model. The case study results demonstrate that the proposed approach can reduce operating costs by more than 15% compared to conventional route and speed strategies while also effectively decreasing emissions of CO₂, NOx, SOx, PM, and CO. Additionally, comparative experiments reveal that the designed algorithm outperforms both the GA and the Linear Interactive and General Optimizer (LINGO) solver for identifying optimal route and speed solutions. This research provides critical insights into the operational dynamics of methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies for conventional fuel vessels are not directly applicable. This study provides critical insights into the optimization of voyage decision-making for methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies designed for conventional fuel vessels are not directly applicable. It further elucidates the impact of methanol fuel tank capacity on voyage planning, revealing that larger tank capacities offer greater operational flexibility and improved economic performance. These findings provide valuable guidance for shipping companies in strategically planning methanol dual-fuel operations, enhancing economic efficiency while reducing vessel emissions. Full article

This paper addresses the optimal conductor selection (OCS) problem in radial distribution networks, aiming to minimize the total costs associated with conductor investment and energy losses while ensuring voltage regulation and power balance as well as observing thermal limits. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model and solved using a hybrid branch-and-bound (B&B), interior-point optimizer (IPO) approach within the Julia-based JuMP framework. Numerical validations on 27-, 33-, and 69-bus test feeders demonstrate cost-efficient conductor configurations. A multi-objective analysis is employed to construct the Pareto front, offering trade-offs between investment and operating costs. The impact of distributed energy resources (DERs) is also assessed, showing cost reductions when said resources provide reactive power support. The results confirm that the proposed MINLP approach outperforms conventional metaheuristics in terms of accuracy and reliability. Full article

The growing interest in soft robotics arises from their unique ability to perform tasks beyond the capabilities of rigid robots, with soft actuators playing a central role in this innovation. Among these, electromagnetic soft actuators (ESAs) stand out for their fast response, simple control mechanisms, and compact design. Analytical and experimental studies indicate that smaller ESAs enhance the force per unit cross-sectional area (F/CSA) without compromising force efficiency. This work uses the magnetic vector potential (MVP) to calculate the magnetic field of an ESA, which is then used to derive the actuator’s generated force. A mixed integer non-linear programming (MINLP) optimization framework is introduced to maximize the ESA’s F/CSA. Unlike prior methods that independently optimized parameters, such as ESA length and permanent magnet diameter, this study jointly optimizes these parameters to achieve a more efficient and effective design. To validate the proposed framework, finite element-based COMSOL 5.4 is used to simulate the magnetic field and generated force, ensuring consistency between MVP-based calculations and the physical model. Additionally, simulation results demonstrate the effectiveness of MINLP optimization in identifying the optimal design parameters for maximizing the F/CSA of the ESA. The data and code are available at GitHub Repository. Full article

This article presents two novel mixed-integer nonlinear programming (MINLP) formulations in the complex variable domain to address the optimal phase-balancing problem in asymmetric three-phase distribution networks. The first employs a matrix-based load connection model (M-MINLP), while the second uses a compact vector-based representation (V-MINLP). Both integrate the power flow equations through the current injection method, capturing the nonlinearities of Delta and Wye loads. These formulations, solved via an interior-point optimizer and the branch-and-cut method in the Julia software, ensure global optima and computational efficiency. Numerical validations on 8-, 25-, and 37-node feeders showed power loss reductions of 24.34%, 4.16%, and 19.26%, outperforming metaheuristic techniques and convex approximations. The M-MINLP model was 15.6 times faster in the 25-node grid and 2.5 times faster in the 37-node system when compared to the V-MINLP approach. The results demonstrate the robustness and scalability of the proposed methods, particularly in medium and large systems, where current techniques often fail to converge. These formulations advance the state of the art by combining exact mathematical modeling with efficient computation, offering precise, scalable, and practical tools for optimizing power distribution networks. The corresponding validations were performed using Julia (v1.10.2), JuMP (v1.21.1), and AmplNLWriter (v1.2.1). Full article