Optimization Model Research Articles

Multi-Objective Coordinated Optimization Method of Active and Reactive Power Considering Power Characteristics of Renewable Energy Converters

In new power systems with a high proportion of renewable energy, optimization criteria based solely on economic efficiency or system stability may lead to a reduction in the static stability domain of the system or lead to long-term deviations from economic operation, thus reducing the overall applicability of such methods. This paper proposes a multi-objective active–reactive power coordinated optimization model that considers both economic efficiency and static stability indicators. The goal of the model is to minimize operating costs while optimizing static stability margins. It combines the reactive power support capabilities of converters and other reactive power compensation equipment to ensure safe and economical dispatch of the system. The proposed method is verified through a case study, which shows that this method can make full use of the potential reactive power regulation capability of the converter. At the same time, the economics and stability of the system are significantly improved by using this method. The overall improvement is about is 12.3%.

A multi-objective Fuzzy Robust Optimization model for open-pit mine planning under uncertainty

A multi-objective Fuzzy Robust Optimization model for open-pit mine planning under uncertainty

Optimization Operation Strategy for Comprehensive Energy System Considering Multi-Mode Hydrogen Transportation

The transformation from a fossil fuel economy to a low-carbon economy has reshaped the way energy is transmitted. As most renewable energy is obtained in the form of electricity, using green electricity to produce hydrogen is considered a promising energy carrier. However, most studies have not considered the transportation mode of hydrogen. In order to encourage the utilization of renewable energy and hydrogen, this paper proposes a comprehensive energy system optimization operation strategy considering multi-mode hydrogen transport. Firstly, to address the shortcomings in the optimization operation of existing systems regarding hydrogen transport, modeling is conducted for multi-mode hydrogen transportation through hydrogen tube trailers and pipelines. This model reflects the impact of multi-mode hydrogen delivery channels on hydrogen utilization, which helps promote the consumption of new energy in electrolysis cells to meet application demands. Based on this, the constraints of electrolyzers, combined heat and power units, hydrogen fuel cells, and energy storage systems in integrated energy systems (IESs) are further considered. With the objective of minimizing the daily operational cost of the comprehensive energy system, an optimization model for the operation considering multi-mode hydrogen transport is constructed. Lastly, based on simulation examples, the impact of multi-mode hydrogen transportation on the operational cost of the system is analyzed in detail. The results indicate that the proposed optimization strategy can reduce the operational cost of the comprehensive energy system. Hydrogen tube trailers and pipelines will have a significant impact on operational costs. Properly allocating the quantity of hydrogen tube trailers and pipelines is beneficial for reducing the operational costs of the system. Reasonable arrangement of hydrogen transportation channels is conducive to further promoting the green and economic operation of the system.

Road side unit deployment optimization for the reliability of internet of vehicles based on information transmission model.

The Internet of Vehicles (IoV) makes it possible to transmit information in real time between vehicles, providing a modern approach for autonomous driving, traffic safety, and other applications. Roadside units (RSUs) contribute to the enhancement of IoV's reliability and transmission efficiency, while mitigating the impact of low IoV penetration. The objective of RSU deployment optimization is to minimize the total cost with the premise of ensuring IoV reliability. We construct a distance-based reliability measure for IoV, which is expressed as the proportion of information transmitted in online mode to the total transmission distance. The distance distribution of the online and offline transmissions is computed using the information transmission model. A bi-objective optimization model is established with the objectives of minimizing the cost of RSU and maximizing the reliability of IoV. Meanwhile, based on variable probabilities of crossover and mutation, a nondomination level-based NSGA-II (NNSGA-II) is designed to improve the solving efficiency. Numerical results show the advantage of the proposed model over the models evaluated with the objective of reducing transmission time can be up to 18% in different traffic scenarios, and NNSGA-II is significantly more computationally efficient.

Optimization of Weight Reduction in Power Transmission Systems Using Bio-Inspired Algorithms and Composite Materials

Traditional optimization approaches for power transmission systems often optimize gear and shaft designs separately, rely on conventional materials, and face challenges handling mixed-variable problems involving discrete and continuous variables. These limitations hinder the potential for significant weight reduction and performance improvements in modern mechanical systems. This research presents an innovative optimization approach for reducing the gear pitch in a power transmission system comprising a shaft with two gears. By employing bio-inspired metaheuristic algorithms and utilizing materials common in additive manufacturing and composite materials, the study aims to minimize system weight while ensuring mechanical integrity. The optimization integrates gear and shaft design variables, accurately models mechanical constraints through penalty functions, and leverages the unique properties of advanced materials to enhance performance. In this work, we developed an integrated optimization model that overcomes the limitations of previous studies by combining advanced materials with bio-inspired algorithms to achieve significant weight reduction in gear systems. This approach effectively addresses the complexities of mixed-variable optimization and mechanical constraints, providing a practical and innovative solution for modern engineering applications.

An optimization model to prioritize fuel treatments within a landscape fuel break network.

We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions. Its mathematical formulation is concise and computationally efficient, allowing for customization and expansion to address more complex and challenging fuel break management problems in diverse landscapes. We constructed test cases for Southern California of the United States to understand model outcomes across a wide range of fire and fuel management scenarios. Results suggest optimal fuel treatment layouts within the Southern California's fuel break network responding to various model assumptions, which offer insights for regional fuel break planning. Comparative tests between the proposed optimization model and a rule-based simulation approach indicate that the optimization model can provide significantly better solutions within reasonable solving times, highlighting its potential to support fuel break management and planning decisions.

Optimization model for reducing inconsistency of pairwise comparison matrices with minimal change

Optimization model for reducing inconsistency of pairwise comparison matrices with minimal change

Optimization of the Frying Process for Maximizing Crispiness of Scallop (Patinopecten yessoensis) Adductor Muscle Snacks Using Vacuum Low-Temperature Frying.

Scallops, an economically important seafood, are popular as fried snacks. Vacuum low-temperature frying creates crispy, healthier foods that meet consumer demand for nutritious snacks with excellent texture. However, research on vacuum frying for shellfish products remains limited. This study aimed to optimize the process for developing a healthy, crispy snack that retains the original shape of the Yesso scallop (Patinopecten yessoensis) adductor muscle using vacuum low-temperature frying technology. The effects of various frying methods on the scallops were analyzed. The vacuum low-temperature frying process was optimized based on evaluations of physical, chemical, and sensory properties. Frying conditions were optimized using response surface methodology, with temperature (X1: 90.1-109.9 °C) and time (X2: 186-774 s) as variables. Based on moisture (5.6 ± 0.1 g/100 g), hardness (1470 ± 5.0 g/cm2), and sensory chewiness (7.6 ± 0.2 points) analyses, 99.9 °C and 480 s were identified as the optimal conditions. Validation was conducted through sensory evaluation by 30 trained panelists. Scallops produced under these optimal frying conditions exhibited low water activity (aw) (0.46), desirable texture (1428 g/cm2), palatability (7.9 points), and high protein content (45.6 g/100 g). The predicted and experimental values for frying temperature and time showed strong agreement, validating the reliability of the optimization model.

Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow

As the penetration of distributed renewable energy increases, the phenomenon of bidirectional power flow in distribution networks becomes increasingly severe. Traditional regulation devices like OLTC (on-load tap changer) and CB (capacitor bank) cannot effectively mitigate reverse power flow in distribution networks due to their limitations. The transmission capacity of the distribution network under reverse power flow is approximately 50% of the rated capacity of the OLTC, leading to issues such as voltage limit violations and high wind and solar curtailment rates. This paper proposes a method for calculating the reverse power flow delivery capacity of distribution networks, quantitatively describing the distribution network’s delivery limits for reverse power flow. Based on this, a joint optimization model for multiple distribution networks with an SOP is established. The SOP is utilized to share reverse power flow delivery capacity among multiple distribution networks, enhancing operational economy and increasing the accommodation of the DG. Finally, the method’s effectiveness and correctness are verified in the IEEE 33-node system. The results validate that while joint operation does not enhance the reverse flow transmission capacity of a single distribution network, it can, through the shared reverse flow transmission capacity approach, elevate the reverse flow transmission capacity to approximately 70% during the majority of time periods.

Assessing the Environmental Kuznets Curve in Agricultural Transport: The Case of Brazilian Soy

Abstract This research investigates the Environmental Kuznets Curve (EKC) hypothesis within Brazil’s soy transportation sector. It aims to determine if CO2 emissions from soy transport show an “Inverted-U” or “N” shaped relationship with income from 2002 to 2017. Using a mathematical optimization model and System GMM for analysis, the study finds an “N” shaped EKC, indicating emissions initially increase, decrease, and then rise again with income. This suggests long-term environmental damage from sector growth, highlighting the need for targeted measures to mitigate emissions and enhance operational productivity, such as investments in intermodal transport and road efficiency improvements.

Real-time cooperative voltage regulation strategy of distribution network based on approximate dynamic programming

Large-scale distributed renewable energy in the distribution network can result in reliability issues such as exceeding voltage limits and overloading power lines. Additionally, the rapid growth of electric vehicles has caused a surge in power demand in the distribution network. Therefore, how to guarantee the real-time stability of distribution network voltage under uncertain environment using available resources is an urgent problem to be solved. To this end, this paper proposes a cooperative voltage regulation of an on-load voltage regulator and electric vehicles for a distribution network considering multiple uncertainties. Firstly, an optimization model of the electric vehicle clusters considering the charging location and power is established, and the traditional on-load voltage regulator of distribution networks is also considered along with network operational constraints. Subsequently, a real-time cooperative voltage regulation strategy based on approximate dynamic programming is proposed, which employs segmented linear functions to process the value function to reduce the distribution network voltage offset and to ensure optimization accuracy. Numerical simulation results validate the feasibility and the effectiveness of the proposed cooperative voltage regulation technology involving electric vehicles and on-load tap changer in the distribution network.

Multi-objective optimization of building envelope of rural housing in the severely cold region of China based on energy consumption, thermal comfort and cost-effectiveness

The performance of rural residential buildings has a great energy-saving potential due to the great spontaneity and ignorance of the rural residential construction. To solve this problem, this paper provides a multi-objective optimization model based on Chebyshev inequality. The model takes energy consumption, indoor thermal comfort, project cost as optimization objectives and considers operating conditions in winter and summer. This study taking a rural residential house as an example and analysed the influence of its envelope parameters on building performance using EnergyPlus. The parameters of the building envelope were optimized, which verified the superiority of the method. Optimization results showed that the heating energy consumption was reduced by 29%, and the air conditioning energy consumption was reduced by 6%. This study analysed the relationship between the building energy consumption, indoor thermal comfort and the cost by multi-objective optimization method, to provide a new research idea for rural residential research. Optimal design solutions were generated under various weighting coefficients. The model aimed to meet the climatic and economic conditions of northern China and similar regions. Therefore, based on these optimization results, decision-makers can choose optimal design and construction solutions according to their own will.

Multi-Objective Distributionally Robust Optimization for Earthquake Shelter Planning Under Demand Uncertainties

Deciding the locations of shelters and how to assign evacuees to these locations is crucial for effective disaster management. However, the inherent uncertainty in evacuation demand makes it challenging to make optimal decisions. Traditional stochastic or robust optimization models tend to be either too aggressive or overly conservative, failing to strike a balance between risk reduction and cost. In response to these challenges, this research proposes a multi-objective distributionally robust optimization (MODRO) model tailored for shelter location and evacuation allocation. First, an ambiguity set (moment-based or distance-based) is constructed to capture the uncertainty in evacuation demand, reflecting the possible range of outcomes based on demand data from a disaster simulation model. Then, the distributionally robust optimization model considers the “worst-case” distribution within this ambiguity set to minimize construction cost, travel distance, and unmet demand/unused capacity, balancing the trade-off between overly conservative and overly optimistic approaches. The model aims to ensure that shelters are optimally located and evacuees are efficiently allocated, even under the most challenging scenarios. Furthermore, Pareto optimal solutions are obtained using the augmented ε-constraint method. Finally, a case study of Ogu, a wooden density built-up area in Tokyo, Japan, compares the DRO model with stochastic and robust optimization models, demonstrating that the cost obtained by the DRO model is higher than a stochastic model while lower than the worst-case robust model, indicating a more balanced approach to managing uncertainty. This research provides a practical and effective framework for improving disaster preparedness and response, contributing to the resilience and safety of urban populations in earthquake-prone areas.

TRIPLE-OBJECTIVE OPTIMIZATION OF SUPERCRITICAL CO2 RECOMPRESSION BRAYTON CYCLE IN SOLAR TOWER SYSTEMS WITH ENERGY, EXERGY AND EXERGOECONOMIC ANALYSIS

This study addresses the energy, exergy and exergoeconomic analyses of the supercritical CO2 recompression Brayton cycle used in solar tower systems. In the study, a three-objective optimization model was developed using artificial neural networks (ANN) to optimize the system performance. The model provides information for the development of sustainable solar energy systems by providing analyses on key factors such as energy efficiency, environmental impact and economic viability. The results show that the supercritical CO2 cycle provides higher thermal efficiency compared to conventional systems and offers cost advantages by reducing the size of system components. In addition, the analyses show that energy and exergy losses can be minimized and the cost effectiveness of the system can be increased, providing important findings in terms of the efficiency and economic viability of solar energy systems.

Hybrid Robotic Arm for Autonomous Aerial Refueling

Autonomous aerial refueling technology is gaining increasing attention to enhance aircraft combat capabilities. Current research on autonomous refueling focuses mainly on flight control laws, with little attention to the automation of refueling pipes. This leads to high demands on control law performance and navigation accuracy, making it difficult to ensure reliability. To address this, we propose a robotic arm system capable of automatic docking during the flexible aerial refueling process. The system uses a conical kinematic space configuration, offering enhanced stability and impact resistance. The frame-type structure achieves a lightweight design. Additionally, we establish a single-objective optimization model for the connecting rod dimensions and apply a genetic algorithm (GA) for their optimization. We also propose a trajectory-fitting calibration theory based on the robotic arm’s special configuration and complete its movement accuracy calibration using a laser tracker. This calibration method reduces the robotic arm’s motion error by 71%, achieving an absolute positioning accuracy better than 3.5 mm, which meets the requirements for autonomous aerial refueling. In summary, this research presents a hybrid robotic arm that meets automatic docking requirements, offering a new approach to autonomous aerial refueling.

Numerical Design Structure Matrix–Genetic Algorithm-Based Optimization Method for Design Process of Complex Civil Aircraft Systems

In the requirement-driven forward design process of civil aircraft, the large number of design tasks of complex systems with varying difficulty and the complex relationships between design tasks lead to unnecessary repetitive design iterations. In order to solve the above problems, the concept of overlap coefficient is proposed to further sort out the forward and backward logical relationships between design tasks and the civil aircraft system design process optimization model based on a numerical design structure matrix. The algorithm NSGA-II is improved and verified with the flight control system design as a case study. The results show that the proposed method can effectively improve the efficiency of complex system design and provide technical support for the optimization of the design process of complex civil aircraft systems.

Modelling a Dual-Objective Optimization Model for Cost Reduction and Disruption Risk Minimization in Automotive Supply Chains

Dual-Objective Optimization model is vital in automotive supply chains (ASC) to emphasize multi-modal transportation under disruption scenarios at minimizing costs and disruption risks. In this context, the study evaluated the hypothetical and real-world data based on the deployment of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to understand the efficacy of incorporating multi-modal transportation to balance cost reduction and risk mitigation. The findings of Dual-Objective Optimization model revealed the model's superiority in identifying cost-effective transportation modes, offering a significant improvement over previous model. This research contributes to the mathematical modelling by providing a comprehensive framework for automotive supply chains, addressing operational efficiency and resilience against disruptions.

Energy Planning and Optimization Model for Campus Buildings: A Case Study of Erciyes University

This study addresses energy consumption and climate change challenges in university campus buildings, focusing on Erciyes University. The research develops a multi-objective optimization model using GAMS software and NEOS Server to enhance campus energy efficiency. The model evaluates various energy-saving measures and their investment costs. Findings indicate that building envelope insulation can reduce heating energy consumption by 35%, while efficient hot water systems and energy-saving technologies can achieve savings up to 75.5%. Model calculations using SCIP and LINDO solvers demonstrate high accuracy, with results differing by only 0.2%. This research provides valuable guidance for university decision-makers in implementing targeted interventions for significant primary energy savings.

Optimization Model of Ship Engine Room Electrical Equipment Layout Based on Deep Learning

Optimization Model of Ship Engine Room Electrical Equipment Layout Based on Deep Learning

Cooperative Low-Carbon Trajectory Planning of Multi-Arrival Aircraft for Continuous Descent Operation

To address the technical challenges of implementing Continuous Descent Operations (CDO) in high-traffic-density terminal control areas, we propose a cooperative low-carbon trajectory planning method for multiple arriving aircraft. Firstly, this study analyzes the CDO phases of aircraft in the terminal area, establishes a multi-phase optimal control model for the vertical profile, and introduces a novel vertical profile optimization method for CDO based on a genetic algorithm. Secondly, to tackle the challenges of CDO in busy terminal areas, a T-shaped arrival route structure is designed to provide alternative paths and to generate a set of four-dimensional (4D) alternative trajectories. A Mixed Integer Programming (MIP) model is constructed for the 4D trajectory planning of multiple aircraft, aiming to maximize the efficiency of arrival traffic flow while considering conflict constraints. The complex constrained MIP problem is transformed into an unconstrained problem using a penalty function method. Finally, experiments were conducted to evaluate the implementation of CDO in busy terminal areas. The results show that, compared to actual operations, the proposed optimization model significantly reduces the total aircraft operating time, fuel consumption, CO2 emissions, SO2 emissions, and NOx emissions. Specifically, with the optimization objective of minimizing total cost, the proposed method reduces the total operation time by 22.4%; fuel consumption, CO2 emissions, SO2 emissions by 22.9%, and NOx emissions by 23.7%. The method proposed in this paper not only produces efficient aircraft sequencing results, but also provides a feasible low-carbon trajectory for achieving optimal sequencing.