Optimization Model Research Articles

A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors. The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.

Integrated energy microgrids participating in voltage regulation ancillary services: An improved ADMM based distributed optimization approach

AbstractThe integrated energy microgrid (IEM) has good potential for both active and reactive power regulation, which are gradually becoming critical resources for participating in power system ancillary services. This paper constructs a distributed optimization model for the IEMs participating in voltage regulation ancillary services by considering multiple physical network constraints. To reduce computational complexity, the DistFlow power flow model and the second‐order cone relaxation method are introduced to transform the optimization model into a second‐order cone programming problem. Moreover, an improved accelerated consensus alternating direction method of multipliers algorithm is proposed to accelerate distributed optimization solutions of the model while protecting user privacy. Finally, the proposed optimization model and solution method are tested in the modified IEEE‐33 node test case to verify their effectiveness and feasibility.

Low-carbon route optimization model for multimodal freight transport considering value and time attributes

As the international community increasingly focuses on climate change, optimizing low-carbon transportation routes in the multimodal freight transport system has become a pressing issue. However, due to the variability in cargo properties and the influence of various factors on transportation route decisions, formulating a low-carbon and economical multimodal freight transport plan remains a significant challenge. To address the issue, this study considered cargoes with different attributes in terms of both value and time attributes. Triangular fuzzy numbers were employed to represent the uncertain demand for cargo, with confidence levels introduced for clarification. A low-carbon route decision optimization model for multimodal freight transport was established to minimize the combined transportation carbon emission and time costs. The catastrophe adaptive genetic algorithm, based on Monte Carlo sampling, was employed to solve the model using arithmetic examples. Finally, parameter sensitivity analysis revealed that adjustments to carbon tax values and changes in the proportion of electric trucks and green electricity supply had the most significant impact on the low-carbon route decision-making plan for multimodal freight transport. For low value-added and timeliness-strong cargo, a 60 % increase in carbon tax value shifted the mode of transportation from road to railway. When the carbon tax increased by more than 140 %, the transportation mode shifted from railway to waterway. Additionally, when the proportion of electric trucks and green electricity supply both exceeded 80 %, the transportation mode between some city nodes shifted from railway to road. When these proportions increased beyond 90 %, road transportation became the predominant mode.

Optimal sampling strategy for probability estimation: An application to the Agricultural Quarantine Inspection Monitoring program.

Imported agricultural pests can cause substantial damage to agriculture, food security, and ecosystems. In the United States, the Agricultural Quarantine Inspection Monitoring (AQIM) program conducts random sampling to estimate the probabilities that cargo and passengers arriving at ports of entry carry pests. Assessing these risks accurately is critical to enable effective policies and operational procedures. This study introduces a pathway-level analysis with an objective function aligned with AQIM's goal, offering a new perspective compared to the current container-by-container approach, which relies on heuristics to set inspection rates. We formulate an optimization model that minimizes the mean squared error of the probability estimates that AQIM obtains. The central decision-making tradeoff that the model explores is whether it is preferable to sample more arriving containers (and fewer boxes per container) or more boxes per container (and fewer containers), given limited resources. We first derive an analytical solution for the optimal sampling strategy by leveraging several approximations. Then, we apply our model to a numerical case study of maritime cargo sampling at the Port of Long Beach. Across a wide range of parameter settings, the optimal strategy samples more containers (but fewer boxes per container) than the current AQIM protocol. The difference between the two strategies and the accuracy improvement with the optimal approach are larger if the pest statuses of boxes in the same container are more strongly correlated. We recommend that AQIM record box-level (beyond only container-level) inspection data, which could be used to estimate this correlation and other modelparameters.

A multi-verse Optimizer Approach Based on a Salp Swarm for Image Feature Selection to Observe Changes in Urban Lands

Many changes have occurred in most urban areas, and it is usually governed by many geographical and social factors, the result will be either the expansion or contraction of these cities. In this paper, an optimizer method called salp swarm optimization (SSO) is used to build a driving force model for urban lands according to the changes in the driving force mechanism. The principle of (SSO) is extracting the driving force classification rules corresponding to different types of land use change samples by imitating the behavior of salp. The classification rules are constructed in the form of "IF ... THEN", and three different fitness functions are selected for simulation verification. The data set used in this search represents images from Global Positioning System (GPS) satellites and remote sensing data. The experimental results show that the overall accuracy evaluation of the salp swarm optimization model is superior to other algorithms, indicating that the SSA is feasible for land use change modeling.

An Optimized Strategy for the Integration of Photovoltaic Systems and Electric Vehicles into the Real Distribution Grid

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

Optimization of process parameters for polishing aero-engine blade with abrasive cloth wheel considering spindle vibration and polished roughness

The vibration of machine tool spindle is very important for machining. Firstly, in order to explore the relationship between spindle vibration and process parameters, the spindle vibration acceleration when polishing aero-engine blade was measured, and the quadratic polynomial models between the spindle vibration acceleration in X, Y and Z direction and the process parameters were established. Secondly, a quadratic polynomial model for the influence of process parameters on polished surface roughness was also determined. Thirdly, through the Analysis of Variance (ANOVA) and main effect analysis, it can be seen that the spindle speed has the greatest impact on the vibration. Finally, a multi-objective optimization model was established with the optimization objective of minimizing spindle vibration acceleration and polished surface roughness, and the optimal process parameters were solved using genetic algorithm. The optimal process parameters were verified and the results show that the polished surface roughness with the optimal process parameters are all less than 0.4 μm, and the deviation rates between the theoretical optimization results of spindle vibration acceleration and the experimental results are all less than 10%, indicating that the optimization results are good.

A Two-Individual-Based Evolutionary Algorithm for Flexible Assembly Job Shop Scheduling Problem with Uncertain Interval Processing Times

The assembly job shop is a prevalent production organization mode in manufacturing enterprises. During the processing and assembly of products, operation processing times are influenced by numerous factors, leading to significant uncertainty. This paper investigates the flexible assembly job shop scheduling problem (FAJSP) with uncertain processing times, where processing times are represented as variable interval numbers. We develop a robust optimization model for the FAJSP, utilizing confidence level estimation to determine the ranges of processing times and reformulating the model based on the chance-constrained method. A two-individual-based master–apprentice evolutionary (MAE) algorithm is proposed. Two effective encoding schemes are designed to prevent the generation of infeasible solutions under assembly sequence constraints. Additionally, a decoding method based on interval scheduling theory is devised to accurately represent interval processing times. Case studies are conducted to validate the effectiveness of the proposed robust optimization model and demonstrate the superiority of the MAE algorithm.

Heuristic Approaches for Using an Exact Model to Obtain the Placement of a Set of Rectangles on a Half-Strip

There is considered the problem of the compact orthogonal placement, which consists in placing of the set of rectangles on the half-strip and its solution by constructing an optimization model that reduces to solving a high-dimensional linear partial Boolean programming problem. The main method of solving the problem is the algorithm of branches and boundaries, providing consistent fiXation of the Boolean variables. The direct use of this algorithm represents significant computational difficulties and is used only for the small size of the initial set of rectangles. In this paper are proposes approaches that reduce the computational complexity of the solution of this optimization model by using heuristic approaches to fix individual Boolean variables. Most of the variables responsible for the relative arrangement of all pairs of rectangles relative to each other are fixed on the basis of generating the permutation of rectangles and special code that determines the direction by which the intersection of the location of the rectangles should be monitored. Thus, the direct computing complexity of the method of branches and the boundaries of solving the problem becomes insignificant. To generate pre-fixation of the Boolean variables, genetic algorithms and local search algorithms are used. Computational experiments showed that the used techniques of fixing the Boolean variables allowed to reduce the volume of calculations and the quality of the resulting approximate solution, in general, it turns out acceptable.

Synergistic solar electricity-water generation through an integration of semitransparent solar cells and multistage interfacial desalination

Energy shortage and freshwater scarcity are critical challenges for the sustainable development of the society. The photovoltaic-thermal (PVT) hybrid system offers a promising strategy by harnessing solar energy for electricity and water cogeneration. However, existing systems suffer from relatively low efficiency due to incomplete solar spectrum utilization. To address this, we propose a novel PVT integrated system that combines semi-transparent solar cells and multistage interfacial stills to maximize solar spectrum utilization, allowing for efficient electricity and freshwater co-production. Experimental results demonstrate a record-high solar-to-vapor efficiency of 210 % with a production rate of 3.17 L m−2 h−1 under one-sun, while maintaining an uncompromised electrical efficiency of 19.57 %. Furthermore, we employ a verified theoretical framework to provide optimized strategies for concurrent enhancement of electricity-water production, by improving internal heat and mass transfer and effectively reducing the thickness of the interstage air gap. Moreover, we introduce a non-contact model for system structure optimization proposed to match the high transmittance of solar cells. This work realizes full solar spectrum utilization to cogenerate electricity and freshwater, offering optimized strategies from the thermal perspective for future research.

Using Gerontology Theory to Guide the Development of Artificial Intelligence to Support Aging-in-Place

If artificial intelligence (AI) is to support aging-in-place, determining how, when, and why to apply AI is a crucial endeavor. A seminal gerontology meta-theory called Se-lection, Optimization, and Compensation (SOC) model has promise to conceptualize how AI can play a role in aging-in-place. The model posits that successful aging re-quires selecting goals/domains to apply resources, opti-mizing means to best achieve those goals, and compen-sating for losses by attaining new resources or tapping into unused resources for alternative means of pursuing those goals. In this short paper, we describe the SOC model, and draw links to domains in which AI can sup-port aging in place. For example, AI can assist with health-related decision making (selection), cognitive training and reminders (optimization), and domestic task assistance (compensation). Human-centered considera-tions are provided for implementation of AI in the home.

Approximating the Pareto frontier for bi-objective preventive maintenance and workshop scheduling: A Lagrangean lower bounding methodology for evaluating contracting forms

AbstractEffective planning of preventive maintenance plays an important role in maximizing the operational readiness of any industrial system. We consider an operating system and a maintenance workshop governed by two stakeholders who collaborate based on a mutual contract: components of the operating system that need maintenance are sent to the maintenance workshop, where necessary maintenance activities are performed and after which the maintained components are returned to the operating systems and ready to be used again. While the maintenance activities must obey the workshop capacity, the components should be returned to the operating system within a contracted time frame. For this problem, we developed in a previous work a mixed-integer linear optimization model incorporating stocks of damaged as well as repaired components, workshop scheduling, and preventive maintenance planning for the operating system. We then investigated an availability contract between the stakeholders and which is in the paper at hand compared with a turn-around time contract type, which is more often used in reality. Since, for real instance sizes, the latter leads to a computationally demanding bi-objective optimization problem, we use Lagrangean relaxation and subgradient optimization to compute local lower bounds on the set of non-dominated points, complemented with math-heuristics to identify good feasible solutions (i.e., local upper bounds). Our suggested method thus provides a bounding of the set of non-dominated points for a turn–around time contract.

Optimization of regional power low-carbon transition pathways in China under differentiated tradable green certificates trading models

In line with China's carbon dioxide peaking and carbon neutrality goals, the widespread adoption of renewable energy has become a key global and national trend. Tradable Green Certificates (TGC), used as a policy tool to implement the Renewable Portfolio Standards (RPS) system, stimulate the supply and demand for renewable energy. China's current TGC trading model comprises Electricity Certificate Combination (ECC) and Electricity Certificate Separation (ECS). These models lead to varying low-carbon transition pathways in the regional power sector. This study investigates the development trends of low-carbon power under different TGC models, aiming to identify the optimal transition path. By formulating policy parameters from a global optimization perspective, this study considers factors such as power balance, natural resource allocation, technological constraints, and carbon reduction policies. Using a bottom-up optimization model for energy expansion, the study analyzes the impacts of different market models and carbon emission reduction pressures on China's low-carbon power transition from 2023 to 2050. The recommendation is first to use the ECC model to promote renewable energy development by 2030, then transition to the ECS model to optimize TGC benefits, achieving a balanced approach to carbon reduction, economic costs, and social costs for the sustainable development of electricity.

An Algorithm for Consolidating Small Shipments to Reduce Costs in International Logistics for E-commerce Businesses

Purpose: This article presents an algorithm designed to optimize the consolidation of small shipments, aiming to reduce costs and improve efficiency in international logistics for e-commerce businesses. The research focuses on the highly trafficked route between China and the USA, particularly the corridor from Shenzhen to Los Angeles. Methodology: By employing a cluster-based optimization model, the algorithm consolidates goods from multiple e-commerce companies into single containers, maximizing space utilization and minimizing transportation costs. Findings: Experimental data reveals a 40% reduction in costs and a 10-day decrease in delivery times compared to traditional methods. Unique Contribution to Theory, Practice and Policy: The algorithm has the potential as a scalable solution for global supply chain logistics.

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical “water-quantity water-scarce region” in China, with those of the Yangtze River Basin, a “water-quality water-scarce region.” The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical–virtual water system challenges within river basins.

Optimum Planning of Carbon Capture and Storage Network Using Goal Programming

Carbon capture and storage (CCS) is a critical technology used for mitigating climate change by capturing carbon dioxide emissions from industrial sources and storing them underground to prevent their release into the atmosphere. Despite its potential, optimizing CCS systems for cost-effectiveness and efficiency improvement remains a significant challenge. In this paper, the optimization of CCS systems through the development and application of two mathematical optimization techniques is introduced. The first technique is based on using a superstructure optimization model, while the second technique relies on applying a goal programming optimization model. These models were solved using LINGO software version API 14.0.5099.166 to enhance the efficiency and cost-effectiveness of CCS systems. The first model, seeking to maximize the exchange of CO2 flowrate from sources to sinks, achieved a CO2 capture rate of 93.36% with an annual total cost of USD 1.175 billion. The second model introduced a novel mixed-integer non-linear programming (MINLP) approach for multi-objective optimization, targeting the minimization of total system cost, alternative storage, and unutilized storage while maximizing CO2 load exchange. The application of the second model, when prioritized to maximize CO2 flowrate exchange using the goal programming technique, resulted in a cost reduction of 36.46% and a CO2 capture rate of 75.87%. In contrast, when the second model prioritized minimizing the total annual cost, a 48% cost reduction was achieved, and the CO2 capture rate was decreased by 68.37%. A comparison of the two models’ results is presented. The results showed that the second model, with the priority of maximizing CO2 capture, provides the best economic–environmental objective balance, which offers notable cost reductions while keeping an efficient CO2 capture rate. This study highlights the potential of advanced mathematical modeling in increasing the feasibility of CCS as one of the very important strategies of mitigating climate change and reducing global warming.

Techno-economic assessment of surfactant Huff-n-Puff EOR in shale plays via multi-objective optimization

Surfactant Huff-n-Puff (HnP) has proven effective for enhanced oil recovery (EOR) in unconventional petroleum reservoirs. Due to the high cost of surfactant injectants, a comprehensive techno-economic analysis is crucial for optimizing development strategies. This study focuses on the Jimusar J305 shale oil reservoir, employing a Pareto-optimal multi-objective optimization (MOO) model to identify the best injection-production strategies for surfactant HnP. Using crude oil recovery and economic performance as objectives, the model combines numerical simulations with the multi-objective particle swarm optimization (MOPSO) algorithm. A decision-making protocol based on Pareto solutions is proposed to balance objectives, alongside an EOR mechanism analysis. Results show the net present value (NPV) improved from −2.98 million to +3.48 million USD without reducing oil recovery, indicating that different surfactant HnP strategies can achieve similar recovery outcomes but vary significantly in profitability. Optimal project design involves adjusting surfactant volume to reverse rock wettability and balancing injection rates and concentrations to enhance production while controlling costs.

LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

Интерактивное приложение, реализующее метод уверенных суждений на массовой программной платформе

The article examines the functionality and architecture of an interactive application designed to support multi-criteria decision-making using S.A. Piyavsky's confident judgment method. It provides a description of key components of this method essential for presenting the topic in a computer-oriented format for data and knowledge representation. The application stores and provides access to a bank of universal importance coefficients for specific criteria and serves as the core of an information methodology for decision-making based on the confident judgment method. This methodology includes the following stages: compiling a list of alternative solutions with evaluations by a set of specific quantitative criteria; establishing a selection policy that orders criteria by importance for the decision-maker; normalizing the multi-criteria problem statement in line with a foundational optimization model; evaluating each alternative solution within this model; and selecting the optimal alternatives. The application automatically coordinates the creation and editing of the list of alternative solutions according to variation boundaries set by the selection policy, integrating all solution stages with user feedback. The application’s functionality is represented by a diagram linking the main activities and their outcomes, along with a UML diagram of application use cases. The architecture of the application is presented as a mind map. The application is implemented on the Excel platform using VBA programming language, chosen to ensure accessibility and support the development of advanced multi-criteria decision-making methods for a broad user base who use Excel in their professional work.

Evaluation and Optimization Model of Green Space Ecosystem Services in Urbanization Process

Evaluation and Optimization Model of Green Space Ecosystem Services in Urbanization Process