A review of optimization modeling and solution methods in renewable energy systems

The advancement of renewable energy(RE)represents a pivotal strategy in mitigating climate change and advancing energy transition efforts.A current of research pertains to strategies for fostering RE growth.Among the frequently proposed approaches,employing optimization models to facilitate decision-making stands out prominently.Drawing from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems(RES)from 1990 to 2023 within the Web of Science database,this study reviews the decision-making optimization problems,models,and solution methods thereof throughout the renewable energy development and utilization chain(REDUC)process.This review also endeavors to structure and assess the contextual landscape of RES optimization modeling research.As evidenced by the literature review,optimization modeling effectively resolves decisionmaking predicaments spanning RE investment,construction,operation and maintenance,and scheduling.Predominantly,a hybrid model that combines prediction,optimization,simulation,and assessment methodologies emerges as the favored approach for optimizing RES-related decisions.The primary framework prevalent in extant research solutions entails the dissection and linearization of established models,in combination with hybrid analytical strategies and artificial intelligence algorithms.Noteworthy advancements within modeling encompass domains such as uncertainty,multienergy carrier considerations,and the refinement of spatiotemporal resolution.In the realm of algorithmic solutions for RES optimization models,a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven optimization.Furthermore,this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps,expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors.

Modeling and Optimization of Solar Collector Design for the Improvement of Solar-Air Source Heat Pump Building Heating System

To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance.

Resilience assessment and optimization method of city road network in the post-earthquake emergency period

The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.

Knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing

Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.

Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Modeling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating

In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surface roughness(Ra and Rz)of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset.The proposed model consists of four nanoparticles(ZnO,ZrO2,SiO2,and NiO)with 2%,4%,6%,and 8%,respectively.Response surface methodology(RSM)was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct.To prove the superiority of the proposed fuzzy model,the model results were compared with those obtained by ANOVA,with the coefficient of determination and the root-mean-square error(RMSE)used as metrics.For Ra,for the first output response,using ANOVA,the coefficient-of-determination values were 0.9137 and 0.4037,respectively,for training and prediction.Similarly,for Rz,the second output response,the coefficient-of-determination results were 0.9695 and 0.4037,respectively,for training and prediction.In the fuzzy modeling of Ra,for the first output response,the RMSE values were 0.0 and 0.1455,respectively,for training and testing.The values for the coefficient of determination were 1.00 and 0.9807,respectively,for training and testing.The results prove the superiority of fuzzy modeling.For modeling the second output response Rz,the RMSE values were 0.0 and 0.0421,respectively,for training and testing,and the coefficient-of-determination values were 1.00 and 0.9959,respectively,for training and testing.

Research on the Multidimensional Constraint Stability of Bridge Structure Modeling based on Optimization Model

In this paper, we conduct research on the multidimensional constraint stability of bridge structure modeling based on the optimization model. The current internal and the external research results to the truss web structure, the high internode the aspect ratio and the stiffness of the middle truss brace of the truss web, deffection of composite beams of the impact of stress is a very important problem in the design of the bridge. Structural health monitoring is the use of the field of the non-destructive sensing technology, including the structural response, including structural system characteristics analysis, to achieve the purpose of monitoring structural damage or degradation. Under this basis, this paper proposes the new idea on the modelling and simulates the performance.

Torsional stiffness modeling and optimization of the rubber torsion bushing for a light tracked vehicle

Taking the rubber torsion bushing of a certain type of all-terrain tracked vehicle as the research object,a theoretical model of torsional stiffness was proposed according to the non-linear characteristics of rubber components and structural feature of the suspension. Simulations were carried out under different working conditions to obtain root mean square of vertical weighted acceleration as the evaluation index for ride performance of the all-terrain tracked vehicle,with a dynamics model of the whole vehicle based on the theoretical model of the torsional stiffness and standard road roughness as excitation input. Response surface method was used to establish the parametric optimization model of the torsional stiffness. The evaluation index showed that ride performance of the vehicle with optimized torsional stiffness model of suspension was improved compared with previous model fromexperiment. The torsional stiffness model of rubber bushing provided a theoretical basis for the design of the rubber torsion bushing in light tracked vehicles.

Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation,a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization(IGWO)is proposed.Firstly,building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system.Secondly,the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function,and the minimum peak-to-valley of the microgrid’s daily output is taken as the inner objective function.By iterating through the outer and inner layers,the system improves operational stability while achieving economic configuration.Then,using the energy-self-smoothness of the microgrid as the evaluation index,a double-layer optimizing configuration method of the microgrid is constructed.Finally,to improve the disadvantages of grey wolf optimization(GWO),such as slow convergence in the later period and easy falling into local optima,by introducing the convergence factor nonlinear adjustment strategy and Cauchy mutation operator,an IGWO with excellent global performance is proposed.After testing with the typical test functions,the superiority of IGWO is verified.Next,using IGWO to solve the double-layer model.The case analysis shows that compared to GWO and particle swarm optimization(PSO),the IGWO reduced the comprehensive cost by 15.6%and 18.8%,respectively.Therefore,the proposed double-layer optimizationmethod of capacity configuration ofmicrogrid with wind-solar-hybrid energy storage based on IGWO could effectively improve the independence and stability of the microgrid and significantly reduce the comprehensive cost.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

A review of uncertain factors and analytic methods in long-term energy system optimization models

A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

A Composite Transformer-Based Multi-Stage Defect Detection Architecture for Sewer Pipes

Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential malfunctioning.In recent years,to relieve the manual efforts by human experts,models based on deep learning have been introduced to automatically identify potential defects.However,these models are insufficient in terms of dataset complexity,model versatility and performance.Our work addresses these issues with amulti-stage defect detection architecture using a composite backbone Swin Transformer.Themodel based on this architecture is trained using a more comprehensive dataset containingmore classes of defects.By ablation studies on the modules of combined backbone Swin Transformer,multi-stage detector,test-time data augmentation and model fusion,it is revealed that they all contribute to the improvement of detection accuracy from different aspects.The model incorporating all these modules achieves the mean Average Precision(mAP)of 78.6% at an Intersection over Union(IoU)threshold of 0.5.This represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network(R-CNN)model and a 6.7% improvement over You Only Look Once version 6(YOLOv6)-large,the highest in the YOLO methods.In addition,for other defect detection models for sewer pipes,although direct comparison with themis infeasible due to the unavailability of their private datasets,our results are obtained from a more comprehensive dataset and have superior generalization capabilities.

Charging Ahead with E-Buses: Benefits, Costs, and Transition Roadmap

This paper reaches a recommendation for the 10-year e-bus transition roadmap for New York City. The lifecycle model of emission reduction demonstrates the ecological and financial impacts of a complete transition from the current diesel bus fleet to an all-electric bus fleet in New York City by 2033. This study focuses on the NOx pollution, which is the highest among all major cities by Environmental Protection Agency (EPA) and greenhouse gases (GHG) with annual emissions of over five million tons. Our model predicts that switching to an all-electric bus fleet will cut GHG emissions by over 390,000 tons and NOx emissions by over 1300 tons annually, in addition to other pollutants such as VOCs and PM 2.5. yielding an annual economic benefit of over 75.94 million USD. This aligns with the city mayor office’s initiative of achieving total carbon neutrality. We further model an optimized transition roadmap that balances ecological and long-term benefits against the costs of the transition, emphasizing feasibility and alignment with the natural replacement cycle of existing buses, ensuring a steady budgeting pattern to minimize interruptions and resistance. Finally, we advocate for collaboration between government agencies, public transportation authorities, and private sectors, including electric buses and charging facility manufacturers, which is essential for fostering innovation and reducing the costs associated with the transition to e-buses.

Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets:Current States and Perspectives

1 Introduction The United States,Japan,Canada,the European Union,and other developed countries and regions have all formulated climate strategies and pledged to achieve net-zero CO_(2) emissions by 2050.China,meanwhile,has announced through the“carbon-peaking and carbon neutrality targets”in September 2020 that it aims to achieve“peak carbon use”by 2030 and“carbon neutrality”by 2060[1].According to statistical data from the International Energy Agency(IEA),Fig.1 illustrates the carbon intensity of electricity generation in various regions in the Announced Pledge Scenario(APS)from 2010 to 2040[2].One can easily observe that each region aims to accomplish a sharp decrease in the carbon intensity of electricity generation after 2020.

Optimisation of Thermal Comfort of Building in a Hot and Dry Tropical Climate: A Comparative Approach between Compressed Earth/Concrete Block Envelopes

Compressed earth blocks (CEB) are an alternative to cement blocks in the construction of wall masonry. However, the optimal architectural construction methods for adequate thermal comfort for occupants in hot and arid environments are not mastered. This article evaluates the influence of architectural and constructive modes of buildings made of CEB walls and concrete block walls, to optimize and compare their thermal comfort in the hot and dry tropical climate of Ouagadougou, Burkina Faso. Two identical pilot buildings whose envelopes are made of CEB and concrete blocks were monitored for this study. The thermal models of the pilot buildings were implemented in the SketchUp software using an extension of EnergyPlus. The models were empirically validated after calibration against measured thermal data from the buildings. The models were used to do a parametric analysis for optimization of the thermal performances by simulating plaster coatings on the exterior of walls, airtight openings and natural ventilation depending on external weather conditions. The results show that the CEB building displays 7016 hours of discomfort, equivalent to 80.1% of the time, and the concrete building displays 6948 hours of discomfort, equivalent to 79.3% of the time. The optimization by modifications reduced the discomfort to 2918 and 3125 hours respectively;i.e. equivalent to only 33.3% for the CEB building and 35.7% for the concrete building. More study should evaluate thermal optimizations in buildings in real time of usage such as residential buildings commonly used by the local middle class. The use of CEB as a construction material and passive means of improving thermal comfort is a suitable ecological and economical option to replace cementitious material.

A New Modified GM (1,1) Model: Grey Optimization Model

Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.

Optimization model of unascertained measurement for underground mining method selection and its application

An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.