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Analysis of green energy regeneration system for Electric Vehicles and Re estimation of carbon emissions in international trade based on evolutionary algorithms

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2024年第5期25卷 641-655页

作者： Gao, Zhongpo Yu, Ao Harbin Univ Sch Econ & Management Harbin 150086 Heilongjiang Peoples R China Harbin Univ Commerce Sch Econ Harbin 150028 Heilongjiang Peoples R China

In order to achieve maximum carbon reduction during the operation of pure electric buses, the author proposes a re estimation of carbon emissions in international trade based on evolutionary algorithm analysis of electric vehicle green energy regeneration systems. On the basis of analyzing single line scheduling, the author focuses on studying the regional scheduling mode of pure electric buses, and establishes a multi-objective optimization model for pure electric bus regional scheduling considering carbon benefits with the goals of minimizing the number of operating vehicles, minimizing the empty distance, and maximizing carbon benefits. Based on the actual operation data of pure electric buses 146 and 149 in a certain city, the author used an improved particle swarm optimization algorithm to solve the regional scheduling problem of pure electric buses according to the characteristics of the model. The results indicate that assuming other conditions remain unchanged, when the price of diesel rises to around 7.9 yuan, its operating costs will exceed those of pure electric buses, and the cost advantage of diesel vehicles will gradually decrease. Other conditions remain unchanged, and when the battery price per vehicle drops to around 300,000 yuan, the operating cost of pure electric buses will be lower than that of diesel vehicles. Conclusion: Under the premise of considering carbon benefits, adopting regional dispatch mode for pure electric buses has better economic efficiency and is more conducive to the promotion of pure electric buses.

关键词： pure electric buses scheduling mode carbon benefits particle swarm optimization algorithm

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optimization of PID controller parameters using a hybrid PSO algorithm

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2024年第10期12卷 3617-3627页

作者： Zhang, Xia Yang, Yue Xiangnan Univ Coll Comp & Artificial Intelligence Chenzhou 423000 Hunan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

PID controller parameter optimization is crucial for improving system stability, reliability, and responsiveness. To improve the efficiency of a PID controller, a hybrid algorithm, HPSO, based on particle swarm optimization algorithm (PSO) and simulated annealing (SA) algorithms is proposed. The HPSO algorithm is used to optimize the control parameters. Adaptive weights and dynamic learning factors are introduced to improve the global optimization ability of the PSO algorithm. The simulated annealing (SA) mechanism is incorporated into the particle swarm optimization (PSO) algorithm. The Metropolis criterion and cooling mechanism are used to guide the population to accept inferior solutions with dynamic jump probabilities, so that the algorithm can escape local optima and effectively improve its global search ability. The experimental results show that compared with the SA algorithm, basic particle swarm optimization algorithm and inertial weight coefficient particle swarm optimization algorithm, the HPSO algorithm has fast convergence speed and strong global search ability. Then, the population particles are defined using the coefficients to implement a unique fitness function, which is consistent with the control objectives of achieving fast system response while minimizing overshoot. Finally, taking a typical second-order temperature control delay system and CNC machine feed servo system as the control objects, the HPSO algorithm is compared with genetic algorithm and seeker optimization algorithm. The simulation results show that the optimized PID parameters obtained from the HPSO algorithm has low overshoot, better steady-state, and dynamic response.

关键词： particle swarm optimization algorithm Simulated annealing algorithm PID controller

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Application of DSAPSO algorithm in Distribution Network Reconfiguration with Distributed Generation

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Energy Engineering 2024年第1期121卷 187-201页

作者： Caixia Tao Shize Yang Taiguo Li School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more *** complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent ***,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local *** tackle this issue,a more advanced particle swarm optimization algorithm is *** address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning *** Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization *** inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search *** incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the *** analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy *** findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the ***,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply ***,the algorithm exhibits a high level of generality and applicability.

关键词： Reconfiguration of distribution network distributed generation particle swarm optimization algorithm simulated annealing algorithm active network loss

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Optimal Volterra-based nonlinear system identification using arithmetic optimization algorithm assisted with Kalman filter

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EVOLVING SYSTEMS 2023年第1期14卷 117-139页

作者： Singh, Sandeep Ashok, Alaknanda Rawat, Tarun Kumar Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttar Pradesh India Netaji Subhas Univ Technol New Delhi India

This paper presents the handling of nonlinear system identification problem based on Volterra-type nonlinear systems. An efficient arithmetic optimization algorithm (AOA) along with the Kalman filter (KF) is being used for the estimation/identification purpose. The KF is proved to be a good state estimator in estimation theory. It is used to estimate the unknown variables with some given measurements observed over time. However, the performance of KF technique degrades while dealing with real-time state estimation problems. To overcome the problem encountered in KF technique, two steps are followed for nonlinear system identification. The first one involves evaluation of the KF parameters using the AOA algorithm by taking a considerable fitness function. The second step is to estimate the parameters of Volterra model using the KF method utilizing the optimal KF parameters attained in first step. In order to prove the efficiency of the proposed KF assisted AOA algorithm is further tested on various benchmark unknown Volterra models. Simulated results are reported in terms of mean square error (MSE), mean square deviation (MSD), Volterra coefficients estimation error, and fitness percentage. The results are compared with other similar algorithms such as sine cosine algorithm (SCA) assisted KF (SCA-KF), cuckoo search algorithm (CSA) assisted KF (CSA-KF), particle swarm optimization (PSO) assisted KF (PSO-KF) and genetic algorithm (GA) assisted KF (GA-KF). The reported results reveal that AOA-KF algorithm is the right choice for nonlinear system identification problem compared to the SCA-KF, CSA-KF, PSO-KF and GA-KF.

关键词： Nonlinear system identification Volterra model Kalman Filter Arithmetic optimization algorithm Sine cosine algorithm Cuckoo search algorithm particle swarm optimization algorithm Genetic algorithm

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Modelling and forecasting non-renewable energy consumption and carbon dioxide emissions in China using a PSO algorithm-based fractional non-linear grey Bernoulli model

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第26期30卷 69651-69665页

作者： Yang, Jie Wu, Zhiqiang Shanghai Elect Power Univ Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China

In China, the consumption of non-renewable energy increases not only in general economic growth but also in large amounts of carbon dioxide (CO2) emissions which cause disasters and catastrophic damages to the environment. To alleviate environmental pressure, it is neccessary to forecast and model the relationship between energy consumption and CO2 emissions. In this study, a fractional non-linear grey Bernoulli (FANGBM(1,1)) model based on particle swarm optimization is proposed to forecast and model non-renewable energy consumption and CO2 emissions in China. Firstly, based on the FANGBM(1,1) model, non-renewable energy consumption in China is predicted. The comparison results of several competitive models show that the FANGBM(1,1) model has the best predictive performance. Then, the relationship between non-renewable energy consumption and CO2 emissions is modeled. On this basis, China's future CO2 emissions are effectively predicted based on the established model. The forecast results show that the growth trend of China's CO2 emissions will continue to grow to 2035, while the prediction results in different scenarios also show that that the different growth rates of renewable energy share lead to different times to peak CO2 emissions. In the end, relevant suggestions are proposed to support China's dual carbon goals.

关键词： Non-renewable energy consumption Carbon dioxide emissions Fractional non-linear grey Bernoulli model particle swarm optimization algorithm

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Low-Scattering-Sidelobe Phased Array Antenna With Improved Radiation Performance

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第10期71卷 7837-7847页

作者： Li, Peng-Fa Qu, Shi-Wei Yang, Shiwen Jiang, Ming Hu, Jun Univ Elect Sci & Technol China UESTC Sch Elect Sci & Engn Chengdu 611731 Peoples R China

In this article, the formulas to compute phased array scattering fields under oblique incidences are derived as the theoretical basis of scattering sidelobe level (SLL) reduction. Meanwhile, in order to improve antenna radiation performance, the way to compute and evaluate array impedance matching performance is proposed based on an equivalent network model. The accuracy and efficiency of the formulas applied to predict array scattering fields and reflection coefficients are well validated by numerical study. Furthermore, a microstrip phased array antenna with a tapered array shape and optimized element feed networks is designed. A modified particle swarm optimization algorithm is proposed to search the solution of feed networks to reduce monostatic scattering SLL and improve antenna impedance matching simultaneously, where the proposed fast prediction approaches are employed. The optimized phased array antenna features scattering SLL reduction by 8.8 dB in the concerned angular range of 10 degrees-30 degrees and in the operating band of 5.5-7.5 GHz against its conventional counterpart, with improved impedance matching and slightly higher gains. Eventually, the numerical results are well validated in experiments.

关键词： Equivalent network model improved radiation performance particle swarm optimization algorithm phased array scattering prediction scattering sidelobe level (SLL) reduction

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The design and optimization of square cascades by PSOA and GOA to provide fresh fuel for a nuclear power reactor

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SEPARATION SCIENCE AND TECHNOLOGY 2023年第11期58卷 2008-2026页

作者： Mirmohammadi, S. L. Safdari, J. Mallah, M. H. Ezazi, F. Atom Energy Org Nucl Sci & Technol Res Inst Nucl Fuel Cycle Res Sch Tehran Iran Atom Energy Org Nucl Fuel Cycle Res Sch Nucl Sci & Technol Res Inst Tehran 8486 Iran

Due to the higher flexibility of square cascades than tapered cascades, this research focuses on the design and optimization of square cascades to provide the enriched uranium used in the fresh fuel of a power reactor with different enrichment levels. In order to design and optimize square cascades, two computational codes based on the particle swarm optimization algorithm and the grasshopper optimization algorithm have been developed for the design and optimization of square cascades. The results show that by using optimal square cascades, it is possible to directly produce the fresh enriched uranium required for a power reactor at different enrichment levels (4.1%, 3.7%, and 3.3%), and there is no need to dilute the products enriched by natural or depleted uranium, and the mixing unit can be removed from enrichment facilities. Also, the results obtained from both algorithms show that the total number of optimized square cascades and gas centrifuges required for the production of the annual fuel for a power reactor are very close to each other and have a difference of about 0.65-1.24%.

关键词： Square cascades power reactor fuel particle swarm optimization algorithm grasshopper optimization algorithms

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Estimation of Weibull Probability Distribution Parameters with optimization algorithms and Foca Wind Data Application

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2024年第3期37卷 1236-1254页

作者： Kose, Bayram Isikli, Ibrahim Sagbas, Mehmet Izmir Bakircay Univ Elect & Elect Engn Dept TR-35665 Izmir Turkiye

In this study, the scale and shape parameters of the Weibull probability distribution function (***) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foca region of Izmir in Turkey. The values of Weibull parameters obtained using particle swarm optimization (PSO), Sine Cosine algorithm (SCA), Social Group optimization (SGO), and Bat algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (chi<^>2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foca were used. As a result of Foca speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and chi<^>2 criteria are evaluated together;it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.

关键词： Weibull distribution parameters particle swarm optimization algorithm Social group optimization algorithm Sine cosine optimization algorithm Bat algorithm

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A novel conformable fractional-order accumulation grey model and its applications in forecasting energy consumption of China

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Chen, Yuzhen Gong, Wenhao Li, Suzhen Guo, Shuangbing Henan Inst Sci & Technol Sch Math Sci Xinxiang 453003 Peoples R China Univ Shanghai Sci & Technol Sch Management Shanghai 200093 Peoples R China

Accurate forecasting of energy consumption demand is crucial to optimize resources and achieve sustainable development goals. However, the energy system is affected by many factors, which are complex and highly uncertain. Therefore, a novel grey model (IBCFGMP (1,1,N)) is proposed, integrating multiple optimization techniques such as background value optimization, initial condition optimization, fractional-order accumulation optimization, and grey action quantity optimization. First, this paper deduces the time response function of the optimization model. The relevant parameters of the model can be found using the particle swarm optimization algorithm. Then, the properties of the model are studied, and it is found that the optimized model have stronger universality and stability. Finally, the model is applied to predict and analyze the energy consumption of China. The prediction results indicate that China's consumption of hydroelectricity, nuclear energy, and coal will be 12.693 exajoules, 5.550 exajoules, and 98.850 exajoules in 2026, respectively. The research results will provide a scientific basis for rationally optimizing resource allocation and realizing the sustainable development of clean energy.

关键词： Grey prediction model Conformable fractional-order particle swarm optimization algorithm Clean energy IBCFGMP (1,1,N) model

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Study on Minimum Miscibility Pressure of CO2-Oil System Based on Gaussian Process Regression and particle swarm optimization Model

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2022年第10期144卷 103002-103002页

作者： Hou, Zhiwei Su, Haizhou Wang, Guodong PetroChina Liaohe Oilfield Explorat & Dev Res Ins Panjin 124010 Liaoning Peoples R China PetroChina Chuanqing Drilling Engn Co Ltd Chengdu 610000 Sichuan Peoples R China

Injecting CO2 into the reservoir can not only improve crude oil recovery but also achieve the goal of CO2 geological storage. It can not only reduce the greenhouse effect but also obtain additional economic benefits. From the perspective of minimum miscible pressure, carbon dioxide flooding can be divided into miscible flooding and immiscible flooding, and miscible flooding is widely used in the oil field. The most important condition for miscible flooding is to achieve the minimum miscible pressure (MMP). In the study, combining the particle swarm optimization (PSO) with Gaussian process regression (GPR), a novel intelligent GPR and particle swarm optimization (GPR-PSO) method was proposed to establish the model of predicting the MMP of the CO2 and oil system. The model uses the database with more data than in the previous literature, with 365 data points, and the value range of the data is also wider. Moreover, the accuracy of GPR-PSO model was evaluated by statistical error and graph error and compared with the prediction results of existing models. The results show that compared with other models, the GPR-PSO model has higher accuracy and wider application range, the mean absolute relative error is only 1.66%. Meanwhile, the reliability of the model is verified by the sensitivity analysis of parameters. The results show that the most influential parameter on the prediction results is the reservoir temperature, and the least influential parameter is the critical temperature of injected gas. The GPR-PSO model can be used not only to predict the MMP of CO2 and oil system but also to predict the MMP of other gases and crude oil system.

关键词： MMP CO2 particle swarm optimization algorithm Gaussian process regression GPR-PSO model

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