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Peak shaving and heat supply flexibility of thermal power plants

APPLIED THERMAL ENGINEERING 2021年 193卷 117030-117030页

作者： Wang, Congyu Song, Jiwei Zhu, Lingkai Zheng, Wei Liu, Zhaozhao Lin, Chengkun Shandong Univ Inst Adv Technol 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Univ Inst Thermal Sci & Technol 17923 Jingshi Rd Jinan 250061 Peoples R China State Grid Corp China Shandong Elect Power Res In 2000 Wangyue Rd Jinan 250003 Peoples R China

The operational flexibility of thermal power plants is important to consume renewable energy generation, especially in the regions where combined heat and power (CHP) units account for a high proportion. Focusing on the relationship between peak-shaving capacity of CHP units and the consumption of renewable energy generation, the problem about operational flexibility of CHP plants is analyzed in this paper. From the perspective of entire CHP plants rather than the renovation of one CHP unit, the problem regarding the operation scheduling without cost or risk is addressed. A plant-level operation domain model is established, which can improve the operational flexibility of CHP plants. The heat and power adjustable ranges of CHP plants were obtained by particle swarm optimization. Also the CHP plant located in northern China was taken as an example. The power load downward adjustment range of four periods in the heating season are 9.21%, 20.44%, 14.09% and 5.31% respectively, compared with the entire plant actual power under the same heat load. Moreover, the operational flexibility of CHP plants can be enlarged by decreasing the pressure of heating extraction. Compared with the entire plant actual operating conditions, the consumption of renewable energy generation could be increased by 268.56 million kWh approximately and about 0.22 million tons CO2 emissions can be reduced per heating season if the reference plant operates with minimum power load constantly.

关键词： CHP Operation domain Flexibility Renewable energy generation particle swarm optimization algorithm

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Recovery trajectory planning for the reusable launch vehicle

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AEROSPACE SCIENCE AND TECHNOLOGY 2021年 117卷 106965-106965页

作者： Cheng, Guanghui Jing, Wuxing Gao, Changsheng Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China

Trajectory planning method for the entire recovery process of the reusable launch vehicle is studied. Firstly, a revised trajectory correction method (RTCM) is proposed to reduce the maximum normal aerodynamic load in the endo-atmosphere unpowered descent phase and improve the vehicle's landing accuracy on the influence of wind field. It consists of two parts, namely moving the ignition point of the vertical landing phase on the horizontal plane and introducing preset steady wind field into trajectory planning. Secondly, a compound trajectory planning method, which combines particle swarm optimization algorithm (PSO) with Polynomial Guidance Law (PGL), is proposed to complete the optimization task of the entire recovery process. Employing PSO algorithm can avoid the complex calculation of additional angle of attack caused by the preset steady wind field in traditional trajectory correction. To overcome the premature convergence of basic PSO, the traditional re-initialization mechanism is improved. Finally, the guidance simulation of two phases greatly affected by wind is accomplished. The effectiveness of the proposed methods is demonstrated with some scenarios and Monte Carlo simulation. (C) 2021 Elsevier Masson SAS. All rights reserved.

关键词： Reusable launch vehicle Trajectory correction Compound trajectory planning method particle swarm optimization algorithm Polynomial guidance law

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Research on Timetabling Problems Based on particle swarm optimization algorithm

Research on Timetabling Problems Based on Particle Swarm Opt...

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2012 3rd International Conference on Information Technology for Manufacturing Systems(ITMS 2012)

作者： Xinmin Ma Linli Wu Basic Courses Department Bengbu Automobile Non-Commissioned Officer Academy NO 4 Department Bengbu Navy Petty Officer Academy

A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

关键词： particle swarm optimization algorithm timetabling fitness function

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Multi-Objective Optimum Design of High-Speed Backplane Connector Using particle swarm optimization

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IEEE ACCESS 2018年 6卷 35182-35193页

作者： Yu, Wenjie Zeng, Zhi Peng, Bei Yan, Shuo Huang, Yueshuang Jiang, Hai Li, Xunbo Fan, Tao Univ Elect Sci & Technol China Sch Mechatron Engn Chengdu 611731 Sichuan Peoples R China UESTC Guangdong Inst Elect & Informat Engn Guangzhou 23808 Guangdong Peoples R China

This paper outlines a new procedure for computer modeling and optimum design for the dynamic mechanical and electrical study of a high-speed backplane connector, which is a key electrical interconnection technology in large communications equipment, ultra-high performance servers, supercomputers, industrial computers, high-end storage devices, and so on. The optimum structure design of contact pairs is important for a backplane connector in meeting multiple challenges in terms of minimizing the maximum insertion force and the contact resistance. Current optimization schemes, such as the quadrature method, are relatively complex. Therefore, we designed the connector contact pairs for simultaneously obtaining the proper insertion force and the contact resistance through a multi-objective particle swarm optimization (MCDPSO) method with simpler settings and faster convergence speed. In this paper, the required insertion force was minimized during the entire process, and the minimum contact resistance was maintained after insertion. To this end, an MCDPSO algorithm was proposed for the connector design. A dynamic weight coefficient was developed to calculate the interval values of the reserved solutions for the selection of the operator, and an external archive update based on roulette wheel selection and gbest selection strategies was developed to increase the diversity of the solutions. A set of optimal structure solutions of the contact pairs was obtained for the subsequent design optimization. The feasibility and effectiveness of the proposed method were verified by comparing with the results from ANSYS finite element simulation.

关键词： High-speed backplane connector contact pairs insertion force contact resistance multi-objective connector design particle swarm optimization algorithm

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A non-dominated sorting based evolutionary algorithm for many-objective optimization problems

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SCIENTIA IRANICA 2021年第6期28卷 3293-3314页

作者： Mane, S. U. Rao, M. R. Narasinga Deemed Univ Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

The optimization problems with more than three objectives are Many-objective optimization Problems (MaOPs) that exist in various scientific and engineering domains. The existing multi-objective evolutionary algorithms are not found effective in addressing the MaOPs. Its limitations initiated the need to develop an algorithm that efficiently solves MaOPs. The proposed work presents the design of the Many-Objective Hybrid Differential Evolution (MaOHDE) algorithm to address MaOPs. Initially, two multi-objective evolutionary algorithms viz. Non-dominated Sorting based Multi-Objective Differential Evolution (NS-MODE) and Non-dominated Sorting based Multi-Objective particle swarm optimization (NS-MOPSO) algorithms were designed. These algorithms were developed by incorporating the non-dominated sorting approach from Non-dominated Sorting-based Genetic algorithm II (NSGA-II), the ranking approach, weight vector, and reference points. Tchebycheff-a decomposition-based approach, was applied to decompose the MaOPs. The MaOHDE algorithm was developed by hybridizing the NS-MODE with the NS-MOPSO algorithm. The strength of the presented approach was determined using 20 instances of DTLZ functions, and its effectiveness and efficiency were verified upon its comparison with the recently developed state of algorithms existing in the literature. From the results, it is observed that the MaOHDE responds better than its competitors or is competitive for most of the test instances and the convergence rate is also good. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Many-objective hybrid differential evolution algorithm Non-dominated sorting Decomposition-based approach Differential evolution algorithm particle swarm optimization algorithm Many-objective optimization problems

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Dynamic space reduction optimization framework and its application in hull form optimization

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APPLIED OCEAN RESEARCH 2021年 114卷 1页

作者： Zheng, Qiang Feng, Bai-Wei Chang, Hai-Chao Liu, Zu-Yuan Wuhan Univ Technol Key Lab High Performance Ship Technol Minist Educ Wuhan Peoples R China Wuhan Univ Technol Sch Transportat Wuhan Peoples R China

Hull form optimization is a typical complex engineering problem. The complex design performance space often results in a low optimization efficiency as an optimal solution cannot be ensured. Presently, several methods, such as efficient optimization algorithms, approximate model technology, and high-performance computing are primarily used to reduce the calculation time. However, these methods cannot satisfy practical application requirements in terms of efficiency and solution accuracy. Thus, we investigated a dynamic space reduction optimization framework (DSROF), in this study, wherein data mining is continuously performed during the optimization process to dynamically reduce the range and number of variables. DSROF enables subsequent optimization only in the range that exhibits a high performance, thereby reducing redundant calculations, improving optimization efficiency, and ensuring a higher degree of accuracy. Furthermore, we applied DSROF to function examples and hull form optimization. The results indicate that the use of the DSROF can reduce the calculation cost in hull form optimization by 23% in comparison with that of the particle swarm optimization algorithm.

关键词： Hull form optimization Data mining Dynamic space reduction particle swarm optimization algorithm

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A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM

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ENERGY 2021年 234卷 121236-121236页

作者： Ren, Xiaoqing Liu, Shulin Yu, Xiaodong Dong, Xia Qilu Univ Technol Shandong Acad Sci Dept Elect Engn & Automat Jinan 250353 Peoples R China

State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions of battery management system. In order to improve the estimation accuracy of SOC, this paper proposes a long shortterm memory neural network based on particle swarm optimization (PSO-LSTM). Firstly, the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology. In addition, random noise is added to the input layer of PSO-LSTM neural network to improve the anti-interference ability of the network. Finally, experiments show that the proposed method can achieve accurate estimation under different conditions. The estimates based on PSO-LSTM converge to the real state-of-charge within an error of 0.5%. (c) 2021 Published by Elsevier Ltd.

关键词： Lithium-ion battery SOC estimation particle swarm optimization algorithm Long short-term memory neural network

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optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115474-115474页

作者： Saadatpour, Motahareh Javaheri, Shima Afshar, Abbas Solis, Samuel Sandoval Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran Univ Lethbridge Environm Engn Lethbridge AB Canada Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA

The selective withdrawal system (SWS) has often been used to alleviate downstream temperature pollutions and in-reservoir unacceptable oxygen concentrations whilst increasing hydropower energy generation. The aim of this study is to a) design the optimal locations of intakes in SWS, and, b) derive optimal parameterized selective withdrawal operation rules. The integration of water quality and quantity aspects to meet long-term environmental services and economic benefits is challenging. The main challenge lies in coupling search-based optimization algorithms with a numerical hydrodynamic and water quality simulation model (i.e., CE-QUAL-W2). To cope with the computational burdens of CE-QUAL-W2, surrogate models have been developed to represent the dynamics of temperature and water quality according to various SWSs. The surrogate models and the hydropower energy computation module were coupled with a multi-objective particle swarm optimization algorithm in an adaptive surrogate-based simulation-optimization framework (ASBSOF). The ASBSOF is applied in Karkheh reservoir, Andimeshk, Khuzestan, Iran, to address problems a and b. The results indicate that the optimal SWSs provide the advantage of sustainable management to balance the energy generation whilst minimizing environmental damages.

关键词： Adaptive surrogate based simulation-optimization CE-QUAL-W2 Karkheh reservoir Surrogate model Multi-objective particle swarm optimization algorithm

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Optimized Design of Steam Turbine Control System

Optimized Design of Steam Turbine Control System

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Chinese Automation Congress (CAC)

作者： Yao, Yuan Gong, Xin Xin, Huang Zhao, Luping Song, Shengjun Liaoning Elect Power Co Ltd Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728176871

The steam turbine regulating system is an important link of a steam turbine generator unit The operating effect of the steam turbine is determined by the quality of control of the regulating system. In this paper, a particle swarm algorithm and fuzzy PID control are combined according to the regulation principle and characteristics of a steam turbine digital electro-hydranlic regulation system(DEM. Respective advantages of two control methods are taken to control the parameters in a loop and regulate the control quality of the system in real time according to the operation deviation change of the DEH control system. Finally, the system is better controlled.

关键词： DEH PID control particle swarm optimization algorithm fuzzy control

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Forecasting the seasonal natural gas consumption in the US using a gray model with dummy variables

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 108002-108002页

作者： Wang, Zheng-Xin He, Ling-Yang Zhao, Yu-Feng Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

To forecast the seasonal fluctuations of US natural gas consumption accurately, a novel gray model based on seasonal dummy variables and its derived model are separately established. Then, the approximate time response formula in the proposed seasonal forecasting model is optimally calculated by using particle swarm optimization algorithm. On this basis, empirical analysis is conducted using data pertaining to natural gas consumption in the US during 2010-2019. The results show that gray model based on seasonal dummy variables and its derived model can recognize seasonal fluctuations in US natural gas consumption, whose prediction performances are much better than that of a traditional gray model, autoregressive integrated moving average (ARIMA), support vector regression (SVR), recurrent neural network (RNN), transformer, and fuzzy time series forecasting models. The mean absolute percentage errors (MAPEs) of the proposed seasonal gray forecasting model and its derived model, the classic gray model, ARIMA, SVR, RNN, Transformer, and Fuzzy time series forecasting models are 3.46%, 2.37%, 12.23%, 3.39%, 2.38%, 3.08%, 3.84%, and 5.02% in the training set, while those are 4.57%, 4.42%, 12.44%, 7.9%, 8.09%, 11.33%, 35.19% and 13.19% in the test set, respectively. The predicted and empirical results obtained by utilizing the proposed gray model implied that natural gas consumption in the US from 2020 to 2022 will maintain its seasonal growth and periodic changes, with the highest and the lowest values in the first and second quarters, respectively. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Gray model Seasonal dummy variables Natural gas consumption particle swarm optimization algorithm

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