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IEEE International Conference on Electro Information Technology (EIT)

作者： Shrestha, Sadhana Hansen, Timothy M. South Dakota State Univ Elect Engn & Comp Sci Dept Brookings SD 57007 USA

ISBN: (纸本)9781467399852

Penetration of a large number of electric vehicles in the grid can have a negative impact to the grid. To prevent a negative effect to the grid, the behavior of electric vehicles must be accurately modeled and their charging schedules must be coordinated. Therefore, it is necessary to determine where and how much charge is available in electric vehicles in the distribution system. In this paper, a state transition algorithm is designed to determine a stochastic model of electric vehicles to simulate electric vehicle movement in an integrated traffic and power network. Dijkstra's algorithm is used to determine the shortest distance between end-user residential and office areas. A naive charging technique is used to charge electric vehicles at different time intervals at different charging stations based on their driving patterns. The probability of finding electric vehicles at different charging stations and available charge using information on driving behavior is determined.

关键词： Dijkstra's algorithm electric vehicle (EV) nodal charging demand power network state transition algorithm

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Multi-objective optimization based optimal setting control for industrial double-stream alumina digestion process

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Journal of Central South University 2022年第1期29卷 173-185页

作者： WANG Xiao-li LU Mei-yu WEI Si-mi XIE Yong-fang School of Automation Central South UniversityChangsha 410083China

The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment ***,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption ***,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the *** a rule based strategy is designed to make the final decision from the Pareto *** method is integrated into an optimal control system for the industrial digestion process and tested in the actual *** show that the proposed method can achieve the technical target while reducing the energy consumption.

关键词： double-stream digestion process optimal setting control multi-objective optimization state transition algorithm rule based decision making

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A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

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Engineering 2019年第6期5卷 1060-1076页

作者： Weichao Yue Weihua Gui Xiaofang Chen Zhaohui Zeng Yongfang Xie School of Information Science and Engineering Central South UniversityChangsha 410083China

In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

关键词： AlF3 addition Fuzzy cognitive maps Learning algorithms state transition algorithm Fuzzy decision trees

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A hybrid robust-interval optimization approach for integrated energy systems planning under uncertainties

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ENERGY 2023年第1期274卷

作者： Dong, Yingchao Zhang, Hongli Ma, Ping Wang, Cong Zhou, Xiaojun Xinjiang Univ Sch Elect Engn Urumqi 830017 Xinjiang Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

The presence of multiple uncertainties in demand response and renewable energy generation significantly impeded the planning of integrated energy systems (IES). To address this challenge, a novel hybrid robust -interval optimization (HRIO) framework was proposed in this study to facilitate flexible and robust uncer-tainty planning while reducing IES operation costs. The framework integrated robust optimization and interval analysis to account for the uncertainties associated with renewable energy generation output and demand response. To model IES planning as a deterministic bi-objective optimization problem with investment operation cost and robustness as the optimization objectives, a constrained multi-objective transition algorithm was developed to solve the problem. Simulation results obtained from a typical IES planning case demonstrated the efficacy of the proposed HRIO method in effectively coordinating the relationship between the system's econ-omy, robustness, and operation reliability. This study provided a theoretical guideline for IES operators and exhibited significant potential for engineering applications.

关键词： Integrated energy system Robust optimization Interval optimization Uncertainty state transition algorithm

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Information gap-based coordination scheme for active distribution network considering charging/discharging optimization for electric vehicles and demand response

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 145卷

作者： Chen, Qian Wang, Weiqing Wang, Haiyun Dong, Yingchao He, Shan Xinjiang Univ Engn Res Ctr Minist Educ Renewable Energy Generat & Grid Connec Urumqi 830017 Xinjiang Peoples R China Xinjiang Univ Sch Elect Engn Urumqi 830017 Xinjiang Peoples R China

The power fluctuation of load and distributed generation is a leading cause of load and voltage fluctuations and directly impacts the safe and efficient operation of the active distribution network in recent years due to the growing penetration of electric vehicles and distributed generation in the network. To optimize the load char-acteristics, a demand response model based on a logistic function is introduced, and the dynamic charging/ discharging electricity price of electric vehicles based on the comprehensive load is considered in order to incentivize electric vehicle users to charge/discharge orderly. Then, a coordinated optimal model based on the information gap decision theory is constructed to cope with the uncertainty of distributed generation and ensure the operational economy and robustness of the active distribution network. Additionally, a population-based multi-objective state transition algorithm with a parallel search mechanism and communication mechanism was proposed to solve the demand response model, and second-order cone programming was utilized to solve the coordinated optimal model with complex constraints. Finally, the proposed comprehensive model and algorithm were investigated based on the IEEE 33 test system. The results show that the proposed model not only reduces the peak-to-valley difference in load by 24.38% but also significantly reduces the total operating cost by 13.41%. Simultaneously, the proposed algorithm increases the solving efficiency, verifying the feasibility and effective-ness of the optimal strategy and algorithm, which promotes the economical and reliable operation of the active distribution network.

关键词： Demand response Electric vehicles Distributed generation Active distribution networks state transition algorithm Multi -objective optimization

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Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Zhou, Xiaojun Tan, Wan Sun, Yan Huang, Tingwen Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance for improving the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process -entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving the effectiveness and superiority of the proposed method.

关键词： Integrated energy system Multi-objective optimization Decision making state transition algorithm Fuzzy TOPSIS

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Hierarchical parameter optimization based support vector regression for power load forecasting

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SUSTAINABLE CITIES AND SOCIETY 2021年 71卷 102937-102937页

作者： Wang, Zeyu Zhou, Xiaojun Tian, Jituo Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar Peng Cheng Lab Shenzhen 518000 Peoples R China

Power load forecasting is an important task of smart grid, which is of great significance to the sustainable development of society. In this paper, a hybrid support vector regression (HSVR) is raised for the medium and long term load forecasting. To further improve prediction accuracy, the coupling and interdependent relationship between hyperparameters and model parameters in the optimization process is focused. A hierarchical optimization method based on nested strategy and state transition algorithm (STA) is proposed to find optimal parameters. The effectiveness of the proposed hierarchical optimization method is confirmed on several benchmarks, and the resulting hierarchical optimization method based SVR is also successfully applied to a real industrial power load forecasting problem in China.

关键词： Power load forecasting Hybrid support vector regression Hierarchical parameter optimization method state transition algorithm

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Functional deep echo state network improved by a bi-level optimization approach for multivariate time series classification

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APPLIED SOFT COMPUTING 2021年 106卷 107314-107314页

作者： Huang, Zhaoke Yang, Chunhua Chen, Xiaofang Zhou, Xiaojun Chen, Guo Huang, Tingwen Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar State Key Lab Synthet Automat Shenyang 110819 Peoples R China

The multivariate time series (MTS) classification is one of the major tasks of time series data mining. Many methods have been proposed to investigate the MTS classification. Among them, the method based on feature representation is the most popular and widely used one. However, there exist some shortcomings for this method, such as unsatisfactory accuracy, being sensitive to noise and not able to fully make use of time series data attributes. In order to overcome these disadvantages, we propose a new method called functional deep echo state network (FDESN) for MTS classification that utilizes two special operators: temporal aggregation and spatial aggregation. In general, the parameters of the FDESN are determined by random selection, human experience or trial and error. This may increase the complexity of the FDESN or reduce the accuracy of the FDESN. In this study, a novel bi-level optimization approach is proposed to optimize the parameters of the FDESN. The parameter selection problem in the FDESN is transformed into the bi-level optimization problem. The state transition algorithm (STA) is used to solve the bi-level optimization problem. Finally, the experimental results show that the proposed method is superior to other methods. In addition, the proposed method is successfully applied to anode condition identification in aluminum electrolysis. For the aluminum electrolysis datasets, the proposed method improved the average classification accuracy by about 3.5% compared with the other methods. For a specific aluminum electrolysis dataset ACS2504, the classification accuracy significantly increased from 77.92% to 82.69% by using the proposed method. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multivariate time series classification Functional deep echo state network Bi-level optimization state transition algorithm Aluminum electrolysis

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A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression

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ENERGIES 2019年第21期12卷 4181-4181页

作者： Huang, Yuansheng Yang, Lei Gao, Chong Jiang, Yuqing Dong, Yulin North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Sch Econ & Management Baoding 071003 Peoples R China

Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed.

关键词： renewable energy consumption Gaussian processes regression state transition algorithm five-year project forecasting

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