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An online cooperative control strategy for enhancing microgrid participation into electricity market

ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Cagnano, A. De Tuglie, E. Introna, A. Montegiglio, P. Passarelli, A. Univ Mediterranea Reggio Calabria Dipartimento Ingn Informaz Infrastrutture & Energi I-89124 Reggio Di Calabria Italy Politecn Bari Dipartimento Ingn Elettr & Informaz I-70125 Bari Italy

This paper develops a cooperative control methodology for the online energy management of grid-connected microgrids. The main aim of this methodology is to actively manage the active power outputs of all dispatchable energy sources available within the microgrid as well as the power exchanged with the utility grid so as to match the total load demand at the minimum operating cost. This implies to solve a constrained multiobjective dynamic optimization problem aimed at minimizing the total microgrid operating costs and ensuring the real-time balance within the microgrid in compliance with its technical-operational constraints. Lyapunov's theorem using sensitivity theory is adopted to solve this optimization. To test the performances of the proposed control methodology, several computer simulations corresponding to different operating scenarios have been conducted on the PrInCE Lab experimental microgrid built at the Polytechnic University of Bari.

关键词： Microgrids Real-time energy management Online economic dispatch multi-objective optimization problem Lyapunov theorem

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A health indicator enabling both first predicting time detection and remaining useful life prediction: Application to rotating machinery

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MEASUREMENT 2024年 235卷

作者： Zhao, Yun-Sheng Li, Pengfei Kang, Yu Zhao, Yun-Bo Univ Sci & Technol China Dept Automat 96 Jinzhai Rd Hefei 230027 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence 5089 Wangjiang W Rd Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Inst Adv Technol 5089 Wangjiang W Rd Hefei 230000 Anhui Peoples R China

Remaining Useful Life (RUL) prediction is vital for system functionality. Non -end -to -end approaches is an important type of RUL prediction approaches for their important application in industrial scenarios. In non -endto -end approaches, Health Indicator (HI) construction is a critical aspect. However, existing HI construction approaches ignore First Predicting Time (FPT) detection, leading to increased domain knowledge demand and system health comprehension difficulty. To address this issue, this paper proposes a multi -objectiveoptimization -based HI construction approach enabling both FPT detection and RUL prediction. A novel metric called the monotonicity strength index is proposed to address the limitation of the conventional monotonicity. The constructed HI possesses the ability to indicate FPT by taking the detectability metric as an optimization objective. The optimization problem is solved by the combination of the multi -objective ant lion optimizer and the entropy weight method. The superiority of this HI is demonstrated through experiments on the widely used IMS bearing dataset and a gearbox dataset.

关键词： Health indicator First predicting time Remaining useful life multi-objective optimization problem

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multi-objective optimization design of rear seat for a passenger car based on GARS and NSGA-III

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Zhou, Xuan Jiang, Hengliang Long, Jiangqi Wenzhou Univ Coll Mech & Elect Engn Chashan St Wenzhou 325035 Peoples R China

A collaborative multi-objective optimization design is conducted for the rear seat of a passenger car. This study introduces a combined optimization strategy that integrates both the multi-objective optimization problem and multi-criteria decision-making approaches. Firstly, a finite element model of the rear seat luggage compartment crash is established, and its accuracy is validated. Secondly, the thickness and material type of the primary stress components of the backrest framework for the rear seat are considered as design variables. The safety test point displacement, material cost, and weight are defined as the optimization objectives, while regulatory standards are taken as constraints to construct a multi-objective optimization problem. Once more, the Pareto frontier solution sets are achieved by constructing the genetic aggregation response surface surrogate model combined with the non-dominated sorting genetic algorithm-III optimization algorithm through experimental design. Finally, the Pareto frontier solution sets are ranked to determine the best compromise solution using the multi-criteria decision-making method, which involves the optimal combination weight and the technique for order preference by similarity to an ideal solution based on the Kullback-Leibler distance. The safety performance, lightweight, and cost-effectiveness of the optimized rear car seat are improved. Specifically, the displacement of the headrest skeleton and backrest skeleton is reduced by 5.96% and 4.47% respectively, the material cost is decreased by 7.1%, and the weight is reduced by 5.54%.

关键词： Rear seat multi-objective optimization problem multi-criteria decision-making optimized design

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Reducing Hovering and Motion Energy Consumptions for UAV-enabled Collaborative Beamforming 27

Reducing Hovering and Motion Energy Consumptions for UAV-ena...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Liang, Shuang Fang, Zhiyi Sun, Geng Li, Jiahui Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781665497923

Unmanned aerial vehicles (UAVs) Communications and networks are of paramount importance in the 5G/6G networks. However, how to deploy the limited on-board energy and restricted transmit power of the UAV so that enhancing the communication efficiency is still a key issue. In this work, we consider a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). For achieving a better energy efficiency, we formulate a hovering and motion energy consumption multi-objective optimization problem (HMECMOP) of UAV-enable CB to simultaneously minimize the total hovering and motion energy consumptions of UAVs by jointly optimizing the positions, excitation current weights of UAVs and the order of communicating with different BSs. Then, we propose an improved multi-objective multi-verse optimizer (IMOMVO) to solve the formulated HMECMOP. IMOMVO uses the vertical and horizontal renewal strategy and nearest neighbor procedure (NNP) to deal with the complex solution space which contains continuous and discrete solutions, so that making the algorithm more suitable for solving the formulated optimization problem. Simulation results demonstrate that the proposed algorithm is effective for solving the HMECMOP and it has better performance than some other comparison algorithms.

关键词： UAV communications collaborative beamforming energy efficiency multi-objective optimization problem swarm intelligence

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multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

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ENERGY 2024年 294卷

作者： Tian, Ai-Qing Wang, Xiao-Yang Xu, Heying Pan, Jeng-Shyang Snasel, Vaclav Lv, Hong-Xia Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

This paper establishes a multi -objective optimization model for railway heavy -haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy -haul trains, introducing multi -objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon -inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi -objective transportation problems, the paper proposes a forward -learning pigeon -inspired optimization algorithm based on a surrogate -assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward -learning strategy to enhance learning from non -dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains.

关键词： Carbon emissions Transport efficiency multi-objective optimization problem Surrogate-assisted model Forward-learning strategy

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A multi-objective optimization approach for beam pattern synthesis of UAV virtual rectangular antenna array

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第2期37卷

作者： Mei, Fang Guo, Xinrong Kang, Hui Sun, Geng Zheng, Tingting Wen, Jianbo Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Peoples R China Jilin Univ Coll Software Changchun Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

Virtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi-objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi-objective multi-verse optimization algorithm based on the inverse S decline curve type (ISDT-MOMVO) which introduces a strategy optimization initialization solution with quasi-opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT-MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

关键词： directivity of mainlobe multi-objective optimization problem sidelobe level UAV virtual rectangular antenna array VAA thinning

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optimization Methods to Improve Efficiency and Fairness in Peer-to-Peer Energy Trading 37

Optimization Methods to Improve Efficiency and Fairness in P...

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37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Kusatake, Eiichi Shinomiya, Norihiko Soka Univ Grad Sch Sci & Engn Hachioji Japan

ISBN: (数字)9781665485593

ISBN: (纸本)9781665485593

In recent years, many countries have been promoting the distributed energy systems for clean energy utilization. Direct energy trading is an efficient way to trade electricity, but not everyone benefits fairly in the market, since some suppliers have limited capacity of supplying electricity. This occasion leads to a decrease in the consumer's benefit. Existing studies are mainly focused on not the equity of consumer's benefit but the efficiency of resource allocation. This paper proposes a multi-objective optimization problem and single-objective one in order to improve both the equity of consumer's benefit and the efficiency of resource allocation. For solving the problem, our study adopts six Evolutionary Algorithms (EAs) and two single-objective algorithms. Simulation results demonstrate that the selected EAs can optimize both the efficiency of resource allocation and the equity of consumer's benefit.

关键词： Peer-to-peer Evolutionary algorithm multi-objective optimization problem Graph theory

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An Improved multi-objective optimization Algorithm Based on Reinforcement Learning 13th

An Improved Multi-objective Optimization Algorithm Based on ...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Liu, Jun Zhou, Yi Qiu, Yimin Li, Zhongfeng Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Yingkou Inst Technol Sch Elect Engn Yingkou 115014 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

multi-objective optimization (MOP) has been widely applied in various applications such as engineering and economics. MOP is an important practical optimization problem, and finding approaches to better solve it is of both practical and theoretical significance. The core of solving an MOP is to find the global optimal solution set efficiently and accurately. The current MOP algorithm has premature convergence or poor population diversity, and the solution set obtained falls easily into the local optimal or clustering phenomenon. In this study, an MOP algorithm based on the non-dominated sorting genetic algorithm based on reinforcement learning (RL-NSGA-II) is proposed;the algorithm is adopted based on the prediction, forecasting, Monte Carlo method, which is based on the action of population genetic information, and environment interaction information and Markov decision process in mathematical modeling. This is because using a non-dominated solution contains valuable information, which can be used to guide the evolution direction of the population and search the optimal solution set more accurately. The proposed RL-NSGA-II algorithm was evaluated on the ZDT and DTLZ test sets, and the experimental results verified the effectiveness of the proposed algorithm in solving MOPs.

关键词： Reinforcement learning NSGA-II Monte Carlo method multi-objective optimization problem

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Air to Air Communications Based on UAV-enabled Virtual Antenna Arrays: A multi-objective optimization Approach

Air to Air Communications Based on UAV-enabled Virtual Anten...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Shi, Xiao Wang, Aimin Sun, Geng Li, Jiahui Zheng, Xiaoya Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781665442664

Due to the flexibility and high line-of-sight (LoS) probability, unmanned aerial vehicles (UAVs) can play an important role in 5G/6G networks. In this work, we study a novel air-to-air (A2A) communication scheme, in which two UAV swarms perform two UAV-enabled virtual antenna arrays (UVAAs) for exchanging data by using collaborative beamforming (CB). In order to improve the transmission efficiency and save the energy consumptions of the UAVs, we formulate an A2A communication multi-objective optimization problem (A2ACMOP) to simultaneously enhance the duplex transmission rates and reduce the total energy consumptions of the UAV swarms by deploying the UAVs and adjusting their excitation current weights. Due to the complexity and NP-hardness of the formulated A2ACMOP, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with opposition-based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed INSGA-III can effectively solve the formulated A2ACMOP and it has better performance than some other benchmark strategies.

关键词： UAV communications collaborative beamforming multi-objective optimization problem evolutionary algorithm

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Comparison of Evolutionary multi-objective optimization Algorithms Using Imitation Game 22

Comparison of Evolutionary Multi-Objective Optimization Algo...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Sato, Yuji Murakawa, Yoshihisa Hosei Univ Fac Comp & Informat Sci Koganei Tokyo Japan

ISBN: (纸本)9781450392686

In this paper, we consider the artificial bird song imitation game as dynamic two objective optimization problem and propose it as the benchmark problem that includes a wide variety of transitions. We have evaluated the speed of evolution towards the edge of chaos using NSGA-II and SPEA2 which are representative evolutionary multi-objective optimization algorithms based on Pareto dominance. As a result, we show that our proposed method is effective, and NSGA-II is better than SPEA2 in convergence speed. We also show that the solution tends to converge around the median of the edge of chaos and the chaos region, as compared with the conventional imitation game using the single objective evolutionary algorithm.

关键词： multi-objective optimization problem NSGA-II SPEA2 Benchmark problem. Imitation Game

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