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Multimodal multi-objective evolutionary algorithm for multiple path planning

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 169卷

作者： Yao, Xingyi Li, Wenhua Pan, Xiaogang Wang, Rui Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Key Lab Multienergy Syst Intel ligent Interc Changsha 410073 Peoples R China

The multi-objective path planning problem has received much attention recently. Traditional solving methods try to find a single optimal path without considering the multiformity of the paths. In this study, we first analyze the situation that several different paths may have the same objective values, termed as multi-modal minimum path problems. To address these problems, we propose a novel solution-encoding method, which decreases the size of decision-space greatly. Then, to maintain the population diversity in the decision space, we propose an environmental selection strategy, in which the duplicate solutions are deleted first and then a second-selection method is adopted. Finally, an effective multi-objective evolutionary algorithm based on the special environmental selection is proposed, termed MMEA-SES. Through the experiments, the proposed method is proved effective and efficient compared to other state-of-the-art algorithms for multimodal multi-objective path planning.

关键词： Evolutionary algorithm Multiple Path planning Multi -modal Discrete optimization Multi-objective optimization

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Optimal energy management for grid-connected microgrids via expected-scenario-oriented robust optimization

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ENERGY 2021年 216卷 119224-119224页

作者： Zhu, Junjie Huang, Shengjun Liu, Yajie Lei, Hongtao Sang, Bo Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China Naval Aeronaut & Astronaut Univ Sch Basic Sci Aviat Yantai 264000 Shandong Peoples R China

In order to facilitate the integration of distributed energy resources (DERs), grid-connected microgrids (MGs) have been rapidly deployed over the last decade. However, their potential negative impact on the security of utility grids cannot be ignored. For the existence of various uncertainties, uncertainty quantification method, e.g., robust optimization (RO), has been proposed for the energy management of MGs. Targeting the conservatism problem presented by the traditional two-stage RO method, which is oriented towards the worst-case scenario, this paper proposes a novel method targeting the expected scenario. Furthermore, in the proposed model, the decision variables representing the charging/discharging states of energy storage system (ESS) is considered in the re-dispatch stage, in contrast to existing models that putting them in the pre-dispatch stage, to increase the operational flexibility of ESS for energy management. Notably, this shift in the treatment of ESS translates the proposed two-stage RO model into a mixed integer programming (MIP) model with recourse. Therefore, the nested column-and-constraint generation (N-C&CG) algorithm is adopted. Results of numerical experiments illustrated the superiorities of the proposed model in minimizing the cost of system operation and reducing the negative impact on the utility grid. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Grid-connected microgrid Energy management Robust optimization Expected-scenario-oriented Two-stage decision process Column-and-constraint generation algorithm

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State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression

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ENERGY 2022年第PartB期239卷 121986-121986页

作者： Zhang, Yajun Liu, Yajie Wang, Jia Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

Accurate state-of-health (SOH) estimation for lithium-ion batteries is of great significance for future intelligent battery management systems. This study proposes a novel method combining voltage -capacity (VC)-model-based incremental capacity analysis (ICA) with support vector regression (SVR) for battery SOH estimation. For accurate and efficient capture of IC curves, 18 VC models are first compared, and then, suitable models are selected for two types of batteries with different chemistries, enabling multitype health features to be obtained by parameterizing the VC models. After correlation analysis of these extracted health features with the reference battery capacity, the SVR algorithm is adopted to construct SOH estimation models. Finally, four aging datasets are employed for validation of the proposed method. The experimental results show that the SVR models achieve high accuracy in SOH estimation, i.e., the respective mean absolute errors (MAEs) and root mean square errors (RMSEs) of all batteries are limited to within 1.1%. Moreover, the method is robust against different initial aging statuses and cycle conditions of the batteries: after migration and fine-tuning, both the MAEs and RMSEs can be confined to within 2.3% by utilizing the established SVR models. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Lithium-ion batteries State-of-health Capacity model Incremental capacity analysis Support vector regression

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A flexible battery capacity estimation method based on partial voltage curves and polynomial fitting

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ENERGY AND BUILDINGS 2023年第1期290卷

作者： Cao, Mengda Liu, Yajie Zhang, Tao Wang, Yu Wang, Ruixi Shi, Zhichao Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interco Changsha Peoples R China

Accurate capacity estimation of lithium-ion batteries is an important topic for electric vehicles. In real cases, electric vehicles are usually not charged from zero to full capacity;thus, the battery management systems (BMSs) of electric vehicles have insufficient data when evaluating battery capacity degradation. Considering the above conditions, we propose a flexible battery capacity estimation method based on partial charging voltage curves and polynomial fitting. First, the charge voltage curve is fitted with a polynomial function with respect to time. Then, the parameters of the polynomial functions are selected as health indicators (HIs) for the battery. Finally, these parameters are input into a deep learning model to estimate the battery capacity. Compared with existing data-driven methods, the proposed method requires less voltage data and is more flexible, allowing the battery charging capacity to be estimated in different phases. Batteries in different working scenarios were compared with advanced features proposed in the literature to validate the effectiveness and superiority of the proposed method.

关键词： Lithium -ion battery Capacity estimation Partial voltage curve Polynomial parameters

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Large-Scale Binary Matrix Optimization for Multimicrogrids Network Structure Design

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IEEE TRANSACTIONS ON systEMS MAN CYBERNETICS-systEMS 2024年第3期54卷 1633-1644页

作者： Li, Wenhua Wang, Rui Huang, Shengjun Zhang, Tao Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Multienergy Syst Intelligent Interco Changsha 410073 Peoples R China Natl Univ Def Technol Xiangjiang Lab Changsha 410073 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The multimicrogrid network structure design problem (MNSDP) represents a binary matrix optimization challenge, targeting the minimization of the cumulative length of power supply circuits within a multimicrogrid system, subject to specific constraints. The optimization of this problem is pivotal for augmenting the stability and resilience of power systems, particularly in remote locales harnessing renewable energy sources. Given its inherent large-scale, sparse, and multimodal nature, the pursuit of the global optimal solution for MNSDP is inherently complex. In this research, we introduce a sophisticated mathematical model of the MNSDP, accommodating three distinct node types, each having disparate reliability prerequisites. We further unveil a benchmark test suite based on real-world scenarios, dubbed MNSDP-LIB. To further our innovations, we present the large-scale binary matrix-based differential evolution (LBMDE) algorithm. This novel algorithm adopts a binary-matrix-centric DE operator with an enhanced feasibility-centric environmental selection strategy. Empirical experiments accentuate the proficiency of LBMDE in addressing large-scale binary matrix optimization challenges. When juxtaposed with extant evolutionary algorithms and a renowned commercial solver, LBMDE demonstrates commendable competitiveness.

关键词： Binary matrix optimization constrained differential evolution algorithm multimicrogrid network structure design renewable energy sources

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Asymmetric Information in Military Microgrid Confrontations-Evaluation Metric and Influence Analysis

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ENERGIES 2020年第8期13卷 1954页

作者： Jiang, Peng Huang, Shengjun Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China China Aerosp Sci & Technol Corp China Acad Launch Vehicle Technol Beijing 100076 Peoples R China Natl Univ Def Technol Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China

Due to the wide integration of information technology in equipment and weapons, a stable and reliable power supply has become one of the pivotal factors in modern warfare to achieve victory. As a critical infrastructure to provide continuous energy supply during long-duration electrical outage, military microgrid always suffers fierce attacks from the enemy. In order to improve the defense effect, a lot of investigation has been made into resource allocation, Distributed Generator (DG) distribution, network reconfiguration, and so forth. Nevertheless, the information gap between defender and attacker has not been considered in the literature. Therefore, this paper is intended to highlight this information mismatch to appeal for community attention and evaluate its capability to improve defensive performance. Firstly, a novel assessment metric is proposed to identify the level of asymmetric information. Then, an Attacker-Defender (AD) model is developed to describe the zero-sum game between two opposite agents, which is subsequently tackled with dual theory and big-M method. Finally, three cases ranging from 6-bus to 57-bus are utilized for numerical experiments to analyze the influence of asymmetric information on military microgrid confrontation. Results on various levels of attack strength validated the effectiveness and significance of asymmetric information in eliminating the attack damage and improving the defensive performance.

关键词： asymmetric information microgrid confrontation attacker-defender model mixed integer linear programming

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A Bayesian optimization hyperband-optimized incremental deep belief network for online battery behaviour modelling for a satellite simulator

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JOURNAL OF ENERGY STORAGE 2023年 58卷

作者： Cao, Mengda Zhang, Tao Liu, Yajie Wang, Yu Shi, Zhichao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interco Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Syst Engn Sanyi Rd Changsha 410073 Hunan Peoples R China

Simulation tools play crucial roles in the stable implementation of space missions during satellite operations;they are typically utilized for satellite behaviour monitoring by comparing obtained telemetry values and predicted values according to pretrained prediction models. However, as telemetry data streams arrive in a chunk-by-chunk manner, a common practice is to retrain the employed simulation tool based on the newly arrived data, which results in the consumption of many computing resources and time lags. Therefore, an incremental learning algorithm is required to achieve accurate and fast satellite behaviour prediction. This paper proposes a Bayesian optimization hyperband-optimized incremental learning-based deep belief network (BOHB-ILDBN) to reproduce battery voltage behaviours, where the BOHB algorithm is utilized to obtain a group of optimal hyperparameter configurations to initialize a DBN model, the DBN model is incrementally updated by a fine-tuning process, and the variance difference between the actual and forecasted values is taken as the criterion for determining the completion of model training. Finally, the effectiveness and robustness of the model are verified on telemetry data obtained from an on-orbit sun-synchronous remote sensing satellite, the China-Brazil Earth Resources Satellite (CBERS-4A).

关键词： Operational satellite simulator Incremental learning Bayesian optimization hyperband (BOHB) Deep belief network (DBN)

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Two-Stage Optimization of Mobile Energy Storage Sizing, Pre-Positioning, and Re-Allocation for Resilient Networked Microgrids with Dynamic Boundaries

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10367页

作者： Lei, Hongtao Jiang, Bo Liu, Yajie Zhu, Cheng Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Informat Syst Engn Changsha 410073 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interco Changsha 410073 Peoples R China

Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. This paper introduces a two-stage optimization framework for MES sizing, pre-positioning, and re-allocation within NMGs. In the first stage, the capacity sizing and pre-positioning of MES devices are optimized before a natural disaster. In the second stage, the re-allocation and active power output of MES devices are adjusted post-disaster, with boundary switches operated based on the damage scenarios. The framework restores unserved loads by either forming isolated microgrids using MES or re-establishing connections between microgrids via smart switches. The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. Extensive experiments on modified IEEE 33-node and 69-node systems demonstrate the model's effectiveness and applicability in improving system resilience.

关键词： resilience networked microgrids mobile energy storage sizing two-stage optimization

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Multimodal multi-objective optimization: Comparative study of the state-of-the-art

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 77卷

作者： Li, Wenhua Zhang, Tao Wang, Rui Huang, Shengjun Liang, Jing Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interco Changsha Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China

Multimodal multi-objective problems (MMOPs) commonly arise in the real world where distant solutions in decision space correspond to very similar objective values. To obtain more Pareto optimal solutions for MMOPs, many multimodal multi-objective evolutionary algorithms (MMEAs) have been proposed. For now, few studies have encompassed most of the representative MMEAs and made a comparative comparison. In this study, we first review the related works during the last two decades. Then, we choose 15 state-of-the-art algorithms that utilize different diversity-maintaining techniques and compared their performance on different types of the existing test suites. Experimental results indicate the strengths and weaknesses of different techniques on different types of MMOPs, thus providing guidance on how to select/design MMEAs in specific scenarios.

关键词： Multimodal multi-objective optimization Evolutionary computation Comparative study Review

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Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression

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ENERGY 2024年 297卷

作者： Zhang, Ran Ji, Chunhui Zhou, Xing Liu, Tianyu Jin, Guang Pan, Zhengqiang Liu, Yajie Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Natl Univ Def Technol Coll Adv Interdisciplinary Studies Changsha Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interco Changsha Peoples R China

Reliable capacity estimation is crucial for safe operation of lithium-ion batteries (LIBs). This work combines the temporal convolutional network (TCN) and Gaussian process regression (GPR) to establish a novel probabilistic capacity estimation method. The proposed TCN-GPR method can not only provide accurate capacity estimation but also quantify the uncertainty of the estimation. Besides, the TCN-GPR method can automatically extract degradation features from partial charging segments, overcoming the limitations of manual experience. In addition, the TCN-GPR method can be applied to different types of LIBs through transfer learning using only a small amount of training data. For validation, the Oxford battery dataset is used to demonstrate the accuracy and robustness of the TCN-GPR method, where a mean absolute percentage error (MAPE) of less than 0.3% can be achieved with only a 15-min partial charging segment. Furthermore, our own experimental dataset is used to demonstrate the generalization ability of the TCN-GPR method through transfer learning, where a MAPE of less than 0.7% can be achieved by using only one battery cell as the training sample.

关键词： Lithium -ion batteries Capacity estimation Temporal convolutional network Gaussian process regression Uncertainty quantification

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