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Federated double DQN based multi-energy microgrid energy management strategy considering carbon emissions 考虑碳排放的基于联邦双 DQN 的多能源微电网能源管理战略
Q1 Engineering Pub Date : 2023-12-01 DOI: 10.1016/j.gloei.2023.11.003
Yanhong Yang , Tengfei Ma , Haitao Li , Yiran Liu , Chenghong Tang , Wei Pei

Multi-energy microgrids (MEMG) play an important role in promoting carbon neutrality and achieving sustainable development. This study investigates an effective energy management strategy (EMS) for MEMG. First, an energy management system model that allows for intra-microgrid energy conversion is developed, and the corresponding Markov decision process (MDP) problem is formulated. Subsequently, an improved double deep Q network (iDDQN) algorithm is proposed to enhance the exploration ability by modifying the calculation of the Q value, and a prioritized experience replay (PER) is introduced into the iDDQN to improve the training speed and effectiveness. Finally, taking advantage of the federated learning (FL) and iDDQN algorithms, a federated iDDQN is proposed to design an MEMG energy management strategy to enable each microgrid to share its experiences in the form of local neural network (NN) parameters with the federation layer, thus ensuring the privacy and security of data. The simulation results validate the superior performance of the proposed energy management strategy in minimizing the economic costs of the MEMG while reducing CO2 emissions and protecting data privacy.

多能源微电网(MEMG)在促进碳中和及实现可持续发展方面发挥着重要作用。本研究探讨了微电网的有效能源管理战略(EMS)。首先,建立了一个允许微网内部能量转换的能源管理系统模型,并提出了相应的马尔可夫决策过程(MDP)问题。随后,提出了一种改进的双深度 Q 网络(iDDQN)算法,通过修改 Q 值的计算方法来增强探索能力,并在 iDDQN 中引入了优先经验重放(PER),以提高训练速度和效果。最后,利用联合学习(FL)和 iDDQN 算法的优势,提出联合 iDDQN,设计 MEMG 能源管理策略,使每个微电网都能以本地神经网络(NN)参数的形式与联盟层共享经验,从而确保数据的隐私性和安全性。仿真结果验证了所提出的能源管理策略在降低 MEMG 经济成本、减少二氧化碳排放和保护数据隐私方面的优越性能。
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引用次数: 0
A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power 利用源-网-荷协作消耗风能的需求响应管理稳健优化模型
Q1 Engineering Pub Date : 2023-12-01 DOI: 10.1016/j.gloei.2023.11.007
Xiangfeng Zhou , Chunyuan Cai , Yongjian Li , Jiekang Wu , Yaoguo Zhan , Yehua Sun

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of wind- driven generators, a min-max-min two-stage robust optimization model is presented, considering the unit commitment, source-network load collaboration, and control of the load demand response. After the constraint functions are linearized, the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method. The minimum-maximum of the original problem was continuously maximized using the iterative method, and the optimal solution was finally obtained. The constraint conditions expressed by the matrix may reduce the calculation time, and the upper and lower boundaries of the original problem may rapidly converge. The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately; otherwise, it is easy to cause excessive accommodation of wind power at some nodes, leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power. Thus, the most economical optimization scheme for the worst scenario of the output power of the generators is obtained, which proves the economy and reliability of the two-stage robust optimization method.

为了尽可能安全地适应风力发电,并处理风力发电机输出功率的不确定性,本文提出了一个最小-最大-最小两阶段鲁棒优化模型,考虑了机组承诺、源网负荷协作和负荷需求响应控制。在对约束函数进行线性化处理后,利用强对偶法将原问题分解为主问题和子问题矩阵。利用迭代法不断求原问题的最小值-最大值,最终得到最优解。矩阵表示的约束条件可以减少计算时间,原问题的上下限可以快速收敛。实例结果表明,应合理选择风电场在电网中的注入节点,否则容易造成部分节点对风电的过度接纳,导致储备成本激增,并不断优化负荷需求响应,降低风电的逆调峰特性。因此,得到了发电机输出功率最坏情况下最经济的优化方案,证明了两阶段鲁棒优化方法的经济性和可靠性。
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引用次数: 0
Automated deep learning system for power line inspection image analysis and processing: Architecture and design issues 用于电力线检测图像分析和处理的自动深度学习系统:架构和设计问题
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.008
Daoxing Li , Xiaohui Wang , Jie Zhang , Zhixiang Ji

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible

无人机在输电线路检测中的应用规模不断增长,导致对无人机检测图像处理的需求相应增加。由于其在计算机视觉方面的优异性能,深度学习已被应用于无人机检测图像处理任务,如电力线识别和绝缘子缺陷检测。尽管性能优异,但基于深度学习的电力无人机检测图像处理模型面临着应用范围小、需要不断重新训练和优化、研发成本高等问题;D由于深度学习的黑匣子和场景数据驱动特性,造成了金钱和时间成本。在本研究中,针对上述问题,提出了一种用于电力无人机检测图像分析和处理的自动化深度学习系统。该系统设计基于可概括性、可扩展性和自动化这三个关键设计原则。回顾了与这些设计原则密切相关的预训练模型、微调(下游任务自适应)和自动机器学习。此外,还提出了一种用于电力无人机检测图像分析和处理的自动化深度学习系统架构。构建了原型系统,并对绝缘子自爆和鸟巢识别两项电力无人机检测图像分析处理任务进行了实验。使用原型系统构建的模型对绝缘体自爆和鸟巢识别的mAP分别达到91.36%和86.13%。这表明系统设计理念是合理的,系统架构是可行的
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引用次数: 0
An evaluation model for smart grids in support of smart cities based on the Hierarchy of Needs Theory 基于需求层次理论的智能电网支持智慧城市的评价模型
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.009
Hongyu Lin , Wei Wang , Yajun Zou , Hongyi Chen

Smart cities depend highly on an intelligent electrical networks to provide a reliable, safe, and clean power supplies. A smart grid achieves such aforementioned power supply by ensuring resilient energy delivery, which presents opportunities to improve the cost-effectiveness of power supply and minimize environmental impacts. A systematic evaluation of the comprehensive benefits brought by smart grid to smart cities can provide necessary theoretical fundamentals for urban planning and construction towards a sustainable energy future. However, most of the present methods of assessing smart cities do not fully take into account the benefits expected from the smart grid. To comprehensively evaluate the development levels of smart cities while revealing the supporting roles of smart grids, this article proposes a model of smart city development needs from the perspective of residents’ needs based on Maslow’s Hierarchy of Needs theory, which serves the primary purpose of building a smart city. By classifying and reintegrating the needs, an evaluation index system of smart grids supporting smart cities was further constructed. A case analysis concluded that smart grids, as an essential foundation and objective requirement for smart cities, are important in promoting scientific urban management, intelligent infrastructure, refined public services, efficient energy utilization, and industrial development and modernization. Further optimization suggestions were given to the city analyzed in the case include strengthening urban management and infrastructure constructions, such as electric vehicle charging facilities and wireless coverage.

智能城市高度依赖智能电网来提供可靠、安全、清洁的电力供应。智能电网通过确保有弹性的能源输送来实现上述电力供应,这为提高电力供应的成本效益和最大限度地减少环境影响提供了机会。系统评估智能电网给智能城市带来的综合效益,可以为城市规划和建设可持续能源未来提供必要的理论基础。然而,目前大多数评估智能城市的方法都没有充分考虑到智能电网的预期效益。为了全面评估智能城市的发展水平,同时揭示智能电网的支撑作用,本文基于马斯洛的需求层次理论,从居民需求的角度提出了一个智能城市发展需求模型,为建设智能城市服务。通过对需求进行分类和整合,进一步构建了支持智慧城市的智能电网评价指标体系。案例分析表明,智能电网作为智慧城市的重要基础和客观要求,对促进城市科学管理、基础设施智能化、公共服务精细化、能源高效利用、产业发展和现代化具有重要意义。对案例中分析的城市提出了进一步的优化建议,包括加强城市管理和基础设施建设,如电动汽车充电设施和无线覆盖。
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引用次数: 0
State-of-the-art review of MPPT techniques for hybrid PV-TEG systems: Modeling, methodologies, and perspectives 混合PV-TEG系统MPPT技术的最新综述:建模、方法和观点
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.005
Bo Yang , Rui Xie , Jinhang Duan , Jingbo Wang

The development of alternative renewable energy technologies is crucial for alleviating climate change and promoting energy transformation. Of the currently available technologies, solar energy has promising application prospects owing to its merits of being clean, safe, and sustainable. Solar energy is converted into electricity through photovoltaic (PV) cells; however, the overall conversion efficiency of PV modules is relatively low, and most of the captured solar energy is dissipated in the form of heat. This not only reduces the power generation efficiency of solar cells but may also have a negative impact on the electrical parameters of PV modules and the service life of PV cells. To overcome the shortcomings, an efficient approach involves combining a PV cell with a thermoelectric generator (TEG) to form hybrid PV-TEG systems, which simultaneously improve the energy conversion efficiency of the PV system by reducing the operating temperature of the PV modules and increasing the power output by utilizing the waste heat generated from the PV system to generate electricity via the TEGs. Based on a thorough examination of the literature, this study comprehensively reviews 14 maximum power point tracking (MPPT) algorithms currently applied to hybrid PV-TEG systems and classifies them into five major categories for further discussion, namely conventional, mathematics-based, metaheuristic, artificial intelligence, and other algorithms. This review aims to inspire advanced ideas and research on MPPT algorithms for hybrid PV-TEG systems.

开发替代可再生能源技术对于缓解气候变化和促进能源转型至关重要。在目前可用的技术中,太阳能具有清洁、安全和可持续的优点,具有很好的应用前景。太阳能通过光伏电池转化为电能;然而,光伏组件的整体转换效率相对较低,并且捕获的大部分太阳能以热的形式耗散。这不仅降低了太阳能电池的发电效率,而且可能对光伏组件的电气参数和光伏电池的使用寿命产生负面影响。为了克服这些缺点,一种有效的方法包括将PV电池与热电发电机(TEG)结合以形成混合PV-TEG系统,其通过降低PV模块的操作温度同时提高PV系统的能量转换效率,并且通过利用从PV系统产生的废热经由TEG发电来增加功率输出。在全面查阅文献的基础上,本研究全面回顾了目前应用于混合PV-TEG系统的14种最大功率点跟踪(MPPT)算法,并将其分为五大类进行进一步讨论,即传统算法、基于数学的算法、元启发式算法、人工智能算法和其他算法。这篇综述旨在启发混合PV-TEG系统的MPPT算法的先进思想和研究。
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引用次数: 0
GIS partial discharge data enhancement method based on self attention mechanism VAE-GAN 基于自关注机制的GIS局部放电数据增强方法
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.007
Qinglin Qian , Weihao Sun , Zhen Wang , Yongling Lu , Yujie Li , Xiuchen Jiang

The reliability of geographic information system (GIS) partial discharge fault diagnosis is crucial for the safe and stable operation of power grids. This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects. First, the non-subsampled contourlet transform (NSCT) algorithm was used to fuse the UHF and optical partial discharge signals to obtain a photoelectric fusion phase resolved partial discharge (PRPD) spectrum with richer information. Subsequently, the VAE structure was introduced into the traditional GAN, and the excellent hidden layer feature extraction ability of the VAE was used to guide the generation of the GAN. Then, the self-attention mechanism was integrated into the VAE-GAN, and the Wasserstein distance and gradient penalty mechanisms were used to optimize the network loss function and expand the sample sets to an equilibrium state. Finally, the KAZE and polar coordinate distribution entropy methods were used to extract the expanded samples. The eigenvectors of the sets were substituted into the long short-term memory (LSTM) network for partial discharge fault diagnosis. The experimental results show that the sample generation quality and fault diagnosis results of this method were significantly better than the traditional data enhancement method. The structure similarity index measure (SSIM) index is increased by 4.5% and 21.7%, respectively, and the average accuracy of fault diagnosis is increased by 22.9%, 9%, 5.7%, and 6.5%, respectively. The data enhancement method proposed in this study can provide a reference for GIS partial discharge fault diagnosis.

地理信息系统局部放电故障诊断的可靠性对电网的安全稳定运行至关重要。本研究提出了一种基于自注意机制的数据增强方法,以优化VAE-GAN方法,解决局部放电样本不足和不同缺陷之间分布不平衡的问题。首先,使用非二次采样轮廓变换(NSCT)算法对超高频和光学局部放电信号进行融合,获得信息更丰富的光电融合相位分辨局部放电(PRPD)光谱。随后,将VAE结构引入到传统的GAN中,并利用VAE优异的隐层特征提取能力来指导GAN的生成。然后,将自注意机制集成到VAE-GAN中,并使用Wasserstein距离和梯度惩罚机制来优化网络损失函数,并将样本集扩展到平衡状态。最后,使用KAZE和极坐标分布熵方法提取扩展样本。将集合的特征向量代入长短期记忆(LSTM)网络进行局部放电故障诊断。实验结果表明,该方法的样本生成质量和故障诊断结果明显优于传统的数据增强方法。结构相似性指数测度(SSIM)指数分别提高了4.5%和21.7%,故障诊断的平均准确率分别提高了22.9%、9%、5.7%和6.5%。本研究提出的数据增强方法可为GIS局部放电故障诊断提供参考。
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引用次数: 0
An adaptive control strategy for microgrid secondary frequency based on parameter identification 基于参数辨识的微电网二次频率自适应控制策略
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.006
Yong Shi , Yin Cheng , Bao Xie , Jianhui Su

Complex microgrid structures and time-varying conditions, among other factors, cause problems in the mechanical modeling of microgrids, making model-based controller optimization difficult. Therefore, this study proposed a secondary frequency adaptive control strategy based on parameter identification, which uses an online parameter identification method to identify the parameters in the microgrid in real-time. The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance. The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters. Finally, a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy

复杂的微电网结构和时变条件等因素导致微电网的机械建模出现问题,使基于模型的控制器优化变得困难。因此,本研究提出了一种基于参数识别的二次频率自适应控制策略,该策略使用在线参数识别方法实时识别微电网中的参数。识别出的参数然后在次级频率自适应控制器中使用,以优化实时控制器性能。该方法实现了微电网运行状态下控制器的自适应优化,并应用于参数未知的微电网,对控制器参数进行调整。最后,通过仿真实验验证了模型的准确性和自适应控制策略的调频效果
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引用次数: 0
Review of multi-objective optimization in long-term energy system models 长期能源系统模型中的多目标优化研究进展
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.010
Wenxin Chen , Hongtao Ren , Wenji Zhou

Modeling and optimizing long-term energy systems can provide solutions to various energy and environmental policies involving public-interest issues. The conventional optimization of long-term energy system models focuses on a single economic goal. However, the increasingly complex demands of energy systems necessitate the comprehensive consideration of multiple dimensional objectives, such as environmental, social, and energy security. Therefore, a multi- objective optimization of long-term energy system models has been developed. Herein, studies pertaining to the multi- objective optimization of long-term energy system models are summarized; the optimization objectives of long-term energy system models are classified into economic, environmental, social, and energy security aspects; and the multi-objective optimization methods are classified and explained based on the preferential expression of decision makers. Finally, the key development direction of the multi-objective optimization of energy system models is discussed.

建模和优化长期能源系统可以为涉及公共利益问题的各种能源和环境政策提供解决方案。长期能源系统模型的传统优化侧重于单一的经济目标。然而,能源系统日益复杂的需求需要综合考虑多个维度的目标,如环境、社会和能源安全。因此,开发了一种长期能源系统模型的多目标优化方法。本文综述了长期能源系统模型的多目标优化研究;长期能源系统模型的优化目标分为经济、环境、社会和能源安全方面;并根据决策者的偏好表达式对多目标优化方法进行了分类和解释。最后,讨论了能源系统模型多目标优化的关键发展方向。
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引用次数: 0
Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression 基于改进k-means算法的电动汽车动态分组控制风力波动抑制
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.003
Yang Yu , Mai Liu , Dongyang Chen , Yuhang Huo , Wentao Lu

To address the significant lifecycle degradation and inadequate state of charge (SOC) balance of electric vehicles (EVs) when mitigating wind power fluctuations, a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm. First, a swing door trending (SDT) algorithm based on compression result feedback was designed to extract the feature data points of wind power. The gating coefficient of the SDT was adjusted based on the compression ratio and deviation, enabling the acquisition of grid-connected wind power signals through linear interpolation. Second, a novel algorithm called IDOA-KM is proposed, which utilizes the Improved Dingo Optimization Algorithm (IDOA) to optimize the clustering centers of the k-means algorithm, aiming to address its dependence and sensitivity on the initial centers. The EVs were categorized into priority charging, standby, and priority discharging groups using the IDOA-KM. Finally, an two-layer power distribution scheme for EVs was devised. The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals. The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles. The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals, smoothing wind power fluctuations, mitigating EV degradation, and enhancing the SOC balance.

为了解决电动汽车在缓解风电波动时生命周期显著退化和荷电状态(SOC)平衡不足的问题,提出了一种基于改进k-means算法的电动汽车动态分组控制策略。首先,设计了一种基于压缩结果反馈的摆动门趋势(SDT)算法来提取风电的特征数据点。SDT的选通系数根据压缩比和偏差进行调整,从而能够通过线性插值获取并网风电信号。其次,提出了一种新的算法IDOA-KM,该算法利用改进的Dingo优化算法(IDOA)来优化k-means算法的聚类中心,旨在解决其对初始中心的依赖性和敏感性。使用IDOA-KM将电动汽车分为优先充电组、备用组和优先放电组。最后,设计了一种电动汽车的双层配电方案。上层确定三个EV组的充电/放电顺序及其相应的功率信号。下层基于最大充电/放电功率或SOC均衡原理将功率信号分配给每个EV。仿真结果证明了所提出的控制策略在准确跟踪电网功率信号、平滑风电波动、缓解电动汽车退化和增强SOC平衡方面的有效性。
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引用次数: 0
Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network 基于改进CEEMDAN方法和生成对抗插值网络的风电数据缺失插值模型
Q1 Engineering Pub Date : 2023-10-01 DOI: 10.1016/j.gloei.2023.10.001
Lingyun Zhao , Zhuoyu Wang , Tingxi Chen , Shuang Lv , Chuan Yuan , Xiaodong Shen , Youbo Liu

Randomness and fluctuations in wind power output may cause changes in important parameters (e.g., grid frequency and voltage), which in turn affect the stable operation of a power system. However, owing to external factors (such as weather), there are often various anomalies in wind power data, such as missing numerical values and unreasonable data. This significantly affects the accuracy of wind power generation predictions and operational decisions. Therefore, developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry. In this study, the causes of abnormal data in wind power generation were first analyzed from a practical perspective. Second, an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components. Finally, a complete wind power generation time series was reconstructed. Compared to traditional methods, the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations

风电输出的随机性和波动可能会导致重要参数(如电网频率和电压)的变化,进而影响电力系统的稳定运行。然而,由于外部因素(如天气),风电数据往往存在各种异常,如数值缺失和数据不合理。这严重影响了风力发电预测和运营决策的准确性。因此,开发和应用可靠的风电插值方法对促进风电行业的可持续发展具有重要意义。本研究首先从实际角度分析了风力发电数据异常的原因。其次,提出了一种改进的带自适应噪声的完全集成经验模式分解(ICEEMDAN)方法,该方法使用生成对抗性插值网络(GAIN)网络对风力发电进行预处理,并对缺失的风力发电子分量进行插值。最后,重构了一个完整的风力发电时间序列。与传统方法相比,所提出的ICEEMDAN-GAIN组合插值模型具有更高的插值精度,可以有效地减少风力发电序列波动带来的误差影响
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引用次数: 1
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Global Energy Interconnection
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