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Synergistic frequency regulation in microgrids: pioneering a controller for seamless integration of wave energy conversion systems 微电网中的协同频率调节:首创无缝集成波浪能转换系统的控制器
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-22 DOI: 10.1007/s00202-024-02582-7
Rohan Kumar Gupta, Amitesh Kumar

Tidal power plants (TPPs) and wave energy conversion systems (WECSs) are emerging as significant contributors to clean energy technologies, with the potential to address energy shortages and mitigate environmental footprints. This necessitates a thorough investigation into their role in supporting ancillary services, particularly in frequency regulation. Integrating intermittent units like TPPs into power systems increases capacity but reduces system inertia due to electronic connections. Unlike other more popular renewable sources, TPP is more consistent, highly predictable, reliable, and has a high energy density. This paper introduces a new wave energy conversion systems (WECS) model incorporated into a microgrid to assess its effects. The presence of WECS leads to a deterioration in the frequency deviation dynamics following disturbances, posing a challenge to frequency regulation services. The microgrid model encompasses a rotational power plant, an electric vehicle aggregator, a TPP, and a standalone solar plant (WECS and capacitor energy storage system (CESS) is added later in the system to see the effect of them). The study considers CESS over battery energy storage system due to its high cycle life and fast response time. The projected microgrid is optimized using a hybrid African vulture optimization salp swarm algorithm in conjunction with a new 1+Fractional order Proportional Derivative controller parallel with Fractional order Proportional Integral controller with filter (1+FOPD-FOPIF controller). The study evaluates the contribution of WECS and CESS to frequency management in microgrid system. The efficacy of these tactics is showcased through simulation-driven experiments and validated using real data reflecting the annual load variation in the Fairbank area (U.S) and for IEEE 5 bus system & IEEE 39 bus system with 60% penetration of renewable sources. For verification benchmark test functions are also used as a statistical analysis of projected optimization method and stability analysis is done for projected controller. The projected technique and controller shows better settling time results and reduces oscillations when WECS and CESS are integrated.

潮汐发电站(TPP)和波浪能转换系统(WECS)正在成为清洁能源技术的重要贡献者,具有解决能源短缺和减少环境足迹的潜力。这就需要深入研究它们在支持辅助服务,特别是频率调节方面的作用。将 TPP 等间歇性机组整合到电力系统中可以增加容量,但由于电子连接的存在,会降低系统惯性。与其他更受欢迎的可再生能源不同,TPP 更为稳定、可预测性高、可靠,而且能量密度高。本文介绍了将波浪能转换系统(WECS)纳入微电网的新模型,以评估其效果。波能转换系统的存在会导致扰动后频率偏差动态恶化,给频率调节服务带来挑战。微电网模型包括一个旋转发电厂、一个电动汽车聚合器、一个 TPP 和一个独立的太阳能发电厂(WECS 和电容器储能系统 (CESS) 稍后加入系统中,以了解它们的影响)。由于电容器储能系统的循环寿命长、响应速度快,因此本研究认为电容器储能系统优于蓄电池储能系统。使用混合非洲秃鹫优化 salp 蜂群算法,结合新的 1+ 分数阶比例微分控制器和带滤波器的分数阶比例积分控制器(1+FOPD-FOPIF 控制器),对预计的微电网进行了优化。研究评估了 WECS 和 CESS 对微网系统频率管理的贡献。通过仿真驱动的实验展示了这些策略的功效,并使用真实数据进行了验证,这些数据反映了费尔班克地区(美国)的年度负荷变化,以及 IEEE 5 总线系统和可再生能源渗透率为 60% 的 IEEE 39 总线系统。为了进行验证,还使用了基准测试功能作为预测优化方法的统计分析,并对预测控制器进行了稳定性分析。当 WECS 和 CESS 集成时,预测的技术和控制器显示出更好的稳定时间结果,并减少了振荡。
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引用次数: 0
Zernike radial basis neural network control of DC–DC power converter driven permanent magnet DC motor: design and experimental validation 直流-直流电源转换器驱动永磁直流电机的泽尼克径向基神经网络控制:设计与实验验证
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-21 DOI: 10.1007/s00202-024-02659-3
Sasank Das Gangula, Tousif Khan Nizami, Ramanjaneya Reddy Udumula, Arghya Chakravarty, Fareed Ahmad

This article presents a novel control architecture for an enhanced closed-loop speed tracking of a DC–DC buck power converter fed Permanent Magnet DC motor (PMDC) motor in face of large exogenous load torque uncertainty. The proposed architecture combines a new self learning Zernike radial polynomial neural network (ZRNN) estimator with the backstepping controller. The design involves a computationally simple online learning based ZRNN to rapidly and accurately estimate the unknown large load torque uncertainties. The proposed control solution concurrently guarantees stability and excellent dynamic performance through an effective neural network based estimation and subsequent compensation of unanticipated load torque perturbations over a wide range. The closed loop stability of the DC–DC buck power converter driven PMDC motor and asymptotic speed tracking with the proposed neuro-adaptive controller is proved using the stability theory for non-autonomous systems. The effectiveness of the proposed controller has been investigated through experimentation on an indigenously developed laboratory prototype of 200 W under closed loop operation using digital signal processors. The tests conducted around different operating conditions include the motor start-up response, step variations in the load torque, and step changes in the reference speed. Experimental results demonstrate a significant improvement in the speed tracking performance achieving (48.13 %) reduction in the settling time and no-change in speed during start-up and load torque perturbations upto (600%), respectively. Experimental validations and extensive tests spanning over a large operating region, substantiate the theoretical claims and real-time suitability of the proposed controller for sensitive applications demanding high performance.

本文提出了一种新的控制架构,用于在面临大的外生负载转矩不确定性时,对馈电永磁直流电机(PMDC)的直流-直流降压功率转换器进行增强型闭环速度跟踪。所提出的架构将新型自学习 Zernike 径向多项式神经网络 (ZRNN) 估计器与反步进控制器相结合。该设计涉及一个计算简单的基于在线学习的 ZRNN,以快速准确地估计未知的大负载转矩不确定性。通过基于神经网络的有效估算,以及随后对大范围内的意外负载转矩扰动进行补偿,所提出的控制解决方案同时保证了稳定性和出色的动态性能。利用非自主系统的稳定性理论,证明了直流-直流降压功率转换器驱动的 PMDC 电机的闭环稳定性,以及使用所提出的神经自适应控制器的渐进速度跟踪。通过使用数字信号处理器对自主开发的 200 W 实验室原型机进行闭环运行实验,研究了所提控制器的有效性。围绕不同运行条件进行的测试包括电机启动响应、负载转矩的阶跃变化和参考转速的阶跃变化。实验结果表明,速度跟踪性能有了明显改善,在启动和负载转矩扰动高达(600%)的情况下,沉降时间缩短了(48.13%),速度没有变化。实验验证和广泛的测试跨越了一个大的工作区域,证实了所提出的控制器的理论主张和实时适用性,适用于要求高性能的敏感应用。
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引用次数: 0
Decentralized energy trading in microgrids: a blockchain-integrated model for efficient power flow with social welfare optimization 微电网中的去中心化能源交易:区块链集成模型与社会福利优化的高效电力流
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-21 DOI: 10.1007/s00202-024-02635-x
Abdullah Umar, Deepak Kumar, Tirthadip Ghose

The paper introduces a novel decentralized electricity market framework tailored for network community microgrid systems, leveraging blockchain technology. It presents a comprehensive model that integrates blockchain with a microgrid energy management system (MEMS) to facilitate peer-to-peer (P2P) energy trading, thereby ensuring optimal power flow and mitigating line congestion. The proposed optimization model takes into account crucial factors such as line flow constraints, market clearance price (MCP) using the double auction method, and social welfare optimization for energy transactions among buyers (consumers) and sellers (prosumers). By incorporating the power transfer distribution factor (PTDF) to calculate service charges associated with distribution network usage, the model safeguards the interests of all market participants while minimizing the risk of line overload. A case study is conducted to illustrate the efficacy of the proposed model, demonstrating the tangible benefits of blockchain integration in effectively managing and optimizing decentralized energy trading within microgrid environments. The proposed blockchain model for P2P energy trading offers a compelling alternative to conventional microgrid energy trading systems. By streamlining trade execution and eliminating intermediaries, it significantly reduces transaction times, with average processing times of around 10 s, highlighting its rapid processing capabilities. Furthermore, its decentralized nature and cryptographic security mechanisms provide robust protection against tampering and fraud, ensuring the integrity of transactions. Additionally, the transparent ledger system guarantees complete audibility and fairness for all participants, distinguishing it from opaque processes typical in traditional models.

本文介绍了利用区块链技术为网络社区微电网系统量身定制的新型去中心化电力市场框架。它提出了一个综合模型,将区块链与微电网能源管理系统(MEMS)整合在一起,促进点对点(P2P)能源交易,从而确保最佳电力流动,缓解线路拥堵。所提出的优化模型考虑了一些关键因素,如线路流量限制、使用双重拍卖方法的市场清算价格(MCP),以及买方(消费者)和卖方(消费者)之间能源交易的社会福利优化。通过将电力传输分配系数(PTDF)用于计算与配电网络使用相关的服务费用,该模型在最大限度降低线路过载风险的同时,也保障了所有市场参与者的利益。通过案例研究说明了所提模型的功效,展示了区块链集成在有效管理和优化微电网环境中分散式能源交易方面的切实优势。针对 P2P 能源交易提出的区块链模式为传统的微电网能源交易系统提供了一个引人注目的替代方案。通过简化交易执行和消除中间环节,它大大缩短了交易时间,平均处理时间约为 10 秒,凸显了其快速处理能力。此外,它的去中心化特性和加密安全机制可提供强大的保护,防止篡改和欺诈,确保交易的完整性。此外,透明分类账系统保证了所有参与者的完全可审计性和公平性,有别于传统模式中典型的不透明流程。
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引用次数: 0
Half-quadratic criterion-based continuous-time adaptive control for robust LCL-filtered grid-tied inverter 基于半二次准则的连续时间自适应控制,用于鲁棒 LCL 滤波并网逆变器
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-21 DOI: 10.1007/s00202-024-02664-6
Danish Khan, Mohammed Qais, Irfan Sami, Pengfei Hu

Grid-tied inverters are essential for seamlessly integrating sustainable energy resources into the electrical grid, yet they also introduce harmonics due to the inherent switching operations of power electronics. While inductance-capacitance-inductance (LCL) filters effectively limit these harmonics, enhancing overall system performance, they come with their own set of challenges. These include design complexity, potential resonance at high frequencies leading to system instability, and variations in grid impedance. This paper addresses the design complexities by employing a circle search algorithm to optimize the LCL filter and control system parameters. Additionally, a continuous time-based adaptive filtering algorithm, the half-quadratic criterion, is implemented to dynamically adjust the gain of the inner capacitor current feedback damping loop and the gains of a proportional resonant controller to address the resonance and grid impedance variation issues. These algorithms aim to minimize a constrained multi-objective optimization function based on total harmonic distortion, including high-frequency harmonic distortion and the absolute amplitude error between the measured and reference currents. The system is tested using MATLAB/SIMULINK and real-time Typhoon HIL simulations. The findings illustrate that the proposed control scheme significantly enhances the damping region by suppressing the resonance frequency in the higher frequency band. Furthermore, the results demonstrate that the proposed control loop maintains robustness against fluctuations in grid-side impedance, accommodating increases up to 400% and decreases down to 75%. The system achieves a nearly negligible steady-state error and maintains a transient error below 0.1% throughout step changes in reference current.

并网逆变器对于将可持续能源无缝集成到电网中至关重要,但由于电力电子固有的开关操作,它们也会引入谐波。虽然电感-电容-电感(LCL)滤波器能有效限制这些谐波,提高系统的整体性能,但它们也面临着一系列挑战。这些挑战包括设计复杂性、导致系统不稳定的高频潜在共振以及电网阻抗变化。本文采用圆搜索算法优化 LCL 滤波器和控制系统参数,以解决设计复杂性问题。此外,还采用了基于时间的连续自适应滤波算法--半二次准则,以动态调整内部电容器电流反馈阻尼环路的增益和比例谐振控制器的增益,从而解决谐振和电网阻抗变化问题。这些算法旨在最小化基于总谐波失真的约束多目标优化函数,包括高频谐波失真以及测量电流和参考电流之间的绝对振幅误差。使用 MATLAB/SIMULINK 和实时 Typhoon HIL 仿真对系统进行了测试。结果表明,所提出的控制方案通过抑制高频段的共振频率,显著增强了阻尼区域。此外,研究结果表明,所提出的控制环路对电网侧阻抗的波动保持稳健性,可承受高达 400% 的增长和低至 75% 的下降。该系统实现了几乎可以忽略不计的稳态误差,并在参考电流阶跃变化过程中保持低于 0.1% 的瞬态误差。
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引用次数: 0
Improved gazelle optimization algorithm (IGOA)-based optimal design of solar/battery energy storage/EV charging station 基于改进的瞪羚优化算法(IGOA)的太阳能/电池储能/电动汽车充电站优化设计
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-21 DOI: 10.1007/s00202-024-02665-5
R. Venkatesh, S. Kalpanadevi, S. M. Kamali, A. Radhika

Small-scale photovoltaic (PV), battery energy storage systems (BESS), and electric vehicle charging stations have all been proposed and implemented as part of an integrated system in numerous cities worldwide to develop sustainable urban efficiency and dramatically increase the rate of utilization of solar energy resources. To scale PV and BESS and define BESS’s charging/discharging pattern, this manuscript demonstrates a grid-connected photovoltaic/battery energy storage/EV charging station optimization model (PBES). To minimize the cost of electricity, this study provides an optimization model for a grid-connected PBES. To solve this model, GOA-BESA is used. The model's optimal size and energy management technique are determined. Therefore, this manuscript proposes an intelligent search technique that combines the gazelle optimization algorithm (GOA) and is improved by utilizing the bald eagle search algorithm (BESA) which is named the improved gazelle optimization algorithm (IGOA). The IGOA is employed to simulate EV charging patterns and to calculate the EV charging demand at each time interval. By then the performance of the proposed methodology will be evaluated using MATLAB, and then, the proposed technique will be compared with existing techniques.

世界上许多城市都提出并实施了小型光伏发电(PV)、电池储能系统(BESS)和电动汽车充电站,将其作为综合系统的一部分,以发展可持续的城市效率,并显著提高太阳能资源的利用率。为了扩大光伏发电和电池储能系统的规模,并确定电池储能系统的充放电模式,本手稿展示了并网光伏发电/电池储能/电动汽车充电站优化模型(PBES)。为了使电力成本最小化,本研究提供了一个并网 PBES 的优化模型。为求解该模型,使用了 GOA-BESA。确定了模型的最佳规模和能源管理技术。因此,本手稿提出了一种智能搜索技术,该技术结合了瞪羚优化算法 (GOA),并利用秃鹰搜索算法 (BESA) 对其进行了改进,命名为改进的瞪羚优化算法 (IGOA)。IGOA 用于模拟电动汽车充电模式,并计算每个时间间隔的电动汽车充电需求。然后,将使用 MATLAB 对建议方法的性能进行评估,并将建议技术与现有技术进行比较。
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引用次数: 0
Faulty bearing diagnostic model based on multi-dimensional signal and multi-analysis domain 基于多维信号和多分析域的故障轴承诊断模型
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-21 DOI: 10.1007/s00202-024-02522-5
Shuo Wang, Bokai Guang, Zihao Wang, Xiaohua Bao

Deep learning and multidimensional signal fusion are utilized to fully extract fault features and integrate them into effective signals to cope with special cases in bearing fault diagnosis. Current mainstream data fusion methods only utilize vibration signals, and the vast majority of signal analysis is limited to the time domain. In addition, in the mainstream data fusion scheme, the fusion capability of the signal collector is relatively low, and the correlation and compatibility between the data cannot be guaranteed. In order to further improve the judging ability of signal features, this paper proposes a bearing fault diagnosis model based on multi-dimensional signals and multi-analysis domain. In this model, a multi-dimensional signal data model with multiple analysis domains is used for feature extraction and fusion. And the independent networks are classified according to their functions, and a single network is used to establish a data feature fusion system, while other networks extract features from different sensors. To ensure the fusion of signal acquisition from different analysis domains, multiple fusion nodes are added between the layers of the fusion network and an attention mechanism is introduced to self-weight the different features. Through experiments, technical comparisons were conducted to improve the efficiency of feature recognition and the accuracy of defect classification, and to verify the effectiveness and feasibility of the proposed method.

利用深度学习和多维信号融合技术充分提取故障特征,并将其整合为有效信号,以应对轴承故障诊断中的特殊情况。目前主流的数据融合方法仅利用振动信号,绝大多数信号分析仅限于时域。此外,在主流数据融合方案中,信号采集器的融合能力相对较低,数据之间的相关性和兼容性无法得到保证。为了进一步提高信号特征的判断能力,本文提出了一种基于多维信号和多分析域的轴承故障诊断模型。在该模型中,采用多分析域的多维信号数据模型进行特征提取和融合。并根据独立网络的功能进行分类,利用单一网络建立数据特征融合系统,其他网络则从不同传感器中提取特征。为确保不同分析领域信号采集的融合,在融合网络的层与层之间增加了多个融合节点,并引入了关注机制对不同特征进行自加权。通过实验进行技术比较,提高了特征识别的效率和缺陷分类的准确性,验证了所提方法的有效性和可行性。
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引用次数: 0
Active disturbance observation rejection control based on port-controlled Hamiltonian with dissipation model for PMSM 基于端口控制哈密顿与耗散模型的 PMSM 主动干扰观测抑制控制
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-21 DOI: 10.1007/s00202-024-02673-5
Bo Fan, Qingwei Hu, Jianxiang Wang, Yi Zhao, Hangyu Zhou, Lifan Sun

The operational performances of the conventional permanent magnet synchronous motor drive systems are affected by complex work conditions, sudden load changes, and external disturbances. For the nonlinear control of a permanent magnet synchronous motor, a passive control method based on ADRC and PCHD with disturbance observation is proposed. The disturbance of the current loop is estimated and compensated by the disturbance observer, and the feedback control law is obtained by interconnection and damping configuration, thus the port-controlled Hamiltonian with dissipation based on perturbation observation is designed. With the introduction of active disturbance rejection control, the system has stronger robustness to external disturbance. The experimental results show that compared with the PI control and ADRC method, the proposed method reduces overshoot effectively and improves the response speed of the system. The control system has the better performance of anti-disturbance.

传统永磁同步电机驱动系统的运行性能会受到复杂工况、负载突变和外部干扰的影响。针对永磁同步电机的非线性控制,提出了一种基于 ADRC 和 PCHD 并带有扰动观测的被动控制方法。通过扰动观测器对电流回路的扰动进行估计和补偿,并通过互联和阻尼配置获得反馈控制律,从而设计出基于扰动观测的带耗散的端口控制哈密顿。引入主动干扰抑制控制后,系统对外部干扰具有更强的鲁棒性。实验结果表明,与 PI 控制和 ADRC 方法相比,所提出的方法能有效减少过冲,提高系统的响应速度。控制系统具有更好的抗干扰性能。
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引用次数: 0
Current decoupling control of linear synchronous motor based on improved extended state observer 基于改进型扩展状态观测器的线性同步电机电流解耦控制
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-21 DOI: 10.1007/s00202-024-02644-w
Peng Leng, Jie Li, Peichang Yu, Lianchun Wang, Tanyi Qiu, Qiang Chen

Linear synchronous motors, with advantages such as high thrust density, fast speed, and strong dynamic response capabilities, are widely used in industrial automation, aerospace, military, transportation, and other fields. The use of vector control in linear synchronous motors can achieve static decoupling of current, but the dynamic coupling relationship still exists. As the speed increases, the impact of dynamic coupling becomes increasingly severe, leading to a decrease in the dynamic performance of the system. Traditional current decoupling control methods, such as current feedback decoupling control and current deviation decoupling control, are sensitive to motor parameters and cannot solve the current decoupling problem caused by changes in inductance parameters during motor operation. Therefore, this paper proposes a current decoupling control strategy based on an improved extended state observer (ESO). By observing the coupling term using the improved ESO and combining it with feedforward control for corresponding compensation, current decoupling control is achieved without relying on accurate inductance parameters, thereby reducing the sensitivity of the strategy to parameters. Furthermore, the stability of the improved ESO was demonstrated using Lyapunov stability theory in the paper. Simulation and experiments have verified that the current decoupling control strategy based on the improved ESO can effectively reduce the dynamic coupling in vector control, enhance the control performance, and significantly improve the system’s robustness.

线性同步电机具有推力密度大、速度快、动态响应能力强等优点,被广泛应用于工业自动化、航空航天、军事、交通等领域。在线性同步电机中使用矢量控制可以实现电流的静态解耦,但动态耦合关系依然存在。随着转速的增加,动态耦合的影响越来越严重,导致系统动态性能下降。传统的电流解耦控制方法,如电流反馈解耦控制和电流偏差解耦控制,对电机参数比较敏感,无法解决电机运行过程中电感参数变化引起的电流解耦问题。因此,本文提出了一种基于改进的扩展状态观测器(ESO)的电流解耦控制策略。通过使用改进型 ESO 观察耦合项并结合前馈控制进行相应补偿,无需依赖精确的电感参数即可实现电流去耦控制,从而降低了该策略对参数的敏感性。此外,本文还利用 Lyapunov 稳定性理论证明了改进型 ESO 的稳定性。仿真和实验验证了基于改进型 ESO 的电流解耦控制策略能有效降低矢量控制中的动态耦合,提高控制性能,并显著改善系统的鲁棒性。
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引用次数: 0
Short-term wind power prediction based on ICEEMDAN decomposition and BiTCN–BiGRU-multi-head self-attention model 基于 ICEEMDAN 分解和 BiTCN-BiGRU-多机头自关注模型的短期风电预测
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-20 DOI: 10.1007/s00202-024-02638-8
Xu Zhang, Jun Ye, Lintao Gao, Shenbing Ma, Qiman Xie, Hui Huang

In order to address the security threats posed by the volatility and stochasticity of large-scale distributed wind power, this paper proposes an attention-based hybrid deep learning approach for more efficient and accurate wind power sequence prediction. Firstly, the Pearson correlation coefficient (PCC) is used to identify the main meteorological variables as input sequences. Secondly, the intrinsic complete ensemble empirical mode decomposition with adaptive noise is used to decompose the sequence of wind power. Then, the hidden information such as wind speed, wind direction, and wind magnitude are extracted by bidirectional temporal convolutional networks (BiTCN), and the acquired information is inputted into bidirectional gated recurrent units (BiGRU) optimized by a multi-head self-attention mechanism for prediction. Finally, the predicted values of each component are summed to obtain the final prediction results. By comparing with the other 12 models, the results show that the two-scale integrated model of BiTCN and BiGRU can obtain better prediction accuracy. Compared with other benchmark models, the RMSE of this paper's model is reduced by more than 9.4%, indicating that this paper's model can fit the wind power data better and achieve better prediction results.

为了应对大规模分布式风电的波动性和随机性带来的安全威胁,本文提出了一种基于注意力的混合深度学习方法,以实现更高效、更准确的风电序列预测。首先,使用皮尔逊相关系数(PCC)来识别作为输入序列的主要气象变量。其次,使用带有自适应噪声的本征完全集合经验模式分解来分解风力发电序列。然后,通过双向时序卷积网络(BiTCN)提取风速、风向和风力大小等隐藏信息,并将获取的信息输入经多头自注意机制优化的双向门控递归单元(BiGRU)进行预测。最后,将各部分的预测值相加得出最终预测结果。通过与其他 12 个模型的比较,结果表明 BiTCN 和 BiGRU 的双尺度集成模型可以获得更好的预测精度。与其他基准模型相比,本文模型的均方根误差降低了 9.4%以上,表明本文模型能够更好地拟合风电数据,取得更好的预测效果。
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引用次数: 0
Optimizing integrated hydrogen technologies and demand response for sustainable multi-energy microgrids 为可持续多能源微电网优化集成氢技术和需求响应
IF 1.8 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-08-19 DOI: 10.1007/s00202-024-02645-9
Xintong Du, Yang Yang, Haifeng Guo

In response to the imperative of achieving net-zero emissions, Multi-Energy Microgrids (MEMGs) have emerged as pivotal infrastructures. This study advocates for precise scheduling of integrated energy resources within MEMGs, incorporating energy conversion facilities and optimizing a hybrid Demand Response (DR) scheme. The integration of hydrogen-based technologies, such as hydrogen power transmission units, hydrogen storage systems (HSSs), fuel cells, and battery electric vehicles (BEVs), offers unprecedented opportunities to mitigate carbon emissions effectively. The approach leverages a novel multi-objective optimization method, the Horse Herd Optimization Algorithm (HOA), complemented by fuzzy sampling and Pareto criteria, to address complex objectives including minimizing operational costs and emissions. The developed energy management model facilitates continuous control mechanisms for MEMG operators, accommodating both flexible and inflexible energy demands. Importantly, the study navigates uncertainties in electricity market prices, energy demand, and renewable power generation through robust stochastic modeling and multiple probabilistic scenarios. This study achieves a significant 18% reduction in operational costs and a remarkable 25% decrease in greenhouse gas emissions, leveraging advanced technologies like HSSs, fuel cells, and BEVs within MEMGs. The integration of these technologies also enables up to 15% improvement in energy efficiency and a 12% increase in revenue generation through optimized energy trading strategies.

为了应对实现净零排放的迫切需要,多能源微电网(MEMGs)已成为举足轻重的基础设施。本研究主张在多能源微电网内精确调度综合能源资源,整合能源转换设施,优化混合需求响应(DR)方案。氢基技术(如氢动力传输装置、氢存储系统(HSS)、燃料电池和电池电动汽车(BEV))的整合为有效减少碳排放提供了前所未有的机遇。该方法利用新颖的多目标优化方法--马群优化算法 (HOA),并辅以模糊采样和帕累托标准,以解决包括运营成本和排放量最小化在内的复杂目标。所开发的能源管理模式可为 MEMG 运营商提供持续控制机制,同时满足灵活和非灵活的能源需求。重要的是,该研究通过稳健的随机建模和多种概率情景,驾驭了电力市场价格、能源需求和可再生能源发电方面的不确定性。这项研究利用 MEMG 中的 HSS、燃料电池和 BEV 等先进技术,使运营成本大幅降低了 18%,温室气体排放量显著减少了 25%。通过优化能源交易策略,这些技术的集成还能使能源效率提高 15%,创收增加 12%。
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引用次数: 0
期刊
Electrical Engineering
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