Global Energy Interconnection最新文献_第6页

Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control 基于先进模型预测控制的水电厂电压自动调节建模与控制

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2024.12.003

Ebunle Akupan Rene , Willy Stephen Tounsi Fokui

Fluctuating voltage levels in power grids necessitate automatic voltage regulators (AVRs) to ensure stability. This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control (MPC), which utilizes an extensive mathematical model of the voltage regulation system to optimize the control actions over a defined prediction horizon. This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints, thereby improving stability and performance under dynamic conditions. The findings were compared with those derived from an optimal proportional integral derivative (PID) controller designed using the artificial bee colony (ABC) algorithm. Although the ABC-PID method adjusts the PID parameters based on historical data, it may be difficult to adapt to real-time changes in system dynamics under constraints. Comprehensive simulations assessed both frameworks, emphasizing performance metrics such as disturbance rejection, response to load changes, and resilience to uncertainties. The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation; however, MPC excelled in controlling overshoot and settling time—recording 0.0 % and 0.25 s, respectively. This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior, which exhibited settling times and overshoots exceeding 0.41 s and 5.0 %, respectively. The controllers were implemented using MATLAB/Simulink software, indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.

电网中波动的电压水平需要自动电压调节器（avr）来保证稳定。本研究利用模型预测控制（MPC）研究了水力发电厂AVR的建模和控制，MPC利用电压调节系统的广泛数学模型在定义的预测范围内优化控制动作。这种预测功能使MPC能够在考虑操作限制的同时最大限度地减少电压偏差，从而提高动态条件下的稳定性和性能。研究结果与使用人工蜂群（ABC）算法设计的最优比例积分导数（PID）控制器的结果进行了比较。ABC-PID方法虽然根据历史数据调整PID参数，但在约束条件下难以适应系统动力学的实时变化。综合模拟评估了这两个框架，强调性能指标，如干扰抑制，对负载变化的响应，以及对不确定性的恢复能力。结果表明，MPC和ABC-PID两种方法均能有效实现精确的电压调节；MPC在控制超调和沉降时间方面表现优异，分别为0.0%和0.25 s。与基于实际系统行为的性能标准优化PID参数的传统控制方法相比，这表明了更强的鲁棒性，其稳定时间和超调分别超过0.41 s和5.0%。控制器是使用MATLAB/Simulink软件实现的，这对于追求最先进的自动电压调节的发电厂工程师来说是一个重大的进步。

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引用次数: 0

Analysis of multi-infeed receiving AC system with incompletely segmented VSC-HVDC 不完全分段vdc - hvdc多馈入接收交流系统分析

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2024.10.015

You Zuo , Minxiao Han , Bing Liu , Tamara Egamnazarova

When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system, voltage dips can easily propagate in the power system, resulting in multiple LCC commutation failures simultaneously. The VSC-HVDC can be used to divide the receiving system into several interconnected sub-partitions and improve the voltage support capability of the receiving system. Compared with asynchronous interconnection, which completely separates the receiving systems with VSC-HVDC, incomplete segmentation with an AC connection is a more pertinent segmenting method for multilayer complex regional power grids. To analyze the voltage support capability of the VSC in incomplete segmentation, a micro-incremental model of the VSC was established, the operating impedance of the VSC was calculated, and the voltage support function of the VSC was quantified. The effect of the fault on the system short-circuit capacity was analyzed, and a calculation method for the multi-infeed short-circuit ratio in an incompletely segmented scenario was obtained. A VSC-segmented model of a two-infeed DC system was built on the EMTDC/PSCAD simulation platform, and the validity of the micro-increment model and accuracy of the proposed conclusions were verified.

当多条LCC- hvdc输电线路密集馈送到接收交流系统中时，电压降很容易在电力系统中传播，导致多条LCC同时换相故障。VSC-HVDC可以将接收系统划分为多个相互连接的小分区，提高接收系统的电压支撑能力。相对于采用直流-直流完全分离接收系统的异步互联，交流连接不完全分段是一种更适合多层复杂区域电网的分段方法。为了分析VSC在不完全分割情况下的电压支撑能力，建立了VSC的微增量模型，计算了VSC的工作阻抗，并对VSC的电压支撑功能进行了量化。分析了故障对系统短路容量的影响，给出了不完全分段情况下多进给短路比的计算方法。在EMTDC/PSCAD仿真平台上建立了双馈直流系统的vsc分段模型，验证了微增量模型的有效性和结论的准确性。

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引用次数: 0

Multiagent, multitimescale aggregated regulation method for demand response considering spatial–temporal complementarity of user-side resources 考虑用户侧资源时空互补性的多智能体、多时间尺度需求响应聚合调控方法

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2025.01.004

Tingzhe Pan , Chao Li , Chen Yang , Zijie Meng , Zongyi Wang , Zean Zhu

The integration of substantial renewable energy and controllable resources disrupts the supply–demand balance in distribution grids. Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels. Current studies typically overlook the spatial–-temporal variations and coordination between these timescales, leading to significant day-ahead optimization errors, high intraday costs, and slow convergence. To address these challenges, we developed a multiagent, multitimescale aggregated regulation method for spatial–-temporal coordinated demand response of user-side resources. Firstly, we established a framework considering the spatial–-temporal coordinated characteristics of user-side resources with the objective to minimize the total regulation cost and weighted sum of distribution grid losses. The optimization problem was then solved for two different timescales: day-ahead and intraday. For the day-ahead timescale, we developed an improved particle swarm optimization (IPSO) algorithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies. For the intraday timescale, we developed an improved alternating direction method of multipliers (IADMM) algorithm that distributes tasks across edge distribution stations, dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision. The simulation results indicate that this method can fully achieve multitimescale spatial–-temporal coordinated aggregated regulation between day-ahead and intraday, effectively reduce the total regulation cost and distribution grid losses, and enhance smart grid resilience.

大量可再生能源和可控资源的整合破坏了配电网的供需平衡。安全运行取决于用户侧资源在前一天和当天的需求响应中的参与。目前的研究通常忽略了这些时间尺度之间的时空变化和协调，导致了严重的日前优化误差、高日内成本和缓慢的收敛。为了应对这些挑战，我们开发了一种多智能体、多时间尺度的用户侧资源时空协调需求响应聚合调节方法。首先，考虑用户侧资源的时空协调特征，以最大限度地降低总调节成本和配电网损失加权总和为目标，建立了一个考虑用户侧资源时空协调特征的框架；然后，针对两个不同的时间尺度（日前和日内）解决了优化问题。对于日前时间尺度，我们开发了一种改进的粒子群优化（IPSO）算法，该算法基于当日结果动态调整粒子数量以优化调节策略。对于日内时间尺度，我们开发了一种改进的交替方向乘法器（IADMM）算法，该算法将任务分配到边缘分配站，通过使用历史日前数据动态调整惩罚因子来同步规则并提高精度。仿真结果表明，该方法能充分实现日、日内多时间尺度时空协同聚合调控，有效降低总调控成本和配电网损失，增强智能电网弹性。

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引用次数: 0

NSGA-II-based load resource management for frequency and voltage support 基于nsga - ii的频率和电压支持负载资源管理

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2025.01.005

Yaxin Wang , Zhihang Zhu , Zhihong Yu , Zigan Wang

Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncertain electricity demand. To address these issues, this study proposes a load resource management (LRM) method to cope with the sudden power disturbances. The LRM method supports primary frequency and voltage regulation, and its integration with network dynamics minimizes the established disutility function caused by load participation. For better control performance, a non-dominated sorting genetic algorithm-II (NSGA-II)-based gain-tuning procedure was utilized for LRM, aiming to enhance the frequency/voltage nadir, reduce the frequency/voltage steady-state error, and minimize the total load control efforts. To validate the effectiveness of the proposed approach, comparative experiments were conducted with three load–resource management technologies for primary regulation auxiliary services in MATLAB/Simulink. Compared to the conventional optimal load control or using LRM alone, the improved NSGA-II-based LRM demonstrates superior performance. It achieves better frequency response, voltage transients, and steady-state responses, while also considering disutility.

确保稳定的频率和电压对现代电力系统来说越来越具有挑战性。这主要是由于可再生能源的波动性和间歇性以及电力需求的不确定性。针对这些问题，本研究提出一种负荷资源管理（LRM）方法来应对突发性电力干扰。LRM方法支持一次频率和电压调节，其与网络动态的集成使负荷参与引起的既定负效用函数最小化。为了获得更好的控制性能，采用基于非支配排序遗传算法ii （NSGA-II）的增益整定程序对LRM进行控制，以提高频率/电压最低点，减小频率/电压稳态误差，使总负载控制工作量最小化。为了验证所提方法的有效性，在MATLAB/Simulink中与三种主要调控辅助业务负载资源管理技术进行了对比实验。与传统的最优负载控制或单独使用LRM相比，改进的基于nsga - ii的LRM具有更优的性能。它实现了更好的频率响应、电压瞬态和稳态响应，同时也考虑了负效用。

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引用次数: 0

Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm 基于贝叶斯神经网络的光伏电池参数高效识别人工生态系统优化算法

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2025.02.001

Bo Yang , Ruyi Zheng , Yucun Qian , Boxiao Liang , Jingbo Wang

Accurate identification of unknown internal parameters in photovoltaic (PV) cells is crucial and significantly affects the subsequent system-performance analysis and control. However, noise, insufficient data acquisition, and loss of recorded data can deteriorate the extraction accuracy of unknown parameters. Hence, this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization (AEO) and a Bayesian neural network (BNN) for PV cell parameter extraction. A BNN is used for data preprocessing, including data denoising and prediction. Furthermore, the AEO algorithm is utilized to identify unknown parameters in the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). Nine other metaheuristic algorithms (MhAs) are adopted for an unbiased and comprehensive validation. Simulation results show that BNN-based data preprocessing combined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing. For instance, under denoised data, the accuracies of the SDM, DDM, and TDM increase by 99.69%, 99.70%, and 99.69%, respectively, whereas their accuracy improvements increase by 66.71%, 59.65%, and 70.36%, respectively.

准确识别光伏电池内部未知参数至关重要，对后续系统性能分析和控制具有重要意义。然而，噪声、数据采集不足和记录数据丢失会降低未知参数提取的准确性。因此，本研究提出了一种集成人工生态系统优化（AEO）和贝叶斯神经网络（BNN）的智能参数识别策略，用于光伏电池参数提取。采用神经网络进行数据预处理，包括数据去噪和预测。此外，利用AEO算法对单二极管模型（SDM）、双二极管模型（DDM）和三二极管模型（TDM）中的未知参数进行了识别。采用其他九种元启发式算法（MhAs）进行无偏和全面的验证。仿真结果表明，与未进行数据预处理的方法相比，基于神经网络的数据预处理与有效的MhAs相结合显著提高了参数提取的精度和稳定性。例如，在去噪条件下，SDM、DDM和TDM的精度分别提高了99.69%、99.70%和99.69%，精度提高幅度分别为66.71%、59.65%和70.36%。

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引用次数: 0

Two-Stage capacity allocation optimization method for user-level integrated energy systems considering user satisfaction and thermal inertia 考虑用户满意度和热惯性的用户级综合能源系统两阶段容量分配优化方法

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2024.03.001

Shunyu Li , Jing Zhang , Yu He , Gang Lv , Ying Liu , Xiangxie Hu , Zhiyang Wang , Xuan Ao

Integrated-energy systems (IESs) are key to advancing renewable-energy utilization and addressing environmental challenges. Key components of IESs include low-carbon, economic dispatch and demand response, for maximizing renewable-energy consumption and supporting sustainable-energy systems. User participation is central to demand response; however, many users are not inclined to engage actively; therefore, the full potential of demand response remains unrealized. User satisfaction must be prioritized in demand-response assessments. This study proposed a two-stage, capacity-optimization configuration method for user-level energy systems considering thermal inertia and user satisfaction. This method addresses load coordination and complementary issues within the IES and seeks to minimize the annual, total cost for determining equipment capacity configurations while introducing models for system thermal inertia and user satisfaction. Indoor heating is adjusted, for optimizing device output and load profiles, with a focus on typical, daily, economic, and environmental objectives. The study findings indicate that the system thermal inertia optimizes energy-system scheduling considering user satisfaction. This optimization mitigates environmental concerns and enhances clean-energy integration.

综合能源系统是推进可再生能源利用和应对环境挑战的关键。国际能源系统的关键组成部分包括低碳、经济调度和需求响应，以最大限度地提高可再生能源消费和支持可持续能源系统。用户参与是需求响应的核心；然而，许多用户并不倾向于积极参与；因此，需求反应的全部潜力仍未实现。在需求-响应评估中必须优先考虑用户满意度。本文提出了一种考虑热惯性和用户满意度的用户级能源系统两阶段容量优化配置方法。该方法解决了IES内部的负载协调和互补问题，并寻求在引入系统热惯性和用户满意度模型的同时，最小化确定设备容量配置的年度总成本。调整室内供暖，以优化设备输出和负载概况，重点关注典型的，日常的，经济的和环境的目标。研究结果表明，在考虑用户满意度的情况下，系统热惯性优化了能源系统调度。这种优化减轻了环境问题，提高了清洁能源的整合。

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引用次数: 0

Distributionally robust optimization-based scheduling for a hydrogen-coupled integrated energy system considering carbon trading and demand response 考虑碳交易和需求响应的氢耦合综合能源系统分布式鲁棒优化调度

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2025.02.002

Zhichun Yang , Lin Cheng , Huaidong Min , Yang Lei , Yanfeng Yang

Addressing climate change and facilitating the large-scale integration of renewable energy sources (RESs) have driven the development of hydrogen-coupled integrated energy systems (HIES), which enhance energy sustainability through coordinated electricity, thermal, natural gas, and hydrogen utilization. This study proposes a two-stage distributionally robust optimization (DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES. The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response (DR) program to adjust flexible multi-energy loads, thereby prioritizing RES consumption. Uncertainties from RES generation and load demand are addressed through an ambiguity set, enabling robust decision-making. The column-and-constraint generation (C&CG) algorithm efficiently solves the two-stage DRO model. Case studies demonstrate that the proposed method reduces operational costs by 3.56%, increases photovoltaic consumption rates by 5.44%, and significantly lowers carbon emissions compared to conventional approaches. Furthermore, the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods, highlighting its potential to advance cost-effective, low-carbon energy systems while ensuring grid stability under uncertainty.

应对气候变化和促进可再生能源（RESs）的大规模整合推动了氢耦合综合能源系统（HIES）的发展，通过协调电力、热能、天然气和氢的利用来提高能源的可持续性。本文提出了一种基于两阶段分布鲁棒优化（DRO）的调度方法，以提高HIES的经济效率和减少碳排放。该框架采用阶梯型碳交易机制来调节排放，并实施需求响应（DR）计划来调整灵活的多能源负荷，从而优先考虑可再生能源的消费。通过模糊集解决可再生能源发电和负荷需求的不确定性，从而实现稳健的决策。柱约束生成（C&；CG）算法有效地求解了两阶段DRO模型。案例研究表明，与传统方法相比，该方法降低了3.56%的运营成本，提高了5.44%的光伏消费率，并显著降低了碳排放。此外，与传统的随机和鲁棒优化方法相比，DRO框架在保守性和鲁棒性之间取得了卓越的平衡，突出了其在推进低成本、低碳能源系统的潜力，同时确保了不确定性下电网的稳定性。

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引用次数: 0

Lessons from the development and operational experiences of international carbon markets for the construction of China’s carbon market 借鉴国际碳市场的发展和运行经验，为中国碳市场的建设提供借鉴

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2024.12.002

Qingkai Sun , Li Ma , Menghua Fan , Xiaojun Wang , Zheng Zhao , Yiru Shi

With the intensifying global climate crisis, carbon emissions trading has emerged as a crucial market-based instrument for emissions reduction, attracting significant attention from government agencies and academia worldwide. As of January 2024, 28 carbon trading markets have been established globally, encompassing approximately 17% of global greenhouse gas emissions and serving approximately 1/3 of the global population. With various nations setting carbon neutrality targets and delineating carbon reduction pathways, the construction, operation, and regulatory frameworks of carbon markets are becoming increasingly refined and comprehensive. This study elucidates the importance and necessity of establishing carbon markets from the perspective of energy system transformation and sustainable economic development. Second, it provides a comparative analysis of the operational mechanisms, trading scales, and emission reduction outcomes of major carbon markets in the European Union, United States, and New Zealand, systematically summarizing their development processes and recent advancements. Finally, this study addresses issues and challenges in the construction of China’s carbon market. Drawing on the successful experiences of leading global carbon markets in institutional design and market operations, we propose development strategies and recommendations for a carbon market with Chinese characteristics. These strategies are intended to align with international standards while meeting China’s national conditions, thereby contributing insights into the global carbon market trading system.

随着全球气候危机的加剧，碳排放交易作为一种重要的以市场为基础的减排手段，受到了各国政府机构和学术界的广泛关注。截至2024年1月，全球已建立28个碳交易市场，约占全球温室气体排放量的17%，服务于全球约三分之一的人口。随着各国制定碳中和目标和碳减排路径，碳市场的建设、运行和监管框架日益完善和全面。本研究从能源体系转型和经济可持续发展的角度阐述了建立碳市场的重要性和必要性。其次，对欧盟、美国和新西兰主要碳市场的运行机制、交易规模和减排成果进行了比较分析，系统总结了其发展历程和最新进展。最后，本文提出了中国碳市场建设中存在的问题和面临的挑战。借鉴全球领先碳市场在制度设计和市场运作方面的成功经验，提出中国特色碳市场的发展策略和建议。这些战略旨在与国际标准接轨，同时符合中国的国情，从而为了解全球碳市场交易体系做出贡献。

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引用次数: 0

Research on the coordinated optimization of energy storage and renewable energy in off-grid microgrids under new electric power systems 新型电力系统下离网微电网储能与可再生能源协同优化研究

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2024.12.004

Zhuoran Song , Mingli Zhang , Yuanying Chi , Jialin Li , Yi Zheng

The supply of electricity to remote regions is a significant challenge owing to the pivotal transition in the global energy landscape. To address this issue, an off-grid microgrid solution integrated with energy storage systems is proposed in this study. Off-grid microgrids are self-sufficient electrical networks that are capable of effectively resolving electricity access problems in remote areas by providing stable and reliable power to local residents. A comprehensive review of the design, control strategies, energy management, and optimization of off-grid microgrids based on domestic and international research is presented in this study. It also explores the critical role of energy storage systems in enhancing microgrid stability and economic efficiency. Additionally, the capacity configurations of energy storage systems within off-grid networks are analyzed. Energy storage systems not only mitigate the intermittency and volatility of renewable energy generation but also supply power support during peak demand periods, thereby improving grid stability and reliability. By comparing different energy storage technologies, such as lithium-ion batteries, pumped hydro storage, and compressed air energy storage, the optimal energy storage capacity configurations tailored to various application scenarios are proposed in this study. Finally, using a typical microgrid as a case study, an empirical analysis of off-grid microgrids and energy storage integration has been conducted. The optimal configuration of energy storage systems is determined, and the impact of wind and solar power integration under various scenarios on grid balance is explored. It has been found that a rational configuration of energy storage systems can significantly enhance the utilization rate of renewable energy, reduce system operating costs, and strengthen grid resilience under extreme conditions. This study provides essential theoretical support and practical guidance for the design and implementation of off-grid microgrids in remote areas.

由于全球能源格局的关键转变，向偏远地区供电是一项重大挑战。为了解决这一问题，本研究提出了一种与储能系统集成的离网微电网解决方案。离网微电网是一种自给自足的电网，能够通过向当地居民提供稳定可靠的电力，有效解决偏远地区的电力接入问题。本文以国内外研究为基础，对离网微电网的设计、控制策略、能源管理和优化进行了综述。它还探讨了储能系统在提高微电网稳定性和经济效率方面的关键作用。此外，还对离网储能系统的容量配置进行了分析。储能系统不仅可以缓解可再生能源发电的间歇性和波动性，还可以在需求高峰期提供电力支持，从而提高电网的稳定性和可靠性。通过对锂离子电池、抽水蓄能、压缩空气储能等不同储能技术的对比，提出了适合不同应用场景的最佳储能容量配置方案。最后，以某典型微网为例，对离网微网与储能一体化进行了实证分析。确定了储能系统的最优配置，探讨了不同场景下风电与太阳能并网对电网平衡的影响。研究发现，合理配置储能系统可以显著提高可再生能源利用率，降低系统运行成本，增强电网在极端条件下的弹性。本研究为偏远地区离网微电网的设计与实现提供了必要的理论支持和实践指导。

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引用次数: 0

Grouping control of electric vehicles based on improved golden eagle optimization for peaking 基于改进金鹰优化的电动汽车调峰分组控制

IF 1.9 Q4 ENERGY & FUELS

Global Energy Interconnection

Pub Date : 2025-04-01 DOI: 10.1016/j.gloei.2024.06.011

Yang Yu , Yuhang Huo , Shixuan Gao , Qian Wu , Mai Liu , Xiao Chen , Xiaoming Zheng , Xinlei Cai

To address the problem of high lifespan loss and poor state of charge (SOC) balance of electric vehicles (EVs) participating in grid peak shaving, an improved golden eagle optimizer (IGEO) algorithm for EV grouping control strategy is proposed for peak shaving scenarios. First, considering the difference between peak and valley loads and the operating costs of EVs, a peak shaving model for EVs is constructed. Second, the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer (GEO) algorithm. Subsequently, IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions. Next, using the k-means algorithm, EVs are dynamically divided into priority charging groups, backup groups, and priority discharging groups based on SOC differences. Finally, a dual layer power distribution scheme for EVs is designed. The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs, whereas the lower layer allocates the charging and discharging instructions for each group to each EV. The proposed strategy was simulated and verified, and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy. The proposed EV grouping control strategy effectively reduces the peak–valley difference in the power grid, reduces the operational life loss of EVs, and maintains a better SOC balance for EVs.

针对参与电网调峰的电动汽车寿命损失大、荷电平衡差的问题，提出了一种改进的针对调峰场景的电动汽车分组控制策略的金鹰优化算法（IGEO）。首先，考虑峰谷负荷差异和电动汽车运行成本，构建电动汽车调峰模型；其次，IGEO的设计提高了金鹰优化器（GEO）算法的全局探索和局部开发能力。随后，利用IGEO求解调峰模型，得到整体电动汽车并网充放电指令。其次，采用k-means算法，根据荷电状态的差异，将电动汽车动态划分为优先充电组、备用组和优先放电组。最后，设计了电动汽车的双层配电方案。上层确定三组电动汽车的充放电顺序和指令，下层将每组电动汽车的充放电指令分配给每辆电动汽车。仿真结果表明，所设计的IGEO具有更快的优化速度和更高的优化精度。所提出的电动汽车分组控制策略有效地减小了电网的峰谷差，降低了电动汽车的运行寿命损失，并保持了电动汽车更好的荷电平衡。

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引用次数: 0