Electric Power Systems Research最新文献

Forecasting data-driven system strength level for inverter-based resources-integrated weak grid systems using multi-objective machine learning algorithms 利用多目标机器学习算法预测基于逆变器的资源整合弱电网系统的数据驱动型系统强度水平

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-30 DOI: 10.1016/j.epsr.2024.111112

Shortage of grid-fault level, known as system strength inadequacy, impacts on grid instability and can lead to blackouts. System strength is generally measured by short circuit ratio index at point of coupling (POC) of inverter-based resources (IBRs) and the grid system. Nowadays, accurate knowledge of system strength forecasting for ‘next day’ to ‘next week’ duration is essential to power system operators, owing to the higher-growth of IBRs. However, releavant publications about this subject remain limited when compared with load demand, active and reactive power prediction. Therefore, a data-driven system strength forecasting scheme is presented in this paper to surmount these issues. Multi-objective machine learning (MOML) algorithms are used to obtain the best result. The designed model uses energy management system (EMS) to collect historical online data and complete the training and testing procedures via learning frameworks such as Hedge-backpropagation neural network-based tangent function (Hedge-BPNNT), support vector machine (SVM) and long short-term memory (LSTM). The methodology is developed to predict up to seven days of system strength forecasting levels by using the last thirty-days data status. The designed model is tested on both simulated and experimented cases, confirming higher accuracy performance with reduced computational time when compared to existing literature.

电网故障等级不足，即系统强度不足，会影响电网的不稳定性，并可能导致停电。系统强度一般通过逆变器资源（IBR）与电网系统耦合点（POC）的短路比指数来衡量。如今，由于 IBR 的快速增长，准确预测 "次日 "至 "下周 "的系统强度对于电力系统运营商来说至关重要。然而，与负荷需求、有功和无功功率预测相比，有关这一主题的出版物仍然有限。因此，本文提出了一种数据驱动的系统强度预测方案来解决这些问题。本文采用多目标机器学习（MOML）算法来获得最佳结果。所设计的模型利用能源管理系统（EMS）收集历史在线数据，并通过基于切线函数（Hedge-BPNNT）、支持向量机（SVM）和长短期记忆（LSTM）等学习框架完成训练和测试程序。所开发的方法可利用过去三十天的数据状况预测长达七天的系统强度预报水平。对所设计的模型进行了模拟和实验测试，结果表明，与现有文献相比，该模型具有更高的准确性，而且计算时间更短。

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引用次数: 0

Short-term load forecasting by GRU neural network and DDPG algorithm for adaptive optimization of hyperparameters 利用 GRU 神经网络和 DDPG 算法自适应优化超参数进行短期负荷预测

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-30 DOI: 10.1016/j.epsr.2024.111119

Short-term load forecasting (STLF) is critical to optimizing power system operation. Deep learning (DL) methods can provide extremely high accuracy for STLF. However, most models in existing research lack adaptive optimization capabilities in the prediction process and suffer from performance degradation. To resolve the above difficulties, we propose a hybrid model (DDPG-GRU) based on gated recurrent units and deep deterministic policy gradients for STLF. First, the GRU network has the advantage of processing multiple time series inputs and can simultaneously consider multi-dimensional load characteristics, thereby making the model more efficient. Since the GRU model structure is relatively complex, choosing a good set of hyperparameters is very difficult. Therefore, the purpose of using DDPG is to optimize the hyperparameters of the GRU model adaptively. The proposed model is a combination of DL methods and reinforcement learning. In order to prove the superiority of the proposed model, it is applied to the load data of Area 1 in China to perform single-step and multi-step load forecasting, respectively. The results show that DDPG-GRU has a better fitting effect than the baseline method. Taking the multi-step prediction results as an example, compared with the classic GRU network, the MAPE, MAE, and RMSE of the proposed model are reduced by 22.75 %, 14.44 %, and 14.02 %, respectively, while the R² coefficient is increased by 13.23 %. At the same time, we use the China Area 2 data set to verify the universality of the proposed model. Furthermore, we compared the proposed method with state-of-the-art methods and achieved better accuracy.

短期负荷预测（STLF）对于优化电力系统运行至关重要。深度学习（DL）方法可为 STLF 提供极高的精度。然而，现有研究中的大多数模型在预测过程中缺乏自适应优化能力，存在性能下降的问题。为解决上述难题，我们提出了一种基于门控递归单元和深度确定性策略梯度的 STLF 混合模型（DDPG-GRU）。首先，GRU 网络具有处理多个时间序列输入的优势，可以同时考虑多维负载特征，从而使模型更加高效。由于 GRU 模型结构相对复杂，选择一组好的超参数非常困难。因此，使用 DDPG 的目的是自适应地优化 GRU 模型的超参数。所提出的模型是 DL 方法与强化学习的结合。为了证明所提模型的优越性，将其应用于中国 1 区的负荷数据，分别进行单步和多步负荷预测。结果表明，DDPG-GRU 的拟合效果优于基准方法。以多步预测结果为例，与经典 GRU 网络相比，所提模型的 MAPE、MAE 和 RMSE 分别降低了 22.75%、14.44% 和 14.02%，R2 系数提高了 13.23%。同时，我们使用中国 2 区数据集验证了所提模型的通用性。此外，我们还将所提出的方法与最先进的方法进行了比较，结果表明所提出的方法具有更高的准确性。

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引用次数: 0

Topology design of variable speed drive systems for enhancing power quality in industrial grids 提高工业电网电能质量的变速驱动系统拓扑设计

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-30 DOI: 10.1016/j.epsr.2024.111114

In the last two decades, a rapid increase in the utilization of non-linear loads within electrical grids has been observed. Consequently, elevated levels of harmonics are found in both voltage and current waves, and adverse effects on power quality are caused. In this context, the variable speed drive (VSD) systems are considered a significant non-linear contributor. To mitigate the harmonic content in VSD systems, various techniques are explored, such as electronic smoothing inductor incorporation in the DC-link, active filters utilization at the grid side, and passive filters integration. A technique centered on the reconfiguration of the DC-link in the VSD systems is proposed in this paper to improve the overall performance of the VSD systems. The configuration comprises two transistors and two diodes, along with smoothing inductors and a capacitor to enhance power quality in the VSD systems. The utilization of three-stage sine pulse width modulation (SPWM) control technology ensures accurate control of the switches, generating optimal control signals that enhance the power quality of the voltage and current waves at the grid side. The effectiveness of the proposed approach is tested via time-domain simulation in MATLAB/Simulink under both constant and variable loading conditions and is verified using a laboratory prototype. The obtained results demonstrate a notable improvement in power quality, showcasing reduced total harmonic distortion (THD) in AC voltage and current waveforms, as well as minimized ripple factor in the DC-link when compared to existing methods.

近二十年来，电网中非线性负载的使用率迅速上升。因此，电压波和电流波中的谐波水平升高，对电能质量造成不利影响。在这种情况下，变速驱动（VSD）系统被认为是一个重要的非线性因素。为了减轻 VSD 系统中的谐波含量，人们探索了各种技术，如在直流链路中加入电子平滑电感器、在电网侧使用有源滤波器和集成无源滤波器。本文提出了一种以 VSD 系统直流链路重新配置为中心的技术，以提高 VSD 系统的整体性能。该配置包括两个晶体管和两个二极管，以及一个平滑电感器和一个电容器，以提高 VSD 系统的电能质量。利用三级正弦脉宽调制（SPWM）控制技术可确保开关的精确控制，产生最佳控制信号，从而提高电网侧电压波和电流波的电能质量。在恒定和可变负载条件下，通过 MATLAB/Simulink 时域仿真测试了所提方法的有效性，并使用实验室原型进行了验证。结果表明，与现有方法相比，该方法显著改善了电能质量，降低了交流电压和电流波形中的总谐波失真（THD），并最大限度地降低了直流链路中的纹波系数。

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引用次数: 0

Physics-informed machine learning for forecasting power exchanges at the interface between transmission and distribution systems 用于预测输电和配电系统接口处电力交换的物理信息机器学习

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-28 DOI: 10.1016/j.epsr.2024.111097

Power exchanges at Transmission–Distribution interfaces are crucial for both the Transmission System Operators (TSOs) and the Distribution System Operators (DSOs). In the past, simple hypothesis as a constant power factor sufficed for characterizing distribution networks and predicting power flows at Transmission–Distribution interfaces. However, the growing integration of distributed energy resources has led to an increased volatility in both active and reactive power flows, rendering traditional models less effective. This study presents a novel Physics-Informed Machine Learning (PIML) model designed to enhance the prediction of power exchanges at Transmission–Distribution interfaces. A novelty of the model lies in its combination of an Inverse Load Flow formulation, which defines an equivalent model of the distribution network (by calculating equivalent resistance and reactance using load flow equations), with classical data-driven regression techniques. Simulation results conducted on a modified version of the Oberrhein MV network highlight the superiority of the proposed PIML approach in front of full ML based methods, as demonstrated by a statistical indicator and an application-oriented evaluation. In addition, this research adopts the TSO perspective through a 2-step Optimal Power Flow analysis that integrates interface power predictions and enables the calculation of production and deviation costs. This multifaceted approach provides valuable insights into the practical implications of the power prediction accuracy on the TSO decision-making process and underscores the significance of accurate power exchange forecasts in the evolving electricity landscape.

输电-配电接口的电力交换对输电系统运营商（TSO）和配电系统运营商（DSO）都至关重要。过去，恒定功率因数这一简单假设足以描述配电网络的特征并预测输电-配电接口处的功率流。然而，分布式能源资源的日益整合导致有功和无功功率流的波动性增加，使传统模型的有效性大打折扣。本研究提出了一种新颖的物理信息机器学习（PIML）模型，旨在增强对输电-配电接口处电力交换的预测。该模型的新颖之处在于将反负载流公式（通过使用负载流方程计算等效电阻和电抗来定义配电网络的等效模型）与经典的数据驱动回归技术相结合。通过统计指标和以应用为导向的评估，在修改版的上莱茵中压网络上进行的仿真结果表明，建议的 PIML 方法优于基于完全 ML 的方法。此外，本研究还从 TSO 的角度出发，通过两步优化功率流分析，整合了接口功率预测，并实现了生产和偏差成本的计算。这种多方面的方法为电力预测准确性对 TSO 决策过程的实际影响提供了宝贵的见解，并强调了准确的电力交换预测在不断变化的电力环境中的重要性。

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引用次数: 0

An AC Z-bus-based distribution factors for contingency analysis of AC–DC networks 用于交直流网络突发事件分析的基于交流 Z 总线的配电因子

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-27 DOI: 10.1016/j.epsr.2024.111104

The AC–DC networks are susceptible to deliberate and unforeseen contingencies, much like any other traditional power network. Due to the complex form of the unified impedance matrix derived for the provided network, the classic Z-bus-based contingency analysis approach cannot comprehend the AC–DC network. The distribution factors thus obtained from a unified complex Z-bus lead to an indication of the flow of complex current for DC lines. Therefore, an alternative method to discover post-contingency consequences is looked for. A streamlined approach for visualizing an equivalent AC network for the given AC–DC network has been proposed in this work. The AC-equivalent network takes the original AC–DC network and recreates it using two parallel AC networks to get the requisite distribution factors. The ultimate current/power distributions of the original AC–DC network can be determined by applying the superposition rule to the parallel networks, by the linear characteristics of the suggested technique. With this, the standard AC-contingency analysis method to complete the steady-state contingency analysis for the AC–DC network can be utilized. The proposed approach has been validated by the encouraging outcomes achieved for AC–DC networks with 10, 13, and 66 buses.

交直流网络与其他传统电力网络一样，容易受到有意和意外突发事件的影响。由于所提供网络的统一阻抗矩阵形式复杂，基于 Z 总线的经典突发事件分析方法无法理解交直流网络。因此，从统一的复杂 Z 总线中获得的分布系数可显示直流线路的复杂电流流向。因此，需要寻找一种替代方法来发现突发事件后的后果。本研究提出了一种简化的方法，可视化给定交直流网络的等效交流网络。交流等效网络采用原始交直流网络，并使用两个并行交流网络重新创建，以获得所需的分布系数。根据所建议技术的线性特征，通过对并联网络应用叠加规则，可确定原始交直流网络的最终电流/功率分布。这样，就可以利用标准交流突发事件分析方法完成交直流网络的稳态突发事件分析。在 10、13 和 66 个母线的交直流网络中取得的令人鼓舞的结果验证了所建议的方法。

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引用次数: 0

Impedance modeling and quantitative stability analysis of grid-connected voltage source converters under complex unbalanced conditions 复杂不平衡条件下并网电压源转换器的阻抗建模和定量稳定性分析

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-27 DOI: 10.1016/j.epsr.2024.111084

The stability issues caused by voltage source converters (VSCs), which are commonly used in renewable energy systems, are investigated in this paper. Much literature has investigated the impedance models and quantitative stability analysis for converters under the weak and unbalanced grid. However, on the one hand, harmonic-transfer-function (HTF)-based modeling methods are complex, and the established infinite-order models may suffer from unreasonable truncation. On the other hand, these existing impedance models without considering all unbalance factors are inaccurate. Both of these may bring wrong stability analysis results. Therefore, this paper proposes a simple impedance extension method and considers all unbalance factors to derive the impedance model of the converter. However, due to the established higher-order impedance model, the traditional impedance-ratio based stability analysis method is not applicable to multi-input multi-output (MIMO) systems. The commonly used eigenvalue-based GNC and diagonalization-based methods applicable to MIMO systems are cumbersome and not suitable for quantitative analysis. Therefore, in this paper, a quantitative analysis tool of system stability based on the phase-frequency characteristics of determinants is developed. Then, stability regions in the multi-parameter space are obtained. Finally, the established impedance model and the quantitative analysis results are validated via both simulations and hardware experiments.

本文研究了可再生能源系统中常用的电压源变流器（VSC）引起的稳定性问题。许多文献研究了弱电网和不平衡电网下变流器的阻抗模型和定量稳定性分析。然而，一方面，基于谐波传递函数（HTF）的建模方法比较复杂，而且已有的无穷阶模型可能存在不合理截断的问题。另一方面，这些现有的阻抗模型没有考虑所有的不平衡因素，是不准确的。这两种情况都可能带来错误的稳定性分析结果。因此，本文提出了一种简单的阻抗扩展方法，并考虑所有不平衡因素来推导变流器的阻抗模型。然而，由于已经建立了高阶阻抗模型，传统的基于阻抗比的稳定性分析方法不适用于多输入多输出 (MIMO) 系统。常用的基于特征值的 GNC 和基于对角线化的方法适用于 MIMO 系统，但这些方法比较繁琐，不适合定量分析。因此，本文开发了一种基于行列式相频特性的系统稳定性定量分析工具。然后，得到多参数空间中的稳定区域。最后，通过模拟和硬件实验验证了所建立的阻抗模型和定量分析结果。保留所有权利。

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引用次数: 0

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station 可再生电网集成充电站中的动态定价策略与 PEV 充电协调

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-27 DOI: 10.1016/j.epsr.2024.111105

The increasing penetration of Plug-in Electric Vehicles (PEV) in the transportation system has increased the burden on the power system. This has made peak load demand management a challenging task for the power grid. To address this issue, a novel dynamic demand response pricing strategy in a grid-renewable generation integrated charging station environment is proposed in this paper. Renewable energy sources reduce the cost of generation and grid integration makes the system reliable. The proposed strategy models a Stackelberg game to provide dynamic prices for charging, discharging and grid power supplied for charging stations. Uncertainty and economics of renewable generation are considered for effective analysis and evaluation of the feasibility of the proposed strategy. The study considers the bidirectional flow of power and the battery degradation cost. Charging coordination is performed to optimize the cost of charging and discharging and support the grid in peak load demand management. Random charging behaviour and other parameters of PEVs are simulated using a random distribution function to resemble the real-time environment. A numerical case study validates that the proposed strategy has optimized the cost of charging and discharging and the serving capabilities of the charging station are enhanced with existing infrastructure.

插电式电动汽车（PEV）在交通系统中的普及率越来越高，加重了电力系统的负担。这使得高峰负荷需求管理成为电网的一项挑战性任务。为解决这一问题，本文提出了一种在电网-可再生能源发电一体化充电站环境下的新型动态需求响应定价策略。可再生能源降低了发电成本，而电网一体化则提高了系统的可靠性。所提出的策略以斯泰克尔伯格博弈为模型，为充电站的充电、放电和电网供电提供动态价格。考虑了可再生能源发电的不确定性和经济性，以有效分析和评估拟议战略的可行性。研究考虑了电力的双向流动和电池退化成本。通过充电协调来优化充放电成本，并支持电网进行高峰负荷需求管理。使用随机分布函数模拟 PEV 的随机充电行为和其他参数，以模拟实时环境。数值案例研究验证了所提出的策略优化了充放电成本，并利用现有基础设施提高了充电站的服务能力。

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引用次数: 0

Low frequency residential load monitoring via feature fusion and deep learning 通过特征融合和深度学习监测低频住宅负载

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-26 DOI: 10.1016/j.epsr.2024.111092

Non-intrusive load monitoring (NILM) is a technique used to disaggregate the total power signal into individual appliance power signals, which plays an important role in smart grid. Recently, deep learning is widely used to deal with the NILM problem. However, current deep learning models are purely data-driven, which do not consider physical mechanisms, making them less effective in extracting useful features. To address these issues, a new approach for feature extraction based on variational mode decomposition (VMD) and a new deep learning model based on variational autoencoder (VAE) are developed in this paper. The proposed feature extraction approach extracts the pulse feature and concatenates it with the original power data to form multiple features, i.e., which achieves feature fusion to improve the performance of deep learning models better than with a single feature. In addition, a feedback variational mode decomposition (FVMD) is proposed to improve the decomposition performance of the original VMD. The channel attention mechanism is introduced to VAE to improve the performance of the model. To verify the accuracy and robustness of the proposed scheme in NILM, it is compared with the state-of-the-art models on the UK-DALE dataset, and the results show that the proposed feature extraction approach can greatly improve the performance of deep learning models and the proposed new deep learning model outperforms some state-of-the-art models in the realm of NILM.

非侵入式负荷监测（NILM）是一种用于将总功率信号分解为单个家电功率信号的技术，在智能电网中发挥着重要作用。最近，深度学习被广泛用于处理 NILM 问题。然而，目前的深度学习模型是纯数据驱动的，没有考虑物理机制，因此在提取有用特征方面效果不佳。为解决这些问题，本文开发了一种基于变异模式分解（VMD）的新特征提取方法和一种基于变异自动编码器（VAE）的新深度学习模型。所提出的特征提取方法可提取脉冲特征，并将其与原始功率数据串联形成多个特征，即实现特征融合，从而比单一特征更好地提高深度学习模型的性能。此外，还提出了反馈变分模式分解（FVMD），以提高原始 VMD 的分解性能。在 VAE 中引入了通道注意机制，以提高模型的性能。为了验证所提方案在NILM中的准确性和鲁棒性，我们在UK-DALE数据集上将其与最先进的模型进行了比较，结果表明所提的特征提取方法可以大大提高深度学习模型的性能，所提的新深度学习模型在NILM领域的表现优于一些最先进的模型。

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引用次数: 0

Quantifying the impact of flexibility asset location on services in the distribution grid: Power system and local flexibility market co-simulation 量化灵活性资产位置对配电网服务的影响：电力系统和本地灵活性市场联合模拟

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-25 DOI: 10.1016/j.epsr.2024.111037

This research investigates the effectiveness of incorporating locational sensitivity factors into local flexibility market clearing mechanisms for effective congestion management and voltage regulation in distribution grids. A centralized local flexibility market optimization model is developed that considers technical and economic constraints. The study aims to explore the requirements for data availability, data quality, and reliable data exchange that can facilitate a broader range of flexibility services, thereby promoting the development of a local flexibility market. Sensitivity factors, including power transfer distribution factors, voltage sensitivity coefficients and transformer sensitivity coefficients, are used to quantify the impact of flexibility asset locations on congestion management and inform clearing rules. These static metrics are insufficient for establishing effective local flexibility market clearing rules when flexibility is procured by distribution system operators. The approach considers the state of the grid when calculating the sensitivity coefficients, which leads to a more accurate evaluation of flexibility bids, especially with regard to the impact of location on congestion management. The proposed mechanism for clearing the local flexibility market assumes continuous communication between the proposed local flexibility market operator and the distribution system operator for dynamic, iterative market clearing, which ensures the protection of grid data and a more accurate bid evaluation. The study demonstrates that the inclusion of locational information significantly increases the effectiveness of the proposed local flexibility market-based congestion management. The developed simulator for the proposed local flexibility market provides valuable insights into the interaction between the proposed local flexibility market and the distribution grid. The research results, derived from selected use cases, emphasize the importance of location-based sensitivity factors in the proposed local flexibility market clearing for distribution grids. The proposed approach offers a promising solution for optimizing congestion management and voltage regulation while ensuring efficient integration of distributed energy resources into distribution grids.

本研究探讨了将地点敏感性因素纳入本地灵活性市场清算机制的有效性，以实现配电网中有效的拥塞管理和电压调节。研究开发了一个集中式本地灵活性市场优化模型，该模型考虑了技术和经济约束条件。该研究旨在探索数据可用性、数据质量和可靠数据交换的要求，以促进更广泛的灵活性服务，从而推动本地灵活性市场的发展。灵敏度系数，包括功率传输分配系数、电压灵敏度系数和变压器灵敏度系数，用于量化灵活性资产位置对拥塞管理的影响，并为清算规则提供依据。当灵活性由配电系统运营商采购时，这些静态指标不足以建立有效的本地灵活性市场清算规则。该方法在计算敏感系数时考虑了电网状态，从而更准确地评估灵活性投标，特别是位置对拥塞管理的影响。拟议的本地灵活性市场清算机制假定，拟议的本地灵活性市场运营商与配电系统运营商之间将进行持续通信，以实现动态、迭代式市场清算，从而确保电网数据的保护和更准确的投标评估。研究表明，纳入定位信息可显著提高基于本地灵活性市场的拥塞管理效率。为拟议的本地灵活性市场开发的模拟器为拟议的本地灵活性市场与配电网之间的互动提供了宝贵的见解。从选定的使用案例中得出的研究结果强调了基于位置的敏感性因素在拟议的配电网本地灵活性市场清算中的重要性。所提出的方法为优化拥塞管理和电压调节提供了一种有前途的解决方案，同时确保了分布式能源资源与配电网的有效整合。

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引用次数: 0

Distributed economic operation control in low-voltage resistive hybrid AC/DC microgrid clusters with interlinking converters 带互联变流器的低压电阻式交直流混合微电网集群的分布式经济运行控制

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research

Pub Date : 2024-09-25 DOI: 10.1016/j.epsr.2024.110971

Connecting adjacent and diverse microgrids to form hybrid microgrid clusters improve the economic viability and the reliability of each microgrid in islanded operations. To realize the global economic operation among low-voltage resistive microgrid clusters, a two-level control strategy for subgrid control and microgrid clusters control is proposed in this study. In the case of subgrids, where the line impedance of the low-voltage grid is primarily resistive, we design the AC voltage increment droop (V_ac-IC) and the DC voltage increment droop (V_dc-IC) to effectively distribute power. The V_ac-IC and V_dc-IC droop controls cannot achieve economic power distribution due to the mismatched line impedances. Therefore, a distributed economic operation control based on the consensus algorithm is proposed in this study to eliminate the influence of mismatched line impedances on economic power distribution. In the context of microgrid clusters, the deviation of increment costs serves as an event-driven signal designed for Interlinking Converters (ILCs), aiming to optimize power exchange between adjacent subgrids. The coordination between the subgrid control and microgrid clusters control achieves global economic operation. Finally, Matlab/Simulink simulation verifies that the islanded hybrid microgrid clusters with the proposed strategy realize the economic operation well.

将相邻的不同微电网连接起来，形成混合微电网群，可以提高经济可行性和每个微电网在孤岛运行时的可靠性。为实现低压电阻微电网群之间的全局经济运行，本研究提出了子电网控制和微电网群控制的两级控制策略。在子电网中，低压电网的线路阻抗主要是电阻性的，我们设计了交流电压增量下垂（Vac-IC）和直流电压增量下垂（Vdc-IC）来有效分配电能。由于线路阻抗不匹配，Vac-IC 和 Vdc-IC 降压控制无法实现经济功率分配。因此，本研究提出了一种基于共识算法的分布式经济运行控制，以消除不匹配线路阻抗对经济功率分配的影响。在微电网集群背景下，增量成本偏差作为事件驱动信号，专为互联转换器（ILC）设计，旨在优化相邻子电网之间的电力交换。子电网控制与微电网集群控制之间的协调实现了全局经济运行。最后，Matlab/Simulink 仿真验证了采用所提策略的孤岛式混合微电网集群能很好地实现经济运行。

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引用次数: 0