IET Energy Systems Integration最新文献_第3页

A distributed robust state estimation method based on alternating direction method of multipliers for integrated electricity‐heat system 基于交变方向乘法的分布式鲁棒状态估计方法，适用于电热一体化系统

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-02-19 DOI: 10.1049/esi2.12133

Yanbo Chen, Yulong Gao, Zhe Fang, Jiaqi Li, Zhenda Hu, Yichao Zou, Jin Ma, Chunlai Li, Qinze Xiao, Zeyu Chen

Integrated electricity‐heat system (IEHS) has been paid more and more attention in recent years for its advantage in improving energy efficiency, reducing carbon emissions and increasing renewable energy penetration. To ensure the safety, reliability and economic operation of IEHS, several centralised state estimation (SE) methods for IEHS have been proposed. However, power systems and heat systems often belong to different management entities, and there are industrial barriers such as information privacy, operational differences, and target differences between them, which leads to less applicability of centralised SE methods. In addition, the robustness of existing distributed SE methods for IEHS is not satisfactory. To this end, a distributed robust state estimation (DRSE) model for IEHS based on the alternating direction method of multipliers (ADMM) is proposed. Firstly, by introducing auxiliary state variables and measurements, a robust linear SE model based on weighted least absolute values (WLAV) is proposed. Then, second‐order cone constraints composed of auxiliary state variables are added to the SE model, leading a SOCP‐based robust SE model. Finally, the ADMM algorithm is used to solve the proposed SOCP‐based robust SE model. Simulations demonstrate that the proposed method has higher estimation accuracy in both general and strongly correlated adverse data tests and also can ensure data privacy, good robustness and high estimation accuracy. This indicates that the method proposed has good robustness and solves the problem of weak robustness of existing distributed static state estimation methods.

近年来，电热一体化系统（IEHS）因其在提高能源效率、减少碳排放和提高可再生能源渗透率方面的优势而受到越来越多的关注。为了确保 IEHS 的安全性、可靠性和经济性，人们提出了几种针对 IEHS 的集中状态估计（SE）方法。然而，电力系统和热力系统往往属于不同的管理实体，两者之间存在信息隐私、运行差异和目标差异等行业障碍，导致集中式状态估计方法的适用性较低。此外，现有的 IEHS 分布式 SE 方法的鲁棒性也不尽如人意。为此，本文提出了一种基于交替乘法（ADMM）的 IEHS 分布式鲁棒状态估计（DRSE）模型。首先，通过引入辅助状态变量和测量值，提出了基于加权最小绝对值（WLAV）的鲁棒性线性 SE 模型。然后，在 SE 模型中加入由辅助状态变量组成的二阶锥约束，从而建立了基于 SOCP 的鲁棒 SE 模型。最后，使用 ADMM 算法求解所提出的基于 SOCP 的鲁棒 SE 模型。仿真结果表明，所提出的方法在一般和强相关的不利数据测试中都具有较高的估计精度，同时还能确保数据的私密性、良好的鲁棒性和较高的估计精度。这表明所提出的方法具有良好的鲁棒性，解决了现有分布式静态状态估计方法鲁棒性较弱的问题。

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引用次数: 0

Research on future trends of electricity consumption based on conditional generative adversarial network considering dual‐carbon target 基于条件生成式对抗网络的未来用电趋势研究（考虑双碳目标

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-02-12 DOI: 10.1049/esi2.12138

Jinghua Li, Zibei Qin, Yichen Luo, Jianfeng Chen, Shanyang Wei

The emergence of novel factors, such as the energy Internet and electricity supply‐side reform within the context of the dual‐carbon target (carbon peaking and carbon neutrality), has heightened the uncertainty surrounding electricity consumption (EC). This increased uncertainty poses challenges for accurate long‐term EC forecasting. Due to the complexities of feature extraction and the absence of labelled data, conventional supervised learning‐based forecasting methods, such as support vector machines (SVM) and long short‐term memory networks (LSTM), struggle to predict EC with precision in situations of heightened uncertainty resulting from the interplay of multiple factors. To address this issue, a novel method based on a conditional generative adversarial network (CGAN) is proposed. Initially, the dominant factors influencing future electricity consumption trends through grey correlation degree analysis and the K‐L information method are identified. Subsequently, an EC forecast model is introduced based on CGAN, adept at capturing essential factors and the non‐linear relationship between EC and exogenous factors. This approach effectively models the uncertainty of EC, accurately approximating the true distribution with only a small dataset. Finally, the proposed method by forecasting China's EC from 2015 to 2020 is validated. The results demonstrate that the authors’ method achieves lower root mean square error and mean absolute percentage error values, specifically 0.177% and 2.39%, respectively, outperforming established advanced methods such as SVM and LSTM.

在双碳目标（碳调峰和碳中和）背景下，能源互联网和电力供应侧改革等新因素的出现，增加了电力消费（EC）的不确定性。这种不确定性的增加给准确的长期用电量预测带来了挑战。由于特征提取的复杂性和标记数据的缺失，传统的基于监督学习的预测方法，如支持向量机（SVM）和长短期记忆网络（LSTM），在多种因素相互作用导致不确定性增加的情况下，很难准确预测用电量。为解决这一问题，我们提出了一种基于条件生成对抗网络（CGAN）的新方法。首先，通过灰色关联度分析和 K-L 信息法确定影响未来用电趋势的主导因素。随后，在 CGAN 的基础上引入了电耗预测模型，该模型善于捕捉本质因素以及电耗与外生因素之间的非线性关系。这种方法有效地模拟了导电率的不确定性，仅用少量数据集就能准确地逼近真实分布。最后，通过预测中国 2015 年至 2020 年的经济增长率，对所提出的方法进行了验证。结果表明，作者的方法取得了较低的均方根误差和平均绝对百分比误差值，分别为 0.177% 和 2.39%，优于 SVM 和 LSTM 等成熟的先进方法。

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引用次数: 0

Research on future trends of electricity consumption based on conditional generative adversarial network considering dual‐carbon target 基于条件生成式对抗网络的未来用电趋势研究（考虑双碳目标

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-02-12 DOI: 10.1049/esi2.12138

Jinghua Li, Zibei Qin, Yichen Luo, Jianfeng Chen, Shanyang Wei

The emergence of novel factors, such as the energy Internet and electricity supply‐side reform within the context of the dual‐carbon target (carbon peaking and carbon neutrality), has heightened the uncertainty surrounding electricity consumption (EC). This increased uncertainty poses challenges for accurate long‐term EC forecasting. Due to the complexities of feature extraction and the absence of labelled data, conventional supervised learning‐based forecasting methods, such as support vector machines (SVM) and long short‐term memory networks (LSTM), struggle to predict EC with precision in situations of heightened uncertainty resulting from the interplay of multiple factors. To address this issue, a novel method based on a conditional generative adversarial network (CGAN) is proposed. Initially, the dominant factors influencing future electricity consumption trends through grey correlation degree analysis and the K‐L information method are identified. Subsequently, an EC forecast model is introduced based on CGAN, adept at capturing essential factors and the non‐linear relationship between EC and exogenous factors. This approach effectively models the uncertainty of EC, accurately approximating the true distribution with only a small dataset. Finally, the proposed method by forecasting China's EC from 2015 to 2020 is validated. The results demonstrate that the authors’ method achieves lower root mean square error and mean absolute percentage error values, specifically 0.177% and 2.39%, respectively, outperforming established advanced methods such as SVM and LSTM.

在双碳目标（碳调峰和碳中和）背景下，能源互联网和电力供应侧改革等新因素的出现，增加了电力消费（EC）的不确定性。这种不确定性的增加给准确的长期用电量预测带来了挑战。由于特征提取的复杂性和标记数据的缺失，传统的基于监督学习的预测方法，如支持向量机（SVM）和长短期记忆网络（LSTM），在多种因素相互作用导致不确定性增加的情况下，很难准确预测用电量。为解决这一问题，我们提出了一种基于条件生成对抗网络（CGAN）的新方法。首先，通过灰色关联度分析和 K-L 信息法确定影响未来用电趋势的主导因素。随后，在 CGAN 的基础上引入了电耗预测模型，该模型善于捕捉本质因素以及电耗与外生因素之间的非线性关系。这种方法有效地模拟了导电率的不确定性，仅用少量数据集就能准确地逼近真实分布。最后，通过预测中国 2015 年至 2020 年的经济增长率，对所提出的方法进行了验证。结果表明，作者的方法取得了较低的均方根误差和平均绝对百分比误差值，分别为 0.177% 和 2.39%，优于 SVM 和 LSTM 等成熟的先进方法。

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引用次数: 0

Supercapacitor‐based coordinated synthetic inertia scheme for voltage source converter‐based HVDC integrated offshore wind farm 基于超级电容器的协调合成惯性方案，用于基于电压源变流器的 HVDC 集成海上风电场

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-02-07 DOI: 10.1049/esi2.12137

Jiebei Zhu, Meiqi Shi, Lujie Yu, Junbo Zhao, Siqi Bu, Chi Yung Chung, Campbell D. Booth

A supercapacitor‐based coordinated synthetic inertia (SCSI) scheme for a voltage source converter‐based HVDC (VSC‐HVDC)‐integrated offshore wind farm (OWF) is proposed. The proposed SCSI allows the OWF to provide a designated inertial response to an onshore grid. Under the SCSI scheme, a supercapacitor is added to the DC side of each wind turbine generator via a bidirectional DC/DC converter, varying its voltage along with the offshore frequency to synthesise the desired inertial response. The HVDC grid side VSC employs a DC voltage/frequency droop control to convey the onshore frequency information to DC voltage without communication. Meanwhile, the wind farm side VSC regulates the offshore frequency to couple with the conveyed onshore frequency, considering voltage drop across the DC cables. An offshore frequency switching algorithm is incorporated to avoid undesired SCSI maloperation under offshore faults. The key parameters of the proposed SCSI are optimised through a small signal stability analysis. The effectiveness of the SCSI scheme is evaluated using a modified IEEE 39‐bus test system. The results show that the proposed SCSI scheme can provide required inertial support from WTG‐installed supercapacitors to the onshore grid through the VSC‐HVDC link, significantly improving the onshore frequency stability.

针对基于电压源变流器的高压直流（VSC-HVDC）集成海上风电场（OWF），提出了一种基于超级电容器的协调合成惯性（SCSI）方案。拟议的 SCSI 允许海上风电场向陆上电网提供指定的惯性响应。在 SCSI 方案中，超级电容器通过双向 DC/DC 转换器被添加到每个风力涡轮发电机的直流侧，其电压随离岸频率变化，以合成所需的惯性响应。HVDC 电网侧 VSC 采用直流电压/频率下降控制，无需通信即可将陆上频率信息转换为直流电压。同时，考虑到直流电缆上的电压降，风电场侧可变电源调节器调节离岸频率，使其与传输的陆上频率耦合。此外，还采用了离岸频率切换算法，以避免在离岸故障情况下出现意外的 SCSI 误操作。通过小信号稳定性分析，对拟议 SCSI 的关键参数进行了优化。利用改进的 IEEE 39 总线测试系统对 SCSI 方案的有效性进行了评估。结果表明，建议的 SCSI 方案可通过 VSC-HVDC 链路从风电机组安装的超级电容器向陆上电网提供所需的惯性支持，从而显著改善陆上频率稳定性。

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引用次数: 0

Supercapacitor-based coordinated synthetic inertia scheme for voltage source converter-based HVDC integrated offshore wind farm 基于超级电容器的协调合成惯性方案，用于基于电压源变流器的 HVDC 集成海上风电场

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-02-07 DOI: 10.1049/esi2.12137

Jiebei Zhu, Meiqi Shi, Lujie Yu, Junbo Zhao, Siqi Bu, Chi Yung Chung, Campbell D. Booth

A supercapacitor-based coordinated synthetic inertia (SCSI) scheme for a voltage source converter-based HVDC (VSC-HVDC)-integrated offshore wind farm (OWF) is proposed. The proposed SCSI allows the OWF to provide a designated inertial response to an onshore grid. Under the SCSI scheme, a supercapacitor is added to the DC side of each wind turbine generator via a bidirectional DC/DC converter, varying its voltage along with the offshore frequency to synthesise the desired inertial response. The HVDC grid side VSC employs a DC voltage/frequency droop control to convey the onshore frequency information to DC voltage without communication. Meanwhile, the wind farm side VSC regulates the offshore frequency to couple with the conveyed onshore frequency, considering voltage drop across the DC cables. An offshore frequency switching algorithm is incorporated to avoid undesired SCSI maloperation under offshore faults. The key parameters of the proposed SCSI are optimised through a small signal stability analysis. The effectiveness of the SCSI scheme is evaluated using a modified IEEE 39-bus test system. The results show that the proposed SCSI scheme can provide required inertial support from WTG-installed supercapacitors to the onshore grid through the VSC-HVDC link, significantly improving the onshore frequency stability.

针对基于电压源变流器的高压直流（VSC-HVDC）集成海上风电场（OWF），提出了一种基于超级电容器的协调合成惯性（SCSI）方案。拟议的 SCSI 允许海上风电场向陆上电网提供指定的惯性响应。在 SCSI 方案中，超级电容器通过双向 DC/DC 转换器被添加到每个风力涡轮发电机的直流侧，其电压随离岸频率变化，以合成所需的惯性响应。HVDC 电网侧 VSC 采用直流电压/频率下降控制，无需通信即可将陆上频率信息转换为直流电压。同时，考虑到直流电缆上的电压降，风电场侧可变电源调节器调节离岸频率，使其与传输的陆上频率耦合。此外，还采用了离岸频率切换算法，以避免在离岸故障情况下出现意外的 SCSI 误操作。通过小信号稳定性分析，对拟议 SCSI 的关键参数进行了优化。利用改进的 IEEE 39 总线测试系统对 SCSI 方案的有效性进行了评估。结果表明，建议的 SCSI 方案可通过 VSC-HVDC 链路从风电机组安装的超级电容器向陆上电网提供所需的惯性支持，从而显著改善陆上频率稳定性。

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引用次数: 0

A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids 多微网分布式合作的两阶段四层稳健优化模型

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-01-31 DOI: 10.1049/esi2.12135

Haobo Rong, Jianhui Wang, Honghai Kuang

As the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG‐CES is presented, taking into account distributed generation under uncertainty. The proposed model follows a two‐stage, four‐layer ‘min‐min‐max‐min’ structure. In the first stage, the initial layer ‘min’ addresses the distributed cooperation problem between MG and CES, while the second stage employs ‘min‐max‐min’ to optimise the scheduling of MG. To enhance the solution process and expedite convergence, the authors introduce a column‐constrained generation algorithm with alternating iterations of U and D variables (CCG‐UD) specifically designed for the three‐layer structure in the second stage. This algorithm effectively decouples subproblems, contributing to accelerated solutions. To tackle the convergence challenges posed by the non‐convex MG‐CES model, the authors integrate the Bregman alternating direction method with multipliers (BADMM) with CCG‐UD in the final solution step. Real case tests are conducted using three zone‐level MGs to validate the efficacy of the proposed model and methodology. The results demonstrate the practical utility and efficiency of the developed approach in addressing distributed cooperation challenges in microgrid systems with energy storage.

随着微电网（MG）与储能技术的不断融合，MG 与共用储能技术（CES）之间的高效分布式合作变得至关重要。考虑到不确定情况下的分布式发电，本文提出了一种用于 MG-CES 分布式合作的稳健优化模型。该模型采用两阶段四层 "最小-最大-最小 "结构。在第一阶段，初始层 "最小 "解决 MG 和 CES 之间的分布式合作问题，第二阶段采用 "最小-最大-最小 "优化 MG 的调度。为了改进求解过程并加快收敛速度，作者在第二阶段引入了专为三层结构设计的列约束生成算法（CCG-UD），交替迭代 U 和 D 变量。该算法有效地解耦了子问题，有助于加速求解。为了解决非凸 MG-CES 模型带来的收敛难题，作者在最后求解步骤中将带乘数的布雷格曼交替方向法（BADMM）与 CCG-UD 相结合。使用三个区级 MG 进行了实际案例测试，以验证所提模型和方法的有效性。结果表明，所开发的方法在应对带储能的微电网系统中的分布式合作挑战方面具有实用性和高效性。

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引用次数: 0

Utility‐scale solar photovoltaic power plant emulating a virtual synchronous generator with simultaneous frequency and voltage control provision 公用事业级太阳能光伏发电站模拟虚拟同步发电机，同时提供频率和电压控制

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-01-19 DOI: 10.1049/esi2.12134

Raja Owais, Sheikh Javed Iqbal

Network operators with significant solar photovoltaic (PV) penetration are having difficulty maintaining grid frequency and voltage within acceptable bounds due to the progressive displacement of synchronous machines. Utility‐scale solar PV plants have a huge potential for participation in frequency and voltage regulation since they are linked to the grid through power electronic interfaces with flexible, decoupled control of active and reactive power. A comprehensive control strategy for a utility‐scale solar PV plant is proposed to simultaneously participate in frequency and voltage control without the aid of any energy storage. The frequency response is accomplished by maintaining some active power reserves that enable the PV plant to participate in both over‐ and under‐frequency events. The active power of the PV plant is modulated by operating the PV as a virtual synchronous generator (VSG). Unlike the classic notion of VSG, an intelligent fuzzy‐based technique is employed to adapt the gains of the VSG controller for improved control performance. Additionally, an adaptive droop‐based voltage control mechanism is proposed to control the reactive power reference for the PV plant. The gain of the droop controller is adapted to the varying maximum reactive power capacity of the PV plant. This ensures that the PV system's unused reactive power capability is fully utilised. Through simulation studies, the efficiency of the proposed frequency and voltage control mechanisms is validated under a range of realistic circumstances. The findings show that the suggested control approach can efficiently leverage the PV plants' capacity to handle both frequency and voltage events.

太阳能光伏发电（PV）渗透率高的电网运营商很难将电网频率和电压维持在可接受的范围内，原因是同步电机会逐渐发生位移。公用事业级太阳能光伏电站通过电力电子接口与电网相连，具有灵活的有功和无功功率解耦控制功能，因此在参与频率和电压调节方面潜力巨大。本文为公用事业级太阳能光伏电站提出了一种综合控制策略，可在不借助任何储能装置的情况下同时参与频率和电压控制。频率响应是通过保持一定的有功功率储备来实现的，使光伏电站能够参与过频和欠频事件。光伏电站的有功功率通过将光伏作为虚拟同步发电机 (VSG) 运行来调节。与传统的 VSG 概念不同，该系统采用了基于模糊的智能技术来调整 VSG 控制器的增益，以提高控制性能。此外，还提出了一种基于下垂的自适应电压控制机制，用于控制光伏电站的无功功率基准。下垂控制器的增益可根据光伏电站最大无功功率容量的变化进行调整。这可确保光伏系统未使用的无功功率能力得到充分利用。通过模拟研究，在一系列实际情况下验证了所建议的频率和电压控制机制的效率。研究结果表明，建议的控制方法可以有效利用光伏电站的能力来处理频率和电压事件。

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引用次数: 0

Design and evaluation of architectural framework for a secured local energy market model based on distributed ledger technologies 设计和评估基于分布式账本技术的安全本地能源市场模型的架构框架

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-01-18 DOI: 10.1049/esi2.12136

Godwin C. Okwuibe, T. Brenner, Muhammad Yahya, P. Tzscheutschler, T. Hamacher

Blockchain‐based local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamper‐proof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the on‐chain features of blockchain with trusted execution environments to develop a transparent, tamper‐resistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.

近年来，人们提出了基于区块链的本地能源市场，为本地能源消费商和消费者提供一个市场平台，以安全、透明和防篡改的方式交换能源。然而，区块链在处理高计算模型/算法/合同的可扩展性方面仍存在一些挑战，因为这可能导致区块链网络区块大小的扩大和非常高的天然气成本。此外，在《通用数据保护条例》方面仍然存在透明度问题，因为在某些情况下，区块链中数据的完全可见性可能会与隐私相冲突。本文介绍了一个框架，该框架将区块链的链上特性与可信执行环境相结合，为本地电力交易开发了一个透明、防篡改、低运营成本、可扩展且有弹性的混合模型架构。该模型架构在德国社区案例场景中针对不同数量的专业用户和消费者进行了模拟，以展示其适用性。仿真结果表明，该模型能够解决区块链在本地能源市场应用中的可扩展性问题，因为市场模型是在可信环境中运行的，参与者可以验证模型的完整性。

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引用次数: 0

Intelligent reinforcement training optimisation of dispatch strategy for provincial power grids with multi-agent systems: Considering operational risks and backup availability 利用多智能体系统对省级电网的调度策略进行智能强化训练优化：考虑运行风险和备用可用性

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-01-04 DOI: 10.1049/esi2.12131

Wenlong Shi, Xiao Han, Xinying Wang, Tianjiao Pu, Dongxia Zhang

In order to optimise resource allocation within the province, a two-stage scheduling model for provincial-level power grids, encompassing day-ahead and intra-day stages is proposed. Firstly, a Conditional Generative Adversarial Network is employed to generate scenarios for load and new energy output. Based on the generated scenario set, the model takes into account the uncertainty and permissible error intervals of new energy and load, utilising conditional value at risk to measure the system scheduling risk. In the day-ahead stage, an optimisation model is proposed, considering intra-provincial power purchase demands, with the goal of minimising system operating costs, including risk costs. It optimises day-ahead scheduling and contingency plans to ensure economic efficiency and robustness of the system based on extreme scenarios. During the training phase, the dataset is enhanced using Conditional Generative Adversarial Network and updated daily, improving the training effectiveness of the multi-agent proximal policy optimisation intra-day scheduling model. In the intra-day stage, the intra-day scheduling model utilises ultra-short-term forecasting data as input to generate contingency plans for dispatching reserve units. Experiments conducted on the IEEE 39-node system validate the feasibility and effectiveness of the proposed approach.

为了优化省内资源配置，提出了省级电网的两阶段调度模型，包括日前和日内阶段。首先，采用条件生成对抗网络生成负荷和新能源输出情景。根据生成的情景集，模型考虑了新能源和负荷的不确定性和允许误差区间，利用条件风险值来衡量系统调度风险。在日前阶段，考虑到省内购电需求，提出了一个优化模型，目标是最大限度地降低系统运营成本，包括风险成本。该模型对日前调度和应急计划进行优化，以确保系统在极端情况下的经济效益和稳健性。在训练阶段，使用条件生成对抗网络对数据集进行增强并每日更新，从而提高多代理近端策略优化日内调度模型的训练效果。在日内调度阶段，日内调度模型利用超短期预测数据作为输入，生成调度备用机组的应急计划。在 IEEE 39 节点系统上进行的实验验证了建议方法的可行性和有效性。

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引用次数: 0

An efficient load forecasting technique by using Holt‐Winters and Prophet algorithms to mitigate the impact on power consumption in COVID‐19 使用 Holt-Winters 和 Prophet 算法的高效负荷预测技术，可减轻 COVID-19 中耗电量的影响

IF 2.4 Q1 Engineering

IET Energy Systems Integration

Pub Date : 2024-01-02 DOI: 10.1049/esi2.12132

W. Waheed, Qingshan Xu

It is strongly recommended to implement effective long‐term load forecasting for future power generation in the new architecture of the smart grid and buildings. This method is essential for the smart grid's stability, power demand estimation, and an improved energy management system, which will enhance integration between efficient demand response and distributed renewable energy sources. However, due to influencing elements including climatic, societal, and seasonal aspects, it is quite challenging to perform energy prediction with high accuracy. To estimate the load demand before and during the time period of the COVID‐19 paradigm with its diversity and complexity, the authors present and integrate time series forecasting techniques such as Holt‐Winters and Prophet algorithms. In comparison to the Holt‐Winters model, the Prophet model has shown to be more noise‐resistant. Additionally, the Prophet model surpasses the Holt‐Winters model according to the generalisability test of the two models by using the hourly driven power consumption data from Houston, Texas, USA. The resultant constraints and influential factors are discussed, and experimental results can be validated from the pivotal outcome.

强烈建议在智能电网和建筑的新架构中对未来发电实施有效的长期负荷预测。这种方法对于智能电网的稳定性、电力需求估算和改进能源管理系统至关重要，将加强高效需求响应与分布式可再生能源之间的整合。然而，由于受到气候、社会和季节等因素的影响，要进行高精度的能源预测具有相当大的挑战性。COVID-19 范例具有多样性和复杂性，为了估算 COVID-19 范例之前和期间的负荷需求，作者提出并整合了 Holt-Winters 和 Prophet 算法等时间序列预测技术。与 Holt-Winters 模型相比，先知模型的抗噪能力更强。此外，通过使用美国德克萨斯州休斯顿市每小时驱动的电力消耗数据，对这两种模型进行了通用性测试，结果表明先知模型优于霍尔特-温特斯模型。讨论了由此产生的制约因素和影响因素，并从关键结果中验证了实验结果。

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引用次数: 0