IET Renewable Power Generation最新文献_第6页

A bi-level optimization model and improved algorithm for wind farm layout 风电场布局的双层优化模型和改进算法

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-29 DOI: 10.1049/rpg2.13005

Erping Song

引用次数: 0

Prediction of power generation and maintenance using AOC-ResNet50 network 使用 AOC-ResNet50 网络预测发电和维护情况

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-29 DOI: 10.1049/rpg2.13081

Yueqiang Chu, Wanpeng Cao, Cheng Xiao, Yubin Song

{"title":"Prediction of power generation and maintenance using AOC-ResNet50 network","authors":"Yueqiang Chu, Wanpeng Cao, Cheng Xiao, Yubin Song","doi":"10.1049/rpg2.13081","DOIUrl":"https://doi.org/10.1049/rpg2.13081","url":null,"abstract":"With the continuous expansion of the photovoltaic industry, the application of solar photovoltaic power generation systems is becoming increasingly widespread. Due to the obvious intermittency and volatility of photovoltaic power generation, integration of large-scale photovoltaic power generation into the power grid can cause certain impacts on the security and stability of the grid. Photovoltaic power prediction is essential to solve this problem, as it can improve the quality of photovoltaic grid connection, optimize grid scheduling, and ensure the safe operation of the grid. In this article, the deep learning method is selected for photovoltaic power prediction. Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC-ResNet50. It is then applied to the prediction of the generation of photovoltaic power. The prediction performance is compared with the ResNet50 network and the Oct-ResNet50 network, and it is found that the AOC-ResNet50 network has the best prediction performance, with an MAE of only 0.176888. Based on the exemplar work, a framework is proposed to illustrate this method. Its general application is discussed.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2381-2393"},"PeriodicalIF":2.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative forecasting management model for multi-energy microgrid considering load response characterization 考虑负荷响应特征的多能源微电网协同预测管理模型

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-29 DOI: 10.1049/rpg2.13076

Huiyu Bao, Yi Sun, Jie Peng, Xiaorui Qian, Peng Wu

{"title":"Collaborative forecasting management model for multi-energy microgrid considering load response characterization","authors":"Huiyu Bao, Yi Sun, Jie Peng, Xiaorui Qian, Peng Wu","doi":"10.1049/rpg2.13076","DOIUrl":"https://doi.org/10.1049/rpg2.13076","url":null,"abstract":"Multi-energy microgrids (MEMG) have become an effective means of integrated energy management due to their unique advantages, including area independence, diverse supply, flexibility, and efficiency. However, the uncertain deviation of the renewable energy generators (REGs) output and the uncertain deviation of the multiple energy load response cumulatively lead to the deterioration of the MEMG model performance. To address these issues, this article proposes a cooperative forecasting management model for MEMG that considers multiple uncertainties and load response knowledge characterization. The model combines a multi-energy load prediction model with a management model based on deep reinforcement learning. It proposes multiple iterations of data, fits the dynamic environment of MEMG by continuously improving the long short-term memory (LSTM) neural network based on knowledge distillation (KD) architecture, and then optimizes the MEMG state space by considering the knowledge of load response characteristics, Furthermore, it combines multi-agent deep deterministic policy gradient (MADDPG) with horizontal federated (hF) learning to co-train multi-MEMG, addressing the issues of training efficiency during co-training. Finally, the validity of the proposed model is demonstrated by an arithmetic example.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2360-2380"},"PeriodicalIF":2.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilience enhancement by line hardening for communication routing considering renewable energy sources in cyber-physical power systems 通过线路加固增强网络物理电力系统中考虑到可再生能源的通信路由的复原力

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-25 DOI: 10.1049/rpg2.13090

Xiaolong Li, Wenyi Li, Nana Wang, Le Li, Xuan Gong

{"title":"Resilience enhancement by line hardening for communication routing considering renewable energy sources in cyber-physical power systems","authors":"Xiaolong Li, Wenyi Li, Nana Wang, Le Li, Xuan Gong","doi":"10.1049/rpg2.13090","DOIUrl":"https://doi.org/10.1049/rpg2.13090","url":null,"abstract":"Communication routing of cyber-physical power systems (CPPS) with the high penetration of renewable energy sources (RES) plays an important role in the resilience enhancement against disasters, natural and man-made alike. Therefore, a trilevel optimization model is proposed for the CPPS resilience enhancement with the RES against extreme events. The upper-level model identifies optimal hardening lines of both the transmission and communication networks with consideration of the communication routing constraints. The middle-level model identifies the attacked lines to maximize load shedding of the power system. The effects of RES uncertainty and communication routings on resilience enhancement are analysed. The lower-level model attains an optimal allocation strategy of power generation to minimize load shedding. The model is solved by the column-and-constraint generation algorithm. Case studies are conducted on the IEEE 14-bus, RTS 24-bus, and 118-bus system. The results show that the proposed hardening strategy can effectively ensure the adaptiveness of the transmission network and communication routing to improve the resilience of CPPS to extreme events. Moreover, it is observed that the load loss and total investment cost are heavily affected without and with RES.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2477-2495"},"PeriodicalIF":2.6,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed optimization control strategy for distribution network based on the cooperation of distributed generations 基于分布式发电合作的配电网络分布式优化控制策略

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-25 DOI: 10.1049/rpg2.13089

Dong Hua, Suisheng Liu, Yiqing Liu, Jian Le, Qian Zhou

引用次数: 0

Grid frequency regulation through virtual power plant of integrated energy systems with energy storage 通过带储能的综合能源系统虚拟发电厂调节电网频率

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-25 DOI: 10.1049/rpg2.13068

Tao Xu, Rujing Wang, He Meng, Mengchao Li, Yu Ji, Ying Zhang, Jianli Zhao, Jiani Xiang

{"title":"Grid frequency regulation through virtual power plant of integrated energy systems with energy storage","authors":"Tao Xu, Rujing Wang, He Meng, Mengchao Li, Yu Ji, Ying Zhang, Jianli Zhao, Jiani Xiang","doi":"10.1049/rpg2.13068","DOIUrl":"https://doi.org/10.1049/rpg2.13068","url":null,"abstract":"Owing to the widespread integration of renewable distributed energy resources (DERs), the system frequency stability has been jeopardized by the non-inertial and stochastic units caused by power electronic devices. The integrated energy system (IES) that combines multi-vector energy resources can provide energy compensation among sub-systems in a coordinated fashion to further alleviate the volatility on the electric grid. Under the framework of IES, a virtual power plant (VPP) can aggregate multi-entities and multi-vector energy resources to participate in the frequency regulation service while pursuing profit maximization. A three-stage optimal scheduling model of IES-VPP that fully considers the cycle life of energy storage systems (ESSs), bidding strategies and revenue settlement has been proposed in this paper under the modified PJM frequency regulation market framework to motivate the aggregated resources to respond to the frequency regulation market actively. The simulation results under various scenarios have verified the feasibility and effectiveness of the proposed model through techno-economic analysis, the advantages of IES-VPP have been demonstrated compared with a single vector energy system.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2277-2293"},"PeriodicalIF":2.6,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability–flexibility integrated optimal sizing of second-life battery energy storage systems in distribution networks 配电网中二次电池储能系统的可靠性-灵活性综合优化选型

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-25 DOI: 10.1049/rpg2.13052

Hui Lu, Kaigui Xie, Bo Hu, Changzheng Shao, Yu Wang, Congcong Pan

{"title":"Reliability–flexibility integrated optimal sizing of second-life battery energy storage systems in distribution networks","authors":"Hui Lu, Kaigui Xie, Bo Hu, Changzheng Shao, Yu Wang, Congcong Pan","doi":"10.1049/rpg2.13052","DOIUrl":"https://doi.org/10.1049/rpg2.13052","url":null,"abstract":"Second-life batteries (SLBs), which are batteries retired from electric vehicles (EVs), can be used as energy storage systems to enhance the performance of distribution networks. Two issues should be addressed particularly for the optimal sizing of SLBs. Compared with fresh batteries, the failure rate of SLBs is relatively high, and timely and preventive replacement is needed. In addition, the flexibility introduced by EVs and installed SLBs should be coordinated to achieve optimal economic benefits. This paper focuses on the efficient utilization of SLBs by highlighting reliability-flexibility concerns in optimal sizing. The model is formulated as a bi-level model. On the upper-level, considering the operational reliability constraints of SLBs, decisions regarding the investment and replacement of SLBs are optimized. Distribution network operations are improved on the lowerlevel, with an effective spatiotemporal flexible dispatch strategy for EVs. Finally, a linearized process for the optimal sizing of SLBs is presented and efficiently implemented. The Sioux Falls network and IEEE 69-node distribution network are coupled as the test system. According to the simulation results, when the state of health of the SLBs decreased to 70%, the conditions were unreliable. The differences in the optimal SLB size and costs considering reliability and flexibility are highlighted.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 13","pages":"2177-2193"},"PeriodicalIF":2.6,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient robust model predictive load frequency control for smart grids with air conditioning loads 针对有空调负荷的智能电网的弹性鲁棒模型预测负荷频率控制

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-25 DOI: 10.1049/rpg2.13075

Shiluo Jike, Guobao Liu, Feng Li, Changyu Zhang, Qi Wang, Mengxia Zhou, Haiya Qian

{"title":"Resilient robust model predictive load frequency control for smart grids with air conditioning loads","authors":"Shiluo Jike, Guobao Liu, Feng Li, Changyu Zhang, Qi Wang, Mengxia Zhou, Haiya Qian","doi":"10.1049/rpg2.13075","DOIUrl":"https://doi.org/10.1049/rpg2.13075","url":null,"abstract":"This paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed <math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 <mo>/</mo>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mi>∞</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$H_2/H_infty$</annotation>\u0000 </semantics></math> performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two-area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi-area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2326-2339"},"PeriodicalIF":2.6,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust bi-level programming approach for optimal planning an off-grid zero-energy complex 离网零能耗建筑群优化规划的稳健双层程序设计方法

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-25 DOI: 10.1049/rpg2.13083

Reza Ghaffarpour, Saeid Zamanian

{"title":"A robust bi-level programming approach for optimal planning an off-grid zero-energy complex","authors":"Reza Ghaffarpour, Saeid Zamanian","doi":"10.1049/rpg2.13083","DOIUrl":"https://doi.org/10.1049/rpg2.13083","url":null,"abstract":"Electric power provision for all the customers is not always possible for distribution companies. Some customers are interested in serving their load through renewable resources regarding the climate situation. Electrically off-grid zero-energy building is an applicable concept, in which the electrical energy provision of the buildings is isolated from the power supply infrastructures. This paper introduces a robust bi-level programming model to create a cost-effective off-grid zero energy complex in Kish Island under risk management. The upper level of the planning is composed of two components, the passive design of buildings within the complex and the design of a stand-alone energy system. The passive design as an energy-saving tool includes the selection of insulation material for building external walls and finding the optimum thickness. Also, the stand-alone energy system design denotes the sizing of diesel generator, photovoltaic, and battery energy storage as the distributed energy resources. The lower-level problem optimally handles the annual scheduling of these resources to meet the complex demand under the impact of passive cooling. The Karush–Kuhn–Tucker condition method is used to solve this bi-level planning problem. Furthermore, the battery degradation is concerned via the throughput model to consider the replacement cost of the problem.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2394-2415"},"PeriodicalIF":2.6,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel wind power forecast diffusion model based on prior knowledge 基于先验知识的新型风能预测扩散模型

IF 2.6 4区工程技术

IET Renewable Power Generation Pub Date : 2024-08-25 DOI: 10.1049/rpg2.13087

Li Han, Yingjie Cheng, Shuo Chen, Shiqi Wang, Junjie Wang

{"title":"A novel wind power forecast diffusion model based on prior knowledge","authors":"Li Han, Yingjie Cheng, Shuo Chen, Shiqi Wang, Junjie Wang","doi":"10.1049/rpg2.13087","DOIUrl":"https://doi.org/10.1049/rpg2.13087","url":null,"abstract":"To improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real-world wind farms.","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"18 14","pages":"2440-2454"},"PeriodicalIF":2.6,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.13087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0