IET Renewable Power Generation最新文献

Estimation of maximum non‐synchronous generation of renewable energy in the South Korea power system based on the minimum level of inertia 基于最小惯性水平的韩国电力系统可再生能源最大非同步发电量估算

IET Renewable Power Generation Pub Date : 2024-02-14 DOI: 10.1049/rpg2.12964

Seunghyuk Im, K. Lee, Byongjun Lee

{"title":"Estimation of maximum non‐synchronous generation of renewable energy in the South Korea power system based on the minimum level of inertia","authors":"Seunghyuk Im, K. Lee, Byongjun Lee","doi":"10.1049/rpg2.12964","DOIUrl":"https://doi.org/10.1049/rpg2.12964","url":null,"abstract":"Managing the output of renewable energy sources considering their uncertainty and variability is crucial for resilience in power system operation. In addition, analyzing stability issues that may arise at the maximum output is important to ensure power system stability. Therefore, the authors propose a method for estimating the maximum non‐synchronous generation (Max NSG) of renewable energy based on the minimum inertia of the power system. The minimum inertia is determined through the correlation between the available and required quantity of inertia and governor resources, satisfying the frequency standards in a South Korean power system. The Max NSG of renewable energy sources at that system inertia level is estimated based on the derived minimum inertia. The proposed method was applied to 22,612 operation data extracted from the Korea energy management system (K‐EMS). The authors estimated a linear relationship between demand levels and Max NSG, ranging from 52.6 to 3.83 GW. The study shows that Max NSG, which is difficult to estimate in many power system operating conditions, can be estimated based on minimum inertia considering the frequency stability in South Korean power systems.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"48 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement learning based two‐timescale energy management for energy hub 基于强化学习的能源枢纽双时标能源管理

IET Renewable Power Generation Pub Date : 2024-02-14 DOI: 10.1049/rpg2.12911

Jinfan Chen, C. Mao, Guanglin Sha, Wanxing Sheng, Hua Fan, Dan Wang, Shushan Qiu, Yunzhao Wu, Yao Zhang

{"title":"Reinforcement learning based two‐timescale energy management for energy hub","authors":"Jinfan Chen, C. Mao, Guanglin Sha, Wanxing Sheng, Hua Fan, Dan Wang, Shushan Qiu, Yunzhao Wu, Yao Zhang","doi":"10.1049/rpg2.12911","DOIUrl":"https://doi.org/10.1049/rpg2.12911","url":null,"abstract":"Maintaining energy balance and economical operation is significant for energy hub (EH) which serves as the central component. Implementing real‐time regulation for heating and cooling equipment within the EH is challenging due to their slow response time in response to the stochastic fluctuation in renewable energy sources and demands while the opposite is true for electric energy storage equipment (EST), a conventional single timescale energy management strategy is no longer sufficient to take into account the operating characteristics of all equipment. With this motivation, this study proposes a deep reinforcement learning based two‐timescale energy management strategy for EH, which controls heating & cooling equipment on a long timescale of 1 h, and EST on a short timescale of 15 min. The actions of the EST are modelled as discrete to reduce the action spaces, and the discrete‐continuous hybrid action sequential TD3 model is proposed to address the problem of handling both discrete and continuous actions in long timescale policy. A joint training approach based on the centralized training framework is proposed to learn multiple levels of policies in parallel. The case studies demonstrate that the proposed strategy reduces the economic cost and carbon emissions by 1%, and 0.5% compared to the single time‐scale strategy respectively.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel deep learning technique to detect electricity theft in smart grids using AlexNet 利用 AlexNet 检测智能电网中窃电行为的新型深度学习技术

IET Renewable Power Generation Pub Date : 2024-02-13 DOI: 10.1049/rpg2.12846

Nitasha Khan, Zeeshan Shahid, M. Alam, Aznida Abu Bakar Sajak, Mobeen Nazar, M. Mazliham

{"title":"A novel deep learning technique to detect electricity theft in smart grids using AlexNet","authors":"Nitasha Khan, Zeeshan Shahid, M. Alam, Aznida Abu Bakar Sajak, Mobeen Nazar, M. Mazliham","doi":"10.1049/rpg2.12846","DOIUrl":"https://doi.org/10.1049/rpg2.12846","url":null,"abstract":"Electricity theft (ET), which endangers public safety, interferes with the regular operation of grid infrastructure, and increases revenue losses, is a significant issue for power companies. To find ET, numerous machine learning, deep learning, and mathematically based algorithms have been published in the literature. However, these models do not yield the greatest results due to issues like the dimensionality curse, class imbalance, inappropriate hyper‐parameter tuning of machine learning, deep learning models etc. A hybrid DL model is presented for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre‐processing techniques are first employed to clean up the data from the smart meters, and then the feature extraction technique, AlexNet is used to address the curse of dimensionality. An actual dataset of Chinese smart meters is used in simulations to assess the efficacy of the suggested approach. To conduct a comparative analysis, various benchmark models are implemented as well. This proposed model achieves accuracy, precision, recall, and F1‐score, up to 86%, 89%, 86%, and 84%, respectively.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"51 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The impact of variation in water flow rate and temperature on reliability analysis of run of the river power plants 水流速度和温度变化对径流式发电厂可靠性分析的影响

IET Renewable Power Generation Pub Date : 2024-02-12 DOI: 10.1049/rpg2.12960

A. Ghaedi, Reza Sedaghati, M. Mahmoudian

{"title":"The impact of variation in water flow rate and temperature on reliability analysis of run of the river power plants","authors":"A. Ghaedi, Reza Sedaghati, M. Mahmoudian","doi":"10.1049/rpg2.12960","DOIUrl":"https://doi.org/10.1049/rpg2.12960","url":null,"abstract":"A run‐of‐the‐river power plant is a renewable energy source used for electricity production. Its power output depends on the varying water flow rate over time, which can impact the reliability of the electric network. Previous research has not studied the effects of water flow rate and temperature variations on the hazard rate of the plant's components. This paper addresses this gap by investigating the impact of these variations on the reliability of electric networks with run‐of‐the‐river power plants. The analysis considers the hazard rates of the plant's components, incorporating the relationship between hazard rate and temperature based on the Arrhenius law. Parameters such as power output, current, power loss, operating temperature, and hazard rate are calculated for different water flow rates and ambient temperatures. Numerical simulations on a test system are conducted to examine the influence of water flow rate and temperature on the reliability indices of the electric network. The results demonstrate that water flow rate and temperature significantly affect the hazard rates of run‐of‐the‐river power plants. This highlights the need to consider these factors in the reliability analysis of electric networks incorporating these renewable resources.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"37 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal design and operation of a wind farm/battery energy storage considering demand side management 考虑需求侧管理的风电场/电池储能优化设计与运行

IET Renewable Power Generation Pub Date : 2024-02-10 DOI: 10.1049/rpg2.12951

Siyu Tao, Chaohai Zhang, Andrés E. Feijóo-Lorenzo, Victor Kim

{"title":"Optimal design and operation of a wind farm/battery energy storage considering demand side management","authors":"Siyu Tao, Chaohai Zhang, Andrés E. Feijóo-Lorenzo, Victor Kim","doi":"10.1049/rpg2.12951","DOIUrl":"https://doi.org/10.1049/rpg2.12951","url":null,"abstract":"Balancing electricity demand and sustainable energy generation like wind energy presents challenges for the smart grid. To address this problem, the optimization of a wind farm (WF) along with the battery energy storage (BES) on the supply side, along with the demand side management (DSM) on the consumer side, should be considered during its planning and operation stages. An optimization framework with two levels to simultaneously decide the layout and operation of the WF/BES is put forward in this paper. The first‐level model consists of determining the WF/BES capacities, the WF configuration, and the connection buses. It is tackled by the mixed‐discrete particle swarm optimization algorithm. The multi‐objective optimization problem (MOOP) model in the second level determines the operation schedule of the WF/BES and other generators taking the DSM into consideration. The MOOP model in the second level is transformed to a single‐objective optimization problem via the maximum fuzzy satisfaction method, and is then solved by the genetic algorithm. The proposed model and the strategy are verified by the Barrow offshore WF test case, which is integrated into the IEEE‐118 system. Simulation results indicate that the wind and load patterns, the DSM and the BES price are the three key factors influencing the WF/BES design optimization.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139846588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells 光伏太阳能电池单二极管模型参数估计的高效、快速和稳健算法

IET Renewable Power Generation Pub Date : 2024-02-09 DOI: 10.1049/rpg2.12958

Husain A. Ismail, A. Diab

{"title":"An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells","authors":"Husain A. Ismail, A. Diab","doi":"10.1049/rpg2.12958","DOIUrl":"https://doi.org/10.1049/rpg2.12958","url":null,"abstract":"Parameter estimation of photovoltaic (PV) solar cells and module models pays attention to researchers owing to their importance in practical considerations. The single diode model (SDM) circuit with five unknown parameters is widely used to model PV solar cells and modules. In this paper, a novel approach called alternate optimization (AO) algorithm based on a discrete search is proposed to estimate the SDM parameters. The proposed algorithm provides efficient and robust performance, considering a limited set of discrete values and increasing the convergence speed. Two practical case studies with actual measurements are considered to assess the proposed AO algorithm: the RTC France solar cell and monocrystalline PV modules with different irradiations and temperatures. The numerical findings underscore the superior performance of the proposed AO algorithm across various metrics. Notably, it achieves an exceptional Root Mean Square Error (RMSE) of 7.7426 × 10−04 for the RTC France PV cell and approximately 1 × 10−03 RMSE for monocrystalline PV modules. Additionally, the algorithm exhibits unparalleled speed, showcasing the fastest convergence with an elapsed time of 1.66 × 10−05—markedly 4.45 times quicker than the fastest method documented in the literature for SDM parameter estimation. Furthermore, the proposed AO algorithm stands out for its efficiency, requiring a maximum of five iterations for parameter estimation, a substantial improvement compared to the more than 10 iterations typically needed by algorithms in the existing literature. Its robustness is also commendable, as evidenced by the stability of final RMSE values across a variety of experiments, distinguishing it from less robust algorithms found in the literature.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"410 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration 多目标多周期分布式发电的最佳地点和规模以及网络重组

IET Renewable Power Generation Pub Date : 2024-02-07 DOI: 10.1049/rpg2.12949

Ghulam Abbas, Zhi Wu, Aamir Ali

{"title":"Multi‐objective multi‐period optimal site and size of distributed generation along with network reconfiguration","authors":"Ghulam Abbas, Zhi Wu, Aamir Ali","doi":"10.1049/rpg2.12949","DOIUrl":"https://doi.org/10.1049/rpg2.12949","url":null,"abstract":"While extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi‐objective network reconfiguration together with siting and sizing of dispatchable and non‐dispatchable DGs. The widely used IEEE 33‐bus and a large‐scale 118‐bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed‐out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi‐objective Bidirectional co‐evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle‐based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi‐objective Evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non‐dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non‐dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"55 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coordinated planning of DGs and soft open points in multi‐voltage level distributed networks based on the Stackelberg game 基于斯泰克尔伯格博弈的多电压等级分布式网络中风电机组和软开路点的协调规划

IET Renewable Power Generation Pub Date : 2024-02-07 DOI: 10.1049/rpg2.12963

Zhihua Chen, Ye He, Yuting Hua, Hongbin Wu, Rui Bi

{"title":"Coordinated planning of DGs and soft open points in multi‐voltage level distributed networks based on the Stackelberg game","authors":"Zhihua Chen, Ye He, Yuting Hua, Hongbin Wu, Rui Bi","doi":"10.1049/rpg2.12963","DOIUrl":"https://doi.org/10.1049/rpg2.12963","url":null,"abstract":"There are significant differences in distributed generators (DGs) distribution and load characteristics between different voltage levels, which makes it difficult to match sources and loads. We focus on the problem of different consumption capacities at different voltage levels and the divergence of interests among investment entities, and propose a coordinated planning model for DGs and soft open points (SOPs) based on the Stackelberg game. Firstly, a model of a multi‐voltage level distribution networks (DNs) is constructed based on SOPs. Next, the source‐load matching degree is proposed as a measure of the degree of matching between sources and loads in the DN, and the source‐load consumption rate is selected as an indicator to evaluate the impact of the load on DG consumption. Following this, the interest demands of DG investors and distribution company (DisCo) in multi‐voltage levels DN are analyzed, a planning mode based on the Stackelberg game is proposed, and this is solved by combining the genetic algorithm with second‐order cone programming. Finally, the effectiveness of the planning model is tested and verified using an improved IEEE 28‐node system. The results show that the proposed model improves the DG consumption capacity of DNs with multiple voltage levels while protecting the interests of DG investors and DisCo.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"26 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence 基于多策略随机加权灰狼优化器与蜂群智能的巨浪高度预报综合方法

IET Renewable Power Generation Pub Date : 2024-02-07 DOI: 10.1049/rpg2.12961

Emrah Dokur, N. Erdogan, Mahdi Ebrahimi Salari, Ugur Yuzgec, Jimmy Murphy

{"title":"An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence","authors":"Emrah Dokur, N. Erdogan, Mahdi Ebrahimi Salari, Ugur Yuzgec, Jimmy Murphy","doi":"10.1049/rpg2.12961","DOIUrl":"https://doi.org/10.1049/rpg2.12961","url":null,"abstract":"While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.","PeriodicalId":507938,"journal":{"name":"IET Renewable Power Generation","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel wasserstein generative adversarial network for stochastic wind power output scenario generation 用于随机风电输出情景生成的新型 Wasserstein 生成式对抗网络

IET Renewable Power Generation Pub Date : 2024-02-01 DOI: 10.1049/rpg2.12932

Xiurong Zhang, Daoliang Li, Xueqian Fu

引用次数: 0