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  • Open Access

    ARTICLE

    Smart Micro Grid Energy System Management Based on Optimum Running Cost for Rural Communities in Rwanda

    Fabien Mukundufite1,*, Jean Marie Vianney Bikorimana1, Alexander Kyaruzi Lugatona2

    Energy Engineering, Vol.121, No.7, pp. 1805-1821, 2024, DOI:10.32604/ee.2024.051398

    Abstract The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024. However, the aforementioned goal is challenged by households that are scattered in remote areas. So far, Solar Home Systems (SHS) have mostly been applied to increase electricity access in rural areas. SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities. The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study. The renewable energy resources available in Remera are the key sources… More >

  • Open Access

    ARTICLE

    Optimal Scheduling Strategy of Source-Load-Storage Based on Wind Power Absorption Benefit

    Jie Ma1, Pengcheng Yue2, Haozheng Yu1, Yuqing Zhang3, Youwen Zhang1, Cuiping Li3, Junhui Li3,*, Wenwen Qin3, Yong Guo1

    Energy Engineering, Vol.121, No.7, pp. 1823-1846, 2024, DOI:10.32604/ee.2024.048225

    Abstract In recent years, the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing, but the peak regulation capacity of the power grid in the three north regions of China is limited, resulting in insufficient local wind power consumption capacity. Therefore, this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid's wind power consumption capacity. The objective of the upper model is to minimize the peak-valley difference of the system load, which is mainly to optimize the system… More >

  • Open Access

    ARTICLE

    Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

    Guanfu Wang1, Yudie Sun1, Jinling Li2,3,*, Yu Jiang1, Chunhui Li1, Huanan Yu2,3, He Wang2,3, Shiqiang Li2,3

    Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794

    Abstract Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which are difficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamic decisions continuously. This paper proposed a dynamic economic scheduling method for distribution networks based on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distribution network is established considering the action characteristics of micro-gas turbines, and the dynamic scheduling model based on deep reinforcement learning is constructed for the new energy distribution network system with a More >

  • Open Access

    ARTICLE

    Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

    Zhishuo Zhang, Xinhui Du*, Yaoke Shang, Jingshu Zhang, Wei Zhao, Jia Su

    Energy Engineering, Vol.121, No.6, pp. 1577-1605, 2024, DOI:10.32604/ee.2024.047706

    Abstract To address the issues of limited demand response data, low generalization of demand response potential evaluation, and poor demand response effect, the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis. Firstly, based on the demand response process and demand response behavior, obtain demand response characteristics that characterize the process and behavior. Secondly, establish a feature extraction and prediction model based on data mining, including similar day clustering,… More >

  • Open Access

    ARTICLE

    Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

    Fuju Zhou*, Li Li, Tengfei Jia, Yongchang Yin, Aixiang Shi, Shengrong Xu

    Energy Engineering, Vol.121, No.6, pp. 1697-1711, 2024, DOI:10.32604/ee.2024.047680

    Abstract When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changing the states of tie-switches and load demands. Computation speed is one of the major performance indicators in power grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault power grids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient. The tedious training process of the reinforcement learning model can be conducted offline, so the model shows satisfactory performance in real-time operation, More >

  • Open Access

    ARTICLE

    Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

    Lin Ma1, Liyong Wang1, Shuang Zeng1, Yutong Zhao1, Chang Liu1, Heng Zhang1, Qiong Wu2,*, Hongbo Ren2

    Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332

    Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

  • Open Access

    ARTICLE

    Field Load Test Based SHM System Safety Standard Determination for Rigid Frame Bridge

    Xilong Zheng*, Qiong Wang, Di Guan

    Structural Durability & Health Monitoring, Vol.18, No.3, pp. 361-376, 2024, DOI:10.32604/sdhm.2024.048473

    Abstract The deteriorated continuous rigid frame bridge is strengthened by external prestressing. Static loading tests were conducted before and after the bridge rehabilitation to verify the effectiveness of the rehabilitation process. The stiffness of the repaired bridge is improved, and the maximum deflection of the load test is reduced from 37.9 to 27.6 mm. A bridge health monitoring system is installed after the bridge is reinforced. To achieve an easy assessment of the bridge’s safety status by directly using transferred data, a real-time safety warning system is created based on a five-level safety standard. The threshold More >

  • Open Access

    ARTICLE

    A Comprehensive Investigation on Shear Performance of Improved Perfobond Connector

    Caiping Huang*, Zihan Huang, Wenfeng You

    Structural Durability & Health Monitoring, Vol.18, No.3, pp. 299-320, 2024, DOI:10.32604/sdhm.2024.047850

    Abstract This paper presents an easily installed improved perfobond connector (PBL) designed to reduce the shear concentration of PBL. The improvement of PBL lies in changing the straight penetrating rebar to the Z-type penetrating rebar. To study the shear performance of improved PBL, two PBL test specimens which contain straight penetrating rebar and six improved PBL test specimens which contain Z-type penetrating rebars were designed and fabricated, and push-out tests of these eight test specimens were carried out to investigate and compare the shear behavior of PBL. Additionally, Finite Element Analysis (FEA) models of the PBL… More >

  • Open Access

    REVIEW

    A Review of NILM Applications with Machine Learning Approaches

    Maheesha Dhashantha Silva*, Qi Liu

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2971-2989, 2024, DOI:10.32604/cmc.2024.051289

    Abstract In recent years, Non-Intrusive Load Monitoring (NILM) has become an emerging approach that provides affordable energy management solutions using aggregated load obtained from a single smart meter in the power grid. Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less of a burden for the energy monitoring process. However, conducted research works have limitations for real-time implementation due to the practical issues. This paper aims to identify the contribution of ML approaches to developing a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,… More >

  • Open Access

    ARTICLE

    Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

    Xueya Wang1, Yiming Zhang2,3,*, Qi Liu4, Huanran Wang1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2907-2922, 2024, DOI:10.32604/cmc.2024.050736

    Abstract Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcing steel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concrete is closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve the resolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. To rapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adopted in this work, which is celebrated for its capabilities in image generation. The open-source dataset used herein… More >

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