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Razieh Keshavarzi, Mehdi Jahangiri^*
Energy Engineering, Vol.121, No.6, pp. 1381-1401, 2024, DOI:10.32604/ee.2024.050029 - 21 May 2024
Abstract Nowadays, the use of renewable energies, especially wind, solar, and biomass, is essential as an effective solution to address global environmental and economic challenges. Therefore, the current study examines the energy-economic-environmental analysis of off-grid electricity generation systems using solar panels, wind turbines, and biomass generators in various weather conditions in Iran. Simulations over 25 years were conducted using HOMER v2.81 software, aiming to determine the potential of each region and find the lowest cost of electricity production per kWh. In the end, to identify the most suitable location, the Technique for Order Preference by Similarity… More >

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Li Deng¹, Jiafei Huan¹, Wei Wang¹, Weitao Zhang¹, Liangbin Xie², Lun Dong², Jingrong Guo², Zhongping Li², Yuan Huang^2,*, Yue Xiang²
Energy Engineering, Vol.121, No.6, pp. 1403-1437, 2024, DOI:10.32604/ee.2024.046393 - 21 May 2024
Abstract As a flexible resource, energy storage plays an increasingly significant role in stabilizing and supporting the power system, while providing auxiliary services. Still, the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage, the principle of market-oriented operation has not been embodied, and there is no unified and systematic analytical framework for the business model. However, the dispatch management model of energy storage in actual power system operation is not clear. Still, the specific scheduling process and energy storage strategy on the source-load-network side could be more… More >

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Aneel Kumar¹, Mahesh Kumar¹, Amir Mahmood Soomro¹, Laveet Kumar^2,*
Energy Engineering, Vol.121, No.6, pp. 1439-1457, 2024, DOI:10.32604/ee.2024.049977 - 21 May 2024
Abstract Electric vehicles (EVs) are the most interesting and innovative technology in the 21st century because of their enormous advantages, both technically and economically. Their emissions rate compared to fuel-based vehicles is negligible as they do not consume fuel and hence do not emit any harmful gases. However, their bulk production, adoption and lack of charging stations increase the stress of power stations due to modern-day lifestyles. If Electric vehicles demand increases drastically then conventional power stations will not bear their demand and if they generate electricity by conventional means it will be very costly and… More >

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ARTICLE

Yu Zhang, Sheng Tian^*, Yongkang Zhang
Energy Engineering, Vol.121, No.6, pp. 1459-1472, 2024, DOI:10.32604/ee.2024.047247 - 21 May 2024
Abstract Aiming at the traditional CUK equalizer can only perform energy equalization between adjacent batteries, if the two single batteries that need to be equalized are far away from each other, there will be the problem of longer energy transmission path and lower equalization efficiency, this paper optimizes the CUK equalizer and optimizes its peripheral selection circuit, which can support the equalization of single batteries at any two positions. The control strategy adopts the open-circuit voltage (OVC) of the battery and the state of charge (SOC) of the battery as the equalization variables, and selects the… More >

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Lin Ma¹, Liyong Wang¹, Shuang Zeng¹, Yutong Zhao¹, Chang Liu¹, Heng Zhang¹, Qiong Wu^2,*, Hongbo Ren²
Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332 - 21 May 2024
（This article belongs to the Special Issue: Innovative Energy Systems Management under the Goals of Carbon Peaking and Carbon Neutrality)
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 >

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ARTICLE

Guanglu Xie¹, Zhimin Xue¹, Bo Xiong¹, Yaowen Huang¹, Chaoming Chen¹, Qing Liao¹, Cheng Yang^2,*, Xiaoqian Ma²
Energy Engineering, Vol.121, No.6, pp. 1495-1519, 2024, DOI:10.32604/ee.2024.047832 - 21 May 2024
Abstract The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems. This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units, the hydraulic and thermodynamic characteristics of the heating network, and the energy loads. Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case, the interaction effect among the source-side prime movers, the heating networks, and the terminal demand thermal parameters were investigated based on the designed values, the… More >

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Yanqun Yu^1,*, Wenhao Xu¹, Yahui Huangfu¹, Jinhai Liu^1,2, Bensheng Wang¹, Kai Liu¹
Energy Engineering, Vol.121, No.6, pp. 1521-1533, 2024, DOI:10.32604/ee.2024.047986 - 21 May 2024
（This article belongs to the Special Issue: Hydraulic Fracturing Theory and Application for Geo-energy Development)
Abstract China’s unconventional gas fields have a large number of low-productivity and low-efficiency wells, many of which are located in remote and environmentally harsh mountainous areas. To address the long-term stable production of these gas wells, plunger-lift technology plays an important role. In order to fully understand and accurately grasp the drainage and gas production mechanisms of plunger-lift, a mechanical model of plunger-liquid column uplift in the plunger-lift process was established, focusing on conventional plunger-lift systems and representative wellbore configurations in the Linxing region. The operating casing pressure of the plunger-lift process and the calculation method… More >

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ARTICLE

Huanan Yu, Hangyu Li, He Wang, Shiqiang Li^*
Energy Engineering, Vol.121, No.6, pp. 1535-1555, 2024, DOI:10.32604/ee.2024.046936 - 21 May 2024
（This article belongs to the Special Issue: Key Technologies of Renewable Energy Consumption and Optimal Operation under )
Abstract The escalating deployment of distributed power sources and random loads in DC distribution networks has amplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimal configuration of measurement points, this paper presents an optimal configuration scheme for fault location measurement points in DC distribution networks based on an improved particle swarm optimization algorithm. Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing. The model aims to achieve the minimum number of measurement points while attaining the best compressive sensing reconstruction effect. It incorporates constraints from… More >

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ARTICLE

Zhang Zhi¹, Haiyu Huang², Wei Xiong², Yijia Zhou³, Mingyu Yan^3,*, Shaolian Xia², Baofeng Jiang², Renbin Su², Xichen Tian⁴
Energy Engineering, Vol.121, No.6, pp. 1557-1576, 2024, DOI:10.32604/ee.2024.047401 - 21 May 2024
Abstract Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existing scenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios, which threatens the robustness of stochastic unit commitment and hinders its application. This paper provides a stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming and Benders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouples the primal problem into the master problem and two types of subproblems. In the master problem, the committed generator is determined, while the feasibility and… More >

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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 - 21 May 2024
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 >

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286

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ARTICLE

Yuanjun Dai, Haonan Li, Baohua Li^*
Energy Engineering, Vol.121, No.6, pp. 1607-1636, 2024, DOI:10.32604/ee.2024.047255 - 21 May 2024
（This article belongs to the Special Issue: Wind Energy Development and Utilization)
Abstract This paper addresses the micro wind-hydrogen coupled system, aiming to improve the power tracking capability of micro wind farms, the regulation capability of hydrogen storage systems, and to mitigate the volatility of wind power generation. A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction, the hydrogen storage state division interval, and the daily scheduled output of wind power generation. The control strategy maximizes the power tracking capability, the regulation capability of the hydrogen storage system, and the fluctuation of the joint output of the wind-hydrogen… More >

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Yuping Bian^*, Xiu Wan, Xiaoyu Zhou
Energy Engineering, Vol.121, No.6, pp. 1637-1656, 2024, DOI:10.32604/ee.2024.047678 - 21 May 2024
Abstract To address uncertainty as well as transient stability constraints simultaneously in the preventive control of wind farm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilistic multi-objective particle swarm optimization based on the point estimate method is employed to cope with the stochastic factors. The transient security region of the system is accurately ensured by the interior point method in the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforced in the last stage. Furthermore, the proposed strategy is a general More >

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Zhengrong Chen^*, Mao Jiang, Chuanzhi Ai, Jianshu Wu, Xin Xie
Energy Engineering, Vol.121, No.6, pp. 1657-1669, 2024, DOI:10.32604/ee.2024.030222 - 21 May 2024
Abstract Based on the actual data collected from the tight sandstone development zone, correlation analysis using the Spearman method was conducted to determine the main factors influencing the gas production rate of tight sandstone fracturing. An integrated model combining geological engineering and numerical simulation of fracture propagation and production was completed. Based on data analysis, the hydraulic fracture parameters were optimized to develop a differentiated fracturing treatment adjustment plan. The results indicate that the influence of geological and engineering factors in the X1 and X2 development zones in the study area differs significantly. Therefore, it is… More >

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Guanfu Wang¹, Yudie Sun¹, Jinling Li^2,3,*, Yu Jiang¹, Chunhui Li¹, Huanan Yu^2,3, He Wang^2,3, Shiqiang Li^2,3
Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794 - 21 May 2024
（This article belongs to the Special Issue: Key Technologies of Renewable Energy Consumption and Optimal Operation under )
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 >

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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 - 21 May 2024
（This article belongs to the Special Issue: Key Technologies of Renewable Energy Consumption and Optimal Operation under )
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 >

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Tao Bao^*, Xiyuan Ma, Zhuohuan Li, Duotong Yang, Pengyu Wang, Changcheng Zhou
Energy Engineering, Vol.121, No.6, pp. 1713-1737, 2024, DOI:10.32604/ee.2023.046150 - 21 May 2024
Abstract The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases. In order to improve and ensure the stable operation of the novel power system, this study proposes an artificial emotional lazy Q-learning method, which combines artificial emotion, lazy learning, and reinforcement learning for static security and stability analysis of power systems. Moreover, this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able More >

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