Caixia Tao, Shize Yang^*, Taiguo Li

Energy Engineering, Vol.121, No.1, pp. 187-201, 2024, DOI:10.32604/ee.2023.042421

Abstract With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima. To tackle this issue, a more advanced particle swarm optimization algorithm is proposed. To address the varying emphases at different stages of the optimization process, a dynamic strategy is implemented to regulate the social and self-learning factors. The Metropolis criterion is introduced into… More >

Hua Leng¹, Silin He², Jian Qiu³, Feng Liu^4,*, Xinfei Huang⁴, Jiran Zhu²

Energy Engineering, Vol.121, No.1, pp. 77-94, 2024, DOI:10.32604/ee.2023.028340

Abstract The distribution network exhibits complex structural characteristics, which makes fault localization a challenging task. Especially when a branch of the multi-branch distribution network fails, the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system. In this paper, the multi-branch mainline is decomposed into single branch lines, transforming the complex multi-branch fault location problem into a double-ended fault location problem. Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines, the endpoint reference time difference matrix S… More >

Lingyu Liang¹, Xiangyu Zhao^1,*, Wenqi Huang¹, Liming Sun^2,3, Ziyao Wang³, Yaosen Zhan²

Energy Engineering, Vol.120, No.12, pp. 2839-2856, 2023, DOI:10.32604/ee.2023.042042

Abstract The N-1 criterion is a critical factor for ensuring the reliable and resilient operation of electric power distribution networks. However, the increasing complexity of distribution networks and the associated growth in data size have created a significant challenge for distribution network planners. To address this issue, we propose a fast N-1 verification procedure for urban distribution networks that combines CIM file data analysis with MILP-based mathematical modeling. Our proposed method leverages the principles of CIM file analysis for distribution network N-1 analysis. We develop a mathematical model of distribution networks based on CIM data and… More >

Xiaobao Yu^*, Kang Yang

Energy Engineering, Vol.120, No.12, pp. 2741-2760, 2023, DOI:10.32604/ee.2023.028041

Abstract Taking an industrial park as an example, this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources (DERs). The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data. To predict and analyze the load growth of the industrial park, an improved back-propagation algorithm is employed. Furthermore, the study classifies users within the industrial park according to their specific power consumption and supply requirements. This user segmentation allows for the introduction of three constraints: node voltage, wire current, and capacity More >

Zihan Qi^*

Energy Engineering, Vol.120, No.11, pp. 2547-2563, 2023, DOI:10.32604/ee.2023.027045

Abstract A novel operation control method for relay protection in flexible DC distribution networks with distributed power supply is proposed to address the issue of inaccurate fault location during relay protection, leading to poor performance. The method combines a fault-tolerant fault location method based on long-term and short-term memory networks to accurately locate the fault section. Then, an operation control method for relay protection based on adaptive weight and whale optimization algorithm (WOA) is used to construct an objective function considering the shortest relay protection action time and the smallest impulse current. The adaptive weight and More >

Feng Yang¹, Zhong Wu^2,*, Xiaoyan Teng¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 719-738, 2024, DOI:10.32604/cmes.2023.028699

Abstract The low efficiency and high cost of fresh agricultural product terminal distribution directly restrict the operation of the entire supply network. To reduce costs and optimize the distribution network, we construct a mixed integer programming model that comprehensively considers to minimize fixed, transportation, fresh-keeping, time, carbon emissions, and performance incentive costs. We analyzed the performance of traditional rider distribution and robot distribution modes in detail. In addition, the uncertainty of the actual market demand poses a huge threat to the stability of the terminal distribution network. In order to resist uncertain interference, we further extend More > Graphic Abstract

Guobin He^*, Rui Su, Jinxin Yang, Yuanping Huang, Huanlin Chen, Donghui Zhang, Cangtao Yang, Wenwen Li

Energy Engineering, Vol.120, No.9, pp. 2133-2154, 2023, DOI:10.32604/ee.2023.029007

Abstract In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization, multi-resource penetration in active distribution networks has been advancing fiercely. In particular, distributed generation (DG) based on renewable energy is critical for active distribution network operation enhancement. To comprehensively analyze the accessing impact of DG in distribution networks from various parts, this paper establishes an optimal DG location and sizing planning model based on active power losses, voltage profile, pollution emissions, and the economics of DG costs as well as meteorological conditions. Subsequently, multi-objective particle swarm optimization (MOPSO) is… More >

Jun Yang^*, Nannan Wang, Jiang Wang, Yashuai Luo

Energy Engineering, Vol.120, No.9, pp. 2079-2096, 2023, DOI:10.32604/ee.2023.028653

Abstract Distribution networks denote important public infrastructure necessary for people’s livelihoods. However, extreme natural disasters, such as earthquakes, typhoons, and mudslides, severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life. Therefore, considering the requirements for distribution network disaster prevention and mitigation, there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions. This paper accesses multi-source data, presents the data quality improvement methods of distribution networks, and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using More >

Zhuohan Jiang¹, Jingyi Tu¹, Shuncheng Liu¹, Jian Peng¹, Guang Ouyang^2,*

Energy Engineering, Vol.120, No.7, pp. 1655-1666, 2023, DOI:10.32604/ee.2023.026981

Abstract The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels. This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions. The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales. More >

Xunyou Zhang^1,2,*, Chuanyang Liu^1,3, Zuo Sun¹

Energy Engineering, Vol.120, No.7, pp. 1667-1683, 2023, DOI:10.32604/ee.2023.025186

Abstract A DC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits, such as high efficiency and easy control. However, a sudden increase in the current after the occurrence of faults in the network may adversely affect network stability. This study proposes an artificial neural network (ANN)-based fault detection and protection method for DC distribution networks. The ANN is applied to a classifier for different faults on the DC line. The backpropagation neural network is used to predict the line current, and the fault detection threshold is obtained on the basis More >