Records with Subject: Energy Management
Showing records 1 to 25 of 1399. [First] Page: 1 2 3 4 5 Last
Multi-Objective Dynamic Reconstruction of Distributed Energy Distribution Networks Based on Stochastic Probability Models and Optimized Beetle Antennae Search
Xin Yan, Yiming Luo, Naiwei Tu, Peigen Tian, Xi Xiao
June 7, 2024 (v1)
Keywords: beetle antennae search algorithm, distribution network optimization, multi-objective optimization, simulated annealing algorithm
In the dynamic optimization problem of the distribution network, a dynamic reconstruction method based on a stochastic probability model and optimized beetle antennae search is proposed. By implementing dynamic reconstruction of distributed energy distribution networks, the dynamic regulation and optimization capabilities of the distribution network can be improved. In this study, a random probability model is used to describe the uncertainty in the power grid. The beetle antennae search is used for dynamic multi-objective optimization. The performance of the beetle antennae search is improved by combining it with the simulated annealing algorithm. According to the results, the optimization success rate of the model was 98.7%. Compared with the discrete binary particle swarm optimization algorithm and bacterial foraging optimization algorithm, it was 9.3% and 26.1% faster, respectively. For practical applications, this model could effectively reduce power grid transmission losses, with... [more]
Line−Household Relationship Identification Method for a Low-Voltage Distribution Network Based on Voltage Clustering and Electricity Consumption Characteristics
Lei Yao, Jincheng Huang, Wei Zhang
June 7, 2024 (v1)
Keywords: electricity consumption characteristic, line–household relationship, low-voltage distribution network, vacant users, voltage clustering
To address the issue of inconspicuous electricity consumption characteristics among vacant users in low-voltage distribution networks (LVDNs), which hinders effective line−household relationship identification (LHRI), a method for identifying line−household relationship based on voltage clustering and electricity consumption characteristics is proposed. Initially, the paper employs Dynamic Time Warping (DTW) to analyze the similarity of user voltage profiles and utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster users. This approach identifies the topological relationship between vacant users and regular users to obtain multiple user categories. Subsequently, by analyzing the electricity consumption characteristic, the connection relationships between different user categories and phase lines are clarified based on the correlation between the electricity consumption characteristic vector of phase lines and the electricity consumption... [more]
A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques
Muhammad Raheel Khan, Zunaib Maqsood Haider, Farhan Hameed Malik, Fahad M. Almasoudi, Khaled Saleem S. Alatawi, Muhammad Shoaib Bhutta
June 7, 2024 (v1)
Keywords: Artificial Intelligence, demand-side management, electric vehicles, energy storage system, microgrid, optimization algorithms, renewable energy resources, smart grid
The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy ma... [more]
Multi-Mode Control of a Hybrid Transformer for the Coordinated Regulation of Voltage and Reverse Power in Active Distribution Network
Xiao Xu, Teng Zhang, Ziwen Qiu, Hui Gao, Haicheng Yu, Zongxiong Ma, Ruhai Zhang
June 7, 2024 (v1)
Keywords: active distribution network, hybrid transformer, multi-mode control, reverse power flow, topology, voltage regulation
The unprecedented growth of distributed renewable generation is changing the distribution network from passive to active, resulting in issues like reverse power flow, voltage violations, malfunction of protection relays, etc. To ensure the reliable and flawless operation of active distribution networks, an electrical device enabling active network management is necessary, and a hybrid distribution transformer offers a promising solution. This study introduces a novel hybrid transformer topology and multi-mode control strategy to achieve coordinated voltage and reverse power regulation in active distribution networks. The proposed hybrid transformer combines conventional transformer windings with a partially rated SiC-MOSFET-based back-to-back converter, reducing additional investment costs and enhancing system reliability. A multi-mode control strategy is proposed to facilitate the concurrent reverse power control and voltage violation mitigation of the presented hybrid transformer, al... [more]
Research on Multi-Objective Energy Management of Renewable Energy Power Plant with Electrolytic Hydrogen Production
Tao Shi, Libo Gu, Zeyan Xu, Jialin Sheng
June 7, 2024 (v1)
Keywords: electrolytic hydrogen, fuzzy chance constraints, improved particle swarm algorithm, peak shaving auxiliary services, power fluctuation smoothing
This study focuses on a renewable energy power plant equipped with electrolytic hydrogen production system, aiming to optimize energy management to smooth renewable energy generation fluctuations, participate in peak shaving auxiliary services, and increase the absorption space for renewable energy. A multi-objective energy management model and corresponding algorithms were developed, incorporating considerations of cost, pricing, and the operational constraints of a renewable energy generating unit and electrolytic hydrogen production system. By introducing uncertain programming, the uncertainty issues associated with renewable energy output were successfully addressed and an improved particle swarm optimization algorithm was employed for solving. A simulation system established on the Matlab platform verified the effectiveness of the model and algorithms, demonstrating that this approach can effectively meet the demands of the electricity market while enhancing the utilization rate o... [more]
Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network
Mingxing Guo, Ran Lv, Zexing Miao, Fei Fei, Zhixin Fu, Enqi Wu, Li Lan, Min Wang
June 7, 2024 (v1)
Keywords: deep-belief neural network, ice-storage air conditioning, load forecasting, operation optimization
The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improve... [more]
Distribution System State Estimation Based on Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition
Xiaomeng Chu, Jiangjun Wang
June 6, 2024 (v1)
Keywords: distribution system, ensemble empirical mode decomposition, kernel ridge regression, state estimation
In the case of strong non-Gaussian noise in the measurement information of the distribution network, the strong non-Gaussian noise significantly interferes with the filtering accuracy of the state estimation model based on deep learning. To address this issue, this paper proposes an enhanced kernel ridge regression state estimation method based on ensemble empirical mode decomposition. Initially, ensemble empirical mode decomposition is employed to eliminate most of the noise data in the measurement information, ensuring the reliability of the data for subsequent filtering. Subsequently, the enhanced kernel ridge regression state estimation model is constructed to establish the mapping relationship between the measured data and the estimation residuals. By inputting the measured data, both estimation results and estimation residuals can be obtained. Finally, numerical simulations conducted on the standard IEEE-33 node system and a 78-node system in a specific city demonstrate that the... [more]
A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction
Ling Xiao, Ruofan An, Xue Zhang
June 5, 2024 (v1)
Keywords: deep learning model, multiple features, power load forecasting, transfer learning
Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optim... [more]
Leveraging Transformer-Based Non-Parametric Probabilistic Prediction Model for Distributed Energy Storage System Dispatch
Xinyi Chen, Yufan Ge, Yuanshi Zhang, Tao Qian
June 5, 2024 (v1)
Keywords: chance-constrained programming, composite quantile regression, distributed energy storage system, low-voltage distribution networks, non-parametric probabilistic prediction, PatchTST
In low-voltage distribution networks, distributed energy storage systems (DESSs) are widely used to manage load uncertainty and voltage stability. Accurate modeling and estimation of voltage fluctuations are crucial to informed DESS dispatch decisions. However, existing parametric probabilistic approaches have limitations in handling complex uncertainties, since they always rely on predefined distributions and complex inference processes. To address this, we integrate the patch time series Transformer model with the non-parametric Huberized composite quantile regression method to reliably predict voltage fluctuation without distribution assumptions. Comparative simulations on the IEEE 33-bus distribution network show that the proposed model reduces the DESS dispatch cost by 6.23% compared to state-of-the-art parametric models.
Study on Temperature Cascade ELM Inversion Method for 110 kV Single-Core Cable Intermediate Joints
Xinhai Li, Bao Feng, Zhengang Wang, Jiangjun Ruan, Chang Xiao
June 5, 2024 (v1)
Keywords: axial inversion, cable joint, cascade inversion, hotspot temperature, radial inversion
The accurate calculation of the hotspot temperature of the cable intermediate joint can effectively guarantee the safe operation of the transmission and distribution network. This paper addresses the limitations of the current method of estimating hotspot temperature solely from surface temperature measurements. Specifically, we focus on a 110 kV single-core cable as our subject of study. We started by establishing a simulation model for the temperature field at the intermediate joint to generate data samples. Subsequently, the NCA (neighborhood component analysis) algorithm was employed to select the optimal measurement points on the cable’s surface. This allowed determination of the quantity and location of characteristic points. Finally, we developed a cascading inversion model, which consists of a radial inversion model and an axial inversion model, based on the extreme learning machine algorithm. The example results show that the mean squared error of hotspot temperature obtained... [more]
Business Process Reengineering with a Circular Economy PDCA Model from the Perspective of Manufacturing Industry
Milena Nebojša Rajić, Zorana Zoran Stanković, Marko V. Mančić, Pedja Miroslav Milosavljević, Rado Maksimović
June 5, 2024 (v1)
Keywords: business process reengineering, circular economy, plan-do-check-act model, resource management, sustainability in manufacturing processes, waste management practices
In times of increasing awareness of sustainability and the need for efficient business processes, this study explores the integration of business process reengineering with circular economy principles within Serbian manufacturing organizations. Addressing the need for sustainable development, the research aims to propose and validate a model that harmonizes business process reengineering with the circular economy to improve environmental and organizational performance. The study conducted an extensive survey and analysis across 135 manufacturing organizations in Serbia, assessing their readiness and current practices in adopting circular economy strategies through business process reengineering, utilizing the Plan-Do-Check-Act (PDCA) model. The findings reveal a moderate level of integration, with an average implementation score of 44.70% across surveyed organizations. Notably, organizations with ISO 9001 and ISO 14001 certifications demonstrated higher levels of model implementation.... [more]
An Improved Dual Second-Order Generalized Integrator Phased-Locked Loop Strategy for an Inverter of Flexible High-Voltage Direct Current Transmission Systems under Nonideal Grid Conditions
Lai Peng, Zhichao Fu, Tao Xiao, Yang Qian, Wei Zhao, Cheng Zhang
January 12, 2024 (v1)
Keywords: DC bias, flexible DC transmission, harmonic voltage, PLL, power quality, unbalance voltage
High-voltage flexible power systems, with their intrinsic characteristics, play an increasingly important role in electronic power systems. Synchronization between the inverter and the grid needs to be achieved by a phase-locked loop (PLL), the performance of which determines the quality of power transmission. This paper proposes a PLL adapted to extremely harsh grid conditions. Firstly, the traditional synchronous reference frame PLL and the dual second-order generalized integrator (DSOGI-PLL) are analyzed, and the errors in phase-locking and the shortcomings of these two methods in the presence of DC components in the grid are pointed out. Secondly, based on the harmonic grid voltage, a repetitive control internal model is introduced by DSOGI to realize the real-time tracking and regulation of the harmonic signals in order to suppress the harmonic voltage disturbance. In addition, a DC bias elimination and frequency adaptive method is proposed to solve the problems of DC bias and gri... [more]
Frequency and Inertial Response Analysis of Loads in The Chilean Power System
Juan Quiroz, Roberto Perez, Héctor Chávez, Carlos Fuentes, Matías Díaz, José Rodriguez
January 12, 2024 (v1)
Keywords: frequency measurement, frequency response, inertia, power systems, smart grids
The integration of power electronics-interconnected generation systems to the grid has fostered a significant number of concerns on power system operations, particularly on the displacement of synchronous generators that leads to a reduction in the grid’s overall inertia and frequency response. These concerns have raised a significant amount of state-of-the-art mathematical proposals on how to estimate system inertia; however, the majority of the proposals do not differentiate generator inertia from load inertia. When inertia prediction for control room applications is required in real-time, the current state-of-the-art proposals use the inertia of generators as a proxy for a minimum, overall inertia estimate, counting the number of units committed in real-time and adding up their inertia. However, as dynamic conditions are becoming challenging with the integration of power electronics-interconnected generation systems, it is important to quantify the amount of inertia from the loads,... [more]
Fault Detection and Location of 35 kV Single-Ended Radial Distribution Network Based on Traveling Wave Detection Method
Xiaowei Xu, Fangrong Zhou, Yongjie Nie, Wenhua Xu, Ke Wang, Jian OuYang, Kaihong Zhou, Shan Chen, Yiming Han
January 5, 2024 (v1)
Keywords: 35 kV, distribution network, Fault Detection, traveling wave method, wavelet conversion method
With the progress of society and the iterative improvement of infrastructure construction, the power grid transmission lines have also entered an era of intelligence. The national distribution system has made ensuring the regular operation of the distribution network as well as prompting troubleshooting and detection its top priority. Research on fault diagnosis for 35 kV single-ended radial distribution networks is still in its infancy compared to other hot topics in the industry, such as short-circuit fault detection and fault node localization. This study adopts the 35 kV single-ended radial distribution network as a model, detects fault lines via the traveling wave method, and accurately locates fault nodes using the wavelet conversion method, hoping to quickly identify and locate fault nodes in distribution networks. The experimental results demonstrate that the research method can quickly identify the faulty line and carry out further fault node location detection. The final obta... [more]
New Technology and Method for Monitoring the Status of Power Systems to Improve Power Quality—A Case Study
Rahim Ildarabadi, Mahmoud Zadehbagheri
January 5, 2024 (v1)
Keywords: data compression, Fourier transform, harmonics, monitoring, power quality, steady-state analysis, transient analysis, wavelet transforms (WTs)
The identification and analysis of harmonics, frequency, and transient events are essential today. It is necessary to have available data relating to harmonics, frequency, and transient events to understand power systems and their proper control and analysis. Power quality monitoring is the first step in identifying power quality disturbances and reducing them and, as a result, improving the performance of the power system. In this paper, while presenting different methods for measuring these quantities, we have made some corrections to them. These reforms have been obtained through the analysis of power network signals. Finally, we introduce a new monitoring system capable of measuring harmonics, frequency, and transient events in the network. In addition, these values are provided for online and offline calculations of harmonics, frequency, and transient events. In this paper, two new and practical methods of the “algebraic method” are used to calculate network harmonics and wavelet... [more]
Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search
Hugo de Oliveira Motta Serrano, Cleberton Reiz, Jonatas Boas Leite
January 5, 2024 (v1)
Keywords: capacity management, distributed generator, distribution network, GRASP, load shedding, Tabu Search
Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving t... [more]
Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models
Tong Lu, Sizu Hou, Yan Xu
January 5, 2024 (v1)
Keywords: feature selection, integrated energy systems, load forecasting, support vector regression, variational mode decomposition
A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Then, each IMF, along with other influential features, is subjected to data dimensionality reduction and clustering denoising using t-distributed stochastic neighbor embedding (t-SNE) and fast density-based spatial clustering of applications with noise (FDBSCAN) to perform major feature selection. Subsequently, the reduced and denoised data are reconstructed, and a time-aware long short-term memory (T-LSTM) artificial neural network is employed to fill in missing data by incorporating time interval information. Finally, the selected multi-factor load time series is used as input into a support vector regression (SVR) model optimized using the quantum particle swarm optimization (QPSO) a... [more]
Development of Ultrasound Piezoelectric Transducer-Based Measurement of the Piezoelectric Coefficient and Comparison with Existing Methods
Chandana Ravikumar, Vytautas Markevicius
September 21, 2023 (v1)
Keywords: acoustic method, dynamic, energy harvesting, interferometric, piezoelectric coefficient, quasi-static, ultrasound transducer
Energy harvesting using the piezoelectric material in the development of compact vibration energy harvesters can be used as a backup power source for wireless sensors or to fully replace the use of fossil-resource-wasting batteries and accumulators to power a device or sensor. Generally, the coefficient is used as the metric for evaluating the property in materials. Recent research reports that accurate measurement and calculation of the coefficient in materials, especially in polymers, can be challenging for various reasons. From the reviewed references, different methods, including the quasi-static, dynamic, interferometric, and acoustic methods, are discussed and compared based on the direct and indirect effect, accuracy, repeatability, frequency range, and so on. A development of an ultrasound piezoelectric transducer is conducted to estimate d33 coefficient with a reference value. The purpose of the method was mainly to measure the values of piezoelectric material in order to meas... [more]
A Novel Power Quality Comprehensive Estimation Model Based on Multi-Factor Variance Analysis for Distribution Network with DG
Haili Ding, Pengyuan Liu, Xingzhi Chang, Bai Zhang
September 21, 2023 (v1)
Keywords: DG, multi-factor analysis of variance, power quality evaluation, significance testing
The power quality estimation for distribution network connected DG (distributed generation) is important in the power system. The significance testing for power quality indicator is less used in traditional power quality evaluation. However, the power quality indicator is affected by various factors of the power system, which seriously impact the power quality evaluation result. To solve this problem, A novel power quality comprehensive estimation model based on multi-factor variance analysis for distribution network with DG is proposed in this paper, in which the significance testing is carried out for power quality indicator with the various system factors, and then to generate the evaluation weights in different levels, further to obtain the power quality assessment results for single node. And then, the dual-significance tests are carried out to generate the weight of node and to obtain the comprehensive estimation result of whole system. At last, an example is developed to validat... [more]
Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature
Jiangpeng Feng, Xiqiang Chang, Yanfang Fan, Weixiang Luo
September 20, 2023 (v1)
Keywords: electric vehicles, load forecasting, Monte Carlo method, spatio-temporal distribution, traffic conditions
The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle’s main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyz... [more]
Optimal Allocation Method of Circuit Breakers and Switches in Distribution Networks Considering Load Level Variation
Guodong Huang, Yi Zhou, Chen Yang, Qiong Zhu, Li Zhou, Xiaofeng Dong, Junting Li, Junpeng Zhu
September 20, 2023 (v1)
Keywords: allocation, circuit breakers, distribution network, load level variation, reliability enhancement, switches
Reliability is a fundamental concept for power systems, and the optimal placement of switchable devices is a valuable tool for improvements in this area. The goal of this paper is to propose an optimal allocation method for circuit breakers and switches that can break the cost−reliability dilemma and simultaneously achieve reliability and economic improvement in terms of the distribution network. Moreover, in view of the fact that variations in the load level can affect the reliability of the distribution network, the variations of different load level scenarios are considered in this paper, where a mixed integer linear programming (MILP) model based on fictitious fault flows is established to derive the optimal allocation scheme that can adapt to the changes of multiple scenarios regarding the load. Meanwhile, due to the constraints of reliability indices, the post-fault reconfiguration scheme of a distribution network under different load level scenarios can also be obtained to enhan... [more]
IMODBO for Optimal Dynamic Reconfiguration in Active Distribution Networks
Naiwei Tu, Zuhao Fan
July 13, 2023 (v1)
Keywords: IMODBO, K-means++, network reconfiguration, renewable energy sources, voltage fluctuations
A dynamic reconfiguration method based on the improved multi-objective dung beetle optimizer (IMODBO) is proposed to reduce the operating cost of the distribution network with distributed generation (DG) and ensure the quality of the power supply, while also minimizing the number of switch operations during dynamic reconfiguration. First, a multi-objective model of distribution network dynamic reconfiguration with the optimization goal of minimizing active power loss and voltage deviation is established. Secondly, the K-means++ clustering algorithm is used to divide the daily load of the distribution network into periods. Finally, using the IMODBO algorithm, the distribution network is reconstructed into a single period. The IMODBO algorithm uses the chaotic tent map to initialize the population, which increases the ergodicity of the initial population and solves the problem of insufficient search space. The algorithm introduces an adaptive weight factor to solve the problem of the alg... [more]
Blank Strip Filling for Logging Electrical Imaging Based on Multiscale Generative Adversarial Network
Qifeng Sun, Naiyuan Su, Faming Gong, Qizhen Du
July 7, 2023 (v1)
Keywords: blank strip filling, electrical imaging logging, GAN, U-Net
The Fullbore Formation Micro Imager (FMI) represents a proficient method for examining subterranean oil and gas deposits. Despite its effectiveness, due to the inherent configuration of the borehole and the logging apparatus, the micro-resistivity imaging tool cannot achieve complete coverage. This limitation manifests as blank regions on the resulting micro-resistivity logging images, thus posing a challenge to obtaining a comprehensive analysis. In order to ensure the accuracy of subsequent interpretation, it is necessary to fill these blank strips. Traditional inpainting methods can only capture surface features of an image, and can only repair simple structures effectively. However, they often fail to produce satisfactory results when it comes to filling in complex images, such as carbonate formations. In order to address the aforementioned issues, we propose a multiscale generative adversarial network-based image inpainting method using U-Net. Firstly, in order to better fill the... [more]
Data-Driven Operation of Flexible Distribution Networks with Charging Loads
Guorui Wang, Zhenghao Qian, Xinyao Feng, Haowen Ren, Wang Zhou, Jinhe Wang, Haoran Ji, Peng Li
July 4, 2023 (v1)
Keywords: charging loads, data-driven operation, flexible distribution networks (FDNs), multi-timescale coordination, soft open point (SOP)
The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can... [more]
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems
Dallatu Abbas Umar, Gamal Alkawsi, Nur Liyana Mohd Jailani, Mohammad Ahmed Alomari, Yahia Baashar, Ammar Ahmed Alkahtani, Luiz Fernando Capretz, Sieh Kiong Tiong
June 13, 2023 (v1)
Keywords: Artificial Intelligence, MPPT, wind energy harvesting system
As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques com... [more]
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