Enhanced Power Supply Research Articles

Application and practice of portable bi-directional DC-AC energy storage converter based on second order generalized integrator phase-locked loop and HERIC circuit

In various industries, including the production of new energy and electric vehicles, portable bidirectional converters are crucial for enhancing power supply reliability. In recent years, these converters have become increasingly normal in daily life. Bidirectional converters have not been as popular or have not been used as much due to issues with phase tracking on grids connected to leakage current in the converter system. The essential features and principles of the portable bidirectional energy storage converter proposed in this paper, which is based on a second-order generalized integrator phase-locked loop, are theoretically investigated. Formulas are also generated using small signals to address this issue. PLECS simulation software was used for parameter design and algorithm simulation to test the effectiveness of the phase-locked loop under various operating situations. Therefore, a set of appropriate phase-locked structure models for this circuit was developed. An experimental verification platform was constructed to confirm the accuracy of the design parameters: the front stage uses the H-bridge; the backstage, which uses the front stage, adopts the H-bridge; and the rear stage adopts the topology platform of the HERIC topology + LCL filter circuit. The test data indicates that, the addition of the HERIC topology and soft switching resulted in a 94 % reduction in leakage current and a 1.6 % improvement in efficiency. Analysis and comparison results of the test data confirmed the consistency between the experimental effect and the design parameters, achieving the expected effect and proving the accuracy of the topology and mathematical model. The design methodology and design parameters of this thesis are useful and instructive for similar or related research.

Design and Feasibility Verification of Novel AC/DC Hybrid Microgrid Structures.

To enhance the power supply reliability of the microgrid cluster consisting of AC/DC hybrid microgrids, this paper proposes an innovative structure that enables backup power to be accessed quickly in the event of power source failure. The structure leverages the quick response characteristics of thyristor switches, effectively reducing the power outage time. The corresponding control strategy is introduced in detail in this paper. Furthermore, taking practical considerations into account, two types of AC/DC hybrid microgrid structures are designed for grid-connected and islanded states. These microgrids exhibit strong distributed energy consumption capabilities, simple control strategies, and high power quality. Additionally, the aforementioned structures are constructed within the MATLAB/Simulink R2023a simulation software. Their feasibility is verified, and comparisons with the existing studies are conducted using specific examples. Finally, the cost and efficiency of the application of this study are discussed. Both the above results and analysis indicate that the structures proposed in this paper can reduce costs, improve efficiency, and enhance power supply stability.

Optimal allocation of renewable energy systems in a weak distribution network

In the context of a real electrical distribution network in Puerto Carreño, Colombia, this paper addresses the challenge of enhancing power supply reliability through the strategic integration of Renewable Energy Sources with Hydrogen Energy Systems. The planning problem is reformulated as a Mixed-Integer Quadratically Constrained Programming problem, enabling the use of efficient solvers with global optimal solution. Considering the extensive curtailment options and numerous allocation components, the optimization problem naturally scales up. To address this computational intensity, representative scenarios are derived from the original high-dimensional probability distribution employing the Gaussian Mixture Model. These scenarios are used in conjunction with the proposed optimization framework to achieve cost-effective power grids while reducing fossil fuel dependency.

Enhancing Power Supply Flexibility in Renewable Energy Systems with Optimized Energy Dispatch in Coupled CHP, Heat Pump, and Thermal Storage

The use of renewable energy by converting it into heat is an important form of storing energy in a usable form and improving the energy supply flexibility; therefore, the electricity–heating system (EHS) can cope with load fluctuations. However, relevant research is lacking on improving the energy supply limitations by the optimal dispatch of energy flow at the typical EHS, such as the coupled CHP–heat pump–thermal storage system (CCHTS). Based on the study of the energy supply characteristics of the CCHTS for extending the energy supply limitation, this study develops an optimal dispatch method using a heat pump (HP) and the thermal storage (TS) of heating networks to improve the flexibility of the CCHTS and the accommodation capacity of renewable energy. The maximum and minimum energy supply limitation model of the CCHTS and the output power characteristic model are established. Based on the piecewise power supply constraint, the energy flow of the EHS is optimized by using the quadratic programming algorithm. The CCHTS can significantly improve the energy supply flexibility; both coupled combined heat and power (CHP) + HP and coupled CHP + TS can improve the power supply flexibility, but the enhanced effect of CHP + HP is better than that of CHP + TS. An increase of 7.6% in wind power consumption is achieved. The consumption of renewable energy increases by 17.9% in the energy flow optimization results.

Investigation of Quantitative Assessment Techniques for Supply-Regulation Capability in Multi-Scenario New-Type Power Systems

This paper offers an in-depth investigation into various quantitative assessment methods used to quantify the supply regulation capacity in new types of power systems under different conditions. As new forms of energy, including renewables, are increasingly becoming the predominant sources of power systems, the traditional systems are undergoing transformative modifications to efficiently address the issue of power generation and consumption fluctuations. In this regard, this paper proposes an original framework that combines advanced statistical methods and machine learning. The primary purpose of the framework is to identify the level of resilience and flexible adaptability of new power systems. The paper presents the results of the simulations and real-world applications of the proposed measurement methods in enhancing power supply reliability and efficiency in all conditions. The implications based on the results will be beneficial to policymakers and other specialists who are making decisions involving designing and optimizing modern power systems. Furthermore, the paper aims to contribute to the existing discussion by providing further insights into the effectiveness of the proposed methods of measurement.

Multi-objective optimization scheduling method considering flexible load integration for distribution network

In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper.

Enhanced power supply circuitry with long duration and high‐efficiency charging for indoor photovoltaic energy harvesting internet of things end device

AbstractLight is a popular choice as an indoor energy source for Internet of Things (IoT) end devices. However, indoor light sources are intermittent, which can disrupt the operation of IoT end devices, potentially leading to safety concerns or inaccurate data. Therefore, there is a growing need to develop a long‐duration power supply for IoT end devices. The energy from ambient light is harnessed to charge a supercapacitor through an energy manager chip. This supercapacitor serves as the power source for IoT nodes when the ambient light is unavailable. Nevertheless, as the voltage of the supercapacitor drops below the operating threshold, the IoT end node will eventually shut down. This paper proposes a circuit that utilizes a Joule Thief circuit, booster converter, and capacitor stack‐up circuit to extract the remaining energy from the supercapacitor and boost the voltage, thereby extending the operational lifespan of IoT end nodes. Additionally, capacitor stack‐up circuits significantly enhance charging efficiency. PSpice design and simulations confirm circuit feasibility. High‐efficiency charging and long‐duration IoT nodes suggest replacing traditional batteries with supercapacitors, reducing environmental impact.

Applications and challenges of Silicon Carbide (SiC) MOSFET technology in electric vehicle propulsion systems: A review

Silicon Carbide (SiC) MOSFET technology plays a pivotal role in the drive systems of electric vehicles (EVs), offering key applications and facing significant challenges. This paper provides an overview of the fundamental principles and characteristics of SiC MOSFETs, highlighting the unique properties of SiC materials. It delves into the critical applications of SiC MOSFETs in electric vehicle drive systems, including motor drives and inverters, and analyzes the advantages of using SiC MOSFETs for efficient energy conversion at high temperatures. The paper also discusses the key challenges faced by SiC MOSFET technology, such as stability issues under high-temperature environments, avalanche breakdown and tolerance issues affecting power supply reliability, and manufacturing cost and consistency issues that limit widespread application. Innovative solutions to these challenges are explored, including strategies for improving stability under high temperatures, techniques for suppressing avalanche effects and enhancing power supply reliability, and innovations in manufacturing processes to reduce costs and improve consistency. The paper concludes by summarizing the crucial role of SiC MOSFET technology in electric vehicle drive systems, emphasizing the importance of innovative solutions to address its challenges, and looking forward to its continued contribution to technological advancements in the field of electric vehicles.

Prediction of SOC for lead-acid battery based on LSTM-Attention and LightGBM

Abstract Accurately estimating the SOC of batteries is crucial for the objective of extending battery life and enhancing power supply reliability. Currently, machine learning methods are commonly used to predict the SOC of batteries, however, their accuracy in capturing the sequential nature of battery charging and discharging is insufficient. To address the problem of SOC prediction, a deep learning model that employs Long Short-Term Memory (LSTM) with Attention mechanism is proposed. The LSTM model is designed to connect the current SOC with historical time data and to extract multi-dimensional features from groups of batteries. Additionally, introducing the Attention mechanism allows for the model to prioritize key information while disregarding insignificant data. This work utilizes two different approaches to the multi-cell case and the single-cell case for several reasons. Considering that the failure of a single cell can affect the entire group of batteries, SOC prediction models for individual batteries need not take a long training time. Thus, the LightGBM model is developed to predict the SOC of a single battery whose training speed surpasses that of the deep learning model and has superior prediction accuracy and greater speed when employed with small-scale data, error within 3%. Conversely, the LSTM-Attention model yields higher prediction accuracy when processing large-scale datasets, error within 5%.Two models are proposed: one for predicting the SOC of groups of batteries and another for a single battery.

Power Quality in a University Campus: The User´s Perspective.

This paper presents preliminary results from a power quality audit conducted at a university’s campus over last year. Voltage and current were measured at various academic buildings; it was found that the main problems for the equipment installed were voltage sags and surges. These phenomena are purely random in nature. The paper examines the causes and effects of power disturbances that affect computer or any other microprocessor based equipment and analyses the auto-protection capabilities of modern power supplies. The convenience of “enhanced power supply” or “low-cost customer-side” protection solutions is also discussed. Finally it is addressed the role of the Standards on the protection of electronic equipment and the implications for the final costumer.

Assessment of the quality of electricity by applying reactive power sources

In the quest to enhance electricity quality, various states and parameters play crucial roles, particularly in assessing the significance and capabilities of reactive power sources. The evolution of electricity systems in developed nations is marked by a vigorous pursuit of cutting-edge technologies to boost energy system efficiency, lower construction and operational costs of network infrastructure, and enhance power supply reliability. The issue of reactive power compensation and electricity quality is intricately linked to diverse consumer loads, voltage, and frequency considerations. This process entails elevating the system's active power factor to harmonize total power within the AC network, maintaining voltage levels, and mitigating harmonic distortions stemming from nonlinear industrial loads. Voltage stability measures are typically essential to curtail fluctuations along power transmission lines. Enhancing the system's operational reliability involves augmenting the maximum active power that reactive power compensation mechanisms can adjust.

Quantitative Analysis of Distribution and Reliability Based on Maximum Capacity Segmented Subscribers on Distribution Lines

The rapid economic progress and widespread use of sophisticated technology elevate the output value per kWh of electricity consumed, underscoring the paramount importance of maintaining an uninterrupted and dependable power supply to avoid substantial economic losses for consumers and society. Investigating the reliability of urban distribution systems emerges not only as a pivotal factor in enhancing power supply quality but also as a cornerstone of electric power modernization, significantly impacting production, technology, and management within the industry while bolstering its economic and social benefits. This study adopts a multifaceted approach: firstly, establishing a methodology for grid-side storage capacity distribution to mitigate substation load factors and implement peak shaving, thereby minimizing load discrepancies. Secondly, it develops a mathematical model encompassing diverse user distributions, employing analytical techniques to derive reliability indices and optimal segment numbers tailored to different user distributions. The research proposes segment optimization based on user distributions, considering both economic viability and reliability, showcasing an interdisciplinary amalgamation of combinatorial principles and scientific computing methodologies. This approach aims to optimize segment distribution, enhancing the reliability and economic feasibility of urban distribution networks through advanced mathematical and computational techniques.

Distributed cooperative grid synchronization strategy for multiple parallel grid-supporting inverters in AC microgrid

In an AC microgrid (MG), the grid-supporting inverters (GSIs) are crucial components, which can regulate the frequency and voltage and enhance power supply reliability. To provide a reliable utilization of distributed generation (DG) and ensure uninterrupted operation of critical loads, a seamless handover between islanded and grid-connected modes is required for an AC MG with multiple parallel operating GSIs. Traditionally, the phase angle and frequency are usually detected by the phase-locked loop (PLL) units. Furthermore, the centralized control structure is widely used to obtain the global information of the system for grid synchronization, which will reduce the system reliability and flexibility. In this paper, a distributed cooperative grid synchronization strategy is proposed for multiple parallel GSIs. A seamless handover can be performed under the proposed control strategy that only needs to transmit the synchronization information to the leader DG without using any PLL units, which is more simple, effective, and flexible. The results of the simulation and StarSim Hardware-in-the-loop experiment are presented to confirm the effectiveness of the proposed control strategy.

Wave Power Prediction Based on Seasonal and Trend Decomposition Using Locally Weighted Scatterplot Smoothing and Dual-Channel Seq2Seq Model

Wave energy has emerged as a focal point in marine renewable energy research. Accurate prediction of wave power plays a pivotal role in enhancing power supply reliability. This paper introduces an innovative wave power prediction method that combines seasonal–trend decomposition using LOESS (STL) with a dual-channel Seq2Seq model. The decomposition model addresses the issue of component redundancy in current input decomposition methods, thereby uncovering key components. The prediction model improves upon the limitations of current prediction models that directly concatenate multiple features, allowing for a more detailed consideration of both trend and periodic features. The proposed approach begins by decomposing the power sequence based on tidal periods and optimal correlation criteria, effectively extracting both trend and periodic features. Subsequently, a dual-channel Seq2Seq model is constructed. The first channel employs temporal pattern attention to capture the trend and stochastic fluctuation information, while the second channel utilizes multi-head self-attention to further enhance the extraction of periodic components. Model validation is performed using data from two ocean buoys, each with a five-year dataset. The proposed model achieves an average 2.45% reduction in RMSE compared to the state-of-the-art method. Both the decomposition and prediction components of the model contribute to this increase in accuracy.

Power system risk assessment strategy based on weighted comprehensive allocation and improved BP neural network

AbstractDuring the operation and maintenance of the power system, power outages and supply‐demand imbalances can disrupt the normal power supply process. This issue must be mitigated or even resolved through the implementation of an appropriate power system risk warning. The article proposes a self‐assessment and early warning strategy for power system hazards based on an enhanced ant colony optimization algorithm (IACO) and a BP neural network. First, a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EWM) is used to assign weights comprehensively to indicators that have a significant impact on the stability and safety of power system operation, thereby avoiding the negative impact of subjective experience or objective factors on the weight allocation results. Secondly, multiple regression analysis is used to calculate the risk assessment results of the selected indicators and weights corresponding to the power system. Training and testing samples for the BP neural network were calculated based on the weight allocation procedure described previously. Then, IACO is employed to global optimize the weights and thresholds of the BP neural network, and an enhanced BP neural network model for independent power system risk assessment is developed. The designed risk assessment and warning strategy was finally evaluated. The results indicate that the proposed power system risk assessment and early warning method can precisely predict the actual operating status of the power system based on weight values, thereby enhancing power supply quality by providing technical personnel with a data reference.

A novel hybrid model for short-term prediction of PV power based on KS-CEEMDAN-SE-LSTM

Accurate prediction of photovoltaic (PV) power plays a pivotal role in ensuring safe and stable grid operation, enhancing power supply quality, and facilitating proactive grid management to mitigate power fluctuations. This paper introduces a novel hybrid model named KS-CEEMDAN-SE-LSTM, which combines K-Shape (KS) clustering, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample entropy (SE), and Long Short-Term Memory (LSTM) techniques. To improve the accuracy of PV power prediction, three advanced preprocessing techniques, KS clustering, CEEMDAN and SE, are employed for feature extraction before prediction. In the proposed approach, KS clustering is employed to capture the similarity features of PV power. Subsequently, CEEMDAN decomposes the PV power into multiple intrinsic mode functions (IMFs) to reduce the influence of non-stationary and noisy features in power prediction. To further enhance prediction accuracy and streamline computational complexity, SE methods and K-means clustering are utilized to reconstruct the decomposed IMFs into new sub-sequences with refined granularity features. Finally, the LSTM model is applied to predict each reconstructed sub-sequence, and the ultimate prediction results are obtained by aggregating the predictions from all sub-sequences. The effectiveness of this method is demonstrated using real-world PV power data collected from China. Comprehensive experiments substantiate that the proposed KS-CEEMDAN-SE-LSTM model outperforms benchmark methods in short-term PV power prediction.

A Review of Optimization Technologies for Large-Scale Wind Farm Planning With Practical and Prospective Concerns

Wind energy utilization is essential for realizing global energy transformation. To capture steady and powerful wind resources, wind turbines tend to be more remotely located with increasingly larger scales. This requires more sophisticated wind farm (WF) planning techniques to increase power production, reduce investment cost, and enhance power supply reliability, or pursue a balance among them. This paper establishes a research framework for large-scale WF planning, with a technical review of the optimization methodologies involved, aiming to provide an updated, broader, and forward-looking vision for WF planners. Beyond the state-of-the-art summary of WF planning in the realm of wind turbines' micro-siting and collector system design techniques, the related considerations on various practical factors are also well assessed including reliability, equipment rating, environment, and landscape. Further, viewing WF as an integrated system with scale expansion, the new research areas including WF joint-planning and re-development are explored and discussed to provide strong technical references for the foreseeable future.

An accurate fault location method for distribution lines based on data fusion of outcomes from multiple algorithms

Accurate fault location technology for distribution lines is of great importance as it can shorten power outage time and enhance power supply reliability. Current and voltage transformer errors and line parameter errors will result in location error of the existing algorithms, which cannot meet the requirements of power supply reliability. To solve this problem, a data-fusion model based on multiple algorithms is proposed in this study, which makes full use of the location results of existing algorithms. First, the complementarity among the four selected algorithms is analyzed. Then the feasibility of improving fault location accuracy by constructing a data-fusion model is explained and the overall structure of the data-fusion model is determined. Finally, an artificial neural network with a strong fitting ability is used to complete the construction of the data-fusion model. The performance of the model is tested for different factors, such as fault positions, line parameters, fault resistances, and fault inception angles. The test cases based on PSCAD/EMTDC show that the constructed model has excellent generalization ability. Additionally, performance of the model is not affected by distributed generation and sampling rate. The proposed method does not use high-sampling devices and is cost-effective.

A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently.

Model-free underground cable incipient fault location using two-terminal zero-sequence measurements

The incipient fault in the underground cable is an intermittent arcing fault with short duration from 1/4 cycle to 4 cycles and can be cleared by itself. It always reoccurs for many times before a permanent fault. The detection and location of incipient faults can help to prevent a permanent fault and enhance power supply reliability. However, accurate incipient fault location is a difficult problem because of its short duration and the non-linearity of the arc model. In this paper, a new idea for accurate underground cable incipient fault location using two-terminal synchronized zero-sequence waveforms is proposed. The two-terminal zero-sequence differential equation is built for fault distance estimation. The proposed algorithm can be applied to locate both sub-cycle and multi-cycle incipient faults. More importantly, an accurate arc model is not required for fault distance estimation. A large number of simulation results in PSCAD/EMTDC validate the effectiveness and accuracy of the proposed method.