Modern Power Systems Research Articles

Achieving ultra-high energy storage performance in simple systems through minimal element substitution

Dielectric capacitors are essential components of modern advanced electronic devices and power systems based on their ultra-fast charging and discharging speeds and supreme power densities. Nevertheless, how to comprehensively boost their energy storage density and storage efficiency is still an insurmountable challenge. Here, we report a simple micro-chemical polarizability modulation strategy that enables SrTiO3-based dielectric materials to achieve excellent energy storage properties. The energy density and energy efficiency are increased to as high as 76 J∙cm−3 and 72 % from 0.4 J∙cm−3 and 46.7 % of pure STO, respectively, which is the largest value in micro-substitution (less than 3 %). Our results show that the introduction of trace amounts of elements with high ionic polarizabilities (Mn, V) facilitates the increase of chemical disorder and the formation of stable and highly dynamic polar nanoregions (PNRs), which reduces the hysteresis of the polarization switching and improves the polarization at the breakdown field strength, synergistically fostering the energy storage performance. The results of X-ray photoelectron spectroscopy and scanning electron microscopy reveal that the micro-substitution of Mn and V promote the reduction of oxygen vacancies and grain size, improving the breakdown resistance and stability of the capacitor. This present approach is expected to be widely used in the development of high-performance dielectrics.

Dynamic Analysis and Approximate Solution of Transient Stability Targeting Fault Process in Power Systems

Modern power systems are high-dimensional, strongly coupled nonlinear systems with complex and diverse dynamic characteristics. The polynomial model of the power system is a key focus in stability research. Therefore, this paper presents a study on the approximate transient stability solution targeting the fault process in power systems. Firstly, based on the inherent sinusoidal coupling characteristics of the power system swing equations, a generalized polynomial matrix description in perturbation form is presented using the Taylor expansion formula. Secondly, considering the staged characteristics of the stability process in power systems, the approximate solutions of the polynomial model during and after the fault are provided using coordinate transformation and regular perturbation techniques. Then, the structural characteristics of the approximate solutions are analyzed, revealing the mathematical basis of the stable motion patterns of the power grid, and a monotonicity rule of the system’s power angle oscillation amplitude is discovered. Finally, the effectiveness of the proposed methods and analyses is further validated by numerical examples of the IEEE 3-machine 9-bus system and IEEE 10-machine 39-bus system.

Decentralized Stochastic Recursive Gradient Method for Fully Decentralized OPF in Multi-Area Power Systems

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 1.0.0.1) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method’s potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems.

Improving grid reliability with grid-scale Battery Energy Storage Systems (BESS)

The modern electric power system is stable because generation and demand are balanced in real-time. To provide grid managers the leeway to maintain this balance, grid-scale energy storage devices are seeing increased deployment. Another existing technique to achieve a stable and reliable power system today is integrating renewable energies with a battery energy storage system (BESS). Integrating grid-scale BESS to improve grid dependability is crucial since renewable energy sources, which may be somewhat unpredictable, are increasingly being integrated into existing power networks. With its massive electrical energy storage and distribution capabilities, BESS contributes to the grid's ability to balance supply and demand. The BESS helps maintain grid stability by storing energy that is not used during peak hours. This energy comes mostly from renewable sources like solar and wind and is then sent back to the system when the demand is highest. Primary function of BESS includes energy storage and time-shifting, regulation of frequency, voltage support, and enhancement of grid reliability. Development in battery technologies and controls has made them cheaper and inevitable for future power networks. The functions and elements of BESS, the types of electrochemical batteries, the implications of their degradation, and their applications for grid optimization and reliability are discussed in this research. In conclusion, BESS is a significant enabler of grid modernization, resilience, and the evolution of the energy system.

Real-Time Simulation and Hardware-in-the-Loop Testing Based on OPAL-RT ePHASORSIM: A Review of Recent Advances and a Simple Validation in EV Charging Management Systems

Phasor-domain (RMS) simulations have become increasingly vital in modern power system analysis, particularly as the complexity and scale of these systems have expanded with the integration of renewable energy sources. ePHASORSIM, an advanced phasor-based simulation tool developed by OPAL-RT, plays a crucial role in this context by enabling real-time phasor-domain simulation and hardware-in-the-loop testing. To keep pace with these evolving needs, continuous efforts are being made to further improve the accuracy, efficiency, and reliability of ePHASORSIM-based simulations. These efforts include automating model conversion processes for enhanced integration with ePHASORSIM, extending ePHASORSIM’s simulation range with custom models, developing hybrid co-simulation techniques involving ePHASORSIM and an EMT simulator, enhancing simulation scalability, and refining HIL testing to achieve more precise validation of control and protection systems. This paper provides a comprehensive review of these recent advances. Additionally, the paper discusses the conversion of models from PowerFactory—a widely used and comprehensive modeling environment—to ePHASORSIM through both automated tools and manual methods using Excel workbooks, which has been discussed little in the literature. Furthermore, as ePHASORSIM is a relatively new tool with limited cross-validation studies, the paper aims to contribute to this area by presenting a comparative validation against DIgSILENT PowerFactory, with a specific emphasis on its application in electric vehicle charging management systems.

Integrating Microgrids into Engineering Education: Modeling and Analysis for Voltage Stability in Modern Power Systems

Integrating Microgrids into Engineering Education: Modeling and Analysis for Voltage Stability in Modern Power Systems

Electric load forecasting under false data injection attacks via denoising deep learning and generative adversarial networks

AbstractAccurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks.

Coordinated frequency control strategy for modern power system considering engagement willingness

The large scale integration of renewable energy sources (RES) is posing unprecedented challenges to the frequency stability of modern power systems, with system inertia being the primary concern. Under such a landscape, this paper introduces a coordinated control strategy aiming at enhancing frequency stability by swiftly adjusting the power output of flexible resources to mitigate power imbalances and further dampen frequency fluctuations. Firstly, a communication-free center of inertia (COI) frequency estimation method is proposed. An equivalent inertia estimation method tailored for modern power systems is then developed by exploiting the correlation between synchronous inertia and active power fluctuations. By integrating the COI frequency variation rate with the estimated equivalent inertia, the imbalance power is calculated and compensated through the proposed coordinated control strategy. In this work, two typical flexible resources including photovoltaic (PV) generation and electric vehicles (EVs) are modeled and integrated into the testing system in PSCAD/EMTDC. Considering the contradiction between maximizing generation profit and reducing power output to mitigate frequency variation, the Corporate Social Responsibility (CSR) of renewable Generation Companies (GENCOs) is modeled to sketch their willingness of engaging into frequency support. Moreover, the frequency modulation potential of EVs in both commercial and residential areas are quantified for practical implementation. The simulations results successfully validate the effectiveness of the proposed control strategy.

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Simulation Framework for Substation Siting Integrating Load, Land Use, Neighborhood, and Cost Analysis

This study presents a comprehensive framework for substation siting to address power demand and urbanization challenges. It integrates economic, social, and environmental dimensions to ensure reliable power supply systems. Using simulation techniques, the paper evaluates substation placement by analyzing load distribution, land use, neighborhood satisfaction, and construction costs. The Analytical Hierarchy Process (AHP) assesses load distribution, while a hierarchical clustering algorithm maximizes land use efficiency. Neighborhood satisfaction is measured using hierarchical analysis and fuzzy logic. Construction costs are optimized via a genetic algorithm. These simulations formulate a multi-criteria decision-making tool, proposing an optimized siting strategy balancing technical, socio-economic, and environmental considerations. The framework enhances the accuracy of substation siting studies and provides practical implementation guidance, offering new insights and refined methodologies for modern power system planning and development.

Research on the Construction of Power System Relay Protection Course System in Electrical Engineering Discipline

Power system relay protection is one of the core courses in the discipline of electrical engineering, which is directly related to the students' professional ability in the field of power system operation and protection. With the rapid development of power system, especially the popularization of smart grid technology, the existing curriculum system gradually exposes problems such as obsolete content, insufficient practical teaching, and disconnection with cutting-edge technology. By analyzing the current situation of the existing curriculum system, this paper proposes key elements such as the combination of theory and practice, the introduction of cutting-edge knowledge, the optimal allocation of teaching resources, and the innovation of teaching methods, and discusses the implementation strategy of curriculum system reform. The study aims to build a relay protection curriculum system that better meets the development needs of modern power systems, and to enhance students' practical ability and innovative thinking to meet the challenges of the future power industry.

Stable energy storage performance of introduced PI-PESU dielectric composite serving at the high electric and thermal fields with the designed layer structure

Excellent energy storage performance of dielectric capacitor is critical in modern electronic devices and power systems. However, the key component of dielectric composite may fail to function properly under severe service conditions, such as elevated temperature and great applied electric field. Here, the linear dielectrics of PI and PESU with high glass transition temperature (Tg) were proposed to improve the energy storage performance of composite, which hold the weakened carrier migration. The large band-gap of PESU and tight molecular chain of PI are beneficial for reducing leakage current density of composite, enhancing inter-molecular stability over a wide temperature and electric field range. Finally, the energy storage performance is improved by constructing a multi-layer structure with alternating outer layers. For instance, the discharge energy density (Ue) of 0.4PI-0.6PESU composite reaches 6.38 J/cm3 at 700 kV/mm and 80 °C. Significantly, the Ue of multi-layer 2/1/PVDF/1/2 composite is increased by 92.36 % at 80 °C and 400 kV/mm (from 2.88 J/cm3 to 5.54 J/cm3), and the efficiency (η) of it keeps at 68.5 %. Meanwhile, it owns a good cycle stability after 50,000 cycles with η > 90 %. This work provides a new direction of developing dielectric composite serving in severe service conditions for capacitors.

Аdaptive Сontrol algorithm Based on a Virtual Synchronous Generator. Part II

An increase in the penetration level of power plants based on renewable energy sources using power converters (PCs) has a direct impact on the dynamic characteristics of modern electric power system (EPS) and, as a consequence, the nature of the transient processes. One of the main problems in such EPS is a significant change in the magnitude of the total inertia of the system over time, which leads to an increase in the rate of change of frequency and the magnitude of its maximum deviation under various disturbances. A promising direction for solving this problem is the synthesis of new structures of PC control systems based on a virtual synchronous generator (VSG) with adaptively changing parameters. The results of the research in this area are presented in the paper, which consists of two parts. In the first part of the paper, the dependence of the adaptive algorithms efficiency for controlling the parameters of the VSG on the structure used is substantiated. A comparative analysis of the developed modified VSG structure with traditional algorithms is performed and its fundamental advantages are proved. The second part of the article presents an analysis of the influence of the modified structure parameters of the VSG on the dynamic response using time domain transient analysis. Based on the results obtained, adaptive algorithms for independent control of virtual inertia and parameters of the VSG damper winding have been developed. The performed mathematical modeling confirmed the reliable and efficient operation of the developed adaptive control algorithms and the modified structure of the VSG as a whole. From the theoretical and experimental results obtained in the paper, it follows that there is a need for simultaneous development and improvement of adaptive control algorithms and the VSG structures used for this.

Power system event detection using Teager Kaiser Energy Operator and autoencoder based algorithm

As the global demand for electricity increases, modern power systems are operating closer to their stability limits. This increases power system events that vary in intensity. It is essential to detect these power system events as early as possible and to take the necessary control actions to prevent escalation. This paper proposes a power system event detection method based on the Teager Kaiser Energy Operator (TKEO) and Autoencoder. In the proposed method, the rules-based algorithm performs primary detection, and the detected events are confirmed using the TKEO and autoencoder-based algorithms. The effectiveness of the proposed method was tested using test signals, simulated power system signals, and real-time power system data obtained from an actual power system. The results reveal the robustness and accuracy of the proposed method in detecting single and multiple power system events in the PMU data.

VOLTAGE FLICKER MITIGATION USING GTOBASED STATCOM TO IMPROVE POWER QUALITY OF A MULTI MACHINE SYSTEM

Transmission networks of modern power systems are becoming increasingly stressed because of growing demand and restrictions on building new lines. One of the consequences of such a stressed system is the threat of losing stability following a disturbance. Flexible ac transmission system (FACTS) devices are found to be very effective in a transmission network for better utilization of its existing facilities without sacrificing the desired stability margin. FACTS such as Static Synchronous Compensator (STATCOM), employ the latest technology of power electronic switching devices in electric power transmission systems to control voltage and power flow. The STATCOM adjusts voltage at its terminal by managing the amount of reactive power injected into or absorbed from the power supply. When the system voltage is low, STATCOM creates reactive power; when the system voltage is high, STATCOM absorbs reactive power. In this paper STATCOM controllers are designed for improving transient stability of multi machine systems. Proposed controllers are implemented under MATLAB/SIMULNK environment. Results of PI based controllers installed with multi machine system is found to be better on comparison with conventional system.

An Enhanced Continuation Power Flow Method Using Hybrid Parameterization

The rapid integration of renewable energy sources and the increasing complexity of modern power systems urge the development of advanced methods for ensuring power system stability. This paper presents a novel continuation power flow (CPF) method that combines two well-known parameterization techniques: natural parameterization and arc-length parameterization. The proposed hybrid approach significantly improves computational efficiency, reducing processing time by 32.76% compared to conventional methods while maintaining high accuracy. The method enables faster and more reliable stability assessments by efficiently managing the complexities and uncertainties, particularly in grids with high penetration of renewable energy.

Optimal utilization of frequency ancillary services in modern power systems

The widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants.

Which coal-fired power units in China should be prioritized for decommissioning?

China's total installed coal power capacity is larger than that of all other countries combined. However, for China to attain its objectives of carbon peak in 2030 and carbon neutrality in 2060, a considerable number of these units will have to be phased out. Therefore, it is crucial to identify which coal-fired power units should be prioritized for decommissioning. This study constructs a stacking evaluation model comprising multiple sub-models, based on the power resource endowment, power generation efficiency, and social responsibility. This model is employed to assess each existing coal-fired power unit in China, with the objective of determining which units should be prioritized for retirement. Additionally, the impact on power supply and heating systems in various Chinese provinces is assessed based on these decommissioning scenarios. The results show that: ① Compared to a single model, the stacking evaluation model developed in this study exhibits higher accuracy and robustness. ② China should prioritize the phasing out of small, inefficient coal-fired power units that do not supply heat and are located in regions with a higher proportion of thermal power generation. Approximately 23.1 % (679 units) of coal-fired power units will need to be retired by 2030. ③ The retirement of outdated coal-fired power units will lead to a more efficient, modern, and healthier Chinese power system. ④ The closure of 679 outdated coal-fired units will not result in a shortage of electricity but will substantially affect urban heating. Northern provinces reliant on district heating anticipate a loss of 516.42 million GJ/year in heating capacity.

Preface

Welcome to the Proceedings of the 2024 3rd International Conference on the Energy Internet and Energy Interactive Technology (EIEIT 2024) held via virtual form in Xi’an, China during May 10th to 12th, 2024! EIEIT 2024 builds upon the success of its two previous editions, underscoring the growing importance of energy internet as a cornerstone technology supporting modern power systems. However, despite its promising potential, the development of energy internet in China remains in its nascent stages, necessitating a concerted effort from academia, industry, and government to propel its progress. The conference was organized to provide a comprehensive forum where participants could present their latest research findings, discuss challenges, and explore opportunities for collaboration. The conference attracted about 80 scholars and researchers from all over the world, covering representatives from academia, industry, research institutes, and government organizations. They have a wide range of professional backgrounds related to energy and power engineering. Such a composition of participants not only facilitated interdisciplinary exchanges and cooperation, but also injected a wealth of academic ideas and perspectives into the conference. The program of EIEIT 2024 is rich and diverse, including keynote speeches, oral presentations, poster presentations, academic discussion, etc. The keynote speeches were delivered by internationally renowned scholars including Professor Sanjeevikumar Padmanaban (University of South-Eastern Norway, Norway) and Associate Professor Cristian Paul Chioncel (Universitatea Babeș-Bolyai, Romania) who have in-depth discussions on the cutting-edge dynamics and future development of energy internet and energy interactive technology; the oral presentations provided an extra platform for researchers to showcase their latest research results; and the poster presentations further promoted academic exchanges and collisions of ideas. These conference agendas were designed to promote international academic exchanges and cooperation, and to push forward the researches and applications in the field of energy internet and energy interactive technology. List of Committee Member is available in this Pdf.

Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses

Accurately predicting electricity load is crucial for maintaining the stability of modern power systems. Most studies focus on deterministic predictions, while uncertainty prediction and demand responses (DR) need further research. This paper proposes Bilinear-DRTFT, a load forecasting model that supports uncertainty and considers DR. To comprehensively evaluate model performance, this study combines five deterministic evaluation metrics and three uncertainty evaluation metrics on the test set. The research findings include:1. Incorporating DR effectively enhances the model's performance in both deterministic and uncertainty prediction; 2. Model performance continually improves with refined DR, achieving better uncertainty reduction when incorporating dynamic DR signals considering critical peak electricity prices, reaching expected performance at 80 % and 90 % confidence levels with narrow PINAW; 3. After adding DR, high-quality feature engineering provides the basis for the simplification of feedforward network; 4. Analyzing monthly uncertainty predictions reveals specific time periods on which DR policies should focus. These conclusions are validated in two experimental regions.