Smart Power Systems Research Articles

A Review of Intelligent Depth Distance Perception Research for Power Transmission Line Corridor Scenarios

Transmission lines are essential carriers for the transmission of electrical energy, and their safe operation is fundamental to ensuring reliable power supply. However, the environment of transmission line corridors is complex and variable, posing numerous risks of external damage. With the development of smart power systems, information technologies such as video monitoring provide effective means for monitoring and managing external damage to transmission lines. Nonetheless, the current technology for identifying external damage and safe distances for transmission lines is weak, leading to frequent false alarms and low efficiency in practical applications. To address this issue, this paper systematically reviews and summarizes the progress of distance recognition technology in power systems. First, it outlines the spatial distance perception performance requirements for models in transmission line corridor scenarios and summarizes the spatial distance data characteristics of these corridors. Second, the paper categorizes existing common safety distance recognition methods from the perspectives of active and passive ranging, analyzing their advantages and disadvantages in the context of transmission line corridors. Finally, this paper summarizes the types and performance characteristics of current deep learning-based distance recognition methods and proposes a potential application framework for these methods in transmission line corridor scenarios. It also discusses the future application prospects and challenges faced by this framework.

A hierarchical framework for integration of smart buildings in fully-renewable multi-microgrids and distribution systems: Towards more sustainable societies

Using smart buildings would be a crucial step towards sustainability. Although in the literature, some research effort has focused on integration of smart buildings into power systems; the integration of fully-renewable smart buildings into fully-renewable microgrids and distribution systems has not been addressed before. In this paper, a tri-level hierarchical stochastic framework is developed for the integration of fully renewable smart buildings in fully renewable multi-microgrids (MGs) and distribution systems. The studied smart buildings have electric water heater, air conditioner, PV and battery. In the first level, the model of each smart building is separately solved to find optimal schedule of its resources and the desired electricity exchange with its corresponding MG. In the second level, each MG solves its operational planning model, considering its transactions with the connected smart buildings and finally, in the third level, distribution system operator solves its operational planning model, accepting the electricity exchange requests taken from all MGs. The proposed methodology is applied to a 69-bus 100 % renewable distribution system with 4 100 % renewable microgrids, integrated with 100 % renewable smart buildings. The results confirm the efficacy of the proposed methodology for the integration of 100 % renewable smart buildings in 100 % renewable multi-MGs. The results testify that that building-to-grid (B2G) capability decreases the operation cost of all smart buildings; the average decrease in operation cost of buildings, caused by B2G, is 38 %. The results on the studied case indicate that that the impact of batteries on operation cost of smart buildings is marginal. The results also show that the higher threshold of comfort index in smart buildings, results in their higher operation cost. The results of this research may be helpful for both smart building operators and power system operators. This research is in line with the global energy transition and decarbonisation targets as the considered smart buildings, microgrids and distribution system are fully renewable.

Mechanical fault diagnosis of high voltage circuit breaker using multimodal data fusion.

A high voltage circuit breaker (HVCB) plays a crucial role in current smart power system. However, the current research on HVCB mainly focuses on the convenience and efficiency of mechanical structures, ignoring the aspect of their fault diagnosis. It is very important to ensure the circuit breaker conducts in a normal state. According to real statistics when HVCB works, most defects and faults in high voltage circuit breakers is caused by mechanical faults such as contact fault, mechanism seizure, bolt loosening, spring fatigue and so on. In this study, vibration sensors were placed at four different locations in the HVCB system to detect four common mechanical faults using vibration signal. In our approach, a convolutional attention network (CANet) was introduced to extract features and determine which mechanical faults occur within a fixed period of time. The results indicate that the mechanical fault diagnosis accuracy rate is up to 94.2%, surpassing traditional methods that rely solely on vibration signals from a single location.

Preface

We are glad to introduce you that the 2024 International Conference on Smart Grids and Power Systems (ICSGPS) was successfully held on January 12–14, 2024 in Zhangjiajie, China. The vision of the ICSGPS 2024 is to provide a premier interdisciplinary platform for researchers, practitioners, and educators to come together and discuss the latest innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Smart Grids and Power Systems. At ICSGPS 2024, participants have the opportunity to present their research findings and engage in meaningful discussions with experts and scholars from universities and research institutions worldwide. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches and Q&A discussion. There were 3 keynote speeches, 4 invited talks both from local and international presenters. The time spent delivering the talks by each keynote speaker was 45 minutes, the oral presentation speaker was 15 minutes following by a Question Answer (Q & A) Session 5 minutes for each. Thanks for wonderful speeches from our keynote speakers Professor Loi Lei Lai, Professor Fushuan Wen and Professor Zhaoyang Dong. ICSGPS 2024 has attracted participants from over 40 universities, their support is greatly appreciated. We would also like to thank all the members of the Technical Program Committee and the Organizing Committee for providing competent, efficient, and friendly help that made this conference success. We also appreciate for all the reviewers for their hard work in providing constructive critical comments, improvements and corrections to the authors. A particularly thank for Professor Wenxuan Yao from Hunan University and Professor Chengxi Liu from Wuhan University for their valuable advice as guest editors to ensure the quality of the submissions. Moreover, thanks for the excellent publishing service from Journal of physics: Conference Series. Finally, many thanks for all the participants and we wish you enjoyed and were inspired by the conference. We are convinced that this gathering of minds will foster collaboration and knowledge sharing, enabling participants to gain valuable insights into practical challenges encountered. The Committee of ICSGPS 2024 List of Committee Member and Workshops are available in this pdf.

A Novel Techno-Economical Control of UPFC against Cyber-Physical Attacks Considering Power System Interarea Oscillations

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.

Adaptive cyber-tolerant finite-time frequency control framework for renewable-integrated power system under deception and periodic denial-of-service attacks

This work concentrates on designing and applying an adaptive cyber-tolerant finite-time frequency control framework for smart power systems under state-dependent sensor deception and periodic denial-of-service (DoS) attacks. An adaptive radial-basis function neural network (RBFNN)-based disturbance observer (DO) is designed to estimate undeniable plant disturbances, which includes structural uncertainties and unmodelled dynamics. Then, the DO-based resilient secondary control law based on the adaptive nonlinear sliding mode strategy for the cyber-physical power system (CPPS) is derived to guarantee global uniform asymptotic stability of the parametric error and the selected nonlinear sliding manifold. Furthermore, an in-depth analysis of uniformly ultimately boundedness (UUB) of the estimation error of RBFNN has been demonstrated. Numerical simulation and qualitative assessment have revealed the proficiency of the applied feedback control framework for the undertaken CPPS and a high-degree of cyber-tolerance against malicious attacks.

Signal processing and artificial intelligence based HVDC network protection: A systematic and state-up-the-art review

In this modern era, the smart grid (SG) is progressively empowered for direct current (DC) power transmission, either at high voltage (HV) or at medium voltage (MV). The high voltage direct current (HVDC) transmission systems have a significant impact on the SG operation even though the integration of add-on green and renewable-based power generation (RPG). In this RPG era, the application of HVDC can be seen in the grid-connected remote offshore wind or photovoltaic (PV) based large RPG, and interconnections amongst the states or countries. In such a context, a smart grid inevitably is ridden with technical complexities (both control and protection viewpoints), and thus signals processing (SP) techniques along with artificial intelligence (AI) methods are considered very much important to understand, effectively plan, draw up plans, and drive the multifaceted electric power system. This paper presents an extensive review of HVDC network protection systems with special attention to SP and AI applications. This paper presents an extensive review of the suggested techniques in the past three decades and discussed the pros and cons of each method. Apart from that critical findings are discussed and analyzed along with the future scope of research toward the development of HVDC protection. This review tries to focus on the gap between the existing protection schemes and topology with the smart grid-based power system perspective and convey to the power engineers and researchers the possibilities of further research as a solution to the associated issues and challenges.

Examining performance calibration in smart power system electricity metering based on environmental perception attention LSTM-network

The operating environment greatly influences the accuracy of power metering devices, resulting in variations and inconsistencies in measurement results across different working situations. A calibration model for power metering devices is proposed in this study, considering a range of environmental circumstances. The first step involves investigating the environmental conditions that impact the accuracy of power metering devices. The mutual information approach is utilized to identify environmental disturbances affecting device accuracy. A machine learning-driven symmetry attention Long Short-Term Memory (LSTM) network addresses measurement errors, capitalizing on the network’s symmetry data knowledge. Ultimately, the efficacy of the suggested approach is substantiated through the utilization of performance indicators, namely, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the proposed method can effectively reduce the errors of the power measurement device in all quarters, and the error reduction effect is over 10% in the spring, which is better than other models, demonstrating exemplary performance in correcting the calibration errors of the power measurement device.

Wavelet Analysis and Machine Learning Approach for Improved Protection of PV-Wind-SVC Integrated Smart Power System

Wavelet Analysis and Machine Learning Approach for Improved Protection of PV-Wind-SVC Integrated Smart Power System

A Deep Learning Framework to Identify Remedial Action Schemes Against False Data Injection Cyberattacks Targeting Smart Power Systems

This article proposes aremedial actionscheme (RAS) based on the concept of deep learning to mitigate the impacts of false data injection (FDI) cyberattacks on smart power systems. As a prerequisite of such a RAS, power system operator is being in attacker's shoe to scrutinize different scenarios of cyberattacks. In design of the RAS, long short-term memory (LSTM) cells have been integrated into a deep recurrent neural network (DRNN) to effectively process the data of an intelligent archive framework (IAF), identifying the proper reaction mechanisms. Power flow analysis has been considered to examine the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interdependence</i> between transmission/distribution sectors to react to the cyberattacks for which similar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pre-investigated</i> remedial actions have not been saved in the IAF. Effectiveness of the proposed RAS is validated on two IEEE transmission/distribution systems, where consequences of FDI cyberattacks are reduced by 30% in case of experiencing attacks, which are not pre-investigated by system operator.

Techno‐economic optimization framework of renewable hybrid photovoltaic/wind turbine/fuel cell energy system using artificial rabbits algorithm

AbstractIn order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.

A review of recent control techniques of drooped inverter‐based AC microgrids

AbstractAs the penetration of distributed generation (DG) systems in the grid is increasing, the challenge of combining large numbers of DGs in the power systems has to be carefully clarified and managed. The control strategy and management concept of the interconnected systems should be flexible and reliable to handle the various types of DGs. This can be suitably met by microgrids. This paper introduces the microgrid structure and elements and states the main objectives that should be achieved by the microgrid controllers and each DG controller in both operation modes (grid‐connected and island mode). It also presents the challenges of having multiple DG units in a microgrid in terms of accurate power control/sharing, voltage and frequency regulation, power management between DGs, different renewable energy sources integration and deployment, seamless mode transfer, and the modeling issues. The centralized and decentralized control techniques as potential solutions have been discussed and compared by highlighting the advantages and disadvantages of each. Furthermore, the recent control techniques for drooped alternating current microgrids and the main proposed solutions and contributions in the literature have been exposed to finally overcome the droop control limitations and obtain a flexible and smart distributed power system.

Energy storage system coordinated with phase-shifting transformer and dynamic rating equipment for optimal operation of wind-rich smart power networks

Emergence of flexibility devices into smart power systems can assist the power system operators in making effective and economical decisions for the power system scheduling. These devices include energy storage system (ESS), phase-shifting transformer (PST), dynamic transformer rating (DTR), and dynamic line rating (DLR). In this paper, an approach is proposed for optimal day-ahead scheduling of power system using coordinated operation of ESS, PST, DTR, and DLR units under high wind power penetration situation. The aim is to minimize operational, load shedding, and wind power curtailment costs by regarding all the problem constraints. All of the model's relations as well as AC power flow equations have been convexified in order to construct a mixed-integer quadratically-constrained programming (MIQCP) model whose solution will obtain global optimum results. The developed model is implemented in GAMS and applied to the IEEE 24-bus system under various case studies, and the obtained results are investigated thoroughly. By coordinated operation of the employed flexibility devices, the loading of transformers and lines reach 159 % and 246 % compared to their nominal rating without violation of temperature limitations. Through releasing the capacity of lines and transformers, the total scheduling cost is reduced by about 52 % compared to traditional operation. Also, the whole energy of wind farms is utilized without any curtailment or constraints violation.

Соціальна відповідальність у сфері енергетики в інтересах розвитку розподіленої генерації та суспільної стабільності

The essence and content of the processes of ensuring social responsibility by entities managing distributed generation, energy business corporations and joint-stock companies and energy regulators are defined and detailed. This made it possible to classify the main types of social responsibility and to formulate the target tasks of energy market participants. A complex of technical and technological procedures and means of coordination of the processes of ensuring the stability of the functioning of the national energy system is proposed for use in order to harmonize the volumes and modes of electricity supply to the network from distributed generation and energy business corporations and joint-stock companies. It includes: smart power system management systems; smart energy management systems; demand forecasting methods; network integration devices; technologies of energy storage and accumulation; remote monitoring and control technologies used by operators; standardization of activities and ensuring mutual compatibility of technologies; development and implementation of contingency plans and emergency scenarios to eliminate violations of energy supply regimes. For public administration entities and regulators in the performance of social responsibility functions and effective control over the activities of economic entities, it is recommended: a) to conduct an analysis of the activities of managers, an assessment of their compliance with the assigned tasks; b) clarify the responsibility of managers; c) approve reporting systems; d) determine rewards and amounts of punishment; e) engage independent experts or audit firms to evaluate activities; g) adapt the existing system of discussion and feedback; h) exercise public pressure and public reaction. The author's work made it possible to develop and propose for use a five-stage Algorithm of management actions for the coordination of the processes of ensuring the stability of the functioning of the national energy system with the rationalization and coordination of the compatibility of volumes and modes of supply to the electricity network from distributed generation enterprises and energy business corporations and joint-stock companies. Within its limits, a number of target tasks are formulated and the deadlines for those responsible for their implementation are determined.

Fault Detection and Isolation in Smart-Grid Networks of Intelligent Power Routers Modeled as Probabilistic Boolean Networks

A self-organizing complex-network modeling method, probabilistic Boolean networks, is presented as a model-based diagnostic system for detecting and isolating different types of faults, failures, and modes of operation in which a network of intelligent power routers is deployed over a standard power test case: the Western System Coordinating Council 9 Bus System. Such a system allows designers and engineering professionals to make educated decisions pertaining to the design of smart-grid systems endowed with intelligent power routers. There is a recurrent necessity to design reliable and fault-tolerant smart power systems, maintaining adequate operation and adherence to performance specifications, while keeping costs at the minimum. This diagnostics system will help achieve such goals: better design through thorough analysis of the conditions that lead to a fault on a smart grid, proper detection of these faults, and isolation of the respective assets.

Description and Analysis of Data Security Based on Differential Privacy in Enterprise Power Systems

The pursuit of environmental sustainability, energy conservation, and emissions reduction has become a global focal point. Electricity is the primary source of energy in our daily lives. Through the analysis of smart power systems, we can efficiently and sustainably harness electrical energy. However, electric power system data inherently contain a wealth of sensitive user information. Therefore, our primary concern is protecting these sensitive user data while performing precise and effective analysis. To address this issue, we have innovatively proposed three granular information models based on differential privacy. In consideration of the characteristics of enterprise electricity consumption data and the imperative need for privacy protection, we implement a reasonable modeling process through data processing, information granulation expression, and the optimization analysis of information granularity. Our datasets encompass enterprise electricity consumption data and related attributes from Hitachi Building Technology (Guangzhou) Co., Ltd’s cloud computing center. Simultaneously, we have conducted experiments using publicly available datasets to underscore the model’s versatility. Our experimental results affirm that granular computation can improve the utility of differential privacy in safeguarding data privacy.

Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus

Multi-agent systems (MAS) are emerging as a promising approach for smart power system restoration. After a fault has been detected and isolated, the maximum out-of-service loads should be restored in a timely manner. This study aims to develop reliable restoration through autonomous, rapid, and stable communication. For this purpose, a multilevel consensus is suggested to coordinate MAS in a fully decentralized fashion. System dynamics emerge in a distributed manner based on the state vectors of the agent and its proximate neighbors. The information matrix (IM) is used by the agents to accumulate and exchange state vectors encompassing common variable interests. In order to weight the influence of agents on voting, an optimized weighting model (Optw) is proposed. The parameters are bound by Lyapunov's assessment to ensure consensus and then optimized for quick convergence. The proposed multilevel topology supports parallel information discovery in local bunches and provides extremely quick convergence. The suggested one-by-one communication architecture facilitates the exchange of information since each agent only interacts with four of its neighbors. The redundant communication prevents a single point of failure. These achievements make it suitable for application in large-scale power systems. The effectiveness of the affirmation method is revealed through numerical simulation in various scenarios.

Pedagogical Perspectives of Interdisciplinary Teaching and Research: An Energy System Modelling Outlook in Relation to Energy Informatics

The purpose of this paper is to present and discuss pedagogical frameworks and approaches to developing, delivering, and evaluating a new interdisciplinary course within the domain of energy informatics at both Master’s and PhD levels. This study is needed because many papers on sustainable energy engineering education concentrate on course content but provide very little information on the pedagogical methods employed to deliver that content. The proposed new course is called “smart energy and power systems modelling” and is aimed at discussing how mathematical optimization, in the context of computer science, can contribute to more effectively managing smart energy and power systems. Different pedagogical frameworks are discussed and adapted for the specific domain of energy informatics. An ASSURE model coupled with Bloom’s taxonomy is presented for the design of the course and identification of learning objectives; self-regulated learning strategies are discussed to enhance the learning process; a novel model called GPD (Gaussian Progression of Difficulty) for lecture planning was proposed; a teaching-research nexus is discussed for the course planning and enhancement. Adopting qualitative analyses and an inductive approach, this paper offers a thorough reflection on the strengths and weaknesses of the new course, together with improvement possibilities based on fieldwork and direct experience with the students and colleagues. Opportunities and challenges of interdisciplinary teaching are presented in light of real-world experience, with a particular focus on the interaction between mathematics and computer science to study the specific application of energy and power systems.

A Cloud-Edge Computing Method for Integrated Electricity-Gas System Dispatch

An integrated electric–gas system (IEGS) is the manifestation and development direction of a modern smart power system. This paper employs the cloud-edge computing method to research IEGS’s optimal dispatch to satisfy data protection requirements between power systems and natural gas systems and reduce data transmission pressure. Based on cloud-edge computing architecture, this paper constructs a cloud-edge computing method based on the Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve optimal dispatch problems. Then, this paper proposes an IEGS dispatch strategy based on cloud-edge computing, which conducts distributed computing independently at the edge of power and natural gas, and the cloud implements global dispatch based on boundary information and edge learning parameters. This method does not require the exchange of all information between the power system and natural gas system, effectively protecting data privacy. This paper takes the improved IEGS of the IEEE 9 node and Gas 8 node as an example to analyze. The equipment output of this dispatch method is within a reasonable range, and the cost is reduced by 0.21% to 1.03% compared with other methods, which verifies the effectiveness of the cloud-edge computing method in solving dispatch problems.

The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.