Development Of Smart Grid Technology Research Articles

An adaptive photovoltaic power interval prediction based on multi-objective optimization

Photovoltaic (PV) power interval prediction can provide a variation range of prediction results, which is of great significance to promoting the optimization of power grid dispatching and maintaining the stability of the power system. However, the existing PV interval prediction fails to accurately reflect error fluctuations in their construction process due to dependency on data distribution assumptions and unreasonable estimation of prediction error standard deviations (PEStds). To address these issues, we propose an adaptive interval prediction method based on multi-objective optimization. Firstly, K-means clustering is used to segment similar days. Subsequently, Convolutional Neural Network-Gated Recurrent Unit-Attention (CNN-GRU-Attention) is utilized for point prediction. Furthermore, Grey Relation Analysis (GRA) is used to screen dates similar to the target date under the same weather type, enabling a more precise estimation of prediction error fluctuations. Ultimately, the estimated values are multiplied by non-fixed parameters to construct intervals, which avoids the dependency on data distribution assumptions. Among them, the optimal values for non-fixed parameters are obtained through multi-objective optimization considering interval coverage probability, interval width, and deviation. To verify the effectiveness of the proposed model, we conduct comparative experiments on two datasets with different resolutions. The results demonstrate that the proposed model offers more flexible and higher-quality intervals. Not only does this research improve the accuracy of PV power generation interval prediction, but it also helps to promote the development of smart grid technology and improve the adaptive ability of power systems facing dynamic environments and complex data, which has an important impact on the future energy management and power market.

Aperiodic small signal stability method for detection and mitigation of cascading failures in smart grids

The occurrence of cascading failures poses significant risks to the stability and reliability of modern smart grids. This article presents a novel hybrid algorithm designed to assess and mitigate these failures. The technique combines advanced clustering algorithms, specifically Affinity Propagation Graph (APG) and Self-Propagating Graph (SPG), to detect critical nodes, and Unified Power Flow Controllers (UPFCs) to provide compensation to grid networks. The algorithm first uses APG to divide the network into clusters and considers the center bus as the critical node. If the center bus is not critical, SPG is applied to identify the critical node. This hybrid approach identifies the critical node in just 0.02 s (for both APG and SPG) and with improved accuracy compared to existing methods. After identifying critical nodes, UPFCs are strategically installed to regulate power flow and reduce the probability of cascading failures, with compensation taking approximately 0.14 s. Simulation results demonstrate the effectiveness of the proposed method in enhancing grid resilience and reducing the likelihood of cascading failures. By strategically deploying UPFCs at critical nodes, this approach ensures resilient grid operation in various scenarios. This research significantly contributes to the development of smart grid technologies by providing a comprehensive framework to address cascading failures in power distribution networks. The proposed method shows potential for improving the reliability and stability of power grids amid changing system dynamics and uncertainties.

Research on calibration method for measurement error of multiple rate carrier energy meter

Abstract With the rapid development of smart grid technology, the multi-rate carrier watt-hour meter is an important measurement tool in power systems; its accuracy directly affects economic benefits and energy management efficiency. This paper proposes a calibration method of measurement error for a multi-rate carrier watt-hour meter to improve the measurement accuracy and reliability of the watt-hour meter under changing load conditions. The multi-rate carrier energy data is collected in real-time by high-precision data acquisition equipment to ensure the comprehensiveness and accuracy of the data. Using advanced data analysis technology to extract the amplitude characteristics of abnormal metering data, on this basis, the output deviation of equipment without input signal is eliminated, and the metering error calibration of the electric energy meter is completed. The experimental results show that this method can significantly improve the metering accuracy of multi-rate carrier watt-hour meters under different power consumption conditions and has good adaptability and practical value.

IOT Based Smart Grid to Remotely Monitor and Control Renewable Energy Sources using Cloud Computing

Abstract: The growing demand for clean and sustainable energy sources necessitates advancements in grid structure. This project explores the potential of an IOT-based smart grid to remotely monitor and control renewable energy sources. In this, we use solar energy as a renewable energy source, and with the help of cloud computing, we monitor and control different types of loads. The paper highlights the key components, functionalities, and benefits of such a system.The increasing demand for sustainable energy solutions fuels the development of smart grid technologies. This project proposes an IOT (Internet of Things)-enabled smart grid architecture for remote monitoring and control of renewable energy sources. The core concept involves integrating sensors with renewable energy sources like solar or wind turbines. These sensors collect real-time data on energy generation, environmental conditions, and system health. This data is then transmitted over a network to a central control system or a cloud platform for further analysis. By implementing remote control capabilities, grid operators can: regulate energy production based on demand; integrate storage solutions for surplus energy; and improve overall grid stability and reliability. The project concludes by outlining the benefits of this approach, including a reduced carbon footprint, cost savings, and a more sustainable energy future.

Multi-objective optimization of regional integrated energy system matrix modeling considering exergy analysis and user satisfaction

Integrated energy system achieves the collaborative optimization of different energy forms such as electricity/gas/heat/cold, and it is an important strategic development direction in the international energy field in the future. However, with the development of smart grid technology and the increase of renewable energy penetration, it needs more flexible load to adapt to the changing energy supply–demand relationship. Considering the impact of user-side load fluctuations on the system, this paper establishes a two-tier closed-loop system model formed by users and the market. The upper-layer is based on the user’s perspective. A comprehensive user satisfaction model is established by the radar chart area method and consumer preference theory. The lower-layer is based on the market perspective. It proposes an improved layering matrix Energy Hub model and conducts the regional integrated energy system exergy efficiency evaluation study. Then, taking economy, carbon emission and exergy efficiency as objective functions, a multi-objective optimization model is constructed. Non-dominated Sorting Genetic Algorithms-II is applied to solve the model, and the output parameters is a Pareto front surface. A combination weighting TOPSIS method is used to select a compromise solution of multi-objective optimization from the optimal solution set. Compared with the primary energy utilization efficiency as the objective function, the exergy efficiency reduces the system cost by 8.64%. Finally, the multi-objective optimization solution based on different user satisfaction is obtained. Through the analysis of the examples, compared with the original load, the introduction of the upper-layer user satisfaction model reduces the system cost by 2.8%. The reasonable setting of user satisfaction and load adjustment amount can improve the economy of system, which verifies the effectiveness of the two-tier closed-loop system based on user satisfaction model.

A hybrid approach based machine learning models in electricity markets

In recent years, integrating renewable and non-renewable energy sources has transformed electric grids, presenting new challenges in predicting energy data due to varying levels of variability. Accurate prediction of both types of energy data is crucial for smart grid technology development and effective renewable energy integration into existing grids. We have introduced an innovative hybrid approach for forecasting both renewable and non-renewable data. This method employs a sophisticated ensemble empirical mode decomposition (EEMD) algorithm, carefully selecting intrinsic mode functions (IMFs) to dissect the original data into distinct IMFs and residuals. The IMFs are predicted utilizing support vector regression (SVR), while the residual series is forecasted using bidirectional long short-term memory with an attention mechanism (BiLSTM-AM). In pursuit of enhanced predictive accuracy, our approach employs an ensemble summation methodology to merge forecasted sub-series effectively. We conducted experiments using two distinct wind speed datasets, generating 24-h forecasts. In comparison to the second-best model, EEMD combined with BiLSTM-AM, our approach demonstrated significant improvement, reducing mean absolute error, root mean square error, and peak percentage of threshold statistics by 7.87 %, 3.91 %, and 23.51 %, respectively. The proposed model accurately captured peak and valley occurrences’ timing and amplitude, surpassing existing models.

Study on a comprehensive indicator and online classification of early warning of low frequency oscillation in power system

With the development of smart grid technology in China, the study of low frequency oscillation early warning has become an important element of power system stability research. Aiming at the problems of single warning indicator with poor accuracy and lack of online warning system in the early warning strategy of low frequency oscillation in power system, a comprehensive low frequency oscillation early warning algorithm based on the evaluation method of kernel matrix hierarchical analysis of fuzzy comprehensive is proposed. Firstly, this method screens out the original data collected by Phasor Measurement Unit (PMU), and extracts the key data volume to form multiple early warning indicators, and generates the trapezoidal fuzzy number judgment matrix. Secondly, by calculating the kernel matrix and consistency test, it is determined whether or not the generated fuzzy matrix is reasonable and calculate the relative weights of the evaluation indicators. The affiliation function is constructed for each early warning indicator. A comprehensive indicator is calculated for multiple indicators, and the reasons of low frequency oscillation in power system are classified by the Random Forest (RF) algorithm. Then, the low-frequency oscillation system is constructed, and the algorithm convergence is proved by the Lyapunov indirect method. The algorithm can automatically determine whether low frequency oscillation occurs or not and classify different types of low frequency oscillations online. Finally, the correctness and validity of this paper’s method is verified by a New England 10-machine 39-node system. Compared with the traditional Analytic Hierarchy Process (AHP) method, the proposed method in this paper greatly improves the accuracy of early warning and meets the need of online warning.

THE CONCEPT OF SMART GRID: TECHNOLOGICAL, ORGANIZATIONAL AND ECONOMIC ASPECTS OF THE DEVELOPMENT OF THE ENERGY SECTOR

Purpose. The aim of the article is to substantiate the concept of Smart Grid application from the point of view of technological, organizational and economic aspects of development in the energy sector, outlining the features of their use in modern conditions and in the future. Methodology of research. The methodological basis of the research is the dialectical method of scientific knowledge. General scientific and special methods were used in the research process, in particular analysis and synthesis, dialectical – to determine the initial conditions and content of the Smart Grid; analytical – when processing literary and Internet sources; causal – to determine the dynamics of Smart Grid technology development in the world; abstract and logical – for outlining the main directions of operation of Smart Grid technologies in the field of energy, forming conclusions and proposals. Findings. Current and prospective Smart Grid technologies in the energy industry are summarized. The need for intellectualization of the electric power industry through the formation of a digital electric power network is substantiated. Measures to create a unified technical and informational infrastructure for electricity producers and consumers thanks to the Smart Grid concept have been determined. An assessment of the dynamics of investment in Smart Grid technologies in the world was carried out, the volume of the world market of Smart Grid technologies was determined, and the income of the world market of electricity supply and management was analysed. Originality. Measures to increase the introduction of Smart Grid in the energy sector are substantiated, which will make it possible to strengthen Ukraine's energy independence and contribute to the introduction of energy-saving technologies. The main vectors of the development of the electric power industry using the Smart Grid concept are proposed: design and use of modernized, new and breakthrough (critical) technologies; development of new energy management systems based on the principles of information interaction of energy facilities; involvement of individual and collective consumers in energy consumption management. Practical value. The substantiated research results can be used for the formation of innovative Smart Grid technologies in the field of energy security of Ukraine. Key words: Smart Grid, intelligent network, energy, energy security, energy supply, renewable energy sources.

Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services

Well-managed demand-side flexibility can effectively alleviate the stress of power systems caused by the increasing renewable energy penetration. The development of smart grid technologies provides win-win opportunities for system operators and demand-side users. Buildings have great flexibility potential through smart control of flexible loads, passive thermal mass storage and distributed active storage. However, a few fundamental problems on flexibility quantification, optimal coordination and profits sharing are faced when aggregating buildings to reach sufficient size for biding multiple flexibility services in electricity markets. Therefore, this paper proposes a multi-level optimal dispatch strategy and profit-sharing mechanism for non-residential building clusters to maximize flexibility profits and reduce the complexity of the optimization. At building level, a model-based quantification method is developed to evaluate flexibility potential of multiple resources. At cluster level, the coordinated bidding strategy of the aggregator is proposed, and flexibility profits are distributed based on the cooperative game theory at the post-market stage. Test results show that the proposed dispatch strategy can reduce up to 20.4% of daily electricity cost at cluster level and ensure profit increase of individual buildings with different flexibility characteristics. The active participation of building clusters in electricity markets could reduce the use of costly backup generators.

A novel cloud-edge collaboration based short-term load forecasting method for smart grid

With the increasing development of smart grid technology, short-term load forecasting becomes particularly important in power system operation. However, the design of accurate and reliable short-term load forecasting methods and models is challenging due to the volatility and intermittency of renewable energy sources, as well as the privacy and individual characteristics of electricity consumption data from user data. To overcome this issue, in this paper, a novel cloud-edge collaboration short-term load forecasting method is proposed for smart grid. In order to reduce the computational load of edge nodes and improve the accuracy of node prediction, we use the method of building a model pre-training pool to train multiple pre-training models in the cloud layer at the same time. Then we use edge nodes to retrain the pre-trained model, select the optimal model and update the model parameters to achieve short-term load forecasting. To assure the validity of the model and the confidentiality of private data, we utilize the model pre-training pool to minimize edge node training difficulty and employ the approach of secondary edge node training. Finally, extensive experiments confirm the efficacy of our proposed method.

Research on Integrated Smart Preventing Mal-Operation System of Intelligent Substation

With the rapid development of smart grid technology, substation comprehensive automation technology has made significant improvements. However, the intelligent operation function of monitoring and operation and maintenance is still not perfect, such as the reversing operation process is still based on manual sequential operation, which is far from being able to meet the intelligent development needs of power system. In this paper, the integrated smart preventing mal-operation system of intelligent substation is studied. Firstly, the "topology preventing mal-operation analysis algorithm" is adopted to capture the charged operation of equipment in real time based on the primary wiring diagram and operation mode of the substation. Secondly, establish wireless micro-power LAN in the substation to achieve full coverage of the micro-power wireless network, upgrade and replace the smart preventing mal-operation host and smart-key so that they have micro-power network access.Through the wireless network mode, the smart-key is connected to the smart preventing mal-operation host in real time,and the real-time communication is realized between the smart preventing mal-operation host and the smart-key as well as the field locks,integrating the means of error-proof measures such as preventing mal-operation, lock control, access control, ground state management, etc.,unifying the authorization and identification, unifying the incorporation of the preventing mal-operation process, realizing the comprehensive coverage of the preventing mal-operation measures in substations to make real-time locking logic judgment and real-time preventing mal-operation, and controlling the operation process of thesmart-key in real time according to the judgment result, effectively improving the work accuracy and efficiency of the operation personnel.

A state‐of‐the‐art review on the impact of fast EV charging on the utility sector

AbstractDeployment of fast‐charging stations for electrified transportation is currently one of the limiting factors in the commercial deployment and paradigm shift of the mobility sector. The rapidly increasing demand for electric vehicles (EVs) brings the demand for accessible and affordable charging infrastructure. The commercial EV fast‐charging load profile depends on the customer's EV charging demand, which poses challenges in determining the overall peak profile. This could lead to unprecedented challenges of harmonic disruption, system losses, supply‐demand balance, and grid reliability. The emergence of bidirectional charging infrastructure and the realization of controlled charging strategies in coordination with the cumulative contribution of renewable energy resources could potentially reduce the challenge of utility grid stability and reliability shortly. Furthermore, the parallel development of smart grid technologies has promising features. This paper aims to provide a comprehensive state‐of‐the‐art review of the major challenges of the utility grid in the swift adoption of fast charging infrastructures available in the open literature, as well as the sustainable implications and potential safety measures. Furthermore, the potential solutions and best practices for the synchronization of electrified transportation with the emerging ecosystem of renewable power systems and various controlled charging strategies are reviewed in detail.

Modern trends of the world market of electrical equipment

Introduction. The world market of electrical equipment is developing very fast. There are many companies in the market that sell electrical equipment, among them there are companies that occupy leading positions. Today, the world market of energy engineering is estimated at 87 billion dollars per year, based on the structural dynamics of growth, the annual volume can reach 110-115 billion dollars per year until 2025. The global market for energy equipment service in 2020 is 31.7 billion dollars, including: LTSA (long-term service) 47 %, modernization – 20 %, field service – 24 %, engineering – 9 %. The purpose of the paper is to analyze the world market of electrical equipment, determine sales, business growth. The list of leaders in electrical equipment on the world market is considered. Results. The main trends in the world today are the following areas: development of DC transmission system, cable lines for underwater laying and cable for connections of renewable energy sources to reduce energy transmission costs through the capabilities of existing transmission lines, through network voltages and innovative design solutions and installation methods. The amendment for these trends shows us the world leaders in the electrical market, such as Legrand, Schneider Electric, ABB, Siemens, DEKraft, SASSIN, EKF, etc., R&D costs are 3.5–5 % of profits (over 60 years). Thus, we can conclude that the global market for cable networks is developing rapidly. This market is expected to grow in the development of smart grid technologies, renewable energy generation and initiatives to modernize the transmission and distribution system. Conclusion. That the global cable ladder market is developing rapidly. This market is expected to grow in the development of smart grid technologies, renewable energy cultivation and government initiatives to modernize transmission and distribution systems.

Development of smart grid technology to maintain the functioning of photoelectric charging stations

An integrated Smart Grid system has been developed for matching the production and consumption of electric power based on a prediction of changes in the battery capacity. Advanced decisions on the change in power transmission capacity have made it possible to regulate voltage in the distribution system by maintaining the power factor of the photoelectric charging station. Voltages at the input to the hybrid inverter and in the distribution system were measured to assess their ratio. Comprehensive mathematical and logical modeling of the photoelectric charging station was performed based on the mathematical substantiation of architecture and operation maintenance. A dynamic subsystem including such components as mains, a photoelectric module, a hybrid inverter, batteries, a two-way counter Smart Meter and a charger formed the basis of the proposed technological system. Time constants and coefficients of mathematical models of dynamics in terms of estimation of changes in the battery capacity and power factor of the photoelectric charging station were determined. A functional estimate of changes in the battery capacity and power factor of the photoelectric charging station was obtained. Maintenance of voltage in the distribution system was realized based on resulting operation data to estimate a change in the battery capacity. Advanced decision-making has made it possible to raise the power factor of the photoelectric charging station up to 40 % due to matching the electric power production and consumption. Maintenance of operation of the photoelectric charging station using the developed Smart Grid technology has enabled prevention of peak loading of the power system due to a 20 % reduction of power consumption from the network.

Research on Demand Response Aggregators Participating in Power Market Based on Bi-level Optimization

With the rapid development of smart grid technology, how demand side resources participate in power market has become a research hotspot. This paper proposes a bi-level optimization model for demand side resource aggregators to participate in electricity market transactions. In the bi-level model, the lower level is the wholesale market clearing model aiming at the maximum social benefit, and the upper level is the bidding decision model aiming at the maximum power consumption benefit of air conditioning load. The case study demonstrates the feasibility and effectiveness of the model.

Research on non-intrusive unknown load identification technology based on deep learning

With the development of smart grid technology, optimization of energy structure, improvement of power efficiency and reduction of power consumption have become the major development trends. As a key process of power structure analysis, load identification is gaining increasing attention in smart grids. Although lots of load identification methods have been proposed, the introduction of unknown states or new load types is still a challenge for precise identification. In this paper, a similarity calculation method of the space convex hull overlap rate is proposed to deal with the identification of unknown loads based on the low-dimensional feature space model of Siamese neural networks. Moreover, transfer learning is introduced to realize the category-added learning of load pre-identification model for unknown load. The ultimate aim was to establish the identification and rapid modeling of unknown loads. The public datasets Plaid is used to verify the performance of the proposed method. As a result, the high accuracy of load identification for unknown load is proved.

Development of smart grid technologies: organizational and communication aspects

This paper concentrates on the organizational and communication aspects of development of the smart grid technologies. The paper highlights the potential of decentralised electricity generation for generating electricity from less energy-intensive and cost-efficient sources. It shows that renewable and unconventional energy sources may be integrated into decentralised electricity grids – the generation lines that have an intelligent grid. In addition, the paper focuses on the benefits and risks of different smart grid applications and their impact. We show that smart grids have the potential to minimise costs, but the use of smart grid technology also affects the level of risk, so the organizational and communication aspects are of a great importance.

Research and Implementation of The Fault Information System FOR Relay Protection Based on Cloud Computing Technology

With the continuous development of smart grid technology, it is of great engineering application value and urgent practical demand to collect and manage more intelligently the operation and fault information of relay protection in 110kV and below substation. With the introduction of “cloud computing” technology, a cloud platform of fault information with good expansibility, high stability, loose coupling, strong reusability and convenient maintenance could be built up, which is the core of the system construction. In order to make full use of the existing data network channels and network resources in 110kV substation, a fault information system of relay protection, with its sub-station modeling and communication management, is presented based on “cloud computing” technology to realize a more efficient and convenient relay protection exchange between 110kV substation and the dispatching control center.

Studying impacts of communication system performance on dynamic stability of networked microgrid

The development of smart grid technologies has resulted in increased interdependence between power and communication systems. Many of the operations in the existing power system rely on a stable and secured communication system. For electrically weak systems and time-critical applications, this reliance can be even greater, where a small degradation in communication performance can degrade system stability. However, despite inter-dependencies between power and communication systems, only a few studies have investigated the impacts of communication system performance on power system dynamics. This study investigates the dependencies of power system dynamics operations on a communication system performance. First, a detailed, dynamic networked microgrid model is developed in the GridLAB-D simulation environment, along with a representative multi-traffic, multi-channel, multi-protocol communication system model, developed in the network simulator (ns-3). Second, a hierarchical engine for large-scale infrastructure co-simulation framework is developed to co-simulate microgrid dynamics, its communication system, and a microgrid control system. The impact of communication system delays on the dynamic stability of networked microgrids is evaluated for the loss of generation using three use-cases. While the example use-cases examine microgrid applications and the impact to resiliency, the framework can be applied to all levels of power system operations.

Development of Smart Grid technology for maintaining the functioning of a biogas cogeneration system

The integrated Smart Grid System of harmonization of production and consumption of electric power and heat with the use of heat-pumping power supply of the biogas plant, which uses fermented wort as a low-potential source of power, was developed. A change in the power factor of the cogeneration system, the temperature of local water is predicted by measuring the voltage at the inlet to the inverter, at the outlet from the inverter and voltage frequency. In the engine cooling circuit, the temperature of cooling water at the inlet to the heat exchanger, at the outlet from the heat exchanger, and the return water temperature are measured. It was proposed to estimate a change in the ratio of voltage at the inlet to the inverter and at the outlet from the inverter. Making forestalling decisions to change the power of the heat pump and the number of plates in the heat exchanger of the engine cooling circuit makes it possible to maintain the voltage at the entrance to the inverter and the temperature of the heated local water. The complex mathematical and logical modeling of the cogeneration system, based on the mathematical substantiation of the architecture of the cogeneration system and mathematical substantiation of the maintenance of functioning of the cogeneration system, was performed. Time constants and coefficients of the mathematical models of dynamics regarding the estimation of a change in the power factor of the cogeneration system, temperature of local water, were determined. Functional estimation of a change in power factor of the cogeneration system in the range of 85–95 %, temperature of local water in the range of 30–55 °С at the compensation of reactive power of up to 40 % was obtained. Determining final functional information provides an opportunity to make forestalling decisions on a change in the power of a heat pump and a change in the number of plates in the heat exchanger of the engine cooling circuit to maintain the functioning of the cogeneration system