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Inverter-driven generation units (IDGUs) based on renewable energy sources (RES) are actively integrated into electric power systems (EPSs) as a part of carbon neutrality development vector. RES units based on solar energy (photovoltaic power plants (PvPPs)) connected to hydrogen-based electric energy storage system (EESS) have been being actively introduced recently. For connection of modern PvPPs and hydrogen-based EESS (HESS) to grid electric power converters (EPC) are used. Large-scale implementation of these devices causes changes of EPS dynamic properties due to properties of inverting EPCs and their automatic control systems. To study and analyze these properties mathematical modelling can be used. Software on calculation of electromechanical transients is usually used for modelling transient processes. In such software generic mathematical models are developed and used to calculate transients in EPSs with PvPPs and HESSs. These models have a number of features due to which the full range of processes accompanying implementation of PvPPs and HESSs into weak grids cannot be simulated. The paper proposes an alternative all-mode variant to model these units in EPS. [ABSTRACT FROM AUTHOR]

Load frequency regulation (LFR) is an indispensable scheme in planning electrical power production to provide consumers with stable, reliable and uninterrupted power. In the face of complicated power system (PS) structures with increasing and intricate power demand, new controllers that offer not only good performance, but also easy commissioning in practice are required. To this end, this research introduces an exponential PID (EXP-PID) controller as a new control scheme to ameliorate the LFR performance of PSs. This controller is simple to design and has a nonlinear feature inherited from two tunable exponential functions, which are placed in front of the PID controller and act on the error signal and its time derivative individually. To achieve the utmost performance, the EXP-PID controller's parameters are procured by a corrected variant of the snake optimizer (co-SO). To validate the proposed control scheme, various single-/multi-area single-/multi-source PSs favored in the area are considered as test benches. A thorough comparison with the state-of-the-art approaches is performed to disclose the true efficacy of our proposal. Among the rivals, co-SO tuned EXP-PID controller, despite its simplicity, is found to render credible and promising performance in mitigating frequency and tie-line power deviations effectively. • An EXP-PID controller is introduced for LFR of power systems. • The new controller enjoys advantages of nonlinear feature and adaptive gains. • Corrected variant of snake optimizer is presented for controller optimization. • Several power systems are studied for performance validation. • The dominance of our proposal is vividly affirmed over the state-of-the-art. [ABSTRACT FROM AUTHOR]

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model's ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is shown that the transformation of the energy sector based on the construction of new architectures of energy systems requires a joint consideration of three layers of two-way energy information exchange: economic, informationmanagement, and physical (energy), and the functioning of modern electric power systems requires the application of process monitoring with enhanced functionality. For such intelligent monitoring, defined as Smart-monitoring, the first and second level monitoring procedures are characterized, in particular, taking into account measurement with a change in signal type, operation with information with syntactic, semantic and pragmatic adequacy, diagnosis with identification and forecasting. It is shown that Smart-monitoring in electric power at the level of medium and low voltages (distribution and consumption systems) should be formed as a technological platform for diagnosis, audit, control, dispatching, energy management of electric power systems, formation of modern energy markets. [ABSTRACT FROM AUTHOR]

The accuracy of the steady-state analysis of a power system network at constant conductor temperature with modern software such as Mathcad, RastrWin3, and MatLab (MatPower and Optimization Toolboxes) is analyzed. The topology of the network and the parameters (impedances and admittances) of its lines, consumption and generation at nodes (buses) of the power system are considered known. The exact solutions for the complex node voltages in an IEEE 5-bus test network are found at which the active power is balanced with an accuracy of 10–307, either in the absence or in the presence of a PV-type node. The errors in the magnitudes (moduli) and phase angles (arguments) of the node voltage vectors calculated with the highest possible accuracy using modern software systems for different operating modes of the power system are analyzed. The results obtained are useful for further development of methods and algorithms for steady-state analysis of a power system and selection of a tool for simulation of its unsteady states. System operators need to perform such analyzes to solve many applied problems. [ABSTRACT FROM AUTHOR]

Vigorously developing new energy sources is an important approach to achieving the carbon peaking and carbon neutrality goals. However, the randomness, volatility, and intermittency of new energy pose severe challenges to the supply-demand balance and stability of the electric power system. Therefore, there is an urgent need to develop diversified flexible resources to guarantee the system’s safe and stable operation. This study summarizes the electricity demand and the power structure development pathways in China under the carbon neutrality goal. Based on this, it analyzes the different flexibility demands of electric power systems with different proportions of wind and solar power generation capacities and elaborates on the characteristics of various types of flexible resources needed for power generation, transmission, load, and storage from four aspects: peak capacity (adequacy), ramping flexibility, stability, and inertia. Drawing from the international experience regarding flexible resource development, we propose the principles for flexible resource development: supply security guarantee, low-carbon development, and economic optimization. Considering the existing problems in the flexible resource development in China, we propose a flexible resource development pathway that aligns with the emission reduction goal of the power sector and the medium- to long-term development trend of the electric power structure of China. Furthermore, key initiatives to ensure the development of flexible resources are proposed from five aspects: power source, power grid, load, energy storage, and market mechanism.

The electric power system must supply energy with quality, security, and reliability. However, the system can operate close to its critical limits due to environmental or economic reasons. Under these circumstances, voltage instability problems, which have been the cause of several significant blackouts worldwide in the last two decades, are prone to happen due to increasing power demand. For this reason, guaranteeing an acceptable voltage security margin is crucial for a safe and reliable power system operation. Over the years, many studies have been made to identify and control voltage instability problems, where voltage stability margin is a parameter used to identify these problems, calculated through continuation power flow. Besides that, to prevent or correct these problems, preventive or corrective controls may be used, such as capacitor switching, for example. Within this context, this paper proposes a method capable of defining a list of capacitor sets that mitigate the criticality of a set of contingencies and maximize the voltage stability margin of the power system under analysis as its main contribution. A continuation power flow embedded in a binary differential evolution algorithm calculates the voltage stability margin. This margin is optimized by selecting capacitor banks to be switched on/off after the contingency. The output of the method can be presented in a suitable format to the system operator, enabling fast choices of corrective control actions after detecting a contingency in real-time operation. [ABSTRACT FROM AUTHOR]

Increasing the generation of electric power from renewable energy sources (RESs) creates important challenges to transmission system operators (TSOs) for balancing the power system. To address these challenges, adequate system flexibility is required. In this context, TSOs carry out flexibility assessment studies to evaluate the flexibility level of the power system and ensure that a stable operation of the transmission system under high RESs integration can be achieved. These studies take into consideration numerous scenarios incorporating different assumptions for temperature, RESs penetration, load growth, and hydraulic conditions. Until now, flexibility studies usually solve the standard unit commitment problem and evaluate if the flexibility level is adequate. Although this approach provides quite accurate results, the computational requirements are significant, resulting in limiting the scenarios chosen for examination. In this paper, deep learning approaches are examined, and more precisely, an integrated system of two recurrent neural networks with long short-term memory cells is designed to carry out the flexibility assessment task, aiming at the reduction in the computational time required by the optimization process. The output of this neural network system is then used to calculate the probability of flexibility shortages. The proposed method is evaluated based on data from the Hellenic transmission system, providing quite promising results in (a) accurately calculating the probability of insufficient flexibility and (b) achieving a significant decrease in computational time. This novel approach could notably facilitate TSOs since more scenarios can be included, exploiting the computational efficiency of the method. In this way, a more complete evaluation of the flexibility level of the power system can be achieved and thus help to ensure the stable and reliable operation of the transmission system. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper. [ABSTRACT FROM AUTHOR]

The analysis of multi-energy flows forms the cornerstone for the study of state estimation, safety assessment, and optimization in integrated smart energy systems (ISES). The interactions between various energy flows and the inherent variability of renewable energy sources often lead to significant challenges to the static stability of ISES. This paper investigates the static voltage stability of ISES under multi-energy coupling conditions through multi-energy flow analysis in electric-gas-thermal energy systems. First, a steady-state model of the ISES is constructed by representing the interconnected energy subsystems as equivalent sources and loads in the power grid. Subsequently, through the coupling elements of ISES, the power flows of the natural gas system (NGS) and the district heating system (DHS) are converted into active power in the electrical power system (EPS), resulting in an equivalent power flow equation for the ISES. Then, using Brouwer’s fixed-point theorem, the analytical sufficient conditions for solving the equivalent power flow equation are derived. Finally, a simulation model based on MATLAB/Simulink is established. The steady-state criterion for ISES is obtained in this paper, and the correctness and effectiveness of the proposed conclusions are verified by the simulation results.

As the energy sector undergoes rapid technological transformation, the resilience of electric power systems has become a focal point in the context of climate change and energy security. This paper examines the interplay between energy resilience and the digital upgradation of electric power systems in China, with a particular emphasis on the role of financial flexibility. Using panel data from 2005 to 2023 across 30 provinces in China, we employ the Generalized Method of Moments (GMM) model to investigate how financial flexibility—measured through capital access and adaptive financial strategies—affects the integration of digital technologies such as smart grids, renewable energy systems, and advanced monitoring infrastructures. Our findings demonstrate that financial flexibility significantly enhances the resilience of electric power systems by facilitating quicker adoption of digital technologies and enabling systems to better withstand external shocks, such as fluctuating energy demands and environmental stressors. The study offers policy insights, underscoring the importance of supportive financial mechanisms to accelerate the digital transformation and sustainability of China's energy infrastructure. This research contributes to the ongoing discourse on energy resilience by highlighting the critical role of financial flexibility in enabling technological advancements within the power sector.

This paper presents A New Methodology for Controlling excitation voltage of a synchronous generator Using Intelligent Controller. This controller depends on Genetic Neural Networks Using Single Phase Semi-converter, To investigate the Effectiveness of the Proposed Controller on the excitation of the synchronous generator, It Was Compared with AC1A Excitation System, The Proposed Controller Processes the angular velocity signal and the terminal voltage signal of the synchronous generator in order to control voltage fed to the excitation circuit of the synchronous generator itself, Finally the model was applied on real time simulator in order to ensure the validity of the results.

The ever-expanding use of complex multifunctional microelectronic and microprocessor systems in electric power industry has led to an increase in the vulnerability of power industry to powerful electromagnetic effects, in particular, to high-altitude electromagnetic pulse (HEMP). Therefore, such use should be accompanied by mandatory parallel use of means of protection against such effects, moreover, specially designed for civilian infrastructure. [ABSTRACT FROM AUTHOR]

Nowadays, renewable energy sources are integral to programs for the development of electric power systems. It is established that modern renewable generation units and energy storage systems utilize power electronic-based interfaces, such as voltage source converters, to convert power. On the one hand, the utilization of a voltage source converter provides certain advantages, such as the absence of a direct link to the network, the ability to operate on all quadrants of the PQ diagram, and the possibility of connecting renewable generation units to a weak network. On the other hand, the widespread use of renewable generation units utilizing voltage source converters reduces the overall inertia of the electric power system. However, excessive use of power electronic converters in the network leads to a significant increase in harmonic pollution, thereby introducing fresh challenges to ensuring safe and stable operation of the electric power system. To ensure the dependable operation of an electric power system with renewable energy sources units and energy storage systems based on the voltage source converter, it is essential to update the control system of the voltage source converter to meet the requirements for traditional generation as part of the electric power system. This paper presents the outcome of the power oscillation damping controller application implemented in the voltage source converter, which enhances the electric power system's stability. [ABSTRACT FROM AUTHOR]

The real-time detection of power quality disturbances (PQDs) in electrical power systems (EPSs) is crucial for prompt actions to protect EPSs from cascade damages that can cause equipment failures, system downtime, and economic losses. The complexity of EPSs makes it challenging to detect the type and location of PQDs accurately. Many previous studies demonstrated that deep learning is a good tool for PQD detection, but the real-time capability of deep learning-based PQD detection has yet to be achieved. In this study, we proposed a voltage signal segmentation approach to use as an input of transformer-based deep learning models. To demonstrate the capability of the proposed method, synthetic voltage signals were prepared from the IEEE 9-bus system with four fault conditions using the PSCAD/EMTDC program. Segmented voltage signals, sampled every 1.67 ms, were used as the input of the deep learning models, and it successfully classified the type and location of PQDs, demonstrating the real-time capability (within 1.67 ms) of the proposed method. Additionally, we showed that the transformer-based model, trained using data obtained from just three locations, achieved high accuracy in detecting PQDs. We also demonstrated that the transformer-based model outperformed convolutional neural networks, which are conventional deep learning models for PQD detection. In conclusion, the suggested data segmentation approach with a transformer-based deep learning model opens up new possibilities in real-time PQD detection.

The new energy is connected to the distribution network in the form of distributed generation, which brings uncontrollability, randomness and volatility to the system. With the help of modern power electronics, information and communication and automatic control technologies, the active distribution network with flexibility and controllability has become a development trend. The active distribution network planning is one of the major research fields in recent years. Based on the research results in this field at home and abroad, the research contents and methods of active distribution network planning were summarized, analyzed and prospected. The basic structure of the active distribution network was described, and the characteristics of the components of the distribution network were introduced. According to the different control variables, the planning models of active distribution network were classified, the optimization objectives in the model were summarized. The commonly used model solving algorithms and their advantages and disadvantages were analyzed and summarized. Through the discussion of key issues, the development trend of active distribution network in the future was analyzed.

. It is a very challenging task to correctly use weather data to calculate the active power loss due to corona in overhead power lines. One of the significant reasons for this was the problem of accuracy of determining weather conditions and the use of the forecast for calculating extended overhead lines. In the USSR (Union of Soviet Socialist Republics), based on field tests and experimental data, recommendations were developed for accounting for losses of electric energy due to corona and interference in overhead lines, the average values of which were given in the relevant instruction for the regulation and justification of the norm of electricity consumption for its transmission over electric networks. According to this regulatory document, the specific cost of active power due to corona in overhead lines, averaged by design, was determined depending on weather conditions, which were divided into four groups. In this paper, the effect of active power losses on corona is estimated using various factors on the example of high-voltage overhead lines in the main electrical networks of the Republic of Belarus of various rated voltages. Dependences of active power losses due to corona have been constructed and modeled, taking into account different climatic and weather conditions observed in the country. The determination of metrological parameters was based on the analysis of weather data and patterns of types of weather conditions. In order to improve the accuracy of calculating the specific losses of active power due to corona, all possible weather conditions were considered, as well as their combinations. Afterwards, the data on weather conditions were processed and their types were reduced to one of the four existing types of weather. A method for determining electric power losses due to corona in overhead lines based on the average relative air density and geometric parameters of the predominant section of overhead lines has been proposed and presented. It is shown that the usage of exclusively rated voltage can cause errors in determining both the specific and actual values of active power losses for corona.

In contemporary society, efficient energy management is becoming crucial for achieving energy sustainability and the reliability of power systems. Microgrids stand out as an essential solution to attain these goals, integrating various energy sources and optimizing transmission costs. A detailed analysis of different methods for microgrid optimization is key to achieving maximum energy efficiency and economic viability in its integration. This paper is based on comprehensive research into optimal energy management in a grid-connected microgrid using a genetic algorithm. The aim of optimal management is to reduce active and reactive power losses, as well as voltage deviation, through precise optimization of electricity production. Research has shown that the proper engagement of production resources and energy consumption can contribute to reducing the cost of electricity production, as well as reducing losses and voltage deviation.

Abstract Background Development finance is vital for low- and middle-income countries to enhance their sustainability agendas, as it provides essential funding necessary to close domestic financing gaps, including in the energy sector. Coal is still a vital power source for the energy sectors in the Western Balkans (i.e., Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia). The energy sector is a critical component in the five countries’ pursuit to decarbonize (i.e., follow the net zero pathways) due to its central role as a primary contributor to greenhouse gas emissions and a critical enabler of sustainability transition. This article presents a mapping exercise of development finance for five Western Balkan countries’ energy sectors. The study conducted a scoping literature review and detailed analysis of the five countries’ energy sector-related development finance flows from 2008 to 2020. This aimed to provide insights into the development finance flows for renewable and non-renewable energy sources in five Western Balkan countries. Results The scoping literature review indicated a significant gap in knowledge about the effects and effectiveness of development finance in the Western Balkans. Data analysis identified US$3.2 billion in energy development finance in the examined countries. The disbursement ratios were above the global average of 63%. Serbia received the highest proportion of the total funding, while Montenegro obtained the highest funding per capita. The data analysis did not establish a connection between adopting the Paris Agreement in 2016 and increasing development finance flows for renewable energy projects. Around one-third of the disbursed development finance was invested in projects for energy supply using non-renewable sources. Official Development Aid loans represented 37% (US$1.2 billion) of the total funding, contributing to the increase in indebtedness in the five countries. European-based bilateral and multilateral development finance providers were the most important actors in the five examined countries. Conclusions The amount of the disbursed development finance was insufficient to cover a significant percentage of the needs of the surveyed countries. Although carbon-intensive energy infrastructure received considerable funding, the total amount of disbursed energy development finance ranged between 0.15 and 0.62% of the average gross domestic product for the analyzed countries during the study period. Based on the research findings, we recommend that development finance providers and recipient countries pay greater attention to planning for strategic funding disbursement.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems. [ABSTRACT FROM AUTHOR]

Addressing the issue of reduced system inertia and significantly increased risk of system frequency deviation due to high penetration of renewable energy sources, this paper proposes a power system disturbance frequency trajectory prediction method based on light gradient boosting machine (LightGBM) Spearman to provide data support for advanced system stability judgment and the initiation of stability control measures. Firstly, the optimal cluster is determined by combining the K-means clustering algorithm with the elbow method to eliminate redundant electrical quantities. Subsequently, the Spearman coefficient is used to analyze feature correlation and filter out electrical quantities that are strongly correlated with frequency stability. Finally, a frequency trajectory prediction model is built based on LightGBM to achieve accurate prediction of disturbed frequency trajectories. The method is validated using a case study on the New England 10-machine 39-bus system constructed on the CloudPSS 4.0 full electromagnetic cloud simulation platform, and the results show that the proposed method has high accuracy in frequency trajectory prediction. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The analysis of the known approach (Kirshtein, B.K. and Litvinov, G.L., Autom. RemoteControl, 2014, vol. 75, no. 10, pp. 1802–1813.) in which tropical geometry over complex multifields of active power balances is used to estimate the region of existence of the electric power system mode. Its limitations are shown and a new approach is proposed, a criterion is also represented for determining the boundary that precedes the violation of the stability of the energy system due to the restructuring of the tropical set of solutions. The developed approach allows to determine the approach of the power system mode to the limit by the known parameters of the lines and the dynamics of changes of the nodes voltage modules and the nodes load. [ABSTRACT FROM AUTHOR]

Modern ships are complex energy systems containing a large number of different elements. Each of these elements is simulated separately. Since all these models form a single system (ship), they are interdependent. The operating modes of some systems influence others, but at the same time, the work of all the systems should be aimed at fulfilling the basic functions of the ship. The work proposes a methodological approach to combining various systems of ships into a single complex model. This model allows combining models of ship systems of various levels (microlevel, macrolevel, metalevel, megalevel). The work provides examples of models of such multi-level energy systems. These are energy systems composed of an electric generator, a diesel engine, a propeller shaft, and algorithms used for operating the common parts of the ship's electric power system and a piston wear process. Analytical, structural, numerical, and object-oriented models were made for these objects. Each of these particular models describes a limited class of problems, has characteristic properties, and a mathematical structure. The work shows how particular models can be interconnected using a set-theoretic description. Particular models are combined into macrolevel models, whose output parameters are quantities that are by no means related. The macrolevel models are interrelated using control models. Control models belong to the metalevel and allow for assigning settings and response thresholds to algorithms used in automation systems. Such a model (megalevel model) allows, ultimately, investigating the dynamics of the entire system as a whole and managing it. [ABSTRACT FROM AUTHOR]

Background: Development finance is vital for low- and middle-income countries to enhance their sustainability agendas, as it provides essential funding necessary to close domestic financing gaps, including in the energy sector. Coal is still a vital power source for the energy sectors in the Western Balkans (i.e., Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia). The energy sector is a critical component in the five countries' pursuit to decarbonize (i.e., follow the net zero pathways) due to its central role as a primary contributor to greenhouse gas emissions and a critical enabler of sustainability transition. This article presents a mapping exercise of development finance for five Western Balkan countries' energy sectors. The study conducted a scoping literature review and detailed analysis of the five countries' energy sector-related development finance flows from 2008 to 2020. This aimed to provide insights into the development finance flows for renewable and non-renewable energy sources in five Western Balkan countries. Results: The scoping literature review indicated a significant gap in knowledge about the effects and effectiveness of development finance in the Western Balkans. Data analysis identified US$3.2 billion in energy development finance in the examined countries. The disbursement ratios were above the global average of 63%. Serbia received the highest proportion of the total funding, while Montenegro obtained the highest funding per capita. The data analysis did not establish a connection between adopting the Paris Agreement in 2016 and increasing development finance flows for renewable energy projects. Around one-third of the disbursed development finance was invested in projects for energy supply using non-renewable sources. Official Development Aid loans represented 37% (US$1.2 billion) of the total funding, contributing to the increase in indebtedness in the five countries. European-based bilateral and multilateral development finance providers were the most important actors in the five examined countries. Conclusions: The amount of the disbursed development finance was insufficient to cover a significant percentage of the needs of the surveyed countries. Although carbon-intensive energy infrastructure received considerable funding, the total amount of disbursed energy development finance ranged between 0.15 and 0.62% of the average gross domestic product for the analyzed countries during the study period. Based on the research findings, we recommend that development finance providers and recipient countries pay greater attention to planning for strategic funding disbursement. [ABSTRACT FROM AUTHOR]

This paper examines the use of manifold learning in the context of electric power system transient stability analysis. Since wide-area monitoring systems (WAMSs) introduced a big data paradigm into the power system operation, manifold learning can be seen as a means of condensing these high-dimensional data into an appropriate low-dimensional representation (i.e., embedding) which preserves as much information as possible. In this paper, we consider several embedding methods (principal component analysis (PCA) and its variants, singular value decomposition, isomap and spectral embedding, locally linear embedding (LLE) and its variants, multidimensional scaling (MDS), and others) and apply them to the dataset derived from the IEEE New England 39-bus power system transient simulations. We found that PCA with a radial basis function kernel is well suited to this type of power system data (where features are instances of three-phase phasor values). We also found that the LLE (including its variants) did not produce a good embedding with this particular kind of data. Furthermore, we found that a support vector machine, trained on top of the embedding produced by several different methods was able to detect power system disturbances from WAMS data. [ABSTRACT FROM AUTHOR]

In order to increase the regime reliability of energy systems, the experience of applying machine learning algorithms and models for various issues of operative-dispatching and counter-accident management was reviewed. It is indicated that an effective solution to this problem is the use of machine learning algorithms and models that are able to learn to predict and control the operating modes of the power system, taking into account many changing influencing factors. The experience of using machine learning technology in the tasks of operational dispatch and emergency control of EPS is presented, which clearly shows the prospects of such studies for subsequent practical implementation in the work of various automated control systems for electric power networks of power systems. Until recently, models based on neural network structures have remained the most popular among machine approaches in predictive problems. The advantages of using this structure are shown, first of all, by the fact that the neural network structure makes it possible to obtain models with good approximating abilities. A comparative analysis of the effectiveness of various models in predicting electricity consumption is given. The issues of voltage and reactive power regulation in the electrical network of power systems using an artificial neural network are considered and the effectiveness of this approach is shown. A model and algorithm for estimating voltage stability in power system nodes under various influencing factors is proposed, as well as results are presented that confirm the reliability of the results obtained. [ABSTRACT FROM AUTHOR]

In today’s world, generating units based on renewable energy sources occupy one of the main places when it comes to the development of power systems. Modern generating units based on renewable energy sources have a power converter in their composition. On the one hand, the use of a power converter gives some advantages: no direct connection to the grid, operation in all quadrants of the PQ diagram. However, on the other hand, widespread use of renewable generation units based on power converter reduces the overall inertia of the EPS, excessive use of power electronic converters in the electric grid will cause many harmonic disturbances and create new problems for safe and stable operation of EPS. In order to ensure the reliable operation of EPS with renewable energy units based on the power converter, the power converters control system should be upgraded to meet the requirements of conventional generation as part of EPS. This paper presents the result of applying a power oscillation damping controller implemented in a power converter to improve the stability of a high-dimensional EPS. The paper presents an analysis of the effect of regulators of different topology for oscillation damping. The results of the application of the power oscillation damping regulator in the following research scenarios are presented. For a more accurate assessment of the impact of the regulator, the transient damping coefficient was calculated. In this paper, the result of using the oscillation damping controller implemented in the power converter for the PV station to improve the stability of the EPS as a whole was demonstrated. The proposed solution is based on the “deloading” strategy of the PV power station and the SI block with two-loop control. The effect of oscillation damping controller on power fluctuations was evaluated according to the AVR oscillator tuning methodology.

The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper.

Based on the Markov model, a fault warning system for power grid operation is proposed. Its main objective is to improve the execution effect of alarm commands. Firstly, the power grid operation reliability evaluation model based on the Markov model is established. According to the set critical value to judge the change of power grid operation state. Develop abnormal alarm commands for power grid operation. Strange events are described in detail using natural language technology, and corresponding instructions are completed in the system. After the comparative test, the new alarm improved more remarkably than the traditional intelligent alarm mode.

A crucial industry for improving society's sustainable development is the power sector. To address issues with the ineffectiveness of electric power emergency response during emergencies and the unclear division of duty among emergency subjects. A prefecture-level city power supply company to respond to the “In-Fa” typhoon, for example, to build a “1 + N” two-level emergency rapid response unit. Furthermore, it is proposed from the emergency response, emergency coordination, emergency material reserves, etc., to build a “full-chain” type of power emergency quick reaction system. Case studies have revealed that the quick response system's emergency combat capability, catastrophe preventive and mitigation capability, and emergency security capability have all improved. The construction of a “full-chain” type of power emergency rapid response system specialized and standardized the power emergency response system and provided a reference basis for the power industry's emergency response.

In a modern and technological world that has a great demand for energy, a versatile energy market, and a renewed electric infrastructure capable of expanding the electric power system under the premise of universal access to electricity, that seeks to minimize the effects of climate change, and that requires an improvement in its reliability, security, and resilience, microgrids are born as one of the systems that have the potential to supply each of these requirements in order to guarantee an adequate decarbonization, decentralization, digitalization, diversification, and democratization of the future grid. However, the integration of microgrids into the electric power system will generate impacts that are currently under study. This paper identifies and analyzes the technical impacts in the electric power system due to the implementation of microgrids, based on what has been recognized in the literature, so that those who have purposes of installation, creation, innovation, and research of microgrids, such as grid operators, technology providers, companies, and researchers, can establish criteria and indicators through which the feasibility of projects involving microgrids can be determined. The concept, importance, and characteristics of microgrids are given, along with a technical justification of the impacts. In addition, technical impacts on some study cases of real microgrids around the globe are identified. Finally, an analysis of the identified technical impacts is offered, and conclusions are drawn. [ABSTRACT FROM AUTHOR]

Electric power system is a complex organizational structure that provides working interaction for its constituent intelligent electronic devices by defining their roles, communication channels, and powers. The control system of a modern electric power system must ensure the coordination of operation of intelligent electronic devices at the technological stages of power generation, transport, distribution, and consumption. The disadvantage of existing process control systems in electric power systems is the use of a hierarchical control structure in relation to the network topology. This fact leads to conflicts of resources and processes at the stages of generation, transport, distribution, and consumption of electricity. Uncoordinated operation of control devices leads to a decrease in the efficiency of power facilities which negatively affects the quality of electricity in the power supply network. To synchronize the work of intelligent electronic devices distributed over the network, it is proposed to provide their joint work through a single information center in a digital environment. At the same time, it is proposed to control the modes of operation of the power supply network using digital twins of its components. Digital twins of electric power system objects control power quality indicators, simulate the modes of interacting devices in a digital environment, and perform control of power supply network components to ensure a rational mode of their operation. To achieve the universality and speed of the control system it is proposed to use the apparatus of fuzzy artificial neural networks, and for better prediction of power quality indicators in the network — ensembles of artificial neural networks. A methodology for controlling the quality of electricity at sections of the electricity distribution network was developed using digital twins that ensure the relationship between the monitored indicators of electricity quality and regulated values of the actuators of intelligent electronic devices.

The article discusses the need to use pumped storage power plants (PSPP) to increase the reliability, stability, maneuverability and energy-economic efficiency of the electric power system (EPS) with power plants based on renewable energy sources (RES), which make it possible to cover the minimum and maximum daily load schedules, and also regulating the capacities of solar-wind power plants, for which the tasks of equalizing power fluctuations and energy storage are especially important. For a technical and economic assessment and determination of the optimal parameters of a PSPP, a mathematical model is proposed under the conditions of its construction with existing structures, taking into account its water-use purpose - low-pressure, daily and seasonal operating modes, with the criterion of minimum investment, payback period, annual costs for PSPP and environmental factors with the maximum generated peak power and fuel savings. On the basis of the proposed mathematical model, a methodology and program have been developed to determine the optimal energy and economic indicators of a PSPP.The schemes of using pumped storage power plants at four energy and water facilities, that is, the Tuyamuyun hydroelectric complex, Arnasai, Talimarjan and Khodjikent reservoirs, were considered, and for these facilities, on the basis of the developed methodology and program, their energy and economic parameters of the PSPP were determined.

The assets of electric power transmission systems are characterized by their high cost and complexity, leading utilities to seek ways to improve the efficiency and economic-financial performance of these assets. Thus, this article proposes a tool to support asset management for electricity transmission systems. This tool considers the useful life of the equipment, its importance to the system, financial aspects, and the forecast of electricity demand. An evaluation of the criteria that impact decision-making on the replacement of assets with specialists was carried out. Thus, the tool uses the Analytic Hierarchy Process method to classify the most critical equipment in the system. For analysis of future scenarios, the Monte Carlo Method was incorporated into the tool to simulate the behavior of equipment during a defined time horizon. As a result, the tool presents a ranking of the most critical equipment in the system under analysis within the simulated time horizon. The tool was applied in a case study with real data in the power transformer of a Brazilian utility. The tool helps in decision-making indicating changes that may be made in the period under review, their likely impact on equipment loading, and the list of critical transformers in the system.

The development of power electronics, including high-efficiency power supply systems, changes the structure of perception of the types of electrical energy receivers connected to the power grid at each supply voltage level. In the past, the dominant type of receivers were constant impedance devices. Currently, more and more devices have power supplies, which are most often constant power receivers. The construction and technological diversity of receivers allows mixed receivers, which are a combination of constant power and constant impedance receivers, to be distinguished. Therefore, there is a need to determine the impact of the type of electrical energy receivers on the power grid, both in the context of the operation of a single receiver and in the case of a branched power grid with a high penetration of distributed generation. This article will discuss the impact of the type of electrical energy receivers on the operation of the power grid, with particular emphasis on the issue of electrical energy losses at changing voltages. To determine the impact of receivers, simulation studies were performed based on two case stages: the first is a fragment of a low-voltage power grid in a household that supplies a heating device, which is analyzed as a different type of receiver; the second is a fragment of a low-voltage power grid with various types of consumers and photovoltaic installations. The research was carried out on the basis of the mathematical model of the low-voltage power network developed by the authors using the electric multipole method and Newton's method. The obtained results show that the type of receiver may have an impact on electrical energy losses. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation of ferroresonance, a dangerous electrical phenomenon that poses significant financial risks. Using real electrical transmission line parameters, we simulated synthetic ferroresonance scenarios on a model and analyzed the resulting data using high-order spectral analysis methods, including the Wigner-Ville method, Welch method, frequency-power analysis, and spectral methods. Our analysis revealed changes in frequency and power before and after ferroresonance, with third and fourth-order cumulants being calculated. We confirmed the accuracy of the power transmission line’s base frequency and power before the ferroresonance event and determined the frequency and power values before and after ferroresonance with frequency-power analysis. Our cumulative analysis results showed symmetrical results that are consistent with the properties of ferroresonance. Additionally, we found that the Wigner Ville method’s high-resolution results were significantly more effective than conventional methods. Our study’s findings provide valuable insights into ferroresonance’s behavior and may inform the development of more effective prevention and mitigation strategies.

The electricity sector has added plenty of new technologies in recent years. Smart Grids are characterized by the use of monitoring and communication technologies almost in whole system. The application and use of such new technologies triggers a significant growth in the data number, increasing the amount of errors and missing data, thus hindering the analysis. In this context, this paper performs the modeling, implementation, validation and comparative analysis of four data imputation techniques: K-Nearest Neighbor, Median Imputation, Last Observation Carried Forward, and Makima. The aim is to verify if they could be applied to the electric segment - more specifically to the Smart Grids environment. The database used in the research is obtained from the electricity utility CEEE and its underground substations, located in southern Brazil. Following this, five simulation scenarios are created and one data set is removed, based on pre-established criteria. Finally, the techniques are applied and the new database is compared with the original one. From the simulation results, the technique which presented the best results is Makima, it is validated as robust to be applied in the Smart Grids environment, especially in electrical data missing from an electric power substation.

Abstract The massive use of small energy resources and storage units causes a transition from a traditionally centralized to a decentralized energy system. To structure and coordinate the emerging changes in the energy system, the concept of Energy Cells (ECs) was developed. Several ECs can be combined to form a hierarchically superordinate EC. These hierarchically superordinate ECs can in turn be combined and thus form a complex hierarchy. An EC encapsulates coherent parts of an energy system and can communicate as well as exchange energy with other ECs at a different or the same level. It follows the idea of local balance of energy provision and demand. A network of ECs forms a Cellular Energy System (CES). In this paper, we develop a concept for modeling and simulating a CES. We accomplish this by beginning with atomic components like consumers, producers, and storage units and aggregating them with Hierarchical Controllers (HCs). Such a hierarchically structured energy system is part of various proposals. However, we are able to add neighborhood relations by introducing Local Controllers (LCs). This is more realistic and also opens many degrees of freedom for control strategies in such a system. Following the recursive structure of the CES itself, we define recursive functions for visiting the CES architecture and realizing various control strategies. We evaluate our approach in a series of partially randomized scenarios, showing notable differences in the performance of the CES regarding different control strategies in a larger example. We also provide a theoretical analysis of the computational complexity of the suggested approach.