Complex Electric Power Research Articles

Tungsten-liquid triboelectric nanogenerator for water-flowing energy harvesting with low-resistance and high-DC density

In the new energy era, triboelectric nanogenerator (TENG) effectively converts weak water energy into electricity to drive sensors and electronic components, which has a very broad research prospect. However, the functional materials of TENG are usually insulating, resulting in low-current and high-resistance problems. Guided by Wang's electrical double layer model, we demonstrate that tungsten presents excellent electricity generation performance in flowing water due to its unique defect structure as evidenced by comparing the electricity generation of tungsten, tantalum, titanium, and copper, and testing their electrochemical impedance spectroscopy and X-ray diffraction characteristics. Hence, the study proposes a novel tungsten-liquid TENG for water-flowing energy harvesting, which has outstanding advantages of high DC density, low internal resistance, simple structure, and no complex electrical power required. A tungsten-liquid TENG continuously outputs 3∼20μA in 0.1–2.0M NaCl liquids at a flow rate of 400 mL/min, with output current densities up to 230 mA/m2 and internal resistances down to 6.4–67 kΩ. Ten-tungsten-liquid TENG outputs nearly 40μA, and multiple tungsten electrodes in parallel can effectively enhance the current. Notably, the study is not only beneficial for capturing the current energy in oceans with high-ionic concentrations, but also broadens the research ideas of TENG for promoting the selection of novel functional materials.

Study of the Stability of a Hybrid Electric Power Complex with Non-Traditional and Renewable Energy Sources of Various Types with a Change in Load Power

Study of the Stability of a Hybrid Electric Power Complex with Non-Traditional and Renewable Energy Sources of Various Types with a Change in Load Power

China's nuclear power in the context of decarbonization of the global energy industry

The global structure of energy resource consumption is analyzed. It is shown that the total consumption of energy resources in the 20th century increased almost 15 times and amounted to about 118 PWh in 2000. Of these, more than 80% were fossil (natural) energy carriers, 13 % were renewable energy sources and more than 6 % were nuclear power. This trend has continued for many years. Renewable energy sources and nuclear power are projected to dominate the growth of global energy consumption, meeting on average more than 90 % of the additional demand. China ranks first in the world in terms of GDP and the rate of development of the electric power complex, as well as in terms of production volume and the rate of development of nuclear power. The structure of electricity generation by fuel type in countries (TOP 5, 2021) with high energy consumption has been analyzed. It is shown that the main players in this market are three countries: China (30 %), USA (13 %) and India (6 %). The volume of electricity production in China is almost three times larger than in the EU countries and almost 7 times larger than in Russia. The structure of nuclear energy and China's achievements in this field are considered. It is shown that in the context of nuclear technologies (creation of fourth-generation reactors, development of technologies close to the full closed cycle of nuclear waste processing) China is already ready to significantly expand the scope of nuclear energy. Special attention is paid to the safety of nuclear facilities operations. The main problem China will face is the absence of an insurmountable barrier between civilian and military use of such technologies. This poses a serious threat to the ecology and life safety of the country. In the long term, nuclear power must also solve the problem of safe, long-term disposal of radioactive waste. It is shown that in the future China needs to re-examine its nuclear program in the context of cooperation with International Nuclear Security Organizations.

Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions.

With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To address this problem, this work develops a robust representation learning approach to enhance DSA performance against multiple input data loss conditions in practice. Specifically, focusing on the short-term voltage stability (SVS) issue, an ensemble representation learning scheme (ERLS) is carefully designed to achieve data loss-tolerant online SVS assessment: 1) based on an efficient data masking technique, various missing data conditions are handled and augmented in a unified manner for lossy learning dataset preparation; 2) the emerging spatial-temporal graph convolutional network (STGCN) is leveraged to derive multiple diversified base learners with strong capability in SVS feature learning and representation; and 3) with massive SVS scenarios deeply grouped into a number of clusters, these STGCN-enabled base learners are distinctly assembled for each cluster via multilinear regression (MLR) to realize ensemble SVS assessment. Such a divide-and-conquer ensemble strategy results in highly robust SVS assessment performance when faced with various severe data loss conditions. Numerical tests on the benchmark Nordic test system illustrate the efficacy of the proposed approach.

Exploration of the double frequency complex plane used in Steinmetz’s method of calculation of complex electrical power in an alternating current circuit

Exploration of the double frequency complex plane used in Steinmetz’s method of calculation of complex electrical power in an alternating current circuit

Adjustment of mutant vector elements in the differential evolution method for solving the problem of power shortage minimization in electric power systems

The article is aimed at increasing the effectiveness of numerical optimization methods in calculations of problems concerning power shortage minimization in electric power systems, specifically the differential evolution (DE) method and its variations–aDE and jDE. Experimental studies and testing of the proposed adjustments were carried out on complex electric power systems of different order. These systems are represented and realized by means of mathematical models of power shortage minimization with the possibility of their analysis using the developed software package as part of adequacy assessment. The performed analysis of the DE method elements and the existing variants of the mutation process revealed that the existing approaches can be further modified. This can subsequently increase the speed of problem-solving. It is shown that the main changes include an additional check that the mutant vectors meet the upper and lower bounds, and if they fail to do so, three adjustment options are considered. Existing approaches propose to generate new vector elements beyond the bounds by applying random numbers within the bounds. The present authors propose to use the “projections” of the found vector elements, i.e., to use the values of upper or lower bounds when they are exceeded for a particular element as mutant vector values. The implemented method involving the adjustment of mutant vector elements is shown to offer an advantage of a 47% reduction in problem-solving time over existing adjustment methods while maintaining the same accuracy. It is noted that aDE and jDE are the most effective variations for solving stated problems. The obtained results of experimental studies confirm the effectiveness and advantages of applying the proposed adjustment method in mutation process of in the form of “projections”, as well as using aDE and jDE variations of the DE method to solve the problems of power shortage minimization in electric power systems.

COMPREHENSIVE CONDITION ANALYSIS AND PERSPECTIVES OF ENERGY DEVELOPMENT IN UKRAINE ACCORDING TO THE SMART GRID CONCEPT

The article examines development trends of the energy complex of Ukraine, possibility and prospects of creating a modern energy complex through the use of an innovative technical base for energy management in accordance with the Smart Grid concept. The general properties of Smart Grid technology and its main advantages are given by authors. It is shown that within the framework of the Smart Grid concept, the intelligent electric power system is considered as a single network of information and control systems. The process of electric power industry modernization in the direction of the creation of "smart" power supply networks in a number of European countries, which aims to ensure stable development, economic growth, growth of living standards and protection of the natural environment, is considered. The paper describes modernization methods of the electric power complex of Ukraine based on foreign experience. It is shown that from the point of view of energy security and sustainable development, Smart Grid is able to ensure the operation of the power grid even in case of damage or destruction of one segment, which is a key advantage during post-war reconstruction. They will also make it possible to effectively integrate renewable generation and energy storage systems into the grid, as well as provide auxiliary services for forecasting the power system operation. The paper analyzes the problematic issues of implementing the Smart Grid concept in Ukraine. They include the lack of technological solutions and methods that provide an effective algorithm for determining the state of power networks cyber security; large length of power distribution networks and insufficiently developed infrastructure. A comparative description of the existing energy systems functional properties and energy systems based on the Smart Grid concept is presented in general. An analysis of possible ways of electric power industry development was carried out, which showed the presence of serious limitations of electric power industry development within the framework of the former concept, based mainly on the improvement of certain types of equipment and technologies. It is shown that Ukraine is at the initial familiarization and formation stage of the first organizational initiatives for Smart Grid, namely the implementation of the government-approved Concept of the "smart grids" introduction in Ukraine for the period up to 2035. The purpose and expected results of this Concept implementation are considered.

A fault recognition method for transmission systems based on independent component analysis and convolutional neural networks

Accurate recognition of transmission line (TL) faults plays a vital role in protection systems, avoiding misoperations and false trips. Several studies in this area have revealed the limitations of existing algorithms, particularly when considering more complex electrical power systems (EPS), where some expected operations (capacitor and load switching, for example) can be misinterpreted as fault events. This work addresses the critical issue of fault recognition (FR) in TLs with a special focus on distinguishing faults from other types of disturbances. The primary objective of this study is to introduce an approach based on Convolutional Neural Networks (CNN) and Continuous Wavelet Transform (CWT) for FR in EPS. This method relies only on voltage signals acquired with a reasonable sampling frequency. The proposed method is enhanced with an Independent Component Analysis (ICA)-based preprocessing, making it robust and versatile in handling different types of disturbances, such as adjacent line faults, load switching and capacitor switching. The evaluation of the proposed method is done considering two different EPS, achieving an accuracy up to 99 %, showing the robustness of the method against different scenarios such as fault events and switching operations.

Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections

Voltage phase angle measurements are often unavailable from sensors in distribution networks and transmission network boundaries. Therefore, this paper addresses the conditions for estimating sensitivities of voltage magnitudes with respect to complex (active and reactive) electric power injections based on sensor measurements. These sensitivities represent submatrices of the inverse power flow Jacobian. We extend previous results to show that the sensitivities of a bus voltage magnitude with respect to active power injections are unique and different from those with respect to reactive power. The classical Newton-Raphson power flow model is used to derive a novel representation of bus voltage magnitudes as an underdetermined linear operator of the active and reactive power injections—parameterized by the bus power factors. Two conditions that ensure the existence of unique complex power injections given voltage magnitudes are established for this underdetermined linear system, thereby compressing the solution space. The first is a sufficient condition based on the bus power factors. The second is a necessary and sufficient condition based on the system eigenvalues. We use matrix completion theory to develop estimation methods for recovering sensitivity matrices with varying levels of sensor availability. Simulations verify the results and demonstrate engineering use of the proposed methods.

Study of the relationship between the parameters of electromagnetic-acoustic transformation and the stress-strain state of the metal to solve the problem of remote monitoring of power equipment

The implementation of Russia’s energy strategy provides for the creation of an intelligent control system for active-adaptive electrical networks of the electric power complex, which contains a system for remote diagnostic monitoring of energy equipment, including metal load-bearing structures of electrical energy generation, transmission and consumption facilities. Promising in this regard is the use of a high-performance electromagnetic-acoustic method, which allows non-contact detection of metal defects and monitoring of their stress-strain state. But existing electromagnetic-acoustic diagnostic tools do not have sufficient sensitivity and information content to solve this problem. The article presents the results of experimental studies aimed at identifying and processing informative parameters of electromagnetic-acoustic transformation for the implementation of remote diagnostic monitoring of the stress-strain state and metal damage of power equipment. Based on the research results, it was proposed to use a frequency model formed as a result of spectral analysis of the electromagnetic-acoustic transducer signal as an integral parameter characterizing the state of the metal.

DEVELOPMENT OF AN INFORMATION-MEASURING SYSTEM FOR DIAGNOSING INSULATORS ON OVERHEAD POWER LINES

The largest percentage of emergency shutdowns falls on overhead lines (overhead lines), which are the most vulnerable objects of the electric power complex. Frequent disconnections of overhead power transmission lines (VLEP) are caused by the overlap of insulators (due to complete destruction or the presence of defects). To change the current situation, it is urgent to develop an information system for determining the location of the damaged insulator and notifying about the (possible upcoming) disconnection of the section of the overhead line. It is proposed to install a piezoelectric contact sensor based on a direct piezoelectric effect on a garland of insulators. The changing acoustic pressure of the ultrasonic wave (formed by a change in the electric field strength caused by the deterioration of the insulating properties of the insulator) will generate an alternating voltage of the same frequency on the piezoelectric element. This indicator is supposed to be used as an indicator of the condition of the predamage of the insulator and transmit information to the operational control point.

GPU-based transient analysis of modern grids deploying a hybrid DDM algorithm

Solving a significant number of differential-algebraic equations is essential for conducting transient stability simulations on a complex and interconnected electrical power system. Because of the increasing size and intricacy of modern grids, traditional sequential simulation methods are becoming more computationally challenging and time-consuming to use for dynamic simulations. This research aims to examine parallelism techniques from an innovative algorithm viewpoint to the hardware level by utilizing multiple graphics processing units as the main computational tool designed to simulate the dynamic behaviour of power systems on a large scale. Our approach features a novel hybrid algorithm to improve the dynamic simulations of grids with significant renewable energy integration. The algorithm employs a Schwarz-based approach in its initial decomposition phase to isolate synchronous machines and renewable sources from the grid. This separation enhances the algorithm's ability to manage these components effectively. Subsequently, each subsystem undergoes further division into subdomains in the second stage, with separate computations carried out using the Schur-complement technique. The simulations were conducted on various test systems, with a maximum of 25,000 buses, 8,000 synchronous generators, and 256 PV farms all modelled in detail. We introduce a GPU-oriented preprocessing and vectorization parallelization method for the implementation of our two-stage algorithm. This approach provides the flexibility to tailor parallelization techniques, including nested for loops and kernel pragmas, to craft a hardware solution that enhances the performance of our dynamic domain decomposition algorithm. Simulation outcomes have verified that this approach can yield a notable 7.8x acceleration in speed when executed on an NVIDIA GeForce RTX 2070 SUPER GPU.

Efficiency evaluation and productivity analysis of complex electric power systems in China: A directional slacks-based network data envelopment analysis approach

The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China’s interprovincial power system.

INCREASING THE RELIABILITY OF ELECTRIC POWER FACILITIES THROUGH THE USE OF UNMANNED AERIAL VEHICLES

The issue of increasing the reliability of the operation of facilities in the energy system of Ukraine is being considered. It is shown that the electric power complex of Ukraine has significant levels of depreciation, which, in turn, leads to an increase in the number of emergency conditions, an increase in the time of post-accident recovery, and as a result, a decrease in the reliability of power supply in general. Accordingly, the loss of electrical energy will be the more, the higher the wear of systems and equipment. Maintenance costs must also increase to keep the power system within regulatory requirements for high wear equipment. Outdated and worn-out equipment leads to an increase in energy losses, an increase in the cost of maintaining the state of working capacity and, as a result, financial losses. Reduction of electricity losses on worn-out equipment cannot be carried out instantly and radically due to the impossibility of the Ukrainian economy to upgrade power equipment in a short time. The authors propose to reduce financial losses by reducing the cost of maintaining existing equipment. And it is proposed to do this through the use of modern technologies, namely the use of UAVs for monitoring power equipment. It is proposed to pay attention to only one parameter, namely the uninterrupted operation of power facilities. Thus, for network companies monitoring the technical condition of lines and control of isolation means is the main type of monitoring. Density of failures of equipment operation is built in graphical form, where a normalized number of inspections is presented, based on reliability requirements. The number of inspections is calculated according to regulatory documents. The paper shows that the graph of the change in the probability of failure-free operation is not straight. Therefore, the frequency of inspections cannot use the frequency that is not related to the wear and tear of the equipment. And to maintain the level of reliability, it is necessary to increase the number of inspections to ensure the standard level of reliability. Therefore, the authors propose to reduce costs with an increased number of inspections using UAVs. The above economic calculations show the high economic efficiency of such a proposal.

INFLUENCE OF FOULING OF HEAT-EXCHANGE SURFACES OF CONDENSERS OF STEAM TURBINES ON THE CARBON OXIDE EMISSIONS

According to the strategy of environmental development of Ukraine until 2030, in order to increase the Environmental Performance Index (EPI), it is planned to reduce the energy intensity of GDP. One of the most polluting industries is the thermal power industry, and therefore reducing the emission of harmful gases, in particular nitrogen dioxide, is an important environmental problem. The share of electricity production by thermal power plants and thermal power plants in the electric power complex is significant, which leads to environmental risks due to large emissions and discharges of harmful substances by these enterprises into the environment. The magnitude of such emissions depends on the efficiency of the circulating cooling systems, which affects the rational use of fuel and water resources and, accordingly, the state of the environment. Increasing the temperature of the exhaust steam by reducing heat transfer through the contaminated heat exchange surface increases the pressure in the condenser of steam turbines and reduces the power of the turbine, which increases fuel consumption and increases the amount of emissions of harmful substances. One of these emissions is nitrogen dioxide, which is a harmful toxic compound and is classified as a greenhouse gas. The Purpose of the work is to calculate oxide carbon emissions depending on the thickness of the deposited layer on the heat exchange surface of the condensers of steam turbines of TPP. The concept of a particle of harmful emissions associated with the emergence and growth of a layer of pollution of the heat exchange surface of the condensers of steam turbines of TPP, expressed in fractions of a unit, is introduced, and an analytical expression is obtained for this value. Based on the theories of fuel combustion, heat transfer, as well as the concept of a part of harmful emissions associated with the emergence and growth of a pollution layer, the dependence of the amount of harmful emissions associated with fuel combustion at TPPs on the thickness of the pollution layer of the heat exchange surface of steam turbine condensers is obtained. Keywords: environmental pollution, thermal power plants, carbon monoxide, heat exchange surfaces.

Analysis of the Energy Complex Member of the EАEU Countries and the Formation of Groups-Technologies of its Digitalization

Due to the increasing internationalization of the member countries of the Eurasian Economic Union (EAEU), it is planned to create a common energy market and an electric energy market. To ensure the reliable functioning of the energy systems of individual states of the Union and their common energy market, it is necessary to increase energy efficiency and reduce costs at each stage of production, transmission, distribution and sale of electricity by optimizing processes in the industry through the introduction of digital technologies. The purpose of this work is to analyze the electric power complexes of the EAEU member states and to consider groups of digitalization technologies of industries in the context of the electric power industry, to form an algorithm for creating a classification of digitalization technologies of the electric power industry and on the basis of the latter to create the classification itself. The article provides an overview of the dynamics of the main economic indicators of the Union countries and the main indicators of their electric power industries, examines the structure of electricity production, energy portfolio, average electricity prices, total electricity consumption by sector. The import-export potential and the level of electricity losses in the networks have been analyzed. The interpretation of digital technologies of the electric power complex in the context of existing groups-digitalization technologies was made, the components of the interconnection of digital technologies were identified; the classification of the above components by production stages has been developed. Recommendations on the further use of the classification for the formation of an indicative system for assessing the level of digitalization of the electric power complex are put forward. It is concluded that digitalization of the electric power system on the technical side increases the energy security of the state and the competitiveness of the energy system on the world market, and on the economic side it helps to reduce costs at all stages of the entire technological cycle.

Steady-State Risk Prediction Analysis of Power System Based on Power Digital Twinning

In essence, electric digital twinning uses artificial intelligence technology to model complex electric power systems, and is the development and supplement of electric power modeling technology. This paper intends to predict and analyze the steady-state risks of complex power systems based on the power digital twin. Firstly, power flow calculation and optimization are carried out for complex large power grid systems. Based on sparse matrix storage and node coding optimization, the power flow calculation speed is improved and the memory usage is reduced. The accuracy and timeliness of the continuous power flow calculation when obtaining the node power and voltage are improved by using the unit processing tangent prediction vector and the internal machine of the prediction vector to determine the prediction direction. Secondly, according to the optimization results of the power flow calculation, the multi-objective optimization problem of power system simulation is solved by using the advantages of neural network modeling, such as self-learning, self-adaptation, fault tolerance, and parallelism. Finally, the power flow calculation optimization and neural network analysis are applied to the integrated stability program of the United States Western Combined Power Grid (WSCC) power system’s nine-node model; this is in order to simulate the regional power grid for simulation analysis. Different risks in the power system under steady–state conditions are predicted and analyzed, the voltage drop in the transient voltage is reduced under multiple working conditions, and the relative power angle is improved, improving the overall stability of the power system.

Analysis of photovoltaic power station (PPS) modeling using artificial neural network and PVsyst software

The possibility of using the method of artificial neural networks to analyze the modes of complex electric power systems with integrated large photovoltaic stations is considered. Based on the correlation analysis, the main factors influencing the energy parameters of photovoltaic power plants were selected and the boundary conditions for the Pearson coefficient were determined. The algorithm of the developed program for calculating the modes of electric power systems using neural networks is described, which makes it possible to more accurately predict generation, taking into account climatic conditions. On the example of calculations of the modes of the South-Western part of the energy system of Uzbekistan, taking into account the change in power flows as the generation of the Navoi photovoltaic plant with a capacity of 100 MW changes, a comparative analysis of the results obtained by calculation with real measurements was carried out.

УПРАВЛЕНИЕ СТРАТЕГИЧЕСКИМ ПЛАНИРОВАНИЕМ НА ОСНОВЕ СДЕЛОК СЛИЯНИЙ И ПОГЛОЩЕНИЙ И ЕГО ВЛИЯНИЕ НА ЭФФЕКТИВНОСТЬ ЭЛЕКТРОЭНЕРГЕТИЧЕСКОГО КОМПЛЕКСА РОССИИ

The article presents a set of solutions proposed by the author for managing strategic plan-ning in the electric power industry based on merger and acquisition deals. The purpose of the study is to develop a methodological approach to the selection of mergers and acquisitions in the implementation of strategies for the development of electric power enterprises. A multi-step algorithm for developing and implementing a strategy is proposed and tested due to the limited number of potential participants in mergers and acquisitions in the electric power industry. The methodological approach is developed using the decision tree method and differs from existing approaches in that it simultaneously takes into account two main indicators of mergers and acquisitions, namely the price of the deal and the probability of its successful completion. The model for assessing the probability of successful completion of merger and acquisition deals as part of the proposed methodological approach is based on the stepwise regression method and confirmed by empirical data on transactions over the previous 20 years. Also, the features of the proposed model for assessing the probability of successful completion of deals include taking into account the type of strategy being implemented, the state of the debt market, as well as a complex of po-litical, economic and social factors formalized in the form of components of the FSI index (fragile state index). To assess the impact of strategies on the state of the industry based on mergers and acquisitions, a system of indicators characterizing the economic and production efficiency of the electric power com-plex has been created. The calculation data have confirmed that an increase in the intensity of mergers and acquisitions as a result of using the proposed methodological approach can lead to solving the prob-lem of updating fixed assets − one of the most important problems in the functioning of the electric power complex of the Russian Federation. The results of the study can be used in the practice of strate-gic planning of Russian power industry enterprises and for the purposes of developing state policy in the electric power industry.

Analysis of the work of the fuel and energy complex of Ukraine in the conditions of military aggression and determination of prospective development directions

The current state and directions of development of the fuel and energy complex of Ukraine are elaborated in the study. The state of the energy complex is analyzed, in particular, the impact of large-scale military aggression on the production and consumption of fuel and energy resources by the economy of Ukraine. The state of the electric power complex was considered. An analysis of damage to energy facilities and infrastructure was carried out by sectors: nuclear energy, thermal energy, large hydropower, renewable types (solar and wind energy). An analysis of damages and their consequences of energy infrastructure — high-voltage substations — was carried out. The state of the oil and gas complex has been worked out. It has been confirmed that the oil industry is not functioning as a result of large-scale aggression. The gas production industry slightly reduced production volumes. The economic conditions in which Ukraine found itself as a result of military actions were considered. The long-term development directions of the energy sector of Ukraine are proposed.