Recloser-based decentralized control of the grid with distributed generation in the Lahsh district of the Rasht grid in Tajikistan, central Asia

Small-scale power generation based on renewable energy sources is gaining popularity in distribution grids, creating new challenges for power system control. At the same time, remote consumers with their own small-scale generation still have low reliability of power supply and poor power quality, due to the lack of proper technology for grid control when the main power supply is lost. Today, there is a global trend in the transition from a power supply with centralized control to a decentralized one, which has led to the Microgrid concept. A microgrid is an intelligent automated system that can reconfigure by itself, maintain the power balance, and distribute power flows. The main purpose of this paper is to study the method of control using reclosers in the Lahsh district of the Rasht grid in Tajikistan with distributed small generation. Based on modified reclosers, a method of decentralized synchronization and restoration of the grid normal operation after the loss of the main power source was proposed. In order to assess the stable operation of small hydropower plants under disturbances, the transients caused by proactive automatic islanding (PAI) and restoration of the interconnection between the microgrid and the main grid are shown. Rustab software, as one of the multifunctional software applications in the field of power systems transients study, was used for simulation purposes. Based on the simulation results, it can be concluded that under disturbances, the proposed method had a positive effect on the stability of small hydropower plants, which are owned and dispatched by the Rasht grid. Moreover, the proposed method sufficiently ensures the quality of the supplied power and improves the reliability of power supply in the Lahsh district of Tajikistan. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Chinese Academy of Sciences, CAS: XDA20060303National Natural Science Foundation of China, NSFC: 41761144079Y848041Ministry of National Infrastructure, Energy and Water ResourcesFunding: This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Grant No. XDA20060303), the International Cooperation Project of the National Natural Science Foundation of China (Grant No. 41761144079), the Xinjiang Tianchi Hundred Talents Program (Grant No. Y848041), and the project of the Research Center of Ecology and Environment in Central Asia (Grant No. Y934031).Acknowledgments: The authors are thankful to the Ministry of Energy and Water Resources of the Republic of Tajikistan and the Rasht electric networks OJSHC “Barqi Tojik” for providing the data for this research work

Optimal Amount of Information Determination for Power System Steady State Estimation

On the basis of literature sources analysis, the paper provides the rationale for the necessity of considering the limited digital devices capabilities when designing closed digital control systems for the complex electrical power grids. The problem of design is decomposed into two subproblems: design of current state observation vector digital transmission systems and current controlled process state estimation; design of digital systems for optimal control vector calculation, transmission and control actions realization. The paper presents consideration of the former problem, i.e. design of current state observation vector digital transmission systems and current controlled process state estimation: the mathematical model of digital system of information transmission and state estimation considering speed and reliability of technical means of implementation is presented; the functional structure of simulation complex is developed; the paper provides the formulation of the problem of estimating the optimal amount of information about the control object state, resulting in a solution of computational experiments simulating complex. © 2021 The Author(s).The reported study was funded by RFBR, Russia, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-38-51007

Expert system application for reactive power compensation in isolated electric power systems

Effective electricity use can be an option which enables to achieve significant economy while generating and transmitting of electricity. One of the most important things is to improve the electricity quality through reactive power correction up to optimum values. The current article presents the solution to compensate the reactive power in the distribution networks, in GornoBadakhshan Autonomous Oblast (GBAO) with the use of the advanced technologies based on the data collection within real time. The article describes the methodology of fuzzy logic application and bio-heuristic algorithms for the suggested solution effectiveness to be determined. Fuzzy logic application to specify the node priority for compensating devices based on the linguistic matrix power loss and voltage gives the possibility to the expert to take appropriate solutions for compensating devices installation location to be determined. The appropriate (correct) determination of the compensating devices installation location in the electric power system ensures the effective regulation of the reactive power with the least economic costs. Optimization problems related to the active power loss minimization are solved as well as the cost minimization with compensating devices to ensure the values tan(φ) not exceeding 0.35 through reducing multi-objective problem to the single-objective one using linear convolution

Optimal Management of Energy Consumption in an Autonomous Power System Considering Alternative Energy Sources

This work aims to analyze and manage the optimal power consumption of the autonomous power system within the Pamir region of Republic of Tajikistan, based on renewable energy sources. The task is solved through linear programming methods, production rules and mathematical modeling of power consumption modes by generating consumers. It is assumed that power consumers in the considered region have an opportunity to independently cover energy shortage by installing additional generating energy sources. The objective function is to minimize the financial expenses for own power consumption, and to maximize them from both the export and redistribution of power flows. In this study, the optimal ratio of power generation by alternative sources from daily power consumption for winter was established to be hydroelectric power plants (94.8%), wind power plant (3.8%), solar photovoltaic power plant (0.5%) and energy storage (0.8%); while it is not required in summer due to the ability to ensure the balance of energy by hydroelectric power plants. As a result, each generating consumer can independently minimize their power consumption and maximize profit from the energy exchange with other consumers, depending on the selected energy sources, thus becoming a good example of carbon-free energy usage at the micro-and mini-grid level. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

The use of Petri computing networks for optimization of the structure of distribution networks to minimize power losses

The paper suggests a self-organizing multi-component computational algorithm as a solution to the problem of optimizing the structure of distribution electrical networks to minimize the loss of power. The suggested algorithm is consistent with the method of branches and borders and uses the apparatus of the Petri computer networks (PCN) apparatus. The PCN apparatus has a universal computational capability to process symbolic-numeric data, which along with the solution of calculating problems, provides for the structural and logical analysis of the systems and processes under study. The structure of the PCN based algorithm is similar to the studied system, which provides for better visualization and convenience of interpretation, modification, and implementation of this algorithm on one or more computers by paralleling computational processes for better system performance. Computing modules within the general text of the algorithm can be arranged in any given order and solve the problem by organizing themselves in the process of functioning. © 2020The reported research was partly funded by Russian Foundation for Basic Research and the government of the Yamal region of the Russian Federation , grant No. 19-48-890001

Analysis of Transient Recovery Voltage and Secondary arc Current in Transposed Extra-High Voltage Lines in a Two-Phase Auto-Reclosing

Extra-high voltage (EHV) lines of 500–750 kV, providing transmission of electricity over long distances and at the same time performing the functions of intersystem communication at the level of the national power system, play an important role not only in normal modes, but also in emergency modes, ensuring the dynamic stability of the power system as a whole. In these lines, the overwhelming proportion of power cuts are caused by single-phase short circuits (90%), a significant part of which, being unstable arc faults, are successfully eliminated in the single-phase auto-reclosing cycle. Also, about 5%–10% of failures can be constituted by two-phase short circuits, which can be eliminated in a two-phase auto-reclosing cycle (TPhAR). The purpose of this paper is to study two-phase auto-reclosing in transposed EHV lines equipped with four-radial shunt reactors (ShR). The paper analyzes the efficiency of using a two-phase auto-reclosing to eliminate two-phase short-circuits in the lines connecting the power systems of Kyrgyzstan and Tajikistan. An algorithm is proposed for calculating the transient recovering voltages (TRV) and secondary arc currents (SAC) in the real transposed line Datka–Khujand–Dushanbe. The obtained results of TRV and SAC, which are within the permissible limits for the Dushanbe–Khujand line section, make it possible to have a dead time of TPhAR of no more than 0.6 s, in order to maintain the dynamic stability of the power system. For lines with a length of about 500 km (Datka–Khujand), equipped with three reactors, a successful TPhAR is impossible due to the appearance of resonant TRV in the circuit. The paper proposes the use of banks of capacitors connected in series in the phases of the ShR for the implementation of a successful TPhAR with the duration of the required pause of about 0.6 s. © 2021 The Authors

Diagnostics of the technical condition of electric network equipment based on fuzzy expert estimates

The paper describes a new possible method of diagnostics of the current technical condition of equipment using a mathematical model based on fuzzy expert estimates and the theory of fuzzy sets. The specifics of the task is determined mainly by the type of the obtained estimates, namely: causal relationships between the controlled parameters of the transformer equipment and defects that could entail their change and the possibility of further operation of the facility. At the same time, attention is paid to the problem of the degree of consistency of expert opinions that affects the quality of the assessment of the current technical condition of the studied object. The paper provides a comparative analysis of the arithmetic mean estimates and median estimates of the consistency of expert opinions. It is shown that the significant drawback of the arithmetic mean approach is its instability towards outliers of individual opinions moving the resulting value under the influence of the “dissident expert opinions”. On the other hand, the median estimate is free of such shortage; it is more outlier-resistant and simply discards a part of radically outlying expert opinions. For the first time, the Kemeny median has been used for technical diagnostics. Kemeny median is based on the introduction of a metric to the set of expert opinions, and axiomatic introduction of the distance between them. Also, the paper formulates a criterion on how to determine the optimal number of experts in the group. © 202

Adaptive Ensemble Models for Medium-Term Forecasting of Water Inflow When Planning Electricity Generation under Climate Change

Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of electricity at hydroelectric power plants, taking into account the regulation in the medium term. Medium-term regulation is necessary to amplify the load in the peak and semi-peak portions of the load curve. The solution to such problems is aggravated by the lack of sufficiently reliable information on water inflow and prospective power consumption, which is of a stochastic nature. In addition, the mid-term planning of electricity generation should consider the seasonality of changes in water inflow, which directly affects the reserves and the possibility of regulation. The paper considers the problem of constructing a model for medium-term forecasting of water inflow for planning electricity generation, taking into account climatic changes in isolated power systems. Taking into account the regularly increasing effect of climate change, the current study proposes using an approach based on machine learning methods, which are distinguished by a high degree of autonomy and automation of learning, that is, the ability to self-adapt. The results showed that the error (RMSE) of the model based on the ensemble of regression decision trees due to constant self-adaptation decreased from 4.5 m3/s to 4.0 m3/s and turned out to be lower than the error of a more complex multilayer recurrent neural network (4.9 m3/s). The research results are intended to improve forecasting reliability in the planning, management, and operation of isolated operating power systems. © 2021 The Author(s).The reported study was funded by RFBR, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-38-51007

Mathematical modelling of mutual electromagnetic influences of related power transmission lines in a transition process mode

A mathematical model is obtained using the variable state method, allowing the simulation of electromagnetic processes in transmission lines and communication lines (both homogeneous and heterogeneous) taking into account the reciprocal inductive and capacitive bonds in transition and established modes. The resulting mathematical model can solve a number of important scientific and practical problems related to the design and operation of electric power systems (EEC). The model also allows for a variety of situations and tasks related, for example, to the calculation of circuit breakage, short circuit (SC) or single-phase SC in isolated neural networks. In particular, it is possible to calculate the steered voltage of the line from high-voltage or high-precision power lines. Based on this methodology, it is also proposed to develop an algorithm for the machine formation of mathematical models for the study of transition processes in complex electrical networks, where the initial data will be their parameters and the structure of graphs of the studied networks. © Published under licence by IOP Publishing Ltd

Medium-Term Load Forecasting in Isolated Power Systems Based on Ensemble Machine Learning Models

Over the past decades, power companies have been implementing load forecasting to determine trends in the electric power system (EPS); therefore, load forecasting is applied to solve the problems of management and development of power systems. This paper considers the issue of building a model of medium-term forecasting of load graphs for EPS with specific properties, based on the use of ensemble machine learning methods. This paper implements the approach of identification of the most significant features to apply machine learning models for medium-term load forecasting in an isolated power system. A comparative study of the following models was carried out: linear regression, support vector regression (SVR), decision tree regression, random forest (Random Forest), gradient boosting over decision trees (XGBoost), adaptive boosting over decision trees (AdaBoost), AdaBoost over linear regression. Isolation of features from a time series allows for the implementation of simpler and more overfitting-resistant models. All the above makes it possible to increase the reliability of forecasts and expand the use of information technologies in the planning, management, and operation of isolated EPSs. Calculations of the total forecast error have proved that the characteristics of the proposed models are high quality and accurate, and thus they can be used to forecast the real load of a power system. © 2021 The Author(s).The reported study was funded by RFBR, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-38-51007