Operation Of Electric Power Systems Research Articles

Cooperative Operation of Power and Hydrogen Energy Systems With HFCV Demand Response

Hydrogen has shown great potential in the renewable power integration and urban mobility decarbonization like hydrogen fuel cell vehicles (HFCVs). The HFCV refueling as an essential hydrogen load is of great flexibility. Considering the HFCV demand response (DR), this article studies the integrated electric power and hydrogen system (IPHS) operation. First, the HFCV refueling load model is formulated with its routing on the transportation network considered. Second, the optimal IPHS operation model is developed in which the electric power operation, tube-trailer based hydrogen delivery and HFCV refueling are coordinated. Third, a Lagrangian relaxation based method is developed to solve the proposed model efficiently, which corresponds to a price-based DR mechanism for HFCVs. Compared with existing works on IPHS, the influence of transportation networks is delicately analyzed on both HFCV refueling and hydrogen delivery. The case studies have proven the effectiveness of the proposed method and demonstrated that the overall operation cost is decreased via the proper guidance of HFCV refueling. The HFCV DR shows great potential in exploring the synergy of energy and transportation systems.

Wind Diesel Hybrid System with Pitch Angle Controller

A wind diesel hybrid system is an autonomous electricity generating system using wind turbines with diesel generators to obtain a maximum contribution by the intermittent wind resource to the total power produced, while providing continuous high quality electric power. The main goal is to control load frequency and reduce fuel consumption enabling reduced system operating costs and environmental impacts. Frequency control in a power system is done to maintain an adequate balance between the consumed and generated active powers, so that the frequency remains within acceptable limits around the nominal frequency. Load–frequency control (LFC) is of importance in electric power system design and operation. The objective of the LFC in an interconnected power system is to maintain the frequency of each area within limits and to keep tie-line power flows within some pre-specified tolerances by adjusting the MW outputs of the generators so as to accommodate fluctuating load demands.

Assessment of the wind power generation uncertainty on the steady-state operation of electric power systems

Nowadays, the large-scale integration of wind energy conversion systems in transmission networks is one of the most effective and practical options for generating electricity from renewable sources. However, the increase in wind energy penetration causes the operation of electrical power systems to become more dependent and vulnerable to variations in wind speed. Within this context, it is necessary to perform probabilistic power flow (PPF) studies to maintain system integrity in the face of stochastic wind power generation. This paper describes and implements a power flow method that considers the uncertainties that arise from wind generation behavior. This PPF approach, based on a point estimation technique, is validated against thousands of Monte Carlo simulations.

Research of current distribution by phases in asynchronous electric motor with a combined winding

THE PURPOSE. Consider the use of propeller electric installations as part of ship electrical complexes with a single electric power system. Highlight the rudder drives as a special type of electric propulsion of ships in northern latitudes. Investigate unified electric power systems with a propeller electric installation for the existence of power exchange oscillations in them. Propose methods and means for eliminating power oscillations in such systems.METHODS. To carry out the research, a single electric power system with electric rudder propellers of the world's only asymmetric icebreaker Baltika was considered. All the main elements of such system have been analyzed in detail. Experimental studies were carried out aimed at studying the operating modes of a unified electric power system.RESULTS. Experimental oscillograms of currents of parallel operating diesel-generator sets in different modes have been obtained. The existence of exchange and in-phase power oscillations during the operation of the unified electric power system of the icebreaker "Baltika" is noted. The data on the negative influence of power oscillations on the operation of the electrical complex of the icebreaker are presented.CONCLUSION. The use of ice-class sea vessels is an extremely important task for the Russian Federation. The installation of blocks that eliminate exchange and in-phase power oscillations will improve the reliability and efficiency of the use of marine vessels with electric rudder propellers when servicing hydrocarbon production on the Arctic shelf.

Feasibility study for the creation and implementation of an instrumental complex for determining and predicting the technical characteristics of locomotives

A modern tool for research and rapid prototyping of new developments are various methods of digital system modeling. When creating or upgrading any electric transport systems and electric rolling stock, the correct choice of all elements of the traction power supply system and electric rolling stock is of great importance. To solve this problem, it is necessary to take into account the movement of a set of moving loads in the form of electric transport in power supply systems of various levels and, at the same time, to study not only the mechanical processes of electric transport movement, but also the electromagnetic processes of interaction of electric transport with the traction power supply system and the electric power system, as well as their mutual interaction. electromagnetic influence on each other. The construction of a software and hardware complex for real-time simulation of the joint operation of electric power systems, traction power supply systems and electric trains corresponds to the modern moment. The implementation of such a system is possible using modern high-performance computing systems or stationary supercomputers with integrated hardware that increase the speed of calculating stiff systems of differential equations that describe large electro-magnetic-mechanical systems. Such computing software and hardware systems are quite expensive. The article describes the composition of such a complex and the feasibility study for its development and implementation.

Automatic Load Model Selection Based on Machine Learning Algorithms

Technology development and decentralized operations create changes in conventional electric systems, where load modeling has been a challenge in dynamic analysis. Consequently, accurate dynamic load models are required to ensure the quality of the studies in current systems. This paper presents an automatic strategy based on clustering, classification, and optimization algorithms, to obtain the load models in the case of several system operating conditions. The obtained load models can be helpful for the planning and operation of electric power systems. The proposed approach validation is performed using the IEEE 14-bus test system, where high performance is obtained. The average obtained cross-validation error for the load models assigned to the 13 clusters of disturbances is 5.36 × 10^-3. The cross-validation error is used as a tolerance value to determine when an online assigned load model is suitable to represent the measured disturbance. The proposed tests show the strategy’s capabilities of defining the load model online, making this approach suitable for field applications.

Artificial Neural Network-based Flexibility Assessment of the Electric Power System with High Wind Energy Penetration

One of the ways to resolve the environmental protection issue is to increase electricity production based on renewable energy sources (RES). High penetration of RES in an electric power system (EPS) induces great uncertainty in EPS operation. In this case, the normal and secure operation of EPS requires information on its flexibility. Algorithms for determining the EPS flexibility in real time are developed in the study. The acceptable time of obtaining the results is ensured by splitting the flexibility computing process into online and offline ones. Offline computation of operating state with minimum flexibility relies on an exact method. Online calculation of operating state with minimum flexibility employs trained artificial neural networks (ANNs). The two machine learning techniques (supervised and unsupervised) are explored and compared. These are the feedforward neural network and Kohonen neural network. In this study, the ANN architecture which fits in with solving the considered problem most is chosen. A new way of interpreting the ANN response is presented. To this end, another ANN is trained to recognize the operating state with minimum flexibility.

Forecasting of Turkey’s Electrical Energy Consumption using LSTM and GRU Networks

Energy demand management is particularly important for developing and emerging economies. Their energy consumptions increase significantly, depending on their growing economies. As a result of Turkey’s rapid economic and population growth, electricity consumption is increasing. Electricity consumption forecasting plays an essential role for energy suppliers, consumers, and policy makers. Therefore, using models to accurately and reliably forecast future electricity consumption trends is a key issue for the planning and operation of electric power systems. This paper focused on forecasting electrical energy consumption by utilizing deep learning methods, i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, for time series data. One-hour and three-hour ahead forecasting are accomplished by using a historical dataset of electrical energy consumption in Turkey. The comparison results show that the GRU model is slightly better than that of the LSTM. Our study also reveals that one-hour ahead predictions are more accurate than three-hour ahead predictions.

Innovative Control of the Development of an Ergatic Electric Power System Based on Fuzzy Logic

This article deals with the search for a palliative optimal solution to the optimization of the electric power problem. A mathematical model of planning by the development of a hierarchical ergatic electric power system is proposed to be presented as an optimization problem of minimizing vectors. An innovative direction in modeling the control system of an ergatic electric power system based on artificial neural networks is also considered. It is noted that intelligent control systems for power supply systems are robust. The innovative concept of a smart power system is due to the integration into a hierarchical power system of renewable energy sources based on modern computer technologies. It is noted that during the operation of an intelligent electric power system, control systems must provide adaptive qualities in relation to the topology of the power supply system. It is proposed to investigate the solution to the optimization of electric power problems using methods of fuzzy mathematical programming.

Laser deicing for high-voltage composite insulator by high-power mid-infrared fiber laser

Ice disaster has always been a threat to the stable operation of electrical power systems, and it is a challenge to melt ice online for high-voltage composite insulator. Here, we proposed a safe and efficient method to deice a high-voltage composite insulator using high-power mid-infrared (mid-IR) laser. The laser source is a Tm-doped fiber laser, delivering a continuous-wave (CW) output power of 75 W at 1940 nm. Due to the strong vibration absorption of H2O molecule at 1940 nm, the mid-IR laser could be absorbed efficiently by ice/water with an absorption coefficient as high as 135 cm − 1. Indoor deicing experiment demonstrated that an 8-mm-diameter ice pillar could be cut off within 28 s under an incident optical intensity of 25 W/cm2. Due to the small penetration depth of 148 μm, the residual water film could prevent the laser from damaging the composite insulator. Our research results show that high-power mid-IR Tm-doped fiber laser is a potential scheme for online deicing high-voltage composite insulator.

Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting

Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consumption patterns. The problems are presented by analyzing the correlation between the power consumption and weather data by season for industrial buildings with different power consumption patterns. Four models were analyzed using the most important factors for predicting power consumption and weather data (temperature, humidity, sunlight, solar radiation, total cloud cover, wind speed, wind direction, humidity, and vapor pressure). The prediction horizon for power consumption forecasting was kept at 24 h. The existing deep learning methods (DNN, RNN, CNN, and LSTM) cannot accurately predict power consumption when it increases or decreases rapidly. Hence, a method to reduce this prediction error is proposed. DNN, RNN, and LSTM were superior when using two-year electricity consumption rather than one-year electricity consumption and weather data.

A novel hybrid approach based on variational heteroscedastic Gaussian process regression for multi-step ahead wind speed forecasting

Accurate wind speed forecasting is the key to safe and economic operation of electric power and energy systems. As a Bayesian nonparametric method, Gaussian process regression (GPR) has provided competitive forecasting results in recent years. However, conventional GPR model assumes that the noise obeys Gaussian distribution and the variance of the noise in the whole data set is a constant, which is not appropriate for some problems. Motivated by this, this study makes the first attempt to study the ability of the variational heteroscedastic GPR (VHGPR) model in wind speed forecasting. The Marginalized Variational (MV) approximation is employed to approximate the heteroscedastic Gaussian process in the VHGPR model. What’s more, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to transfer the nonstationary wind speed series into a certain number of subseries with more regularity such that the forecasting performance of VHGPR can be enhanced. The proposed method is compared with the other twelve benchmark models for 1- to 3- step ahead wind speed forecasting. Experiments results on four real-world datasets demonstrate that VHGPR with the CEEMDAN decomposition strategy is able to obtain better forecasting results for wind speed time series.

Mechanism analysis and mitigation strategies for oscillatory instability of synchronous generators affected by voltage source converters

The electric power systems are experiencing a transformation to be characterized by power electronic devices, for example, voltage source converters (VSCs). However, the large-capacity VSC will interact with the synchronous generators (SGs) and could lead to low-frequency oscillation (LFO). It becomes a hurdle for the secure and stable operation of modern electric power systems. Therefore, this paper analyses the oscillatory instability mechanism of the SG with the penetration of grid-forming (GFM) and grid-following (GFL) VSC. The distinctive transfer function model of the SG-VSC system is first developed. Then, through parameter sensitivity and transfer function analysis, the impact of three types of VSC is investigated, encompassing GFM-VSC and GFL-VSCs with two conventional reactive control schemes. It proves that the voltage control of the VSC can be detrimental to the damping torque of the SG. According to the theoretical analysis, a supplementary compensator is proposed to suppress the LFO induced by the VSC. Finally, case studies in a two-area test system validate the analytical results and robustness of the proposed enhancement method.

Decision Making in Planning and Operation of Electric Power System based on Operations Research: From the Viewpoint of Multiobjective Optimization

1．はじめに

Determining the Residual Magnetic Flux Value of a Single-Phase Transformer Using a Minor Hysteresis Loop

Residual magnetic flux can cause significant problems in electric power system operation if not taken into consideration. The laboratory setup and experimental procedure for determining the residual magnetic flux value of a single-phase transformer using a minor hysteresis loop are presented in this paper. Using the proposed method, residual magnetic flux could be determined without prior knowledge about the last de-energization of the transformer. Only voltage and current measurements are needed, and any physical intervention in the core or in some other parts of the transformer is unnecessary. Also, a significantly lower voltage than the transformer's nominal is used to determine the residual magnetic flux value in the proposed method. The pros and cons of the proposed method, as well as plans for future research are given.

A model for power shortage minimization in electric power systems given constraints on controlled sections

This paper considers the problem of modifying the mathematical models of power shortage minimization as used in the adequacy assessment of electric power systems. A review and analysis of existing software packages were conducted, in particular, the power shortage minimization mathematical models that are part of them were considered. The mathematical models were modified to correctly account for the maximum allowable active power flow in the controlled sections. In the experimental part of this study we tested the proposed modifications. As a result, it was determined that the most appropriate results from the standpoint of the physical laws of operation of electric power systems are yielded by the model of power shortage minimization with quadratic losses, which takes into account the constraints of power transmission through the controlled sections.

Research on insulator defect detection algorithm of transmission line based on CenterNet.

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

Challenges for Future Flexible Electric Power Systems

This paper describes some of the challenges that face the operation of future electric power systems. These systems are becoming more flexible and agile. Their physical structures and connections are continuously changing as microgrids, electric vehicles, and other generation and storage devices are connected/disconnected from the grid, which result in new challenges for the operation, management, and control of the systems of the future that incorporate active participation of the consumers, and high penetration of intermittent nature renewable resources such as wind and solar.

Economic analysis and configuration design for the energy storage unit of photovoltaic virtual synchronous generator based on the inertia support and primary frequency control

With the increasing proportion of distributed generation (DG) in electrical power systems (EPSs), the lack of grid inertia and damping has posed great challenges to the stability and operation of EPSs. To address these problems, the concept of a virtual synchronous generator (VSG) has been proposed. As the physical basis of virtual inertia, the energy storage unit directly affects the performance and operating cost of the VSG. In this paper, the energy change of the energy storage unit during the frequency control process is calculated. Based on this calculation, the charge and discharge behavior of the energy storage unit can be inferred according to the VSG parameters and the frequency deviation data. Then, combined with the improved battery cycle life model, the operating cost of the energy storage unit when the VSG participates in frequency control can be obtained. Based on the data provided by the British electricity system operator (ESO), a case is studied, and the effects of the control parameters and battery rated capacity on the operating cost is analyzed. The analysis results can provide an economic reference for the parameter selection and energy storage unit capacity configuration of the VSG. Moreover, the simulation results validate the accuracy and efficiency of the proposed method.

Investment equilibrium of an integrated multi–stakeholder electricity–gas–hydrogen system

The coordinated planning and operation of electric power and natural gas systems contribute significantly toward the integration of renewables and decarbonization of energy systems. The coupling between the two systems will be further increased due to the potential use of hydrogen that represents an indispensable terminal energy source. Thus, this study proposes an investment equilibrium model for an integrated electricity–gas–hydrogen system (IEGHS) constituting electric power systems, natural gas systems, and hydrogen fuel stations. Specifically, electricity, gas, and hydrogen energy trading are considered in this model. A triangular coupling relationship is formulated in the IEGHS with the objective of maximizing each stakeholder's profit. Furthermore, two trading mechanisms with different energy prices are built and compared. In particular, the equilibrium model with locational marginal energy prices (LMEPs)-based trading is further solved via a direct approach based on the Karush–Kuhn–Tucker conditions and strong-duality theory. Case studies indicate that the LMEP-based trading provides the highest overall benefit in the case of multiple stakeholders. Additionally, the impact of energy price, investment equipment price, and equipment conversion efficiency on the investment equilibrium of IEGHS is investigated.