Ilia Shushpanov

This article examines the performance of negative-sequence overcurrent protection during short circuits in the presence of current asymmetry caused by single-phase loads, such as those encountered in AC railway traction systems. The impact of unbalanced loads on the generation of negative-sequence currents is analyzed using field test data and a mathematical model. Various operating modes of an electric power network under unbalanced loading conditions are simulated in MATLAB Simulink R2015a. It is shown that under significant load asymmetry, negative-sequence currents can reach magnitudes comparable to those of short-circuit currents, thereby increasing the risk of false protection operation. To address this issue, a modified negative-sequence overcurrent protection scheme is proposed that ensures both sensitivity and selectivity. The modification is based on analyzing the ratio of negative-sequence to positive-sequence current phasors and monitoring the rate of change of the negative-sequence current. A faulted phase selector is also incorporated into the protection scheme. Simulation results confirm the effectiveness of the modified protection in reliably identifying unsymmetrical short circuits under varying unbalanced load conditions, including remote faults with high fault resistance.

Currently, research aimed at optimizing the power rating and energy capacity of electrical energy storage (EES) systems while accounting for multiple sources of uncertainty remains underrepresented in the scientific literature, primarily due to the complexity of solving multidimensional uncertainty problems in microgrids. With regard to the comprehensive assessment of EES parameters under the influence of various factors, despite numerous studies devoted to the evaluation and rational selection of these parameters, the problem remains largely unresolved. This paper proposes a methodology for selecting EES parameters that accounts for the uncertainty of wind power plant (WPP) generation and electric vehicle charging station (EVCS) load and EES performance degradation, as well as the reliability and cost of microgrid implementation. The goal is to ensure the uninterrupted operation of electric vehicle supply equipment within a distribution network with limited available power capacity. The developed method, together with the EVCS load profile model, enables the generation of a time-based power profile under input data uncertainty. The work presents a mathematical model of microgrid operation that considers the integrated performance of the EES, WPP, and EVCS. The proposed EES parameter selection methodology is demonstrated using examples of various system configuration scenarios.

Currently, research aimed at optimizing the power rating and energy capacity of electrical energy storage (EES) systems while accounting for multiple sources of uncertainty remains underrepresented in the scientific literature, due to the complexity of solving multidimensional uncertainty problems in microgrids. Regarding the comprehensive assessment of EES parameters considering the influence of various factors, despite numerous studies dedicated to the evaluation and rational selection of EES parameters, this task remains largely unresolved. This paper proposes a methodology for selecting EES parameters that accounts for the uncertainty of wind power plant (WPP) generation and electric vehicle charging station (EVCS) load, EES performance degradation, as well as the reliability and cost of microgrid implementation to ensure uninterrupted operation of EV supply equipment within a distribution network with limited available power capacity. The developed method and EVCS load profile model enable the generation of a time-based power profile under input data uncertainty. The work presents a mathematical model of microgrid operation that considers the integrated performance of EES, WPP, and EVCS. The EES parameter selection methodology is demonstrated using examples of various system configuration scenarios.

Relevance. This study addresses the need to develop a method for modeling the load profile of electric vehicle charging stations (EVCS) while accounting for parameter uncertainties, including the stochastic intensity of electric vehicle (EV) connections over a given period.The Purpose. Analyze challenges in modeling EVCS load profiles under uncertainty. Develop a simulation method for EVCS load profiles that incorporates multiple stochastic components. Simulate load profiles reflecting the intensity of charging start times using empirical data. Evaluate EVCS load uncertainty, model convergence, and sensitivity to input parameter variations.Methods. The study analyzes existing methods for modeling EVCS load profiles. A Monte Carlo method, implemented in MatLab®, was used to construct a mathematical model of the EVCS load profile.Results. Experimental data on the number of connected EVs are processed to derive an average daily EVCS load profile. A combined probability distribution law, aligned with empirical data and reflecting EV connection intensity, is applied. A parametric model is developed to generate the temporal load profile of EVCS, incorporating key uncertainty factors: charging start time, EV power consumption, charging duration, and the number of EVs.Conclusions. A method and model for simulating EVCS load profiles are proposed, enabling the generation of temporal power profiles under input data uncertainty. The model can be applied to plan EVCS placement, evaluate power imbalance and select parameters for energy storage systems to integrate EVCS and ensure their stable operation in distribution grids.

The modeling of battery energy storage systems (BESS) remains poorly researched, especially in the case of taking into account the power loss due to degradation that occurs during operation in the power system with a large penetration of generation from renewables and stochastic load from electric vehicles (EV). Meanwhile, the lifetime varies considerably from the manufacturer’s claim due to different operating conditions, and also depends on the level of renewable energy sources (RES) penetration, cyclic operation, temperature, discharge/charge rate, and depth of discharge. Choosing a simplistic approach to the degradation model can lead to unreliable conclusions in choosing the best management strategy and significant investment and operating costs. Most existing BESS models in stationary applications either assume zero degradation costs for storage or simplify battery life to a linear function of depth of discharge (DOD), which can lead to additional error in estimating the cost of BESS degradation. The complexity of constructing a lifetime model of BESS is due to the presence of nonlinear degradation of BESS at the beginning and at the end of the lifetime, as well as the difficulty in obtaining a large amount of experimental data that are close to the real-world operating conditions for the construction of most models. This article analyzes the features of BESS that are specific to their operation in microgrids in terms of the influence of the main stress factors on the degree of BESS degradation. This study also provides a review of existing models for assessing battery degradation.

At present, the entire world is moving towards digitalization, including in the electric power industry. Digitalization is in its heyday and a lot of articles and reports are devoted to this topic. At the same time, the least digitalized of the electrical networks are distribution networks that account for a very large share in electric power systems. The article proposes a methodology for creating a flexible distribution network based on the use of digital technology. Additionally, we elaborate a methodology with the identification and collection of the necessary information to create digital networks, develop ways to adapt the required equipment, and suggest methods of recognition of some short circuits. Furthermore, we address the issue of reliability of the information obtained from digital devices, develop a technique for arranging the devices to cover the entire network as required to improve the power system protection of electrical power distribution networks. The above measures make it possible to ensure the flexibility of the active distribution network, as well as to adjust the parameters of the actuation of power system protection depending on changes in external conditions and in the event of emergencies. We propose a technique for controlling the distribution network, based on the factoring-in of the type of damage during an emergency in real time, as well as a technique for arranging the measuring devices and the creation of an information and communication network. We provide recommendations for the design and operation of electric power distribution networks with digital network control technology.

Global electricity companies have embarked on a transition to digital microprocessor devices. Digitalization penetrates every aspect of power industry, whether it is power generation, transmission or distribution. Being the most widespread, the distribution electrical networks are nevertheless the least digitalized. Some issues about them are still insufficiently studied. These are the collection of necessary information, adaptation methods, and the effect of the unrecognizability of some short circuits. This can be explained by improper placement of devices, which causes lack of data, which, in turn, leads to the failure of relay protection and emergency control systems of distribution electrical networks to provide selectivity. The paper proposes algorithms for placing measuring devices so that the information will be collected properly. Placement of the devices will allow adjusting the operating parameters of the relay protection and emergency control systems depending on changes in external weather conditions and load fluctuations in the network. We propose a technique for control of distribution network, taking into account the type of damage in case of emergency in real time, and a technique for placement of measuring devices and creation of an information communication network.

ИСПОЛЬЗОВАНИЕ ВОЗОБНОВЛЯЕМЫХ ИСТОЧНИКОВ ЭНЕРГИИ ДЛЯ ПИТАНИЯ СОБСТВЕННЫХ НУЖД НЕФТЕПРОВОДА

A mathematical model for distribution network reliability calculation is given in this paper. A topological method is used determining such reliability indices as probability of element failure, frequency of element failures, duration of element failure. The method is universal for determination of reliability indices and applicable to calculations of any other (e.g. autonomic) electric power networks.