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.

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.

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.