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This study describes a laboratory model of a battery energy storage system (BESS) designed for testing algorithms aimed at reducing peak power consumption in railway traction substations. The system comprises a DC/DC converter and battery energy storage. This article details a laboratory model of a bidirectional buck-boost DC/DC converter, which is used to transfer energy between the battery energy storage and a DC line. It presents an analysis of DC/DC converter systems along with simulation studies. Furthermore, the results of laboratory tests on the DC/DC converter model are also provided. The control algorithm of the system in the traction substation is focused on reducing peak power, offering benefits such as lower charges for the railway operator due to the possibility of reducing contracted power requirements. From the perspective of the power grid, the reduction in power fluctuations and, consequently, voltage sags, is advantageous. This paper includes a description of a hardware simulator for verifying the system's control algorithms. The verification of the control algorithms was performed through experimental tests conducted on a laboratory model (a hardware simulator) of the system for dynamic load reduction in traction substations, on a power scale of 1:1000 (5.5 kW). The experimental tests on the laboratory model (hardware simulator) demonstrated the effectiveness of the algorithm in reducing the peak power drawn from the power source. [ABSTRACT FROM AUTHOR]

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions. [ABSTRACT FROM AUTHOR]

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity. [ABSTRACT FROM AUTHOR]

The optimal allocation and sizing of distributed generation (DG) resources are important in installing these resources, to improve the technical parameters of the network, including the power losses, voltage profile, and short-circuit level, as well as to increase economic factors. In this paper, a new multi-criteria algorithm and objective function are proposed for the optimal sizing and allocation of renewable and non-renewable DG resources simultaneously. The proposed algorithm is implemented on 63/20 kV substations at 20 kV levels. In the proposed objective function, all important technical and economic factors as well as important constraints, such as penetration level of DGs and budget constraint, are considered in a way that all factors are assigned to monetary values. Moreover, a new mathematical formulation is introduced for the allocation of renewable DG resources to reduce run-time optimization. The genetic algorithm (GA) is employed in the proposed algorithm to minimize the objective function. For renewable DG resources, photovoltaic panels and wind turbines, and for non-renewable DG resources, gas turbines are considered. The 115 buses network of Bakhtar Regional Electric Company (BREC) in Iran is used to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm improves technical factors efficiently and maximizes the profitability of the investment. [ABSTRACT FROM AUTHOR]