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The use of battery energy storage system (BESS) is one of the methods employed in solving the major challenge of overvoltage, experienced on distribution networks with high penetration of photovoltaics (PV). The overvoltage problem limits the penetration levels of PV into the distribution network, and the benefits that could be gained. This study presents three loosely‐related schemes for the coordination of multiple BESSs in such networks. Through the efficient selection, coordination and timing of charge and discharge operations of the BESS, the scheme maintains bus voltages within statutory ranges during periods of high PV power generation and high network load demand. Network segmentation was used in two of the schemes to encourage more even utilisation of the BESS in order to maximise the economic benefits of the BESS. The algorithms for the schemes were implemented and demonstrated on two different distribution networks. Simulation results showed that the schemes met the objectives of mitigating overvoltage and more even cycling of the BESSs during their operating lifetimes.

Under the twin forces of global energy transition and transportation electrification in the 21st century, the railway system, as an efficient backbone transportation mode, has witnessed the optimization of power supply technology and energy efficiency emerging as a central challenge driving industrial innovation [...]

To meet the increasing demand for resilient infrastructure in seismic and high-impact areas, accurate prediction and reliability analysis of the performance of composite structures under impact loads is essential. Conventional techniques, including experimental testing and high-quality finite element simulation, require considerable time and resources. To address these issues, this study investigated individual and hybrid models including support vector regression (SVR), Gaussian process regression (GPR), and improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) models for predicting the failure strength of composite concrete developed by combining normal concrete (NC) with ultra-high performance concrete (UHPC) and polyurethane-based polymer concrete (PUC), considering different surface treatments and subjected to various static and impact loads. An experimental dataset was utilized to train the ML models and perform the reliability analysis on the impact dataset. Key parameters included compressive strength (Cfc), flexural load of the U-shaped specimens (P), density (ρ), first crack strength (N1), and splitting tensile strength (ft). Results revealed that all the developed models had high prediction accuracy, achieving NSE values above acceptable thresholds greater than 90% across all the datasets. Statistical errors such as RMSE, MAE, and PBIAS were calculated to fall within acceptable limits. Hybrid IEPANN appeared to be the most effective model, demonstrating the highest NSE value of 0.999 and the lowest RMSE, PBIAS, and MAE values of 0.0013, 0.0018, and 0.001, respectively. The reliability analysis revealed that impact times (N1 and N2) reduced as the survival probability increased.

In this paper, a solar and wind renewable energies-based hybrid AC/DC microgrid (MG) is proposed for minimizing the number of DC/AC/DC power conversion processes. High penetration rates of renewable energy increase MG instability. This instability can be mitigated by maintaining a balance between consumption demand and production levels. Coordination control is proposed in this study to address coordinated electricity flowing through both AC and DC links and to achieve system stability under variability of generation, load, and fault conditions. The MG adopts a bidirectional main converter that is controlled using a digital proportional resonant (PR) current controller in a synchronous reference frame. The PR controller plays a role as a digital filter with infinite impulse response (IIR) characteristics by virtue of its high gain at the resonant frequency, thereby reducing harmonics. Moreover, the applied PR controller quickly follows the reference signal, can adapt to changes in grid frequency, is easy to set up, and has no steady-state error. Moreover, the solar photovoltaic (PV)-based distribution generation (DG) is supported by a maximum power point tracker (MPPT)-setup boost converter to extract maximum power. Due to the usage of converter-connected DG units in MGs, power electronic converters may experience excessive current during short circuit faults. Fault detection is critical for MG control and operation since it empowers the system to quickly isolate and recover from faults. This paper proposed an intelligent online fault detection, diagnostic, and localization information system for hybrid low voltage AC/DC MGs using an artificial neural network (ANN) due to its accuracy, robustness, and quickness. The proposed scheme enables rapid detection of faults on the AC bus, resulting in a more reliable MG. To ensure the neural network’s validity, it was trained on various short circuit faults. The performance of the MG was evaluated using MATLAB software. The simulation findings indicate that the suggested control strategy maintains the dynamic stability of the MG, meets the load demand, and achieves energy balance as well as properly predicts faults.

Increasing use of distributed generation (DG), mainly roof‐top photovoltaic (PV) panels and electric vehicle (EV) charging would cause over‐ and under‐voltage problems generally at the remote sections of the low‐voltage (LV) distribution feeders. As these voltage problems are sustained for a few hours, power electronic compensators (PECs) with input voltage control, i.e. electric springs cannot be used due to the unavailability of non‐critical loads that can be subjected to non‐rated voltages for a long duration of time. However, PECs in output voltage control mode could be used to inject a controllable series voltage either somewhere on the feeder (mid‐feeder compensation, MFC) or between the feeder and each customer (point‐of‐load compensation, PoLC) both of which are effective in tackling the voltage problem without disrupting PV power output and EV charging. In this study, a comparison between the MFC and PoLC option is presented in terms of their voltage control capability, required compensator capacity, network losses, PV throughput, and demand response capability. The criteria for selection of the optimal location of these compensators are also discussed. Stochastic demand profile for different types of residential customers in the UK and a typical European LV network is used for the case study.

Currently, energy saving has become an acute problem all over the world. Due to the rapid development of both the energy and information technology sectors, as well as an increase in the electricity demand, the electric distribution system is facing problems caused by stricter requirements for electricity quality, reliability, efficiency, and sustainability. Therefore, the use of energy-saving technologies, both the improvement of existing ones and the application of new ones, is one of the main reserves for electricity saving in power supply systems. This study is devoted to the evaluation of the operating modes of transformer substations of the 6–10 kV electric distribution network in Dushanbe city, based on the results of control winter measurements and the dependence of the low coefficient of active power in transformer substations. It has been observed that, with a low coefficient of active power in transformer substations, when transmitting electricity from substation busbars to transformer substations of a final consumer via overhead transmission lines (an average length of 5 km), the voltage loss exceeds the maximum permissible values. When transmitting electricity via cable lines, the voltage losses are within the limit of 5%. However, a low active power factor may be the reason for an increase in capacitive currents, followed by the command of single-phase earth fault currents. Due to the low active power factor, the increase in voltage losses and useful power losses leads to the inefficient operating modes of the transformer substations.

Consumers may decide to modify the profile of their demand from high price periods to low price periods in order to reduce their electricity costs. This optimal load response to electricity prices for demand side management generates different load profiles and provides an opportunity to achieve power loss minimisation in distribution systems. In this study, a new method to achieve power loss minimisation in distribution systems by using a price signal to guide the demand side management is proposed. A fuzzy adaptive particle swarm optimisation is used as a tool for the power loss minimisation study. Simulation results show that the proposed approach is an effective measure to achieve power loss minimisation in distribution systems.

Regulation of power transfer between High-voltage Side (HVS) and Distribution Power Systems (DPS) is an urgent issue for modern power grids. During light loading, active DPS can export the extra power to the transmission system and may create voltage rise at HVS. In other words, active DPS are highly required to provide ancillary services to HVS during its unexpected failures. Therefore, this study suggests an online centralized control approach to satisfy the international Demand Connection Codes (DCC) at Transmission/Distribution interconnection point or fulfill the power requests from HVS. A linear power-flow taking into account the voltage-dependence characteristics of electric loads is used for problem formulation. The problem is formulated as a mixed integer quadratic optimization problem (MIQP). The MIQP problem can easily be solved to its optimality solution while reducing the complex computations. The proposed method can regulate both active and reactive power transfer at the interconnection point and thus it can be operated at different modes. The proposed control method uses the Distributed Generation (DGs), Capacitor Banks (CBs), and controllable loads as control variables. The use of controllable loads can increase the responsibility level of DPS in satisfying the DCC requirements. The suggested approach was checked on a 11 kV 77-bus active DPS.

YAs China advances its transition towards green and low-carbon energy, the proportion of new energy generation in the power grid is gradually increasing, leading to a significant rise in the demand for power resource scheduling. However, due to the scarcity of historical load response data from users, it is challenging to effectively predict user-responsive loads. To address this issue, this study proposes a method of augmenting historical load response data in a weakly supervised manner. Taking into account the unique circumstances of high-voltage users, a sparse CNN for anomaly detection is introduced, along with a multi-branch parallel CNN model capable of weighted output of prediction results from both global and local perspectives. Subsequently, effective iterative training of the model is performed using the EM algorithm. Ultimately, accurate prediction of user-responsive loads is achieved. Based on historical 96-point load data and load response data from high-voltage users in a specific city in China, the predicted results are compared with actual load response data, validating the rationality and accuracy of this method in predicting user-responsive loads.

This study considers a power system with half the demand comprises of industrial loads with large rotating machines. The dynamic behaviour of these loads during disturbance is crucial to system stability. This study describes a practical approach to develop Laplace transfer function (TF) model of a mill load connected to this power system. A PSCAD/EMTDC™ simulation model of the mill (‘the detailed model’) is built from information on the types of load within the mill and verified in a piece-wise manner, i.e., against individual motor starts. Instead of using generic load models, system identification theory is applied to evaluate the TF load model from simulated disturbance responses for both voltage and frequency changes. The main objective is to accurately represent the load behaviour particularly during small signal disturbances. It is recognised that large signal requirements of a load model for transient stability studies are important but the inevitable non-linearity of such a model would tend to saturate the loads' behaviour during smaller disturbances. The developed small signal load model is then tested using practical disturbance profiles from this power system. This modelling approach allows development work to commence before real-life disturbance measurements are available since such data are currently scant.