International Transactions on Electrical Energy Systems

The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard.

In recent years, renewable resources have widely been used to provide necessary energy due to the increasing fossil fuel prices, environmental pollution concerns, and the necessity to meet the growth in energy demands. The output of renewable resources especially solar and wind energies is associated with meteorological parameters, so their reliability creates many challenges for the energy sector. The consumption peak of the gas network is taken into account to adjust the frequency of the microgrid (MG). Both gas network load and electric load distributions are adjusted at the same time. In a multicarrier network, the frequency is regulated in a nonlinear manner. Meanwhile, new necessary loads for production and electric vehicles have imposed new loads on the power network; if proper management is not performed to respond to these new loads, the increase of network frequency deviations may lead the network to fail and even break down. In this paper, a network of various sources including the wind turbine, solar panel, storage (battery and flywheel), electric vehicle (EV), diesel generator (DG) electric power generation, and multicarrier energy hub (MCEH) with combined heat and power (CHP) was designed to examine vehicle-to-grid (V2G) electric vehicles. The ANFIS adaptive fuzzy control method was used to provide a fine-tuning frequency of the network. A comparison between the suggested approach and a fuzzy controller system was carried out to examine the superiority of the introduced approach to the frequency control. The simulations were obtained using MATLAB/SIMULINK software. The simulation outcomes indicated that the SMART controller can achieve good efficiency in frequency regulation and reliable output power in the examined microgrid. Further comparison in terms of effective (RMS) values and maximum frequency deviation indicates the superior performance of the proposed method over the fuzzy method.

In a solar power plant, a solid phase transformer and an optimization coordinated controller are utilized to improve transient responsiveness. Transient stability issues in a contemporary electrical power system represent one of the difficult tasks for an electrical engineer due to the rise in uncertain renewable energy sources (RESs) as a result of the need for green energy. The potential for terminal voltage to be adversely impacted by this greater RES raises the possibility of electrical device damage. It is possible to use a solid state transformer (SST) or smart transformer to address a transient response issue. These devices are frequently employed to interact between RES and a power grid. SST features a variety of regulated converters to maintain the necessary voltage levels. This method can therefore simultaneously lessen power fluctuations and transient responsiveness. In order to improve the quality of RES power injections and the electrical system’s transient stability, this work provides a controller design for a solar photovoltaic (SPV) system that is connected to the grid by SST. The optimization of a controller model is proposed by modifying a PI controller taken from a commercial one. With the use of IEEE 39 standard buses, the proposed controller is tested. When evaluating the effectiveness of a suggested controller, it is important to take into account a variety of solar radiation patterns as well as a time delay uncertainty that can range from 425 ms to 525 ms. According to simulation results, the proposed controller can be employed to lessen power fluctuation brought on by unpredictable RES. Additionally, the proposed coordinated regulation of SPV and SST can prevent catastrophic damage in the event of substantial disturbances like a circuit breaker collapsing to expand a power line due to a fault by inhibiting significant voltage cycles within an electronic appliance’s rated voltage limit. The results indicate that a transitory stability issue in a modern power system caused by an unforeseen increase in RES may be addressed utilizing the suggested controllers as alternatives.

Smart grids must detect cyber-attacks early to ensure their safety and reliability. There have been many outlier detection methods presented in the studies, varying from those requiring instance-by-instance decisions t the online diagnosing methods that require the use of accurate models of an attack. This study proposes a novel intelligent online anomaly or attack detection method based on the partially observable Markov decision procedure (POMDP). The proposed model may be categorized as a general detection method according to the reinforcement learning (RL) architecture for POMDP which can help the learning process based on the award concept. The performance of the proposed model is verified using the IEEE test system. Based on numerical results, the suggested RL-based algorithm shows to be very effective in detecting cyber-attacks against the smart grid quickly and accurately.

This study presents the boost converter-based cascaded H-bridge (CHB) multilevel inverter with improved reliability for solar PV (photovoltaic) applications. The solar PV is associated with the boost converter to enhance DC link voltage by using the maximum power point tracking-perturb and observe (MPPT-P & O) technique. The proposed configuration is aimed toward the performance analysis of the boost converter-based CHB MLI by reducing the number of components, low total harmonic distortion (THD), reduced power, less cost function, low total standing voltage (TSV), improved reliability, and switching losses for solar PV application. In this study, a CHB multilevel inverter is used to obtain stepped pure sinusoidal AC from the solar PV array. The proposed boost converter extracts maximum power and enhances higher DC link voltage which provides high efficiency. The boost converter is integrated with a 27-level CHB multilevel inverter to generate near-sinusoidal output voltage with lower THD. The inverter is tested with linear and nonlinear loads for robustness, and during dynamic loads, inverter is stable and well suited to grid-connected applications. A detailed comparison is presented on the component count and reliability aspects with existing MLIs and 27-level MLIs. The simulation outcomes of the implemented arrangement are presented with the help of MATLAB/Simulink, an experimental prototype is developed using a dSPACE RTI1104 controller and also tested in the research laboratory for checking the possibility of the implemented arrangement.

Consumption growth demands power system expansion, and changes in a network lead to changes in operation indices of the upstream network. Hence, to establish a reliable planning of the distribution network, simultaneous planning of the subtransmission network should also be taken into account. It is predicted that this planning impacts different technical indices; therefore, formulation of various technical indices need to be included in the planning model. Therefore, in this study, the stochastic problem model of simultaneous planning of distribution and super distribution network development considering the reliability and security indicators is presented. The aforementioned problem is in the form of an optimization problem whose objective function is to minimize the costs of construction, replacement, operation, maintenance, reliability, and security. Furthermore, the mentioned problem is bound to the equations of power distribution, reliability, and security of voltage in addition to the limitation of network indicators. It is worth mentioning that, in the proposed problem, the active and reactive loads in addition to the operating price are in the form of uncertainty. Thus, the mentioned problem is in the form of a stochastic problem in which the point estimation method is used to evaluate the scenarios. Simultaneous expansion planning of distribution and subtransmission networks along with the placement of distributed generations and capacitors, as well as modeling security and reliability indices, is the novelty of the proposed scheme. In the end, the proposed problem is applied to the test network by GAMS optimization software, and then the capabilities of the proposed problem are extracted. Accordingly, based on numerical results, the expansion of distribution networks imposes considerable cost. Moreover, expansion planning of both distribution and subtransmission networks helps voltage to be in its permissible range, lower power loss, and improve reliability and security to 100%.