International Transactions on Electrical Energy Systems最新文献

This paper proposes a coordinated frequency regulation strategy for grid-forming (GFM) type-4 wind turbine (WT) and energy storage system (ESS) controlled by DC voltage synchronous control (DVSC), where the ESS consists of a battery array, enabling the power balance of WT and ESS hybrid system in both grid-connected (GC) and stand-alone (SA) modes. The newly developed GFM framework, i.e., DVSC, is adopted in both WT and ESS, which utilizes DC voltage dynamics for synchronization purposes. In this paper, the GC mode and SA mode are transferred by changing the status of the series-connected switch, and it is necessary to meet the grid connection conditions when the system is transferred to the GC mode, namely, voltage, frequency, and phase sequence. For the GC mode, the inertia response from WT can be realized using the reserved energy of the DC capacitor, while the ESS serves to eliminate the steady-state frequency deviation and reduce the DC voltage fluctuation of WT using the designed secondary frequency regulation scheme. For the SA mode, the proposed strategy can keep the power balance without external power sources. The small-signal model of the WT and ESS hybrid system is derived. The stability analysis in both GC and SA modes is fully conducted utilizing the modal analysis method, where the impacts of control parameters on stability are assessed. The performance of the proposed strategy in a weak system is evaluated. Simulation studies are carried out under various power system contingencies to verify the effectiveness of the proposed strategy and validate the correctness of the theoretical analysis.

In offshore stand-alone microgrids, due to the lack of access to the main power grid, energy supply has always been vulnerable to many risks, such as uncertainty in fuel supply for diesel generators (DEGs) and unforeseen changes in renewable energy sources (RESs) and loads. As a result, this paper proposes a novel framework based on the hybrid renewable energy system (HRES) concept to optimize the capacity design and operation of HRES. The proposed framework is implemented on one of the Persian Gulf islands as a case study. This paper uses hybrid IGDT/stochastic optimization techniques for load and PV uncertainties and a novel wind-based method for fuel procurement uncertainties. Using MIP modeling in GAMS, the paper shows that renewable and storage systems can reduce the total cost by 38% and the energy supply cost by 43% compared to conventional energy supply system. Accounting for uncertainties can boost the system robustness and reduce the expected total cost by 16% compared to the deterministic model.

The surging electricity demand in Pakistan has led to frequent blackouts, prompting government initiatives to expand power plant capacities and improve the national grid. The government prioritizes integrating large-scale renewable energy sources, such as wind and solar power, to reduce dependence on conventional power plants. However, the intermittency of renewables leads to forecasting errors, requiring extra power reserves from conventional units, thereby escalating operational costs and CO₂ emissions. The country currently utilizes a manual mechanism for power balancing operations, overlooking critical grid constraints of the transmission line loadings. In such conditions, injecting large-scale power from renewables can lead to significant fluctuations in line power flows, risking transmission line loadings and compromising the system’s secure operation. Hence, this paper has developed an automatic generation control (AGC) model for the highly wind-integrated power system to alleviate line congestions in the network and enhance the economic operation of the system. The study utilizes the Pakistan power system as a case study to simulate the proposed model. The developed real-time power dispatch strategy for the AGC system considers the constraints of the transmission line to avoid congestion. It integrates wind energy as operating reserves to enhance the economic operation of the system. When managing line congestion, it identifies overloaded bus lines and adjusts power regulation accordingly while compensating for shortfalls by augmenting transmitted power from regional grid stations. However, it maintains a constant dispatch ratio without line overloads, aligned with generation capacities. Additionally, the strategy integrates reserve power from the wind power plant and traditional generating units to further improve economic operations. Simulations have been conducted using PowerFactory software, employing the eight-bus and five-machine models to replicate the characteristics of the Pakistan power system. The results demonstrate the effectiveness of the proposed AGC design in mitigating transmission line congestion of power systems that are heavily integrated with wind energy sources while simultaneously ensuring the economic operation of generating units.

This work presents a novel architecture for the 31-level asymmetrical DC voltage source configured diode switched multilevel inverter, which has a single phase and fewer components. Using asymmetric DC sources and an H-bridge, the proposed topology generates a maximum output voltage of 31 levels. This 31-level topology is suitable for both renewable energy source conversion (RES) and electric vehicle (EV) applications. This topology requires fewer total components, lower cost, and smaller size. Along with the numerous benefits of MLIs, reliability issues are critical due to the larger number of devices required to minimize THD. Several characteristics, such as total standing voltage (TSV), reliability, cost function (CF), efficiency, and overall power losses, are investigated for the developed 31-level MLIs. The TSV and CF of the proposed MLI are critical factors in demonstrating that the proposed topology is cost-effective when compared to other recent topologies. Many parameters are thoroughly compared and tabulated, as well as represented graphically. The suggested MLI has lower TSV and component demand. Total harmonic distortion complies with IEEE specifications. The reliability aspects were also calculated and validated. The proposed MLI is controlled by a fuzzy logis controller (FLC) to achieve efficient results. The topology is simulated in MATLAB/Simulink software under a variety of conditions and dynamic load changes, and a prototype with a dSPACE controller is also implemented.

Accurate power load forecasting is critical to maintaining the stability and efficiency of power systems. However, due to the complex and fluctuating nature of power load patterns, physical calculations are often inefficient and time-consuming. In addition, traditional methods, known as statistical learning methods, require not only mathematical background and understanding but also statistical background and understanding. To overcome these difficulties, the authors proposed a simpler way to predict load by using artificial intelligence. This study investigated the performance of forecasting techniques, including three single-layer and seven hybrid multilayer deep learning models that combine them. This study also analyzed the effect of hyperparameters on the learning results by varying the epoch and activation functions. To evaluate and analyze the performance of the deep learning model, this study used load data from the power system in Jeju Island, Korea. In addition, this study included weather factors that may affect the load to improve the prediction performance. The prediction process is performed on the Python platform, and the model that showed the highest accuracy was LSTM-CNN, a hybrid combination of LSTM and CNN models. Considering both the maximum and minimum error, the error value was low at 0.231%. By providing detailed insights into the entire research process, including data collection, preprocessing, scaling, prediction, and analysis, this study provided valuable guidance for future research in this area.

With the development of new energy industries, the demand for the driving range and power quality of electric vehicle (EV) drive systems is growing rapidly. The drive motor is faced with the challenge of continuously improving power density and performance. This paper proposes a multiobjective optimization method for an interior permanent magnet synchronous motor for a traction drive (IPMSMTD). Based on the flat wire winding technology, the multiobjective optimization design of the IPMSMTD is carried out to improve the motor power density and high-efficiency range, reduce the torque ripple, and suppress the electromagnetic vibration and noise. The structure and size equation of the IPMSMTD are described. The mathematical model considering iron losses is established, and the optimization objectives are determined. Based on the genetic algorithm, a multiobjective optimization mechanism of the magnetic pole structure is established. The operation performance of the motor is analyzed by the finite element simulation and efficiency map. In order to ensure the comprehensive operation index of the IPMSMTD, the vibration noise and modal analysis are carried out, which verifies the rationality of the designed motor and the optimization method.

To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO.

Rotor blades are the main part for generating electrical energy and the primary source of stresses in a wind turbine (WT). The stresses caused by the blades increase the load on the hub, tower, and foundation of the WTs. In this research, the asymmetry of the blade angle with each other has been investigated as one of the factors affecting the stress distribution using Monte Carlo (MC) simulation. The focus of this study is on the stresses caused by the asymmetry of the blades angle when there is the fault in the sensors. A deep understanding of the blade stress distribution due to sensor faults can improve control designs, increase WT operating time, and reduce energy generation costs when these faults occur.

The integration of renewable energy sources into power systems has increased significantly in recent years. Among various types of renewable energy, the use of wind energy is growing rapidly due to its low operating cost, wide distribution worldwide, and no greenhouse gas emissions. However, power systems integrated with wind energy may face stability and reliability issues due to the intermittent nature of wind power. Therefore, in power systems connected to wind farms, it is usually required to use some compensators such as static synchronous series compensator (SSSC) to increase the system performance under abnormal conditions. On the other hand, for an SSSC to be effective in improving the system performance, it must be equipped with a suitable controller. In this paper, a fuzzy logic controller (FLC) is used for the SSSC because of its advantages over conventional controllers. Extensive research has been conducted in power systems with wind turbines in which SSSC or FLC has been used; however, their simultaneous application in such systems has received less attention. Therefore, this article aims to fill this gap. The proposed method is implemented on two power systems and the simulation results are analyzed. In both systems, the dynamic behavior of three different wind farms is examined. In the first and second wind farms, either a squirrel cage induction generator (SCIG) or doubly-fed induction generator (DFIG) are used, whereas in the third one which is a combined wind farm (CWF), an equal number of SCIG and DFIG are employed. In wind farms with SCIG or DFIG, an SSSC is also utilized. Furthermore, an FLC is employed for the SSSC to improve its efficacy. A proportional integral (PI) controller is also considered for the SSSC, and its results are compared with FLC results. The simulation results confirm the superiority of FLC over PI controller.