IET Renewable Power Generation最新文献

The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non-convex engineering optimization issues. The traditional LCA (t-LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t-LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t-LCA based on Weibull flight operator, mutation-based approach, quasi-opposite-based learning and gorilla troops exploitation-based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non-parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable-based (wind turbines + PVs) optimal power flow problem using a modified RER-based IEEE 57-bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57-bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u.

This research proposes the application of fractional-order sliding mode control (FOSMC) at the primary controller level to improve the stability of an islanded microgrid by adjusting its voltage and frequency. The control strategies used in the microgrid are performed in two levels (primary and secondary) in the islanded mode. Practically, most previous studies have worked to improve the primary controller. Droop control is one of the most commonly used methods at the primary level and is adopted in this study as well. The sliding mode control (SMC) strategy is normally used to control linear equations. Thus, the non-linear microgrid equations were transformed into some linear ones using the input-output feedback linearization technique. Further, a fractional sliding surface was acquainted. The sliding surface and FOSMC were designed to reject system uncertainties and organize the voltage and frequency. Design parameters were chosen using the Lyapunov stability theorem. The validation of the proposed method using Simulink-MATLAB confirms its effectiveness in enhancing level power sharing, regulating frequency, and maintaining voltage stability across the system.

A soft switching non-isolated bidirectional DC–DC converter with an improved voltage conversion ratio without any additional auxiliary switch is presented in this paper. In the proposed converter, improved step-up/step-down gain conversion is achieved by employing the coupled inductors method. Also, the auxiliary circuit provides soft switching conditions for all the semiconductor elements, regardless of the power flow direction and without any extra voltage stress. The other switch helps provide soft switching conditions for the main switch. Moreover, the switch used for providing soft switching conditions operates as a synchronous rectifier as well. The additional circuit added to attain soft switching is composed of an inductor, coupled with the converter's main inductor, and two auxiliary diodes. The auxiliary diodes benefit from zero-current-switching conditions. Fully soft switching conditions for all semiconductor devices, removing the reverse recovery problem, and a low number of components have led to mitigating switching losses and improving efficiency. Detailed operating principles and a theoretical analysis of the proposed converter are presented. Also, the experimental results of a 220 W prototype circuit are provided to confirm the validity of the proposed topology.

Eight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%.

With the high penetration and flexible access of inverter-interfaced distributed generators (IIDGs), it is gradually becoming difficult for traditional protection schemes to meet the requirements for the safe operation of distribution networks (DNs). Active protection schemes based on power electronic equipment provide a new approach. On the basis of the controllability of voltage source converters, a method for active high-frequency signal injection and a selection principle for the corresponding control parameters are proposed. Considering the impact of T-connected load branches on the protected line, the high-frequency current characteristics at the three terminals during internal and external faults are analysed. On this basis, an active protection scheme based on high-frequency current is proposed for DNs with IIDGs. The performance of the proposed scheme is verified via PSCAD/EMTDC simulation software. The test results show that the proposed scheme can reliably trip during internal faults and identify faulty phases, which has better endurance to fault resistance.

The virtual power plant integrating the flexible resources in the distribution network can provide additional adjustment capacity for the auxiliary services of distribution network. However, the actual internal situation of distribution network including insufficient adjustable capacity of energy storage, unreasonable power allocation, and voltage overrun leads to the difficulties in optimization scheduling. Therefore, this paper proposes a power allocation optimization strategy of distributed electricity-H₂ virtual power plants (EHVPPs) with aggregated flexible resources. Specifically, a distributed EHVPP division method based on the granular K-medoids clustering algorithm is proposed to realize the independent autonomy and coordinated interaction between EHVPPs, and in order to quantify the operation and regulation capacity of distributed EHVPPs, an aggregation approach of regulating feasible domains of flexibility resources based on the improved zonotope approximations is developed. Moreover, a power allocation strategy based on the flexibility weight factor is proposed to handle the calculated minimum deviation between the total active output of PV and the dispatching power command, realizing the self-consistency of distributed EHVPPs. Comparative studies have demonstrated the superior performance of the proposed methodology in economic merits and self-consistency efficiency.

This article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph-based representation of the wind farm, capturing wind turbines as vertices and the inter-turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high-fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts.

Under the influence of environmental pollution and energy scarcity, integrated energy systems (IES) have received extensive attention in the field of energy supply due to their ability to consume renewable energy and enhance energy utilization. In the context of low-carbon scheduling for IES, numerous studies calculate the system's carbon emissions based on the carbon emission coefficients of energy devices. However, IES is a multi-energy coupling system in which a device's energy input can originate from multiple sources with varying degrees of carbon emissions, making it difficult to accurately calculate the resulting carbon emissions using fixed coefficients. Consequently, a carbon emission flow (CEF) model is constructed for the system to calculate carbon emissions. In addition to the basic input–output CEF model, the CEF model for energy storage devices is considered, and carbon emission constraints during system operation are formulated based on the CEF model. Furthermore, many studies on low-carbon scheduling of IES overlook the uncertainties associated with load and renewable energy. Therefore, a two-stage scheduling model consisting of day-ahead stage and intra-day stage is developed to achieve reliable energy supply. Finally, through experiments, the low-carbon performance and reliability of the model are validated.

This paper proposes an active and reactive power dispatch method for a wind farms (WF) considering the real-time service quality and the available power to achieve the fair power allocation within the WF. The goal is to ensure operational safety while meeting the power demand instructions from the dispatch centre. This paper generates WF service quality metrics based on real-time monitoring data of key components to characterize the operational stability of the wind turbines (WTs). Specifically, service quality metrics related to active/reactive power are constructed separately to improve the accuracy of service quality assessment and power dispatch. The available power is then integrated into the service quality metrics to derive active/reactive power allocation factors, considering operational safety and power margin. Active power allocation is constrained by the power demand instructions, while reactive power allocation aims to maintain the WT terminal voltage within a feasible range. The proposed method can ensure the fair power allocation within WFs to meet the requirements of equipment reliability, voltage safety, and power supply simultaneously, which can improve resource utilization and avoid the potential failure. Case studies in MATLAB/Simulink confirm the effectiveness of the proposed method.