Research Output
Articles
Publication Date: 2025
Chinese Journal of Electrical Engineering (20961529)11(4)pp. 195-206
Power systems operate close to their voltage stability margin owing to the high load demand. To prevent voltage collapse, the system operator must be aware of the system functionality and be prepared to implement preventive and corrective control measures. Hence, an appropriate algorithm to determine the time required by the operator is crucial, making it necessary to adopt appropriate models for effective implementation. Based on the load prediction curve for each bus, a new method is proposed to estimate the remaining time before voltage instability. Furthermore, an algorithm is developed to select the most suitable control action (preventive or corrective) based on the remaining time until the power system voltage instability occurs. In this study, two models based on voltage stability-constrained optimal power flow (VSC-OPF) are proposed, incorporating voltage stability index constraints for both control types. The effectiveness of the proposed algorithm and models is assessed using the IEEE 14- and 57-bus power systems, thereby demonstrating promising results. © 2017 CMP.
Publication Date: 2025
Results in Engineering (25901230)27
Optimal Power Flow (OPF) plays a fundamental role in the secure and efficient management of power systems, both in system design and real-time operation. Existing OPF approaches often struggle with the problem's non-linearity, non-convexity, and mixed-variable characteristics, which hinder convergence and compromise solution diversity. This paper addresses these challenges by applying a multi-objective evolutionary algorithm based on decomposition (MOEA/D) enhanced with stable matching theory. The proposed method ensures a balanced and effective trade-off between solution accuracy and diversity in multi-objective optimization. Comparative evaluations against well-established algorithms demonstrate the superior performance of the proposed approach in approximating the Pareto front, improving computational efficiency, and maintaining solution diversity. The results highlight the effectiveness of the method in addressing OPF problems with conflicting objectives such as cost minimization, loss reduction, and voltage stability enhancement. This research provides a new perspective on applying stable matching mechanisms into evolutionary algorithms for power system optimization. © 2025 The Author(s)
Publication Date: 2024
IET Generation, Transmission and Distribution (17518687)18(17)pp. 2776-2792
By increasing the number of electric vehicles (EVs) to achieve a less carbon environment, not only they consume power from the grid to be charged, but also do they deliver power to the grid, and this can play a significant role in load frequency control. However, EVs take part in frequency regulation based on their state of charge (SoC) which will cause uncertainties. In order to manage these uncertainties, EV aggregator (EVA) concept has been introduced. The EV owner participates in the demand response program provided by the EVA arbitrarily considering her/his requirements. Accordingly, EVA calculates the up and down power reserve for the power system operator. Following any frequency deviation, EVA sends the proper commands to each EV to consume or to inject power to the system. There are also communication delays between different parts, uncertainties, and non-linearities that existed in the power system. To overcome these issues, this study proposes a new robust load frequency controller based on feedback theory and artificial neural network which is designed through the non-linear multi-machine power system. Simulation results on IEEE 39-bus power system show that the proposed controller regulates frequency more desirably in comparison with other methods. © 2024 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
