S. Malek; A. Khodabakhshian; R. Hooshmand
Abstract
This paper proposes a robust state feedback controller for Electric Vehicle aggregators to solve the challenging problem caused by the participation of Electric Vehicles in the load frequency control of the power system. The Lyapunov-Krasovskii functional method is used to achieve two objectives ...
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This paper proposes a robust state feedback controller for Electric Vehicle aggregators to solve the challenging problem caused by the participation of Electric Vehicles in the load frequency control of the power system. The Lyapunov-Krasovskii functional method is used to achieve two objectives of the robust performance and stability. Then, by using teaching learning based optimization algorithm, both primary and secondary participation gains of EV aggregators in LFC are optimally determined. The Generation Rate Constraint and time delay, as nonlinear elements, are also taken into account. Simulations are carried out on two nonlinear power systems by using the power system simulation software. The results show that the designed controller gives a desirable robust performance for frequency regulation at the presence of uncertainties.
Distribution Systems
S. Ghasemi; A. Khodabakhshian; R. Hooshmand
Abstract
After extreme events such as floods, thunderstorms, blizzards and hurricanes there will be devastating effects in the distribution networks which may cause a partial or complete blackout. Then, the major concern for the system operators is to restore the maximum critical loads as soon as possible by ...
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After extreme events such as floods, thunderstorms, blizzards and hurricanes there will be devastating effects in the distribution networks which may cause a partial or complete blackout. Then, the major concern for the system operators is to restore the maximum critical loads as soon as possible by available generation units. In order to solve this problem, this paper provides a restoration strategy by using Distributed Generations (DGs). In this strategy, first, the shortest paths between DGs and critical loads are identified. Then, the best paths are determined by using a decision-making method, named PROMOTHEE-II to achieve the goals. The uncertainties for the output power of DGs are also considered in different scenarios. The IEEE 123-node distribution network is used to show the performance of the suggested method. The simulation results clearly show the efficiency of the proposed strategy for critical loads restoration in distribution networks.
