A modified GWO technique based cascade PI-PD controller for AGC of power systems in presence of Plug in Electric Vehicles

A Modified Grey Wolf Optimization (MGWO) based cascade PI-PD controller is suggested in this paper for Automatic Generation Control (AGC) of power systems in presence of Plug in Electric Vehicles (PEV). The modification in original Grey Wolf Optimization (GWO) algorithm is introduced by strategy which maintains a proper balance between exploration and exploitation stages of the algorithm and gives more importance to the fittest wolves to find the new position of grey wolves during the iterations. Proposed algorithm is first tested using four bench mark test functions and compared with original GWO, Differential Evolution (DE), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO) to show its superiority. The proposed technique is then used to tune various conventional controllers in a single area three-unit power system consisting of thermal hydro and gas power plants for AGC. The superiority of proposed MGWO algorithm over some recently proposed approaches has been demonstrated. In the next step, different controllers like PI, PID, and cascaded PI-PD controller are taken and Plug in Electric Vehicles (PEVs) are assumed. The proposed approach is also extended to a two-area six-unit power system. Lastly, a five unequal area nonlinear power system with PEVs and dissimilar cascade PI-PD controller in each area is considered and proposed MGWO technique is employed to optimize the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay. It is observed that PEVs contribute in the AGC to control system frequency

Participation of Renewable Energy Sources in the Frequency Regulation Issues of a Five-Area Hybrid Power System Utilizing a Sine Cosine-Adopted African Vulture Optimization Algorithm

In this article, a novel methodology is proposed by utilizing a technique which, in light of the change in the African vulture optimization known as Sine Cosine, adopted an African vulture optimization algorithm (SCaAVOA)-based tilt integral derivative (TID) regulator for the load frequency control (LFC) of a five-area power system with multi-type generations. At first, the execution of the Sine Cosine-adopted calculation is tried by contrasting it with the standard AVOA calculation while considering different standard benchmark functions. To demonstrate the superiority of the proposed SCaAVOA algorithm, the results are contrasted using different standard approaches. In the next stage, the proposed method is used in a five-area thermal power system and is likewise applied to a five-area, ten-unit system comprising different conventional sources as well as some renewable energy sources. The performance analysis of the planned regulator is completed for various system boundaries and loading conditions. It is seen that the said regulator is more viable in comparison to the other standard controllers