Certain investigation of power system reliability and cost reduction hybrid optimization techniques in renewable pv wind fc system

Abstract

The modern power systems include a large number of dispersed and small power generating sources, which are slowly replacing larger and more concentrated power sources to satisfy energy demand. Generally, high emissions, higher cost, low power quality, low reliability and high power losses are the major issues of power systems. The proposed power generation can be done through the renewable energy resources like Photovoltaic (PV), Wind Turbine (WT) and Fuel Cell (FC). The power system must provide the reliable power delivery to various loads with minimum cost. A variety of strategies are implemented here to improve power system reliability while lowering costs. A number of research works have been completed and focused on the enhancement of power system reliability and cost minimization in Hybrid Renewable Energy Systems (HRES) using various optimization methods. This proposed work introduces some new optimization algorithms for improving the efficiency and lowering the cost and loss of HRES systems. Initially, the Tunicate Swarm Optimization (TSA) is considered as proposed optimization technique. The TSA approach produces the gain parameters of control signals and manages the energy sources of the system. The main objective of this proposed approach is reduction of cost in different hybrid source condition such as PV, WT, and FC and reduces the cost with high reliability for the supply of load. The proposed system considers reliability indices such as expected load loss and energy loss. In addition, a new hybrid technique of Mayfly Algorithm (MA) and Radial Basis Function Neural Network (RBFNN) is introduced for reducing the cost and maximizing the power system reliability. This proposed hybrid control system is named as MA-RBFNN technique. newline