Optimal power flow management Using adaptive quasi oppositional Differential migrated biogeographybased Optimization

Abstract

In a modern power system, the power market operator plays a vital role to deliver electrical energy for the consumer. He has the responsibility to consider certain norms, such that quality of supply, operating costs, environmental impacts and network constraints. The electrical power system is a complex and wide area network, mathematically which deals with a large number of control and dependent variables associated with nonlinear, non-convex and discrete functions. So, the market operator is in the position to solve this complex problem, to maintain the system with the safest and economic operation newlineHere, the optimization technique is very essential to solve such a complex problem. Earlier decades, traditional optimization techniques were used to solve these problems, such as quadratic programming, linear and nonlinear programming, sequential quadratic programming, gradient descent method, Newton s method, interior point algorithm, Bender s decomposition technique, Broyden s method, augmented Lagrangian method, etc. These techniques are not suitable for some complex problems with more local optima. It takes more computation time and memory space because of its computational procedures. Hence, researchers move towards meta-heuristic algorithms to solve such complex problems with practical constraints. newlineLast decades, many meta-heuristic algorithms are developed to solve optimal power flow problems. Still, the researchers have more attention on developing a new algorithm for better solutions. In the present work, a novel hybrid meta-heuristic algorithm is developed to find a better solution for optimal power flow management in the different power systems newline