A Novel Hybrid Swarming Based Computational Paradigm for Optimizing Fuel Generation Cost and Copper Losses in Power System

Abstract

Optimal power system planning play the part of key importance in operation and management of dynamics in power network. In this regard optimal power flow addresses two sub problems that includes Economic load dispatch (ELD) to optimally allocate real power generation output from thermal power plants along with Optimal Reactive Power Dispatch (ORPD) to fne tune controlled variables in terms of generator voltages, transformers tap changer settings and shunt reactive power compensators play a key role in reducing generation cost and active power losses respectively. Furthermore, power system network, power handling capability, system security and transmission effciency are improved. When nonrenewable fuels are used to power systems, harmful gas emissions occur and resources for future generations are depleted. The answer to this problem is to provide generators the best possible power allocation. Recently developed technique, whale optimization algorithm (WOA) is a revolutionary approach for concurrently lowering fuel costs and pollution levels. The (ELD) issue is solved utilizing WOA backed by Sequential Quadratic Programming (SQP) and Interior Point Algorithm (IPA) in a hybrid computer environment for speedy local convergence. In the present work evolutionary strategy approach based on hybrid approach to balance the exploration and exploitation search abilities is incorporated to address the stiff scenarios involved with non-linear, transcendental and multimodal objective functions to reduce generation cost and active power losses. In the case of 3, 13, and 14 thermal producing units, the design scheme is assessed for resolving Economic Load Dispatch (ELD) concerns while accounting for and eliminating valve point loading impacts. The system is further explored for the Combined Economic Emission Load Dispatch (CEELD) issue for 6 generators using simply the global search approach, omitting the influence of valve point loading. Entropy function is incorporated in the classical W.O.A algorithm to enhance its memory and divergence towards optimal allocation of control variables towards minimization of transmission losses on IEEE-30 standard bus system. The results will be validated through statistical performance indices along with comparison with state-of-the-art counterparts.