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.
