Backtracking Search Algorithm with Single and Multi-Objective Function for the Solution of Optimal Power Flow Problem

This manuscript investigates the performance of the backtracking search algorithm (BSA) to minimize various objectives for an economical and secure power system. A variety of single and multi - objectives are delineated and solved. This manuscript also includes the valve-point loading effect alongside the objectives considered. The simulation has been computed in the IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test network. The simulation outcomes as obtained by the proposed BSA and various algorithms are compared. Convergence curves are plotted to testify the characteristics of the proposed BSA for proceeding towards the global minima.

Economic Emission Load Dispatch Problem with Valve-Point Loading Using A Novel Quasi-Oppositional-Based Political Optimizer

In the present paper, a novel meta-heuristic algorithm, namely quasi-oppositional search-based political optimizer (QOPO), is proposed to solve a non-convex single and bi-objective economic and emission load dispatch problem (EELDP). In the proposed QOPO technique, an opposite estimate candidate solution is performed simultaneously on each candidate solution of the political optimizer to find a better solution of EELDP. In the bi-objective EELDP, QOPSO is applied to simultaneously minimize fuel costs and emissions by considering various constraints such as the valve-point loading effect (VPLE) and generator limits for a generation. The effectiveness of the proposed QOPO technique has been applied on three units, six units, 10-units, 11-units, 13-units, and 40-unit systems by considering the VPLE, transmission line losses, and generator limits. The results obtained using the proposed QOPO are compared with those obtained by other techniques reported in the literature. The relative results divulge that the proposed QOPO technique has a good exploration and exploitation capability to determine the optimal global solution compared to the other methods provided in the literature without violation of any constraints and bounded limits.

Cuckoo search algorithm to solve the problem of economic emission dispatch with the incorporation of facts devices under the valve-point loading effect

The essential objective of optimal power flow is to find a stable operating point which minimizes the cost of the production generators and its losses, and keeps the power system acceptable in terms of limits on the active and reactive powers of the generators. In this paper, we propose the nature-inspired Cuckoo search algorithm (CSA) to solve economic/emission dispatch problems with the incorporation of FACTS devices under the valve-point loading effect (VPE). The proposed method is applied on different test systems cases to minimize the fuel cost and total emissions and to see the influence of the integration of FACTS devices. The obtained results confirm the efficiency and the robustness of the Cuckoo search algorithm compared to other optimization techniques published recently in the literature. In addition, the simulation results show the advantages of the proposed algorithm for optimizing the production fuel cost, total emissions and total losses in all transmission lines.

Sensitivity Analysis of Load Frequency Control Problem considering impact of Wind Penetration using improved Harris Hawks Optimizer

This paper suggest a new technique known as Harris’s Hawk Optimizer which is used to solve multi objective constraints. This optimizer is predicated on gray wolf multi objective optimisation approach and intended by symbiotic trapping behavior of Harris’s Hawk. These hawks are called as wolf bundle of azure. Here in this paper the sensitivity analysis to judge robustness with Harris hawk optimization technique for load frequency control is effectually and consistently presented. The result indicates unimodel and multimodel for various benchmarking functions examining sensitivity analysis and the valve point loading effect. The concluding results gained using improved HHO are compared with other algorithms and found to be encouraging.

Solution of Economically Load Scheduling Problem using Teaching Learning and Bio-Geography Based Hybrid Optimization Algorithm

The article presents a new optimization algorithm view namely hybridization of teaching learning (TLBO) and biogeography based (BBO) optimization algorithm used to solve the convex economic dispatch (ED) problem with non-linear constraints like ramp rate limit, valve point loading effect etc. Hybridization of TLBO and BBO is the mixed combination of superior properties of TLBO and BBO. Teaching learning algorithm (TLBO) is based on teacher learner relationship in class and bio-geography algorithm (BBO) is based on geographical representation of biological species. The main goal of ED is to allocate power allocation economically meeting load demand. The proposed algorithm is tested for 13-unit, 15-unit, 40-unit and 140 unit systems. For proving superiority properties of proposed algorithm, obtained result are compared with recent algorithm. It gives optimum fuel cost compared to other optimization algorithm.