A novel quantum inspired hybrid metaheuristic for dispatch of power system including solar photovoltaic generation

ABSTRACT

This manuscript proposes a novel metaheuristic, which is an amalgamation of the quantum concept and the gravitational search particle swarm optimization technique. The inclusion of quantum concepts in the algorithm enhances its capability, as in quantum space there is no restriction on the movement of particle and the solution can be obtained with smaller population and faster convergence. An adaptive contraction expansion factor is also introduced which ensures better exploration of the algorithm. The technique is applied to solve the combined economic emission dispatch of a hybrid solar thermal unit operating in New Delhi, India for full and reduced solar radiation. To verify the effectiveness of the proposed technique, it is also applied to solve the combined economic emission dispatch of a 10 unit, 2000 MW system. It is observed that the proposed approach provides a fuel cost saving of up to 1300$ when compared with other referred techniques.

KEYWORDS:

• Binary encoded problems
• combinatorial optimization
• combined economic emission dispatch
• mixed integer optimization problem
• quantum evolutionary algorithm
• rotation quantum gate

List of abbreviations

CEED Combined EEDCLQPSO Quantum-behaved PSO with collaborative attractors DCEED Dynamic CEEDDE Differential evolution EAE volutionary algorithm EED Economic emission dispatch ELD Economic load dispatch EP Evolutionary programing FPA Flower pollination algorithm GA Genetic algorithm GP Goal programming GSA Gravitational search algorithm HSP Hybrid sequential programming LCOE Levalized cost of electricity LMNN Levenberg Marquardt neural network MBA Mine blasting algorithm MIOP Mixed integer optimization problem MODE Multi-objective DENSGA-II Non-dominated sorting GA-IIPDE Pareto DEPOZ Prohibited operating zone PSO Particle swarm optimization QBA Quantum behaved bat algorithm QI-TVIW-GSA-PSO Quantum inspired time varying inertia weight gravitational search algorithm PSORRL Ramp rate limit SCEED Static CEEDSPEA-2 Strength Pareto EA 2TLBO Teaching-learning-based optimization

Disclosure statement

The authors declare that they have no conflict of interest.

List of symbols

Table