Quantum Genetic Algorithm for Dynamic Economic Dispatch of Active Distribution Network with Microgrid Including Renewable Energy Source

This paper presents a Quantum Genetic Algorithm (QGA) for the Dynamic Economic Dispatch (DED) of Active Distribution Networks (ADNs) with a Microgrid (MG) including renewable energy source. The economic dispatch problem aims to minimize the operating cost of the power system while meeting the load demand and satisfying operational constraints. The integration of renewable energy sources, such as wind and solar, into the power system presents new challenges due to their intermittent and uncertain nature. The proposed QGA-based DED approach considers the uncertainties of renewable energy source and enables the effective optimization of the system operation. The QGA combines the advantages of both quantum computing and genetic algorithm to provide a more efficient and effective solution. Therefore, quantum-bits (qubits) offer a much wider computational capability than the classical bits used in traditional genetic algorithms. The proposed approach is tested on the IEEE 37 bus system with two photovoltaic systems, and the results demonstrate its superior performance compared to other well-known methods which are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed QGA provides an effective solution for the DED problem in ADNs with MGs and renewable energy sources, which can contribute to the development of sustainable and efficient power systems.