Quantum Differential Evolution Algorithm for Power Dispatch in Active Distribution Networks

With the high proportion of distributed power sources connected to active distribution networks, the complexity and uncertainty of their operation and scheduling have significantly increased. This article proposes an improved differential evolution algorithm that integrates quantum computing principles. Firstly, a multi-objective optimization model is constructed with the objectives of minimizing operating costs, minimizing network losses, and minimizing voltage deviations, taking into account constraints such as power balance, node voltage, and distributed power output. Secondly, the quantum bit encoding mechanism is introduced into the standard difference evolution algorithm framework, utilizing the quantum superposition state characteristics to expand population diversity. A dynamic mutation strategy based on quantum rotation gates and an adaptive crossover probability adjustment mechanism are designed to effectively balance global search and local development capabilities. The simulation experiment is based on a 32 node active distribution network, and the results show that compared with the traditional differential evolution algorithm, the proposed method improves the convergence speed by about 40 % and reduces the fluctuation range of the optimal solution by 65 %. This verifies the advantages of the algorithm in solving accuracy and stability, and provides a new solution for the optimization scheduling of distribution networks containing high penetration renewable energy.