Intelligent planning and scheduling strategies for low-carbon distribution networks under high proportions of renewable energy integration

This article proposes an intelligent planning and scheduling strategy for low-carbon distribution networks under the condition of high-penetration renewable energy. Initially, based on the deep neural network and particle swarm optimization algorithm models, it forecasts the power loads as well as photovoltaic and wind generation capacities. Through the multitime scale optimal scheduling theory, it dynamically adjusts the operational state of the distribution network, striving to minimize carbon emissions and enhance energy utilization efficiency. This holds significant guiding implications for the realization of intelligent planning in distribution networks.