Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties

A community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on users’ willingness is proposed for the uncertainty of users’ demand response behavior; a training scenario set of a reinforcement learning agent is generated with a Latin hypercube sampling method for the uncertainties of power, load, temperature, and electric vehicle trips. Based on the proposed demand response model, low-carbon economic dispatch of the community-integrated energy system under multiple uncertainties is achieved by training the agent to interact with the environment in the training scenario set and reach convergence after 250 training rounds. The simulation results show that the reinforcement learning agent achieves low-carbon economic dispatch under 5%, 10%, and 15% renewable energy/load fluctuation scenarios, temperature fluctuation scenarios, and uncertain scenarios of the number of trips, time periods, and mileage of electric vehicles, with good generalization performance under uncertain scenarios

PHYSICA [x

, respectively. The space group is 14/mmm which is consistent with that of Pb-1222. The resistivity measurement showed that the two compounds were narrow-gap semiconductors

MOESM2 of Properties of STAT1 and IRF1 enhancers and the influence of SNPs

Additional file 2. Additional figures