Community Microgrid Energy Co-Scheduling Based on Deep Reinforcement Learning and Contribution Mechanisms

Realizing energy-distributed cooperative scheduling while guaranteeing user privacy is challenging for community microgrid energy management. A dual-interaction deep deterministic policy gradient (DI-DDPG) method based on the contribution mechanism is proposed in this paper to realize distributed co-scheduling of energy in community microgrids under user privacy, electricity demand, and uncertainty of renewable energy sources and loads. To avoid privacy risks, each residential microgrid is defined as an agent with learning capabilities, and model training and decision-making are executed locally. The contribution mechanism encourages users to participate in co-scheduling in the community through contribution evaluation and benefit distribution. Meanwhile, to cope with the uncertainty of renewable energy and loads, a mid-day scheduling algorithm is introduced to adjust the day-ahead scheduling strategy and combine it with the demand response mechanism, greatly improving the model’s performance under uncertainty. Finally, simulation experiments using open-source real data and periodic data are conducted, and the results verified the effectiveness of the contribution mechanism and the mid-day scheduling algorithm in DI-DDPG and its excellent performance in energy co-scheduling.