Generalized Load Collaborative and Interactive Dispatching Strategy in Smart Communities Based on Reinforcement learning
With the advancement of the low-carbon economy in the power sector,there has been a gradual increase in residents'awareness of energy conservation and environmental sustainability.This study addresses the generalized load scheduling issue in smart communities and investigates a smart community's generalized load coordination and interactive scheduling strategy based on a reinforcement learning framework.By leveraging data from intelligent meters and devices,the study aims to optimize load management and energy consumption.By analyzing the load characteristics of smart communities and users'electricity preferences,a generalized load and energy storage charging/discharging model is developed.Subsequently,an energy management model based on a deep reinforcement learning framework is constructed,and a community energy management approach utilizing the Soft Actor-Critic(SAC)algorithm is proposed to derive optimized scheduling strategies for low-carbon communities.The effectiveness of the proposed model and approach is validated through illustrative examples.The research findings demonstrate that the smart community's generalized load coordination and interactive scheduling strategy significantly reduce energy consumption costs and effectively mitigate carbon emissions within the community.
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