Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

TL;DR

This paper introduces a multi-agent deep reinforcement learning model for optimal scheduling of multi-community energy systems, effectively reducing wind curtailment and operating costs under uncertain conditions.

Contribution

It presents a novel data-driven scheduling approach that models the problem as a Markov decision process without complex energy coupling modeling.

Findings

Wind curtailment reduced from 16.3% to 0%

Overall operating cost decreased by 5445.6 Yuan

Effective coordination of multi-community energy interactions

Abstract

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.