Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning

TL;DR

This paper introduces a novel deep reinforcement learning algorithm, HAFH-DDPG, for joint energy dispatch and unit commitment in microgrids, effectively reducing costs while managing hybrid action spaces.

Contribution

It proposes a hybrid DRL algorithm combining DQN and DDPG within a finite-horizon framework for MG energy management, addressing hybrid action space challenges.

Findings

HAFH-DDPG outperforms baseline algorithms in cost reduction.

The diesel generator selection strategy simplifies the action space.

Experimental results validate the algorithm's effectiveness with real-world data.

Abstract

Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management. In this paper, deep reinforcement learning (DRL) is applied to learn an optimal policy for making joint energy dispatch (ED) and unit commitment (UC) decisions in an isolated MG, with the aim for reducing the total power generation cost on the premise of ensuring the supply-demand balance. In order to overcome the challenge of discrete-continuous hybrid action space due to joint ED and UC, we propose a DRL algorithm, i.e., the hybrid action finite-horizon DDPG (HAFH-DDPG), that seamlessly integrates two classical DRL algorithms, i.e., deep Q-network (DQN) and deep deterministic policy gradient (DDPG), based on a finite-horizon dynamic programming (DP) framework. Moreover, a diesel generator (DG) selection strategy is presented to support a simplified action space for reducing the computation complexity of this algorithm. Finally, the effectiveness of our proposed algorithm is verified through comparison with several baseline algorithms by experiments with real-world data set.