Work

Reward shaping-based deep reinforcement learning for look-ahead dispatch with rolling-horizon

Year: 2025

Type: article

Abstract: The increasing penetration of renewable energy exacerbates the challenges in designing an effective and adaptable model-driven Look-ahead Dispatch (LAD) method. Recently, deep reinforcement learning (DRL) methods show enormous potential in developing a dispatching agent with self-learning ability, attributed to their superior generalization, adaptability, and computational efficiency. However, existing DRL-based LAD methods overlook the discounting effect when calculating the immediate total reward for LAD and lack attention to trial-and-error reward design and expected discounted returns that could reflect the true performance metrics of LAD. Therefore, this paper proposes novel reward shaping (RS)-based DRL algorithms for the rolling-horizon LAD problem. We propose the method for accurately estimating the look-ahead discounted factor that best matches different look-ahead horizons (LAHs). The shaped reward functions are designed and an RS-based regularization is also proposed by employing a potential function. Case studies on the SG 126-bus and IEEE 118-bus systems demonstrate the effectiveness of the proposed improved measures, as well as the superiority and adaptability of the proposed improved DRL algorithms in training and testing performance. © 2017 Elsevier Inc. All rights reserved.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.110673

Authors: Hongsheng Xu, Yungui Xu, Ke Wang, Yaping Li, Abdullah Al Ahad

Institutions: Hohai University, China Electric Power Research Institute

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,920