Reward shaping-based deep reinforcement learning for look-ahead dispatch with rolling-horizon
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, a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002248 |
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| Summary: | 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. |
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| ISSN: | 0142-0615 |