Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning

Awol Seid Ebrie, Chunhyun Paik, Yongjoo Chung and Young Jin Kim ()
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Awol Seid Ebrie: Major in Industrial Data Science and Engineering, Department of Industrial and Data Engineering, Pukyong National University, Busan 48513, Republic of Korea
Chunhyun Paik: Department of Industrial Management and Big Data Engineering, Dongeui University, Busan 47340, Republic of Korea
Yongjoo Chung: Department of Global Marketing, Busan University of Foreign Studies, Busan 46234, Republic of Korea
Young Jin Kim: Department of Systems Management and Engineering, Pukyong National University, Busan 48513, Republic of Korea

Energies, 2023, vol. 16, issue 16, 1-12

Abstract: A novel approach to power scheduling is introduced, focusing on minimizing both economic and environmental impacts. This method utilizes deep contextual reinforcement learning (RL) within an agent-based simulation environment. Each generating unit is treated as an independent, heterogeneous agent, and the scheduling dynamics are formulated as Markov decision processes (MDPs). The MDPs are then used to train a deep RL model to determine optimal power schedules. The performance of this approach is evaluated across various power systems, including both small-scale and large-scale systems with up to 100 units. The results demonstrate that the proposed method exhibits superior performance and scalability in handling power systems with a larger number of units.

Keywords: power scheduling; unit commitment; reinforcement learning; agent-based simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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