Review and outlook on reinforcement learning: Its application in agricultural energy internet

• Published in: IET renewable power generation 2024-12, Vol.18 (16), p.3678-3690
• Main Authors: Fu, Xueqian, Zhang, Jing, Bai, Xiang, Chang, Xinyue, Xue, Yixun
• Format: Article
• Language: English
• Subjects: agricultural energy internet; agriculture; artificial intelligence; distribution network; energy management systems; Flexible operation; photovoltaic; photovoltaic power systems; reinforcement learning
• ISSN: 1752-1416; 1752-1424
• DOI: 10.1049/rpg2.13019

Agricultural Energy Internet (AEI), representing a key evolutionary direction in the integrated energy landscape of rural regions, holds a vital position in advancing the electrification of agricultural sectors. However, the disjointed control between agricultural loads and grid operations hinders the collaborative development of agriculture and energy. Addressing these issues, this paper investigates the current applications of artificial intelligence in the fields of agriculture and energy. The authors examine the evolutionary path of AEI, particularly emphasizing the critical technologies emerging from the intersection of agriculture, energy, and digital networks. Furthermore, the authors examine the critical technologies of reinforcement learning in the context of smart grid applications. In response to the challenges posed by low energy efficiency in rural areas, a reinforcement learning framework is proposed for coordinating fisheries, agriculture, livestock farming, and rural distribution networks. This framework provides a clear pathway for the application of reinforcement learning in AEI. This research acts as a conduit, merging agricultural and energy domains to promote a cohesive progression that markedly aids in the enhancement of rural electrification and the adoption of sustainable energy methodologies through reinforcement learning. Recognizing the diverse demands associated with fisheries, crop cultivation, and livestock farming loads, this paper introduces distinct reinforcement learning models tailored to address these variations. The unique contribution of this study lies in its comprehensive analysis of the AEI in conjunction with reinforcement learning, offering valuable insights into the future trajectory of reinforcement learning within the AEI domain.