部分可观测马尔可夫决策过程
数学优化
调度(生产过程)
计算机科学
可再生能源
马尔可夫决策过程
趋同(经济学)
约束(计算机辅助设计)
分布式计算
马尔可夫过程
马尔可夫链
工程类
马尔可夫模型
数学
经济
机器学习
电气工程
统计
机械工程
经济增长
作者
Yanting Zhou,Zhongjing Ma,Xingyu Shi,Suli Zou
出处
期刊:Energy
[Elsevier]
日期:2024-02-01
卷期号:288: 129732-129732
被引量:16
标识
DOI:10.1016/j.energy.2023.129732
摘要
In a multi-regional integrated energy system (MIES), optimal scheduling under random renewable supply and user demand is crucial to promote the process of carbon neutrality. Further, the total carbon emission of multiple regions is expected to strictly restricted under a threshold, while intensifying the complex coupling of multiple agents. To address the optimal dispatching problem, we establish a typical MIES model with the global carbon emission constraint, which is formulated as a partially observable Markov decision-making process (POMDP). Then we propose an improved multi-agent deep deterministic policy gradient (MADDPG) method, which utilizes a centralized training and decentralized execution (CTDE) framework to effectively improve the multi-agent stationarity. Moreover, an attention mechanism is employed to enhance the efficiency of communication and coordination among agents. Experiments are carried out on multi-regional datasets, and the results certify that the proposed algorithm can decrease system operation costs, reduce carbon emissions, and speed up the convergence of the multi-agent system.
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