Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management
文献类型：期刊论文
| 作者 | Boaro, Matteo1; Fuselli, Danilo1; De Angelis, Francesco1; Liu, Derong2; Wei, Qinglai2 ; Piazza, Francesco1
|
| 刊名 | COGNITIVE COMPUTATION
 |
| 出版日期 | 2013-06-01 |
| 卷号 | 5期号:2页码:264-277 |
| 关键词 | Adaptive dynamic programming Approximate dynamic programming Neural networks Energy scheduling Battery management |
| 英文摘要 | The employment of intelligent energy management systems likely allows reducing consumptions and thus saving money for consumers. The residential load demand must be met, and some advantages can be obtained if specific optimization policies are taken. With an efficient use of renewable sources and power imported from the grid, an intelligent and adaptive system which manages the battery is able to satisfy the load demand and minimize the entire energy cost related to the scenario under study. In this paper, an adaptive dynamic programming-based algorithm is presented to face dynamic situations, in which some conditions of the environment or habits of customer may vary with time, especially using renewable energy. Based on the idea of smart grid, we propose an intelligent management scheme for renewable resources combined with battery implemented with a faster and simpler scheme of dynamic programming, by considering only one critic network and some optimization policies in order to satisfy the load demand. Since this kind of problem is suitable to avoid the training of an action network, the training loop among the two neural networks is deleted and the training process is greatly simplified. Computer simulations confirm the effectiveness of this self-learning design in a typical residential scenario. |
| WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Neurosciences |
| 研究领域[WOS] | Computer Science ; Neurosciences & Neurology |
| 关键词[WOS] | PARTICLE SWARM OPTIMIZATION ; TIME NONLINEAR-SYSTEMS ; STORAGE SYSTEM ; CRITIC DESIGNS ; WIND ; SMART |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000318648900012 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3828]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 作者单位 | 1.Univ Politecn Marche, Dipartimento Ingn Informaz, I-60131 Ancona, Italy 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Boaro, Matteo,Fuselli, Danilo,De Angelis, Francesco,et al. Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management[J]. COGNITIVE COMPUTATION,2013,5(2):264-277. |
| APA | Boaro, Matteo,Fuselli, Danilo,De Angelis, Francesco,Liu, Derong,Wei, Qinglai,&Piazza, Francesco.(2013).Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management.COGNITIVE COMPUTATION,5(2),264-277. |
| MLA | Boaro, Matteo,et al."Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management".COGNITIVE COMPUTATION 5.2(2013):264-277. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。