PMID- 36275978
OWN - NLM
STAT- MEDLINE
DCOM- 20221025
LR  - 20221025
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - Distributed Cluster Regulation Strategy of Multipark Integrated Energy System 
      Using Multilayer Deep Q Learning.
PG  - 5151369
LID - 10.1155/2022/5151369 [doi]
LID - 5151369
AB  - The power system is evolving from a single energy system to an integrated energy 
      system. In order to further improve the power generation and consumption balance 
      capacity of the park integrated energy system (PIES), the park integrated energy 
      system is gradually transitioning from the single park energy system operation 
      mode to the multipark energy system operation mode. The design of multipark 
      integrated energy system (MPIES) collaborative control strategy will become an 
      important part to improve the power generation and consumption balance ability of 
      the integrated energy system. In order to fully tap the regulation capacity of 
      each PIES, we propose a coordinated control strategy for the integrated energy 
      system in multiple parks considering the flexible substitution interval of 
      multiple types of energy. Firstly, we analyze the influence of the types of 
      regulation resources and the regulation incentive mechanism of the PIES on the 
      regulation flexible range of the PIES. Then, based on the Markov decision 
      process, a distributed cluster regulation model of MPIES considering regulation 
      demand and regulation flexible interval is established. Finally, using multilayer 
      deep Q networks (MLDQN), the distributed cluster regulation optimization 
      algorithm of MPIES is given. The simulation results show that the proposed method 
      can coordinate the regulation ability of each park integrated energy system in 
      the MPIES, give full play to the large-scale advantage of the interconnection of 
      the park integrated energy system, and improve the overall stability of the 
      multipark integrated energy system.
CI  - Copyright (c) 2022 Chaoqun Zhu et al.
FAU - Zhu, Chaoqun
AU  - Zhu C
AUID- ORCID: 0000-0002-2671-3784
AD  - State Grid Corporation of China, Marketing Department of Suzhou Branch, Suzhou 
      215000, China.
FAU - Shen, Jie
AU  - Shen J
AUID- ORCID: 0000-0002-1224-6847
AD  - State Grid Corporation of China, Marketing Department of Suzhou Branch, Suzhou 
      215000, China.
FAU - Li, Jie
AU  - Li J
AUID- ORCID: 0000-0002-1144-9407
AD  - State Grid Corporation of China, Marketing Department of Suzhou Branch, Suzhou 
      215000, China.
FAU - Zhang, Xiaoming
AU  - Zhang X
AUID- ORCID: 0000-0003-1864-3322
AD  - State Grid Corporation of China, Marketing Department of Suzhou Branch, Suzhou 
      215000, China.
FAU - Zhou, Lei
AU  - Zhou L
AUID- ORCID: 0000-0001-5158-5924
AD  - State Grid Corporation of China, Marketing Department of Suzhou Branch, Suzhou 
      215000, China.
FAU - Zhu, Dan
AU  - Zhu D
AUID- ORCID: 0000-0002-2051-1348
AD  - State Grid Corporation of China, Marketing Department of Suzhou Branch, Suzhou 
      215000, China.
FAU - Li, Yafei
AU  - Li Y
AUID- ORCID: 0000-0001-6280-7828
AD  - State Grid Corporation of China, Marketing Department of Suzhou Branch, Suzhou 
      215000, China.
LA  - eng
PT  - Journal Article
DEP - 20221012
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - *Algorithms
MH  - Computer Simulation
PMC - PMC9581593
COIS- The authors declare no conflicts of interest.
EDAT- 2022/10/25 06:00
MHDA- 2022/10/26 06:00
PMCR- 2022/10/12
CRDT- 2022/10/24 04:09
PHST- 2022/08/25 00:00 [received]
PHST- 2022/09/21 00:00 [revised]
PHST- 2022/09/27 00:00 [accepted]
PHST- 2022/10/24 04:09 [entrez]
PHST- 2022/10/25 06:00 [pubmed]
PHST- 2022/10/26 06:00 [medline]
PHST- 2022/10/12 00:00 [pmc-release]
AID - 10.1155/2022/5151369 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Oct 12;2022:5151369. doi: 10.1155/2022/5151369. 
      eCollection 2022.