PMID- 36118952
OWN - NLM
STAT- Publisher
LR  - 20240216
IS  - 1387-3326 (Print)
IS  - 1572-9419 (Electronic)
IS  - 1387-3326 (Linking)
DP  - 2022 Sep 14
TI  - Scenario-Based Distributionally Robust Unit Commitment Optimization Involving 
      Cooperative Interaction with Robots.
PG  - 1-15
LID - 10.1007/s10796-022-10335-9 [doi]
AB  - With the increasing penetration of renewable energy, uncertainty has become the 
      main challenge of power systems operation. Fortunately, system operators could 
      deal with the uncertainty by adopting stochastic optimization (SO), robust 
      optimization (RO) and distributionally robust optimization (DRO). However, 
      choosing a good decision takes much experience, which can be difficult when 
      system operators are inexperienced or there are staff shortages. In this paper, a 
      decision-making approach containing robotic assistance is proposed. First, 
      advanced clustering and reduction methods are used to obtain the scenarios of 
      renewable generation, thus constructing a scenario-based ambiguity set of 
      distributionally robust unit commitment (DR-UC). Second, a DR-UC model is built 
      according to the above time-series ambiguity set, which is solved by a hybrid 
      algorithm containing improved particle swarm optimization (IPSO) and mathematical 
      solver. Third, the above model and solution algorithm are imported into robots 
      that assist in decision making. Finally, the validity of this research is 
      demonstrated by a series of experiments on two IEEE test systems.
CI  - (c) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 
      part of Springer Nature 2022, Springer Nature or its licensor holds exclusive 
      rights to this article under a publishing agreement with the author(s) or other 
      rightsholder(s); author self-archiving of the accepted manuscript version of this 
      article is solely governed by the terms of such publishing agreement and 
      applicable law.
FAU - Song, Xuanning
AU  - Song X
AD  - School of Management and Engineering, Nanjing University, Nanjing, 210093 China. 
      GRID: grid.41156.37. ISNI: 0000 0001 2314 964X
FAU - Wang, Bo
AU  - Wang B
AD  - School of Management and Engineering, Nanjing University, Nanjing, 210093 China. 
      GRID: grid.41156.37. ISNI: 0000 0001 2314 964X
FAU - Lin, Pei-Chun
AU  - Lin PC
AD  - Department of Information Engineering and Computer Science, Feng Chia University, 
      Taichung, Taiwan. GRID: grid.411298.7. ISNI: 0000 0001 2175 4846
FAU - Ge, Guangyu
AU  - Ge G
AD  - School of Business, Jiangsu Second Normal University, Nanjing, 211200 China. 
      GRID: grid.449520.e. ISNI: 0000 0004 1800 0295
FAU - Yuan, Ran
AU  - Yuan R
AD  - School of Management and Engineering, Nanjing University, Nanjing, 210093 China. 
      GRID: grid.41156.37. ISNI: 0000 0001 2314 964X
FAU - Watada, Junzo
AU  - Watada J
AD  - Graduate School of Information, Production and Systems, Waseda University, 
      Kitakyushu, 808-0135 Japan. GRID: grid.5290.e. ISNI: 0000 0004 1936 9975
LA  - eng
PT  - Journal Article
DEP - 20220914
PL  - United States
TA  - Inf Syst Front
JT  - Information systems frontiers : a journal of research and innovation
JID - 101685853
PMC - PMC9472199
OTO - NOTNLM
OT  - Distributionally robust unit commitment
OT  - Hybrid solution algorithm
OT  - Renewable generation
OT  - Robotic assistance
OT  - Scenario-based ambiguity set
COIS- Conflict of InterestsNone.
EDAT- 2022/09/20 06:00
MHDA- 2022/09/20 06:00
PMCR- 2022/09/14
CRDT- 2022/09/19 04:19
PHST- 2022/08/18 00:00 [accepted]
PHST- 2022/09/19 04:19 [entrez]
PHST- 2022/09/20 06:00 [pubmed]
PHST- 2022/09/20 06:00 [medline]
PHST- 2022/09/14 00:00 [pmc-release]
AID - 10335 [pii]
AID - 10.1007/s10796-022-10335-9 [doi]
PST - aheadofprint
SO  - Inf Syst Front. 2022 Sep 14:1-15. doi: 10.1007/s10796-022-10335-9.