Scenario-Based Distributionally Robust Unit Commitment Optimization Involving Cooperative Interaction with Robots

Xuanning Song, Bo Wang (), Pei-Chun Lin, Guangyu Ge, Ran Yuan and Junzo Watada
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Xuanning Song: Nanjing University
Bo Wang: Nanjing University
Pei-Chun Lin: Feng Chia University
Guangyu Ge: Jiangsu Second Normal University
Ran Yuan: Nanjing University
Junzo Watada: Waseda University

Information Systems Frontiers, 2024, vol. 26, issue 1, No 2, 9-23

Abstract: Abstract 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.

Keywords: Renewable generation; Scenario-based ambiguity set; Distributionally robust unit commitment; Hybrid solution algorithm; Robotic assistance (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10796-022-10335-9

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