Data-Driven Stochastic Scheduling for Energy Integrated Systems

Heng Yang, Ziliang Jin, Jianhua Wang, Yong Zhao, Hejia Wang and Weihua Xiao
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Heng Yang: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Ziliang Jin: Department of Production Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
Jianhua Wang: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yong Zhao: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Hejia Wang: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Weihua Xiao: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

Energies, 2019, vol. 12, issue 12, 1-21

Abstract: As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed data-driven model converges to a risk-neutral model with historical data.

Keywords: data-driven; stochastic optimization; scheduling optimization; unit commitment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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