Multi-uncertain rolling optimization scheduling based on information gap decision theory

Abstract

Abstract: Focusing on the uncertainty problems existing in the operation of theregional integrated energy system (RIES), a bi-level robust optimal scheduling model of RIES is constructed by means of the rolling optimization scheduling method and information gap decision theory (IGDT) to translate it into operational economics. The upper layer of the model solves the uncertainty of the system, while the lower layer quantifies these uncertainties through the model revenue base value to ensure that the model operation income is not lower than the expectation, thereby realizing the dynamic scheduling. By adjusting the level factor of the model, different scheduling schemes can be obtained, so as to acquire different scheduling benefit expectations. Decision-makers can choose the appropriate scheduling scheme according to the degree of risk aversion. Finally, the RIES system composed of an improved IEEE33-node distribution network, 19-node heat network, and 20-node natural gas network is tested. The results show that the robust model can improve the system's risk-avoidance by 5% compared to the deterministic model in a given scenario.

Key words: regional integrated energy system, multi-source coordinated scheduling, rolling optimization, information gap decision theory, uncertainty of source-load

CLC Number:

TM73

ZHANG Ming-guang, GAO Yan-xia, ZHANG Fei-xiang, WANG Hai-bin. Multi-uncertain rolling optimization scheduling based on information gap decision theory[J]. Journal of Lanzhou University of Technology, 2025, 51(1): 72-82.

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