Bi-layer energy optimal scheduling of regional integrated energy system considering variable correlations. (June 2023)

• Record Type: Journal Article
• Authors: Zhang, Jing; Kong, Dezheng; He, Yu; Fu, Xiaofan; Zhao, Xiangyu; Yao, Gang; Teng, Fei; Qin, Yuan
• Is Part Of: International journal of electrical power & energy systems. Volume 148(2023)
• Publication Date: 2023-06
• Subjects: Regional integrated energy system -- Model predictive control -- Information gap decision theory -- Two-point estimation method -- Cornish-Fisher series expansion -- Nataf transformation -- Singular Value Decomposition; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108840
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: This paper employs IGDT to model the uncertain factors in the day-ahead scheduling plan to reduce the large deviation between the day-ahead scheduling plan and the actual scheduling caused by source and load prediction errors. To support IGDT in dealing with multiple uncertainties, this work implements a two-point estimation method and Cornish-Fisher series expansion to transform the multiple uncertainties (in terms of the source and the load) into a single power shortage uncertainty, thereby improving the solving efficiency of the IGDT model. the Nataf inverse transformation and singular value decomposition techniques are introduced so that the 2PEM-Cornish-Fisher method could handle input variables that are correlated, thereby improving the applicability of the IGDT model. This paper uses MPC rolling optimization within a day to correct the day-ahead scheduling deviation and compensate for the shortcomings of IGDT open-loop control. Abstract: As the capacity of new energy sources connected to the regional integrated energy system increases, it is necessary to consider (i) the energy imbalance caused by multiple uncertainty factors and (ii) the correlation between the power output of those uncertainty sources in the overall system. This paper proposes a bi-level optimal energy scheduling strategy, which combines information gap decision theory (IGDT) with model predictive control (MPC). In order to reduce the large deviation between the day-ahead scheduling plan and the actual scheduling caused by prediction errors of source and load, the IGDT is adopted in the upper layer strategy to model the uncertain factors in the day-ahead scheduling plan. To support IGDT in dealing with multiple uncertainties, the two-point estimation method and Cornish-Fisher series expansion is used to transform the uncertainties of the sources and the loads into uncertainties of power unbalance. Moreover, the Nataf transformation and singular value decomposition (SVD) technique are introduced, so that the two-point estimation and Cornish-Fisher method could handle correlative variables, thereby the solving efficiency of the IGDT model can be improved much more. The MPC rolling optimization is adopted within a day to correct the day-ahead scheduling deviation and compensate for the shortcomings of IGDT open-loop control. Finally, the proposed method is applied to a numerical example to verify its effectiveness.