Bi-layer energy optimal scheduling of regional integrated energy system considering variable correlations

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 pre-dictive 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 trans -formation 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.

Automated summary

As the capacity of new energy sources increases in the regional integrated energy system, it becomes necessary to consider the energy imbalance caused by uncertainties and the correlation between power outputs of these uncertain sources. This paper proposes a bi-level optimal energy scheduling strategy that combines information gap decision theory (IGDT) with model predictive control (MPC) to address these challenges. The IGDT is used to model uncertainties in the day-ahead scheduling plan, while the MPC rolling optimization corrects deviations and compensates for shortcomings. The proposed method is tested in a numerical example to demonstrate its effectiveness.