Distributionally robust optimization scheduling of electricity and natural gas integrated energy system considering confidence bands for probability density functions

Abstract

The rapid growth of gas-fired units and the development of power-to-gas (PtG) technology have strengthened the interdependency of power system and natural gas system and provided a new way for the absorption of renewable energy. This paper proposes a distributionally robust optimization (DRO) scheduling model for the electricity-gas coupled integrated energy system considering wind power uncertainty and PtG technology. Combining the advantages of stochastic programming and robust optimization, the proposed DRO model describes the uncertainty by an ambiguity set constructed based on the confidence bands of its probability density function, and aims to minimize the expectation of the re-dispatch cost under the worst-case distribution. Moreover, a novel affine adjustable strategy with the allocation ratio pairs is developed to enhance the flexibility of reserve configuration. Benefiting from the special structure of the ambiguity set, the proposed model with uncertainties can be reformulated as a mixed integer linear program problem to solve. Case studies are implemented on three coupled systems with different scales, and simulation results demonstrate that DRO with proposed affinely adjustable strategy can obtain the scheduling solution with lower conservatism and higher economical performance compared to the adjustable robust optimization and DRO with the single adjustment strategy.