Stochastic Day-Ahead Scheduling of Integrated Energy Distribution Network with Identifying Redundant Gas Network Constraints

Considering uncertainties from wind power and day-ahead market price, this paper proposes a scenario-based two-stage model for the day-ahead scheduling of integrated energy distribution network which couples various energy systems such as electricity, heat, and natural gas. However, gas network constraints pose a great challenge to the computation because the piecewise linear approximation (PLA) method linearizes the nonlinear flow-pressure constraints (i.e., Weymouth equations) by adding a large number of additional auxiliary variables and constraints into the model, which increases the complexity of the model. To address this problem, we propose a sufficient condition for identifying redundant gas network constraints. We also develop a bounds tightening strategy that combines Weymouth equation relaxation and an optimality-based bounds tightening method to mitigate the conservatism of the proposed sufficient condition. Numerical case studies show that the proposed method can greatly improve the computational performance of the optimization model, making it superior to the conventional PLA method. For a larger-scale test system, the improvement of computational efficiency is more significant.