A Two-Stage Robust Optimization Scheduling Strategy Dispatching Uncertainties of Integrated Energy Systems

The inherent uncertainties associated with renewable energy sources and loads present risks to the optimal control of microgrids. In response, this paper proposes a two-stage robust optimization scheduling strategy specifically designed to address these uncertainties in renewable energy sources and loads. Initially, an objective function is established to minimize the daily operational costs of microgrid scheduling. Subsequently, the strategy integrates operational constraints for energy storage, demand-side loads, and controllable distributed generation, leading to a two-stage robust optimization model. Uncertainty adjustment parameters are introduced to modulate the conservatism of the model. The model is then decomposed into a master problem and subproblems using the column-and-constraint generation algorithm, with the inner model decoupled through dual theory, resulting in a scheduling plan that minimizes operational costs under the worst-case scenario. Finally, simulation analysis is conducted to validate the effectiveness of the proposed model and solution algorithm.