Robust Optimization in Enabling Optimal Economic Dispatch of IES Based on Information Interaction

Abstract

At present, The Internet of Things is an important support for the construction of the energy Internet, and it is of great significance to realize the intelligent coordination, promote the transformation of the country's energy structure. On this basis, small-scale wind and solar power are increasingly integrated into modern power system through integrated energy system. Taking into account the uncertainty of wind-solar power, this paper establishes a two-stage robust optimization model to achieve the minimum operating cost. Before the uncertainty is realized, the day-ahead stage as the first stage determines operating strategy that can withstand the worst-case uncertainty. As long as the uncertainty is observed, the operating units will be adjusted in the real-time stage to compensate for errors caused by the operating strategy. Due to the difficulties of solving the model, this paper further adopts the duality theory, Big-M method, and column-and-constraint generation (C&CG) decomposition to transform the model into two tractable mixed integer linear programming problems. In addition, C&CG iterative algorithm is also used to solve MILP, which ultimately provides the optimal economic day-ahead scheduling strategy. The experimental results demonstrate the effectiveness of the proposed method.