Two-stage coordinated energy scheduling of intelligent connected transportation systems considering nonanticipativity

The traditional transportation system is transforming into an Intelligent Connected Transportation System (ICTS), in which a large number of connected devices are distributed across the road system. To better utilize distributed energy resources in ICTS, this paper proposes a two-stage coordinated scheduling model for ICTS considering nonanticipativity. Firstly, the energy consumption characteristics of ICTS loads are comprehensively modeled, with a mathematical model describing the intricate coupling relationship between power demand and traffic flow. Subsequently, the representative scenarios of wind and solar power output are identified using the Minimum Volume Enclosing Ellipsoid(MVEE) algorithm. Simultaneously, nonanticipativity constraints are introduced to satisfy nonanticipativity requirements in energy storage decision-making, leading to the development of a two-stage coordinated scheduling model for ICTS multi-microgrids. In the day-ahead stage, the model aims to minimize interconnection costs, electricity purchase costs, and wind/solar curtailment costs under representative scenarios. In the intraday stage, rolling optimization is employed to determine the scheduling strategy to minimize the operational costs of the ICTS multi-microgrids based on the day-ahead interconnection solution. Finally, an adaptive PJ-ADMM algorithm is applied to solve the model, and simulation case studies demonstrate that the proposed model effectively facilitates the economically efficient and stable operation of ICTS.