Abstract:

A hierarchical real-time optimization (HRTO) based on economic model predictive control is designed to address the issues of randomness and uncertainty of renewable energy and demand-side in integrated energy systems (IES) with electric vehicles. The optimization problem of the entire system is divided into three sub-problems: day-ahead rolling optimization, real-time rolling optimization, and tracking control. The day-ahead optimization strategy based on economic model predictive control is constructed to ensure that the operational units can meet users' demands. The optimal steady-state operating points of the entire IES are obtained through the real-time optimization layer. The tracking model predictive controller is implemented to track the optimal reference values transmitted from the upper layer. This strategy introduces electric vehicles to participate in the optimization and scheduling of the IES, which can fully utilize the energy storage characteristics and flexibility of electric vehicles, thereby achieving the collaborative optimization between electric vehicles and various energy systems. Simulation and analysis results demonstrate that the proposed HRTO effectively tracks electric power loads and building temperature setpoints.

Key words: integrated energy system, hierarchical real-time optimization, economic model predictive control, electric vehicles, rolling optimization