Model predictive control based real-time scheduling for balancing multiple uncertainties in integrated energy system with power-to-x

Integration of the electric power system, natural gas system, and district heating system can reduce the operational cost and improve the utilization of renewable energy sources. The day-ahead schedule for the optimal operation of the integrated energy system may not be economically optimal in real-time due to the prediction errors of multiple uncertainty sources.

To balance the real-time prediction errors economically, this paper proposes a model predictive control (MPC) based real-time scheduling strategy to optimize the real-time operation of the integrated energy system, which makes real-time operational decisions based on the measured state of the system and future information of uncertainties.

In the MPC based real-time scheduling, the penalty for the deviation between the day-ahead and realtime schedules is considered to minimize the regulation cost. In addition, multiple uncertainty sources are taken into account. An online learning method is utilized in MPC to predict the future information of these uncertainties.

Besides, the power-to-x technology and thermal energy and gas storage devices are considered to improve the capability of the system to balance these uncertainties. The simulation results show that the MPC based real-time scheduling outperforms the traditional real-time scheduling on economic efficiency and wind power utilization.