The district cooling system (DCS) belongs to a class of centralized air-conditioning loads and has frequency regulation potential. This paper proposed an auxiliary frequency regulation control strategy of DCS based on model predictive control (MPC) with terminal constraints, which controls the power consumption of the DCS by adjusting the chilled water flow rate and the number of chiller shutdowns. Firstly, the study established a dynamic model of DCS and traditional units considering the relationship between chilled water flow rate and chilled water outlet temperature, and constructed the state space expression of the system. Then, based on MPC with terminal constraints, it established a joint frequency regulation control model for DCSs and traditional units, with the objective function of minimizing frequency deviation, building temperature deviation from human comfort temperature, chilled water flow’s control instructions, and traditional unit’s control instructions. The terminal constraints include terminal cost function and terminal set. Moreover, it was proved that the MPC problem with terminal constraints is asymptotically stable by constructing the Lyapunov function of the system. Finally, simulations on a 10-unit 39-bus system and an actual power system were carried out. The results verify that adding terminal constraints can improve system stability, and the use of DCS to assist in grid frequency regulation can help the system to quickly restore the rated frequency and improve regulation performance. In addition, the participation of DCSs in grid frequency regulation have no significant impact on comfort.

As a clean, pollution-free secondary energy source with high energy density, hydrogen energy is an ideal energy storage carrier for large-scale consumption of new energy. The electric-heat-hydrogen integrated energy system (EHH-IES), which couples hydrogen energy storage system (HESS) and renewable energy, provides new ideas and solutions for the consumption of new energy. Therefore, this paper focused on how to put in hydrogen energy storage equipment in an economically rational way, and aims to solve the problem of reasonable allocation of hydrogen energy storage equipment capacity and consider the impact of source and load uncertainty on the operation of electrothermal hydrogen integrated energy system. This paper proposed a method for optimizing the capacity of HESS in an EHH-IES considering seasonal storage and source-load uncertainty. Aiming at the relatively large prediction error of wind power and high forecasting accuracy of electric, heat and gas loads at first, the uncertain set and sampling scenario were used to elaborate source and load uncertainty, respectively. Then a bi-level robust stochastic optimization model for configuring hydrogen energy storage considering source-load uncertainty and seasonal storage was constructed, where the upper model optimizes the capacities of devices in hydrogen energy storage with the objective of minimizing total cost of annualized investment costs and operating costs, and the lower model was constructed as a two-stage robust stochastic optimization model to simulate the optimal operation scheme of the EHH-IES under the worst scenario of output wind power in typical days. Since the model is difficult to solve directly, particle swarm optimization and column and constraint generation algorithms were used to solve this type of complex model. Finally, through the analysis of case studies of an EHH-IES, the effectiveness of the proposed method was verified. The obtained solution for the optimal configuration of hydrogen energy storage system can promote the consumption of wind power and improve the economics of the system operation.