Distributionally Robust Optimization of Electricity-Heat-Hydrogen Integrated Energy System with Wind and Solar Uncertainties

The operation flexibility of China's power system is insufficient, and the energy low-carbon transformation faces the problem of how to realize the coordinated development of multiple energy systems. Hydrogen energy storage (HES) can realize power transfer and storage of power system through peak shaving and valley filling of charging and discharging, which is the key to solve the flexibility problem. The existing research on the optimization of integrated energy system containing HES is aimed at the collaborative optimization of renewable energy and hydrogen energy storage, mainly focusing on the peak shaving and valley filling function of HES as energy storage. However, the research results of multiple energy coupling in HES are few, and most of them are electric energy and thermal energy, electric energy and hydrogen energy coupling. The HES coupling model containing electric, thermal and hydrogen energy is worth in-depth study. In view of the above situation, an electricity-heat-hydrogen integrated energy system (EHH-IES) model is established to study the coupling of electricity, heat and hydrogen. In addition, renewable energy such as wind and solar in the integrated energy system is uncertain, and the high proportion of renewable energy access makes the safety and economic operation of the power system face great challenges, which seriously restricts the development of the integrated energy system. Therefore, combined with the characteristics of stochastic programming and robust optimization, a distributionally robust conditional value at risk (DRCVaR) method, based on moment information such as expectation, variance, and covariance, is proposed to quantify the risk of wind and solar uncertainties. Considering the game relationship between renewable energy and artificial decision makers, aiming at the maximum system revenue, the minimum carbon emissions in the whole life cycle and the minimum uncertainty risk cost, a zero-sum game model of EHH-IES distributed robust optimization problem is established. The model is transformed into a semi-definite programming problem by Lagrange duality principle and then solved. Finally, the effectiveness of the model and method is verified by numerical simulation. In the case simulation, a comparative analysis is made on whether the HES coupling model is used and whether the DRCVaR method is used to quantify the risk of wind and solar uncertainties. The following conclusions can be drawn: (1) After applying the HES coupling model with multiple energy sources including electricity, heat and hydrogen, the total revenue of the system has increased by 1.4%, and the carbon emissions in the whole life cycle have decreased by 2.9%, which proves that the development and promotion of EHH-IES can give consideration to both economy and low carbon. (2) When adopting the optimization strategy of DRCVaR method to quantify the risk of wind and solar uncertainties, the economic risk of the system is less than the optimization strategy of conditional value at risk (CVaR) method, which shows that DRCVaR method effectively reduces the risk of wind and solar uncertainties to the system. (3) With the improvement of the confidence level, the total income of the system is reduced, the uncertainty risk cost is increased, and the carbon emissions of the whole life cycle are increased. This means that the decision-maker needs to measure according to the actual situation, and set a reasonable confidence level to achieve the coordination and unification of the system economy, low carbon and robustness. © 2023 Chinese Machine Press. All rights reserved.