| Popis: |
Integrated Energy System (IES) is far-reaching significance for improving energy utilization efficiency and reducing carbon dioxide emissions. However, due to the lack of consideration of construction sequencing, carbon trading development trends and uncertainty risks in the current IES planning scheme, problems such as mismatch between supply and demand, excess carbon emissions and weak renewable energy carrying capacity have emerged. To address these problems, this study proposes a multi-stage time sequence robust planning model for IES that considers carbon trading and extreme scenarios. First, the synergistic and complementary energy framework of IES is introduced, and the operation principle of each unit in the system is briefly shown by matrix. Then, considering the construction sequence of multiple stages and the load difference in different seasons, a life cycle planning model is established, which includes carbon trading cost, renewable energy curtailment penalty cost, integrated demand response cost. In order to improve the system’s ability to withstand the uncertainty of power and load, the extreme scenario is obtained according to the confidence level, and the improved robust optimization model is used to enhance the robustness of the system, which plays a dual role of risk prevention and control. Finally, a green ecological park is used as the object to implement simulation, and a safer planning scheme is obtained under extreme scenarios. The case study shows that: (1) Compared with the single-stage planning method, the method proposed in this study can reduce the depreciation cost in the whole life cycle by 5.44%, and improve the phenomenon that the curtailment rate of wind power exceeds 20%. (2) After participating in the carbon market, the cumulative income generated in the three stages is 17.92 million ¥. (3) After implementing demand response, the cumulative configuration capacity of wind turbine, ground source heat pump and energy storage decreased by 1.02%, 7.85% and 57.6%, respectively. (4) The improved robust optimization model improves the risk resistance of the system. |
