| The proposal of the goals of peak carbon dioxide emission and carbon neutralization has accelerated China’s pace in the transformation and upgrading of energy infrastructure.Through reducing energy consumption per unit GDP and increasing the proportion of non-fossil energy consumption,China aims at paving a green,low-carbon and high-quality development path.Through the complementarity between multiple energy suppliers and various energy forms,integrated energy system improves the energy utilization rate,and provides favorable conditions for renewable energy.Integrated energy system is an important path to achieve the "double carbon goal".However,the current study on the multi-energy complementarity effect of the integrated energy system mostly focuses on the qualitative analysis.There is no research on its quantitative evaluation,which leads to the difficulty of applying the multi-energy complementarity effect in the system planning stage to enhance the complementarity of system structure.Therefore,starting from the multi-energy complementary effect,which is the key feature of integrated energy system,this paper carries out the research on the quantitative evaluation model of complementarity,improves the current typical day selection method and expands the optimization indicators of integrated energy system planning.The study in this paper can effectively improve the accuracy of planning and strengthen the complementarity of system structure.The main contents and study results of this paper are summarized as follows.According to the idea of energy hub modeling,this paper models the conversion unit,storage unit and transmission unit of the integrated energy system,which provides a model basis for system planning and operation.Meanwhile,this paper establishes a quantitative model of complementarity effect of integrated energy system based on net load fluctuation after comparing different complementarity indicators.Furthermore,the advantages of the proposed model for integrated energy system are explained through qualitative analysis.Based on the model of integrated energy system,this paper proposes a dynamic selection method of typical days in integrated energy system planning based on complementary structure configuration.Through dynamic selection of typical days for the specific planning scheme,this method can reduce the solution error in the operation stage,so as to improve the accuracy of the system planning.The case study shows that compared with the traditional method,the method proposed in this paper reduces the annual cost calculation error by 1.53% in the stage of system planning,especially in the calculation of operation cost,the error is reduced by 9.43%.The calculation time is the same order of magnitude as the traditional method,which proves the effectiveness and practicability of the proposed method.Combined with the above research,a multi-objective optimized planning of integrated energy system based on economic index and complementarity index is carried out.Through the case study,this paper studies the mechanism of multi-energy complementarity effect and analyzes the benefit of multi-energy complementarity.This paper proves that the system with stronger complementarity has stronger ability of peak cutting and valley filling,and can effectively promote the communication and interaction among various energy flows.At the same time,this paper finds that in the scenario of load growth,the allocation of gas turbines and electric energy storage units with appropriate capacity can effectively improve the system complementarity and reduce the economic cost in the planning cycle.The allocation of heat storage units with larger capacity is a more economical means to enhance complementarity,and has a better performance in long-term economic benefits.This paper makes innovation for the quantification of multi-energy complementarity and planning method in the field of integrated energy system.Furthermore,case study is carried out to validate the proposed model and method.Therefore,this paper has obvious innovation and application value. |
