Research On Multi-Energy Flow Model And District-Level Integrated Energy System Optimization

In the context of the new round of energy revolution,the direction of world energy development has gradually shifted from expanding energy supply to how to adjust the structure of the energy system,the optimal operation of the system and the improvement of the quality and efficiency of the energy development.Multi-energy coupling and the integrated energy system are important technical means and application support to promote the energy revolution.This paper focuses on the district-level integrated energy system(DIES)optimization strategy and analyzes from three dimensions:DIES multi-energy coupling model basis,a general scenario analysis method to quantify multiple uncertain factors of DIES,and a DIES optimization method based on energy-information system integration.The key work content and innovations of this paper are as follows:(1)The basic physical model of DIES is discussed.From the perspective of energy station,the energy hub and its extended model are abstracted,which describe the conversion and balance relationship of multi-energy flow;and the subdivision units of the energy hub are analyzed in detail.As for the energy network,in order to reduce the complexity of DIES calculation and analysis,a unified modeling method based on heterogeneous energy flow forms is discussed,which quantitatively describing the physical characteristics of energy transmission.Also,the steady and dynamic state estimation of multi-energy network are analyzed respectively.(2)A multi-energy adaptive dynamic scenario analysis method is proposed which effectively quantifies the impact of uncertainties on power system from a multi-temporal and spatial perspective.It can provide power system strong modeling and scenario support for applications such as multi-dimensional cooperative operation and decision-making.First,the multi-energy prediction error probability distribution fitting is performed driven by forecast error,which can fully reflect the random power output information,thus improving the generalization performance of the model;using time-series related range parameters as data-driven correlation variables,the fluctuation intensity can be controlled efficiently and dynamically;final scenario generation adopts the mapping idea of inverse transformation to ensure the confidence.Second,as for the optimal scenario reduction,a recursion&cluster multi-stage nested reduction algorithm is proposed,which combines the improved Wasserstein probability distance index,possessing comprehensive advantages in accuracy,timeliness and stability aspects.Finally,the frontier and effectiveness of the method are verified through comparative experiments.(3)As for the energy-information-system-integrating DIES optimization,based on the cyber-physical system(CPS)framework,considering the interdisciplinary optimization problems of energy flow and information flow,the multidisciplinary design optimization strategy is introduced to ensure the autonomy and the consistency of each system.Specifically,an improved concurrent subspace optimization(CSSO)algorithm is used to complete the construction and solution of this integrated optimization model.The model first analyzes the energy and information system independently from their own disciplines aspects,and then introduces the radical basis function neural network to build the multidisciplinary response surface relationship between the dual system,that is,establishes a two-way coupling relationship,and the global optimization is finally completed at the integrated system level.In general,the idea of decomposition and coordination is used to realize the interconnecting and interacting global optimization of the DIES energy&information system under the CPS framework.And the economic advantages of this method are demonstrated by comparative experiments.