Research On Integrative Design Method Of Distributed Integrated Energy System

With economic development and urban expansion,the energy consumed to meet indoor comfort is gradually increasing,and the energy demand is increasing in a diversified manner.Traditional buildings receive electricity through the power grid and are heated and cooled by local HVAC equipment.The generation and supply of energy products are independent of each other.Although the conversion efficiency of single equipment continues to improve,the energy consumption and emission of building energy supply remain in high level.This is mainly due to multiple energy conversion and long-distance transmission from energy resources to energy consumers.Integrated energy system(IES)is a distributed energy hub that consumes multiple energy sources and supplies energy product for local energy customer.IES can integrate a variety of renewable energy equipment,energy conversion equipment,and energy storage equipment to increase the economic and environmental benefits of energy supply effectively.Although the high proportion of renewable energy and energy cascade utilization contributes to the energy saving and emission reduction,they also bring challenges to the optimal design of IES.In view of the randomness of renewable energy,the uncertainty of energy load,and the nonlinearity of energy equipment,this paper combines theoretical mechanism analysis and artificial intelligence algorithm into the study of optimal design method of IES.The multienergy flow model of energy supply,storage and demand is established,and the energy cascade utilization mechanism under variable working conditions,multi-step integrated optimal design method,and time-space conversion solution method are proposed,which improves the accuracy and reliability of IES design.Then,the optimal design software of IES is developed,and the technical application of above models and methods is realized.The main contents are as follows:(1)Multi-energy flow model of energy supply,storage and demand.The working condition of energy supply equipment is constrained by energy resources and user demand.The realization of peak shaving and valley filling of energy storage equipment needs to predict and analyze the change trend on both sides of resource and demand.The power elasticity and time elasticity of flexible load increase the diversity of supply,storage and demand balance.Therefore,in this paper,the full working condition model of energy supply equipment is established with the load rate as the core variable,the active energy storage model is established with the peak valley parameters as the core variable,and the flexible load model is established with the elastic constraint of flexible load as the core variable,so as to finely describe the energy balance relationship of multi-energy flow connected supply,storage,and demand in the IES,and provide model support for the research of energy consumption mechanism and optimal design method.(2)Energy cascade utilization mechanism under variable working conditions.The traditional cascade utilization mechanism of following electric load and following heating load is limited to rated working conditions and rated efficiency,which weakens the cooperative relationship between equipments.Combined with the first and second laws of thermodynamics,this paper calculates the energy grade with exergy density,takes exergy loss rate as the evaluation index of energy cascade utilization,cooperatively manages the complementary conversion of multiple energy flows and the real-time balance of supply,storage and demand,so as to puts forward the energy cascade utilization mechanism under variable working conditions,realizes the flexible and efficient energy conversion of IES,improves the economic and environmental benefits,and provides theoretical support for the research of optimal design method.(3)Multi-step integrated optimal design method.In the traditional design method of IES,steps of designing system structure,configuring equipment capacity,and selecting operation parameters is optimized in turn.This separated design method is easy to fall into local optimum.This paper analyzes the space-time spiral characteristics of the IES and the interactive relationship between design steps,and puts forward the multi-step integrated optimal design method.The serial structure model and modular operation mechanism are adopted to jointly optimize the connection sequence,type capacity,and start-up parameters of the equipment,which avoids the local optimum caused by the separated design,and realize the global optimization of multiple design step of IES.(4)Time-space conversion solution method.There are many high-dimensional variables and nonlinear constraints caused by a large number of operation scenarios in the optimal design of IES.Solution in traditional numerical algorithm resulted in long calculation time and large solution error.This paper analyzes the mutual conversion mechanism between single system multi-time operation and multi-system single time operation,puts forward the optimization solution method based on space-time conversion,and constructs the distributed gradient stochastic gradual descent algorithm.Under the scenario of continuous operation throughout the year,this method improves the solution speed of the optimal design of IES,and ensures the accuracy and reliability of the solution results.(5)Optimal design software of IES.Simulation software is an important tool to verify and promote the integrated design method of IES.Following the process of functional requirements analysis,module and algorithm design,architecture and interface design,program coding,and debugging,this paper integrates the above models,strategies,methods and algorithms,and develops the optimal design software of IES.At the same time,the problem of insufficient source load data is solved by training neural network to generate operation scenarios.This software is not only a research tool for optimal design theory and method of IES,but also can provide accurate and reliable data and schemes for project management and decision-making.