CN111626587B - A comprehensive energy system topology optimization method considering energy flow delay characteristics - Google Patents

Abstract

The invention relates to a comprehensive energy system topology optimization method that takes into account the energy flow delay characteristics. The optimization method includes the following steps: Step S1, establishing a transportation network including steam, hot water, chilled water, cold air and compressed air transportation. The network mechanism model of the integrated energy system including the network; Step S2, calculate the response characteristics of key parameters such as temperature and flow rate of the working fluid at the end of each energy flow transport network with the fluctuation of the source end; Step S3, establish the lowest comprehensive delay of various energy flows. Objective function, wherein the weight coefficient of each energy flow delay is obtained by the fuzzy analytic hierarchy process; Step S4: construct a combination set of supply and demand conditions, set a variety of energy flow supply and demand matching constraints and comprehensive energy system network node spatial distribution constraints; Step S5 , using the particle swarm optimization algorithm to solve the optimization problem composed of the objective function and the constraints of the above steps, and obtain the optimal topology planning scheme of the integrated energy system.

Description

A comprehensive energy system topology optimization method considering energy flow delay characteristics

technical field

The invention relates to a comprehensive energy system optimization method, belongs to the field of comprehensive energy systems, and specifically covers a comprehensive energy system topology optimization method that takes into account energy flow delay characteristics.

technical background

At present, my country's energy consumption continues to grow, and the total primary energy production in 2017 reached 3.585 billion tons of standard coal. From the perspective of regional energy, how to solve the problem of diversified energy demand in the region, solve the problem of improving regional energy efficiency, and promote the sustainable development of regional energy is the focus of research in the energy field. Driven by the policy guidance of energy transition and the technological drive of cyber-physical integration, the concept of comprehensive energy system for the above problems was born. Integrated energy system refers to the use of advanced physical information technology and innovative management models in a certain area to integrate various energy sources such as electric energy, heat energy, cold energy, compressed air, etc. in a certain area to achieve coordinated planning, collaborative management, and A new integrated energy system that responds and complements each other.

Existing research on integrated energy systems focuses on the direction of system optimization planning and optimal dispatching strategies. With the continuous deepening of the urbanization process, the urban spatial layout and energy structure are constantly being reconstructed, and the planning of a new comprehensive energy system has attracted more and more scholars' attention. However, the evaluation methods of integrated energy system planning are relatively simple, dominated by economic and emission characteristics, and few methods consider the delay characteristics of different energy flows. Taking steam energy flow as an example: under the action of various factors such as steam parameter fluctuation, compressibility, state change, friction and heat transfer, the complex transient characteristics of the working fluid flow in the steam pipe network cause the steam at the source and user ends. Temperature and flow response is slow.

Considering the different delay response characteristics of various energy flows such as cold, heat, compressed air, etc., how to establish a model of each energy flow in an integrated energy system from the perspective of mechanism, and establish a node network topology optimization method for an integrated energy system based on the model is to achieve. Key technical issues for efficient utilization of regional resource endowments.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a comprehensive energy system topology optimization method that takes into account the energy flow delay characteristics, and uses the mechanism-based comprehensive energy system model and the optimization method based on the particle swarm algorithm to form the internal source side and the user side of the comprehensive energy system. The node topology network planning scheme is optimized, and a comprehensive energy system planning strategy that meets the minimum delay requirement is obtained at a lower labor cost.

In order to solve the technical problem, the present invention adopts the following technical solutions to realize:

A comprehensive energy system topology optimization method considering energy flow delay characteristics, comprising the following steps:

Step S1, establishing a network mechanism model of a comprehensive energy system including a steam, hot water transportation network, a chilled water, cold air transportation network and a compressed air transportation network. There are certain differences in the construction methods of different energy flow transmission models. Cold air and compressed air transportation networks only need to establish hydraulic models, while steam, hot water, and chilled water transportation networks need to establish thermal models in addition to hydraulic models. The following steps describe the construction of general hydraulic and thermal models:

Step S110, the hydraulic model requires that the flow of the working fluid in the integrated energy network should satisfy the fundamental theorem of the network: the flow of each pipeline should satisfy the flow continuity equation at each node, that is, the inflow flow at the node is equal to the outflow flow. In a closed transport loop, the sum of the head losses of the working fluid flowing in each pipeline is 0, that is,

In the formula, A _s is the node-branch correlation matrix of the integrated energy system network; m _t is the flow of each pipeline; m _q,t is the outflow flow of each node; B _h is the loop-branch correlation matrix of the integrated energy system network; h _f,t is the head loss vector, and its calculation method is

h _f,t = Km _t |m _t | (2)

In the formula, K is the resistance coefficient matrix of the pipeline.

In step S120, the thermal model can be represented by the following three equations: that is, equation (3) the expression of node thermal power φ _t , equation (4) the relationship between the pipe end temperature T _{end, t} and the starting end temperature T _{start, t} , and the equation (5) The temperature relationship expression of the working fluid before and after mixing at the node.

φ _t =C _p m _q,t (T _s,t -T _o,t ) (3)

(∑m _out,t )T _out,t =∑m _in,t T _in,t (5)

In the formula, the heating temperature T _s,t is the temperature before the working medium is injected into the load node; the output temperature T _o,t is the temperature when the working medium flows out of the load node; C _p is the specific heat capacity of the working medium; m _q,t is the temperature at the node Mass flow of working medium; m _a,t is the average mass flow of working medium in the pipeline; T _{a, t} is the ambient temperature; λ is the thermal conductivity of the pipeline; L is the length of the pipeline; m _out,t , T _out,t and min _{in , t} and T _{in, t} are the mass flow and temperature of the working medium flowing out and flowing into the pipeline, respectively.

Step S2, calculating the response characteristics of the key parameters of the temperature and flow rate of the working medium at the end of each energy flow transport network with the fluctuation of the source end. The specific process is as follows:

In step S210, for the transmission network of steam, hot water and chilled water, since all of them include delays in flow and temperature response, the following steps are adopted:

Step S211, input the temperature and flow parameters of the working fluid at the source side and the user side into the network mechanism model of the integrated energy system, and perform corresponding transformations on the influence factors such as the thermal insulation layer and the thickness of the pipeline of the energy flow transmission network, so as to obtain the integrated energy system. Temperature, pressure and flow distribution throughout the network.

Step S212, keeping the source-side working medium parameters unchanged, so that the mass flow rate of the working medium in the energy flow network is increased in a stepwise manner. Reduce to the original flow level, calculate the time for the user side working medium to complete the response, so as to obtain the flow response characteristics under this working condition.

Step S213, keeping the pressure and flow rate of the source side working medium unchanged, so that the source side working medium temperature is increased in a stepwise manner. To the original temperature supply level, calculate the time for the user-side working fluid to complete the response, so as to obtain the temperature response characteristics under this working condition.

In step S220, for the cold air and compressed air transportation network, since there is only a delay in the flow response, the following steps are adopted:

Step S221 , input the working fluid flow between the source side and the user side under the required calculation conditions in the network mechanism model of the integrated energy system, and obtain the pressure and flow distributions of various parts of the energy network.

Step S222, under the condition that other parameters on the source side remain unchanged, the flow rate of the source side working medium is increased in a stepwise manner, and after the working medium parameter is close to stable (the fluctuation amplitude does not exceed 5%), it is reduced in a stepwise manner To the original level, calculate the time for the working fluid on the user side to complete the response.

Step S3, establishing a variety of objective functions of the lowest comprehensive delay of the power flow, wherein the weight coefficient of each power flow delay is obtained by the fuzzy analytic hierarchy process. The specific process includes:

In step S310, the sum of the normalized delay response times of each energy flow of each node user of the integrated energy system topology network is used as the objective function:

In the formula, L, M, and N represent the number of user nodes, energy flow category and response type, respectively; D _ijk is the completion time of the k-th response of the j-th energy flow of the i-th user; D _jkmax and D _jkmin are all The maximum and minimum completion time of the kth response of the jth energy flow in the user; w _j is the weight of the objective function occupied by the response of different energy flow delays, and the method for determining the weight includes the following steps:

Step S311, compare the importance of each energy flow pairwise, and construct a fuzzy complementary judgment matrix A:

0≤a_pq≤1，a_pq+a_qp＝1；a_pq为p能流相对于q能流的重要性，a_qp为q能流相对于p能流的重要性，二者取值均精确到十分位。

0≤a _pq ≤1, a _pq +a _qp =1; a _pq is the importance of p energy flow relative to q energy flow, a _qp is the importance of q energy flow relative to p energy flow, both values are Accurate to tenths.

若有：Step S312, determine whether the fuzzy complementary judgment matrix A has complementary consistency, that is, the elements in the matrix A are

If any:

a _pr a _rq a _qp = a _pq a _qr a _rp (8)

Then matrix A has complementary consistency. If the fuzzy complementary judgment matrix A does not satisfy the consistency, but has:

Then A is said to have satisfactory consistency, where _spq is the allowable deviation of the judgment matrix.

Step S313 , in order to find the minimum deviation of the judgment matrix elements that satisfies the allowable deviation, assuming that the error of the judgment matrix elements is s' _pq , the matrix E composed of them is called the error matrix of the judgment matrix A.

In the formula, s' _pq can be regarded as a random variable with a mean value of 0; w _p and w _q are the weights of the objective function occupied by different energy flow delay responses.

Define the error optimal objective function:

p,q＝1,2,...,Y。stw _p > 0, and

p,q=1,2,...,Y.

Solve the optimization problem of Equation (11) to obtain the weight w, and then inversely deduce the judgment matrix A ^* with complementary consistency according to Equation (7). Consistency tests are performed by means of statistical hypothesis testing of the element-wise differences of matrices A and A ^* .

In step S4, a combination set of supply and demand conditions is constructed, and a variety of energy flow supply and demand matching constraints and spatial distribution constraints of integrated energy system network nodes are set. The specific process includes:

Step S410, obtain the annual operation data in the comprehensive energy supplier database, and construct a combination set of supply and demand conditions. It is assumed that there are S energy flow supply and demand conditions in the optimization period, and each operation condition includes the energy flow supply and demand data at T times. All data of the energy flow supply process in the cycle is represented by an S×T order matrix. Using the k-means clustering algorithm, the unit of day is used as the basic unit of clustering, and the historical data of supply and demand of each energy flow in the cycle is clustered and divided, and then a set of typical supply and demand conditions for the whole year is obtained.

Step S420, establishing a variety of energy flow supply and demand matching constraints, the constraint is: applying the operation data collected in the annual typical supply and demand conditions to the integrated energy system network mechanism model and the multiple energy system network mechanism model under the existing integrated energy system topology design conditions. After the objective function of the lowest comprehensive delay of the flow is obtained, it is judged whether an effective solution of the temperature, flow and delay of the whole network can be obtained.

Step S430, establishing the spatial distribution constraints of the network nodes of the integrated energy system, that is, determining the feasible region of the topology structure of the integrated energy system at the spatial level, and the specific method is as follows:

Use enumeration algorithm to determine each energy flow transmission trunk line constructed along the highway;

Select the shortest distance between the user node and the energy flow transmission trunk line to establish the energy flow transmission branch line, thus forming the feasible region of the source-load network topology of the integrated energy system.

In step S5, the particle swarm optimization algorithm is used to solve the optimization problem formed by the objective function of step S3 and the constraints of step S4. The solution process consists of the following sub-steps:

Step S510: Determine the number of initial planning schemes, combine the connection schemes in the feasible domain into d-dimensional particles, and initialize the acceleration factor, the maximum number of iterations, and the maximum particle velocity parameters.

Step S520: Initialize the velocity vectors and position information of various energy flow topology network connection schemes, so that the position of the current scheme is the individual historical global optimal position of each scheme. Calculate the optimal position of all connection planning schemes at the same time;

Step S530, the particles fly in the search space. Define the fitness function, compare the current global optimal position with the historical global optimal position, and use the update formula to update the velocity and position of the particles in the energy flow topology network scheduling scheme

v _ij (t+1)=v _ij (t)+c ₁ r ₁ (pbest _ij (t)-x _ij (t))+c ₂ r ₂ (gbest _j (t)-x _ij (t)) ( 12)

x _ij (t+1)=x _ij (t)+v _ij (t+1) (13)

Among them, i represents the ith particle; j represents the jth dimension of the particle; v _ij (t) represents the jth dimension flight velocity component of particle i when it evolves to the t generation; x _ij (t) represents the particle i evolves to the t generation. The jth dimension position component in t generation; pbest _ij (t) represents the jth dimension individual optimal position pbest _i component of particle i when it evolves to t generation; gbest _j (t) represents the entire particle swarm when it evolves to t generation The j-th dimension component of the optimal position gbest; c ₁ , c ₂ are called learning factors, the former is the individual learning factor of each particle, the latter is the social learning factor of each particle; r ₁ , r ₂ are [0 ,1] is a random number.

In step S540, the objective function value of the position of each connection scheme is calculated, and the individual historical optimal position of each connection scheme and the optimal position of all scheduling schemes are updated by using the update formula.

Step S550, when the set maximum number of iterations is reached, the calculation is stopped and the result is output. Otherwise, return to step S530 to continue searching.

According to the method of the present invention, a topology optimization system for an integrated energy system that takes into account the energy flow delay characteristics can also be obtained. The system includes a comprehensive energy system mechanism model calculation module; multiple energy flow integrated minimum delay time calculation modules; Topology optimization planning module for the target integrated energy system;

The integrated energy system mechanism model calculation module provides calculation model support for the multiple energy flow integrated minimum delay time calculation module, and the multiple energy flow integrated minimum delay time calculation module is an integrated energy system aiming at the lowest delay of the system. The topology optimization planning module provides delay data support.

Compared with the prior art, the beneficial effects of the present invention are:

The invention innovatively considers different delay characteristics of different energy flows, and establishes a network mechanism model and a delay calculation method of an integrated energy system. In addition, the present invention takes the engineering ease of use of the weighting method into consideration, and adopts the fuzzy analytic hierarchy process combining qualitative and quantitative methods to determine the weight coefficients of each energy flow delay. The invention proposes a method for optimizing the topology planning of the integrated energy system based on the network mechanism model of the integrated energy system and the delay calculation model. question.

Description of drawings

Figure 1, the solution steps of particle swarm optimization algorithm;

Figure 2, the main implementation steps of the method of the present invention.

Detailed ways

The present invention will now be described in further detail with reference to the accompanying drawings.

Figure 2 is the main implementation steps of the method of the present invention.

A comprehensive energy system topology optimization method considering energy flow delay characteristics, comprising the following steps:

Step (1): the established comprehensive energy system transportation network mechanism model;

Step (2): Calculate the response characteristics of the key parameters of the temperature and flow rate of the working fluid at the end of each energy flow transport network fluctuating with the source end;

Step (3): establish the objective function of the lowest comprehensive delay of various energy flows based on the fuzzy analytic hierarchy process;

Step (4): construct a combination set of supply and demand conditions, set a variety of energy flow supply and demand matching constraints and comprehensive energy system network node spatial distribution constraints;

Step (5): Use the particle swarm optimization algorithm to solve the topology optimization problem of the integrated energy system with the lowest delay (as shown in Figure 1), and then the optimal topology planning scheme of the integrated energy system can be obtained.

In the present invention, described step (1) is realized in the following way:

a. Establish a comprehensive energy system network mechanism model including steam, hot water transportation network, chilled water, cold air transportation network and compressed air transportation network. The cold air and compressed air transportation network only needs to establish a hydraulic model, while the steam, hot water, and chilled water transportation network needs to establish a thermal model in addition to the hydraulic model. The following steps describe the construction of general hydraulic and thermal models:

b. Establish a hydraulic model of an integrated energy system. The hydraulic model requires that the flow of working fluid in the comprehensive energy network should satisfy the basic theorem of the network: the flow of each pipeline should satisfy the flow continuity equation at each node, that is, the inflow flow at the node is equal to the outflow flow. In a closed transport loop, the sum of the head losses of the working fluid flowing in each pipeline is 0, that is,

In the formula, A _s is the node-branch correlation matrix of the integrated energy system network; m _t is the flow of each pipeline; m _q,t is the outflow flow of each node; B _h is the loop-branch correlation matrix of the integrated energy system network; h _f,t is the head loss vector, and its calculation method is

h _f,t = Km _t |m _t | (2)

In the formula, K is the resistance coefficient matrix of the pipeline.

c. Establish a comprehensive energy system thermal model. The thermal model can be represented by the following three equations: namely, equation (3) the expression of thermal power φ _t at the node, equation (4) the relationship between the pipe end temperature T _{end, t} and the starting temperature T _{start, t} , and the equation (5) the working fluid in An expression of the temperature relationship before and after mixing at the node.

φ _t =C _p m _q,t (T _s,t -T _o,t ) (3)

(∑m _out,t )T _out,t =∑m _in,t T _in,t (5)

In the formula, the heating temperature T _s,t is the temperature before the working medium is injected into the load node; the output temperature T _o,t is the temperature when the working medium flows out of the load node; C _p is the specific heat capacity of the working medium; m _q,t is the temperature at the node Mass flow of working medium; m _a,t is the average mass flow of working medium in the pipeline; T _{a, t} is the ambient temperature; λ is the thermal conductivity of the pipeline; L is the length of the pipeline; m _out,t , T _out,t and min _{in , t} and T _{in, t} are the mass flow and temperature of the working medium flowing out and flowing into the pipeline, respectively.

In the present invention, described step (2) is realized in the following way:

a. For the transmission network of steam, hot water and chilled water, because all of them include flow and temperature response delay, the following steps are adopted:

Input the temperature and flow parameters of the working fluid at the source side and the user side into the network mechanism model of the integrated energy system, and convert the influence factors of the thermal insulation layer and the thickness of the pipeline in the energy flow transmission network accordingly, so as to obtain the various parts of the integrated energy system network. temperature, pressure and flow distribution.

Keep the source-side working medium parameters unchanged, so that the mass flow rate of the working medium in the energy flow network increases in steps. Calculate the time for the working fluid on the user side to complete the response, so as to obtain the flow response characteristics under this working condition.

Keep the source-side working fluid pressure and flow unchanged, so that the source-side working fluid temperature increases in a stepwise manner. After the fluctuation range of the working fluid parameters does not exceed 5%, it is reduced to the original supply temperature level in a stepwise manner. Calculate The time for the working fluid on the user side to complete the response, so as to obtain the temperature response characteristics under this working condition.

b. For the cold air and compressed air delivery network, because there is only a delay in the flow response, the following steps are adopted:

Input the working fluid flow between the source side and the user side under the required calculation conditions in the network mechanism model of the integrated energy system, and obtain the pressure and flow distribution of the energy network.

Under the condition that other parameters on the source side remain unchanged, the flow rate of the source side working medium is increased in a stepwise manner. After the fluctuation range of the working medium parameter does not exceed 5%, it is reduced to the original level in a stepwise manner. Calculate the user side The time for the working fluid to complete the response.

In the present invention, described step (3) is realized in the following way:

a. Take the sum of the normalized delay response time of each energy flow of each node user of the integrated energy system topology network as the objective function:

In the formula, L, M, and N represent the number of user nodes, energy flow category and response type, respectively; D _ijk is the completion time of the k-th response of the j-th energy flow of the i-th user; D _jkmax and D _jkmin are all The maximum and minimum completion time of the kth response of the jth energy flow in the user; w _j is the weight of the objective function occupied by the response of different energy flow delays, and the method for determining the weight includes the following steps:

b. Compare the importance of each energy flow pairwise, and construct a fuzzy complementary judgment matrix A:

0≤a_pq≤1，a_pq+a_qp＝1；a_pq为p能流相对于q能流的重要性，a_qp为q能流相对于p能流的重要性，二者取值均精确到十分位。

0≤a _pq ≤1, a _pq +a _qp =1; a _pq is the importance of p energy flow relative to q energy flow, a _qp is the importance of q energy flow relative to p energy flow, both values are Accurate to tenths.

若有：Judging whether the fuzzy complementary judgment matrix A has complementary consistency, that is, for the elements in matrix A

If any:

a _pr a _rq a _qp = a _pq a _qr a _rp (8)

Then matrix A has complementary consistency. If the fuzzy complementary judgment matrix A does not satisfy the consistency, but has:

Then A is said to have satisfactory consistency, where _spq is the allowable deviation of the judgment matrix.

c. In order to find the minimum deviation of the elements of the judgment matrix that satisfies the allowable deviation, assuming that the error of the elements of the judgment matrix is s' _pq , the matrix E composed of them is called the error matrix of the judgment matrix A.

In the formula, s' _pq can be regarded as a random variable with a mean value of 0; w _p and w _q are the weights of the objective function occupied by different energy flow delay responses.

Define the error optimal objective function:

p,q＝1,2,...,Y。stw _p > 0, and

p,q=1,2,...,Y.

Solve the optimization problem of Equation (11) to obtain the weight w, and then inversely deduce the judgment matrix A ^* with complementary consistency according to Equation (7). Consistency tests are performed by means of statistical hypothesis testing of the element-wise differences of matrices A and A ^* .

In the present invention, described step (4) is realized in the following way:

a. Obtain the annual operation data in the comprehensive energy supplier database, and construct a combination set of supply and demand conditions. Taking steam energy flow as an example, it is assumed that there are S steam supply and demand conditions in the optimization period, and each condition includes the steam supply and demand at T times. Data, that is, all data of the steam supply process in the cycle can be represented by an S×T order matrix. Using the k-means clustering algorithm, the unit of day is used as the basic unit of clustering, and the historical data of supply and demand of each energy flow in the cycle is clustered and divided, and then a set of typical supply and demand conditions for the whole year is obtained.

b. Establish a variety of energy flow supply and demand matching constraints, which is: applying the operating data collected under typical supply and demand conditions throughout the year to the integrated energy system network mechanism model and multiple energy flows under the existing integrated energy system topology design conditions After the objective function of the lowest comprehensive delay is determined, it is judged whether an effective solution for the temperature, traffic and delay of the whole network can be obtained.

c. Establish the spatial distribution constraints of the integrated energy system network nodes, that is, determine the feasible region of the integrated energy system topology structure at the spatial level. The specific method is to use the enumeration algorithm to determine each energy flow transmission trunk line constructed along the highway, and then select the shortest distance between the user node and the energy flow transmission trunk line to establish the energy flow transmission branch line, so as to form the source-load network topology structure of the integrated energy system in space. level feasible domain.

In the present invention, described step (5) is realized in the following way:

a. Determine the number of initial planning schemes, combine the connection schemes in the feasible domain into d-dimensional particles, and initialize the acceleration factor, the maximum number of iterations, and the maximum particle velocity parameters.

b. Initialize the velocity vector and position information of various energy flow topology network connection schemes, so that the position of the current scheme is the individual historical global optimal position of each scheme. Calculate the optimal position of all connection planning schemes at the same time;

c. Particles fly within the search space. Define the fitness function, compare the current global optimal position with the historical global optimal position, and use the update formula to update the velocity and position of the particles in the energy flow topology network scheduling scheme

v _ij (t+1)=v _ij (t)+c ₁ r ₁ (pbest _ij (t)-x _ij (t))+c ₂ r ₂ (gbest _j (t)-x _ij (t)) ( 12)

x _ij (t+1)=x _ij (t)+v _ij (t+1) (13)

Among them, i represents the ith particle; j represents the jth dimension of the particle; v _ij (t) represents the jth dimension flight velocity component of particle i when it evolves to the t generation; x _ij (t) represents the particle i evolves to the t generation. The jth dimension position component in t generation; pbest _ij (t) represents the jth dimension individual optimal position pbest _i component of particle i when it evolves to t generation; gbest _j (t) represents the entire particle swarm when it evolves to t generation The jth dimension component of the optimal position gbest; c ₁ , c ₂ are learning factors, the former is the individual learning factor of each particle, the latter is the social learning factor of each particle; r ₁ , r ₂ are [0, 1] random number within.

d. Calculate the objective function value of the position of each connection scheme, and use the update formula to update the individual historical optimal position of each connection scheme and the optimal position of all scheduling schemes.

e. When the set maximum number of iterations is reached, the calculation stops and the result is output. Otherwise, return to step c to continue searching.

Based on the method of the present invention, a topology optimization system for an integrated energy system that takes into account the energy flow delay characteristics, the system includes a comprehensive energy system mechanism model calculation module; a variety of energy flow integrated minimum delay time calculation modules; The integrated energy system topology optimization planning module;

The integrated energy system mechanism model calculation module provides calculation model support for the multiple energy flow integrated minimum delay time calculation module, and the multiple energy flow integrated minimum delay time calculation module is an integrated energy system aiming at the lowest delay of the system. The topology optimization planning module provides delay data support.

Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (7)

1. a comprehensive energy system topology optimization method considering energy flow delay characteristics, is characterized in that, comprises the steps: Step S1, establishing a comprehensive energy system network mechanism model including steam, hot water transportation network, chilled water, cold air transportation network and compressed air transportation network; Step S2, calculating the response characteristics of the temperature and flow rate of the working fluid at the end of each energy flow transport network fluctuating with the source end; Step S3, establishing a variety of objective functions of the lowest comprehensive delay of the energy flow, wherein the weight coefficient of each energy flow delay is obtained by the fuzzy analytic hierarchy process; Step S4: constructing a combination set of supply and demand conditions, establishing constraints for the topology optimization problem of the integrated energy system, and setting a variety of energy flow supply and demand matching constraints and spatial distribution constraints of the integrated energy system network nodes; Step S5, adopt the particle swarm optimization algorithm to solve the optimization problem formed by the objective function of the above-mentioned step S3 and the constraint condition of the step S4, and obtain the optimal topology planning scheme of the integrated energy system; In the described step S2, for the transmission network of steam, hot water and chilled water, because all of them include flow and temperature response delays, the following steps are used to calculate the response characteristics of the temperature and flow of the working medium at the end of the transmission network fluctuating with the source. : Firstly, the temperature and flow parameters of the working fluid at the source side and the user side are input into the network mechanism model of the integrated energy system, and the influence factors of the thermal insulation layer and the thickness of the pipeline in the energy flow transmission network are converted accordingly, so as to obtain the parameters of the integrated energy system network. temperature, pressure and flow distribution at Second, keep the working fluid parameters on the source side unchanged, so that the mass flow rate of the working fluid in the energy flow network increases in steps. The time for the side working medium to complete the response, so as to obtain the flow response characteristics under this working condition; Finally, keep the source-side working fluid pressure and flow unchanged, so that the source-side working fluid temperature increases in steps. The time for the working fluid to complete the response, so as to obtain the temperature response characteristics under this working condition; In the described step S2, for the cold air and compressed air conveying network, because only the flow response is delayed, the following steps are used to calculate the response characteristics of the temperature and flow of the working medium at the end of the conveying network fluctuating with the source end: First, input the working fluid flow between the source side and the user side under the required calculation conditions in the network mechanism model of the integrated energy system, and obtain the pressure and flow distribution of the energy network; Then, under the condition that other parameters on the source side remain unchanged, the flow rate of the source side working medium is increased in a stepwise manner. the time to complete the response; In the step S3, the sum of the normalized delay response times of each energy flow of each node user of the integrated energy system topology network is used as the objective function:

In the formula, L, M, and N represent the number of user nodes, energy flow category and response type, respectively; D _ijk is the completion time of the k-th response of the j-th energy flow of the i-th user; D _jkmax and D _jkmin are all The maximum and minimum completion time of the kth response of the jth energy flow among users; w _j is the weight of the objective function occupied by the response of different energy flow delays. 2. The comprehensive energy system topology optimization method considering energy flow delay characteristic according to claim 1, is characterized in that: in described step S1, cold air and compressed air transport network only need to establish hydraulic model, and steam In addition to the hydraulic model, the hot water and chilled water transportation network also needs to establish a thermal model. 3. the comprehensive energy system topology optimization method considering energy flow delay characteristic according to claim 2, it is characterized in that: establish the general hydraulic model of the comprehensive energy system, the hydraulic model requires that the working fluid flows in the comprehensive energy network and should meet the network requirements. Basic theorem: The flow of each pipeline should satisfy the flow continuity equation at each node, that is, the inflow flow at the node is equal to the outflow flow; in a closed transport loop, the sum of the head loss of the working fluid flowing in each pipeline is 0 ,Right now

In the formula, A _s is the node-branch correlation matrix of the integrated energy system network; m _t is the flow of each pipeline; m _q,t is the outflow flow of each node; B _h is the loop-branch correlation matrix of the integrated energy system network; h _f,t is the head loss vector, and its calculation method is h _f,t = Km _t |m _t | (2) In the formula, K is the resistance coefficient matrix of the pipeline. 4. The integrated energy system topology optimization method according to claim 2, wherein: A universal thermodynamic model of the integrated energy system is established. The thermodynamic model is represented by the following three formulas: formula (3) the expression of thermal power φ _t at the node, formula (4) the relationship between the temperature at the end of the pipeline T _end,t and the temperature at the start end T _start,t , and the expression of the temperature relationship of the working fluid before and after mixing at the node of formula (5); φ _t =C _p m _q,t (T _s,t -T _o,t ) (3)

(∑m _out,t )T _out,t =∑m _in,t T _in,t (5) In the formula, the heating temperature T _s,t is the temperature before the working medium is injected into the load node; the output temperature T _o,t is the temperature when the working medium flows out of the load node; C _p is the specific heat capacity of the working medium; m _q,t is the temperature at the node Mass flow of working medium; m _a,t is the average mass flow of working medium in the pipeline; T _{a, t} is the ambient temperature; λ is the thermal conductivity of the pipeline; L is the length of the pipeline; m _out,t , T _out,t and min _{in , t} and T _{in, t} are the mass flow and temperature of the working medium flowing out and flowing into the pipeline, respectively. 5. The comprehensive energy system topology optimization method according to claim 1, wherein the method for determining the weight of the objective function occupied by different energy flow delay responses comprises the following steps: First, compare the importance of each energy flow pairwise, and construct a fuzzy complementary judgment matrix A:

q=1,2,...,Y, 0≤a _pq ≤1, a _pq +a _qp =1; a _pq is the importance of p energy flow relative to q energy flow, a _qp is q energy flow relative to The importance of p energy flow, both values are accurate to tenths; Secondly, judge whether the fuzzy complementary judgment matrix A has complementary consistency, that is, the elements in the matrix A are

If any: a _pr a _rq a _qp = a _pq a _qr a _rp (8) Then the matrix A has complementary consistency; if the fuzzy complementary judgment matrix A does not satisfy the consistency, but there are:

Then A is said to have satisfactory consistency, where _spq is the allowable deviation of the judgment matrix; Again, in order to find the minimum deviation of the judgment matrix elements that satisfies the allowable deviation, assuming that the error of the judgment matrix elements is s' _pq , then the matrix E composed of them is called the error matrix of the judgment matrix A;

In the formula, s' _pq can be regarded as a random variable with a mean value of 0; w _p and w _q are the weights of the objective function occupied by different energy flow delay responses; Finally, define the error optimal objective function:

stw _p > 0, and

Solve the optimization problem of Equation (11) to obtain the weight w, and then inversely deduce the judgment matrix A ^* with complementary consistency according to Equation (7) ^. test. 6. The integrated energy system topology optimization method according to claim 1, wherein: In step S4, the annual operation data in the comprehensive energy supplier database is obtained, and a combination set of supply and demand conditions is constructed: it is assumed that there are S energy flow supply and demand conditions in the optimization period, and each condition contains the energy flow supply and demand data at T times, that is, All the data of the energy flow supply process in the cycle can be represented by the S×T order matrix; the k-means clustering algorithm is used, and the daily unit is used as the basic unit of clustering, and the historical data of each energy flow supply and demand in the cycle is clustered and divided. Then, a set of typical supply and demand conditions throughout the year is obtained; Establish a variety of energy flow supply and demand matching constraints: After applying the concentrated operating data of typical supply and demand conditions throughout the year to the integrated energy system network mechanism model under the existing integrated energy system topology design conditions and the objective function of the lowest integrated delay of various energy flows , to judge whether the effective solution of the temperature, flow and delay of the whole network can be obtained; Establish the spatial distribution constraints of the network nodes of the integrated energy system, that is, determine the feasible region of the topology structure of the integrated energy system at the spatial level. The energy flow transmission branch line is established at the shortest distance from the main line, thus forming a feasible region of the source-load network topology of the integrated energy system at the spatial level. 7. The integrated energy system topology optimization method according to claim 6, wherein: The step S5 specifically includes the following steps: (1) firstly determine the number of initial planning schemes, combine the connection schemes in the feasible domain into d-dimensional particles, and initialize the acceleration factor, the maximum number of iterations and the maximum particle velocity parameters; (2) Initialize the velocity vectors and position information of various energy flow topology network connection schemes, so that the position of the current scheme is the individual historical global optimal position of each scheme; at the same time, the optimal positions of all connection planning schemes are calculated; (3) Define the fitness function, compare the current global optimal position with the historical global optimal position, and use the update formula to update the velocity and position of the particles in the energy flow topology network scheduling scheme v _ij (t+1)=v _ij (t)+c ₁ r ₁ (pbest _ij (t)-x _ij (t))+c ₂ r ₂ (gbest _j (t)-x _ij (t)) ( 12) x _ij (t+1)=x _ij (t)+v _ij (t+1) (13) Among them, i represents the ith particle; j represents the jth dimension of the particle; v _ij (t) represents the jth dimension flight velocity component of particle i when it evolves to the t generation; x _ij (t) represents the particle i evolves to the t generation. The jth dimension position component in t generation; pbest _ij (t) represents the jth dimension individual optimal position pbest _i component of particle i when it evolves to t generation; gbest _j (t) represents the entire particle swarm when it evolves to t generation The jth dimension component of the optimal position gbest; c ₁ , c ₂ are learning factors, the former is the individual learning factor of each particle, the latter is the social learning factor of each particle; r ₁ , r ₂ are [0, 1] random number within; (4) Calculate the objective function value of the position of each connection scheme, and use the update formula to update the individual historical optimal position of each connection scheme and the optimal position of all scheduling schemes: when the set maximum number of iterations is reached, the calculation stops and the result is output ; otherwise, return to step (3) to continue searching.

Images