CN111626587B - Comprehensive energy system topology optimization method considering energy flow delay characteristics - Google Patents
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Abstract
The invention relates to a comprehensive energy system topology optimization method considering energy flow delay characteristics, which comprises the following steps: step S1, establishing a comprehensive energy system network mechanism model including a steam and hot water delivery network, a chilled water and cold air delivery network and a compressed air delivery network; step S2, calculating the response characteristics of key parameters such as the temperature and the flow of the working medium at the tail end of each energy flow transport network along with the fluctuation of the source end; step S3, establishing a plurality of objective functions of energy flow minimum comprehensive delay, wherein the weight coefficient of each energy flow delay is obtained by a fuzzy analytic hierarchy process; step S4: constructing a supply and demand working condition combination set, and setting various energy flow supply and demand matching constraints and comprehensive energy system network node space distribution constraints; and step S5, solving an optimization problem formed by the objective function and the constraint condition in the step by adopting a particle swarm optimization algorithm to obtain an optimal topological planning scheme of the comprehensive energy system.
Description
Technical Field
The invention relates to an optimization method of a comprehensive energy system, belongs to the field of comprehensive energy systems, and particularly relates to a topological optimization method of a comprehensive energy system considering energy flow delay characteristics.
Technical Field
Currently, the energy consumption of China is continuously increased, and the total amount of primary energy production in 2017 reaches 35.85 hundred million tons of standard coal. How to solve the problem of diversified energy demand in the region, solve the problem of regional energy efficiency improvement and promote the sustainable development of regional energy from the perspective of regional energy is the research focus in the energy field. Under the technical drive of policy guidance and information physical fusion of energy transformation, an integrated energy system concept aiming at the problems is generated immediately. The comprehensive energy system is a novel integrated energy system which integrates various energy sources such as electric energy, heat energy, cold energy, compressed air and the like in a certain area by utilizing an advanced physical information technology and an innovative management mode and realizes coordinated planning, cooperative management, interactive response and complementary mutual assistance among energy subsystems.
The existing research on the comprehensive energy system focuses on the direction of system optimization planning and optimization scheduling strategies. Along with the continuous deepening of the urbanization process, the urban spatial layout and the energy structure are continuously reconstructed, and the planning of a novel comprehensive energy system is concerned by more and more scholars. But the comprehensive energy system planning evaluation means is single, the economy and the emission characteristics are taken as the leading factors, and the delay characteristics of different energy flows are considered in the fresh method. Taking steam energy flow as an example: the complex transient characteristics of the steam pipe network working medium flow process cause slow response speed of the temperature and flow of steam at the source end and the user end under the action of various factors such as steam parameter fluctuation, compressibility, state change, friction, heat transfer and the like.
Considering different delay response characteristics of various energy flows such as cold, heat, compressed air and the like, from the mechanism perspective, how to establish a model of each energy flow of the comprehensive energy system, and establishing a comprehensive energy system node network topology optimization method based on the model is a key technical problem for realizing the efficient utilization of regional resources.
Disclosure of Invention
The invention aims to provide a comprehensive energy system topology optimization method considering energy flow delay characteristics, which optimizes a node topology network planning scheme consisting of an inner source side and a user side of a comprehensive energy system by a comprehensive energy system model based on a mechanism and an optimization means based on a particle swarm algorithm, and obtains a comprehensive energy system planning strategy meeting the lowest delay requirement with lower labor cost.
In order to solve the technical problem, the invention adopts the following technical scheme:
a topology optimization method of an integrated energy system considering energy flow delay characteristics comprises the following steps:
and step S1, establishing a comprehensive energy system network mechanism model including a steam and hot water delivery network, a chilled water and cold air delivery network and a compressed air delivery network. The construction methods of different energy flow transmission models have certain differences, the cold air and compressed air transportation network only needs to establish a hydraulic model, and the steam, hot water and chilled water transportation network also needs to establish a thermal model besides the hydraulic model. The construction of a general hydraulic model and a thermal model is introduced in the following steps:
step S110, the hydraulic model requires that the working medium flows in the comprehensive energy network to meet the basic network theorem: the flow of each pipe should satisfy the flow continuity equation at each node, i.e., the inflow flow at a node is equal to the outflow flow. In a transport closed loop, the sum of head losses of the working medium flowing in each pipeline is 0, namely
In the formula, A s A node-branch incidence matrix for the integrated energy system network; m is t Flow rate for each pipeline; m is q,t Flow out for each node; b is h A loop-branch correlation matrix for the integrated energy system network; h is a total of f,t Is an indenter loss vector calculated by
h f,t ＝Km t |m t | (2)
In the formula, K is a resistance coefficient matrix of the pipeline.
In step S120, the thermodynamic model can be represented by the following three formulas: i.e. formula (3) node thermal power phi t Expression, formula (4) pipe end temperature T end,t And the starting temperature T start,t Relational expression, and the temperature relational expression before and after the working medium of the expression (5) is mixed 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 Representing the temperature of the working medium before the working medium is injected into the load node; output temperature T o,t Representing the temperature of the working medium flowing out of the load node; c p Is working medium specific heat capacity; m is a unit of q,t Is the working medium mass flow at the node; m is a,t The average mass flow of the working medium in the pipeline; t is a,t Is ambient temperature; λ is the heat transfer coefficient of the pipe; l is the length of the pipeline; m is out,t 、T out,t And m in,t 、T in,t Mass flow and temperature of the working medium in the outflow and inflow pipelines respectively.
Step S2, calculating the response characteristics of the terminal working medium temperature and the flow key parameters of each energy flow transport network along with the fluctuation of the source end, and the specific flow is as follows:
step S210, for the transportation networks of steam, hot water and chilled water, since they all include flow and temperature response delays, the following steps are adopted:
and S211, inputting the working medium temperature and flow parameters at the source side and the user side into the comprehensive energy system network mechanism model, and correspondingly converting the influence factors such as the heat insulation layer, the pipeline thickness and the like of the energy flow transmission network, so as to obtain the temperature, pressure and flow distribution at each position in the comprehensive energy system network.
Step S212, keeping the source side working medium parameters unchanged, enabling the mass flow of the working medium in the energy flow network to increase in a step mode, reducing the working medium parameters to the original flow level in a step mode after the working medium parameters are close to stable (the fluctuation amplitude is not more than 5%), and calculating the time for the user side working medium to complete response, so that the flow response characteristic under the working condition is obtained.
Step S213, keeping the pressure and the flow of the working medium on the source side unchanged, increasing the temperature of the working medium on the source side in a step mode, reducing the temperature to the original temperature supply level in a step mode after the working medium parameters are close to stable (the fluctuation amplitude is not more than 5%), and calculating the time for the working medium on the user side to complete response so as to obtain the temperature response characteristic under the working condition.
Step S220, for the cold air and compressed air delivery network, since there is only a flow response delay, the following steps are adopted:
and step S221, working medium flow of the source side and the user side under the working condition required to be calculated is input into the comprehensive energy system network mechanism model, and pressure and flow distribution of the energy network at all positions are obtained.
And step S222, under the condition that other parameters at the source side are not changed, the flow of the working medium at the source side is increased in a step mode, after the working medium parameters are close to be stable (the fluctuation amplitude does not exceed 5 percent), the working medium parameters are reduced to the original level in a step mode, and the time for the working medium at the user side to complete response is calculated.
And step S3, establishing a plurality of objective functions of energy flow minimum comprehensive delay, wherein the weight coefficient of each energy flow delay is obtained by a fuzzy analytic hierarchy process. The specific process comprises the following steps:
step S310, taking the sum of the standardized delay response time of each energy flow of each node user of the topology network of the comprehensive energy system as an objective function:
l, M, N respectively represents the number of user nodes, the energy flow category and the response category; d ijk Completion time of kth response for jth fluence of ith user; d jkmax 、D jkmin Maximum and minimum completion times of the kth response of the jth power flow in all users respectively; w is a j The weight of the objective function occupied by the delay response of different energy flows is determined by the method comprising the following steps:
step S311, the importance of each energy flow is compared pairwise, and a fuzzy complementary judgment matrix A is constructed:
Step S312, judging whether the fuzzy complementary judging matrix A has complementary consistency, namely, the elements in the matrix A are matched
If so:
a pr a rq a qp ＝a pq a qr a rp (8)
then matrix a has complementary identity. If the fuzzy complementation judgment matrix A does not meet the consistency, but the following steps are carried out:
then A is said to have satisfactory consistency, where s pq To determine the allowable deviation of the matrix.
In step S313, in order to find the minimum deviation of the judgment matrix elements satisfying the allowable deviation, it is assumed that the error of the judgment matrix elements is S' pq Then its constituent matrix E is referred to as the error matrix of decision matrix a.
S 'in the formula' pq A random variable that can be considered to have a mean value of 0; w is a p 、w q The delay response for different energy flows takes the weight of the objective function.
Defining an error optimal objective function:
s.t.w p is greater than 0, and
p,q＝1,2,...,Y。
solving the optimization problem of the formula (11) to obtain the weight w, and then reversely deducing the judgment matrix A with complementary consistency according to the formula (7) * . By aligning matrices A and A * The element difference of (2) is tested for consistency by a statistical hypothesis test.
And step S4, constructing a supply and demand working condition combination set, and setting various energy flow supply and demand matching constraints and comprehensive energy system network node space distribution constraints. The specific process comprises the following steps:
and step S410, acquiring annual operation data in a comprehensive energy supplier database, constructing a supply and demand working condition combination set, assuming that S energy flow supply and demand working conditions exist in an optimization period, wherein each working condition comprises energy flow supply and demand data at T moments, and all data of the energy flow supply process in the period can be represented by an S x T order matrix. And clustering and dividing historical data of energy flow supply and demand in a period by using a k-means clustering algorithm and taking a day as a basic clustering unit so as to obtain a group of annual typical supply and demand working condition sets.
Step S420, establishing various energy flow supply and demand matching constraints, wherein the constraints are as follows: after the operating data concentrated in the annual typical supply and demand working conditions are applied to the comprehensive energy system network mechanism model and the objective functions of the minimum comprehensive delay of various energy flows under the existing comprehensive energy system topology design condition, whether effective solutions of the whole network temperature, the whole network flow and the whole network delay can be obtained or not is judged.
Step S430, establishing a spatial distribution constraint of network nodes of the comprehensive energy system, namely determining a feasible region of a topological structure of the comprehensive energy system on a spatial level, wherein the specific method comprises the following steps:
determining each energy flow transmission trunk line constructed along the road by adopting an enumeration algorithm;
and selecting 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 a feasible domain of the source-load network topology of the comprehensive energy system.
And step S5, solving the optimization problem formed by the objective function of the step S3 and the constraint condition of the step S4 by adopting a particle swarm optimization algorithm. The solving process comprises the following substeps:
step S510, determining the number of initial planning schemes, combining feasible intra-domain connection schemes into d-dimensional particles, and initializing acceleration factors, maximum iteration times and maximum particle speed parameters.
Step S520, initializing the speed vectors and the position information of the multiple energy flow topology network connection schemes, and enabling the position of the current scheme to be the individual historical global optimal position of each scheme. Calculating the optimal positions of all the connection planning schemes simultaneously;
in step S530, the particles fly in the search space. Defining a fitness function, comparing the global optimal position with the historical global optimal position, and updating the speed and the position of the energy flow topological network scheduling scheme particles by using an updating formula
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)
Wherein i represents the ith particle; j represents the j-th dimension of the particle; v. of ij (t) represents the j-th dimensional flight velocity component of the particle i when it evolves to the t generation; x is the number of ij (t) represents the j-th dimensional position component of the particle i when evolving to the t generation; pbest ij (t) individual optimal positions pbest of j-dimension when particle i evolves to t generation i A component; gbest j (t) a j-dimensional component representing the optimal position gbest of the entire particle population when evolving to the t generation; c. C 1 ，c 2 The former is an individual learning factor of each particle, and the latter is a social learning factor of each particle; r is a radical of hydrogen 1 ，r 2 Is [0,1 ]]The random number in (c).
And step S540, calculating an objective function value of the position of each connection scheme, and updating the individual historical optimal position of each connection scheme and the optimal positions of all scheduling schemes by using an updating formula.
And step S550, when the set maximum iteration number is reached, stopping calculation and outputting a result. Otherwise, returning to step S530 to continue searching.
According to the method, a topology optimization system of the comprehensive energy system considering the energy flow delay characteristic can be obtained, and the system comprises a comprehensive energy system mechanism model calculation module; a multi-energy flow comprehensive minimum delay time calculation module; a comprehensive energy system topology optimization planning module which takes the lowest system delay as a target;
the comprehensive energy system mechanism model calculation module provides calculation model support for the multiple energy flow comprehensive minimum delay time calculation module, and the multiple energy flow comprehensive minimum delay time calculation module provides delay data support for the comprehensive energy system topology optimization planning module taking the system minimum delay as a target.
Compared with the prior art, the invention has the beneficial effects that:
the invention creatively considers different delay characteristics of different energy flows and establishes a comprehensive energy system network mechanism model and a delay calculation method. In addition, the invention considers the engineering usability of the weight method and adopts a fuzzy analytic hierarchy process combining qualitative analysis and quantitative analysis to determine the weight coefficient of the delay of each energy flow. The invention provides a comprehensive energy system topology planning optimization method based on a comprehensive energy system network mechanism model and a delay calculation model.
Drawings
FIG. 1 is a diagram of solving steps of a particle swarm optimization algorithm;
FIG. 2 shows the main implementation steps of the method of the invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
FIG. 2 shows the main steps of the method of the present invention.
A topology optimization method of an integrated energy system considering energy flow delay characteristics comprises the following steps:
step (1): establishing a transport network mechanism model of the comprehensive energy system;
step (2): calculating the response characteristics of the terminal working medium temperature and the flow key parameters of each energy flow transport network along with the fluctuation of the source end;
and (3): establishing a target function of the minimum comprehensive delay of various energy flows based on a fuzzy analytic hierarchy process;
and (4): constructing a supply and demand working condition combination set, and setting various energy flow supply and demand matching constraints and comprehensive energy system network node space distribution constraints;
and (5): and solving the comprehensive energy system topology optimization problem with the lowest delay by adopting a particle swarm optimization algorithm (as shown in figure 1), so as to obtain the optimal topology planning scheme of the comprehensive energy system.
In the present invention, the step (1) is implemented by:
a. and establishing a comprehensive energy system network mechanism model comprising a steam and hot water delivery network, a chilled water and cold air delivery network and a compressed air delivery network. The cold air and compressed air transportation network only needs to establish a hydraulic model, and the steam, hot water and chilled water transportation network needs to establish a thermal model besides the hydraulic model. The construction of a general hydraulic model and a thermal model is introduced in the following steps:
b. and establishing a comprehensive energy system hydraulic model. The hydraulic model requires that working media flow in the comprehensive energy network to meet the basic network theorem: the flow of each pipe should satisfy the flow continuity equation at each node, i.e., the inflow flow at a node is equal to the outflow flow. In a transport closed loop, the sum of head losses of the working medium flowing in each pipeline is 0, namely
In the formula, A s A node-branch incidence matrix for the integrated energy system network; m is t For each pipeline flow; m is q,t Flow out for each node; b is h For integrated energy system networkA loop-branch correlation matrix of the network; h is f,t Is an indenter loss vector calculated by
h f,t ＝Km t |m t | (2)
In the formula, K is a resistance coefficient matrix of the pipeline.
c. And establishing a comprehensive energy system thermodynamic model. The thermodynamic model can be represented by the following three formulas: i.e. formula (3) node thermal power phi t Expression, formula (4) pipe end temperature T end,t And the starting temperature T start,t Relational expression, and the temperature relational expression before and after the working medium of the expression (5) is mixed 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 Representing the temperature before the working medium is injected into the load node; output temperature T o,t Representing the temperature of the working medium flowing out of the load node; c p The specific heat capacity of the working medium; m is a unit of q,t Is the working medium mass flow at the node; m is a,t The average mass flow of the working medium in the pipeline; t is a unit of a,t Is ambient temperature; lambda is the heat transfer coefficient of the pipe; l is the length of the pipeline; m is out,t 、T out,t And m in,t 、T in,t Mass flow and temperature of the working medium in the outflow and inflow pipelines respectively.
In the present invention, the step (2) is implemented by:
a. aiming at the delivery networks of steam, hot water and chilled water, the following steps are adopted because the delivery networks all comprise flow and temperature response delays:
and inputting the working medium temperature and flow parameters at the source side and the user side into the comprehensive energy system network mechanism model, and correspondingly converting the influence factors of the heat insulation layer and the pipeline thickness of the energy flow transmission network, thereby obtaining the temperature, pressure and flow distribution of each part in the comprehensive energy system network.
Keeping the source side working medium parameters unchanged, increasing the mass flow of the working medium in the energy flow network in a step mode, reducing the mass flow to the original flow level in a step mode after the fluctuation range of the working medium parameters does not exceed 5%, and calculating the time for completing response of the working medium at the user side so as to obtain the flow response characteristic under the working condition.
Keeping the pressure and the flow of the working medium on the source side unchanged, enabling the temperature of the working medium on the source side to increase in a step mode, reducing the temperature to the original temperature supply level in a step mode after the fluctuation amplitude of the working medium parameters is not more than 5%, and calculating the time for the working medium on the user side to complete response so as to obtain the temperature response characteristic under the working condition.
b. For cold air and compressed air delivery networks, the following steps are taken because of the only flow response delay:
working medium flow of a source side and a user side under a working condition required to be calculated is input into the comprehensive energy system network mechanism model, and pressure and flow distribution of all parts of the energy network are obtained.
Under the condition that other parameters at the source side are not changed, the flow of the working medium at the source side is increased in a step mode, after the fluctuation amplitude of the working medium parameters does not exceed 5%, the working medium parameters are reduced to the original level in a step mode, and the time for the working medium at the user side to complete response is calculated.
In the present invention, the step (3) is implemented by:
a. taking the sum of the standardized delay response time of each energy flow of each node user of the topology network of the comprehensive energy system as a target function:
l, M, N respectively represents the number of user nodes, the energy flow category and the response category; d ijk Completion time of kth response for jth fluence of ith user; d jkmax 、D jkmin Maximum and minimum completion times of the kth response of the jth power flow in all users respectively; w is a j The weight of the objective function occupied by the delay response of different energy flows is determined by the method comprising the following steps:
b. and (3) carrying out pairwise comparison on the importance of each energy flow to construct a fuzzy complementary judgment matrix A:
Judging whether the fuzzy complementary judgment matrix A has complementary consistency, namely, elements in the matrix A are subjected to complementary consistency
If so:
a pr a rq a qp ＝a pq a qr a rp (8)
then matrix a has complementary identity. If the fuzzy complementation judgment matrix A does not meet the consistency, but the following steps are carried out:
then A is said to have satisfactory consistency, where s pq To determine the allowable deviation of the matrix.
c. To find the minimum variance of the decision matrix elements that satisfies the allowable variance, assume that the error of the decision matrix elements is s' pq Then its constituent matrix E is referred to as the error matrix of decision matrix a.
Of formula (II) s' pq Can be considered as a random variable with a mean value of 0; w is a p 、w q The delay response for different energy flows takes the weight of the objective function.
Defining an error optimal objective function:
s.t.w p is greater than 0, and
p,q＝1,2,...,Y。
solving the optimization problem of the formula (11) to obtain the weight w, and then reversely deducing the judgment matrix A with complementary consistency according to the formula (7) * . By aligning matrices A and A * The element difference of (2) is tested for consistency by a statistical hypothesis test method.
In the present invention, the step (4) is implemented by:
a. annual operation data in a comprehensive energy supplier database are obtained, a supply and demand working condition combination set is constructed, steam energy flow is taken as an example, S steam supply and demand working conditions are assumed in an optimization period, each working condition comprises T steam supply and demand data at time, and all data of a steam supply process in the period can be represented through an S x T order matrix. And clustering and dividing historical data of energy flow supply and demand in a period by using a k-means clustering algorithm and taking a day as a basic clustering unit so as to obtain a group of annual typical supply and demand working condition sets.
b. Establishing various energy flow supply and demand matching constraints, wherein the constraints are as follows: and applying the operating data concentrated by the annual typical supply and demand working conditions to the comprehensive energy system network mechanism model and the multiple objective functions with the lowest comprehensive delay of energy flow under the topological design condition of the conventional comprehensive energy system, and judging whether effective solutions of the whole network temperature, the whole network flow and the whole network delay can be obtained or not.
c. And establishing the space distribution constraint of the network nodes of the comprehensive energy system, namely determining the feasible region of the topological structure of the comprehensive energy system at the space level. The method specifically comprises the steps of determining each energy flow transmission trunk line constructed along a road by adopting an enumeration algorithm, and then selecting a user node and establishing an energy flow transmission branch line at the shortest distance from the energy flow transmission trunk line, so as to form a feasible region of a source-load network topological structure of the comprehensive energy system at a spatial level.
In the present invention, the step (5) is implemented by:
a. determining the number of initial planning schemes, combining feasible intra-domain connection schemes into d-dimensional particles, and initializing acceleration factors, maximum iteration times and particle maximum speed parameters.
b. And initializing the speed vectors and the position information of the various energy flow topology network connection schemes to enable the position of the current scheme to be the individual historical global optimal position of each scheme. Calculating the optimal positions of all the connection planning schemes simultaneously;
c. the particles fly within the search space. Defining a fitness function, comparing the global optimal position with the historical global optimal position, and updating the speed and the position of the energy flow topological network scheduling scheme particles by using an updating formula
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)
Wherein i represents the ith particle; j represents the j-th dimension of the particle; v. of ij (t) represents the j-th dimensional flight velocity component of particle i as it evolves to the t generation; x is the number of ij (t) represents the j-th dimensional position component of the particle i when evolving to the t generation; pbest ij (t) individual optimal positions pbest of j-dimension when particle i evolves to t generation i A component; gbest j (t) a j-dimensional component representing the optimal position gbest of the entire particle population when evolving to the t generation; c. C 1 ，c 2 The former is an individual learning factor of each particle, and the latter is a social learning factor of each particle; r is a radical of hydrogen 1 ，r 2 Is [0,1 ]]The random number in (c).
d. And calculating an objective function value of the position of each connection scheme, and updating the individual historical optimal position of each connection scheme and the optimal positions of all scheduling schemes by using an updating formula.
e. And when the set maximum iteration times is reached, stopping calculation and outputting a result. Otherwise, returning to the step c to continue searching.
The invention relates to a topology optimization system of an integrated energy system considering energy flow delay characteristics, which comprises an integrated energy system mechanism model calculation module; a multi-energy flow comprehensive minimum delay time calculation module; a comprehensive energy system topology optimization planning module which takes the lowest system delay as a target;
the comprehensive energy system mechanism model calculation module provides calculation model support for the multiple energy flow comprehensive minimum delay time calculation module, and the multiple energy flow comprehensive minimum delay time calculation module provides delay data support for the comprehensive energy system topology optimization planning module taking the system minimum delay as a target.
Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A comprehensive energy system topology optimization method considering energy flow delay characteristics is characterized by comprising the following steps:
step S1, establishing a comprehensive energy system network mechanism model including a steam and hot water delivery network, a chilled water and cold air delivery network and a compressed air delivery network;
step S2, calculating the response characteristics of the fluctuation of the temperature and the flow of the working medium at the tail end of each energy flow transport network along with the source end;
step S3, establishing a plurality of objective functions of energy flow minimum comprehensive delay, wherein the weight coefficient of each energy flow delay is obtained by a fuzzy analytic hierarchy process;
step S4: constructing a supply and demand working condition combination set, establishing constraint conditions of a topological optimization problem of the comprehensive energy system, and setting various energy flow supply and demand matching constraints and comprehensive energy system network node space distribution constraints;
step S5, solving an optimization problem formed by the objective function of the step S3 and the constraint condition of the step S4 by adopting a particle swarm optimization algorithm to obtain an optimal topological planning scheme of the comprehensive energy system;
in the step S2, for the transmission networks of steam, hot water and chilled water, since they all include flow rate and temperature response delay, the response characteristics of the transmission network end working medium temperature and flow rate fluctuating with the source end are calculated by the following steps:
firstly, working medium temperature and flow parameters at a source side and a user side are input into a comprehensive energy system network mechanism model, and influence factors of the heat insulation layer and the pipeline thickness of an energy flow transmission network are correspondingly converted, so that the temperature, pressure and flow distribution of all parts in the comprehensive energy system network are obtained;
secondly, keeping the source side working medium parameters unchanged, enabling the mass flow of the working medium in the energy flow network to increase in a step mode, reducing the working medium parameters to the original flow level in a step mode after the working medium parameters are close to stable, and calculating the time for the user side working medium to complete response so as to obtain the flow response characteristic under the working condition;
finally, keeping the pressure and the flow of the working medium on the source side unchanged, enabling the temperature of the working medium on the source side to increase in a step-by-step manner, reducing the temperature to the original temperature supply level in a step-by-step manner after the working medium parameters are close to be stable, and calculating the time for the working medium on the user side to complete response so as to obtain the temperature response characteristic under the working condition;
in the step S2, for the cold air and compressed air delivery network, because there is only flow response delay, the response characteristics of the delivery network end working medium temperature and flow fluctuating with the source end are calculated by the following steps:
firstly, inputting working medium flow of a source side and a user side under a working condition to be calculated in a comprehensive energy system network mechanism model to obtain pressure and flow distribution of each part of an energy network;
then, under the condition that other parameters at the source side are not changed, the flow of the working medium at the source side is increased in a step mode, after the working medium parameters are close to be stable, the working medium flow is reduced to the original level in a step mode, and the time for the working medium at the user side to complete response is calculated;
in step S3, the sum of the normalized delay response times of the energy flows of the users at the nodes of the topology network of the integrated energy system is used as an objective function:
l, M, N respectively represents the number of user nodes, the energy flow category and the response category; d ijk The completion time of the kth response of the jth power flow of the ith user; d jkmax 、D jkmin Maximum and minimum completion times of the kth response of the jth power flow in all users respectively; w is a j The delay response for different energy flows takes the weight of the objective function.
2. The method for optimizing topology of integrated energy systems taking into account energy flow delay characteristics according to claim 1, wherein: in step S1, the cold air and compressed air transportation network only needs to establish a hydraulic model, and the steam, hot water and chilled water transportation network needs to establish a thermal model in addition to the hydraulic model.
3. The method for optimizing topology of an integrated energy system taking into account energy flow delay characteristics of claim 2, wherein: establishing a universal hydraulic model of the comprehensive energy system, wherein the hydraulic model requires that working media flow in a comprehensive energy network to meet the basic network theorem: the flow of each pipeline at each node should satisfy a flow continuity equation, that is, the inflow flow at the node is equal to the outflow flow; in a transport closed loop, the sum of head losses of working medium flowing in each pipeline is 0, namely
In the formula, A s A node-branch incidence matrix for the integrated energy system network; m is t For each pipeline flow; m is q,t Flow out for each node; b is h A loop-branch correlation matrix for the integrated energy system network; h is f,t Is an indenter loss vector calculated by
h f,t ＝Km t |m t | (2)
In the formula, K is a resistance coefficient matrix of the pipeline.
4. The integrated energy system topology optimization method of claim 2, wherein:
establishing a universal thermodynamic model of the comprehensive energy system, wherein the thermodynamic model is represented by the following three formulas: i.e. formula (3) node thermal power phi t Expression, formula (4) pipe end temperature T end,t And a starting temperature T start,t Relational expression and temperature relational expression before and after mixing of working medium at the node of the 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 Representing the temperature of the working medium before the working medium is injected into the load node; output temperature T o,t Representing the temperature of the working medium flowing out of the load node; c p The specific heat capacity of the working medium; m is q,t Is the working medium mass flow at the node; m is a,t The average mass flow of the working medium in the pipeline; t is a unit of a,t Is ambient temperature; λ is the heat transfer coefficient of the pipe; l is the length of the pipeline; m is out,t 、T out,t And m in,t 、T in,t Mass flow and temperature of the working medium in the outflow and inflow pipelines respectively.
5. The integrated energy system topology optimization method of claim 1, wherein: the method for determining the objective function weight occupied by the delay responses of different energy flows comprises the following steps:
firstly, pairwise comparison is carried out on the importance of each energy flow, and a fuzzy complementary judgment matrix A is constructed:
secondly, judging whether the fuzzy complementary judging matrix A has complementary consistency, namely, the elements in the matrix A are matched
If so:
a pr a rq a qp ＝a pq a qr a rp (8)
then matrix a has complementary identity; if the fuzzy complementation judgment matrix A does not meet the consistency, but the following steps are carried out:
then A is said to have satisfactory consistency, where s pq Judging the allowable deviation of the matrix;
again, to find the minimum deviation of the decision matrix elements that satisfy the allowable deviation, assume that the error of the decision matrix elements is s' pq Then the matrix E formed by the matrix E is called an error matrix of the judgment matrix A;
of formula (II) s' pq A random variable that can be considered to have a mean value of 0; w is a p 、w q The weight of the objective function occupied by the delay response of different energy flows;
finally, defining an error optimal objective function:
s.t.w p is greater than 0, and
solving the optimization problem of the formula (11) to obtain the weight w, and then reversely deducing the judgment matrix A with complementary consistency according to the formula (7) * (ii) a By aligning matrices A and A * The element difference of (2) is tested for consistency by a statistical hypothesis test.
6. The integrated energy system topology optimization method of claim 1, wherein:
in step S4, year-round operation data in the integrated energy supplier database is obtained, and a supply and demand working condition combination set is constructed: assuming that S energy flow supply and demand working conditions exist in an optimization period, each working condition comprises energy flow supply and demand data at T moments, namely, all data of the energy flow supply process in the period can be represented by an S multiplied by T order matrix; clustering and dividing historical data of energy flow supply and demand in a period by using a k-means clustering algorithm and taking a day as a basic clustering unit so as to obtain a group of annual typical supply and demand working condition sets;
establishing multiple energy flow supply and demand matching constraints: applying operating data concentrated by annual typical supply and demand working conditions to a comprehensive energy system network mechanism model and a target function of minimum comprehensive delay of multiple energy flows under the topological design condition of the conventional comprehensive energy system, and judging whether effective solutions of the whole network temperature, the whole network flow and the whole network delay can be obtained or not;
establishing a space distribution constraint of network nodes of the comprehensive energy system, namely determining a feasible domain of a topological structure of the comprehensive energy system on a space level, wherein the specific method comprises the following steps: and determining each energy flow transmission trunk line constructed along the highway by adopting an enumeration algorithm, and selecting a user node and establishing an energy flow transmission branch line at the shortest distance from the energy flow transmission trunk line, thereby forming a feasible region of the source-load network topological structure of the comprehensive energy system on a spatial level.
7. The integrated energy system topology optimization method of claim 6, wherein:
the step S5 specifically includes the following steps: (1) firstly, determining the number of initial planning schemes, combining feasible intra-domain connection schemes into d-dimensional particles, and initializing parameters of an acceleration factor, the maximum iteration times and the maximum particle speed;
(2) initializing speed vectors and position information of various energy flow topological network connection schemes, and enabling the position of the current scheme to be the individual historical global optimal position of each scheme; calculating the optimal positions of all the connection planning schemes simultaneously;
(3) defining a fitness function, comparing the global optimal position with the historical global optimal position, and updating the speed and the position of the energy flow topological network scheduling scheme particles by using an updating formula
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)
Wherein i represents the ith particle; j represents the j-th dimension of the particle; v. of ij (t) represents the j-th dimensional flight velocity component of the particle i when it evolves to the t generation; x is the number of ij (t) represents the j-th dimensional position component of particle i when evolving to t generation; pbest ij (t) individual optimal positions pbest of j-dimension when particle i evolves to t generation i A component; gbest j (t) a j-dimensional component representing the optimal position gbest of the entire particle population when evolving to the t generation; c. C 1 ，c 2 The former is the individual learning factor for each particle,the latter is the social learning factor of each particle; r is 1 ，r 2 Is [0,1 ]]A random number within;
(4) calculating an objective function value of the position of each connection scheme, and updating the individual historical optimal position of each connection scheme and the optimal positions of all scheduling schemes by using an updating formula: when the set maximum iteration times are reached, stopping calculation and outputting a result; otherwise, returning to the step (3) to continue searching.
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