A hub modeling method for integrated energy system based on radial network

Abstract

The present invention relates to the field of integrated energy systems, and in particular to a network hub modeling method for an integrated energy system based on a radial network: the method includes: proposing a matrix modeling method for a network hub of an electric-gas-heat regional integrated energy system based on a distributed radial network; establishing a power grid network model of an electric-gas-heat regional integrated energy system based on a distributed radial network; establishing a gas grid network model of an electric-gas-heat regional integrated energy system based on a distributed radial network; establishing a heat grid network model of an electric-gas-heat regional integrated energy system based on a distributed radial network; processing a power grid network model, a gas grid network model and a heat grid network model based on a distributed radial network to complete the hub matrix modeling. The present invention protects the privacy of the intermediate distributed system and retains the accuracy at the same time.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy systems, and in particular to a modeling method for a network hub of an integrated energy system based on a radial network. Background Art

[0002] The regional integrated energy system takes the power system as the core and adopts distributed power generation to promote the interaction of supply and demand of electricity, heat, cooling and gas, and improve the regional energy utilization efficiency and renewable energy consumption level. Distributed computing can decompose the optimal scheduling task concentrated in the regional joint scheduling center into multiple subtasks, and assign the optimal scheduling task to each user. The research on coordinated control of integrated energy systems based on distributed algorithms refers to the use of distributed computing methods in the region to achieve collaborative optimization and fault emergency control of multi-user integrated energy systems.

[0003] However, in a radial network, there are many terminal distributed systems that are not directly connected to the upper network. They may be connected to the upper network through the same intermediate distributed system. If the intermediate distributed system does not participate in distributed optimization, and these terminal distributed systems do not directly perform distributed optimization through new communication lines, it is necessary to couple the ports of the two, thereby increasing the risk of privacy leakage in the intermediate distributed system. Summary of the invention

[0004] The purpose of the present invention is to provide a network hub modeling method for an integrated energy system based on a radial network, which protects the privacy of the intermediate distributed system and retains accuracy at the same time.

[0005] In order to solve the above technical problems, the technical solution of the present invention is: a network hub modeling method for an integrated energy system based on a radial network, comprising:

[0006] Step 1: Propose a matrix modeling method for the network hub of the electricity-gas-heat regional integrated energy system based on a distributed radial network; Step 2: Establish a power grid network model of the electricity-gas-heat regional integrated energy system based on a distributed radial network;

[0007] Step 3: Establish a gas network model of the electricity-gas-heat regional integrated energy system based on a distributed radial network;

[0008] Step 4: Establish a heat network model of the electricity-gas-heat regional integrated energy system based on a distributed radial network;

[0009] Step 5: Based on the matrix modeling method, process the power grid network model, gas grid network model and heat grid network model to complete the hub matrix modeling.

[0010] Further, in step 1, the matrix modeling method is a matrix modeling method based on linear equality constraints;

[0011] The linear equality constraint is expressed as:

[0012]

[0013] In the formula: x is a variable; a and b are constants; m is the number of linear equality constraints; n is the number of variables; a mn represents the constant coefficient of the nth variable in the mth linear equality constraint; x n represents the nth variable; b m represents the constant parameter of the mth linear equality constraint;

[0014] Introduce a set of variables c1…c m , rewrite formula (1) as follows:

[0015]

[0016] Among them, c m represents the coefficient variable of the mth linear equality constraint;

[0017] Express equation (2) in matrix form:

[0018]

[0019] If you want to keep the variables x1…x k , eliminating the variable x k+1 …x n , where k ≥ n-m+1, then there exists a set of c1…c that are not all zero. m satisfy:

[0020]

[0021] Further eliminate the variable x k+1 …x n ,get:

[0022]

[0023] In the formula: x is a variable; d and e are constants; (m-n+k) is the number of linear equality constraints; k is the number of variables; x k represents the kth variable;

[0024] When k=n-m+1, d and e satisfy:

[0025]

[0026]

[0027] Among them, d 1jrepresents the constant coefficient of the jth variable in the first linear equality constraint in equation (5); e1 represents the constant coefficient of the jth variable in the ith linear equality constraint in equation (1);

[0028] If the variables x1…x k In x1…x t is the state variable, x t+1 …x k is the control variable, then formula (5) can be further expressed as:

[0029]

[0030] Where: x is a variable; d and e are constants; (m-n+k) is the number of linear equality constraints; t is the number of state variables; (kt) is the number of control variables;

[0031] Finally, for linear inequality constraints, they are divided into the following four categories according to the types of variables contained in the constraints:

[0032] 1) When the variable type is "variable of a port node or a branch between system ports", the linear inequality constraints of the relevant variables are directly retained;

[0033] 2) When the variable type is "variables of coupling devices and energy storage devices", the linear inequality constraints of the relevant variables are directly retained;

[0034] 3) When the variable type is "variable of internal nodes or branches of the system", for the radial network, after the source node and its adjacent branches meet the inequality constraints of the upper and lower limits, the remaining nodes and branches meet the inequality constraints of the upper and lower limits accordingly;

[0035] 4) When the variable types do not belong to the above situations, but are other situations, the equality constraint relationship between the current variable and the retained variable is established through the matrix modeling method based on linear equality constraints, and the inequality constraint for the current variable is rewritten into the inequality constraint for the retained variable.

[0036] Furthermore, in step 2, the variables retained are: the voltage amplitude U of the port node (let W = 1 / U), the active power flow P and reactive power flow Q of the port branch; the constants retained are: the active load P of each node L , reactive load Q L , the resistance R and reactance X of each branch;

[0037] The distribution network model is as follows:

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] Where: subscript p is the parent node of the node; c(i) is the set of child nodes of node i; is the active power flow transformation variable from the parent node of i to the branch of node i; is the transformation variable of reactive power flow from parent node i to branch node i; R ij , X ij are the resistance and reactance of branch ij respectively; W i is the inverse of the voltage amplitude at node i; P i , Q i are the active load and reactive load of node i respectively; P ij , Q ij are the active power flow and reactive power flow of branch ij respectively; P i , Q i Transformed variables; P ij , Q ij Transformed variables; They are the upper and lower limits of the purchased power respectively; are the upper and lower limits of branch power flow respectively; are the upper and lower limits of the node voltage respectively.

[0049] Furthermore, in step 3, the variables retained are: the pressure p of the port node, the flow q of the port branch; the constants retained are: the flow load q of each node L , the flow-pressure coefficient K of each branch;

[0050] The gas distribution network model is as follows:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] ε k+1 ≤λ k ≤ε k k=1,2,…,num-2(26)

[0059] 0≤ε k ≤1k=1,2,…,num-1(27)

[0060] λ k ∈{0,1}k=1,2,…,num-2(28)

[0061] Where: Ω j- ,Ω j+ are the pipeline sets with node j as the end and start respectively; q ij is the flow rate of branch ij; q j is the traffic load of node j; is the square of the pressure at node i; K ij is the natural gas pipeline transmission coefficient of branch ij; They are the upper and lower limits of gas purchase flow respectively; They are the upper and lower limits of branch flow respectively; are the upper and lower limits of the square of the node pressure, respectively; k is a binary variable; q k represents the flow of the kth segment; ε k Represents a random number between 0 and 1; k represents the number of segments.

[0062] Furthermore, in step 4, the heat network adopts a quality regulation mode, and the retained variables are: the thermal power Q and temperature T of the port branch, and the retained constants are: the ambient temperature T e , specific heat capacity of water c, temperature drop coefficient of each branch J, pipeline flow q;

[0063] The heat distribution network model is as follows:

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] Where: Ω i- ,Ω i+ are the pipeline sets with node i as the end and start respectively; q k is the flow rate in pipe k; are the thermal powers at the inlet and outlet of pipe k respectively; are the temperatures at the inlet and outlet of pipe k respectively; T i is the temperature of node i; J k is the Sukhov temperature drop correction coefficient of pipeline k; T e is the ambient temperature; c is the specific heat capacity of water; They are the upper and lower limits of purchased heat power respectively; are the upper and lower limits of node temperature respectively.

[0072] The present invention has the following beneficial effects:

[0073] 1. The present invention establishes the relationship between the port information of the intermediate distributed system by retaining the port information and necessary internal messages of the intermediate distributed system, thereby protecting the privacy of the intermediate distributed system while completing the optimization of non-adjacent distributed systems; and the network hub matrix modeling method can protect the privacy of the intermediate distributed system;

[0074] 2. The present invention simplifies the calculation of distributed networks by using matrix modeling, while ensuring the accuracy of the relationship between port parameters, thereby simplifying distributed calculations while retaining accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is a flow chart of the method of the present invention;

[0076] Figure 2 This is a framework diagram of the radial network equivalence method for distributed regional integrated energy systems. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0078] Please refer to Figure 1,The present invention is a network hub modeling method for an integrated energy system based on a radial network, comprising: step 1: proposing a matrix modeling method for a network hub of an electric-gas-heat regional integrated energy system based on a distributed radial network; step 2: establishing a power grid network model of an electric-gas-heat regional integrated energy system based on a distributed radial network;

[0079] Step 3: Establish a gas network model of the electricity-gas-heat regional integrated energy system based on a distributed radial network;

[0080] Step 4: Establish a heat network model of the electricity-gas-heat regional integrated energy system based on a distributed radial network;

[0081] Step 5: Based on the matrix modeling method, process the power grid network model, gas grid network model and heat grid network model to complete the hub matrix modeling.

[0082] In step 1, first, the electricity-gas-heat regional integrated energy system network in the present invention is a distributed network, that is, a network that is solved by a distributed algorithm instead of a centralized algorithm. The electricity-gas-heat regional integrated energy system network in the present invention is a radial network, that is, it does not include a ring network and belongs to a distribution network level network. The electricity-gas-heat regional integrated energy system network in the present invention simultaneously retains the information of the distributed network ports and the necessary internal information. Therefore, this equivalence method is applicable to most integrated energy system optimization problems, including but not limited to integrated energy system planning problems, integrated energy system operation problems and integrated energy system optimal power flow problems; thereby realizing the interaction between terminal distributed systems.

[0083] Secondly, for optimization problems with more variables than linear equality constraints, a matrix modeling method for linear equality constraints is proposed as follows:

[0084] The linear equality constraint is expressed as:

[0085]

[0086] In the formula: x is a variable; a and b are constants; m is the number of linear equality constraints; n is the number of variables; a mn represents the constant coefficient of the nth variable in the mth linear equality constraint; x n represents the nth variable; b m represents the constant parameter of the mth linear equality constraint;

[0087] Introduce a set of variables c1…c m , rewrite formula (1) as follows:

[0088]

[0089] Among them, c mrepresents the coefficient variable of the mth linear equality constraint;

[0090] Express equation (2) in matrix form:

[0091]

[0092] If you want to keep the variables x1…x k , eliminating the variable x k+1 …x n , where k ≥ n-m+1, then there exists a set of c1…c that are not all zero. m satisfy:

[0093]

[0094] Further eliminate the variable x k+1 …x n ,get:

[0095]

[0096] In the formula: x is a variable; d and e are constants; (m-n+k) is the number of linear equality constraints; k is the number of variables; x k represents the kth variable;

[0097] When k=n-m+1, d and e satisfy:

[0098]

[0099]

[0100] Among them, d 1j represents the constant coefficient of the jth variable in the first linear equality constraint in equation (5); e1 represents the constant coefficient of the jth variable in the ith linear equality constraint in equation (1);

[0101] If the variables x1…x k In x1…x t is the state variable, x t+1 …x k is the control variable, then formula (5) can be further expressed as:

[0102]

[0103] Where: x is a variable; d and e are constants; (m-n+k) is the number of linear equality constraints; t is the number of state variables; (kt) is the number of control variables;

[0104] Finally, for linear inequality constraints, they are divided into the following four categories according to the types of variables contained in the constraints:

[0105] 1) When the variable type is "variables of port nodes or branches between system ports", most of these variables are retained state variables, so the linear inequality constraints of the relevant variables can be directly retained;

[0106] 2) When the variable type is "variables of coupling equipment and energy storage equipment", most of these variables are retained control variables, so the linear inequality constraints of the relevant variables can be directly retained;

[0107] 3) When the variable type is "variable of internal nodes or branches of the system", for the radial network, after the source node and its adjacent branches meet the inequality constraints of the upper and lower limits, the remaining nodes and branches meet the inequality constraints of the upper and lower limits accordingly;

[0108] 4) When the variable types do not belong to the above situations, but are other situations, the equality constraint relationship between the current variable and the retained variable is established through the matrix modeling method based on linear equality constraints, and the inequality constraint for the current variable is rewritten into the inequality constraint for the retained variable.

[0109] In step 2, the specific contents of the grid network modeling method of the electric-gas-heat regional integrated energy system based on the distributed radial network include:

[0110] The variables retained in this step are: the voltage amplitude U of the port node (let W = 1 / U), the active power flow P and reactive power flow Q of the port branch; the constants retained are: the active load P of each node L , reactive load Q L , the resistance R and reactance X of each branch;

[0111] The distribution network model is as follows:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122] Where: subscript p is the parent node of the node; c(i) is the set of child nodes of node i; is the active power flow transformation variable from the parent node of i to the branch of node i; is the transformation variable of reactive power flow from parent node i to branch node i; R ij , X ij are the resistance and reactance of branch ij respectively; W i is the inverse of the voltage amplitude at node i; P i , Q i are the active load and reactive load of node i respectively; P ij , Q ij are the active power flow and reactive power flow of branch ij respectively; P i , Q i Transformed variables; P ij , Q ij Transformed variables; They are the upper and lower limits of the purchased power respectively; are the upper and lower limits of branch power flow respectively; are the upper and lower limits of the node voltage respectively.

[0123] In step 3, the specific contents of the gas network modeling method of the electric-gas-heat regional integrated energy system based on the distributed radial network include:

[0124] In this step, the variables retained are: the pressure p of the port node, the flow q of the port branch; the constants retained are: the flow load q of each node L , the flow-pressure coefficient K of each branch;

[0125] The gas distribution network model is as follows:

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] ε k+1 ≤λ k≤ε k k=1,2,…,num-2(26)

[0134] 0≤ε k ≤1k=1,2,…,num-1(27)

[0135] λ k ∈{0,1}k=1,2,…,num-2(28)

[0136] Where: Ω j- ,Ω j+ are the pipeline sets with node j as the end and start respectively; q ij is the flow rate of branch ij; q j is the traffic load of node j; is the square of the pressure at node i; K ij is the natural gas pipeline transmission coefficient of branch ij; They are the upper and lower limits of gas purchase flow respectively; They are the upper and lower limits of branch flow respectively; are the upper and lower limits of the square of the node pressure, respectively; k is a binary variable; q k represents the flow of the kth segment; ε k Represents a random number between 0 and 1; k represents the number of segments.

[0137] In step 3, the variables retained are: the pressure p of the port node, the flow q of the port branch; the constants retained are: the flow load q of each node L , the flow-pressure coefficient K of each branch;

[0138] The gas distribution network model is as follows:

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] ε k+1 ≤λ k ≤ε k k=1,2,…,num-2(26)

[0147] 0≤ε k ≤1k=1,2,…,num-1(27)

[0148] λ k ∈{0,1}k=1,2,…,num-2(28)

[0149] Where: Ω j- ,Ω j+ are the pipeline sets with node j as the end and start respectively; q ij is the flow rate of branch ij; q j is the traffic load of node j; is the square of the pressure at node i; K ij is the natural gas pipeline transmission coefficient of branch ij; They are the upper and lower limits of gas purchase flow respectively; They are the upper and lower limits of branch flow respectively; are the upper and lower limits of the square of the node pressure, respectively; k is a binary variable; q k represents the flow of the kth segment; ε k Represents a random number between 0 and 1; k represents the number of segments.

[0150] In step 4, the specific contents of the heat network modeling method of the electric-gas-heat regional integrated energy system based on the distributed radial network include:

[0151] In this step, the heat network adopts the quality regulation mode. The variables retained are: the thermal power Q and temperature T of the port branch, and the constants retained are: the ambient temperature T e , specific heat capacity of water c, temperature drop coefficient of each branch J, pipeline flow q;

[0152] The heat distribution network model is as follows:

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160] Where: Ω i- ,Ω i+are the pipeline sets with node i as the end and start respectively; q k is the flow rate in pipe k; are the thermal powers at the inlet and outlet of pipe k respectively; are the temperatures at the inlet and outlet of pipe k respectively; T i is the temperature of node i; J k is the Sukhov temperature drop correction coefficient of pipeline k; T e is the ambient temperature; c is the specific heat capacity of water; They are the upper and lower limits of purchased heat power respectively; are the upper and lower limits of node temperature respectively.

[0161] The present invention is introduced below with an example:

[0162] First, for Figure 2 The system shown in the figure is matrix modeled using the method proposed in the present invention. The distributed system A is matrix modeled as a network hub, the topology of A is hidden, and only the relationship between the ports of the upper network and B, and the upper network and C is retained, and the following can be obtained:

[0163]

[0164]

[0165] [Q 1,2 ]=[5.383][Q 4,5 ]-[279.203](38)

[0166] [Q 1,2 ]=[10.766][Q 4,11 ]-[279.203](39)

[0167] According to the method in step 1, the formulas in steps 2, 3, and 4 in the actual situation are processed to obtain formulas (36)-(39); where P 1,2 Represents the active power of the branch between nodes 1 and 2; q 1,2 represents the natural gas flow rate of the branch between nodes 1 and 2; Q 1,2 Indicates the thermal power of the branch between nodes 1 and 2.

[0168] Secondly, the initial port variable values ​​of distributed systems A, B, and C in Table 1 after centralized optimization are compared with the port relationships obtained by matrix modeling of the network hub, i.e., equations (36)-(39).

[0169] Table 1 Variable values ​​of each port after centralized optimization

[0170]

[0171] It can be seen from Table 1 that the relationships between variables obtained by the two methods (centralized optimization method and distributed optimization method) are consistent, which verifies the effectiveness of the method proposed in the present invention.

[0172] The parts not involved in the present invention are the same as the prior art or are implemented by using the prior art.

[0173] The above contents are further detailed descriptions of the present invention in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A network hub modeling method for an integrated energy system based on a radial network, characterized by: include Step 1: Propose a matrix modeling method for the network hub of the electric-gas-heat regional integrated energy system based on a distributed radial network; the matrix modeling method is a matrix modeling method based on linear equality constraints; The linear equality constraint is expressed as: In the formula: x is a variable; a and b are constants; m is the number of linear equality constraints; n is the number of variables; a mn represents the constant coefficient of the nth variable in the mth linear equality constraint; x n represents the nth variable; b m represents the constant parameter of the mth linear equality constraint; Introduce a set of variables c1…c m , rewrite formula (1) as follows: Among them, c m represents the coefficient variable of the mth linear equality constraint; Express equation (2) in matrix form: If you want to keep the variables x1…x k , eliminating the variable x k+1 …x n , where k ≥ n-m+1, then there exists a set of c1…c that are not all zero. m satisfy: Further eliminate the variable x k+1 …x n ,get: In the formula: x is a variable; d m-n+k,k 、e m-n+k is a constant; m-n+k is the number of linear equality constraints; k is the number of variables; x k represents the kth variable; When k = n-m+1, d m-n+k,k 、e m-n+k Respectively meet: Among them, d 1j represents the constant coefficient of the jth variable in the first linear equality constraint in equation (5); e1 represents the constant coefficient of the jth variable in the ith linear equality constraint in equation (1); If the variables x1…x k In the equation x1…x t is the state variable, x t+1 …x k is the control variable, then formula (5) can be further expressed as: In the formula: t is the number of state variables; kt is the number of control variables; Finally, for linear inequality constraints, they are divided into the following four categories according to the types of variables contained in the constraints: 1) When the variable type is "variable of port node or branch between system ports", the linear inequality constraints of the relevant variables are directly retained; 2) When the variable type is "variables of coupling devices and energy storage devices", the linear inequality constraints of the relevant variables are directly retained; 3) When the variable type is "variable of internal nodes or branches of the system", for radial networks, after the source node and its adjacent branches satisfy the inequality constraints of the upper and lower limits, the remaining nodes and branches also satisfy the inequality constraints of the upper and lower limits accordingly; 4) When the variable types do not belong to the above types, the equality constraint relationship between the current variable and the retained variable is established through the matrix modeling method based on linear equality constraints, and the inequality constraint for the current variable is rewritten into the inequality constraint for the retained variable; Step 2: Establish a power grid network model of the electric-gas-heat regional integrated energy system based on a distributed radial network; the retained variables are: the voltage amplitude U of the port node, the active power flow P and reactive power flow Q of the port branch, let W = 1 / U; the retained constants are: the active load P of each node L , reactive load Q L , the resistance R and reactance X of each branch; The distribution network model is as follows: Where: subscript p is the parent node of the node; c(i) is the set of child nodes of node i; is the active power flow transformation variable from the parent node of i to the branch of node i; is the transformation variable of reactive power flow from parent node i to branch node i; R ij , X ij are the resistance and reactance of branch ij respectively; W i is the inverse of the voltage amplitude at node i; P i , Q i are the active load and reactive load of node i respectively; P ij , Q ij are the active power flow and reactive power flow of branch ij respectively; P i , Q i Transformed variables; P ij , Q ij Transformed variables; They are the upper and lower limits of purchased power respectively; are the upper and lower limits of branch power flow respectively; are the upper and lower limits of node voltage respectively; Step 3: Establish a gas network model of the electric-gas-heat regional integrated energy system based on a distributed radial network; the retained variables are: the pressure p of the port node, the flow q of the port branch; the retained constants are: the flow load q of each node L , the flow-pressure coefficient K of each branch; The gas distribution network model is as follows: ε k+1 ≤λ k ≤ε k k=1,2,…,num-2 (26) 0≤ε k ≤1 k=1,2,…,num-1 (27) λ k ∈{0,1} k=1,2,…,num-2 (28) Where: Ω j- ,Ω j+ are the pipeline sets with node j as the end and start respectively; q ij is the flow rate of branch ij; q j is the traffic load of node j; is the square of the pressure at node i; K ij is the natural gas pipeline transmission coefficient of branch ij; They are the upper and lower limits of gas purchase flow respectively; They are the upper and lower limits of branch flow respectively; are the upper and lower limits of the square of the node pressure, respectively; k is a binary variable; q k represents the flow of the kth segment; ε k represents a random number between 0 and 1; k represents the number of segments; Step 4: Establish a heat network model of the electric-gas-heat regional integrated energy system based on a distributed radial network; the heat network adopts a quality regulation method, and the retained variables are: the thermal power Q and temperature T of the port branch, and the retained constants are: the ambient temperature T e , specific heat capacity of water c, temperature drop coefficient of each branch J, pipeline flow q; The heat distribution network model is as follows: T i min ≤T i ≤T i max (35) Where: Ω i- ,Ω i+ are the pipeline sets with node i as the end and the start respectively; q k is the flow rate in pipe k; are the thermal powers at the inlet and outlet of pipe k respectively; are the temperatures at the inlet and outlet of pipe k respectively; T i is the temperature of node i; J k is the Sukhov temperature drop correction coefficient of pipeline k; T e is the ambient temperature; c is the specific heat capacity of water; are the upper and lower limits of purchased heat power respectively; T i max 、T i min are the upper and lower limits of node temperature respectively; Step 5: Based on the matrix modeling method, process the power grid network model, gas grid network model and heat grid network model to complete the hub matrix modeling.

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