Distributed energy cluster optimization scheduling method, medium and electronic equipment

Abstract

The present invention discloses a distributed energy cluster optimization scheduling method, medium and electronic equipment, the method includes establishing a power network transmission model, determining the power transmission loss cost according to the power network transmission model; establishing a heat network heat storage equivalent micro-element dynamic model, determining the thermal transmission loss cost according to the heat network heat storage equivalent micro-element dynamic model; determining the distribution network power purchase cost and equipment operation and maintenance cost, and constructing a regional multi-energy optimization objective function according to the distribution network power purchase cost, equipment operation and maintenance cost, power transmission loss cost and thermal transmission loss cost; establishing the constraint conditions of the regional multi-energy optimization objective function, solving the regional multi-energy optimization objective function based on the constraint conditions, obtaining the optimal output value of the heat pump and cogeneration equipment in the distributed energy cluster in each time period, and realizing the distributed energy cluster optimization scheduling. The present invention can realize the peak-shifting sharing of thermal energy among multiple distributed energy clusters, which is convenient for improving the overall regional energy supply economy.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy systems, and in particular to a distributed energy cluster optimization scheduling method, medium and electronic equipment. Background Art

[0002] The fourth generation of regional heating networks makes full use of local energy resources such as solar energy, wind energy, and low-grade waste heat to form a distributed intelligent multi-energy cooperative supply network. Therefore, it is necessary to achieve regional autonomy of each cluster and multi-energy mutual assistance between clusters, which is conducive to improving the overall regional energy supply economy. The heat storage advantage of the regional low-temperature heat ring network can bring great flexibility to the energy interaction between clusters, which is usually ignored due to the complex thermal dynamic model of the heat network. The heat storage advantage of the heat ring network has attracted widespread attention due to its large adjustable flexibility. Therefore, it is necessary to provide an effective operation and scheduling scheme that utilizes the heat storage characteristics of the regional heat ring network to achieve multi-energy cooperation of regional distributed energy clusters, so as to improve the overall regional energy supply economy. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first object of the present invention is to provide a distributed energy cluster optimization scheduling method, which utilizes the heat storage characteristics of the heat ring network to achieve peak-shifting sharing of heat energy among multiple distributed energy clusters, thereby improving the overall energy supply economy of the region.

[0004] A second object of the present invention is to provide a computer-readable storage medium.

[0005] A third object of the present invention is to provide an electronic device.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A distributed energy cluster optimization scheduling method, comprising:

[0008] Establishing a power network transmission model, and determining power transmission loss costs according to the power network transmission model;

[0009] Establishing a dynamic model of heat storage equivalent micro-element of the heat network, and determining the heat transmission loss cost according to the dynamic model of heat storage equivalent micro-element of the heat network;

[0010] Determine the power purchase cost and equipment operation and maintenance cost of the distribution network, and construct a regional multi-energy optimization objective function based on the power purchase cost, equipment operation and maintenance cost, power transmission loss cost and thermal transmission loss cost of the distribution network;

[0011] Establishing constraint conditions of the regional multi-energy optimization objective function, solving the regional multi-energy optimization objective function based on the constraint conditions, obtaining the optimal output values ​​of heat pumps and cogeneration equipment in the distributed energy cluster in each time period, and realizing the optimal scheduling of the distributed energy cluster;

[0012] The power network transmission model is a model for calculating the power network transmission power. The power network transmission power is expressed as follows:

[0013]

[0014] in, Transmit power to the electricity network, Transmits voltage to the power grid, is the transmission current of the power network, θ is the phase angle, g represents the imaginary unit, and t represents the time;

[0015] The dynamic model of the heat storage equivalent microelement of the heat network is expressed as follows:

[0016]

[0017] in, is the heat storage capacity of the kth microelement in the pipeline between nodes i and j of the heating network at time t, Δt is the unit scheduling time, is the heat storage capacity of the kth microelement in the pipeline between nodes i and j of the heating network at time t-Δt, c is the specific heat capacity of water, is the water flow mass of the kth microelement in the pipeline between nodes i and j of the heating network at time t, are the temperatures on both sides of the kth microelement in the pipeline between the heating network nodes i and j at time t, λ ij is the heat transfer coefficient of the pipeline between nodes i and j in the heating network, Δx is the length of the microelement, T a is the ambient temperature;

[0018] The regional multi-energy optimization objective function is expressed as follows:

[0019]

[0020] Among them, F is the regional multi-energy optimization objective function, W D is the internal decision variable of the distributed energy cluster, P h is the heat exchange power between the distributed energy cluster and the heat network, P e is the electric power exchanged between distributed energy clusters, J is the system node, including grid nodes and heating network nodes, n is the total number of system nodes, ∑ is the summation function, is the total cost of operating a distributed energy cluster, is the total cost of power and heat transmission loss, H is the dispatch period, are the operation and maintenance costs of the gas turbine and heat source at system node J, The operation and maintenance costs of the heating network. They are the purchased power and the online power, are the unit costs of purchased power and grid-connected power, To purchase natural gas power, is the unit price of natural gas power, P e,loss,t , P VHS,loss,t are respectively the power transmission loss and the heat transmission loss, c loss,e 、c loss,h They are the unit cost of power loss in electricity transmission and the unit cost of power loss in heat transmission respectively.

[0021] Preferably, the constraints of the regional multi-energy optimization objective function include heat network transmission constraints, maximum charge and discharge power constraints of the heat network equivalent energy storage model, and charge balance constraints of the heat network equivalent energy storage model.

[0022] Preferably, the heat network transmission constraint condition includes a heat exchange power balance constraint sub-condition between the heat network node and the distributed energy cluster and a constraint sub-condition that a heat exchange node exists between micro-elements.

[0023] Preferably, the method also includes: determining the heat network flow parameters, and determining the heat network equivalent energy storage model according to the heat network flow parameters, so as to determine the maximum charging and discharging power constraints and the charge balance constraints of the heat network equivalent energy storage model.

[0024] Preferably, the heat network flow parameters at least include the injected water flow and the outflow water flow of the heat network nodes, the charging and discharging power of the heat network equivalent energy storage model, and the heat network node temperature and pipeline temperature.

[0025] Preferably, the step of solving the regional multi-energy optimization objective function based on the constraint conditions includes:

[0026] Solve the heat network flow parameters;

[0027] Determine the heating network equivalent energy storage model according to the heating network power flow parameters, and determine the charge balance constraint condition and the maximum charge and discharge power constraint condition of the heating network equivalent energy storage model;

[0028] Solving the regional multi-energy optimization objective function to obtain the decision variable value of the current iteration, wherein the decision variable value is the output value of the heat pump and the cogeneration equipment in the distributed energy cluster in each time period;

[0029] Return to solve the heat network power flow parameters for the next round of iteration and obtain the decision variable values ​​after the latest round of iteration;

[0030] Determine the difference between the decision variable value after the latest round of iteration and the decision variable value after the previous round of iteration. If the difference is less than the convergence threshold, the decision variable value after the latest round of iteration is determined to be the optimal value, and the decision variable value after the latest round of iteration is output.

[0031] To achieve the above-mentioned purpose, the second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned distributed energy cluster optimization scheduling method is implemented.

[0032] To achieve the above-mentioned purpose, the third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned distributed energy cluster optimization scheduling method is implemented.

[0033] The present invention has at least the following technical effects:

[0034] The present invention provides a distributed energy cluster optimization scheduling method, which obtains the power transmission loss cost by establishing a power network transmission model, then establishes a dynamic model of the heat storage equivalent micro-element of the heat network to obtain the thermal transmission loss cost, and then constructs a regional multi-energy optimization objective function based on each transmission loss cost. Then, the constraints of the regional multi-energy optimization objective function are determined, and the constraints include the heat network transmission constraints, the maximum charge and discharge power constraints of the heat network equivalent energy storage model, and the charge balance constraints of the heat network equivalent energy storage model, wherein the heat storage equivalent micro-element dynamic model of the heat network and the various constraints of the heat network reflect the heat storage characteristics of the heat network, and then, based on these constraints, the regional multi-energy optimization objective function is solved to obtain the optimal decision variable value of the distributed energy cluster, thereby utilizing the heat storage characteristics of the heat ring network, i.e., the heat network, to realize the peak-shifting sharing of heat energy among multiple distributed energy clusters, thereby facilitating the improvement of the overall regional energy supply economy.

[0035] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a distributed energy cluster optimization scheduling method according to an embodiment of the present invention.

[0037] Figure 2 Schematic diagram of the structure of adjacent micro-elements of a heating network pipeline according to an embodiment of the present invention.

[0038] Figure 3 Schematic diagram of the influence of the length of the microelement on the maximum relative error of the equivalent energy storage model of the heating network according to an embodiment of the present invention.

[0039] Figure 4 FIG. 4 is a schematic diagram of various parameters of RHN according to an embodiment of the present invention.

[0040] Figure 5 Schematic diagram of heat inflow and heat outflow of each distributed energy cluster in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present embodiment is described in detail below, and examples of the embodiment are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0042] The distributed energy cluster optimization scheduling method, medium and electronic device of this embodiment are described below with reference to the accompanying drawings.

[0043] Figure 1 Flow chart of the distributed energy cluster optimization scheduling method according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0044] Step S1: Establish a power network transmission model, and determine the power transmission loss cost according to the power network transmission model.

[0045] In this embodiment, establishing the power network transmission model mainly refers to calculating the power network transmission power, and the power network transmission power is expressed as:

[0046]

[0047] in, Transmit power to the electricity network, Transmits voltage to the power grid, is the transmission current of the power network, θ is the phase angle, g represents the imaginary unit, and t represents the time.

[0048] Based on the power network transmission power, the power transmission loss power can be obtained:

[0049]

[0050] Among them, P e,loss,t is the power transmission loss, R d is the line impedance.

[0051] Step S2: Establish a dynamic model of the heat storage equivalent micro-element of the heat network, and determine the heat transmission loss cost according to the dynamic model of the heat storage equivalent micro-element of the heat network.

[0052] The one-dimensional flow process of water in a pipe can be expressed by the energy conservation equation:

[0053]

[0054] in, is the partial derivative symbol, T(t,x) is the pipe temperature function, x is the pipe position, m is the pipe water mass, ρ is the water density, A is the cross-sectional area of ​​the pipe, λ is the pipe heat transfer coefficient, c is the specific heat capacity of water, T a is the ambient temperature.

[0055] Divide the pipeline into micro-units. When the length is small enough, equation (3) can be approximated as:

[0056]

[0057] Among them, Δt is the unit scheduling time, are the temperatures on both sides of the kth microelement in the pipeline between the nodes i and j of the heating network at time t, are the temperatures on both sides of the kth microelement in the pipeline between the heating network nodes i and j at the time t-Δt, is the water flow mass of the kth microelement in the pipeline between nodes i and j of the heating network at time t, Δx is the length of the microelement, λ ij is the heat transfer coefficient of the pipeline between heating network nodes i and j.

[0058] Furthermore, the heat storage capacity of the microelement k is expressed as:

[0059]

[0060] in, is the heat storage capacity of the kth microelement in the pipeline between nodes i and j of the heating network at time t, M ij,k is the intermediate coefficient of heat storage capacity, d ij,k is the diameter of the pipe where the infinitesimal body k is located.

[0061] Substituting equations (5) and (6) into equation (4), we can obtain the dynamic model of the equivalent micro-element of the thermal network, which is expressed as follows:

[0062]

[0063] in, is the heat storage capacity of the kth microelement in the pipeline between the heating network nodes i and j at the time t-Δt.

[0064] The left side of equation (7) is the change in heat storage capacity, the first term on the right side is the heat exchange value of the heating network node, and the second term on the right side is the heat transfer loss value of the kth microelement in the pipeline between heating network nodes i and j, which is recorded as It is expressed as follows:

[0065]

[0066] Combining equations (5) and (8) we can further obtain:

[0067]

[0068] Among them, T re It is the reference water flow temperature of the heating network.

[0069] Therefore, the heat transmission loss value of the heat network can be expressed as:

[0070]

[0071] Among them, Q h,loss,t is the heat transmission loss value of the heat network, and the heat transmission loss power P can be obtained from this energy loss value VHS,loss,t .

[0072] Step S3: Determine the power purchase cost of the distribution network and the equipment operation and maintenance cost, and construct a regional multi-energy optimization objective function based on the power purchase cost of the distribution network, the equipment operation and maintenance cost, the power transmission loss cost, and the heat transmission loss cost.

[0073] The goal of dispatch optimization is to minimize the overall comprehensive cost, including the power purchase cost of the distribution network, the equipment operation and maintenance cost, and the transmission loss cost caused by the shared power and heat among clusters, that is, the power transmission loss cost and the heat transmission loss cost. Based on this, the regional multi-energy optimization objective function can be constructed. The regional multi-energy optimization objective function is expressed as follows:

[0074]

[0075] Among them, F is the regional multi-energy optimization objective function, W D is the internal decision variable of the distributed energy cluster, W D =[P HP,e,in ,P HP,h,out ,P CHP,h,out ,P CHP,e,out ], P HP,e,in represents the electrical input power of the heat pump, P HP,h,out Indicates the heat output of the heat pump, P CHP,h,out represents the thermal output of the cogeneration equipment, P CHP,e,out Represents the electrical output of the cogeneration equipment, P h is the heat exchange power between the distributed energy cluster and the heat network, P e is the electric power exchanged between distributed energy clusters, J is the system node, including grid nodes and heating network nodes, n is the total number of system nodes, ∑ is the summation function, is the total cost of operating a distributed energy cluster, is the total cost of power and heat transmission loss, H is the dispatch period, are the operation and maintenance costs of the gas turbine and heat source at system node J, The operation and maintenance costs of the heating network. They are the purchased power and the online power, They are the unit cost of purchased power and the unit cost of grid-connected power, To purchase natural gas power, is the unit price of natural gas power, P e,loss,t , P VHS,loss,t As mentioned above, they are respectively the power loss in electric power transmission and the power loss in thermal power transmission, c loss,e 、c loss,h are the unit cost of power loss in electric power transmission and the unit cost of power loss in thermal power transmission respectively; where P h and P e It is also the decision quantity required to solve.

[0076] Step S4: Establish the constraints of the regional multi-energy optimization objective function, solve the regional multi-energy optimization objective function based on the constraints, obtain the optimal output value of the heat pump and cogeneration equipment in the distributed energy cluster in each time period, and realize the optimal scheduling of the distributed energy cluster.

[0077] In one embodiment of the present invention, the constraints of the regional multi-energy optimization objective function include heat network transmission constraints, maximum charging and discharging power constraints of the heat network equivalent energy storage model, and charge balance constraints of the heat network equivalent energy storage model.

[0078] Among them, the heat network transmission constraints include the heat exchange power balance constraint sub-conditions between the heat network nodes and the distributed energy clusters and the constraint sub-conditions that there are heat exchange nodes between micro-elements.

[0079] Specifically, the heat network transmission model is first explained.

[0080] When water flows into the heating network, the water flow model of the heating network node is:

[0081]

[0082] in, is the water mass injected into the heating network node at time t, i.e., the water flow into the node. is the water flow mass of the k+1th microelement in the pipeline between nodes i and j of the heating network at time t, m ij,k They are the upper and lower limits of the water flow quality of the kth microelement in the pipeline between the heating network nodes i and j, respectively.

[0083] When water flows into the heating network, the temperature mixing model of the heating network node is:

[0084]

[0085] in, is the temperature of the water injected into the node in the heating network node, It is the temperature on one side of the k+1th microelement in the pipeline between the heating network nodes i and j at time t.

[0086] When water flows out of the heating network, the water flow model of the heating network node and the temperature mixed model of the heating network node are:

[0087]

[0088] in, is the water mass flowing out of the outflow node in the heating network at time t, It is the temperature of the water flowing out of the outflow node in the heating network node.

[0089] Under this heat network transmission model, the heat exchange power balance constraint sub-condition between the heat network node and the distributed energy cluster can be obtained:

[0090]

[0091] in, They are respectively the heat exchange power between the heat network and the distributed energy cluster at the source node, i.e. the injection node, and the load node, i.e. the outflow node.

[0092] The constraint sub-conditions for the existence of heat exchange nodes between micro-elements are expressed as follows:

[0093]

[0094] Where, ΔT max The maximum allowable temperature change of the pipeline.

[0095] When there is no heat transfer node between two micro-elements, such as Figure 2 As shown, the temperature on one side of the kth microelement at time t is equal to the temperature on the other side of the k+1th microelement at time t.

[0096]

[0097] The maximum charging and discharging power constraints of the heating network equivalent energy storage model and the charge balance constraints of the heating network equivalent energy storage model are explained below.

[0098] First, the heat network equivalent energy storage model is a model used to describe the heat network energy storage process. The maximum heat storage capacity of the heat network equivalent energy storage model is expressed as follows:

[0099]

[0100] in, is the maximum thermal storage capacity of the equivalent energy storage model of the heat network, M r is the mass capacity of the equivalent energy storage model of the heat network, which is related to the heat network structure, T max is the maximum pipe temperature, and VHS represents the equivalent energy storage model of the heating network.

[0101] The loss factor of the equivalent energy storage model of the heat network is expressed as follows:

[0102]

[0103] in, are the first linear loss factor and the second linear loss factor of the equivalent energy storage model of the thermal network, are the first linear loss factor and the second linear loss factor of the pipeline between the heating network nodes i and j, respectively, RHN is the length of the heating network pipeline.

[0104] The maximum charging and discharging power constraints of the heat network equivalent energy storage model are expressed as follows:

[0105]

[0106] in, are the charging and discharging powers of the equivalent energy storage model of the heat network, They are respectively the charging and discharging power of the equivalent energy storage model of the thermal network.

[0107] Based on the parameters of the above heat network equivalent energy storage model, the charge balance constraint condition of the heat network equivalent energy storage model can be obtained, which is expressed as follows:

[0108]

[0109] in, are the charges of the equivalent energy storage model of the thermal network at time t and t-Δt, respectively. q and l represent the injection node and the outflow node, respectively.

[0110] In one embodiment of the present invention, the method further includes: determining the heat network flow parameters, and determining the heat network equivalent energy storage model according to the heat network flow parameters, so as to determine the maximum charging and discharging power constraints and charge balance constraints of the above-mentioned heat network equivalent energy storage model.

[0111] The heat network flow parameters include at least the injected water flow and the outflow water flow of the heat network nodes, the charging and discharging power of the heat network equivalent energy storage model, and the heat network node temperature and pipeline temperature.

[0112] In this embodiment, the heat network flow parameters can be solved based on the node and pipeline calculation sequence. Since the flow solution has nonlinear characteristics, the hydraulic-thermal decomposition method can be used for solution. The calculation sequence is to first calculate the temperature of a certain node, and then calculate the temperature of the downstream pipeline of the node on the premise of knowing the temperature of the upstream node, and then calculate the temperature of the node located downstream of the pipeline, that is, the end node of the pipeline, until the temperature of all nodes is calculated. The final calculation sequence of the heat network node temperature and pipeline temperature is:

[0113] O RHN =F find,index {F rank (o pipe ,o node )} (27)

[0114] Among them, O RHN The temperature calculation sequence for the pipes and nodes of the heat network, F find,index is a function that searches for the pipe number or node number corresponding to the computation sequence, F rank is the order function from large to small displacement, o pipe , o node Compute order values ​​for each pipeline and node respectively.

[0115] In one embodiment of the present invention, the step of solving the regional multi-energy optimization objective function based on the constraint conditions includes: solving the heating network flow parameters; determining the heating network equivalent energy storage model according to the heating network flow parameters, and determining the charge balance constraint conditions and the maximum charging and discharging power constraint conditions of the heating network equivalent energy storage model; solving the regional multi-energy optimization objective function to obtain the decision variable value of the current iteration, and the decision variable value is the output value of the heat pump and cogeneration equipment in the distributed energy cluster in each time period; returning to solve the heating network flow parameters for the next round of iteration, and obtaining the decision variable value after the latest round of iteration; determining the difference between the decision variable value after the latest round of iteration and the decision variable value after the previous round of iteration, if the difference is less than the convergence threshold, then determining that the decision variable value after the latest round of iteration is the optimal value, and outputting the decision variable value after the latest round of iteration.

[0116] Specifically, in order to effectively solve the regional multi-energy optimization problem, the regional multi-energy optimization problem can be divided into an inner problem and an outer problem. The inner problem is the problem of solving the heat network flow parameters, and the outer problem is the problem of solving the regional multi-energy optimization objective function. Since the heat network flow parameters are dynamically updated, the inner problem can be solved first, and then the outer problem can be solved based on the solution of the inner problem, and then the inner problem can be returned to solve, so as to alternately iterate and solve the optimal output value of the heat pump and cogeneration equipment in the distributed energy cluster in each time period, that is, the optimal distributed energy cluster optimization scheduling strategy. The overall solution order of the regional multi-energy optimization problem is:

[0117] Step S41: Initialize equipment configuration, source-load information and heat network flow parameters, that is, use the hydraulic-thermal decomposition method to solve the heat network flow parameters.

[0118] Step S42: Determine the heating network equivalent energy storage model that describes the heating network energy storage process based on the solved heating network flow parameters, and calculate the maximum heat storage capacity, loss factor and other parameters of the heating network equivalent energy storage model, and then determine the charge balance constraint conditions of the heating network equivalent energy storage model based on the calculated parameters.

[0119] Step S43: setting the maximum charging and discharging power of the heating network equivalent energy storage model, that is, determining the maximum charging and discharging power constraint conditions of the heating network equivalent energy storage model.

[0120] Step S44: Solve the regional multi-energy optimization objective function based on the above constraints to obtain the decision variable values ​​of the current iteration.

[0121] Step S45: Return to step S41, perform the next round of iteration, and obtain the decision variable value after the latest round of iteration, and then determine whether the convergence condition is met. If not, return to step S41; if satisfied, output the optimization result, that is, output the decision variable value after the latest round of iteration, and output the optimal optimization result. The convergence condition is expressed as follows:

[0122] |W D(v+1) -W D(v) |≤ε out (28)

[0123] Among them, W D(v+1) is the internal decision variable value of the distributed energy cluster after the v+1th iteration, W D(v) is the internal decision variable value of the distributed energy cluster after the vth iteration, ε out is the convergence threshold.

[0124] An analysis was conducted by taking an area with a large number of low-grade heat sources in a certain place as an example. In this area, the amount of low-grade heat sources in power plants, steel plants, data centers and sewage treatment plants were all measured on site. Among them, the waste heat temperature of a power plant is 43.2℃, and the daily output is 3025kW.h. The waste heat temperature of the data center is 31.5℃, and the daily output is 2063kW.h. The waste heat temperature of the steel plant is 52.1℃, and the daily output is 3863kW.h. The waste heat temperature of another power plant is 48.3℃, and the daily output is 3135kW.h. The waste heat temperature of the sewage treatment plant is 18℃, and the daily output is 842kW.h. There are 5 distributed energy clusters. The unit cost of power transmission loss and the unit cost of thermal transmission loss are set to 0.03CHY / kW, and the scheduling time scale is 1 hour. RHN, the heat ring network (referred to as heat network), connects the energy station nodes of each distributed energy cluster. The distances between adjacent heat network pipelines of cluster 1 to cluster 5 are 2000m, 2000m, 3200m, 4000m and 3000m respectively.

[0125] The influence of the length of the microelement on the maximum relative error of the equivalent energy storage model of the heating network is as follows: Figure 3 As shown, in order to control the maximum relative error to less than 2%, the length of the microelement is set to 200m.

[0126] RHN parameters such as Figure 4 As shown. Positive power indicates that the distributed energy cluster injects heat flow into the RHN, and negative power indicates that the heat flow is injected from the RHN to the distributed energy cluster. The results show that heat is released from 2:00 to 8:00 and 12:00 to 20:00, and heat is charged in other periods.

[0127] The heat inflow and heat outflow of each distributed energy cluster are as follows: Figure 5 As shown in the figure, clusters 1 and 4 completely input heat to the RHN instant heating network, and cluster 5 obtains heat from RHN. Clusters 2 and 3 show different interaction modes with RHN at different time periods. This shows that RHN can achieve peak-shifting complementarity among multiple distributed energy clusters.

[0128] In summary, the present invention provides a distributed energy cluster optimization scheduling method, which obtains the power transmission loss cost by establishing a power network transmission model, then establishes a dynamic model of the heat storage equivalent micro-element of the heat network to obtain the thermal transmission loss cost, and then constructs a regional multi-energy optimization objective function based on each transmission loss cost. Then, the constraints of the regional multi-energy optimization objective function are determined, and the constraints include the heat network transmission constraints, the maximum charge and discharge power constraints of the heat network equivalent energy storage model, and the charge balance constraints of the heat network equivalent energy storage model, wherein the heat storage equivalent micro-element dynamic model of the heat network and the various constraints of the heat network reflect the heat storage characteristics of the heat network. After that, the regional multi-energy optimization objective function is solved based on these constraints to obtain the optimal decision variable value of the distributed energy cluster, thereby utilizing the heat storage characteristics of the heat ring network, i.e., the heat network, to realize the peak-shifting sharing of heat energy among multiple distributed energy clusters, thereby facilitating the improvement of the overall regional energy supply economy.

[0129] Furthermore, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned distributed energy cluster optimization scheduling method can be implemented.

[0130] Furthermore, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned distributed energy cluster optimization scheduling method can be implemented.

[0131] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0132] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A distributed energy cluster optimization scheduling method, characterized in that: include: Establishing a power network transmission model, and determining power transmission loss costs according to the power network transmission model; Establishing a dynamic model of heat storage equivalent micro-element of the heat network, and determining the heat transmission loss cost according to the dynamic model of heat storage equivalent micro-element of the heat network; Determine the power purchase cost and equipment operation and maintenance cost of the distribution network, and construct a regional multi-energy optimization objective function based on the power purchase cost, equipment operation and maintenance cost, power transmission loss cost and thermal transmission loss cost of the distribution network; Establishing constraint conditions of the regional multi-energy optimization objective function, solving the regional multi-energy optimization objective function based on the constraint conditions, obtaining the optimal output values ​​of heat pumps and cogeneration equipment in the distributed energy cluster in each time period, and realizing the optimal scheduling of the distributed energy cluster; The power network transmission model is a model for calculating the power network transmission power. The power network transmission power is expressed as follows: in, Transmit power to the electricity network, Transmits voltage to the power grid, is the transmission current of the power network, θ is the phase angle, g represents the imaginary unit, and t represents the time; The dynamic model of the heat storage equivalent microelement of the heat network is expressed as follows: in, is the heat storage capacity of the kth microelement in the pipeline between nodes i and j of the heating network at time t, Δt is the unit scheduling time, is the heat storage capacity of the kth microelement in the pipeline between nodes i and j of the heating network at time t-Δt, c is the specific heat capacity of water, is the water flow mass of the kth microelement in the pipeline between nodes i and j of the heating network at time t, are the temperatures on both sides of the kth microelement in the pipeline between the heating network nodes i and j at time t, λ ij is the heat transfer coefficient of the pipeline between nodes i and j in the heating network, Δx is the length of the microelement, T a is the ambient temperature; The regional multi-energy optimization objective function is expressed as follows: Among them, F is the regional multi-energy optimization objective function, W D is the internal decision variable of the distributed energy cluster, P h is the heat exchange power between the distributed energy cluster and the heat network, P e is the electric power exchanged between distributed energy clusters, J is the system node, including grid nodes and heating network nodes, n is the total number of system nodes, Σ is the summation function, is the total cost of operating a distributed energy cluster, is the total cost of power and heat transmission loss, H is the dispatch period, are the operation and maintenance costs of the gas turbine and heat source at system node J, The operation and maintenance costs of the heating network. They are the purchased power and the grid-connected power, are the unit costs of purchasing electricity and connecting to the grid, To purchase natural gas power, is the unit price of natural gas power, P e,loss,t , P VHS ,loss ,t are respectively the power transmission loss and the heat transmission loss, c loss,e 、c loss,h They are the unit cost of power loss in electricity transmission and the unit cost of power loss in heat transmission respectively.

2. The distributed energy cluster optimization scheduling method according to claim 1, characterized in that: The constraints of the regional multi-energy optimization objective function include heat network transmission constraints, maximum charging and discharging power constraints of the heat network equivalent energy storage model, and charge balance constraints of the heat network equivalent energy storage model.

3. The distributed energy cluster optimization scheduling method according to claim 2, characterized in that: The heat network transmission constraint conditions include heat exchange power balance constraint sub-conditions between heat network nodes and distributed energy clusters and constraint sub-conditions that heat exchange nodes exist between micro-elements.

4. The distributed energy cluster optimization scheduling method according to claim 2, characterized in that: The method also includes: determining a heat network flow parameter, and determining the heat network equivalent energy storage model according to the heat network flow parameter, so as to determine the maximum charge and discharge power constraint condition and the charge balance constraint condition of the heat network equivalent energy storage model.

5. The distributed energy cluster optimization scheduling method according to claim 4, characterized in that: The heat network flow parameters at least include the injected water flow and the outflow water flow of the heat network nodes, the charging and discharging power of the heat network equivalent energy storage model, and the heat network node temperature and pipeline temperature.

6. The distributed energy cluster optimization scheduling method according to claim 5, characterized in that: The step of solving the regional multi-energy optimization objective function based on the constraint conditions comprises: Solve the heat network flow parameters; Determine the equivalent energy storage model of the heat network according to the heat network flow parameters, and determine the charge balance constraint condition and the maximum charge and discharge power constraint condition of the equivalent energy storage model of the heat network; solve the regional multi-energy optimization objective function to obtain the decision variable value of the current iteration, and the decision variable value is the output value of the heat pump and cogeneration equipment in the distributed energy cluster in each time period; Return to solve the heat network power flow parameters for the next round of iteration and obtain the decision variable values ​​after the latest round of iteration; Determine the difference between the decision variable value after the latest round of iteration and the decision variable value after the previous round of iteration. If the difference is less than the convergence threshold, the decision variable value after the latest round of iteration is determined to be the optimal value, and the decision variable value after the latest round of iteration is output.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed energy cluster optimization scheduling method according to any one of claims 1 to 6 is implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the distributed energy cluster optimization scheduling method according to any one of claims 1 to 6 is implemented.

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