A county energy internet planning method and planning system

Abstract

The present invention discloses a county energy internet planning system and a planning method, including an energy equipment model dynamic library with built-in data of various energy equipment, an interactive module, a data acquisition module, and a planning module. The planning module has a built-in planning model, including a cost optimization objective function, county type constraints, and energy equipment working constraints. The planning module calculates the imported data based on the planning model and obtains a planning scheme. The data acquisition and processing module is used to collect and process the basic data of the county to be planned. The interactive module is used to manage the energy equipment model dynamic library and select energy equipment data, and display the planning scheme. Constraints for various county types and various energy equipment are pre-built to meet the customized needs of county energy internet planning in various situations. The planning method has a wide range of applications. Planning optimization is carried out based on the cost of the entire life cycle of the county energy internet, which effectively improves the economic efficiency of the planned county energy internet.

Description

Technical Field

[0001] The present invention relates to energy internet, and in particular to an energy internet planning method and planning system. Background Art

[0002] In the energy internet, unified planning of multiple energy systems can make up for the shortcomings of the original independent planning of various forms of energy, and fully consider the coupling and complementary relationship of various forms of energy, thereby improving the efficiency of energy resource utilization and reducing energy supply costs. However, the county energy internet not only includes new energy sources such as wind power and photovoltaics, but also includes special energy sources such as industrial waste heat power generation and biomass energy, forming a diversified scenario. However, the existing planning tools have not fully considered the county's special resource endowments and energy needs, and have given less consideration to the energy characteristics and corresponding scenarios of the county energy internet, making it difficult to meet the planning needs of the county energy internet in multiple scenarios. Summary of the invention

[0003] Purpose of the invention: In view of the above shortcomings, the present invention provides a county energy Internet planning method for multiple scenarios based on county types and energy equipment constraints.

[0004] The present invention also provides a county energy internet planning system.

[0005] Technical solution: To solve the above problems, the present invention adopts a county energy internet planning method, which includes the following steps:

[0006] (1) Establish a county energy internet planning model; the county energy internet planning model aims to minimize the cost of the county energy internet over its entire life cycle;

[0007] And classify the county types, establish the county type classification constraints according to the energy characteristics and load data of different county types; and establish the energy equipment working constraints according to the county types;

[0008] (2) Collect energy characteristics and load data of the county to be planned; and determine the types and quantities of energy equipment to be planned;

[0009] (3) According to the types and quantities of energy equipment to be planned, the collected basic data are substituted into the county energy Internet planning model for solution to obtain the types, capacities and locations of energy equipment in the county to be planned.

[0010] Furthermore, the objective function of the county energy internet planning model in step (1) is:

[0011]

[0012] Among them, C CEIis the life cycle cost of the county energy internet, N is the number of energy equipment in the county to be planned, c n is the initial investment cost per unit capacity of the nth energy equipment, p n is the capacity of the nth energy device, R n is the total number of resets of the nth energy device in the planning period, j represents the jth reset of the nth energy device in the planning period, L n is the service life of the nth energy device, L e is the total planning cycle of the county energy internet planning, k represents the kth planning cycle of the county energy internet planning, C M is the annual maintenance cost, C B is the annual energy cost, C D is the annual depreciation cost of energy equipment, i is the interest rate; t r is the tax rate;

[0013] The total number of resets of the nth energy device in the planning period R n The calculation formula is:

[0014]

[0015] in, Indicates that the value is not greater than The largest integer of ;

[0016] Annual maintenance cost C M and annual depreciation cost of energy equipment C D The calculation formula is:

[0017]

[0018]

[0019] Among them, r M is the equipment maintenance rate, r D is the equipment depreciation rate.

[0020] Annual energy cost C B The calculation formula is:

[0021]

[0022] Among them, q e,d,t is the electricity consumption in the t period on the dth day, ρ e,t is the electricity price in period t, q g,d,t is the natural gas usage in the t period on the dth day, ρ g For natural gas prices.

[0023] Furthermore, the county types in step (1) include at least one of the following: industrially and commercially developed urban counties, digital economy emerging urban counties, industrial and tourism integrated counties, energy-receiving ecological counties, and low-carbon transformation island counties; the energy characteristics of each county type include at least one of the following: economic development, energy production side, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling indicators; the energy equipment working constraints include at least one of the following: energy equipment capacity constraints, energy equipment operating power constraints, power system flow constraints, natural gas system flow constraints, and thermal system flow constraints.

[0024] Furthermore, the step (1) of establishing the county type division constraint condition includes the following steps:

[0025] (1.1) Establish county-level energy characteristics matrix C:

[0026] C=[a,b,c,d,e,f,g]

[0027] Among them, a, b, c, d, e, f, and g represent the values ​​of economic development, energy production side, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling indicators, respectively;

[0028] Establish the county type constraint matrix D:

[0029] D = [w,x,y,z]

[0030] Among them, w represents the county type; x represents the proportion of electric energy, y represents the proportion of multi-energy coupling, and z represents the growth rate of cost investment;

[0031] (1.2) The transfer functions of matrix C and matrix D are established with the first-order inertia link:

[0032]

[0033] Among them, T is the time constant, K is the inertia link gain;

[0034] (1.3) By solving T and K through step response, we can obtain the constraints of county type division.

[0035] Furthermore, in step (3), the capacity constraint condition that the energy equipment needs to meet is:

[0036] p w,n,min ≤p w,n ≤p w,n,max

[0037] Among them, p w,n represents the capacity of the nth energy device under the w county type, p w,n,max and pw,n,min They represent the maximum and minimum capacity of the nth energy equipment under the w county type respectively;

[0038] The power constraints that energy equipment needs to meet are:

[0039] p w,n,r,min ≤p w,n,r ≤p w,n,r,max

[0040] Among them, p w,n,r represents the operating power of the nth energy device under the w county type, p w,n,r,max and p w,n,r,min They represent the maximum operating power and minimum operating power of the nth energy equipment under the w county type respectively.

[0041] Furthermore, the power system flow constraints that energy equipment needs to meet are:

[0042]

[0043]

[0044] -p w,hm,max ≤p w,hm,t ≤p w,hm,max

[0045] Among them, p w,hm,t is the electric power of the power branch hm in the period t under the w county type; θ w,h,t ,θ w,m,t are the phase angles of nodes h and m on the power branch hm in the w county type in period t; X w,hm is the reactance of the power branch hm under the w county type; represents the power of the energy device injected into node h under the county type w; L e,w,h,t represents the load of node h in time period t under county type w; p w,hm,max It represents the maximum electric power of the power branch hm under the county type w.

[0046] Furthermore, the natural gas system flow constraints that energy equipment needs to meet are:

[0047]

[0048]

[0049]

[0050] -f w,kl,max ≤f w,kl,t ≤f w,kl,max

[0051] Among them, fw,kl,t is the natural gas flow of natural gas pipeline kl in period t under county type w; p p,w,k 、p p,w,l are the pressures at nodes k and l on the natural gas pipeline kl under county type w; D w is the pipe diameter under the w county type; F w is the pipeline friction coefficient under the w county type; S w is the gas density under the w county type; L w is the length of the pipeline; represents the flow of energy equipment injected into node k under county type w; L g,w,k,t is the natural gas load at node k during period t; f w,kl,max is the maximum natural gas flow of natural gas pipeline kl under county type w.

[0052] Furthermore, the thermal system flow constraints that the energy equipment needs to meet are:

[0053] AM=M q

[0054] Bh f =0

[0055] h f =KM|M|

[0056] Φ+∑p w,n,r =C p M q (T s -T o )

[0057]

[0058] (∑m out )T out =∑(m in T in )

[0059] Among them, A is the network association matrix; M is the water flow rate of the heating network pipeline, M q is the water flow rate flowing into the load node or out of the heat source node; B is the loop association matrix; h f is the pipeline pressure drop caused by friction loss, K is the pipeline resistance coefficient; Φ is the heat load, C p is the specific heat of water, T s is the node water supply temperature, T o is the node return water temperature; T end is the pipe end temperature, T start is the temperature at the beginning of the pipeline; λ is the heat transfer coefficient; L is the length of the pipeline; T a is the ambient temperature; m inis the pipe flow into the node, m out is the pipe flow out of the node, T in is the temperature at the end of the input pipe, T out is the node mixing temperature. ∑p w,n,r It represents the heat consumed by the energy equipment on the heating network node under the w county type.

[0060] The present invention also adopts a county energy internet planning system, including a data acquisition module, a planning model building module, and a planning calculation module, wherein:

[0061] The planning model establishment module is used to establish a county energy internet planning model; the county energy internet planning model aims to minimize the cost of the entire life cycle of the county energy internet and divides the county types, and establishes county type division constraints based on the energy characteristics and load data of different county types; and establishes energy equipment working constraints based on the county types;

[0062] The data collection module is used to collect energy characteristics and load data of the county to be planned; and determine the types and quantities of energy equipment to be planned;

[0063] The planning calculation module is used to substitute the collected data into the county energy Internet planning model for solution according to the type and quantity of energy equipment to be planned, so as to obtain the type, capacity and location of energy equipment in the county to be planned.

[0064] Furthermore, the county types in the planning model establishment module include at least one of the following: industrially and commercially developed urban counties, digital economy emerging urban counties, industrial and tourism comprehensive counties, energy-receiving ecological counties, and low-carbon transformation island counties.

[0065] Furthermore, the energy characteristics of each of the county types include at least one of the following: economic development, energy production side, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling indicators.

[0066] Beneficial effects: Compared with the prior art, the significant advantage of the present invention is that it pre-builds constraints for various county types and various energy equipment to meet the customized needs of county energy Internet planning in various situations. The planning method has a wide range of applications. The planning is optimized from the perspective of the full life cycle cost of the county energy Internet, which effectively improves the economic efficiency of the planned county energy Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 Shown is the overall structural block diagram of the planning system of the present invention;

[0068] Figure 2 Shown is the overall structural block diagram of the planning method of the present invention;

[0069] Figure 3 The figure shows the energy network structure and planning status of a certain county to be planned in the present invention;

[0070] Figure 4 Shown is the multi-energy load data of a certain county to be planned in the present invention;

[0071] Figure 5 The figure shows the annual capital investment during the entire life cycle of the county energy Internet in a certain county to be planned in the present invention. DETAILED DESCRIPTION

[0072] Example 1

[0073] like Figure 1 As shown, a county energy internet planning system in this embodiment includes an energy setting model dynamic library, an interaction module, a data acquisition module, and a planning module. The main framework of the county energy internet planning system is developed and planned based on the C# language. The functional modules of the county energy internet planning system are developed and used in the form of desktop software. Based on the DevExpress suite, the configuration drawing and icon display of the user interface are realized. The module interface supports two methods: subroutine calling and C# dynamically calling C++.

[0074] The dynamic library of energy equipment models includes basic data of several types of energy equipment, including 12 types of power component models, 54 cold and hot component models, 19 natural gas component models, and 6 energy station models. The interactive module can realize the compilation management, display management, parameter setting, parameter storage and other functions of the energy equipment required for the county energy Internet planning in the dynamic library of energy equipment models. Compilation management mainly includes the functions of adding energy equipment models, modifying models, and deleting models. Display management can set different display icons and connection anchors for models of the same type, and support the addition, deletion and modification of icons / anchors for specified models. Parameter setting adopts a WINDOWS-like property box mode to provide end users with operations such as setting, modifying, and viewing model parameters, and provides a summary of each field. Parameter storage is to store the specific parameter values ​​after the parameter setting and modification are completed, and the storage method adopts an object-based relational database storage mode. The interactive module selects energy equipment in the dynamic library of energy equipment models and imports the corresponding energy equipment data into the planning module.

[0075] The data acquisition module is used to collect basic data of the county to be planned, establish basic data filling templates such as the county energy Internet cold and hot electrical multi-type loads, existing energy network structure, equipment parameters to be planned, energy prices, etc. After completing the filling of the county energy Internet data template, it is transmitted to the county energy Internet planning system through the set data interface, and visual data is obtained through decoding, basic data collection is completed, and data sample files are generated. Abnormal data and vacant values ​​in the sample file are deleted and supplemented to complete data correction.

[0076] The planning module includes a planning model building module and a planning calculation module. The planning model building module pre-extracts the conditional elements such as county resource endowment, energy consumption characteristics, and energy transformation, and constructs the county type element set of the county energy Internet. It sorts out the county type construction indicators from the aspects of economic development, energy production measurement, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling. According to the county's energy endowment, supply status, load demand, development positioning and other customized planning needs, a county type library is formed, which mainly covers six types of counties: industrial and commercial developed urban counties, digital economy emerging urban counties, industrial tourism comprehensive counties, energy-incoming ecological counties, and low-carbon transformation island counties. And with the goal of minimizing the cost of the entire life cycle of the county energy Internet, a county energy Internet planning model is established. The planning calculation module calculates the imported data and obtains the county energy Internet planning scheme according to the built-in county energy Internet planning model objective function, county type constraints, and energy equipment working constraints. The interactive module displays the planning scheme.

[0077] Example 2

[0078] like Figure 2 As shown, a county energy Internet planning method in this embodiment includes the following steps:

[0079] (1) With the goal of minimizing the cost of the entire life cycle of the county energy Internet, a county energy Internet planning model is established, and the model objective function is:

[0080]

[0081] Among them, C CEI is the life cycle cost of the county energy internet, N is the number of energy equipment in the county to be planned, c n is the initial investment cost per unit capacity of the nth energy equipment, p n is the capacity of the nth energy device, R n is the total number of resets of the nth energy device in the planning period, j represents the jth reset of the nth energy device in the planning period, L n is the service life of the nth energy device, L eis the total planning cycle of the county energy internet planning, k represents the kth planning cycle of the county energy internet planning, C M is the annual maintenance cost, C B is the annual energy cost, C D is the annual depreciation cost of energy equipment, i is the interest rate; t r For the tax rate.

[0082] The total number of resets of the nth energy device in the planning period R n The calculation formula is:

[0083]

[0084] in, Indicates that the value is not greater than The maximum integer.

[0085] Annual maintenance cost C M , Annual depreciation cost of energy equipment C D , annual energy cost C B The calculation formulas are:

[0086]

[0087]

[0088]

[0089] Among them, r M is the equipment maintenance rate, r D is the equipment depreciation rate; q e,d,t is the electricity consumption in the t period on the dth day, ρ e,t is the electricity price in period t, q g,d,t is the natural gas usage in the t period on the dth day, ρ g For natural gas prices.

[0090] The county types are divided into industrial and commercial developed urban counties, digital economy emerging urban counties, industrial tourism comprehensive counties, energy-integrated ecological counties, and low-carbon transformation island counties; according to the energy characteristics and energy data of different county types, the county type division constraint conditions are established; the county energy characteristics include economic development, energy production side, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling indicators; county energy data include the proportion of electricity, multi-energy coupling ratio, and cost input growth rate; establishing the county type division constraint conditions includes the following steps:

[0091] (1.1) Establish county-level energy characteristics matrix C:

[0092] C=[a,b,c,d,e,f,g]

[0093] Among them, a represents the value of economic development, which is divided into three situations: fast, medium and slow, and the corresponding values ​​are 1, 0 and -1 respectively; b represents the value of the energy production side, which is divided into three situations: high proportion of electricity, medium proportion of electricity and low proportion of electricity, and the corresponding values ​​are 1, 0 and -1 respectively; c represents the value of the energy transmission network, which is divided into three situations: strong, medium and weak, and the corresponding values ​​are 1, 0 and -1 respectively; d represents the value of the energy demand side, which is divided into three situations: high proportion of electricity, medium proportion of electricity and low proportion of electricity, and the corresponding values ​​are 1, 0 and -1 respectively; e represents the value of the basic conditions of information support, which is divided into three situations: high, medium and low, and the corresponding values ​​are 1, 0 and -1 respectively; f represents the value of the basic conditions of value creation, which is divided into three situations: high, medium and low, and the corresponding values ​​are 1, 0 and -1 respectively; g represents the value of the multi-energy coupling index, which is divided into three situations: strong, medium and weak, and the corresponding values ​​are 1, 0 and -1 respectively;

[0094] Establish the county type constraint matrix D:

[0095] D = [w,x,y,z]

[0096] Among them, w represents the county type, and the numbers 1 to 6 represent industrially and commercially developed urban counties, digital economy emerging urban counties, industrial tourism comprehensive counties, energy-integrated ecological counties, and low-carbon transformation island counties; x represents the proportion of electricity, y represents the proportion of multi-energy coupling, and z represents the growth rate of cost input;

[0097] (1.2) Through the system identification method, the transfer function of matrix C and matrix D is established with the first-order inertia link:

[0098]

[0099] Among them, T is the time constant, K is the inertia link gain;

[0100] (1.3) Solve T and K through step response, and then determine the county type and the corresponding electricity share, multi-energy coupling ratio, and cost input growth rate according to the value of county energy characteristics;

[0101] According to the county type, considering the physical meaning of the optimization variables and the actual situation, under the specified county type, the energy equipment parameters need to be kept within a certain range, so as to establish the working constraints of the energy equipment, including energy equipment capacity constraints, energy equipment operating power constraints, and flow constraints; energy equipment includes gas turbines, electric boilers, gas boilers, electric refrigeration, absorption chillers, etc.

[0102] The capacity constraints that energy equipment needs to meet are:

[0103] p w,n,min ≤p w,n ≤p w,n,max

[0104] Among them, p w,n represents the capacity of the nth energy device under the w county type, p w,n,max and p w,n,min They represent the maximum and minimum capacity of the nth energy equipment under the w county type respectively;

[0105] The power constraints that energy equipment needs to meet are:

[0106] p w,n,r,min ≤p w,n,r ≤p w,n,r,max

[0107] Among them, p w,n,r represents the operating power of the nth energy device under the w county type, p w,n,r,max and p w,n,r,min They represent the maximum operating power and minimum operating power of the nth energy equipment under the w county type respectively.

[0108] The power flow constraints that energy equipment needs to meet include power flow constraints of the power system, natural gas system and thermal system.

[0109] Energy equipment such as gas turbines, electric boilers, and electric refrigeration that interact with the power system need to meet the power system flow constraints. The power system flow constraints that energy equipment needs to meet are:

[0110]

[0111]

[0112] -p w,hm,max ≤p w,hm,t ≤p w,hm,max

[0113] Among them, p w,hm,t is the electric power of the power branch hm in the period t under the w county type; θ w,h,t ,θ w,m,t are the phase angles of nodes h and m on the power branch hm in the w county type in period t; X w,hm is the reactance of the power branch hm under the w county type; represents the power of the energy device injected into node h under the county type w, and the gas turbine is the device that injects power. is a positive value, and electric boilers, electric refrigeration and other equipment with outflow power are is a negative value; L e,w,h,t represents the load of node h in time period t under county type w; pw,hm,max It represents the maximum electric power of the power branch hm under the county type w.

[0114] Energy equipment such as gas turbines and gas boilers that interact with the natural gas system need to meet the natural gas system flow constraints. The natural gas system flow constraints that energy equipment needs to meet are:

[0115]

[0116]

[0117]

[0118] -f w,kl,max ≤f w,kl,t ≤f w,kl,max

[0119] Among them, f w,kl,t is the natural gas flow of natural gas pipeline kl in period t under county type w; p p,w,k 、p p,w,l are the pressures at nodes k and l on the natural gas pipeline kl under county type w; D w is the pipe diameter under the w county type; F w is the pipeline friction coefficient under the w county type; S w is the gas density under the w county type; L w is the length of the pipeline; represents the flow of energy equipment injected into node k under the w county type, gas turbines and gas boilers as the equipment with outflow flow, is a negative value; L g,w,k,t is the natural gas load at node k during period t; f kl,max is the maximum natural gas flow of natural gas pipeline kl under county type w.

[0120] The thermal system flow constraints that energy equipment such as gas turbines, electric boilers, and absorption chillers that interact with the thermal system need to meet are:

[0121] AM=M q

[0122] Bh f =0

[0123] h f =KM|M|

[0124] Φ+∑p w,n,r =C p M q (T s -T o )

[0125]

[0126] (∑m out )T out =∑(m in T in )

[0127] Among them, A is the network association matrix; M is the water flow rate of the heating network pipeline, M q is the water flow rate flowing into the load node or out of the heat source node; B is the loop association matrix; h f is the pipeline pressure drop caused by friction loss, K is the pipeline resistance coefficient; Φ is the heat load, C p is the specific heat of water, T s is the node water supply temperature, T o is the node return water temperature; T end is the pipe end temperature, T start is the temperature at the beginning of the pipeline; λ is the heat transfer coefficient; L is the length of the pipeline; T a is the ambient temperature; m in is the pipe flow into the node, m out is the pipe flow out of the node, T in is the temperature at the end of the input pipe, T out is the node mixing temperature. ∑p w,n,r represents the heat consumed by the energy equipment at the heating network node under the w county type, and the absorption chiller is used as the heat consumption device, ∑p w,n,r is a positive value, gas turbines and electric boilers are heat release devices, ∑p w,n,r Is a negative value.

[0128] (2) Collect basic data of the county to be planned, including energy characteristics and load data;

[0129] and determine the types and quantities of energy equipment to be planned;

[0130] (3) According to the objective function and constraints, the basic data collected are solved according to the types and quantities of energy equipment to be planned. According to the construction process of the above objective function and constraints, it can be seen that the model to be solved is a mixed integer nonlinear programming model. After obtaining the 24-hour typical daily load curve of electricity, gas, heat and cold of the county energy Internet to be planned, the branch and bound method is first used to transform the mixed integer programming problem into multiple pure integer programming problems, that is, the 0-1 planning problem of county type determination and energy equipment selection is transformed into a 0 or 1 existence problem. Then, the second-order cone programming is used to continuously process the discontinuous derivatives existing in the power, natural gas and thermal power flow constraints, and the non-convex optimization is transformed into convex optimization. Finally, the Lagrange multiplier method is used to linearize the nonlinear terms involved in the objective function, and finally the type of county energy Internet to which the county to be planned belongs, as well as the type and capacity of its energy equipment to be planned and the node location of the access to the power, natural gas and thermal network are solved.

[0131] like Figure 3 As shown in the figure, the energy network structure of a county to be planned includes a 14-node power distribution network, an 11-node gas distribution network, and an 11-node heat distribution network. The energy equipment to be planned includes gas turbines, gas boilers, electric boilers, electric refrigeration, and absorption chillers. Figure 4 The following table shows the multi-energy load data for the planned county, including typical load curves for summer, spring and autumn, and winter. The economic development, energy production side, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling index values ​​are fast, high, strong, high, medium, medium, and strong. The basic data of the energy equipment to be planned are shown in Table 1:

[0132] Table 1

[0133]

[0134]

[0135] The planning results show that the county's energy Internet development scenario is a digital economy emerging urban county, with the values ​​of electricity share, multi-energy coupling ratio, and cost investment growth rate being 53.54%, 62.56%, and -41.43% respectively. Figure 5 As shown in Table 2, during the entire life cycle of the county's energy internet planning, the annual capital investment has shown a decreasing trend year by year, indicating that the funds spent are mainly on early equipment investment. The results of energy equipment planning are shown in Table 2.

[0136] Table 2

[0137]

[0138] In Table 2, the first four columns are the selected energy equipment, their capacities and construction costs; the fifth column indicates the grid, gas and heat network nodes to which the selected energy equipment is connected, indicating that the planned energy equipment is connected to 4 nodes of the grid, 1 node of the gas network and 8 nodes of the heat network; the sixth column indicates the full life cycle cost of the county's energy Internet planning.

[0139] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims of the invention.

Claims

1. A county energy internet planning method, characterized in that: The following steps are involved: (1) Establish a county energy internet planning model; the county energy internet planning model aims to minimize the cost of the entire life cycle of the county energy internet; and classify counties into different types, establish county type classification constraints based on the energy characteristics and load data of different county types; and establish energy equipment working constraints based on the county types; Establishing the constraints for county classification includes the following steps: (1.1) Establish county-level energy characteristics matrix C: C=[a,b,c,d,e,f,g] Among them, a, b, c, d, e, f, and g represent the values ​​of economic development, energy production side, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling indicators, respectively; Establish the county type constraint matrix D: D = [w,x,y,z] Among them, w represents the county type; x represents the proportion of electric energy, y represents the proportion of multi-energy coupling, and z represents the growth rate of cost investment; (1.2) The transfer functions of matrix C and matrix D are established with the first-order inertia link: Among them, T is the time constant, K is the inertia link gain; (1.3) Solve T and K by step response to obtain the constraint conditions of county type division; (2) Collect energy characteristics and load data of the county to be planned; and determine the types and quantities of energy equipment to be planned; (3) According to the types and quantities of energy equipment to be planned, the collected data is substituted into the county energy Internet planning model for solution to obtain the type, capacity and location of energy equipment in the county to be planned.

2. The planning method according to claim 1, characterized in that: The objective function of the county energy internet planning model in step (1) is: Among them, C CEI is the life cycle cost of the county energy internet, N is the number of energy equipment in the county to be planned, c n is the initial investment cost per unit capacity of the nth energy equipment, p n is the capacity of the nth energy device, R n is the total number of resets of the nth energy device in the planning period, j represents the jth reset of the nth energy device in the planning period, L n is the service life of the nth energy device, L e is the total planning cycle of the county energy internet planning, k represents the kth planning cycle of the county energy internet planning, C M is the annual maintenance cost, C B is the annual energy cost, C D is the annual depreciation cost of energy equipment, i is the interest rate; t r is the tax rate; The total number of resets of the nth energy device in the planning period R n The calculation formula is: in, Indicates that the value is not greater than The largest integer of ; Annual maintenance cost C M and annual depreciation cost of energy equipment C D The calculation formula is: Among them, r M is the equipment maintenance rate, r D is the equipment depreciation rate; Annual energy cost C B The calculation formula is: Among them, q e,d,t is the electricity consumption in the t period on the dth day, ρ e,t is the electricity price in period t, q g,d,t is the natural gas usage in the t period on the dth day, ρ g For natural gas prices.

3. The planning method according to claim 1, characterized in that: The county type in step (1) includes at least one of the following: an industrially and commercially developed urban county, a digital economy emerging urban county, an industrial tourism integrated county, an energy-receiving ecological county, and a low-carbon transformation island county; The energy characteristics of each county type include at least one of the following: economic development, energy production side, energy transmission network, energy demand side, basic conditions for information support, basic conditions for value creation, and multi-energy coupling indicators; The energy equipment working constraint conditions include at least one of the following: energy equipment capacity constraint, energy equipment operating power constraint, power system flow constraint, natural gas system flow constraint and thermal system flow constraint.

4. The planning method according to claim 3, characterized in that: In step (3), the capacity constraint condition that the energy equipment needs to meet is: p w,n,min ≤p w,n ≤p w,n,max Among them, p w,n represents the capacity of the nth energy device under the w county type, p w,n,max and p w,n,min They represent the maximum and minimum capacity of the nth energy equipment under the w county type respectively; The power constraints that energy equipment needs to meet are: p w,n,r,min ≤p w,n,r ≤p w,n,r,max Among them, p w,n,r represents the operating power of the nth energy device under the w county type, p w,n,r,max and p w,n,r,min They represent the maximum operating power and minimum operating power of the nth energy equipment under the w county type respectively.

5. The planning method according to claim 3, characterized in that: The power system flow constraints that energy equipment needs to meet are: -p w,hm,max ≤p w,hm,t ≤p w,hm,max Among them, p w,hm,t is the electric power of the power branch hm in the period t under the w county type; θ w,h,t ,θ w,m,t are the phase angles of nodes h and m on the power branch hm in the w county type in period t; X w,hm is the reactance of the power branch hm under the w county type; represents the power of the energy device injected into node h under the county type w; L e,w,h,t represents the load of node h in time period t under county type w; p w,hm,max It represents the maximum electric power of the power branch hm under the county type w.

6. The planning method according to claim 3, characterized in that: The natural gas system flow constraints that energy equipment needs to meet are: -f w,kl,max ≤f w,kl,t ≤f w,kl,max Among them, f w,kl,t is the natural gas flow of natural gas pipeline kl in period t under county type w; p p,w,k 、p p,w,l are the pressures at nodes k and l on the natural gas pipeline kl under county type w; D w is the pipe diameter under the w county type; F w is the pipeline friction coefficient under the w county type; S w is the gas density under the w county type; L w is the length of the pipeline; represents the flow of energy equipment injected into node k under county type w; L g,w,k,t is the natural gas load at node k during period t; f w,kl,max is the maximum natural gas flow of natural gas pipeline kl under county type w; The thermal system flow constraints that energy equipment needs to meet are: AM=M q Bh f =0 h f =KM|M| Φ+∑p w,n,r =C p M q (T s -T o ) (∑m out )T out =∑(m in T in ) Among them, A is the network association matrix; M is the water flow rate of the heating network pipeline, M q is the water flow rate flowing into the load node or out of the heat source node; B is the loop association matrix; h f is the pipeline pressure drop caused by friction loss, K is the pipeline resistance coefficient; Φ is the heat load, C p is the specific heat of water, T s is the node water supply temperature, T o is the node return water temperature; T end is the pipe end temperature, T start is the temperature at the beginning of the pipeline; λ is the heat transfer coefficient; L is the length of the pipeline; T a is the ambient temperature; m in is the pipe flow into the node, m out is the pipe flow out of the node, T in is the temperature at the end of the input pipe, T out is the node mixing temperature; ∑p w,n,r It represents the heat consumed by the energy equipment on the heating network node under the w county type.

7. A county energy internet planning system using the method of claim 1, characterized in that: It includes data acquisition module, planning model building module and planning calculation module, among which: The planning model establishment module is used to establish a county energy internet planning model; the county energy internet planning model aims to minimize the cost of the entire life cycle of the county energy internet; and to divide the county types, and establish county type division constraints based on the energy characteristics and load data of different county types; and to establish energy equipment working constraints based on the county types; The data collection module is used to collect energy characteristics and load data of the county to be planned; and determine the types and quantities of energy equipment to be planned; The planning calculation module is used to substitute the collected data into the county energy Internet planning model for solution according to the type and quantity of energy equipment to be planned, so as to obtain the type, capacity and location of energy equipment in the county to be planned.

8. The planning system according to claim 7, characterized in that The county types in the planning model establishment module include at least one of the following: industrially and commercially developed urban counties, digital economy emerging urban counties, industrial and tourism comprehensive counties, energy-receiving ecological counties, and low-carbon transformation island counties.

9. The planning system according to claim 8, characterized in that The energy characteristics of each county type include at least one of the following: economic development, energy production side, energy transmission network, energy demand side, information support basic conditions, value creation basic conditions, and multi-energy coupling indicators.

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