A method and system for constructing an energy station scenario

Abstract

A method and system for constructing an energy station scenario, comprising: constructing a preset number of load centers in a pre-established energy point and importing the predicted annual cooling, heating, and power load demand; determining the number of resource points in the energy point according to the predicted annual cooling, heating, and power load demand, and determining the optimal positions of the resource points in the energy point; optimizing the power supply relationship between the load centers and the resource points in the energy point to achieve supply-demand balance. The present invention optimizes the siting and sizing of a regional energy point, determines the number and positions of resource points within the regional energy point based on big data analysis, realizes siting and sizing configuration, comprehensively distributes energy to the load centers within the energy point, and ensures the supply-demand balance between the load centers and the energy point.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and particularly relates to a method and system for constructing an energy station scenario. Background Art

[0002] At present, energy and environment have become the main bottlenecks restricting the sustainable development of the national economy. The transformation from the traditional extensive energy utilization mode to the refined, decentralized, and sustainable low-carbon energy utilization mode has become a trend. Therefore, the development of distributed low-carbon energy points plays an important role in improving the overall efficiency of energy and the consumption capacity of renewable energy. The development of various energy conversion devices (such as combined heat and power, heat pumps, electric heating, power-to-hydrogen, etc.) provides a means for the coordinated complementarity of multiple types of energy. The distributed low-carbon energy point integrates high technologies and devices, and is a new type of energy system that can realize the cascade utilization of energy and improve the energy utilization rate. The development of distributed low-carbon energy points can reduce environmental pollution, strengthen energy security, optimize the energy structure, and improve the energy utilization rate. Studying the joint optimization regulation technology of distributed energy points is a prerequisite for the efficient operation of the distributed energy system, and is also the technical basis for the demonstration project of low-carbon energy points with electricity as the core and comprehensive energy optimization configuration on the demand side. It provides support for realizing the transformation of the energy allocation mode, provides a prerequisite for promoting the change of production and living modes, and becomes an important part of promoting the energy revolution and the third industrial revolution.

[0003] In reality, meeting the resource allocation requirements needs to consider the overall planning and regulatory planning, analyze the load types and load demands of each plot, the local renewable energy, external clean energy, resource endowment, resource quantity, calculate and analyze the energy production costs of different energy subsystems, as well as the energy consumption analysis of each energy subsystem, energy storage, etc., and the overall energy consumption analysis of the system, including mechanical kinetic energy loss, electric energy loss, and heat energy loss. However, on this basis, the links of energy production and distribution are lacking in consideration, so the realization of regional customized capacity optimization configuration by means of big data analysis cannot be achieved preferentially. Summary of the Invention

[0004] In order to solve the problem of lacking consideration of the links of energy production and distribution in the prior art, the present invention provides a method and system for constructing an energy station scenario.

[0005] Compared with the prior art, the beneficial effects of the present invention are as follows: A method and system for constructing an energy station scenario provided by this solution include: importing the predicted annual cooling, heating, and power load demand in the load center of the pre-established initial scenario; determining the number of energy points in the initial scenario according to the predicted annual cooling, heating, and power load demand, and determining the optimal positions of the energy points in the initial scenario; formulating a power supply plan for the energy points based on the predicted annual cooling, heating, and power load demand to complete the construction of the energy station scenario. The present invention optimizes the location and capacity of regional energy points, determines the number and positions of energy points in the regional energy points based on big data analysis, realizes the location and capacity configuration, comprehensively distributes energy to the load centers in the energy points, and ensures the supply-demand balance between the load centers and the energy points. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 It is a flowchart of the method for constructing an energy station scenario of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0007] To better understand the present invention, the content of the present invention will be further described below in conjunction with the accompanying drawings of the specification and examples.

[0008] Example 1: As Figure 1 shown,

[0009] S1: Set multiple load centers in the preset initial scenario, and set the predicted annual cooling, heating, and power load demand for each load center:

[0010] First, create a new energy point scenario, enter relevant information, place 10 load centers in the scenario, and import the predicted annual cooling, heating, and power load demands L1 - L10 (load data for the annual working hours of the load centers) according to the load center numbers. Select three addresses as potential energy points E1, E2, and E3; obtain the power upper limit Qini,j according to the energy resource data management

[0011] Then calculate the maximum energy supply potential of the three potential energy points

[0012]

[0013] Among them, Qin i,j is the power upper limit of the potential energy point; p is the total number of energy supply subsystems in the potential energy point; i is the potential energy point number; k is the electricity consumption type, and k = 1, 2, 3 respectively represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption; η j,k represents the conversion efficiency of the corresponding j - type energy supply subsystem and the k - type electricity consumption type.

[0014] S2: Determine the number of energy points in the initial scenario according to the predicted annual cooling, heating, and power load demand, and determine the optimal locations of the energy points in the initial scenario:

[0015] First, determine the predicted annual cooling, heating, and power load demand:

[0016] First, calculate the maximum energy supply potential based on the available resources of the land where ABC is located, import the annual cooling, heating, and power demand of 10 load centers, calculate the total load demand of the 10 load centers, and obtain the sum of the load demands L. Then, compare L with the maximum energy supply potentials E1m, E2m, and E3m of E1, E2, and E3.

[0017] If L is less than the maximum demands of all three potential energy points, only one energy point needs to be built to meet the demand. If L is greater than the maximum demands of all three potential energy points, it means that two or more energy points are required.

[0018] And so on, finally calculate how many energy points are needed, or whether none of the cases are satisfied.

[0019] For the case of needing energy points: if one, two, three, or the sum of three all cannot meet the demand, it means there is a problem with the plan, terminate the calculation and prohibit the execution of the next step. In the above example, there are eight cases where (E1), (E2), (E3), (E1, E2), (E1, E3), (E2, E3), and (E1, E2, E3) are all not satisfied, and it is determined how many energy points are needed, En = (1, 2, 3).

[0020] The energy points are determined from the potential energy points, and the numbers of the determined energy points remain unchanged.

[0021] Secondly, determine the optimal locations of the energy points in the initial scenario with the economically optimal plan as the priority. If the economically optimal plans are the same, determine the optimal locations of the energy points in the initial scenario with the carbon emission optimal plan.

[0022] (1) Formulate the objective function of the economically optimal plan through the following sub-formulas:

[0023] Distance formula:

[0024]

[0025]

[0026]

[0027] (2) represents the price cost of a single device.

[0028] (3) represents the carbon emission of a single device.

[0029] Equation (4) represents the energy efficiency of a single device

[0030]

[0031] Equation (5) represents the production cost of a single energy (cooling, heating, and electricity) at Energy Point i.

[0032]

[0033] Equation (6) represents the carbon emissions of a single energy (cooling, heating, and electricity) produced at Energy Point i.

[0034]

[0035] Equation (7) represents the energy efficiency of a single energy (cooling, heating, and electricity) produced at Energy Point i.

[0036]

[0037] Equation (8) represents the average unit capacity construction and operation and maintenance costs of the jth type of energy supply subsystem; where K takes 1, 2, 3 to represent the unit capacity production cost, correction factor, and operation and maintenance cost;

[0038]

[0039] Equation (9) represents the average unit capacity construction cost and operation and maintenance cost of the ith potential energy point (average construction / operation and maintenance cost)

[0040] Location[N][2] represents the distance matrix;

[0041] Where: N = m + n, including energy points and load centers; 2 represents the geometric center coordinates x, y of each plot (potential energy point and load center), respectively;

[0042] Pi,j,k Pi,j,k is the sum of the energy undertakings of the kth energy produced by the jth functional subsystem in the ith energy point.

[0043] Pipe_Info[k][k`]

[0044] Where: k = 1, 2, 3 represent cooling, heating, and electricity respectively, and k` = 4, 5, 6 represent transmission power consumption, pipeline loss, and construction cost respectively.

[0045] According to the most economical site selection:

[0046]

[0047] (10) The formula represents the initial investment; where Pi,j,k is the sum of the energy borne by the electricity consumption of the k-th type of electricity used by the j-th energy supply subsystem in the i-th energy point; m is the total number of energy points; n is the total number of load centers; Device_Cost_Avg_P i,k,1 is the equipment correction coefficient for the electricity consumption of the k-th type of electricity used by the i-th energy point; Device_Cost_Avg_P i,k,2 is the energy flow borne by a single device for the electricity consumption of the k-th type of electricity used by the i-th energy point; Location i - Location 负荷 is the distance matrix from the energy point to the load center; Pipe_Info k,3 is the unit price of the pipeline for meeting the electricity consumption of the k-th type of electricity.

[0048] Co_m i，k = Ratio * Cini i，k (11)

[0049] (11) The formula represents the operation and maintenance cost. Among them, Ratio * Cini i，k represents the production cost of the electricity consumption of the k-th type of electricity used by the i-th energy point;

[0050]

[0051] (12) The formula represents the production cost, among which, the pumping power cost for the electricity consumption of the k-th type of electricity used by the i-th energy point;

[0052]

[0053] (13) The formula represents the transportation cost = (sum of (distance * transportation power consumption * borne energy flow) / conversion efficiency * local industrial electricity price), where the borne energy flow represents how much cooling, heating and electricity are used in this load center, the conversion efficiency = 60%, and the local industrial electricity price is subject to the local situation.

[0054] The total economic cost of cooling, heating and electricity of each potential energy point is

[0055]

[0056] Among them: r is the annual interest rate of the bank in the region where the project is located; ntot is the expected service life of the equipment; according to the above formula, calculate each energy point F1, if only one energy point is needed, then sort from small to large; F1E1, F1E2, F1E3.

[0057] Take the minimum value as the optimal condition. If multiple energy points are required, first combine multiple cases according to permutations and combinations. For example, if two energy points are needed, there are three cases: (E1, E2), (E1, E3), and (E2, E3). According to the resource analysis of the maximum energy supply potential and load demand, calculate how many load centers the first energy point can supply energy to, and then calculate how many load centers the second energy point can supply energy to. For example, if it is calculated that the first one can supply energy to load centers 1, 2, 3, 4, 5, 6, and the second one can supply energy to load centers 7, 8, 9, 10, then calculate the corresponding distances, pipeline costs, transportation costs, initial investments, etc., add them together and sum them up, and then compare the total economic costs of the three groups to find the optimal one.

[0058] Calculation of the optimal energy efficiency: First, based on the total load data of each load center's annual working hours, determine the guaranteed load D of each load center with a specified non-guarantee rate k , where k is the type of electricity consumption, and k = 1, 2, 3 represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption respectively.

[0059] Load matrix:

[0060] D[n][k]

[0061] where: n represents the total number of load centers; k is the type of electricity consumption, and k = 1, 2, 3 represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption respectively.

[0062]

[0063] F2 = total load demand / (energy flow borne * (1 / energy efficiency of a single energy point) + distance * transmission power consumption / conversion efficiency / local industrial electricity price)

[0064] Calculate F2 for each energy point according to the above formula. If only one energy point is needed, sort F2E1, F2E2, and F2E3 from small to large.

[0065] Take the maximum value (the higher the efficiency, the better) as the optimal condition. If multiple energy points are required, first combine multiple cases according to permutations and combinations. For example, if two energy points are needed, there are three cases: (E1, E2), (E1, E3), and (E2, E3). According to the resource analysis of the maximum energy supply potential and load demand, calculate how many load centers the first energy point can supply energy to, and then calculate how many load centers the second energy point can supply energy to. For example, if it is calculated that the first one can supply energy to load centers 1, 2, 3, 4, 5, 6, and the second one can supply energy to load centers 7, 8, 9, 10 (special case, when there is a load center that cannot be satisfied by any energy point, then calculate which energy point can supply energy to this load center with higher energy efficiency, and the insufficient part is supplied by the energy point closest to this load center), then calculate their respective efficiencies, take the average efficiency, and then compare the average efficiencies of the three groups to find the optimal one.

[0066] (2) Formulate the objective function for optimal carbon emissions through the following formula:

[0067]

[0068] First, sum up (the sum of energy flows borne by a single energy point * the carbon emissions of a single energy point), which is the sum of the production of one type of energy (one of cooling, heating, and electricity) by a single energy point; second, sum up the carbon emissions of all combined energy points; finally, sum up the cooling, heating, and electricity. If only one energy point is needed, m = 1, if two are needed, m = 2, and so on;

[0069] Calculate the carbon emissions F3 of each energy point according to the above formula. If only one energy point is needed, sort F3E1, F3E2, F3E3 from small to large.

[0070] Take the minimum value as the optimal condition. If multiple energy points are needed, first combine multiple cases according to permutations and combinations. For example, if two energy points are needed, there are three cases: (E1, E2), (E1, E3), (E2, E3). According to the maximum energy supply potential and load demand analysis of resources, calculate how many energy points the first energy point can supply energy to, and then calculate how many energy points the second energy point can supply energy to. For example, if it is calculated that the first can supply energy to load centers No. 1, 2, 3, 4, 5, 6, and the second can supply energy to load centers No. 7, 8, 9, 10, then calculate the carbon emissions of each energy point respectively, add them together and sum, and then compare the carbon emissions of the three groups to find the optimal one.

[0071] S3: Based on the predicted annual cooling, heating, and electricity load demand, formulate the power supply plan for the energy points to complete the construction of the energy station scenario:

[0072] Load forecasting technology can provide the hourly cooling, heating, and electricity loads for 8760 hours throughout the year. Please refer to the load sample table. According to the supply-demand balance relationship, the cooling, heating, and electricity loads at each moment throughout the year are satisfied. Taking the supply-demand balance of the kth type of energy at time t as an example, the kth type of load (cooling or heating or electricity load) on all plots can be provided by all potential energy points together, and the following supply-demand balance formula is obtained.

[0073]

[0074] Among them, k is the electricity consumption type, and k = 1, 2, 3 represent cooling electricity consumption, heating electricity consumption, and electricity load electricity consumption respectively; T i,j,k is the transmission time of the jth energy supply subsystem in the ith energy point for transmitting electricity to the kth electricity consumption type; η j,k is the transmission efficiency of the jth energy supply subsystem for transmitting electricity to the kth electricity consumption type; D t,j,k$P_{j,k}(t)$ is the hourly load of the $k$-th type of electricity consumption produced by the $j$-th energy supply subsystem, $t$ represents the working hours, and $H$ is the annual working hours. Determine the location of the energy points and the energy supply relationship, and build an energy station at the energy points to complete the construction of the energy station scenario.

[0075] Embodiment 2:

[0076] Based on the same inventive concept, the present invention also provides an energy station scenario construction system, including:

[0077] Load demand analysis module: Set multiple load centers in a preset initial scenario, and set the annual predicted load demand for cooling, heating, and electricity for each load center;

[0078] Energy network layout module: According to the annual predicted load demand for cooling, heating, and electricity, determine the number of energy points in the initial scenario, and determine the optimal location of the energy points in the initial scenario;

[0079] Resource analysis module: Based on the annual predicted load demand for cooling, heating, and electricity, formulate the power supply plan for the energy points to complete the construction of the energy station scenario.

[0080] The energy network layout module includes:

[0081] First network layout calculation sub-module: In the initial scenario, select a preset number of potential energy points, and obtain the power upper limit of each potential energy point;

[0082] Second network layout calculation sub-module: Obtain the maximum energy supply potential of each potential energy point according to the power upper limit;

[0083] Scheme saving sub-module: Based on the maximum energy supply potential, select a combination of potential energy points that can meet the annual predicted load demand for cooling, heating, and electricity and the sum of the maximum energy supply potentials is the lowest, and determine the number of energy points; if the sum of the maximum energy supply potentials of all potential energy points cannot meet the annual predicted load demand for cooling, heating, and electricity, terminate the construction of the energy station scenario.

[0084] The second network layout calculation sub-module calculates the maximum energy supply potential of each potential energy point through the following formula:

[0085]

[0086] where $Q_{in}$ i,j is the power upper limit of the potential energy point; $p$ is the total number of energy supply subsystems in the potential energy point; $i$ is the potential energy point number; $k$ is the electricity consumption type, and $k = 1, 2, 3$ respectively represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption; $\eta$ j,k represents the conversion efficiency of the corresponding $j$-th type of energy supply subsystem to supply electricity to the $k$-th type of electricity consumption.

[0087] The energy network layout module includes:

[0088] An optimization objective formulation sub-module: formulating an economically optimal solution and a carbon emission optimal solution for the energy points;

[0089] A priority selection sub-module: preferentially selecting the location of the energy points according to the economically optimal solution, and when the economically optimal solutions are the same, selecting the location through the carbon emission optimal solution.

[0090] In the optimization objective formulation sub-module, the economically optimal solution is formulated by the following formula:

[0091]

[0092] where i is the number of the energy point, k is the type of electricity consumption, and k = 1, 2, 3 respectively represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption; Cini i,k is the economically optimal function for the production of the k-th type of energy at the i-th energy point; Co_m i,k is the operation and maintenance cost of the electricity consumption for the production of the k-th type of electricity consumption at the i-th energy point; Cpump i,k is the transportation cost of the electricity consumption for the production of the k-th type of electricity consumption at the i-th energy point; Cprod i,k is the production cost of the electricity consumption for the production of the k-th type of electricity consumption at the i-th energy point.

[0093] In the optimization objective formulation sub-module, the carbon emission optimal solution is formulated by the following formula:

[0094]

[0095] where Pi,j,k is the sum of the energy commitments of the electricity consumption for the production of the k-th type of electricity consumption by the j-th functional subsystem in the i-th energy point; P_Cost i,k,2 is the carbon emission of the electricity consumption for the production of the k-th type of electricity consumption at the i-th energy point.

[0096] The priority selection sub-module includes:

[0097] A solution formulation unit: formulating an initial investment optimal solution according to the economically optimal solution;

[0098] A location selection unit: determining the location of the energy point according to the initial investment optimal solution.

[0099] The location selection unit includes:

[0100] An initial location selection sub-unit: obtaining the distance matrix from the energy point to the load center according to the initial investment optimal solution;

[0101] Specific site selection sub - unit: Solve the distance matrix to obtain the specific locations of the energy points.

[0102] The initial site selection sub - unit determines the distance matrix from the energy points to the load centers through the following formula:

[0103]

[0104] Where, Pi,j,k is the sum of the energy undertakings of the power consumption of the k - th type of electricity consumption produced by the j - th functional subsystem in the i - th energy point; m is the total number of energy points; n is the total number of load centers; Device_Cost_Avg_P i,k,1 is the equipment correction coefficient for the power consumption of the k - th type of electricity consumption produced by the i - th energy point; Device_Cost_Avg_P i,k,2 is the energy flow volume borne by a single device for the power consumption of the k - th type of electricity consumption produced by the i - th energy point; Location i -Location 负荷 is the distance matrix from the energy point to the load center; Pipe_Info k,3 is the unit price of the pipeline for meeting the electricity consumption of the k - th type of electricity consumption.

[0105] The resource analysis module includes:

[0106] Load demand analysis sub - module: Determine the equipment capacity and working hours of the functional subsystems in the energy points according to all the annual cooling, heating and power load demand quantities.

[0107] The load demand analysis sub - module determines the working hours of the equipment in the energy supply subsystems in the energy points through the following formula:

[0108]

[0109] Where, k is the type of electricity consumption, k = 1, 2, 3 respectively represent cooling electricity consumption, heating electricity consumption and electrical load electricity consumption; T i,j,k is the transmission time of the j - th energy supply subsystem in the i - th energy point for transmitting electricity to the k - th type of electricity consumption; η j,k is the efficiency of the j - th energy supply subsystem for transmitting electricity to the k - th type of electricity consumption; D t,j,k is the hourly load of the j - th energy supply subsystem for producing the power consumption of the k - th type of electricity consumption, t represents the working hours, and H is the annual working hours.

[0110] Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0111] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0113] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0115] The above are only the embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention pending approval.

Claims

1. A method for constructing an energy station scenario, characterized in that, Including: Set multiple load centers in a preset initial scenario, and set the predicted annual cooling, heating and power load demand for each load center; According to the predicted annual cooling, heating and power load demand, determine the number of energy points in the initial scenario, and determine the optimal locations of the energy points in the initial scenario; Based on the predicted annual cooling, heating and power load demand, formulate the power supply plan for the energy points to complete the construction of the energy station scenario; The determining the number of energy points in the initial scenario includes: In the initial scenario, select a preset number of potential energy points and obtain the power upper limit of each potential energy point; Obtain the maximum energy supply potential of each potential energy point according to the power upper limit; Based on the maximum energy supply potential, select a combination of potential energy points that can meet the predicted annual cooling, heating and power load demand and has the lowest sum of maximum energy supply potentials to determine the number of energy points; if the sum of the maximum energy supply potentials of all potential energy points cannot meet the predicted annual cooling, heating and power load demand, terminate the construction of the energy station scenario; The determining the optimal locations of the energy points in the initial scenario includes: Formulate the economically optimal plan and the carbon emission optimal plan for the energy points; Prioritize the site selection of the energy points according to the economically optimal plan. When the economically optimal plans are the same, conduct the site selection through the carbon emission optimal plan; The formulating the power supply plan for the energy points based on the predicted annual cooling, heating and power load demand includes: According to all the predicted annual cooling, heating and power load demand, determine the working hours of the equipment in the functional subsystems of the energy points; The economically optimal plan is determined by the following formula: Among them, i is the number of energy points, j is the number of energy supply subsystems, k is the type of electricity consumption, where k = 1, 2, 3 represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption respectively; m is the total number of energy points; is the economic optimal function for the production of the k-th type of energy at the i-th energy point; is the operation and maintenance cost of the electricity consumption for the production of the k-th type of electricity consumption at the i-th energy point; is the transportation cost of the electricity consumption for the production of the k-th type of electricity consumption at the i-th energy point; is the production cost of the electricity consumption for the production of the k-th type of electricity consumption at the i-th energy point; r is the annual interest rate of the bank in the project area; ntot is the expected service life of the equipment; The carbon emission optimal plan is determined by the following formula: Among them, is the sum of the energy undertakings of the electricity consumption of the kth type of electricity consumption produced by the jth energy supply subsystem in the ith energy point; is the carbon emission of the electricity consumption of the kth type of electricity consumption produced by the ith energy point; among them, N = m + n , including energy points and load centers; The calculation formula for the working hours is as follows: Among them, k is the type of electricity consumption, where k = 1, 2, and 3 represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption, respectively; is the transmission time of the j-th energy supply subsystem in the i-th energy point for transmitting electricity of the k-th type of electricity consumption; is the efficiency of the j-th energy supply subsystem for transmitting electricity to the k-th type of electricity consumption; is the hourly load of the electricity consumption produced by the j-th energy supply subsystem for the k-th type of electricity consumption, t represents the working hours, and H is the annual working hours.

2. The method according to claim 1, characterized in that, The maximum power supply potential is calculated as follows: Among them, is the power upper limit of the j-th energy supply subsystem of the i-th potential energy point; p is the total number of energy supply subsystems in the potential energy point; j is the energy supply subsystem number; i is the energy point number; k is the type of electricity consumption, where k = 1, 2, 3 represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption respectively; represents the conversion efficiency of the corresponding j-th energy supply subsystem for supplying power to the k-th type of electricity consumption.

3. The method according to claim 1, characterized in that The site selection of the energy points according to the economically optimal plan includes: According to the economically optimal plan, formulate the optimal initial investment plan; According to the optimal initial investment plan, obtain the distance matrix from the energy points to the load centers; Solve the distance matrix to obtain the specific locations of the energy points.

4. The method according to claim 3, wherein The calculation formula for the distance matrix from the energy points to the load centers is as follows: Among them, is the sum of the energy bearers for the electricity consumption of the k-th type of electricity consumption produced by the j-th functional subsystem in the i-th energy point; m is the total number of energy points; n is the total number of load centers; N = m + n , including energy points and load centers; is the equipment correction factor for the electricity consumption of the k-th type of electricity consumption produced by the i-th energy point; is the energy flow amount borne by a single device for the electricity consumption of the k-th type of electricity consumption produced by the i-th energy point; is the distance matrix from energy points to load centers; is the unit price of the pipeline for meeting the electricity consumption of the k-th type of electricity consumption.

5. A system for implementing the energy station scenario construction system as described in claim 1, characterized in that, Including: Load demand analysis module: Set multiple load centers in a preset initial scenario, and set the predicted annual cooling, heating and power load demand for each load center; Energy network layout module: According to the predicted annual cooling, heating and power load demand, determine the number of energy points in the initial scenario, and determine the optimal locations of the energy points in the initial scenario; Resource analysis module: Based on the predicted annual cooling, heating and power load demand, formulate the power supply plan for the energy points to complete the construction of the energy station scenario; The energy network layout module includes: First network layout calculation sub-module: In the initial scenario, select a preset number of potential energy points and obtain the power upper limit of each potential energy point; Second network layout calculation sub-module: Obtain the maximum energy supply potential of each potential energy point according to the power upper limit; Scenario Saving Sub-module: Based on the maximum energy supply potential, select a combination of potential energy points that can meet the annual cooling, heating, and power prediction load demand and have the lowest sum of maximum energy supply potentials, and determine the number of energy points; if the sum of the maximum energy supply potentials of all potential energy points cannot meet the annual cooling, heating, and power prediction load demand, terminate the construction of the energy station scenario. The energy network layout module includes: Optimization Objective Formulation Sub-module: Formulate the economically optimal scenario and the carbon emission optimal scenario for the energy points. Priority Selection Sub-module: Prioritize the location selection of the energy points according to the economically optimal scenario, and when the economically optimal scenarios are the same, conduct location selection through the carbon emission optimal scenario. Based on the annual cooling, heating, and power prediction load demand in the resource analysis module, formulating the power supply scenario for the energy points includes: According to all the annual cooling, heating, and power prediction load demands, determine the working hours of the equipment in the functional subsystems of the energy points.

6. The system according to claim 5, wherein The second network layout calculation sub-module calculates the maximum energy supply potential through the following formula : Among them, is the power upper limit of the potential energy point; p is the total number of energy supply subsystems in the potential energy point; j is the number of the energy supply subsystem; i is the number of the potential energy point; k is the type of electricity consumption, where k = 1, 2, 3 represent cooling electricity consumption, heating electricity consumption, and electrical load electricity consumption respectively; represents the conversion efficiency of the j-th energy supply subsystem to supply power to the k-th type of electricity consumption.

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