A method and system for configuring equipment capacity of a thermal-electric decoupling system of an energy station

Abstract

The present invention discloses a method and system for configuring the equipment capacity of a thermal-electric decoupling system of an energy station, including a data acquisition module, a calculation module, an optimization module and a judgment module. The data acquisition module obtains the energy network information of the area under the jurisdiction of the energy station to be built; the calculation module calculates the energy station site selection and capacity planning scheme; and the energy supply and demand relationship evaluation index in the planning scheme; and the capacity configuration of the internal equipment of the energy station; the judgment module determines whether the energy supply and demand relationship evaluation index reaches the optimal level, and if it reaches the optimal level, the calculation module configures the capacity of the internal equipment of the energy station; if it does not reach the optimal level, the optimization module is fed back to optimize the site selection and capacity configuration of the energy station. Unify the site optimization selection and capacity optimization configuration, and optimize the site selection of the energy station by considering the impact of capacity configuration on the site selection after site selection and capacity determination, so as to optimize the energy efficiency ratio of the energy station, promote the local consumption of new energy, reduce carbon emissions, and ensure the efficient and stable operation of the regional energy network.

Description

Technical Field

[0001] The present invention relates to energy station construction, and in particular to an equipment capacity configuration method and system for a thermal-electric decoupling system of an energy station. Background Art

[0002] Capacity configuration is based on the known total capacity of the energy station, and reasonable capacity allocation is made to its internal key equipment, such as cogeneration units, so that the energy efficiency ratio of the energy station can be optimized.

[0003] At present, addressing and capacity allocation are widely understood as two independent planning contents. However, the capacity configuration of energy stations based on thermal-electric decoupling, while seeking the optimal energy efficiency ratio, will indirectly affect the economy and reliability of the energy Internet, thereby changing the site selection. Independent planning of the addressing and capacity allocation of energy stations cannot achieve the optimal energy efficiency ratio of energy stations. Summary of the invention

[0004] Purpose of the invention: In view of the above shortcomings, the present invention provides a method and system for equipment capacity configuration of a thermal-electric decoupling system of an energy station with unified site optimization selection and capacity optimization configuration.

[0005] The present invention adopts a method for configuring the equipment capacity of a thermal-electric decoupling system of an energy station, comprising the following steps:

[0006] (1) Obtain energy network information in the area where the energy station is to be built;

[0007] (2) Based on the acquired energy network information, the energy station is sited and capacity planned to obtain a planning scheme for the energy station;

[0008] (3) Based on the planning scheme of the energy station, the energy supply and demand relationship within the jurisdiction of the energy station is calculated to obtain the score value of the energy supply and demand relationship evaluation index within the jurisdiction of the energy station;

[0009] (4) Determine the optimal score value and judge whether the calculated score value is optimal: if it is optimal, proceed to step (6);

[0010] (5) Based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, the capacity of the internal equipment of the energy station is configured with the lowest economic cost of the energy station and the highest wind power / photovoltaic power absorption rate as the optimization goals.

[0011] Furthermore, in step (4), if the score value of the energy supply and demand relationship evaluation index within the jurisdiction of the energy station is not optimal, the location and capacity of the energy station are optimized according to the obtained score value to obtain an optimized planning scheme, and then return to step (3).

[0012] Furthermore, the energy supply and demand relationship evaluation index in step (3) is the energy supply and demand balance within the current cluster, and the calculation formula is:

[0013]

[0014] Among them, E s (i) is the capacity of the i-th energy supply node, E d (i) is the capacity of the i-th energy-consuming node, C j is the number of nodes in the jth cluster, M is the total number of clusters, and a cluster is the set of energy supply / consumption nodes in the energy grid where the energy station is located.

[0015] Furthermore, in step (5), all current energy supply / consumption nodes are reclassified according to the obtained score values ​​to optimize the location and capacity of the energy station. The specific steps include:

[0016] (5.1) Calculate the BSED value of energy supply and demand balance of each cluster;

[0017] (5.2) Compare the BSED values ​​of two clusters with adjacent center Euclidean distances. When the BSED is less than 1, the points with the smallest energy moments from the cluster with the smaller BSED value to the cluster with the larger BSED value are assigned to the cluster with the larger BSED value.

[0018] (5.3) Compare the BSED values ​​of two clusters with adjacent Euclidean distances from their centers. When the BSED is greater than 1, the points in the cluster with the larger BSED value whose distance to the cluster with the smaller BSED value is the smallest are assigned to the cluster with the smaller BSED value.

[0019] Furthermore, the thermoelectric decoupling model is a thermoelectric decoupling model with heat storage, including a first model applicable to a first typical scenario and a second model applicable to a second typical scenario; the first typical scenario and the second typical scenario have different renewable energy outputs, and the thermoelectric decoupling model responds differently to the energy grid;

[0020] The first model is:

[0021]

[0022]

[0023] Wherein, P1(s) is the output of the thermoelectric decoupling power balance system in the first typical scenario, ξ is the insulation coefficient of the thermoelectric decoupling device, η is the insulation efficiency of the thermoelectric decoupling device, v is the intake volume of the thermoelectric decoupling device, R is the volume ratio of the thermoelectric decoupling device, H is the enthalpy drop coefficient, K c is the opening coefficient of the thermoelectric decoupling device, ΔP1(s) is the expected output of the thermoelectric decoupling device in the first typical scenario, G1(s) is the transfer function of the first model, and K1 is the equivalent coefficient of the proportional link of the first model;

[0024] The second model is:

[0025]

[0026]

[0027] Wherein, P2(s) is the output of the thermoelectric decoupling power balance system in the second typical scenario, ξ is the insulation coefficient of the thermoelectric decoupling device, η is the insulation efficiency of the thermoelectric decoupling device, v is the intake volume of the thermoelectric decoupling device, R is the volume ratio of the thermoelectric decoupling device, p is the intake pressure of the thermoelectric decoupling device, and K rot is the torque coefficient, ω is the rotor speed, T s is the integral link equivalent coefficient of the second model, s is the number of typical scenarios, ΔP2(s) is the expected output of the thermoelectric decoupling device in the second typical scenario, and G2(s) is the transfer function of the second model;

[0028] Furthermore, the conditions for capacity configuration of the internal equipment of the energy station include:

[0029] (1) Objective function for minimizing the economic cost of energy stations:

[0030]

[0031] Among them, s is the number of typical scenes, C ic is the investment cost, C oc is the operation and maintenance cost, C fc is the fuel cost, C pw Penalty cost for energy waste, ω s is the weight coefficient of the typical scenario;

[0032] (2) Objective function for maximizing wind power / photovoltaic power consumption rate:

[0033]

[0034] Where t is time, P Eload,t is the time sequence component of the electric load, P EC,t is the electric heating power time series component, P CHPe,t is the time series component of the thermal power of the CHP unit, is the wind power / photovoltaic output timing component;

[0035] (3) Constraints include equipment capacity configuration constraints, equipment power constraints, and heat storage upper limit constraints.

[0036] The present invention also adopts an equipment capacity configuration system of a thermal-electric decoupling system of an energy station, comprising a data acquisition module, a calculation module and a judgment module, wherein:

[0037] The data acquisition module is used to obtain energy network information in the area of ​​the energy station to be built;

[0038] The calculation module is used to perform site selection and capacity planning for the energy station according to the energy network information obtained by the data acquisition module, and obtain a planning scheme for the energy station; and based on the planning scheme of the energy station, calculate the energy supply and demand relationship within the jurisdiction of the energy station, and obtain a score value of the energy supply and demand relationship evaluation index within the jurisdiction of the energy station;

[0039] The calculation module is also used to configure the capacity of the internal equipment of the energy station based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic power consumption rate as the optimization goals;

[0040] The judgment module is used to judge whether the score value reaches the optimal value. If it reaches the optimal value, the feedback calculation module configures the capacity of the internal equipment of the energy station based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic absorption rate as the optimization goals.

[0041] Furthermore, it also includes an optimization module, which optimizes the site selection and capacity determination of the energy station according to the obtained energy supply and demand relationship evaluation index to obtain an optimized planning scheme; the judgment module is used to judge whether the score value of the optimized planning scheme has reached the optimal value; the calculation module is used to configure the capacity of the internal equipment of the energy station based on the capacity determination results in the optimized planning scheme and the thermal-electric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic absorption rate as the optimization goals.

[0042] Beneficial effect: Compared with the prior art, the significant advantage of the present invention is the unified site optimization selection and capacity optimization configuration. By considering the impact of capacity configuration on site selection after site selection and capacity determination, the site selection of the energy station is optimized, the energy efficiency ratio of the energy station is optimized, the local consumption of new energy is promoted, carbon emissions are reduced, and the efficient and stable operation of the regional energy network is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the overall process of the device capacity configuration method of the present invention;

[0044] Figure 2 It is a structural block diagram of the equipment capacity configuration system of the present invention;

[0045] Figure 3 This is the solution step of the hybrid multi-objective PSO algorithm in the present invention. DETAILED DESCRIPTION

[0046] Example 1

[0047] like Figure 1 As shown, the equipment capacity configuration method of the thermal-electric decoupling system of the energy station in this embodiment includes the following steps:

[0048] (1) Based on multi-source data sets, obtain the energy network information of the area under the jurisdiction of the energy station to be built; the multi-source data sets include internal and external data of the regional energy network. The energy network information includes the geographical location information of the supply / consumption nodes, the supply / consumption data, and the power grid system information; the supply / consumption data includes wind power generation data, photovoltaic power generation data, thermal power plant data, cogeneration data, natural gas surplus data that can be supplied to cogeneration, actual user electricity load data, and predicted values ​​of user electricity load data; the power grid system information includes the power grid topology, heat network topology, and natural gas network topology;

[0049] (2) Based on the acquired energy network information, the energy station is sited and capacity planned to obtain a planning scheme for the energy station;

[0050] (3) Based on the planning scheme of the energy station, the energy supply and demand relationship within the energy station area is calculated to obtain the score of the energy supply and demand relationship evaluation index; the energy supply and demand relationship evaluation index within the energy station area is described by the current energy supply and demand balance within the cluster. The closer the balance is to 100%, the better the energy supply and demand relationship within the energy station area is, and the better the energy supply and demand relationship evaluation index score is; the calculation formula of the energy supply and demand balance is:

[0051]

[0052] Among them, E s (i) is the capacity of the i-th energy supply node, E d (i) is the capacity of the i-th energy-consuming node, C j is the number of nodes in the jth cluster, M is the total number of clusters, a cluster is a collection of energy supply / consumption nodes in the energy grid, the initial position of the cluster center (energy station) is random, and the geometric center (energy station) of M energy supply and consumption nodes can be taken to speed up convergence.

[0053] (4) determining the optimal score value of the energy supply-demand relationship evaluation index according to the actual situation, and judging whether the calculated score value of the energy supply-demand relationship evaluation index reaches the optimal value: if it reaches the optimal value, proceed to step (6); if it does not reach the optimal value, proceed to step (5);

[0054] (5) Optimize the location and capacity of the energy station according to the obtained score of the energy supply and demand relationship evaluation index, reclassify all current supply / consumption nodes, obtain the optimized planning scheme, and then return to step (3); the specific optimization steps include:

[0055] The specific steps include:

[0056] (5.1) Calculate the BSED value of energy supply and demand balance of each cluster;

[0057] (5.2) Compare the BSED values ​​of two clusters with adjacent center Euclidean distances. When the BSED is less than 1, the points with the smallest energy moments from the cluster with the smaller BSED value to the cluster with the larger BSED value are assigned to the cluster with the larger BSED value.

[0058] (5.3) Compare the BSED values ​​of two clusters with adjacent center Euclidean distances. When the BSED is greater than 1, the points with the smallest energy moments from all nodes in the cluster with the larger BSED value to the cluster with the smaller BSED value are assigned to the cluster with the larger BSED value.

[0059] (6) Based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, the optimization objectives are to minimize the economic cost of the energy station and maximize the wind power / photovoltaic power consumption rate. Figure 3 The hybrid multi-objective PSO algorithm shown is used to solve the capacity configuration of the internal equipment of the energy station.

[0060] The thermoelectric decoupling model is a thermoelectric decoupling model with heat storage, including a first model applicable to the first typical scenario and a second model applicable to the second typical scenario; the renewable energy outputs of the first typical scenario and the second typical scenario are different, and the thermoelectric decoupling model responds differently to the energy grid; in the first typical scenario, the output of the renewable energy system is high-frequency and oscillates in a small amplitude, and the thermoelectric decoupling device only responds to the pulsating component of the system power difference; in the second typical scenario, the output of the renewable energy system is low-frequency and oscillates in a large amplitude, and the thermoelectric decoupling device only responds to the stable component of the system power difference, but there is a response delay.

[0061] The first model is:

[0062]

[0063]

[0064] Wherein, P1(s) is the output of the thermoelectric decoupling power balance system in the first typical scenario, ξ is the insulation coefficient of the thermoelectric decoupling device, η is the insulation efficiency of the thermoelectric decoupling device, v is the intake volume of the thermoelectric decoupling device, R is the volume ratio of the thermoelectric decoupling device, H is the enthalpy drop coefficient, K c is the opening coefficient of the thermoelectric decoupling device, ΔP1(s) is the expected output of the thermoelectric decoupling device in the first typical scenario, G1(s) is the transfer function of the first model, and K1 is the equivalent coefficient of the proportional link of the first model.

[0065] The second model is:

[0066]

[0067]

[0068] Wherein, P2(s) is the output of the thermoelectric decoupling power balance system in the second typical scenario, ξ is the insulation coefficient of the thermoelectric decoupling device, η is the insulation efficiency of the thermoelectric decoupling device, v is the intake volume of the thermoelectric decoupling device, R is the volume ratio of the thermoelectric decoupling device, p is the intake pressure of the thermoelectric decoupling device, and K rot is the torque coefficient, ω is the rotor speed, T s is the integral link equivalent coefficient of the second model, s is the number of typical scenarios, ΔP2(s) is the expected output of the thermoelectric decoupling device in the second typical scenario, and G2(s) is the transfer function of the second model.

[0069] The conditions for capacity configuration of internal equipment of the energy station include:

[0070] (1) Objective function for minimizing the economic cost of energy stations:

[0071]

[0072] Among them, s is the number of typical scenes, C ic is the investment cost, C oc is the operation and maintenance cost, C fc is the fuel cost, C pw Penalize cost for energy waste, ω s is the weight coefficient of the typical scenario.

[0073] (2) Objective function for maximizing wind power / photovoltaic power consumption rate:

[0074]

[0075] Among them, t is time, P Eload,t is the time sequence component of the electric load, P EC,t is the electric heating power time series component, P CHPe,t is the time series component of the thermal power of the CHP unit, It is the timing component of wind power / photovoltaic output.

[0076] (3) Constraints include equipment capacity configuration constraints, equipment power constraints, and heat storage upper limit constraints. Equipment capacity configuration constraints include capacity configuration constraints for wind turbines, photovoltaics, combined heat and power (CHP), heat storage, and expansive power generation (EPG); equipment power constraints include CHP electric power constraints and CHP thermal power constraints. Constraints also include EPG minimum air intake constraints and EPG minimum speed constraints.

[0077] The configuration of key equipment inside the energy station should also meet the following constraints:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] Among them, P CHP_e，t P is the electric power of the cogeneration unit; CHP_h，t is the thermal power of the cogeneration unit, P HS_eh，t is the electric heating power of the electric heating device, P HS_ht，t is the heat exchange power of the heat exchange device, W HS，t is the heat capacity of the heat storage device, P EPG，t is the electric power of the expansion power generation device, P SG，t is the thermal power of the steam generating device, is the maximum output of the cogeneration unit, v CHP_e is the heat-to-electricity ratio of the cogeneration unit, v CHP_h The heat-to-electricity ratio of the cogeneration unit, The maximum output of the electric heating device, The maximum output of the heat exchange device, is the maximum energy storage capacity of the heat storage device, The maximum output of the expansion power generation device, It is the maximum output of the steam generating device.

[0086] Example 2

[0087] like Figure 2 As shown, in this embodiment, a device capacity configuration system of a thermal-electric decoupling system of an energy station includes a data acquisition module, a calculation module, an optimization module and a judgment module, wherein:

[0088] The data acquisition module obtains the energy network information of the area under the jurisdiction of the energy station to be built based on the multi-source data set; the multi-source data set includes the internal and external data of the regional energy network. The energy network information includes the geographical location information of the supply / consumption nodes, the supply / consumption data and the power grid system information; the supply / consumption data includes wind power generation data, photovoltaic power generation data, thermal power plant data, cogeneration data, natural gas surplus data that can be supplied to cogeneration, actual user load data and user load data forecast value; the power grid system information includes the power grid topology, heat network topology and natural gas network topology.

[0089] The calculation module uses the energy network information to carry out site selection and capacity planning for the energy station, and obtains the planning scheme of the energy station; and based on the planning scheme of the energy station, calculates the energy supply and demand relationship within the jurisdiction of the energy station, and obtains the score value of the energy supply and demand relationship evaluation index;

[0090] The calculation module configures the capacity of the internal equipment of the energy station based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic absorption rate as the optimization goals;

[0091] The optimization module is used to optimize the location and capacity of the energy station according to the obtained score values ​​of the energy supply and demand relationship evaluation index to obtain an optimized planning scheme;

[0092] The judgment module is used to determine whether the energy supply and demand relationship evaluation index has reached the optimal level. If it has reached the optimal level, the feedback calculation module will configure the capacity of the internal equipment of the energy station based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic absorption rate as the optimization goals; if it has not reached the optimal level, the feedback optimization module will optimize the site selection and capacity determination of the energy station according to the obtained energy supply and demand relationship evaluation index to obtain the optimized planning scheme.

[0093] Based on the same inventive concept, in another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium, which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the analysis method of the impact of the user-side energy management system access on the distribution network in the above embodiment.

[0094] 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.

[0095] 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.

[0096] 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 comprising 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.

[0097] 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.

[0098] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for configuring equipment capacity of a thermal-electric decoupling system of an energy station, characterized in that: The following steps are involved: (1) Obtain energy network information in the area where the energy station is to be built; (2) Based on the acquired energy network information, the energy station is sited and capacity planned to obtain a planning scheme for the energy station; (3) Based on the planning scheme of the energy station, the energy supply and demand relationship within the jurisdiction of the energy station is calculated to obtain the score value of the energy supply and demand relationship evaluation index within the jurisdiction of the energy station; (4) Determine the optimal score value and judge whether the calculated score value reaches the optimal value: if it reaches the optimal value, proceed to step (5); if the score value of the energy supply and demand relationship evaluation index within the jurisdiction of the energy station does not reach the optimal value, optimize the site selection and capacity of the energy station according to the obtained score value, obtain the optimized planning scheme, and then return to step (3); (5) Based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, the capacity of the internal equipment of the energy station is configured with the lowest economic cost of the energy station and the highest wind power / photovoltaic power consumption rate as the optimization goal; Reclassify all current energy supply / consumption nodes according to the obtained score values ​​to optimize the location and capacity of energy stations. The specific steps include: (5.1) Calculate the BSED value of energy supply and demand balance of each cluster; (5.2) Compare the BSED values ​​of two clusters with adjacent center Euclidean distances. When the BSED is less than 1, the points with the smallest energy moments from the cluster with the smaller BSED value to the cluster with the larger BSED value are assigned to the cluster with the larger BSED value. (5.3) Compare the BSED values ​​of two clusters with adjacent Euclidean distances. When the BSED is greater than 1, the points with the smallest energy moments from the cluster with the larger BSED value to the cluster with the smaller BSED value are assigned to the cluster with the smaller BSED value. The energy supply and demand relationship evaluation index in step (3) is the energy supply and demand balance within the current cluster, and the calculation formula is: Among them, E s (i) is the capacity of the i-th energy supply node, E d (i) is the capacity of the i-th energy-consuming node, C j is the number of nodes in the jth cluster; M is the total number of clusters, and a cluster is the set of energy supply / consumption nodes in the energy grid where the energy station is located.

2. The device capacity configuration method according to claim 1, characterized in that: The thermoelectric decoupling model is a thermoelectric decoupling model with heat storage, including a first model applicable to a first typical scenario and a second model applicable to a second typical scenario; the first typical scenario and the second typical scenario have different renewable energy outputs, and the thermoelectric decoupling model responds differently to the energy grid; The first model is: Wherein, P1(s) is the output of the thermoelectric decoupling power balance system in the first typical scenario, ξ is the insulation coefficient of the thermoelectric decoupling device, η is the insulation efficiency of the thermoelectric decoupling device, v is the intake volume of the thermoelectric decoupling device, R is the volume ratio of the thermoelectric decoupling device, H is the enthalpy drop coefficient, K c is the opening coefficient of the thermoelectric decoupling device, ΔP1(s) is the expected output of the thermoelectric decoupling device in the first typical scenario, G1(s) is the transfer function of the first model, and K1 is the equivalent coefficient of the proportional link of the first model; The second model is: Wherein, P2(s) is the output of the thermoelectric decoupling power balance system in the second typical scenario, ξ is the insulation coefficient of the thermoelectric decoupling device, η is the insulation efficiency of the thermoelectric decoupling device, v is the intake volume of the thermoelectric decoupling device, R is the volume ratio of the thermoelectric decoupling device, p is the intake pressure of the thermoelectric decoupling device, and K rot is the torque coefficient, ω is the rotor speed, T s is the integral link equivalent coefficient of the second model, s is the number of typical scenarios, ΔP2(s) is the expected output of the thermoelectric decoupling device in the second typical scenario, and G2(s) is the transfer function of the second model.

3. The device capacity configuration method according to claim 1, characterized in that: The conditions for capacity configuration of the internal equipment of the energy station include: (1) Objective function for minimizing the economic cost of energy stations: Among them, s is the number of typical scenes, C ic is the investment cost, C oc is the operation and maintenance cost, C fc is the fuel cost, C pw Penalty cost for energy waste, ω s is the weight coefficient of the typical scenario; (2) Objective function for maximizing wind power / photovoltaic power consumption rate: Where t is time, P Eload,t is the time sequence component of the electric load, P EC,t is the electric heating power time series component, P CHPe,t is the time series component of the thermal power of the CHP unit, is the wind power / photovoltaic output timing component; (3) Constraints include equipment capacity configuration constraints, equipment power constraints, and heat storage upper limit constraints.

4. An equipment capacity configuration system for a thermal-electric decoupling system of an energy station, characterized in that: It includes a data acquisition module, a calculation module and a judgment module, wherein: The data acquisition module is used to obtain energy network information in the area of ​​the energy station to be built; The calculation module is used to perform site selection and capacity planning for the energy station according to the energy network information obtained by the data acquisition module, and obtain a planning scheme for the energy station; and based on the planning scheme of the energy station, calculate the energy supply and demand relationship within the jurisdiction of the energy station, and obtain a score value of the energy supply and demand relationship evaluation index within the jurisdiction of the energy station; The calculation module is also used to configure the capacity of the internal equipment of the energy station based on the capacity determination results in the planning scheme and the thermal-electric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic power consumption rate as the optimization goals; The judgment module is used to judge whether the score value reaches the optimum. If it reaches the optimum, the feedback calculation module is used to configure the capacity of the internal equipment of the energy station based on the capacity determination results in the planning scheme and the thermoelectric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic absorption rate as the optimization goals; if the score value of the energy supply and demand relationship evaluation index within the jurisdiction of the energy station does not reach the optimum, the site selection and capacity determination of the energy station is optimized according to the obtained score value, and the optimized planning scheme is obtained, and the score value is recalculated by the calculation module; Reclassify all current energy supply / consumption nodes according to the obtained score values ​​to optimize the location and capacity of energy stations. The specific steps include: (5.1) Calculate the BSED value of energy supply and demand balance of each cluster; (5.2) Compare the BSED values ​​of two clusters with adjacent center Euclidean distances. When the BSED is less than 1, the points with the smallest energy moments from the cluster with the smaller BSED value to the cluster with the larger BSED value are assigned to the cluster with the larger BSED value. (5.3) Compare the BSED values ​​of two clusters with adjacent Euclidean distances. When the BSED is greater than 1, the points with the smallest energy moments from the cluster with the larger BSED value to the cluster with the smaller BSED value are assigned to the cluster with the smaller BSED value. The energy supply and demand relationship evaluation index in step (3) is the energy supply and demand balance within the current cluster, and the calculation formula is: Among them, E s (i) is the capacity of the i-th energy supply node, E d (i) is the capacity of the i-th energy-consuming node, C j is the number of nodes in the jth cluster; M is the total number of clusters, and a cluster is the set of energy supply / consumption nodes in the energy grid where the energy station is located.

5. The equipment capacity configuration system of the thermal-electric decoupling system of the energy station according to claim 4, characterized in that: It also includes an optimization module, which optimizes the site selection and capacity determination of the energy station according to the obtained energy supply and demand relationship evaluation index to obtain an optimized planning scheme; the judgment module is used to judge whether the score value of the optimized planning scheme has reached the optimal value; the calculation module is used to configure the capacity of the internal equipment of the energy station based on the capacity determination results in the optimized planning scheme and the thermal-electric decoupling model, with the lowest economic cost of the energy station and the highest wind power / photovoltaic absorption rate as the optimization goals.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

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 steps of the method according to any one of claims 1 to 3 are implemented.

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