A power distribution network energy storage and data center planning method considering information-physical coupling

Abstract

The application discloses a power distribution network energy storage and data center planning method considering information-physical coupling, and belongs to the field of electric power energy, and comprises the following contents: firstly, based on the information-physical coupling mechanism of the power distribution network, distributed photovoltaic and energy storage in the power distribution network are modeled; further, considering the demand response capability of data in the power distribution network, a data center physical model is established, and a data load flexible regulation strategy is proposed; considering the comprehensive utilization of the regulation characteristics of distributed energy storage and the space-time transfer potential of data load of the data center, the physical side energy storage configuration, the space-time transfer mode of data load and the information side communication network topology are collaboratively planned, a power distribution network fusion planning model considering information-physical coupling is proposed, the planning cost minimization is realized, and the power distribution network communication topology is optimized; finally, the economy and actual operation effect of the fusion planning scheme are verified through examples.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically a method for planning energy storage and data centers in distribution networks that takes into account cyber-physical coupling. Background Technology

[0002] With the rapid development of the global economy and the substantial increase in energy demand, energy development faces enormous challenges from resource shortages and environmental pollution. The effective utilization of clean energy and energy transformation have become necessary ways to alleviate the current energy and environmental crisis.

[0003] In recent years, the development of informatization and intelligentization has gradually transformed traditional distribution networks into highly coupled information and physical systems (CPDS). Furthermore, the increasing diversification of distribution network resources and the increasingly stringent control requirements have provided new ideas for distribution network planning through the high-level perception, deep extension, and collaborative control capabilities of the information side of CPDS. In addition, the deployment of large-scale internet data centers has become an inevitable trend in distribution network development. Data centers have become an important demand response resource, capable of actively participating in system operation and achieving peak shaving, valley filling, or ancillary services.

[0004] Against this backdrop, the problem of distribution network planning needs to take into account the coordinated cooperation of multiple types of flexible resources and the coupling between information and physical systems, so as to achieve the best economic efficiency of the distribution information and physical system while ensuring the safety and reliability of the distribution system. Summary of the Invention

[0005] To address the shortcomings mentioned in the background section, the present invention aims to provide a method for planning energy storage and data centers in power distribution networks that considers cyber-physical coupling.

[0006] The objective of this invention can be achieved through the following technical solution: a method for planning energy storage and data centers in power distribution networks that considers cyber-physical coupling, the method comprising the following steps:

[0007] From the perspective of distribution network flexibility resource planning, distributed resources in the distribution network are modeled, and a physical-side distributed energy storage regulation characteristic model is established to provide a physical-side resource model basis for the distribution network integrated planning model that takes into account information-physical coupling.

[0008] From the perspective of cyber-physical integration of distribution networks, and taking into account the demand response capabilities of data centers on the information side, a physical model of a single data center is established, and a spatiotemporal transfer model of data load for data centers on the information side is proposed. ， This provides an information-side resource model foundation for a distribution network integration planning model that takes into account cyber-physical coupling;

[0009] Based on the established physical-side distributed energy storage regulation characteristic model and the information-side data center data load spatiotemporal transfer model, the physical-side energy storage configuration, data load spatiotemporal transfer mode, and information-side communication network topology are planned collaboratively. Objective functions and constraints in the distribution network planning scheme are established, and a distribution network fusion planning model that takes into account information-physical coupling is established accordingly.

[0010] The actual effectiveness of the distribution network integration planning model based on the actual distribution network and cyber-physical coupling was verified.

[0011] Preferably, the power distribution network flexibility resource planning perspective is as follows:

[0012] The overall architecture of the Distribution Cyber-Physical System (CPDS) is divided into three layers: the power physical layer, the communication network layer, and the information control layer.

[0013] From the perspective of distribution network flexibility resource planning and cross-space signal transmission, it is as follows: First, the power physical layer collects the basic parameters, operating status or fault information of various physical devices through sensors, and converts the physical signals into electrical signals. Then, the distribution network side intelligent terminal transmits the acquired electrical signals through the communication channel in the communication network layer. After receiving the electrical signals, the controller in the information control layer issues corresponding control command signals, thereby regulating the operating status or working condition of the physical devices in the power physical layer.

[0014] Preferably, the process of modeling distributed resources in the distribution network includes:

[0015] Distributed resources in the power distribution network include photovoltaic systems and energy storage systems;

[0016] The photovoltaic (PV) system is used to power the distribution information physical system and data center. The PV system directly converts solar energy into DC power. When operating in grid-connected mode, the current-controlled inverter converts the DC power into AC power with the same frequency and phase as the distribution network and connects it to the grid. The inverter of the PV grid-connected system mainly adopts the voltage source current control method. It is only necessary to control the inverter output current to track the grid voltage to achieve the purpose of parallel operation. When the PV power is connected to the grid, the power factor is kept at 1. In the power flow calculation, only the active power is considered and it is treated as a PQ node.

[0017] When a high proportion of distributed power sources are connected, the energy storage system (ESS) uses droop control to adjust the power absorbed or emitted by the ESS, thereby achieving power flow and voltage regulation of the system. When the node voltage exceeds the upper limit of the normal voltage, the ESS is in a charging state, absorbing a portion of active power to alleviate the voltage rise problem; when the node voltage is below the lower limit of the normal voltage, the ESS is in a discharging state, releasing a portion of the stored active power to improve the node voltage.

[0018] The power regulation requirements for energy storage systems in CPDS are as follows:

[0019]

[0020] Among them, P n This represents the active power actually absorbed or emitted by the nth ESS. This indicates the droop control power adjustment value, ΔSOC. n,t E represents the change in the state of charge (SOC) of the nth ESS during time period t. n This represents the rated capacity of the nth ESS;

[0021] For a single ESS, the active power adjustment needs to be evenly distributed to prevent the active power adjustment of the ESS from remaining at a consistently high level; that is, to maintain a consistent relative amount of active power stored in each ESS.

[0022]

[0023] Preferably, the physical model of the single data center includes:

[0024] In CPDS, the data load processing flow of a data center can be simplified into three key parts:

[0025] Local users issue task requests, forming a data load model;

[0026] The data center collects, analyzes, and processes the data load in the data load model;

[0027] The data center distributes data load to each node, forming a physical model of a single data center.

[0028] Preferably, the spatiotemporal transfer model of data load in the information-side data center includes:

[0029] In CPDS, data loads are divided into two categories: latency-sensitive and latency-tolerant. Latency-sensitive loads require real-time processing within a short period of time and use an M / M / 1 queuing model to model the queuing delay within a time period, ensuring that the data loads received by the data center in each time period must be processed within that time period. Latency-tolerant loads have a high tolerance for processing time requirements and can be processed within a specified time. Latency-tolerant data loads between different data centers can also be spatially transferred, thus the data load has spatiotemporal adjustment characteristics.

[0030] Preferably, the objective function is:

[0031]

[0032] Where F is the objective function, Indicates the investment cost of energy storage. Indicates the investment cost of smart terminals. Indicates the investment cost of the communication network. Indicates the operating cost of energy storage. This indicates the operating cost of the data center. Indicates the cost of network loss. This indicates the EENS loss cost due to insufficient power supply;

[0033] Preferably, in the objective function, the energy storage investment cost is:

[0034]

[0035] in, E represents the investment cost per unit capacity of energy storage. n For the nth energy storage capacity, N E y1 represents the number of planned energy storage facilities, d represents the operating life of the energy storage facilities, and d represents the discount rate.

[0036] Investment costs for smart terminals:

[0037]

[0038] in, y2 represents the investment cost of a single smart terminal, K represents the planned number of smart terminals, y2 represents the operating life of the smart terminal, and d represents the discount rate.

[0039] Communication network investment costs:

[0040]

[0041] In this context, it is assumed that the investment cost of the communication network is proportional to the straight-line distance between nodes where smart terminals are installed. y3 represents the investment cost per unit length of the communication network, and L represents the operating life of the communication network. ij Let G represent the straight-line distance between node i and node j, and let G represent the set of lines between nodes with installed smart terminals.

[0042] Energy storage operating costs:

[0043]

[0044] in, The cost of operation and dispatching per unit capacity of energy storage. This represents the charging or discharging power of the i-th energy storage unit during time period t, where T represents the total number of energy storage charging and discharging periods. The default value for a year is 365 days. This converts the energy storage operating cost into an annual value.

[0045] Data center operating costs:

[0046]

[0047] Among them, MP t N represents the nodal marginal electricity price of the distribution network at time t. D For the number of data centers, This represents the electrical energy required by a data center to process a unit of data load per unit of time.

[0048] Network loss cost:

[0049]

[0050] in, The network loss during time period t;

[0051] EENS losses due to insufficient power supply:

[0052]

[0053] in, L represents the amount of insufficient power supply at node l on day t, where L represents the total number of physical nodes in the distribution network.

[0054] Preferably, the constraint condition is:

[0055] Active and reactive power flow equality constraints; node voltage and branch current equality constraints; branch current and node voltage inequality constraints; energy storage charge state transition equality constraints; energy storage capacity equality constraints; energy storage charge state inequality constraints.

[0056] Preferably, an apparatus includes:

[0057] One or more processors;

[0058] Memory, used to store one or more programs;

[0059] When one or more of the programs are executed by one or more of the processors, the one or more processors implement a power distribution network energy storage and data center planning method that takes into account cyber-physical coupling, as described above.

[0060] Preferably, a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a power distribution network energy storage and data center planning method considering cyber-physical coupling as described above.

[0061] The beneficial effects of this invention are:

[0062] This invention comprehensively utilizes the power and voltage regulation characteristics of distributed energy storage and the potential for spatiotemporal data load transfer in data centers. It collaboratively plans the physical-side energy storage configuration, data load spatiotemporal transfer methods, and information-side communication network topology, proposing a CPDS integrated planning model to minimize planning costs and optimize the distribution network communication topology. This invention also considers the impact of cyber-physical coupling, which can improve the absorption capacity of distributed energy resources in the distribution network and reduce system power operating costs. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart of the method of the present invention;

[0065] Figure 2 This is a schematic diagram of the data center physical model and processing flow of the present invention;

[0066] Figure 3 This is the IEEE-33 node distribution network model of the present invention;

[0067] Figure 4 The present invention provides typical daily photovoltaic power output and load curves.

[0068] Figure 5 This is a typical daily data load curve for the data center of this invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] like Figure 1 As shown, a method for planning energy storage and data centers in power distribution networks that considers cyber-physical coupling is presented. The method includes the following steps:

[0071] From the perspective of distribution network flexibility resource planning, distributed resources in the distribution network are modeled, and a physical-side distributed energy storage regulation characteristic model is established to provide a physical-side resource model basis for the distribution network integrated planning model that takes into account information-physical coupling.

[0072] From the perspective of cyber-physical integration of power distribution networks, and taking into account the demand response capabilities of data centers on the information side, a physical model of a single data center is established, and a spatiotemporal transfer model of data load for data centers on the information side is proposed. ， This provides an information-side resource model foundation for a distribution network integration planning model that takes into account cyber-physical coupling;

[0073] Based on the established physical-side distributed energy storage regulation characteristic model and the information-side data center data load spatiotemporal transfer model, the physical-side energy storage configuration, data load spatiotemporal transfer mode, and information-side communication network topology are planned collaboratively. Objective functions and constraints in the distribution network planning scheme are established, and a distribution network fusion planning model that takes into account information-physical coupling is established accordingly.

[0074] The actual effectiveness of the integrated planning model for distribution networks based on the actual distribution network and cyber-physical coupling was verified.

[0075] A planning method for the integration of distribution network energy storage and data center considering cyber-physical coupling, wherein the cyber-physical coupling mechanism of the distribution network includes:

[0076] The overall architecture of the Distribution Cyber-Physical System (CPDS) is divided into the power physical layer, the communication network layer, and the information control layer.

[0077] The information-physical coupling mechanism and cross-space signal transmission in the power distribution network are as follows: First, the power physical layer collects basic parameters, operating status, or fault information of various physical devices through sensors and converts the physical signals into electrical signals. Then, the intelligent terminal on the distribution network side transmits the acquired electrical signals through the communication channel in the communication network layer. After receiving the electrical signals, the controller in the information communication layer issues corresponding control command signals, thereby regulating the operating status or condition of the physical devices in the power physical layer.

[0078] A planning method for the integration of energy storage and data centers in a distribution network that considers cyber-physical coupling, wherein distributed resource modeling in the distribution network includes:

[0079] Photovoltaic (PV) systems can power distribution cyber-physical systems and data centers, effectively reducing operating costs and improving the economic efficiency of distribution networks. PV systems directly convert solar energy into direct current (DC). During grid-connected operation, a current-controlled inverter converts the DC energy into alternating current (AC) with the same frequency and phase as the distribution network. PV grid-connected system inverters primarily use voltage source current control; simply controlling the inverter's output current to track the grid voltage achieves parallel operation. When PV power is connected to the grid, it can maintain a power factor of 1, allowing active power to be considered only in power flow calculations, treating it as a PQ node.

[0080] Energy Storage System (ESS): When a high proportion of distributed power sources are connected, droop control is used to adjust the power absorbed or emitted by the ESS to achieve power flow and voltage regulation of the system. When the node voltage exceeds the upper limit of the normal voltage, the ESS is in a charging state, absorbing some active power to alleviate the voltage rise problem; when the node voltage is below the lower limit of the normal voltage, the ESS is in a discharging state, releasing some of the stored active power to improve the node voltage. Furthermore, the power regulation requirements for the energy storage system in CPDS are as follows:

[0081] Overall, sufficient active power resources must be ensured, meaning the active power absorbed or generated by the ESS must meet the following requirements:

[0082]

[0083] Among them, P n This represents the active power actually absorbed or emitted by the nth ESS. This indicates the droop control power adjustment value, ΔSOC. n,t E represents the change in the State of Charge (SOC) of the nth Energy Storage System (ESS) during time period t. n This represents the rated capacity of the nth ESS.

[0084] For a single ESS (Electronic Power Supply), it is necessary to ensure the even distribution of active power adjustment as much as possible to prevent some ESSs from maintaining a consistently high active power adjustment level, which would degrade their service life and operational performance. In other words, it is essential to maintain a consistent relative amount of active power stored in each ESS.

[0085]

[0086] A planning method for the integration of distribution network energy storage and data centers that considers cyber-physical coupling, wherein the physical model of a single data center includes:

[0087] In CPDS, the data load processing flow of a data center can be simplified into three key parts:

[0088] (1) Local users issue task requests, forming a data load model;

[0089] (2) The data center collects, analyzes, and processes data loads;

[0090] (3) The data center distributes the data load to each node to form a computing node model.

[0091] Data center physical model and processing flow diagram as follows Figure 2 As shown.

[0092] A planning method for the integration of distribution network energy storage and data center considering cyber-physical coupling, wherein the data load flexible spatiotemporal control strategy includes:

[0093] In CPDS, data loads are typically categorized into two types: latency-sensitive and latency-tolerant. The former requires real-time processing within a short timeframe, usually employing an M / M / 1 queuing model to model queuing latency over a given period, ensuring that data loads received by the data center within each time period are processed within that period. The latter has a higher tolerance for processing time, requiring only completion within a specified time. Latency-tolerant data loads can also be spatially transferred between different data centers, thus exhibiting spatiotemporal adjustment characteristics. Without loss of generality, this invention primarily considers latency-tolerant loads.

[0094] A planning method for the integration of energy storage and data centers in power distribution networks considering cyber-physical coupling, wherein the objective function of the power distribution network integration planning model considering cyber-physical coupling includes:

[0095] objective function

[0096] in, Indicates the investment cost of energy storage. Indicates the investment cost of smart terminals. Indicates the investment cost of the communication network. Indicates the operating cost of energy storage. This indicates the operating cost of the data center. Indicates the cost of network loss. This indicates the cost of power shortage (EENS) losses.

[0097] Energy storage investment costs:

[0098] in, E represents the investment cost per unit capacity of energy storage. n For the nth energy storage capacity, N E y1 represents the number of planned energy storage facilities, y1 represents the operating life of the energy storage facilities (since the energy storage model considers the uniform distribution of the active power adjustment of each ESS, it is assumed that the operating life of each ESS is the same), and d represents the discount rate.

[0099] Investment costs for smart terminals:

[0100] in, Let y2 be the investment cost of a single smart terminal, K be the planned number of smart terminals, y2 be the operating life of the smart terminal, and d be the discount rate.

[0101] Communication network investment costs:

[0102] It is assumed that the investment cost of the communication network is proportional to the straight-line distance between nodes where smart terminals are installed. y3 represents the investment cost per unit length of the communication network, and L represents the operating life of the communication network. ij Let G represent the straight-line distance between node i and node j, and let G represent the set of lines between nodes with installed smart terminals.

[0103] Energy storage operating costs:

[0104] in, The cost of operation and dispatching per unit capacity of energy storage. This represents the charging or discharging power of the i-th energy storage unit during time period t, where T represents the total number of energy storage charging and discharging periods. A year is assumed to be 365 days, and the energy storage operating cost is converted to an annual value.

[0105] Data center operating costs:

[0106] Among them, MP t N represents the marginal price at each node of the distribution network at time t (assuming the price is the same at each node in the distribution network). D For the number of data centers, This represents the electrical energy required by a data center to process a unit of data load per unit of time.

[0107] Network loss cost:

[0108] in, Let t be the network loss during time period t.

[0109] Power shortage (EENS) losses:

[0110] in, L represents the amount of insufficient power supply at node l on day t, where L represents the total number of physical nodes in the distribution network.

[0111] A planning method for the integration of energy storage and data centers in power distribution networks considering cyber-physical coupling, wherein the constraints of the power distribution network integration planning model considering cyber-physical coupling include:

[0112] Constraints include: active and reactive power flow equations; node voltage and branch current equations; branch current and node voltage inequalities; energy storage state of charge transition equations; energy storage capacity equations; and energy storage state of charge inequalities.

[0113] The energy storage constraint is expressed as:

[0114]

[0115]

[0116]

[0117] For energy storage droop control, the active power absorbed or released; E n The rated capacity of the nth energy storage unit; ΔSOC n,t Let N be the change in state of charge (SOC) of the nth energy storage unit during time period t; N is the planned number of energy storage units; SOC j [k] represents the SOC value of energy storage during time period k; SOC j Indicates the lower limit of the energy storage SOC; Indicates the upper limit of the energy storage SOC; Let i be the in-degree of node i. This indicates that i is a subordinate node of j in the communication network, meaning that node i can receive communication information sent by node j.

[0118] The following is a detailed description of an optional embodiment of the present invention.

[0119] In one embodiment of the present invention: the above-described topology identification method is applied to, for example... Figure 3 The IEEE-33 node distribution network model shown has a rated voltage of 12.66 kV, a total active power demand of 3715 kW, and a total reactive power demand of 2300 kvar.

[0120] Figure 3 The nodes marked in the middle are distributed photovoltaic (PV) access nodes and planned energy storage nodes, with a rated PV output of 600 kW and a maximum energy storage capacity of 200 kWh. The load power factor for both is 0.95. Typical daily PV output and load curves are shown below. Figure 4 As shown, the typical daily data load curve of a data center is as follows: Figure 5 Three data centers are installed at nodes 9, 17, and 25. Data loads in different data centers can be spatially transferred, and data loads within each data center can be temporally transferred. In addition, other energy consumption within the data centers is not considered.

[0121] In addition, the other parameter settings in the CPDS multi-resource multi-objective primary and secondary fusion planning model are shown in the table below.

[0122] Table 1 Parameter Settings

[0123]

[0124] To verify the economic efficiency of the proposed cyber-physical collaborative planning method, the following two planning schemes are compared:

[0125] Option 1: Independent planning of physical system and communication topology;

[0126] Option 2: The present invention proposes a planning method for the integration of distribution network energy storage and data center with cyber-physical coupling.

[0127] Table 2 Energy Storage Locations and Quantities

[0128]

[0129] Table 3 Comprehensive Costs

[0130]

[0131] As shown in Table 3, the proposed integrated planning method for distribution network energy storage and data centers, which considers cyber-physical coupling, is more economical than the independent cyber-physical planning method. The main reason is that the proposed method configures energy storage and communication terminals as a whole, avoiding overly dispersed energy storage configuration and thus reducing communication investment.

[0132] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0133] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0134] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0135] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A method for planning energy storage and data centers in power distribution networks considering cyber-physical coupling, characterized in that, The method includes the following steps: From the perspective of distribution network flexibility resource planning, distributed resources in the distribution network are modeled, and a physical-side distributed energy storage regulation characteristic model is established to provide a physical-side resource model basis for the distribution network integrated planning model that takes into account information-physical coupling. From the perspective of cyber-physical integration of distribution networks, taking into account the demand response capability of data centers on the information side, a physical model of a single data center is established, and a spatiotemporal transfer model of data load of data centers on the information side is proposed, providing a resource model basis for distribution network integration planning model that takes into account cyber-physical coupling. Based on the established physical-side distributed energy storage regulation characteristic model and the information-side data center data load spatiotemporal transfer model, the physical-side energy storage configuration, data load spatiotemporal transfer mode, and information-side communication network topology are planned collaboratively. Objective functions and constraints in the distribution network planning scheme are established, and a distribution network fusion planning model that takes into account information-physical coupling is established accordingly. The objective function is: + + + + + + Where F is the objective function, Indicates the investment cost of energy storage. Indicates the investment cost of smart terminals. Indicates the investment cost of the communication network. Indicates the operating cost of energy storage. This indicates the operating cost of the data center. Indicates the cost of network loss. This indicates the EENS loss cost due to insufficient power supply; In the objective function, the energy storage investment cost is: in, The cost per unit capacity of energy storage investment, For the nth energy storage capacity, The number of energy storage units to be planned. For the lifespan of energy storage operation, The discount rate; Investment costs for smart terminals: in, For the investment cost of a single smart terminal, Plan the number of smart terminals. For the service life of smart terminals, The discount rate; Communication network investment costs: In this context, it is assumed that the investment cost of the communication network is proportional to the straight-line distance between nodes where smart terminals are installed. The cost of investment per unit length of communication network For the service life of the communication network, This represents the straight-line distance between node i and node j. This represents the set of lines between installed smart terminal nodes; Energy storage operating costs: in, The cost of operation and dispatching per unit capacity of energy storage. This represents the charging or discharging power of the i-th energy storage unit during time period t, where T represents the total number of energy storage charging and discharging periods. The default value for a year is 365 days. This converts the energy storage operating cost into an annual value. Data center operating costs: in, This represents the nodal marginal electricity price of the distribution network at time t. For the number of data centers, This represents the electrical energy required by a data center to process a unit of data load per unit of time. Network loss cost: in, The network loss during time period t; EENS losses due to insufficient power supply: in, In the first Daily Node Insufficient power supply Indicates the total number of physical nodes in the distribution network; The actual effectiveness of the distribution network integration planning model based on the actual distribution network and cyber-physical coupling was verified.

2. The method for planning energy storage and data centers in a distribution network considering cyber-physical coupling as described in claim 1, characterized in that, The perspective of distribution network flexibility resource planning is as follows: The overall architecture of the Distribution Cyber-Physical System (CPDS) is divided into three layers: the power physical layer, the communication network layer, and the information control layer. From the perspective of distribution network flexibility resource planning and cross-space signal transmission, it is as follows: First, the power physical layer collects the basic parameters, operating status or fault information of various physical devices through sensors, and converts the physical signals into electrical signals. Then, the distribution network side intelligent terminal transmits the acquired electrical signals through the communication channel in the communication network layer. After receiving the electrical signals, the controller in the information control layer issues corresponding control command signals, thereby regulating the operating status or working condition of the physical devices in the power physical layer.

3. The method for planning energy storage and data centers in a distribution network considering cyber-physical coupling as described in claim 1, characterized in that, The process of modeling distributed resources in the distribution network includes: Distributed resources in the power distribution network include photovoltaic systems and energy storage systems; The photovoltaic (PV) system is used to power the power distribution information physical system and data center. The PV system directly converts solar energy into DC power. When operating in grid-connected mode, the current-controlled inverter converts the DC power into AC power with the same frequency and phase as the distribution network and connects it to the grid. The inverter of the PV grid-connected system mainly adopts the voltage source current control method. It is only necessary to control the inverter output current to track the grid voltage to achieve the purpose of parallel operation. When the PV power is connected to the grid, the power factor is kept at 1. In the power flow calculation, only the active power is considered and it is treated as a PQ node. When a high proportion of distributed power sources are connected, the energy storage system (ESS) uses droop control to adjust the power absorbed or emitted by the ESS, thereby achieving power flow and voltage regulation of the system. When the node voltage exceeds the upper limit of the normal voltage, the ESS is in a charging state, absorbing a portion of active power to alleviate the voltage rise problem; when the node voltage is below the lower limit of the normal voltage, the ESS is in a discharging state, releasing a portion of the stored active power to improve the node voltage. The power regulation requirements for energy storage systems in CPDS are as follows: in, This represents the active power actually absorbed or emitted by the nth ESS. This indicates the droop control power adjustment value. This represents the change in the State of Charge (SOC) of the nth Energy Storage System (ESS) during time period t. This represents the rated capacity of the nth ESS; For a single ESS, the active power adjustment needs to be evenly distributed to prevent the active power adjustment of the ESS from remaining at a consistently high level; that is, to maintain a consistent relative amount of active power stored in each ESS. 。 4. The method for planning energy storage and data centers in a distribution network considering cyber-physical coupling as described in claim 1, characterized in that, The physical model of the single data center includes: In CPDS, the data load processing flow of a data center can be simplified into three key parts: Local users issue task requests, forming a data load model; The data center collects, analyzes, and processes the data load in the data load model; The data center distributes data load to each node, forming a physical model of a single data center.

5. The method for planning energy storage and data centers in a distribution network considering cyber-physical coupling according to claim 1, characterized in that, The spatiotemporal transfer model for data load in the information-side data center includes: In CPDS, data loads are divided into two categories: latency-sensitive and latency-tolerant. Latency-sensitive loads require real-time processing within a short period of time and use an M / M / 1 queuing model to model the queuing delay within a time period, ensuring that the data loads received by the data center in each time period must be processed within that time period. Latency-tolerant loads have a high tolerance for processing time requirements and can be processed within a specified time. Latency-tolerant data loads between different data centers can also be spatially transferred, thus the data load has spatiotemporal adjustment characteristics.

6. The method for planning energy storage and data centers in a distribution network considering cyber-physical coupling according to claim 1, characterized in that, The constraints are as follows: Active and reactive power flow equality constraints; node voltage and branch current equality constraints; branch current and node voltage inequality constraints; Energy storage state of charge transition equation constraints; energy storage capacity equation constraints; Constraints on the state of charge inequality of energy storage.

7. A distribution network energy storage and data center planning device considering cyber-physical coupling, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more of the programs are executed by one or more of the processors, the one or more processors implement a distribution network energy storage and data center planning method that takes into account cyber-physical coupling as described in any one of claims 1-6.

8. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform a distribution network energy storage and data center planning method that takes into account cyber-physical coupling as described in any one of claims 1-6.

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