Power distribution network operation optimization method and device considering data center, electronic equipment and storage medium

Abstract

The application discloses a power distribution network operation optimization method and device considering a data center, electronic equipment and a storage medium. The method comprises the following steps: constructing a total power consumption model of the data center; constructing a total data load model of the data center; taking the minimum system operation cost and system carbon emission cost of the power distribution system as the target, constructing a corresponding target function and a constraint condition of the target function; under the constraint of the constraint condition, solving the target function to obtain the main grid power purchase power of the power distribution system in each period, the transferable load adjustment amount, the migratable load adjustment amount and the reducible load adjustment amount of the data center in each period when the system operation cost and the system carbon emission cost of the power distribution system are the minimum, generating an operation optimization scheme of the power distribution network, and then optimizing the operation of the power distribution network according to the operation optimization scheme. Through the application, the overall economy and low-carbon benefit of the DC power distribution network system can be optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a power distribution network operation optimization method considering data centers, a device, an electronic device and a storage medium. BACKGROUND

[0002] In recent years, with the accelerated promotion of digital economy and new infrastructure construction, as a key infrastructure for information load analysis and processing, data centers (DC) have shown an explosive growth trend in quantity and development scale. DC power consumption has gradually become an important new load in the power system. Compared with conventional power users, DC has a larger power consumption, and often needs to run uninterruptedly throughout the year to meet user service requirements. Therefore, a large number of data centers are connected to the power distribution network, which brings many challenges to the safe and economic operation of the power distribution network. At the same time, since data centers have the potential to flexibly adjust data load processing, data centers also have flexible and adjustable characteristics, which brings opportunities to improve the flexibility of the power distribution system. However, from the perspective of coordinated operation of DC and power distribution network, the current research lacks the interactive integration relationship between data centers and power distribution network, and there is no discussion on the coordinated optimization operation of data centers and power distribution network, and there is no corresponding means for the operation optimization of power distribution network containing data centers. SUMMARY

[0003] The present application provides a power distribution network operation optimization method considering data centers, a device, an electronic device and a storage medium, to solve the technical problem that the current research lacks the interactive integration relationship between data centers and power distribution network, and there is no discussion on the coordinated optimization operation of data centers and power distribution network, and there is no corresponding means for the operation optimization of power distribution network containing data centers.

[0004] In order to solve the above technical problems, the present application embodiment provides a power distribution network operation optimization method considering data centers, comprising:

[0005] According to the constituent equipment of the data center, a total power consumption model of the data center is constructed; wherein the constituent equipment includes: servers, air conditioning equipment and auxiliary equipment;

[0006] According to the data load type of the data center, a data load model of each type of data load is constructed, and then according to the data load model of each type of data load, a total data load model of the data center is constructed; wherein the data load type includes: time transferable data load, time reducible data load, space migratable data load and rigid data load;

[0007] According to the total power consumption model and the total data load model, the system operation cost and the system carbon emission cost of the power distribution system are minimized as the target, a corresponding objective function and the constraint condition of the objective function are constructed.

[0008] solving the objective function under the constraint condition, obtaining the main grid power purchase of the power distribution system in each period, the transferable load adjustment amount of the data center in each period, the migratable load adjustment amount of the data center in each period, and the reducible load adjustment amount of the data center in each period when the system operation cost and the system carbon emission cost of the power distribution system are the minimum;

[0009] generating an operation optimization scheme of the power distribution network according to the main grid power purchase of the power distribution system in each period, the transferable load adjustment amount of the data center in each period, the migratable load adjustment amount of the data center in each period, and the reducible load adjustment amount of the data center in each period, and then optimizing the operation of the power distribution network according to the operation optimization scheme.

[0010] As a preferred solution, the device total power consumption model of the data center is constructed according to the constituent devices of the data center, including:

[0011] The server power consumption model of a single server in the data center is constructed, and the number of powered-on servers in the data center is obtained, and then the server total power consumption model of the powered-on servers in the data center is obtained according to the server power consumption model and the number of powered-on servers;

[0012] The refrigeration power and the energy efficiency coefficient of the air conditioning equipment in the data center are obtained, and then the air conditioning power consumption model of the air conditioning equipment in the data center is constructed according to the refrigeration power and the energy efficiency coefficient;

[0013] The power consumption coefficient of the data center is obtained, and then the device total power consumption model of the data center is constructed according to the power consumption coefficient, the server total power consumption model, and the air conditioning power consumption model.

[0014] As a preferred solution, the corresponding objective function and the constraint condition of the objective function are constructed with the minimum system operation cost and the minimum system carbon emission cost of the power distribution system as the target according to the device total power consumption model and the total data load model, including:

[0015] The system operation cost model and the system carbon emission cost model of the power distribution system are constructed according to the device total power consumption model and the total data load model;

[0016] The corresponding objective function and the constraint condition of the objective function are constructed with the minimum system operation cost and the minimum system carbon emission cost of the power distribution system as the target according to the system operation cost model and the system carbon emission cost model.

[0017] As a preferred solution, the device total power consumption model is:

[0018]

[0019]

[0020]

[0021] wherein, is the total energy consumption of data center i at time t; is the energy consumption of server devices of data center i at time t; is the energy consumption of refrigeration devices of data center i at time t, η DC is the power consumption coefficient of data center; u i,t is the CPU utilization rate of server; is the number of servers that are turned on; is the refrigeration power of air conditioning refrigeration devices of data center i at time t; η ACS is the energy efficiency coefficient of air conditioning devices.

[0022] As a preferred solution, the data load model is:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] wherein, TSDL is time transferable data load; TRDL is time reducible data load; STDL is space transferable data load; RDL is rigid load; is the capacity proportion of TSDL, is the time transferable data load after task migration; is the initial total data load received by DC; β TSDL is the proportion of TSDL in total data tasks; is the initial amount of TSDL; is the data load amount transferred from time t' to time t; is the data load amount transferred from time t to time t"; is the TSDL data load amount of data center at time t; β TRDLis the data load reduction amount at time t; is the initial data load reduction amount at time t; is the actual data load reduction amount; is the STDL capacity ratio; is the STDL data load after task migration; β STDL is the STDL ratio in total data tasks; is the STDL data amount before scheduling; is the data amount migrated between data centers i and i'; is the RDL capacity corresponding to time t.

[0032] As a preferred solution, the objective function is:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] wherein C O is the system operation cost model; is the system carbon emission cost model; is the power purchase from the main grid at time t; c Grid is the power purchase price of the distribution grid; c DR is the unit compensation price of data load delay or reduction; p s is the probability of scenario s occurring; θ is the number of days; Ω T is the set of time in a day; Ω DC is the set of data centers; e THG is the unit carbon emission of conventional units of the upper grid; c e is the unit carbon emission cost, ζ is the proportion of power purchase from the main grid for conventional coal-fired units.

[0039] As a preferred solution, the constraint conditions include: distributed power output constraint, distribution system operation constraint, and data center flexibility constraint;

[0040] The distribution system operation constraint includes: node power balance constraint, node voltage constraint, grid flow constraint, line load flow constraint, and power purchase capacity constraint;

[0041] The data center flexibility constraints include: server data load constraints, time-shiftable data load constraints, time-reducible data load constraints, space-migratable data load constraints, and air conditioning device power constraints;

[0042] The distributed power supply output constraints are:

[0043]

[0044] The node power balance constraints are:

[0045]

[0046]

[0047] The node voltage constraints are:

[0048]

[0049] The power grid power flow constraints are:

[0050]

[0051] The line current-carrying capacity constraints are:

[0052]

[0053] The power purchase capacity constraints are:

[0054]

[0055] The server data load constraints are:

[0056]

[0057]

[0058]

[0059] The time-shiftable data load constraints are:

[0060]

[0061]

[0062] The time-reducible data load constraints are:

[0063]

[0064] The space-migratable data load constraints are:

[0065]

[0066]

[0067]

[0068] The air conditioning equipment power constraint is:

[0069]

[0070] Wherein, is the initial amount of TSDL of the data center; is the data load amount of the data center i transferred from time t' to time t; is the data load amount of the data center i transferred from time t to time t"; is the TSDL data amount of the data center i at time t; is the initial reducible data load amount of the data center i at time t; is the actually reduced data load amount, is the STDL data amount before scheduling; is the migrated data amount between the data center i and i'; is the virtual machine transmission capacity occupied by a unit data amount; is the upper limit of the rated power of the air conditioning refrigeration equipment.

[0071] On the basis of the above embodiment, another embodiment of the present application provides a power distribution network operation optimization device considering a data center, comprising: a device total power consumption model construction module, a total data load model construction module, a target function construction module, a target function solving module and a power distribution network operation optimization module;

[0072] The device total power consumption model construction module is used for constructing a device total power consumption model of the data center according to constituent devices of the data center; wherein the constituent devices include: servers, air conditioning equipment and auxiliary equipment;

[0073] The total data load model construction module is used for constructing data load models of various types of data loads according to data load types of the data center, and then constructing a total data load model of the data center according to the data load models of various types of data loads; wherein the data load types include: time transferable data load, time reducible data load, space migratable data load and rigid data load;

[0074] The target function construction module is used for constructing a corresponding target function and constraint conditions of the target function according to the device total power consumption model and the total data load model, with the minimum system operation cost and system carbon emission cost of the power distribution system as the target;

[0075] The target function solving module is configured to solve the target function under the constraint condition, and obtain the main grid power purchase of the power distribution system in each time period, the transferable load adjustment amount of the data center in each time period, the migratable load adjustment amount of the data center in each time period, and the reducible load adjustment amount of the data center in each time period when the system operation cost and the system carbon emission cost of the power distribution system are the lowest.

[0076] The power distribution network operation optimization module is configured to generate an operation optimization scheme of the power distribution network according to the main grid power purchase of the power distribution system in each time period, the transferable load adjustment amount of the data center in each time period, the migratable load adjustment amount of the data center in each time period, and the reducible load adjustment amount of the data center in each time period, and then optimize the operation of the power distribution network according to the operation optimization scheme.

[0077] On the basis of the above-mentioned embodiments, a further embodiment of the application provides an electronic device, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the power distribution network operation optimization method considering data centers when executing the computer program.

[0078] On the basis of the above-mentioned embodiments, a further embodiment of the application provides a storage medium, which comprises a stored computer program, and the computer program controls a device where the storage medium is located to execute the power distribution network operation optimization method considering data centers when the computer program is running.

[0079] Compared with the prior art, the embodiments of the application have the following beneficial effects:

[0080] The application provides a power distribution network operation optimization method considering data centers, a total equipment power consumption model of a data center is constructed according to constituent equipment of the data center; a data load model of each type of data load is constructed according to a data load type of the data center, and then a total data load model of the data center is constructed according to the data load model of each type of data load; a corresponding objective function and a constraint condition of the objective function are constructed with the minimum system operation cost and system carbon emission cost of the power distribution system as the target according to the total equipment power consumption model and the total data load model; the objective function is solved under the constraint of the constraint condition, and the main grid power purchase power of each period of the power distribution system, the transferable load adjustment amount of each period of the data center, the migratable load adjustment amount of each period of the data center and the reducible load adjustment amount of each period of the data center are obtained when the system operation cost and the system carbon emission cost of the power distribution system are minimum; and the operation optimization scheme of the power distribution network is generated according to the main grid power purchase power of each period of the power distribution system, the transferable load adjustment amount of each period of the data center, the migratable load adjustment amount of each period of the data center and the reducible load adjustment amount of each period of the data center, so as to optimize the operation of the power distribution network.

[0081] The application explores the role of DC flexibility in improving the operation economy and low-carbon benefit of the power distribution system, considers the integrated and collaborative operation of DC and the power distribution network, constructs a corresponding objective function and a constraint condition of the objective function with the minimum system operation cost and system carbon emission cost of the power distribution system as the target according to the total equipment power consumption model and the total data load model of the DC, and then optimizes the operation of the power distribution network according to the solving result of the objective function, so as to effectively improve the operation efficiency of the DC-containing power distribution network and realize the collaborative optimization of the overall economy and low-carbon benefit of the DC-containing power distribution network system. BRIEF DESCRIPTION OF DRAWINGS

[0082] Figure 1 is a flowchart of a power distribution network operation optimization method considering data centers provided by an embodiment of the application;

[0083] Figure 2 is an active power distribution network optimization operation architecture diagram containing data centers;

[0084] Figure 3 is a modified ieee-33 power distribution system architecture diagram;

[0085] Figure 4 is a photovoltaic output curve diagram under each scenario;

[0086] Figure 5 is a DC power load curve diagram under each scenario;

[0087] Figure 6is a structural schematic diagram of a power distribution network operation optimization device considering a data center provided by an embodiment of the present application. DETAILED DESCRIPTION

[0088] For the purposes of the present application, the technical solutions and advantages are more clearly, the technical solutions in the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0089] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0090] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise explicitly and specifically limited.

[0091] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, or necessarily alternatives to other embodiments. It will be explicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0092] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this document generally represents an "or" relationship between the front and rear associated objects.

[0093] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two), and similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0094] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0095] Example 1

[0096] Please refer to Figure 1 The following is a flowchart illustrating a method for optimizing the operation of a power distribution network considering a data center, as provided in an embodiment of the present invention, comprising the following specific steps:

[0097] S1. Based on the constituent equipment of the data center, construct a total power consumption model for the data center equipment; wherein, the constituent equipment includes: servers, air conditioning equipment, and auxiliary equipment;

[0098] Preferably, the step of constructing the total power consumption model of the data center based on the constituent equipment of the data center includes: constructing a server power consumption model for a single server in the data center, and obtaining the number of servers that are powered on in the data center, and then obtaining the total power consumption model of the servers that are powered on in the data center based on the server power consumption model and the number of servers that are powered on; obtaining the cooling power and energy efficiency coefficient of the air conditioning equipment in the data center, and then constructing an air conditioning power consumption model of the air conditioning equipment in the data center based on the cooling power and energy efficiency coefficient; obtaining the power consumption coefficient of the data center, and then constructing the total power consumption model of the data center based on the power consumption coefficient, the total power consumption model of the servers, and the air conditioning power consumption model.

[0099] Preferably, the total power consumption model of the device is as follows:

[0100]

[0101]

[0102]

[0103] in, Let t be the total energy consumption of data center i at time t; The energy consumption of server equipment in data center i at time t; η is the energy consumption of the cooling equipment in data center i at time t. DC The power consumption factor for the data center; u i,t This refers to the server's CPU utilization. This refers to the number of servers that are currently running. Let η be the cooling power of the air conditioning unit in data center i at time t; ACS The energy efficiency coefficient of air conditioning equipment.

[0104] Specifically, this invention aims at low-carbon economic operation, considering the efficient integration and coordinated operation of DC and distribution networks, and proposes a low-carbon economic operation framework for an active distribution network that takes into account the spatiotemporal flexibility of data centers. Unlike existing technologies, this invention aims to explore the spatiotemporal flexibility of data centers and investigate the potential value of DC flexibility in promoting low-carbon and economic efficiency improvements in distribution systems. By comprehensively considering constraints such as DC energy consumption and flexibility adjustments, a data center energy consumption and spatiotemporal flexibility model is established. Based on this, the balance between low-carbon and economic operation of data centers is coordinated, and an optimization model for low-carbon operation of an active distribution network that takes into account the spatiotemporal flexibility of data centers is constructed. Through joint optimization of source, network, and load resources, a synergistic optimization of the overall system's economic and low-carbon benefits is achieved. The specific implementation is as follows:

[0105] 1. Optimized operation framework for power distribution networks including data centers:

[0106] Please refer to Figure 2 This is a diagram illustrating the optimized operation architecture of an active distribution network including a data center. The DC power distribution system mainly consists of three parts: distribution network lines, data center network, and distributed power sources. The electrical equipment within the DC mainly includes IT equipment, represented by servers and virtual machines (VMs), as well as other auxiliary equipment such as cooling and lighting.

[0107] Data centers can adjust their energy consumption characteristics by leveraging the adjustability of their data resources, thus exhibiting excellent flexibility. Therefore, data centers can participate in the interactive operation of the distribution network through this flexibility. On one hand, by flexibly adjusting their data resources and coordinating with the distribution network, data centers can promote the absorption of distributed renewable energy within the distribution network and reduce its operating costs. On the other hand, by fully absorbing and utilizing distributed renewable energy in the distribution network, they can reduce electricity purchases from the main grid, achieving low-carbon operation of the distribution network.

[0108] Based on the operational characteristics of data centers and distribution networks, distribution network operators aim to simultaneously reduce overall system operating costs and improve system economics, while also maximizing the utilization of distributed energy resources within the distribution network area to promote carbon emission reduction in the power system. Therefore, decision-making requires balancing both economic and low-carbon optimization objectives.

[0109] Based on the above analysis, the power distribution network operation optimization problem involving data centers is constructed into an optimization model, such as... Figure 2As shown, this model considers both operating costs and carbon emission costs. Objective 1 aims to minimize system operating costs, while objective 2 aims to minimize system carbon emission costs. By optimizing the operating strategies of DC (including information load migration, server start / stop status, and cooling equipment output) and distribution network (including RES output and mains power purchase) at different time periods, a low-carbon and economical operation of the distribution network that takes into account the spatiotemporal flexibility of data centers is achieved.

[0110] 2. Data center energy consumption and spatiotemporal flexibility modeling:

[0111] To fully understand the energy consumption characteristics and spatiotemporal flexibility of data centers, the following model is used to model these characteristics:

[0112] 2.1 Data Center Energy Consumption Characteristics Modeling:

[0113] The power consumption of a DC mainly consists of three parts: servers, air conditioning equipment, and other auxiliary equipment. The power consumption of servers and air conditioning equipment usually accounts for more than 90% of the total power consumption of a DC

[11] . Therefore, when modeling the total power consumption of its equipment, it can be expressed by the following formula:

[0114]

[0115] In the formula, This represents the total energy consumption of data center i at time t; This represents the energy consumption of server i in the corresponding data center at time t. This represents the energy consumption of the cooling equipment in data center i at time t. This represents the energy consumption of other auxiliary equipment in the corresponding data center at time t.

[0116] Because there are many other auxiliary devices and their energy consumption is relatively small, the energy consumption factor of the data center is often used for energy consumption modeling, as shown in the following formula:

[0117]

[0118] In the formula, η DC The power consumption factor represents the power consumption of a data center, which is the ratio of the sum of the power consumption of servers and air conditioning equipment to the total power consumption of the data center.

[0119] The power consumption of a server is generally proportional to the CPU utilization. Let Pmin be the power of the CPU when its utilization is 0%, i.e., its silent power when not processing network tasks, and Pmax be the power of the CPU when its utilization is 100%, i.e., its peak power under full load. Based on this, the power consumption of a single server in a DC system at time t can be calculated as follows:

[0120]

[0121] In the formula, u i,t This refers to the server's CPU utilization.

[0122] When modeling the total power consumption of a data center, considering the linear relationship between server power consumption and the number of servers in operation at real time, the power consumption of all servers in the data center can be defined as a linear mathematical model. Therefore, the total power of the servers in the data center at time t is:

[0123]

[0124] In the formula, This represents the number of servers that are currently running.

[0125] To ensure the ambient temperature meets the operating requirements of servers and other equipment, a large number of air conditioning and refrigeration units are required within the DC (Distributed Control Center). The air conditioning and refrigeration system converts electrical energy into low-temperature internal energy through a compressor, and its operating characteristics are as follows:

[0126]

[0127] In the formula, η represents the cooling power of the air conditioning unit in data center i at time t; ACS The coefficient of performance (COP) represents the energy efficiency ratio of an air conditioning unit, which is the ratio of the cooling capacity to the electrical power consumed by the unit.

[0128] S2. Based on the data load types of the data center, construct data load models for each type of data load, and then construct the overall data load model of the data center based on the data load models for each type of data load; wherein, the data load types include: time-transferable data load, time-reducible data load, spatially transferable data load, and rigid data load.

[0129] Preferably, the data load model is:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] Among them, TSDL stands for Time-Transferable Data Load; TRDL stands for Time-Reducible Data Load; STDL stands for Spatially Transferable Data Load; and RDL stands for Rigid Load. This represents the capacity percentage of TSDL. The amount of data load that can be transferred after the task migration; The initial total data load received by the DC; β TSDL This represents the percentage of TSDL in the total data tasks. This is the initial value for TSDL; This refers to the amount of data load transferred from time t' to time t; The data load transferred from time t to time t'; β represents the TSDL data load in the data center at time t. TRDL To reduce the proportion of data load; The initial data load can be reduced at time t; This represents the actual amount of data load reduced. β represents the percentage of STDL capacity; β represents the STDL data load after task migration; β represents the percentage of STDL capacity. STDL This represents the percentage of STDL in the total data tasks. This represents the amount of STDL data before scheduling. This represents the amount of data migrated between data centers i and i'. Let be the RDL capacity at time t.

[0139] 2.2 Data Center Spatiotemporal Flexibility Modeling Based on Data Load:

[0140] The demand responsiveness of a data center (DC) primarily stems from its flexibility in data load scheduling and the spatial transferability of information tasks between DCs in different geographical locations. Based on response methods, data loads in a DC can be broadly categorized into four types for modeling: Time-Shiftable Data Load (TSDL), Time-Reducable Data Load (TRDL), Spatially Transferable Data Load (STDL), and Rigid Data Load (RDL). The characteristics of each type of load will be modeled below:

[0141] Time-transferable data workloads (TSDL) refer to data workloads with a fixed amount of data but flexible processing time within a certain time frame. Data distribution centers (DCs) can postpone the processing time of TSDL tasks to a certain extent within the scheduling cycle based on relevant needs; that is, TSDL demands generated at time t can be postponed to the time period [t+1, T] for processing. This can be modeled as follows:

[0142]

[0143]

[0144] Formula (6) represents the capacity proportion of TSDL, and Formula (7) represents the time-transferable data load after considering TSDL. Wherein, The initial total data load received by the DC; β TSDL This represents the percentage of TSDL in the total data tasks. This is the initial value for TSDL; This refers to the amount of data load transferred from time t' to time t; The data load transferred from time t to time t'; The data load of the data center at time t is the TSDL data load.

[0145] DC can reduce data load TRDL to a certain extent by directly reducing time based on relevant needs within the scheduling cycle. Based on the above description of TRDL, it can be modeled as follows:

[0146]

[0147]

[0148] Where: β TRDL To reduce the proportion of data load; The initial data load can be reduced at time t; This represents the actual amount of data load reduced.

[0149] Spatially portable data workloads (STDL) refer to data workloads with fixed request processing times but capable of flexibly migrating between different geographic data centers (DCs) as needed. Based on the above description of spatially portable data workloads (TRDL), they can be modeled as follows:

[0150]

[0151]

[0152] Formula (10) represents the STDL capacity ratio, and formula (11) represents the STDL data load after task migration. Wherein, β STDLThis represents the percentage of STDL in the total data tasks. This represents the amount of STDL data before scheduling. This represents the amount of data migrated between data centers i and i'.

[0153] Rigid data loads lack operational adjustability potential, and the operational characteristics of RDL can be represented as follows:

[0154]

[0155] In the formula, Let be the RDL capacity at time t.

[0156] Based on the modeling descriptions of the above four types of data load, the total data load that the DC needs to process in each time period after considering demand response can be expressed as follows:

[0157]

[0158] S3. Based on the total power consumption model of the equipment and the total data load model, construct the corresponding objective function and the constraints of the objective function with the goal of minimizing the system operating cost and system carbon emission cost of the power distribution system;

[0159] Preferably, the step of constructing a corresponding objective function and constraints for the objective function based on the total power consumption model and the total data load model of the equipment, with the goal of minimizing the system operating cost and system carbon emission cost of the power distribution system, includes: constructing a system operating cost model and a system carbon emission cost model of the power distribution system based on the total power consumption model and the total data load model of the equipment; and constructing a corresponding objective function and constraints for the objective function based on the system operating cost model and the system carbon emission cost model, with the goal of minimizing the system operating cost and system carbon emission cost of the power distribution system.

[0160] Preferably, the objective function is:

[0161]

[0162]

[0163]

[0164]

[0165]

[0166] Among them, C O For system operating cost model; For the system's carbon emission cost model; c is the power purchased from the main grid at time t;Grid c is the electricity purchase price for the distribution network; DR The unit compensation price for data load delays or reductions; p s Ω represents the probability of scenario s occurring; θ represents the number of days; T A collection of moments throughout the day; Ω DC For data center collection; e THG Carbon emissions per unit output of conventional generating units in the upper-level power grid; c e ζ represents the unit carbon emission cost, and ζ represents the proportion of electricity purchased from the main grid for generating electricity for conventional coal-fired units.

[0167] Preferably, the constraints include: distributed power generation output constraints, power distribution system operation constraints, and data center flexibility constraints; the power distribution system operation constraints include: node power balance constraints, node voltage constraints, power grid flow constraints, line current carrying capacity constraints, and power purchase capacity constraints; the data center flexibility constraints include: server data load constraints, time-transferable data load constraints, time-reducible data load constraints, spatially transferable data load constraints, and air conditioning equipment power constraints; the distributed power generation output constraints are:

[0168]

[0169] The node power balance constraint is:

[0170]

[0171]

[0172] The node voltage constraint is:

[0173]

[0174] The power flow constraints of the power grid are:

[0175]

[0176] The line current carrying capacity constraint is:

[0177]

[0178] The power purchase capacity constraint is as follows:

[0179]

[0180] The server data load constraint is:

[0181]

[0182]

[0183]

[0184] The time-transferable data load constraint is:

[0185]

[0186]

[0187] The time-reducible data load constraint is:

[0188]

[0189] The spatially portable data load constraint is:

[0190]

[0191]

[0192]

[0193] The power constraint of the air conditioning equipment is:

[0194]

[0195] in, This is the initial value for the data center TSDL; The data load in data center i that is transferred from time t' to time t; The data load of data center i transferred from time t to time t”; Let be the amount of TSDL data in data center at time i and time t. The initial data load of data center i can be reduced at time t; This represents the actual reduction in data load. This represents the amount of STDL data before scheduling. The amount of data migrated between data centers i and i' The virtual machine's transmission capacity per unit of data; This refers to the upper limit of the rated power of air conditioning and refrigeration equipment.

[0196] 3. Optimization model for low-carbon operation of active distribution networks considering the spatiotemporal flexibility of data centers:

[0197] 3.1 Objective Function:

[0198] In this invention, two objectives are considered: economic operation and low-carbon operation of the power distribution network. At the economic operation level, the objective function is constructed to minimize the annual operating cost of the power distribution system, as follows:

[0199]

[0200] System operating cost C O This mainly includes the cost of purchasing electricity from the upper-level power grid and the related costs resulting from DC flexible response:

[0201]

[0202] In the formula, c represents the power purchased from the main grid at time t; Grid Indicates the electricity purchase price in the distribution network; c DR This represents the unit compensation price for data load delays or reductions; p s Ω represents the probability of scenario s occurring; θ represents the number of days; T A collection of moments in a day.

[0203] At the low-carbon operation level, leveraging the operational flexibility of DC (Distributed Generator) can improve power flow distribution in the distribution network, reduce network losses, and promote the absorption and utilization of distributed renewable energy, thereby reducing generation-side emissions and bringing considerable low-carbon benefits. Here, it is assumed that the upstream power grid purchases electricity from conventional coal-fired units, and the carbon cost of system operation is characterized by the generation-side carbon cost incurred by the system in purchasing electricity from the upstream grid.

[0204]

[0205] In the formula, e THG This indicates the carbon emissions per unit output of conventional generating units in the upstream power grid; c e ζ represents the unit carbon emission cost, and ζ represents the proportion of electricity purchased from the main grid for generating electricity for conventional coal-fired units.

[0206] To comprehensively consider the balance between the two objectives, a weighting system is established to represent the weight differences between the different objectives, and the overall objective function is established as follows:

[0207]

[0208] As can be seen from the above equation, the objective function is determined by the system's economic operating cost C. O and carbon emission costs It is a weighted sum of two aspects.

[0209] 3.2 Constraints:

[0210] Regarding constraints, the main considerations include power distribution system operation constraints, distributed power generation output constraints, and data center flexibility constraints. Specifically:

[0211] (1) The power generation of distributed generation in the distribution network system at any given time is constrained by its installed capacity and predicted output.

[0212]

[0213] (2) System operation constraints:

[0214] Node power balancing:

[0215]

[0216]

[0217] Node voltage constraints:

[0218]

[0219] Power flow constraints:

[0220]

[0221] Line current carrying capacity constraints:

[0222]

[0223] Power purchase capacity constraints:

[0224]

[0225] (3) Demand-side constraints:

[0226] To reduce the complexity of problem solving, this paper assumes that all DC servers are of the same model and that during the operation phase, tasks are distributed evenly to each active server. Based on this, according to the M / M / 1 queuing theory

[13] , the data load corresponding to each server can be determined as follows:

[0227]

[0228]

[0229]

[0230] To ensure service requests for system user data load, the DC also needs to strictly meet data load constraints during operation. Constraints are imposed on Time Transferable Data Load (TSDL), Time Reduceable Data Load (TRDL), and Spatially Transferable Data Load (STDL), mainly considering the capacity limitations of each type of data load.

[0231] For Time Transferable Data Loading (TSDL):

[0232]

[0233]

[0234] In the formula, This is the initial value for the data center TSDL; The data load in data center i that is transferred from time t' to time t; The data load of data center i transferred from time t to time t”; Let t represent the amount of TSDL data at time i in data center t.

[0235] TRDL for time-reducing data load:

[0236]

[0237] In the formula: The initial data load of data center i can be reduced at time t; This represents the actual amount of data load reduced.

[0238] For spatially portable data workloads (STDL), considering that the migration volume of STDL must meet the virtual machine capacity limit:

[0239]

[0240]

[0241]

[0242] In the formula, This represents the amount of STDL data before scheduling. The amount of data migrated between data centers i and i' This indicates the virtual machine's transmission capacity per unit of data.

[0243] The air conditioning and refrigeration equipment in the DC unit also needs to operate at its rated power:

[0244]

[0245] In the formula, This refers to the upper limit of the rated power of air conditioning and refrigeration equipment.

[0246] S4. Under the constraints of the above constraints, solve the objective function to obtain the main grid power purchase, transferable load adjustment, relocatable load adjustment and load reduction adjustment of the data center in each time period when the system operating cost and carbon emission cost of the power distribution system are minimized.

[0247] Then, under the constraints, the objective function is solved, and the final solution is obtained when the system operating cost and system carbon emission cost of the power distribution system are minimized, the power purchased from the main grid in each time period, and the load transfer, load migration, and load reduction adjustment amount of the data center in each time period.

[0248] S5. Based on the main grid power purchase capacity of the power distribution system in each time period, the transferable load adjustment amount of the data center in each time period, the relocatable load adjustment amount of the data center in each time period, and the load reduction adjustment amount of the data center in each time period, generate an operation optimization scheme for the power distribution network, and then optimize the operation of the power distribution network according to the operation optimization scheme.

[0249] Finally, based on the main grid power purchase capacity of the power distribution system at each time period, the transferable load adjustment amount of the data center at each time period, the relocatable load adjustment amount of the data center at each time period, and the load reduction adjustment amount of the data center at each time period, an operation optimization scheme for the power distribution network is generated, and then the operation of the power distribution network is optimized according to the operation optimization scheme.

[0250] In a specific embodiment, to verify the effectiveness of the above-mentioned distribution network operation optimization method considering data centers, a simulation analysis is performed using a modified IEEE-33 node distribution network as an example. Please refer to... Figure 3 This is a modified IEEE-33 power distribution system architecture diagram. The system contains 32 branches and 33 load nodes, with a rated voltage of 12.66kV. The data center access nodes in the distribution network are nodes 13, 19, 25, and 26, and the distributed photovoltaic access nodes are nodes 13, 19, 23, and 26. The system contains four types of loads: commercial, industrial, residential, and data center. To reveal the benefits of low-carbon collaborative operation between the DC and distribution networks, the optimal operating results obtained in the following three scenarios are compared and analyzed. The relevant results are shown in Table 1.

[0251] Scenario I: Without considering the spatiotemporal flexibility of the data center, i.e., the data center and distribution network operate independently. This scenario reflects the cost-effectiveness of optimizing the distribution network operation without taking into account the spatiotemporal flexibility of the data center.

[0252] Scenario II: Low-carbon optimization operation of the power distribution network considering the spatiotemporal flexibility of data centers, i.e., only considering the data center TSDL's participation in scheduling and control. This scenario reflects the cost-effectiveness of the system when only partial data center flexibility is considered in operational decisions;

[0253] Scenario III: The model presented in this paper considers the low-carbon optimized operation of the power distribution network, taking into account the full spatiotemporal flexibility of the data center. In this scenario, the DC can schedule TSDL, TRDL, and STDL separately, meaning that all types of flexible resources in the data center can participate in demand response simultaneously.

[0254]

[0255] Table 1 Cost Situation in Different Scenarios

[0256] As shown in Table 1, the distribution network operating costs from Scenario I to Scenario III exhibit a gradually decreasing trend, with Scenario III's total annual operating cost decreasing by 11.49% compared to Scenario I. This is mainly because Scenario III considers various data loads of the DC to participate in demand response, reducing the distribution network's reliance on the main grid for electricity purchases, thus leading to a corresponding reduction in system carbon emissions and electricity purchase costs. The differences between the various scenarios are analyzed below:

[0257] Compared to Scenario I, Scenario II reduces annual electricity purchase costs and carbon emission costs. Compared to Scenario I, Scenario II considers partial demand response in the DC power grid, specifically the demand response of Time Transferable Data Load (TSDL) for 30% of the data center load. This adjustment improves the absorption of distributed photovoltaic power in the system and reduces system operating costs. The fundamental reason is that by adjusting the processing time of the DC data load, the DC power load is adjusted, changing the timing of electricity demand. TSDL-based demand response improves the matching degree between renewable energy generation output and end-user load demand, promoting the utilization of renewable energy and reducing the purchase of electricity from the upper-level grid during peak hours, thus giving the system better operational economy and low-carbon benefits.

[0258] Comparing Scenario III and Scenario II, Scenario III further enhances the spatiotemporal flexibility of DC (Distributed Power Generation) based on Scenario II. It further considers 10% of the time-based data load reduction (TRDL) and 30% of the spatially transferable data load (STDL), making DC more flexible. It can be seen that after comprehensively utilizing the demand response capabilities of DC resources in both time and space dimensions, although the demand response cost increases by 155,800 yuan, the reduction in electricity purchase cost and carbon emission cost brought about by demand response is more significant, reaching 1,370,600 yuan. The system operation can utilize more distributed photovoltaic power, further reducing electricity purchase cost and carbon emission cost. Therefore, investing more adjustable and flexible resources in DC demand response can reduce the operating cost of the distribution network and effectively improve the economic and environmental benefits of the distribution system.

[0259] Please refer to Figure 4 This involves generating photovoltaic (PV) output curves for various scenarios, and analyzing and comparing the distributed PV output and DC operation under different scenarios. Based on... Figure 4It can be seen that, between 12:00 and 16:00, the distributed photovoltaic (PV) grid connection performance is better in Scenario III compared to Scenario I and Scenario II. During peak PV output periods, more distributed PV can be grid-connected through spatiotemporal adjustments to the data load of more data centers. At other times, distributed PV can achieve full grid connection. This is because PV output is mainly concentrated during the daytime, which is also the period of most concentrated load. Therefore, PV output can be effectively matched with load characteristics, achieving efficient PV grid connection.

[0260] Please refer to Figure 5 This is a DC power load curve diagram for various scenarios, based on... Figure 5 It can be seen that in Scenario III, the number of units started and the total power output increased significantly from 0:00 to 6:00 and from 18:00 to 24:00, while the output decreased relatively evenly from 8:00 to 18:00. This adjustment can promote the balance between power grid supply and demand and reduce the power grid's electricity purchase cost, thus further improving its economic benefits.

[0261] Therefore, this invention provides a method for optimizing power distribution network operation considering data centers. Compared with existing technologies, this invention focuses on exploring the role of DC flexibility in improving the economic efficiency and low-carbon benefits of power distribution system operation, based on the data adjustment characteristics of data center demand response. Based on the simulation results of the above examples, the following main conclusions are drawn:

[0262] (1) Compared with the optimized operation of the distribution network that does not consider the spatiotemporal flexibility of the data center, the optimized operation method proposed in this invention can effectively improve the operating efficiency of the distribution network with DC and play an important role in promoting the utilization of renewable energy and carbon emission reduction.

[0263] (2) The expected benefits of optimized operation of distribution network are affected by the spatiotemporal mobility of DC data load. That is, the stronger the spatiotemporal adjustment flexibility of data load of data center, the more significant the economic and low-carbon benefits of the power grid.

[0264] Example 2

[0265] Please refer to Figure 6 This is a schematic diagram of a power distribution network operation optimization device for data centers provided in an embodiment of the present invention. The device includes: a total power consumption model construction module, a total data load model construction module, an objective function construction module, an objective function solving module, and a power distribution network operation optimization module.

[0266] The total power consumption model construction module is used to construct a total power consumption model of the data center based on the constituent devices of the data center; wherein, the constituent devices include: servers, air conditioning equipment, and auxiliary equipment;

[0267] The total data load model construction module is used to construct data load models for each type of data load according to the data load type of the data center, and then construct the total data load model of the data center according to the data load models for each type of data load; wherein, the data load types include: time-transferable data load, time-reducible data load, spatially transferable data load, and rigid data load.

[0268] The objective function construction module is used to construct a corresponding objective function and the constraints of the objective function based on the total power consumption model of the equipment and the total data load model, with the goal of minimizing the system operating cost and system carbon emission cost of the power distribution system.

[0269] The objective function solving module is used to solve the objective function under the constraints of the constraints to obtain the main grid power purchase, transferable load adjustment, relocatable load adjustment and load reduction adjustment of the data center at each time period when the system operating cost and carbon emission cost of the power distribution system are minimized.

[0270] The power distribution network operation optimization module is used to generate an operation optimization scheme for the power distribution network based on the main grid power purchase capacity, the transferable load adjustment amount of the data center, the relocatable load adjustment amount of the data center, and the load reduction adjustment amount of the data center in each time period, and then optimize the operation of the power distribution network according to the operation optimization scheme.

[0271] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0272] Those skilled in the art will clearly understand that, for convenience and simplicity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0273] Example 3

[0274] Accordingly, embodiments of the present invention provide an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power distribution network operation optimization method for data centers described in the above embodiments of the invention.

[0275] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0276] The processor can 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0277] Example 4

[0278] Accordingly, embodiments of the present invention provide a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the power distribution network operation optimization method for data centers described in the above embodiments of the invention.

[0279] The memory can be used to store the computer program. The processor implements various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0280] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0281] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing the operation of a power distribution network considering data centers, characterized in that, include: Based on the components of a data center, a total power consumption model for the data center's equipment is constructed; wherein, the components include: servers, air conditioning equipment, and auxiliary equipment; Based on the data load types of the data center, data load models for each type of data load are constructed, and then based on the data load models for each type of data load, a total data load model for the data center is constructed; wherein, the data load types include: time-transferable data load, time-reducible data load, spatially transferable data load, and rigid data load. Based on the total power consumption model and the total data load model, with the goal of minimizing the system operating cost and carbon emission cost of the power distribution system, a corresponding objective function and constraints for the objective function are constructed. Under the constraints of the above conditions, the objective function is solved to obtain the main grid power purchase, transferable load adjustment, relocatable load adjustment, and load reduction adjustment of the data center at each time period when the system operating cost and carbon emission cost of the power distribution system are minimized. Based on the main grid power purchase capacity of the power distribution system at each time period, the transferable load adjustment amount of the data center at each time period, the relocatable load adjustment amount of the data center at each time period, and the load reduction adjustment amount of the data center at each time period, an operation optimization scheme for the power distribution network is generated, and then the operation of the power distribution network is optimized according to the operation optimization scheme. The total power consumption model of the device is as follows: ； ； ； in, Let t be the total energy consumption of data center i at time t; Let t be the energy consumption of the server equipment in data center i at time t. Let t be the energy consumption of the cooling equipment in data center i at time t. This refers to the power consumption factor of the data center. This refers to the server's CPU utilization. This refers to the number of servers that are currently running. Let be the cooling power of the air conditioning unit in data center i at time t; The energy efficiency coefficient of air conditioning equipment; The data load model is as follows: ； ； ； ； ； ； ； ； Among them, TSDL stands for Time-Transferable Data Load; TRDL stands for Time-Reducible Data Load; STDL stands for Spatially Transferable Data Load; and RDL stands for Rigid Load. The time taken for data center i at time t can reduce the data load; This represents the capacity percentage of TSDL; This represents the initial total data load received by the DC. This represents the percentage of TSDL in the total data tasks. This is the initial value for TSDL; This refers to the amount of data load transferred from time t' to time t; The data load transferred from time t to time t'; Let be the TSDL data load of data center i at time t; To reduce the proportion of data load; The initial data load can be reduced at time t; The actual reduction in data load; the STDL data load after task migration; This represents the percentage of STDL in the total data tasks. This represents the amount of STDL data before scheduling. This represents the amount of data migrated between data centers i and i'. Let be the RDL capacity at time t.

2. The method for optimizing the operation of a power distribution network considering a data center as described in claim 1, characterized in that, The step of constructing a total power consumption model for the data center based on its constituent devices includes: Construct a server power consumption model for a single server in a data center, obtain the number of servers that are powered on in the data center, and then obtain a total server power consumption model for the servers that are powered on in the data center based on the server power consumption model and the number of servers powered on. The cooling power and energy efficiency coefficient of the air conditioning equipment in the data center are obtained, and then the air conditioning power consumption model of the air conditioning equipment in the data center is constructed based on the cooling power and energy efficiency coefficient. Obtain the power consumption coefficient of the data center, and then construct the total power consumption model of the data center equipment based on the power consumption coefficient, the server total power consumption model, and the air conditioning power consumption model.

3. The method for optimizing the operation of a power distribution network considering a data center as described in claim 2, characterized in that, Based on the total power consumption model and the total data load model of the equipment, and with the goal of minimizing the system operating cost and carbon emission cost of the power distribution system, a corresponding objective function and constraints for the objective function are constructed, including: Based on the total power consumption model of the equipment and the total data load model, construct the system operation cost model and the system carbon emission cost model of the power distribution system. Based on the system operation cost model and the system carbon emission cost model, with the goal of minimizing the system operation cost and system carbon emission cost of the power distribution system, a corresponding objective function and constraints for the objective function are constructed.

4. The method for optimizing the operation of a power distribution network considering a data center as described in claim 3, characterized in that, The objective function is: ； ； ； ； in, For system operating cost model; For the system's carbon emission cost model; The power purchased from the main grid at time t; The electricity purchase price for the distribution network; The unit compensation price for data load delays or reductions; This represents the actual amount of data load reduced. Let i be the amount of data load transferred from time t to time t' in data center i. Let θ be the probability of scenario s occurring; θ be the number of days. A collection of moments throughout the day; For data center collection; Carbon emissions per unit output of conventional generating units in the upper-level power grid; ζ represents the unit carbon emission cost, and ζ represents the proportion of electricity purchased from the main grid for generating electricity for conventional coal-fired units.

5. A power distribution network operation optimization device for data centers, characterized in that, The power distribution network operation optimization method for data centers as described in any one of claims 1-4 includes: a total power consumption model construction module, a total data load model construction module, an objective function construction module, an objective function solving module, and a power distribution network operation optimization module; The total power consumption model construction module is used to construct a total power consumption model of the data center based on the constituent devices of the data center; wherein, the constituent devices include: servers, air conditioning equipment, and auxiliary equipment; The total data load model construction module is used to construct data load models for each type of data load according to the data load type of the data center, and then construct the total data load model of the data center according to the data load models for each type of data load; wherein, the data load types include: time-transferable data load, time-reducible data load, spatially transferable data load, and rigid data load. The objective function construction module is used to construct a corresponding objective function and the constraints of the objective function based on the total power consumption model of the equipment and the total data load model, with the goal of minimizing the system operating cost and system carbon emission cost of the power distribution system. The objective function solving module is used to solve the objective function under the constraints of the constraints to obtain the main grid power purchase, transferable load adjustment, relocatable load adjustment and load reduction adjustment of the data center at each time period when the system operating cost and carbon emission cost of the power distribution system are minimized. The power distribution network operation optimization module is used to generate an operation optimization scheme for the power distribution network based on the main grid power purchase capacity, the transferable load adjustment amount of the data center, the relocatable load adjustment amount of the data center, and the load reduction adjustment amount of the data center in each time period, and then optimize the operation of the power distribution network according to the operation optimization scheme.

6. An electronic device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power distribution network operation optimization method for data centers as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform the power distribution network operation optimization method considering any one of claims 1 to 4.

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