An economic and low-carbon operation optimization method for a data center in a power distribution system

Abstract

This invention discloses a method for optimizing the economical and low-carbon operation of data centers in a power distribution system. The method includes: establishing a power distribution system operation optimization model; solving the power distribution system operation optimization model to obtain the power flow results and marginal electricity prices at distribution nodes; solving for the carbon intensity at distribution system nodes based on the power flow results and carbon emission flow theory; and sending the marginal electricity prices and carbon intensity at distribution system nodes to the data center. The data center, aiming to minimize overall costs, establishes and solves its own economical and low-carbon operation optimization model to obtain the power purchase and sale results with the power distribution system, and then sends these results to the power distribution system. This invention uses the marginal electricity prices and carbon intensity at distribution nodes to perform time-based scheduling of workloads, reducing the overall operating costs of the data center. Furthermore, the power distribution system operator and the data center operator only need to exchange boundary variable results, effectively protecting data privacy.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon and economical operation and scheduling technology of data centers, and particularly relates to an optimization method for the economical and low-carbon operation of data centers in power distribution systems. Background Technology

[0002] The goal of optimizing the economic operation of a power distribution system is to improve grid efficiency and reduce system costs through the rational allocation of resources and optimized operation methods. In this process, the nodal marginal price, as a key indicator, reflects the temporal and spatial characteristics of power distribution system pricing. The nodal marginal price not only accurately characterizes the marginal operating costs of different nodes but also guides distributed energy resources and loads to participate rationally in market transactions, improving resource utilization efficiency. Simultaneously, it provides a scientific basis for the optimized dispatch of regional energy resources, contributing to improving the economy, flexibility, and sustainability of the power grid.

[0003] Carbon emission flows, as a tool for quantifying load carbon emissions, can intuitively reflect the environmental impact of energy consumption and have significant application value. Carbon emission flows can assess the carbon emission levels of loads at different times and locations, providing data support for identifying high-emission loads and optimizing energy consumption structures. Furthermore, analysis of carbon emission flows can guide users to prioritize the use of low-carbon or renewable energy sources, reducing carbon emissions, and provide a basis for developing carbon emission-based pricing mechanisms, thus promoting the achievement of decarbonization goals.

[0004] Within power distribution networks, data centers offer temporal and spatial scheduling flexibility due to their workloads, allowing for reduced energy and carbon emission costs through optimized scheduling. When developing operational plans for data centers, their interaction with the power distribution system must be fully considered. Furthermore, since data center operators and power distribution system operators are separate entities, data privacy protection is also a crucial issue that must be addressed.

[0005] In summary, there is an urgent need to propose an optimization method for the economical and low-carbon operation of data centers in power distribution systems. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes an optimization method for the economical and low-carbon operation of data centers in power distribution systems, thereby resolving the issues present in the existing technologies.

[0007] To achieve the above objectives, the present invention provides an optimization method for the economical and low-carbon operation of data centers in a power distribution system, characterized by comprising the following steps:

[0008] With the goal of minimizing the energy economic cost of the power distribution system, a power distribution system operation optimization model is established using DIST-flow power flow constraints;

[0009] The power distribution system operation optimization model is solved to obtain the power flow results of the power distribution system and the marginal electricity price of the distribution nodes;

[0010] Based on the power flow results of the power distribution system and the carbon emission flow theory, the carbon intensity of the power distribution system nodes is solved.

[0011] The marginal electricity price and carbon intensity of the distribution nodes of the power distribution system are sent to the data center. The data center establishes an economic and low-carbon operation optimization model with the goal of minimizing the overall cost.

[0012] The economic and low-carbon operation optimization model of the data center is solved to obtain the power purchase and sale results with the power distribution system, and then sent to the power distribution system.

[0013] The power distribution system and the data center iteratively solve their respective optimization models until the interaction power between the two converges, thus completing the optimization of the data center's economical and low-carbon operation.

[0014] Optionally, the objective function of the power distribution system operation optimization model is as follows:

[0015]

[0016] In the formula, c g,t This indicates the wholesale market electricity price within the higher-level power grid; P represents the electricity price within the power distribution system. g,t This indicates the power consumption of the power distribution system in the wholesale market; This indicates the electrical energy purchased from distributed generators; This indicates the power purchased from the data center.

[0017] Optionally, the DIST-flow power flow constraint is as follows:

[0018]

[0019]

[0020] In the formula, P ij,t and Q ij,t These represent the active and reactive power on branch ij, respectively; and These represent the active and reactive power injected at node j, respectively; ij,t U represents the current in branch ij; j,t The voltage magnitude at node j is represented by r. ij and x ij Let represent the resistance and reactance of line ij, respectively; and These represent the connection relationships between node j in the power distribution system and the distributed generator and the data center, respectively. and These represent the active and reactive loads of node j, respectively. and λ represents the upper and lower limits of the voltage amplitude at node j, respectively; j,t P represents the dual variable of the corresponding constraint. jk,t and Q jk,t These represent the active and reactive power on line jk, respectively. P is a binary parameter representing the connection status between node j and the upstream power grid. g,t P represents the active power interacting with the upstream power grid. dg,t P represents the output active power of a distributed generator. d,t This indicates the active power load of the data center. Q represents the output reactive power of a distributed generator. g,t Q represents the reactive power interacting with the upstream power grid. d,t This indicates the reactive power load of the data center.

[0021] Optionally, the carbon intensity of energy at node i and line ij in the power distribution system is calculated using the following formula:

[0022]

[0023] In the formula, ρ ij and ρ i Let be the carbon intensity of line ij and node i, respectively; The energy flowing from node i to line ij; The energy loss generated on line ij; The energy flowing from line ij to node j; and They are respectively and The corresponding carbon emission rate.

[0024] Optionally, the process of solving for the carbon intensity of the distribution system nodes also includes: calculating the carbon emission flow in the distribution system based on the carbon emission conservation law, the proportional allocation principle, and the energy merging principle.

[0025] Optionally, the objective function of the data center economic and low-carbon operation optimization model is shown in the following equation:

[0026]

[0027] In the formula, and These represent the power purchased and sold by the data center, respectively; λ d,t and These represent the electricity purchase price and the electricity sales price for the data center, respectively; ce This is the carbon emission cost coefficient; This indicates the carbon intensity of data center nodes in the power distribution system.

[0028] Optionally, the constraints of the data center operation optimization model include: workload operation constraints, server startup constraints, energy storage operation constraints in the UPS, and data center power constraints.

[0029] Optionally, during the process of iteratively solving the corresponding optimization models for the power distribution system and the data center, an iterative solution algorithm based on the bisection method is adopted.

[0030] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0031] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0032] Compared with the prior art, the present invention has the following advantages and technical effects:

[0033] This invention proposes an economical and low-carbon operation optimization method for data centers in power distribution systems. First, an operation optimization model for the power distribution system is established, and the power flow results and marginal electricity prices at distribution nodes are obtained and sent to the data center. Based on the power flow results and carbon emission flow theory, the carbon intensity of the distribution system nodes is calculated and sent to the data center. The data center, aiming to minimize overall costs, establishes and solves an economical and low-carbon operation optimization model for itself, obtains the electricity purchase and sale results, and sends them to the power distribution system.

[0034] This invention uses marginal electricity prices and carbon intensity at distribution nodes to schedule workloads, thereby reducing the overall operating costs of data centers. Furthermore, the exchange between distribution system operators and data center operators only requires the results of boundary variables, effectively protecting data privacy. Attached Figure Description

[0035] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0036] Figure 1 This is a topology diagram of a data center and an IEEE 33-node power distribution system according to an embodiment of the present invention;

[0037] Figure 2 This is a diagram illustrating the interaction framework between the power distribution system and the data center according to an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the convergence process of the iterative solution based on the bisection method in an embodiment of the present invention, wherein (a) is a schematic diagram of the convergence process of the iterative solution based on the bisection method in data center 2, and (b) is a schematic diagram of the convergence process of the iterative solution based on the bisection method in data center 3.

[0039] Figure 4 This is a schematic diagram of the marginal electricity price results at the distribution node in an embodiment of the present invention;

[0040] Figure 5 This is a schematic diagram showing the results of the node carbon intensity of the power distribution system according to an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram of the spatial scheduling results for a latency-sensitive workload according to an embodiment of the present invention;

[0042] Figure 7 This is a schematic diagram of the time scheduling results for a latency-tolerant workload according to an embodiment of the present invention. Detailed Implementation

[0043] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0044] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0045] Example 1

[0046] This embodiment provides an optimization method for the economical and low-carbon operation of a data center in a power distribution system. In this method, the power distribution system optimizes its operation with the goal of minimizing energy economic costs, obtaining the marginal electricity price at each distribution node as the electricity price for the loads in the system. Based on the power flow results of the power distribution system and using carbon emission flow theory, the carbon intensity of each node is calculated to quantify the carbon emissions of the loads in the system. Simultaneously, the data center, based on the received marginal electricity price and carbon intensity at each distribution node, formulates an operation plan with the goal of minimizing both energy and carbon emission economic costs, schedules the workload in a timely manner, and sends the electricity purchase and sale results to the power distribution system. To address the potential oscillation problem between the power distribution system and the data center during the iterative solution process, an iterative solution method based on the bisection method is proposed.

[0047] To achieve the above objectives, such as Figure 1As shown, this embodiment employs a power distribution system consisting of three data centers and IEEE 33 nodes, and constructs an interaction framework between the power distribution system and the data centers, as follows: Figure 2 As shown in the diagram. Within this framework, the power distribution system and the data center sequentially formulate their operational plans and interact with each other based on the results of boundary variables. Specifically, this includes the following steps:

[0048] This embodiment aims to minimize the energy economic cost of the power distribution system and establishes an optimization model for the operation of the power distribution system using DIST-flow power flow constraints.

[0049] The power distribution system operation optimization model is solved to obtain the power flow results of the power distribution system and the marginal electricity price of the distribution nodes;

[0050] Based on the power flow results of the power distribution system and the carbon emission flow theory, the carbon intensity of the power distribution system nodes is solved.

[0051] The marginal electricity price and carbon intensity of the distribution nodes of the power distribution system are sent to the data center. The data center establishes an economic and low-carbon operation optimization model with the goal of minimizing the overall cost.

[0052] The economic and low-carbon operation optimization model of the data center is solved to obtain the power purchase and sale results with the power distribution system, and then sent to the power distribution system.

[0053] The power distribution system and the data center iteratively solve their respective optimization models, schedule the workload, solve for their own economical and low-carbon operation plans, obtain the power purchase and sale results with the power distribution system, and send them to the power distribution system. The power distribution system and the data center iteratively formulate their operation plans until the interactive power converges.

[0054] As a specific implementation method, with the goal of minimizing the energy economic cost of the power distribution system, a power distribution system operation optimization model is established using DIST-flow constraints. The process of solving the power flow results of the power distribution system and the marginal electricity price of the distribution nodes includes:

[0055] First, with the goal of minimizing energy economic costs, the distribution network establishes an objective function, which includes the cost of purchasing and selling electricity from the upper-level grid, the cost of purchasing electricity from local distributed generators, and the cost of purchasing electricity from data centers. The specific objective function of the distribution system operation optimization model is shown in the following formula:

[0056]

[0057] In the formula, c g,t This indicates the wholesale market electricity price within the higher-level power grid; P represents the electricity price within the power distribution system. g,t This refers to the power consumption of the power distribution system in the wholesale market. Electricity purchased from distributed generators; This indicates the power purchased from the data center.

[0058] The power flow constraints of the power distribution system are characterized using the Dist-flow model, as shown below:

[0059]

[0060] In the formula, P ij,t and Q ij,t These represent the active and reactive power on branch ij, respectively; and These represent the active and reactive power injected at node j, respectively; ij,t U represents the current in branch ij; j,t The voltage magnitude at node j is represented by r. ij and x ij Let represent the resistance and reactance of line ij, respectively; and These are binary parameters, representing the connection relationships between power distribution system node j and distributed generators and data centers, respectively. and These represent the active and reactive loads of node j, respectively. and λ represents the upper and lower limits of the voltage amplitude at node j, respectively; j,t P represents the dual variable of the corresponding constraint. jk,t and Q jk,t These represent the active and reactive power on line jk, respectively. P is a binary parameter representing the connection status between node j and the upstream power grid. g,t P represents the active power interacting with the upstream power grid. dg,t P represents the output active power of a distributed generator. d,t This indicates the active power load of the data center. Q represents the output reactive power of a distributed generator. g,t Q represents the reactive power interacting with the upstream power grid. d,t This indicates the reactive power load of the data center.

[0061] The established power distribution system operation optimization model is a second-order cone programming problem. By solving the power distribution system operation optimization model, the power flow results of the system and the dual variable λ are obtained. j,t As a result, the marginal electricity price of the distribution node is sent to the data center.

[0062] As a specific implementation method, based on the power flow results of the distribution system and the carbon emission flow theory, a calculation model for the carbon intensity of the distribution system nodes is established. The calculation process for the carbon intensity of the distribution system nodes includes:

[0063] The carbon emission rate (denoted by R, in ton CO2 / h) represents the carbon emission rate corresponding to an energy flow (denoted by E, in MW). The energy flowing from node i to line ij is... The energy loss generated on line ij is The energy flowing from line ij to node j is and The corresponding carbon emission rates are respectively and Then, on line ij, the following equation must be satisfied:

[0064]

[0065] Carbon intensity (denoted as ρ, unit ton CO2 / MWh) represents the carbon emissions corresponding to a unit of energy. Clearly, the relationship between carbon emission rate and carbon intensity is: R = ρE. Therefore, the carbon intensity of energy at node i and line ij is calculated using the following formula:

[0066]

[0067] In the formula, ρ ij and ρ i Let be the carbon intensity of line ij and node i, respectively.

[0068] Carbon emission flows in the power distribution system are calculated based on the following three principles:

[0069] (1) Law of conservation of carbon emissions: This means that the total amount of carbon flowing into each node of the energy network is equal to the total amount of carbon flowing out, as shown in the following formula:

[0070]

[0071] In the formula, R s and R d Representing the energy production E at node j respectively s and demand E d The corresponding carbon emission rate, This represents the carbon emission rate flowing into line jk.

[0072] (2) Proportional distribution principle: The distribution of carbon outflow is proportional to the energy outflow, as shown in the following formula:

[0073]

[0074] In the formula, This represents the energy flowing into line jk.

[0075] (3) Energy Consolidation Principle: When energy from different branches or producers is injected into a node, the corresponding carbon emissions are consolidated. Therefore, the carbon emission intensity of node j is calculated using the following formula:

[0076]

[0077] In the formula, X = 1 represents the carbon emissions corresponding to energy transmission losses allocated to energy consumers.

[0078] After calculating the node carbon intensity in the power distribution system, the result is sent to the data center.

[0079] As a specific implementation method, based on the marginal electricity price and carbon intensity of the distribution nodes of the power distribution system, the data center operator aims to minimize its overall cost by establishing an economic and low-carbon operation optimization model for the data center. The model solves for the data center's operation plan and workload scheduling strategy, determines the power purchase and sale between the data center and the power distribution system, and sends the results to the power distribution system operator.

[0080] The objective function of the data center economic and low-carbon operation optimization model is as follows:

[0081]

[0082] In the formula, and These represent the power purchased and sold by the data center, respectively; λ d,t and These represent the electricity purchase price and the electricity sales price for the data center, respectively; c e This is the carbon emission cost coefficient; This indicates the carbon intensity of data center nodes in the power distribution system.

[0083] The data center operation optimization model includes the following constraints:

[0084] (1) Workload operating constraints:

[0085]

[0086]

[0087] In the formula, and These represent flexible latency-sensitive workloads and fixed latency-sensitive workloads on the front-end server, respectively. and These represent the upper and lower limits, respectively, for flexible latency-sensitive workloads allocated to the data center; This represents the number of the q-th latency-tolerant workload in the data center; and These represent the start and end times of the q-th latency-tolerant workload processing period within the data center, respectively. This indicates latency-sensitive workloads scheduled to data center d; This indicates a latency-tolerant workload being processed by data center d; Indicates the total number of workloads processed. This indicates that the operation and scheduling time period is taken into consideration. This represents the processable time period of the q-th latency-tolerant workload within data center d.

[0088] (2) Enable server constraints:

[0089]

[0090] In the formula, N represents the number of servers in the data center. d,q Indicates the number of latency-tolerant workloads; Indicates the number of servers started to handle latency-sensitive workloads; Indicates the number of servers started to handle latency-tolerant workloads; S d,t Indicates the total number of servers that are active; μ d Indicates the server's processing speed; t D Indicates the processing latency limit for flexible latency-sensitive workloads; This indicates the transmission latency for latency-sensitive workloads. This indicates flexible latency-sensitive workloads in the data center. This indicates a fixed latency-sensitive workload in the data center.

[0091] (3) Operational constraints of energy storage in UPS:

[0092]

[0093] In the formula, E d,t Indicates stored electrical energy; and These represent the charge and discharge efficiencies, respectively. and These represent the charging and discharging power, respectively. and These represent the lower and upper limits for storing electrical energy, respectively. Indicates energy storage capacity; E d,0 Indicates the initial stored electrical energy; and These represent the upper limits of the charging and discharging power of energy storage, respectively. and These represent the lower limits of energy storage.

[0094] (4) Data center power constraints:

[0095]

[0096] In the formula, Indicates the power of IT equipment. and These represent the output electrical power of photovoltaic and wind turbines, respectively; Indicates other power levels in the data center; P d,t P represents the power exchange between the data center and the power distribution system. peak,d and P idle,d These represent the peak and idle power of the IT equipment, respectively; pue represents the PUE value of the data.

[0097] As a specific implementation method, an iterative solution algorithm based on the bisection method is proposed in the iterative solution process for power distribution system operators and data center operators to address the oscillation problem that may occur during the iteration process and ensure the convergence of the iterative solution process. The specific details of the proposed bisection method iterative solution algorithm are shown in Table 1:

[0098] Table 1

[0099]

[0100] The results of the iterative solution process based on the bisection method are as follows: Figure 3 As shown. The results of the marginal electricity price at the distribution node are as follows. Figure 4 As shown. The results for the carbon intensity at the nodes of the power distribution system are as follows. Figure 5 As shown. The spatial scheduling results for latency-sensitive workloads in the data center are as follows. Figure 6 As shown. The time scheduling results for latency-tolerant workloads in the data center are as follows. Figure 7 As shown in Table 2, the operating costs of the data center are as follows.

[0101] Table 2

[0102]

[0103] Example 2

[0104] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0105] Example 3

[0106] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0107] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the economical and low-carbon operation of data centers in a power distribution system, characterized in that, Includes the following steps: With the goal of minimizing the energy economic cost of the power distribution system, a power distribution system operation optimization model is established using DIST-flow power flow constraints; The power distribution system operation optimization model is solved to obtain the power flow results of the power distribution system and the marginal electricity price of the distribution nodes; Based on the power flow results of the power distribution system and the carbon emission flow theory, the carbon intensity of the power distribution system nodes is solved. The marginal electricity price and carbon intensity of the distribution nodes of the power distribution system are sent to the data center. The data center establishes an economic and low-carbon operation optimization model with the goal of minimizing the overall cost. The economic and low-carbon operation optimization model of the data center is solved to obtain the power purchase and sale results with the power distribution system, and then sent to the power distribution system. The power distribution system and the data center iteratively solve the corresponding optimization models until the interaction power between the two converges, thus completing the optimization of the data center's economical and low-carbon operation. The objective function of the data center economic and low-carbon operation optimization model is shown in the following equation: ， In the formula, and These represent the power purchased and sold by the data center, respectively. and These represent the electricity purchase price and the electricity sales price for the data center, respectively. This is the carbon emission cost coefficient; This indicates the carbon intensity of data center nodes in a power distribution system. The constraints of the data center operation optimization model include: workload operation constraints, server startup constraints, energy storage operation constraints in UPS, and data center power constraints. In the process of iteratively solving the corresponding optimization models of the power distribution system and the data center, an iterative solution algorithm based on the bisection method is adopted.

2. The method according to claim 1, characterized in that, The objective function of the power distribution system operation optimization model is shown in the following equation: ， In the formula, This indicates the wholesale market electricity price within the higher-level power grid; This indicates the electricity price sold within the power distribution system; This indicates the power consumption of the power distribution system in the wholesale market; This indicates the electrical energy purchased from distributed generators; This indicates the power purchased from the data center.

3. The method according to claim 1, characterized in that, The formulas for calculating the carbon intensity of energy at node i and line ij in a power distribution system are as follows: ， In the formula, and Let be the carbon intensity of line ij and node i, respectively; The energy flowing from node i to line ij; The energy loss generated on line ij; The energy flowing from line ij to node j; , and They are respectively , and The corresponding carbon emission rate.

4. The method according to claim 3, characterized in that, The process of solving for the carbon intensity of nodes in a power distribution system also includes calculating the carbon emission flow in the power distribution system based on the law of conservation of carbon emissions, the principle of proportional allocation, and the principle of energy merging.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-4.

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