Calculating power task time sequence scheduling-considered computing power network energy collaborative planning method and computing power task time sequence scheduling-considered computing power network energy collaborative planning device

Abstract

The invention relates to the technical field of computers, in particular to a computing power task time sequence scheduling-considered computing power network energy collaborative planning method and device. According to the method, calculation network system planning is carried out by considering the energy supply characteristics of the energy supply system and flexible allocation of the computing power task, energy supply system planning is carried out by considering the load demand of the calculation network system, calculation network system planning design and energy supply system planning design interact with each other through the computing power task and energy supply as coupling points and boundaries, and through multiple iterations, the calculation network system planning design and the energy supply system planning design are optimized. The calculation network energy integrated optimal planning scheme can overcome the problem of inconsistency of a traditional independent planning configuration scheme, realizes high coupling degree of a calculation power task and renewable energy power generation, improves the consumption rate of renewable energy, reduces the energy consumption cost of a data center, and is suitable for large-scale popularization and application. The computing power resource-computing power task-load demand-energy supply multi-level interaction is realized, the overall configuration scheme of the computing power network energy is optimal, and computing power task time sequence scheduling computing power network energy collaborative planning can be realized.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a computing network collaborative planning method and device that takes into account the timing scheduling of computing tasks. Background Art

[0002] Computing network energy planning includes two parts: computing power network system planning and energy supply system planning. Computing power network system planning is used to meet the needs of various computing power tasks and formulate the optimal computing power equipment construction plan. Energy supply system planning is used to meet the energy needs of the computing power system and formulate the optimal electricity, cooling, heat and other energy system supply plans to ensure that the computing power network system uses energy safely, economically and reliably.

[0003] In the current problem of computing network energy planning, computing network system planning and energy supply system planning are usually carried out separately. During the planning process, both the computing network system and the energy supply system are unknown. When allocating computing tasks in the computing network system planning, it is necessary to consider the energy supply characteristics of the energy supply system. When planning the energy supply system, it is necessary to consider the changes in the load demand of the computing network system.

[0004] Due to the flexible and adjustable characteristics of computing tasks, batch workloads can be time-adjusted. According to energy price policies and renewable energy output curves, a high degree of coupling between computing tasks and renewable energy generation can be achieved, and the renewable energy consumption rate can be improved. Under the premise of meeting the latency constraints of computing tasks, computing tasks can be allocated to periods with low electricity prices to reduce data center energy costs. The flexible allocation of computing tasks directly affects the configuration plan of computing network system equipment, and will also cause computing network system load demand, indirectly affecting the energy supply system configuration plan. The energy supply system configuration plan in turn affects the allocation plan of computing tasks, and then affects the configuration plan of computing network system equipment.

[0005] Regarding the current problem of computing network energy planning, computing power network system planning and energy supply system planning are usually carried out separately. Usually, the computing power network system plans and then proposes energy demand, and the energy supply system matches it, making it impossible for the two modules to interact and coordinate.

[0006] Based on this, there is an urgent need for a computing network collaborative planning method and device that takes into account the timing scheduling of computing power tasks to solve the technical problem of how to achieve computing network collaborative planning with timing scheduling of computing power tasks. Summary of the invention

[0007] In order to solve the technical problem of how to achieve computing power task timing scheduling and computing network collaborative planning, the embodiments of the present invention provide a computing power task timing scheduling and computing network collaborative planning method and device.

[0008] In a first aspect, an embodiment of the present invention provides a computing network collaborative planning method considering computing task timing scheduling, the method being applied to a single-node computing network, comprising:

[0009] Establishing a computing power network planning model and an energy supply planning model; wherein the computing power network planning model includes a first upper layer model and a first lower layer model, and the energy supply planning model includes a second upper layer model and a second lower layer model;

[0010] The first upper-layer model takes the maximum data center revenue and the minimum carbon emission as the objective function, takes the latency constraint and the computing power scale constraint as the constraint conditions, determines the center computing power scale, and sends the center computing power scale to the first lower-layer model;

[0011] The first lower-layer model takes the maximum revenue and minimum carbon emissions of the data center as the objective function, takes the energy constraint and the computing power scale constraint as the constraint conditions, determines the carbon emissions and profits of the data center, and sends the carbon emissions and profits of the data center to the first upper-layer model;

[0012] The first upper model and the first lower model are repeatedly iterated to determine a first optimal solution; wherein the first optimal solution includes a computing power equipment planning scheme and a computing power energy consumption load curve, and the computing power energy consumption load curve is used as an input of an energy supply planning model to perform coupling interaction;

[0013] The second upper model takes the maximum profit and the minimum carbon emission in the planning period as the objective function, takes the upper limit constraint of the equipment planning capacity and the equipment commissioning constraint as the constraint conditions, determines the equipment configuration strategy and the planning capacity, and sends the equipment configuration strategy and the planning capacity to the second lower model;

[0014] The second lower-layer model takes the minimum operating cost as the objective function, and takes the power balance constraint, the equipment operation constraint, the power exchange constraint between the energy system and the upper network, and the green power proportion constraint as the constraint conditions to determine the optimal operating plan and the total operating cost, and sends the optimal operating plan and the total operating cost to the second upper-layer model;

[0015] The second upper model and the second lower model are repeatedly iterated to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a computing power network planning model for coupling interaction;

[0016] The computing power network planning model and the energy supply planning model are cyclically coupled and interacted, and computing network energy collaborative planning for computing power task timing scheduling has been realized.

[0017] In a second aspect, an embodiment of the present invention further provides a computing network collaborative planning device that considers computing task timing scheduling, including:

[0018] A model building module, used to establish a computing power network planning model and an energy supply planning model; wherein the computing power network planning model includes a first upper layer model and a first lower layer model, and the energy supply planning model includes a second upper layer model and a second lower layer model;

[0019] A first data processing module is used for the first upper-layer model to determine the center computing power scale with the maximum data center revenue and the minimum carbon emission as the objective function, and with the delay constraint and the computing power scale constraint as the constraint conditions, and send the center computing power scale to the first lower-layer model;

[0020] A second data processing module is used for the first lower-layer model to determine the carbon emissions and profits of the data center with the maximum data center revenue and the minimum carbon emissions as the objective function, and with the energy constraint and the computing power scale constraint as the constraint conditions, and to send the carbon emissions and profits of the data center to the first upper-layer model;

[0021] A third data processing module is used for repeatedly iterating the first upper model and the first lower model to determine a first optimal solution; wherein the first optimal solution includes a computing power equipment planning scheme and a computing power energy consumption load curve, and the computing power energy consumption load curve is used as an input of an energy supply planning model to perform coupling interaction;

[0022] A fourth data processing module is used for the second upper-layer model to determine the equipment configuration strategy and planning capacity with the maximum profit and the minimum carbon emission in the planning period as the objective function, and the upper limit constraint of the equipment planning capacity and the equipment commissioning constraint as the constraint conditions, and send the equipment configuration strategy and planning capacity to the second lower-layer model;

[0023] A fifth data processing module is used for the second lower-layer model to determine the optimal operation plan and the total operation cost with the minimum operation cost as the objective function, the power balance constraint, the equipment operation constraint, the power exchange constraint between the energy system and the upper network, and the green power proportion constraint as the constraint conditions, and send the optimal operation plan and the total operation cost to the second upper-layer model;

[0024] a sixth data processing module, configured to repeatedly iterate the second upper model and the second lower model to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a computing power network planning model for coupling interaction;

[0025] The loop coupling interaction module is used for loop coupling interaction between the computing power network planning model and the energy supply planning model, and has realized the computing power network collaborative planning of the computing power task timing scheduling.

[0026] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of the present invention is implemented.

[0027] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method described in any embodiment of the present invention.

[0028] The embodiment of the present invention provides a method and device for collaborative planning of computing network energy considering the timing scheduling of computing power tasks. The present invention considers the energy supply characteristics of the energy supply system and the flexible allocation of computing power tasks to plan the computing network system, and considers the load demand of the computing network system to plan the energy supply system. The computing network system planning and design and the energy supply system planning and design use computing power tasks and energy supply as coupling points and boundary interactions. Through multiple iterations, the optimal planning scheme for the integration of computing network energy is finally optimized. It can overcome the inconsistency problem of configuration schemes of traditional independent planning, achieve a high degree of coupling between computing power tasks and renewable energy power generation, improve the renewable energy absorption rate, reduce the energy cost of data centers, and realize multi-level interaction of computing power resources-computing power tasks-load demand-energy supply, so as to achieve the optimal overall configuration scheme of computing network energy, and realize collaborative planning of computing network energy with timing scheduling of computing power tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 It is a flow chart of a computing network collaborative planning method considering computing power task timing scheduling provided by an embodiment of the present invention;

[0031] Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0032] Figure 3 This is a structural diagram of a computing network collaborative planning device that takes into account the timing scheduling of computing tasks provided by an embodiment of the present invention;

[0033] Figure 4This is a diagram of a computing network collaborative planning architecture that takes into account the timing scheduling of computing tasks, provided by an embodiment of the present invention;

[0034] Figure 5 It is a structural diagram of a computing power network planning model provided by an embodiment of the present invention;

[0035] Figure 6 It is a structural diagram of an energy supply planning model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0037] Please refer to Figure 1 The embodiment of the present invention provides a computing network collaborative planning method considering the timing scheduling of computing tasks, the method is applied to a single-node computing network, and the method includes:

[0038] Step 100: Establish a computing power network planning model and an energy supply planning model; wherein the computing power network planning model includes a first upper layer model and a first lower layer model, and the energy supply planning model includes a second upper layer model and a second lower layer model;

[0039] Step 102: The first upper-layer model takes the maximum data center revenue and the minimum carbon emission as the objective function, takes the latency constraint and the computing power scale constraint as the constraint conditions, determines the center computing power scale, and sends the center computing power scale to the first lower-layer model;

[0040] Step 104: The first lower-layer model takes the maximum revenue of the data center and the minimum carbon emission as the objective function, and takes the energy constraint and the computing power scale constraint as the constraint conditions, determines the carbon emission and profit of the data center, and sends the carbon emission and profit of the data center to the first upper-layer model;

[0041] Step 106: the first upper model and the first lower model are repeatedly iterated to determine a first optimal solution; wherein the first optimal solution includes a computing power equipment planning scheme and a computing power energy consumption load curve, and the computing power energy consumption load curve is used as an input of the energy supply planning model to perform coupling interaction;

[0042] Step 108: The second upper-layer model takes the maximum profit and the minimum carbon emission in the planning period as the objective function, takes the upper limit constraint of the equipment planning capacity and the equipment commissioning constraint as the constraint conditions, determines the equipment configuration strategy and the planning capacity, and sends the equipment configuration strategy and the planning capacity to the second lower-layer model;

[0043] Step 110: The second lower-layer model takes the minimum operating cost as the objective function, and takes the power balance constraint, the equipment operation constraint, the power exchange constraint between the energy system and the upper network, and the green power ratio constraint as the constraint conditions to determine the optimal operating plan and the total operating cost, and sends the optimal operating plan and the total operating cost to the second upper-layer model;

[0044] Step 112: the second upper model and the second lower model are repeatedly iterated to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of the computing power network planning model for coupling interaction;

[0045] Step 114: The computing power network planning model and the energy supply planning model are cyclically coupled and interacted to realize the computing network energy collaborative planning of computing power task timing scheduling.

[0046] In an embodiment of the present invention, a computing power network planning model and an energy supply planning model are established; wherein the computing power network planning model includes a first upper model and a first lower model, and the energy supply planning model includes a second upper model and a second lower model; the first upper model takes the maximum data center revenue and the minimum carbon emission as the objective function, takes the delay constraint and the computing power scale constraint as the constraint conditions, determines the center computing power scale, and sends the center computing power scale to the first lower model; the first lower model takes the maximum data center revenue and the minimum carbon emission as the objective function, takes the energy constraint and the computing power scale constraint as the constraint conditions, determines the carbon emissions and profits of the data center, and sends the carbon emissions and profits of the data center to the first upper model; the first upper model and the first lower model are repeatedly iterated to determine the first optimal solution; wherein the first optimal solution includes a computing power equipment planning scheme (computing power equipment type, quantity + scale FLOPS and computing power task timing optimization curve) and a computing power energy consumption load curve, and uses the computing power energy consumption load curve as the input of the energy supply planning model for coupling interaction; the second upper model Taking the maximum profit and minimum carbon emission in the planning period as the objective function, and the upper limit constraint of equipment planning capacity and the equipment commissioning constraint as the constraint conditions, the equipment configuration strategy and planning capacity are determined, and the equipment configuration strategy and planning capacity are sent to the second lower-level model; the second lower-level model takes the minimum operating cost as the objective function, and takes the power balance constraint, equipment operation constraint, energy system and upper network power exchange constraint, and green electricity proportion constraint as the constraint conditions to determine the optimal operation plan and total operating cost, and send the optimal operation plan and total operating cost to the second upper-level model; the second upper-level model and the second lower-level model are repeatedly iterated to determine the second optimal solution; wherein, the second optimal solution includes the energy supply planning plan (energy system construction configuration type + capacity), the power curve of power generation equipment, the public grid thermal power curve and the green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve and the green electricity trading curve are used as the input of the computing power network planning model for coupling interaction; the computing power network planning model and the energy supply planning model are cyclically coupled and interacted, and the computing network energy collaborative planning of computing power task timing scheduling has been realized.

[0047] Described below Figure 1 How the various steps are performed.

[0048] Regarding step 102:

[0049] In one embodiment of the present invention, the objective function is to maximize the data center revenue and minimize the carbon emissions, including the following formula:

[0050] max f ntp

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] S t,c =C t,c ·(E t,nom -E t,co2 ) / 10000

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] f ep =f in -f c

[0079] f tp =f ep -f ep ·λ vat ·λ umt -f ep ·λ vat ·λ est

[0080] f ntp =(f ep -f ep ·λ vat ·λ umt -f ep ·λ vat ·λ est )·(1-λ int )

[0081] In the formula, The number of production facilities supplying the i-th energy source put into operation in year t; The capacity of the selected model of production equipment for the i-th energy supply; is the number of the i-th energy storage device put into operation in year t; is the capacity of the model selected for the i-th energy storage device; C inv is the initial investment cost; T is the life cycle; r is the capital discount rate; is the investment cost in year t; α lr,t is the loan ratio in year t; i is the number of energy supply production equipment; Ω 1 Assembling of production equipment for energy supply; Unit capacity investment cost of production equipment for the i-th energy supply; is the investment and construction capacity of the i-th energy supply production equipment in year t; j is the energy storage equipment number; Ω 2 Assemble for energy storage equipment; is the unit capacity investment cost of the jth energy storage device; is the investment capacity of the j-th energy storage device in year t, The annual operation and maintenance cost per unit capacity of the production equipment for the i-th energy supply; is the annual operation and maintenance cost per unit capacity of the j-th energy storage device, is the purchase cost of public grid thermal power in year t, is the medium- and long-term transaction cost of the i-th green electricity in the t-th year, is the normal green electricity purchase cost in year t, is the penalty cost for the deviation of the i-th green power medium- and long-term transaction in the t-th year, is the natural gas purchase cost in year t, is the hydrogen purchase cost in year t, S is the number of typical daily scenarios each year; N s is the number of days of a typical day scene s in a year; H is the total number of time periods in a day, C e,t,s,h P is the unit price of public grid electricity purchased in the h period of the typical day scenario in the tth year; e,t,s,h is the amount of electricity purchased from the public grid in the typical day scenario h period in year t, C le,t,i,s,h P is the purchase price of the i-th green power medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; le,t,i,s,h is the mid- to long-term green electricity purchase volume in the i-th period of the typical day scenario in the t-th year, C ge,t,s,h P is the normal green electricity purchase price in the h period of the typical day scenario in the tth year; ge,t,s,h is the normal green electricity purchase amount in the h period of the typical day scenario in the tth year, C g,t,s,h G is the natural gas purchase price in the typical day scenario h period in year t; g,t,s,h is the natural gas purchase volume in the typical day scenario h period in year t, is the unit price of hydrogen purchased in the typical day scenario h period in year t; is the amount of hydrogen purchased in the typical day scenario h period in year t, C le,t,i,s,h,dp The penalty unit price for deviation of the i-th green power medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; P le,t,i,s,h,dp P is the deviation of the i-th green electricity medium- and long-term transaction caused by the power purchaser’s responsibility in the h-th period of the typical day scenario in the t-th year; lest,t,i,s,h The transaction volume of the i-th green power medium- and long-term trading contract in the h-th period of the typical day scenario in the t-th year; P legm,t,i,s,h The amount of green electricity provided by medium- and long-term transactions in the i-th period of time in the typical day scenario of the t-th year, C fc,t is the financial cost in year t; r i,tt is the annual loan interest rate of the loan in year tt; C inv,tt is the investment cost in year tt; α lr,tt is the loan ratio in year tt; N tt is the repayment period of the loan in year tt, f c is the total investment cost of the integrated energy system; t,c is the investment cost in year t, SR t is the energy supply income in year t, C e,t,s,h,S are the electricity selling price in the typical day scenario h period in year t; P eL,t,s,h P is the other electrical load in the typical day scenario h period in year t; cpeL,t,s,h HPR is the power load consumed by computing equipment in the typical day scenario h period of year t;FJ is the heating price per unit area; CPR FJ is the cooling price per unit area; A r,HFJ is the heating area; A r,CFJ is the cooling area, S t,up is the surplus Internet access income in the tth year; S is the number of typical daily scenarios each year; N s is the duration of the typical day scene s in a year; H is the total number of time periods in a day, C up,e,t,s,h is the unit price of surplus electricity on-grid in the h period of the typical day scenario in the tth year; P up,e,t,s,h is the online power consumption in the typical day scenario h period in year t, formula S inv Subsidy for total investment; is the investment subsidy for year t; i is the number of the energy supply production equipment; Ω 1 A collection of production equipment for energy supply; Subsidy for initial investment per unit capacity of production equipment for the i-th energy supply; is the investment and construction capacity of the i-th energy supply production equipment in year t; j is the energy storage equipment number; Ω 2 Assemble for energy storage equipment; is the initial investment subsidy per unit capacity of the jth energy storage device; is the investment capacity of the j-th energy storage device in year t, S t,sub is the power generation subsidy income in the tth year; S is the number of typical daily scenarios each year; N s is the number of days of a typical day scene s in a year; H is the total number of time periods in a day, C wt,e,t,s,h , C pv,q,t,s,h are the electricity subsidies for wind power generation and photovoltaic power generation in the h period of the typical day scenario in the tth year; P wt,e,t,s,h , P pv,e,t,s,h are the wind power generation and photovoltaic power generation in the typical day scenario h period in year t, S t,c is the environmental benefit in year t; C t,c is the carbon trading price in year t; E t,nom is the carbon quota for year t; E t,co2 is the carbon dioxide emissions in year t; α e,CO2 , is the emission coefficient of carbon dioxide generated by the consumption of electricity; α g, i,CO 2 is the emission coefficient of carbon dioxide produced by the i-th energy supply production equipment consuming natural gas, S t,i,le,dp is the deviation income of the i-th green power medium- and long-term transaction in the t-th year, C le,t,i,s,h,dp The penalty unit price for deviation of the i-th green power medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; P le,t,i,s,h,dp P is the deviation of the i-th green electricity medium- and long-term transaction caused by the power purchaser’s responsibility in the h-th period of the typical day scenario in the t-th year;lest,t,i,s,h The transaction volume of the i-th green power medium- and long-term trading contract in the h-th period of the typical day scenario in the t-th year; P legm,t,i,s,h The amount of green electricity provided by medium- and long-term transactions in the i-th period of time in the typical day scenario of the t-th year, E RV is the total residual value of the comprehensive energy system equipment at the end of the planning period, M n is the total number of equipment in the integrated energy system, δ i is the residual value rate of the ith equipment, C INV,i is the initial investment of the ith equipment, f in is the total benefit of the integrated energy system over its entire life cycle; t,in is the total income in year t, f ep Provide comprehensive energy services for the full life cycle economic benefits, ntp is the total net profit over the entire life cycle, λ vat is the value added tax rate, λ umt Maintaining tax rates for urban construction, λ est is the education fee surcharge rate, which is 5%, int is the income tax rate, f ntp is the total net profit over the entire life cycle, f e is the carbon dioxide emission, T is the planning period, i.e. the total planning period; is the CO2 emission in the tth year; S is the number of typical daily scenes each year; N s is the number of days of the typical day scene s in a year; H is the total number of time periods in a day; The emission coefficient of carbon dioxide generated by consuming public grid electricity; is the emission coefficient of carbon dioxide produced by natural gas.

[0082] In one embodiment of the present invention, the computing power task demand model is:

[0083] Meaning: The time within a day can be expressed as The task arriving at time slot t can be expressed as:

[0084]

[0085] Where:

[0086] As can be seen from 3.2.3 Task Feature Model;

[0087] x∈{delay-tolerant, delay-sensitive}, indicating the type of tasks divided according to their sensitivity to delay;

[0088] sce∈{working day, holiday, weekend}, represents a typical day divided according to the amount of computation;

[0089] I represents the number of routers, i = [1, I] represents the number of routers;

[0090] Indicates the task The corresponding computing resource requirements;

[0091] Indicates the task The corresponding network resource demand;

[0092] Indicates the task The corresponding energy resource demand;

[0093] N i，x represents the number of tasks of type x that arrive at router i, n = [1, N i，x ] indicates the number of a specific task;

[0094] t∈{1,2,...,24}, represents the arrival time slot.

[0095] In a single data center, there is no cross-data center scheduling of tasks, so it is task-oriented.

[0096] Task, only time decision variable, determines the task The scheduling decision for forwarding time is

[0097] Delay Constraint

[0098]

[0099] Meaning: The total latency of each task is defined as the sum of transmission latency, propagation latency and processing latency.

[0100] The delay under scheduling decision Z can be expressed as:

[0101]

[0102] Where: is the transmission delay, that is, the sum of the tasks passing through the servers on the transmission path, is the propagation delay (actual physical distance divided by the speed of light), and is the computation delay. The computation delay of each task can be expressed as:

[0103]

[0104] Where: Indicates the computing power obtained by each task, Num task (t) is the number of tasks in time slot t. gen(FLOPS) is the computing resource of the general-purpose server, and Num is the number of general-purpose servers in the data center.

[0105] Computing power scale constraints:

[0106] Computing power scale model

[0107] Meaning: The total computing power scale needs to consider the sum of the computing power scales corresponding to delay-tolerant and delay-sensitive tasks. The final computing power scale of the data center can be expressed as:

[0108] K total =K+K′

[0109] Where: K is the computing power scale corresponding to the delay-tolerant task. K′ is the computing power scale corresponding to the delay-sensitive task. Since delay-sensitive tasks cannot be scheduled, the maximum task computing power received by the data center is Assume that the average processing delay of each delay-sensitive task is t sen , we assume that t sen =0.1s. Then the computing power scale of a single data center is K′=Request sen / t sen .

[0110] C2: K′≤K max

[0111] Where: The constraints indicate that the task processing time must be before the tolerable time point, and the data center scale cannot exceed the maximum computing resource capacity. max Indicates the maximum computing capacity that a data center can support.

[0112] Computing power scale constraints:

[0113]

[0114] Regarding step 104:

[0115] In one embodiment of the present invention, the energy supply constraint is:

[0116] C2:E total ≤Q total

[0117] Where: Q total It is the total amount of energy that can be provided by the energy supplier.

[0118] Regarding step 106:

[0119] like Figure 5As shown, in one embodiment of the present invention, it can be seen from the above model that the decision variables include task allocation strategy, data center computing power scale strategy and data center computing power equipment construction strategy. Since the task allocation strategy is related to the computing power equipment configuration of the data center, and the computing power scale of the data center is the boundary condition of the computing power equipment construction strategy of the data center, the decisions are coupled with each other and it is difficult to optimize and solve them at the same time, so the above variables need to be split and converted. Considering the logical relationship of the variables, the task allocation strategy and the data center computing power scale strategy can be first determined based on the computing power requirements of the task. After obtaining the data center computing power scale strategy, the computing power scale is used as the boundary condition to further obtain the computing power equipment construction strategy. It can be seen that the two-layer optimization model is suitable for solving the computing power planning model of this project.

[0120] The original optimization model is converted into a multi-objective two-layer joint optimization computing power configuration model. The upper model is the main model, with the goal of minimizing carbon emissions and maximizing profits, to obtain the task allocation strategy and data center computing power scale strategy; the lower model is the sub-model, with the data center computing power scale determined by the upper model as the boundary condition, with the goal of minimizing carbon emissions and maximizing profits, to formulate the data center computing power equipment construction plan.

[0121] The data center computing power scale obtained by the upper-level model optimization is passed to the lower-level model. The lower-level model optimizes the construction of data center computing power equipment and passes the optimization target of one iteration to the upper-level model for subsequent iterations. Through the transmission from the upper to the lower layer, the two-layer joint solution obtains the optimal computing power configuration strategy for the computing power system.

[0122] 1) Upper model

[0123] The upper model is the main model for computing system planning. The decision variables are task allocation strategy and data center computing scale strategy. Its objective function is to minimize carbon emissions and electricity prices, as shown below.

[0124] The last generation adds weighted values ​​and normalizes the economic and environmental goals and then sums them up. The comprehensive objective function is as follows.

[0125]

[0126]

[0127]

[0128] ω 1 +ω 2 =1

[0129] In the formula, ω 1 ,ω 2 is the weight coefficient, is the normalized value of the economic objective of the i-th solution of the last generation, is the normalized value of the environmental protection target of the i-th solution in the last generation, is the maximum value of the economic objective among all solutions of the last generation, is the minimum value of the economic objective among all solutions of the last generation, is the maximum value of the environmental protection target among all solutions of the last generation, is the minimum value of the environmental protection target among all solutions of the last generation, and M is the number of solutions of the last generation.

[0130] After the upper-level model determines the scale of the data center, it is transmitted to the lower-level model to complete the configuration of the computing equipment in the data center.

[0131] (2) Lower-level model

[0132] The lower model is a sub-model of the upper model, which is used to solve the sub-problem of optimizing the construction of computing power equipment in the computing power system. It coordinates the joint processing tasks of various computing power equipment according to the differences in cost, income, environmental protection, etc. of different computing power equipment. The data center computing power scale of the upper model is passed to the lower model as the boundary condition of the lower model.

[0133] The last generation adds weighted values ​​and normalizes the economic and environmental goals and then sums them up. The comprehensive objective function is as follows.

[0134]

[0135]

[0136]

[0137] ω 1 +ω 2 =1

[0138] In the formula, ω 1 ,ω 2 is the weight coefficient, is the normalized value of the economic objective of the i-th solution of the last generation, is the normalized value of the environmental protection target of the i-th solution in the last generation, is the maximum value of the economic objective among all solutions of the last generation, is the minimum value of the economic objective among all solutions of the last generation, is the maximum value of the environmental protection target among all solutions of the last generation, is the minimum value of the environmental protection target among all solutions of the last generation, and M is the number of solutions of the last generation.

[0139] In each optimization process, the upper-level model optimizes the computing power scale of the data center, and the lower-level model uses this as the boundary condition to optimize the optimal computing power configuration of the data center.

[0140] Regarding step 108:

[0141] In one embodiment of the present invention, the maximum profit during the planning period is determined by the following formula:

[0142]

[0143]

[0144]

[0145]

[0146] Cost e (t) = E(t)P(t)

[0147]

[0148]

[0149]

[0150]

[0151]

[0152] Income t =P task *T running

[0153]

[0154]

[0155]

[0156] TrueProfit Total =Profit Total *(1-λ)

[0157] In the formula, Cost Device Indicates the total cost of initial equipment purchase, Cost Device,l represents the total initial equipment acquisition cost of the data center, Indicates the number of CPU computing devices of type a in the data center, Indicates the number of GPU computing devices of type b in the data center, Indicates the number of c types of network devices in the data center, Indicates the number of type d cabinets in the data center, Indicates the price of type a CPU computing equipment in a data center, Indicates the price of GPU computing equipment of type b in the data center, represents the price of type c network equipment in the data center, Indicates the price of d types of cabinet equipment in the data center, represents the planned maintenance cost in year t, Indicates the number of CPU computing devices of type a in the data center, Indicates the number of GPU computing devices of type b in the data center, Indicates the number of c types of network devices in the data center, Indicates the number of type d cabinets in the data center, represents the maintenance cost of type a CPU computing equipment in the data center in year t, represents the maintenance cost of type b GPU computing equipment in the data center in year t, represents the maintenance cost of type c network equipment in the data center in year t, represents the maintenance cost of type d cabinet equipment in the data center in year t, Indicates the annual equipment power consumption cost, HPR FJ A represents the heating price per unit area of ​​the data center. r,HFJ Indicates the heating area of ​​the data center, CPR FJ Table 2. Cooling price per unit area, A r,CFJ represents the cooling area of ​​the data center, w type represents the energy efficiency coefficient of the data center in different seasons, where w type The default value is 1.05, c e,t represents the terminal power supply price of the data center in year t, P request represents the energy consumption of the computing power request equipment in the data center in year t, P basic represents the basic energy consumption of data center equipment in year t, Cost e,total is the total electricity price, T cycle is the life cycle in years, is the total electricity price of the data center corresponding to a typical day, T seai is the number of days of a typical day, Cost is the total electricity price of the data center in one day. e (t) The electricity price of data center m at time slot t,

[0158] represents the electricity price of the data center, PCI) represents the electricity price of the data center at time t, S t,vs is the value-added service revenue in year t, β vs The value-added service revenue ratio coefficient for the data center. The comprehensive energy purchase cost of the data center. Represents the total cost of the data center, Energy trepresents the total power consumption of all devices in the data center in year t, Expense t Cost represents the construction cost of the data center in year t. Inv Indicates the initial investment cost, Cost Device represents the total initial equipment acquisition cost of the data center, represents the total cost of the data center. Considering the delay in project planning and construction, the discount rate needs to be increased. r is the capital discount rate, α is the data center loan ratio, is the financial cost in year t, is the financial cost of the data center in year t, rate is the annual loan interest rate of the data center loan, Cost Inv is the data center investment cost, α is the data center loan ratio, lim is the repayment period of the data center loan, Cost Total The total cost of the planning cycle of the comprehensive computing network system, Income t represents the computing power income in the tth year, P task represents the unit price of computing power in the data center in year t, T running represents the planned computing power demand in the center in year t, Residual is the total residual value of the computing network system equipment at the end of the planning period, H is the total number of equipment in the computing network system and H = A + B + C + D, β i is the residual value rate of the i-th equipment, Price i is the initial purchase price of the i-th device, Income Total is the total income of the whole life cycle of the network system, r is the capital discount rate, To calculate the economic benefits of the entire life cycle of the network system, TrueProfit Total is the total net profit over the entire life cycle, and λ is the income tax rate.

[0159] The upper limit constraint of equipment planning capacity is determined by the following formula:

[0160]

[0161]

[0162] In the formula, are the planned capacity upper limits of energy production equipment and energy storage equipment, respectively.

[0163] Equipment operation constraints include upper and lower output limits of energy supply equipment and charging and discharging power constraints of energy storage equipment.

[0164] Regarding step 110:

[0165] In one embodiment of the present invention, the power balance constraint includes an electric power balance constraint, a thermal power balance constraint, a cold power balance constraint, a natural gas power balance constraint, and a hydrogen power balance constraint. The power balance constraint is determined by the following formula:

[0166] Electric power balance constraints

[0167]

[0168] Thermal power balance constraints

[0169]

[0170] Cold power balance constraint

[0171]

[0172] Natural Gas Power Balance Constraints

[0173]

[0174] Hydrogen power balance constraints

[0175]

[0176] In the formula, Ω 4 A collection of energy supply production equipment for generating electrical energy, Ω 5 is the collection of power storage devices, Ω 6 A collection of energy supply production equipment that consumes electrical energy, P e,m,t,s,h P is the power generated by the mth energy supply production equipment generating electricity in the hth period of the typical day scenario in the tth year, e,j,ES-dis,t,s,h is the discharge power of the jth storage device in the hth period of the typical day scenario in the tth year, P e,k,t,s,h P is the power consumed by the kth energy supply device consuming electricity in the hth period of the typical day scenario in the tth year, eL,t,s,h is the other power load in the typical day scenario h period in year t, P e,j,ES-ch,t,s,h is the charging power of the jth energy storage device in the hth period of the typical day scenario in the tth year, P cpeL,t,s,h is the power load consumed by the computing equipment in the typical day scenario h period of year t, Ω 7 A collection of energy supply production equipment for generating heat, Ω 8 is the collection of heat storage devices, Ω 9 A collection of energy supply production equipment that consumes heat energy, Q q,i,t,s,h Q is the thermal power generated by the i-th energy supply production equipment generating heat energy in the typical day scenario h period in the t-th year, q,j,HS-dis,t,s,h Q is the heat release power of the jth heat storage device in the hth period of the typical day scenario in the tth year, q,k,t,s,hQ is the heat load of the kth energy supply production equipment consuming heat energy in the typical day scenario h period in year t, qL,t,s,h is the heat load of the typical day scenario h period in year t, Q q,j,HS-ch,t,s,h is the charging power of the jth heat storage device in the hth period of the typical day scenario in the tth year, Ω 10 A collection of energy supply production equipment for generating cold energy, Ω 11 C is a collection of cold storage equipment. c,i,t,s,h is the cooling power generated by the i-th energy supply production equipment generating cooling energy in the typical day scenario h period in year t, C c,j,CS-dis,t,s,h is the cooling power of the jth cold storage device in the hth period of the typical day scenario in the tth year, C cL,t,s,h is the cooling load of the typical day scenario h period in year t, C c,j,CS-ch,t,s,h is the cooling power of the jth cold storage device in the hth period of the typical day scenario in the tth year, Ω 3 A collection of energy supply production equipment consuming natural gas, Ω 12 G is the gas storage equipment set. g,t,s,h is the natural gas power purchased in the typical day scenario h period in year t, G g,i,GS-dis,t,s,h G is the gas discharge power of the jth gas storage device in the hth period of the typical day scenario in the tth year, g,j,t,s,h is the natural gas power of the energy supply production equipment consuming natural gas in the jth natural gas consumption period in the typical day scenario of the tth year, G g,i,GS-ch,t,s,h The charging power of the jth gas storage device in the hth period of the typical day scenario in the tth year, Ω 14 A collection of energy supply production equipment for consuming hydrogen, Ω 13 For hydrogen storage equipment collection, is the amount of hydrogen purchased in the typical day scenario h period in year t, is the hydrogen discharge power of the jth hydrogen storage device in the hth period of the typical day scenario in the tth year, The hydrogen power of the energy supply production equipment for the jth energy consumption of hydrogen energy in the typical day scenario h period in year t is The hydrogen charging power of the jth hydrogen storage device in the h period of the typical day scenario in the tth year.

[0177] The power exchange constraint between the energy system and the upper network is determined by the following formula:

[0178]

[0179]

[0180]

[0181]

[0182] 0≤P le,t,i,s,h ≤Plegm,t,i,s,h

[0183] In the formula, are the minimum and maximum power purchases from the thermal power grid respectively; The minimum and maximum purchased natural gas power respectively; The minimum and maximum purchased hydrogen power respectively; are the minimum and maximum constant green electricity purchase power respectively; P le,t,i,s,h P is the green electricity mid- and long-term transaction purchase volume of the ith green electricity in the h-th period of the s-th typical day scenario in the t-th year; legm,t,i,s,h The amount of green electricity provided in the i-th green electricity medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year.

[0184] The equipment operation constraints are determined by the following formula:

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191] In the formula, is the minimum value of the amount of electricity provided by the i-th green electricity medium- and long-term transaction; The maximum electric power consumed by the kth energy supply device that consumes electric energy in the tth year; The maximum thermal power of the production equipment supplied by the kth energy source consuming heat energy in year t; The maximum natural gas power of the energy supply production equipment for the jth natural gas consuming energy in year t; The maximum hydrogen power of the production equipment supplied by the jth energy source consuming hydrogen in year t; are the rated capacities of the electricity, heat and cold energy production equipment in year t, They are the peak values ​​of other power loads, power load consumed by computing equipment, heat load, and cold load in year t respectively.

[0192] Green power ratio constraints

[0193] GE rate ≥GE rate_nor

[0194] In the formula, GE rate is the proportion of green electricity, GE rate_nor The lower limit of green electricity ratio.

[0195] Regarding step 112:

[0196] like Figure 6 As shown, in one embodiment of the present invention, it can be seen from the above model that the decision variables include equipment configuration strategy and system operation optimization variables. The system operation optimization problem takes the system equipment configuration strategy as the boundary condition. It is difficult to optimize both at the same time, and the two parts of the variables need to be split and converted. From the logical relationship of the variables, the equipment configuration strategy can be determined first, and the system operation optimization decision can be made on this basis. The optimal solution is output by repeated iterations. It can be seen that the two-layer optimization model is suitable for solving the planning model of this project.

[0197] The original optimization model is converted into a multi-objective two-layer joint optimization configuration model for the integrated energy system. The upper model is the main model, which is an equipment selection and capacity optimization model, used to select the optimal equipment configuration combination from the selected equipment set, with the goal of minimizing total cost and pollutant gas emissions; the lower model is a sub-model, which is an operation and scheduling optimization model. The equipment configuration scheme determined by the upper model is used as a boundary condition to formulate the optimal plan for the output of each equipment under the premise of meeting the regional energy supply and demand balance, with the goal of minimizing the total operating cost.

[0198] The upper-level model optimizes the configuration strategies and planned capacities of various types of equipment and passes them to the lower-level model. The lower-level model optimizes the scheduling and operation of the comprehensive energy system, and returns the optimal operation plan and total operating cost to the upper-level model. Through the optimization iteration of the upper and lower layers, the optimal configuration strategy of the energy system is solved.

[0199] The upper model is the main model for energy system planning. The decision variables are the configuration and planned capacity of various types of equipment, namely the investment capacity of energy supply production equipment, the investment capacity of energy storage equipment and the rated power of energy storage equipment. Its objective function is to minimize the total planning cost and the emission of pollutants.

[0200] By adding weighted values ​​and normalizing the economic and environmental goals and then summing them up, the comprehensive objective function is as follows.

[0201]

[0202]

[0203]

[0204] ω 1 +ω 2 =1

[0205] In the formula, ω 1 ,ω 2 is the weight coefficient, is the normalized value of the economic objective of the ith solution of the current iteration, is the normalized value of the environmental protection target of the i-th solution in the current iteration, max i (f i,ntp ) is the maximum value of the economic objective among all solutions in the current iteration, min i (f i,ntp ) is the minimum value of the economic objective among all solutions in the current iteration, max i (f i,e ) is the maximum value of the environmental protection target among all solutions in the current iteration, min i (f i,e ) is the minimum value of the environmental protection target among all solutions in the current iteration, and M is the number of solutions in the current iteration.

[0206] After the lower-level model determines the operating strategy, it is transmitted to the upper-level model to complete the cost and benefit calculation related to the operating strategy.

[0207] (2) Lower-level model

[0208] The lower model is a sub-model of the upper model, which is used to solve the scheduling and operation optimization sub-problems of various energy supply production equipment and energy storage equipment in the energy system. According to the differences in the supply and demand of different energy sources, various energy needs are met by coordinating the operation of various energy supply production equipment and the charging and discharging of energy storage equipment. The configuration and planned capacity of various equipment produced by the upper model are passed to the lower model as the boundary conditions of the lower model. Its objective function is to minimize the total operating cost, as shown below.

[0209]

[0210] In the nth iteration of the optimization process, the upper-level model optimizes the configuration and planned capacity of various equipment and passes it to the lower-level model. The lower-level model uses this as the boundary condition to optimize the optimal scheduling operation plan, and returns to the upper level to calculate the profit and pollutant gas emissions during the planning period as the initial value of the n+1th iteration.

[0211] Regarding step 114:

[0212] like Figure 4As shown, in one embodiment of the present invention, the energy supply of the data center is jointly completed by the local integrated energy station and the external power grid, natural gas network, hydrogen network, etc. The multi-objective planning and design of computing power aims to minimize carbon emissions, maximize profits, and minimize energy consumption. With the various energy supply curves and prices of the data center as boundary conditions, the timing optimization of computing power tasks is carried out to determine the initial scale of the data center and the computing power load demand curve. The energy multi-objective planning and design module calculates the cold load, heat load and other electrical loads of the data center according to the computing power load demand curve, and uses this as the boundary condition, with the maximum profit and the minimum carbon emissions during the planning period as the goal, to optimize the energy supply facility capacity of the data center energy supply system, the scale of public grid thermal power and green power transactions, the scale of natural gas and hydrogen transactions, etc. The multi-objective planning and design of computing power and the multi-objective planning and design of energy use computing power tasks and energy supply as coupling points and boundary interactions, and through multiple iterations, finally optimize the optimal planning scheme for computing network energy integration.

[0213] like Figure 2 , Figure 3 As shown, an embodiment of the present invention provides a computing network collaborative planning device that considers the timing scheduling of computing tasks. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown, a hardware architecture diagram of an electronic device where a computing network collaborative planning device is located considering the timing scheduling of computing tasks provided by an embodiment of the present invention is provided. Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the electronic device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, the CPU of the electronic device in which it is located reads the corresponding computer program in the non-volatile memory into the internal memory and runs it.

[0214] like Figure 3 As shown, this embodiment provides a computing network collaborative planning device that considers the timing scheduling of computing tasks, and the device includes:

[0215] A model building module 300 is used to establish a computing power network planning model and an energy supply planning model; wherein the computing power network planning model includes a first upper layer model and a first lower layer model, and the energy supply planning model includes a second upper layer model and a second lower layer model;

[0216] The first data processing module 302 is used for the first upper-layer model to determine the center computing power scale with the maximum data center revenue and the minimum carbon emission as the objective function, and with the delay constraint and the computing power scale constraint as the constraint conditions, and send the center computing power scale to the first lower-layer model;

[0217] The second data processing module 304 is used for the first lower-layer model to determine the carbon emissions and profits of the data center with the maximum revenue and the minimum carbon emissions of the data center as the objective function, and the energy constraint and the computing power scale constraint as the constraint conditions, and send the carbon emissions and profits of the data center to the first upper-layer model;

[0218] The third data processing module 306 is used to repeatedly iterate the first upper model and the first lower model to determine a first optimal solution; wherein the first optimal solution includes a computing power equipment planning scheme and a computing power energy consumption load curve, and the computing power energy consumption load curve is used as an input of an energy supply planning model to perform coupling interaction;

[0219] The fourth data processing module 308 is used for the second upper model to determine the equipment configuration strategy and planning capacity with the maximum profit and the minimum carbon emission in the planning period as the objective function, and the upper limit constraint of the equipment planning capacity and the equipment commissioning constraint as the constraint conditions, and send the equipment configuration strategy and planning capacity to the second lower model;

[0220] A fifth data processing module 310 is used for the second lower-layer model to determine the optimal operation plan and the total operation cost with the minimum operation cost as the objective function, the power balance constraint, the equipment operation constraint, the power exchange constraint between the energy system and the upper network, and the green power proportion constraint as the constraint conditions, and send the optimal operation plan and the total operation cost to the second upper-layer model;

[0221] The sixth data processing module 312 is used for repeatedly iterating the second upper model and the second lower model to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of a power generation device, a public grid thermal power curve, and a green electricity trading curve, and the power curve of the power generation device, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a computing power network planning model for coupling interaction;

[0222] The loop coupling interaction module 314 is used for loop coupling interaction between the computing power network planning model and the energy supply planning model, and has realized the computing power network energy collaborative planning of the computing power task timing scheduling.

[0223] In an embodiment of the present invention, the model building module 300 can be used to execute step 100 in the above method embodiment, the first data processing module 302 can be used to execute step 102 in the above method embodiment, the second data processing module 304 can be used to execute step 104 in the above method embodiment, the third data processing module 306 can be used to execute step 106 in the above method embodiment, the fourth data processing module 308 can be used to execute step 108 in the above method embodiment, the fifth data processing module 310 can be used to execute step 110 in the above method embodiment, the sixth data processing module 312 can be used to execute step 112 in the above method embodiment, and the loop coupling interaction module 314 can be used to execute step 114 in the above method embodiment.

Claims

1. A computing network collaborative planning method considering the timing scheduling of computing tasks, characterized in that: The method is applied to a single-node computing network, and includes: Establishing a computing power network planning model and an energy supply planning model; wherein the computing power network planning model includes a first upper layer model and a first lower layer model, and the energy supply planning model includes a second upper layer model and a second lower layer model; The first upper-layer model takes the maximum data center revenue and the minimum carbon emission as the objective function, takes the latency constraint and the computing power scale constraint as the constraint conditions, determines the center computing power scale, and sends the center computing power scale to the first lower-layer model; The first lower-layer model takes the maximum revenue and minimum carbon emissions of the data center as the objective function, takes the energy constraint and the computing power scale constraint as the constraint conditions, determines the carbon emissions and profits of the data center, and sends the carbon emissions and profits of the data center to the first upper-layer model; The first upper model and the first lower model are repeatedly iterated to determine a first optimal solution; wherein the first optimal solution includes a computing power equipment planning scheme and a computing power energy consumption load curve, and the computing power energy consumption load curve is used as an input of an energy supply planning model to perform coupling interaction; The second upper model takes the maximum profit and the minimum carbon emission in the planning period as the objective function, takes the upper limit constraint of the equipment planning capacity and the equipment commissioning constraint as the constraint conditions, determines the equipment configuration strategy and the planning capacity, and sends the equipment configuration strategy and the planning capacity to the second lower model; The second lower-layer model takes the minimum operating cost as the objective function, and takes the power balance constraint, the equipment operation constraint, the power exchange constraint between the energy system and the upper network, and the green power proportion constraint as the constraint conditions to determine the optimal operating plan and the total operating cost, and sends the optimal operating plan and the total operating cost to the second upper-layer model; The second upper model and the second lower model are repeatedly iterated to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a computing power network planning model for coupling interaction; The computing power network planning model and the energy supply planning model are cyclically coupled and interacted, and computing network energy collaborative planning for computing power task timing scheduling has been realized.

2. The method according to claim 1, characterized in that: The objective function is to maximize the data center's revenue and minimize carbon emissions, including the following formula: max f ntp S t,c =C t,c ·(E t,nom -E t,co2 ) / 10000 f ep =f in -f c f tp =f ep -f ep ·l vat ·l umt -f ep ·l vat ·l est f ntp =(f ep -f ep ·l vat ·l umt -f ep ·l vat ·l est )·(1-λ int ) In the formula, The number of production facilities supplying the i-th energy source put into operation in year t; The capacity of the selected model of production equipment for the i-th energy supply; is the number of the i-th energy storage device put into operation in year t; is the capacity of the model selected for the i-th energy storage device; C inv is the initial investment cost; T is the life cycle; r is the capital discount rate; is the investment cost in year t; α lr,t is the loan ratio in the tth year; i is the number of energy supply production equipment; Ω1 is the set of energy supply production equipment; Unit capacity investment cost of production equipment for the i-th energy supply; is the investment and construction capacity of the i-th energy supply production equipment in year t; j is the energy storage equipment number; Ω2 is the energy storage equipment set; is the unit capacity investment cost of the jth energy storage device; is the investment capacity of the j-th energy storage device in year t, The annual operation and maintenance cost per unit capacity of the production equipment for the i-th energy supply; is the annual operation and maintenance cost per unit capacity of the j-th energy storage device, is the purchase cost of public grid thermal power in year t, is the medium- and long-term transaction cost of the i-th green electricity in the t-th year, is the normal green electricity purchase cost in year t, is the penalty cost for the deviation of the i-th green power medium- and long-term transaction in the t-th year, is the natural gas purchase cost in year t, is the hydrogen purchase cost in year t, S is the number of typical daily scenarios each year; N s is the number of days of a typical day scene s in a year; H is the total number of time periods in a day, C e,t,s,h P is the unit price of public grid electricity purchased in the h period of the typical day scenario in the tth year; e,t,s,h is the amount of electricity purchased from the public grid in the typical day scenario h period in year t, C le,t,i,s,h P is the purchase price of the i-th green power medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; le,t,i,s,h is the mid- to long-term green electricity purchase volume in the i-th period of the typical day scenario in the t-th year, C ge,t,s,h P is the normal green electricity purchase price in the h period of the typical day scenario in the tth year; ge,t,s,h is the normal green electricity purchase amount in the h period of the typical day scenario in the tth year, C g,t,s,h G is the natural gas purchase price in the typical day scenario h period in year t; g,t,s,h is the natural gas purchase volume in the typical day scenario h period in year t, is the unit price of hydrogen purchased in the typical day scenario h period in year t; is the amount of hydrogen purchased in the typical day scenario h period in year t, C le,t,i,s,h,dp The penalty unit price for deviation of the i-th green power medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; P le,t,i,s,h,dp P is the deviation of the i-th green electricity medium- and long-term transaction caused by the responsibility of the power purchaser in the h-th period of the typical day scenario in the t-th year; lest,t,i,s,h The transaction volume of the i-th green power medium- and long-term trading contract in the h-th period of the typical day scenario in the t-th year; Pl egm,t,i,s,h The amount of green electricity provided by medium- and long-term transactions in the i-th period of time in the typical day scenario of the t-th year, C fc,t is the financial cost in year t; r i,tt is the annual loan interest rate of the loan in year tt; C inv,tt is the investment cost in year tt; α lr,tt is the loan ratio in year tt; N tt is the repayment period of the loan in year tt, f c is the total investment cost of the integrated energy system; f t,c is the investment cost in year t, SR t is the energy supply income in year t, C e,t,s,h,S are the electricity selling price in the typical day scenario h period in year t; P eL,t,s,h P is the other electrical load in the typical day scenario h period in year t; cpeL,t,s,h HPR is the power load consumed by computing equipment in the typical day scenario h period of year t; FJ is the heating price per unit area; CPR FJ is the cooling price per unit area; A r,HFJ is the heating area; A r,CFJ is the cooling area, S t,up is the surplus Internet access income in the tth year; S is the number of typical daily scenarios each year; N s is the number of days of a typical day scene s in a year; H is the total number of time periods in a day, C up,e,t,s,h is the unit price of surplus electricity on-grid in the h period of the typical day scenario in the tth year; P up,e,t,s,h is the online power consumption in the typical day scenario h period in year t, where: S inv Subsidy for total investment; is the investment subsidy for the tth year; i is the number of the energy supply production equipment; Ω1 is the energy supply production equipment set; Subsidy for initial investment per unit capacity of production equipment for the i-th energy supply; is the investment and construction capacity of the i-th energy supply production equipment in year t; j is the energy storage equipment number; Ω2 is the energy storage equipment set; is the initial investment subsidy per unit capacity of the jth energy storage device; is the investment capacity of the j-th energy storage device in year t, S t,sub is the power generation subsidy income in the tth year; S is the number of typical daily scenarios each year; N s is the number of days of a typical day scene s in a year; H is the total number of time periods in a day, C wt,e,t,s,h , C pv,q,t,s,h are the electricity subsidies for wind power generation and photovoltaic power generation in the h period of the typical day scenario in the tth year; P wt,e,t,s,h , P pv,e,t,s,h are the wind power generation and photovoltaic power generation in the typical day scenario h period in year t, S t,c is the environmental benefit in year t; C t,c is the carbon trading price in year t; E t,nom is the carbon quota for year t; E t,co2 is the carbon dioxide emissions in year t; The emission coefficient of carbon dioxide generated by the consumption of electricity; is the emission coefficient of carbon dioxide produced by the i-th energy supply production equipment consuming natural gas, S t,i,le,dp is the deviation income of the i-th green power medium- and long-term transaction in the t-th year, C le,t,i,s,h,dp The penalty unit price for deviation of the i-th green power medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year; P le,t,i,s,h,dp P is the deviation of the i-th green electricity medium- and long-term transaction caused by the responsibility of the power purchaser in the h-th period of the typical day scenario in the t-th year; lest,t,i,s,h The transaction volume of the i-th green power medium- and long-term trading contract in the h-th period of the typical day scenario in the t-th year; P legm,t,i,s,h The amount of green electricity provided by medium- and long-term transactions in the i-th period of time in the typical day scenario of the t-th year, E RV is the total residual value of the comprehensive energy system equipment at the end of the planning period, M n is the total number of equipment in the integrated energy system, δ i is the residual value rate of the ith equipment, C INV,i is the initial investment of the ith equipment, f in is the total benefit of the integrated energy system over its entire life cycle; t,in is the total income in year t, f ep Provide comprehensive energy services for the full life cycle economic benefits, ntp is the total net profit over the entire life cycle, λ vat is the value added tax rate, λ umt Maintaining tax rates for urban construction, λ est is the education fee surcharge rate, which is 5%, int is the income tax rate, f ntp is the total net profit over the entire life cycle, f e is the carbon dioxide emission, T is the planning period, i.e. the total planning period; is the CO2 emission in the tth year; S is the number of typical daily scenes each year; N s is the number of days of the typical day scene s in a year; H is the total number of time periods in a day; The emission coefficient of carbon dioxide generated by consuming public grid electricity; is the emission coefficient of carbon dioxide produced by natural gas.

3. The method according to claim 2, characterized in that The maximum profit during the planning period is determined by the following formula: Cost e (t)=E(t)P(t) Income t =P task *T running TrueProfit Total =Profit Total *(1-λ) In the formula, Cost Device Cost represents the total initial equipment purchase cost. Device,l represents the total initial equipment acquisition cost of the data center, Indicates the number of CPU computing devices of type a in the data center, Indicates the number of GPU computing devices of type b in the data center, Indicates the number of c types of network devices in the data center, Indicates the number of type d cabinets in the data center, Indicates the price of type a CPU computing equipment in a data center, Indicates the price of GPU computing equipment of type b in the data center, represents the price of type c network equipment in the data center, Indicates the price of d types of cabinet equipment in the data center, represents the planned maintenance cost in year t, Indicates the number of CPU computing devices of type a in the data center, Indicates the number of GPU computing devices of type b in the data center, Indicates the number of c types of network devices in the data center, Indicates the number of type d cabinets in the data center, represents the maintenance cost of type a CPU computing equipment in the data center in year t, represents the maintenance cost of type b GPU computing equipment in the data center in year t, represents the maintenance cost of type c network equipment in the data center in year t, represents the maintenance cost of type d cabinet equipment in the data center in year t, Indicates the annual equipment power consumption cost, HPR FJ represents the heating price per unit area of ​​the data center, A r,HFJ Indicates the heating area of ​​the data center, CPR FJ Table 2. Cooling price per unit area, A r,CFJ represents the cooling area of ​​the data center, w type represents the energy efficiency coefficient of the data center in different seasons, where w type The default value is 1.05, c e,t represents the terminal power supply price of the data center in year t, P request represents the energy consumption of the computing power request equipment in the data center in year t, P basic represents the basic energy consumption of data center equipment in year t, Cost e，total is the total electricity price, T cycle is the life cycle in years, is the total electricity price of the data center corresponding to the typical day scei, T scei is the number of days of a typical day, Cost is the total electricity price of the data center in one day. e (t) The electricity price of data center m at time slot t, represents the electricity price of the data center, P(t) represents the electricity price of the data center at time t, S t,vs is the value-added service revenue in year t, β vs The value-added service revenue ratio coefficient for the data center. The comprehensive energy purchase cost of the data center. Represents the total cost of the data center, Energy t represents the total power consumption of all devices in the data center in year t, Expense t Cost represents the construction cost of the data center in year t. Inv Indicates the initial investment cost, Cost Device represents the total initial equipment acquisition cost of the data center, represents the total cost of the data center. Considering the delay in project planning and construction, the discount rate needs to be increased. r is the capital discount rate, and α is the data center loan ratio. is the financial cost in year t, is the financial cost of the data center in year t, rate is the annual loan interest rate of the data center loan, Cost Inv is the data center investment cost, α is the data center loan ratio, lim is the repayment period of the data center loan, Cost Total The total cost of the planning cycle of the comprehensive computing network system, Income t represents the computing power income in the tth year, P task represents the unit price of computing power in the data center in year t, T running represents the planned computing power demand in the center in year t, Residual is the total residual value of the computing network system equipment at the end of the planning period, H is the total number of equipment in the computing network system and H = A + B + C + D, β i is the residual value rate of the i-th equipment, Price i is the initial purchase price of the i-th device, Income Total is the total income of the whole life cycle of the network system, r is the capital discount rate, To calculate the economic benefits of the entire life cycle of the network system, TrueProfit Total is the total net profit over the entire life cycle, and λ is the income tax rate.

4. The method according to claim 3, characterized in that The power balance constraints include electric power balance constraints, thermal power balance constraints, cold power balance constraints, natural gas power balance constraints and hydrogen power balance constraints, and the power balance constraints are determined by the following formula: Where Ω4 is the energy supply production equipment set that generates electricity, Ω5 is the energy storage equipment set, Ω6 is the energy supply production equipment set that consumes electricity, and P e,m,t,s,h P is the power generated by the mth energy supply production equipment generating electricity in the hth period of the typical day scenario in the tth year, e,j,ES-dis,t,s,h is the discharge power of the jth storage device in the hth period of the typical day scenario in the tth year, P e,k,t,s,h P is the power consumed by the kth energy supply device consuming electricity in the hth period of the typical day scenario in the tth year, eL,t,s,h is the other power load in the typical day scenario h period in year t, P e,j,ES-ch,t,s,h is the charging power of the jth energy storage device in the hth period of the typical day scenario in the tth year, P cpeL,t,s,h is the power load consumed by the computing equipment in the typical day scenario h period of year t, Ω7 is the set of energy supply and production equipment that generates heat energy, Ω8 is the set of heat storage equipment, Ω9 is the set of energy supply and production equipment that consumes heat energy, Q q,i,t,s,h Q is the thermal power generated by the i-th energy supply production equipment generating heat energy in the typical day scenario h period in the t-th year, q,j,HS-dis,t,s,h Q is the heat release power of the jth heat storage device in the hth period of the typical day scenario in the tth year, q,k,t,s,h Q is the heat load of the kth energy supply production equipment consuming heat energy in the typical day scenario h period in year t, qL,t,s,h is the heat load of the typical day scenario h period in year t, Q q,j,HS-ch,t,s,h is the charging power of the jth heat storage device in the hth period of the typical day scenario in the tth year, Ω 10 A collection of energy supply production equipment for generating cold energy, Ω 11 C is a collection of cold storage equipment. c,i,t,s,h is the cooling power generated by the i-th energy supply production equipment generating cooling energy in the typical day scenario h period in year t, C c,j,CS-dis,t,s,h is the cooling power of the jth cold storage device in the hth period of the typical day scenario in the tth year, C cL,t,s,h is the cooling load of the typical day scenario h period in year t, C c,j,CS-ch,t,s,h is the charging power of the jth cold storage device in the hth period of the typical day scenario in the tth year, Ω3 is the set of energy supply production equipment consuming natural gas, and Ω 12 G is the gas storage equipment set. g,t,s,h is the natural gas power purchased in the typical day scenario h period in year t, G g,i,GS-dis,t,s,h G is the gas discharge power of the jth gas storage device in the hth period of the typical day scenario in the tth year, g,j,t,s,h is the natural gas power of the energy supply production equipment consuming natural gas in the jth natural gas consumption period in the typical day scenario of the tth year, G g,i,GS-ch,t,s,h The charging power of the jth gas storage device in the hth period of the typical day scenario in the tth year, Ω 14 A collection of energy supply production equipment for consuming hydrogen, Ω 13 For hydrogen storage equipment collection, is the amount of hydrogen purchased in the typical day scenario h period in year t, is the hydrogen discharge power of the jth hydrogen storage device in the hth period of the typical day scenario in the tth year, The hydrogen power of the energy supply production equipment for the jth energy consumption of hydrogen energy in the typical day scenario h period in year t is The hydrogen charging power of the jth hydrogen storage device in the h period of the typical day scenario in the tth year.

5. The method according to claim 4, characterized in that The upper limit constraint of the equipment planning capacity is determined by the following formula: In the formula, They are the planned capacity upper limits of energy production equipment and energy storage equipment respectively.

6. The method according to claim 5, characterized in that The power exchange constraint between the energy system and the upper network is determined by the following formula: 0≤P le,t,i,s,h ≤P legm,t,i,s,h In the formula, are the minimum and maximum power purchases from the thermal power grid respectively; The minimum and maximum purchased natural gas power respectively; The minimum and maximum purchased hydrogen power respectively; are the minimum and maximum constant green electricity purchase power respectively; P le,t,i,s,h P is the green electricity mid- and long-term transaction purchase volume of the ith green electricity in the h-th period of the s-th typical day scenario in the t-th year; legm,t,i,s,h The amount of green electricity provided in the i-th green electricity medium- and long-term transaction in the h-th period of the typical day scenario in the t-th year.

7. The method according to claim 6, characterized in that The equipment operation constraints are determined by the following formula: In the formula, is the minimum value of the amount of electricity provided by the i-th green electricity medium- and long-term transaction; The maximum electric power consumed by the kth energy supply device that consumes electric energy in the tth year; The maximum thermal power of the production equipment supplied by the kth energy source consuming heat energy in year t; The maximum natural gas power of the energy supply production equipment for the jth natural gas consuming energy in year t; The maximum hydrogen power of the production equipment supplied by the energy source for the jth hydrogen-consuming energy source in year t; are the rated capacities of the electricity, heat and cold energy production equipment in year t, They are the peak values ​​of other power loads, power load consumed by computing equipment, heat load, and cold load in year t respectively.

8. A computing network collaborative planning device considering the timing scheduling of computing tasks, characterized in that: include: A model building module, used to establish a computing power network planning model and an energy supply planning model; wherein the computing power network planning model includes a first upper layer model and a first lower layer model, and the energy supply planning model includes a second upper layer model and a second lower layer model; A first data processing module is used for the first upper-layer model to determine the center computing power scale with the maximum data center revenue and the minimum carbon emission as the objective function, and with the delay constraint and the computing power scale constraint as the constraint conditions, and send the center computing power scale to the first lower-layer model; A second data processing module is used for the first lower-layer model to determine the carbon emissions and profits of the data center with the maximum data center revenue and the minimum carbon emissions as the objective function, and with the energy constraint and the computing power scale constraint as the constraint conditions, and to send the carbon emissions and profits of the data center to the first upper-layer model; A third data processing module is used for repeatedly iterating the first upper model and the first lower model to determine a first optimal solution; wherein the first optimal solution includes a computing power equipment planning scheme and a computing power energy consumption load curve, and the computing power energy consumption load curve is used as an input of an energy supply planning model to perform coupling interaction; A fourth data processing module is used for the second upper-layer model to determine the equipment configuration strategy and planning capacity with the maximum profit and the minimum carbon emission in the planning period as the objective function, and the upper limit constraint of the equipment planning capacity and the equipment commissioning constraint as the constraint conditions, and send the equipment configuration strategy and planning capacity to the second lower-layer model; A fifth data processing module is used for the second lower-layer model to determine the optimal operation plan and the total operation cost with the minimum operation cost as the objective function, the power balance constraint, the equipment operation constraint, the power exchange constraint between the energy system and the upper network, and the green power proportion constraint as the constraint conditions, and send the optimal operation plan and the total operation cost to the second upper-layer model; a sixth data processing module, configured to repeatedly iterate the second upper model and the second lower model to determine a second optimal solution; wherein the second optimal solution includes an energy supply planning scheme, a power curve of power generation equipment, a public grid thermal power curve, and a green electricity trading curve, and the power curve of power generation equipment, the public grid thermal power curve, and the green electricity trading curve are used as inputs of a computing power network planning model for coupling interaction; The loop coupling interaction module is used for loop coupling interaction between the computing power network planning model and the energy supply planning model, and has realized the computing power network collaborative planning of the computing power task timing scheduling.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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