Computing network energy multi-objective collaborative optimization operation method considering computing power task time sequence scheduling

Abstract

The invention relates to the technical field of computers, in particular to a computing network energy multi-objective collaborative optimization operation method considering computing power task time sequence scheduling. According to the method, a calculation network energy multi-target collaborative optimization operation model considering the adjustable calculation power task time sequence is established by taking the maximum benefit of a data center and the minimum carbon emission as targets, and calculation network system optimization operation and energy system optimization operation interact with each other by taking calculation power tasks and energy supply as coupling points and boundaries; the global most solution is realized through computing power-energy collaborative iteration, the problem of inconsistency of operation schemes of traditional independent optimization operation can be overcome, high coupling degree of computing power tasks and renewable energy power generation is realized, the consumption rate of renewable energy is improved, and the energy consumption cost of a data center is reduced. And multi-stage interaction of computing power resource-computing power task-load demand-energy supply is realized, and the overall operation scheme of computing network energy is optimal.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a computing network energy multi-objective collaborative optimization operation method taking into account the timing scheduling of computing tasks. Background Art

[0002] The coordinated optimization operation of computing network and energy includes two parts: the optimization operation of computing power network system and the optimization operation of energy supply system. The optimization of computing power network system is used to meet the needs of various computing power tasks and formulate the optimal computing power task allocation plan. The optimization operation of energy supply system is used to meet the energy needs of computing power system and formulate the optimal electricity, cooling, heat and other energy system supply plans to ensure the safe, economical and reliable use of computing power network system.

[0003] In the current problem of optimizing the operation of computing network, the optimization operation of computing power network system and the optimization operation of energy supply system are usually carried out separately. When the computing power network system is optimized, the computing power task demand is used as the boundary condition, and the economy, environmental protection, and energy consumption are considered to give the optimal computing power task allocation plan, without considering the power supply type and equipment output curve characteristics in the energy supply system. When the energy supply system is optimized, the load demand of the computing power network system is used as the boundary condition, and the economy and environmental protection are considered to give the optimal energy system supply plan. When the computing network is optimized, there is no data interaction and coordination between the optimization operation of computing power network system and the optimization operation of energy supply system.

[0004] In the operation of a single-node computing network, all computing tasks are completed by a single-node data center alone, and 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 scheduling and operation of computing tasks and energy systems in a single-node data center requires comprehensive consideration of the timing optimization of computing tasks and the impact of energy price policies, new energy output, and energy system equipment to avoid independent formulation of operation strategies for computing tasks and energy systems, which results in the inability to achieve optimal matching between computing demand and energy supply systems.

[0005] Based on this, there is an urgent need for a multi-objective collaborative optimization operation method for a computing network that takes into account the timing scheduling of computing tasks to solve the technical problem of how to enable a single-node computing network to achieve collaborative operation. Summary of the invention

[0006] In order to solve the technical problem of how to enable a single-node computing network to achieve collaborative operation, an embodiment of the present invention provides a method for multi-objective collaborative optimization operation of a computing network that takes into account the timing scheduling of computing tasks.

[0007] In a first aspect, an embodiment of the present invention provides a method for multi-objective collaborative optimization operation of a computing network considering the timing scheduling of computing tasks, the method being applied to a single-node computing network, comprising:

[0008] Establishing a multi-objective collaborative optimization model for computing network energy; wherein the multi-objective collaborative optimization model for computing network energy includes an upper layer model and a lower layer model;

[0009] The upper-layer model takes maximum benefit and minimum carbon emission as objective functions, delay constraints, computing power scale constraints and energy supply constraints as constraints, and task timing allocation as decision variables to determine the computing power task allocation plan, and sends the computing power task allocation plan to the lower-layer model;

[0010] The lower-level model takes maximum benefit and minimum carbon emission as the objective function, power balance constraint, power exchange constraint between energy system and upper-level network, surplus power access constraint, and green power ratio constraint as the constraint conditions, and equipment operation strategy as the decision variable to determine the energy supply plan, carbon emissions and operating income, and sends the energy supply plan, carbon emissions and operating income to the upper-level model;

[0011] The upper model and the lower model are repeatedly iterated to determine the optimal solution; wherein the optimal solution includes computing task timing optimization and energy supply curve.

[0012] In a second aspect, an embodiment of the present invention further provides a computing network multi-objective collaborative optimization operation device considering the timing scheduling of computing tasks, including:

[0013] A model building module is used to establish a multi-objective collaborative optimization model for computing network energy; wherein the multi-objective collaborative optimization model for computing network energy includes an upper model and a lower model;

[0014] The first data processing module is used for the upper-layer model to determine the computing power task allocation plan with maximum benefit and minimum carbon emission as the objective function, delay constraint, computing power scale constraint and energy supply constraint as the constraint conditions, and task timing allocation as the decision variable, and send the computing power task allocation plan to the lower-layer model;

[0015] The second data processing module is used for the lower-level model to take maximum benefit and minimum carbon emission as the objective function, power balance constraint, power exchange constraint between energy system and upper-level network, surplus power access constraint, green power proportion constraint as constraint conditions, and equipment operation strategy as decision variables to determine the energy supply plan, carbon emissions and operating income, and send the energy supply plan, carbon emissions and operating income to the upper-level model;

[0016] The third data processing module is used to repeatedly iterate the upper model and the lower model to determine the optimal solution; wherein the optimal solution includes computing task timing optimization and energy supply curve.

[0017] 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.

[0018] 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.

[0019] The embodiment of the present invention provides a method for multi-objective collaborative optimization operation of computing network energy taking into account the timing scheduling of computing power tasks. The present invention takes maximizing the benefits of data centers and minimizing carbon emissions as the goals, and establishes a multi-objective collaborative optimization operation model of computing network energy taking into account the adjustable timing of computing power tasks. The computing network system optimization operation and the energy system optimization operation use computing power tasks and energy supply as coupling points and boundary interactions, and achieves the global optimal solution through computing power-energy collaborative iteration. It can overcome the inconsistency problem of operation schemes of traditional independent optimization operations, achieve a high degree of coupling between computing power tasks and renewable energy power generation, improve the renewable energy consumption rate, reduce the energy consumption 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 operation scheme of computing network energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1 It is a flow chart of a computing network multi-objective collaborative optimization operation method considering computing power task timing scheduling provided by an embodiment of the present invention;

[0022] Figure 2 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0023] Figure 3 It is a structural diagram of a computing network multi-objective collaborative optimization operation device that takes into account the timing scheduling of computing tasks, provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] Please refer to Figure 1 The embodiment of the present invention provides a method for multi-objective collaborative optimization operation of a computing network considering the timing scheduling of computing tasks. The method is applicable to a single-node computing network. The method includes:

[0026] Step 100: Establishing a multi-objective collaborative optimization model for network computing; wherein the multi-objective collaborative optimization model for network computing includes an upper model and a lower model;

[0027] Step 102: The upper model takes maximum benefit and minimum carbon emission as the objective function, takes delay constraint, computing power scale constraint and energy supply constraint as the constraint conditions, takes task timing allocation as the decision variable, determines the computing power task allocation plan, and sends the computing power task allocation plan to the lower model;

[0028] Step 104: The lower-level model takes maximum benefit and minimum carbon emission as the objective function, power balance constraint, power exchange constraint between energy system and upper-level network, surplus power access constraint, and green power ratio constraint as the constraint conditions, and equipment operation strategy as the decision variable to determine the energy supply plan, carbon emission and operation benefit, and sends the energy supply plan, carbon emission and operation benefit to the upper-level model;

[0029] Step 106: The upper model and the lower model are repeatedly iterated to determine the optimal solution; wherein the optimal solution includes computing task timing optimization and energy supply curve.

[0030] In the embodiment of the present invention, the present invention takes maximizing the benefits of data centers and minimizing carbon emissions as the goal, and establishes a multi-objective collaborative optimization operation model of computing network energy that takes into account the adjustable timing of computing power tasks. The optimization operation of the computing network system and the optimization operation of the energy system use computing power tasks and energy supply as coupling points and boundary interactions, and achieves the global optimal solution through computing power-energy collaborative iteration. It can overcome the inconsistency problem of operation plans of traditional independent optimization operations, achieve a high degree of coupling between computing power tasks and renewable energy power generation, improve the renewable energy consumption rate, reduce the energy consumption 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 operation plan of computing network energy.

[0031] Described below Figure 1 How the various steps are performed.

[0032] Regarding step 102:

[0033] In one embodiment of the present invention, the objective function is to maximize the benefits and minimize the carbon emissions, including the following formula:

[0034] max f i (y,e)

[0035] mix f e (y, e)

[0036] f i (y,e)=f in -f c

[0037] f in =f cp_profit +S up +S le,dp +S sub +S c

[0038]

[0039]

[0040] Where max f i (y,e) is the objective function with the maximum benefit, mix f e (y, e) is the objective function of minimizing carbon emissions, f in is the total revenue of the data center, f c is the total cost of the data center, f cp_profit is the computing power income, S up S is the income from the surplus electricity grid connection, le,dp is the deviation income of green power medium and long term trading, S sub is the power generation subsidy income, S c Carbon trading benefits Representative request m The computing power required, stands for data center dc n The unit price of computing resources, Represents the data center dc of the day n The percentage of computing power requests processed in the total number of requests, H is the total number of time periods in a day, C up,e,h is the unit price of surplus electricity on-grid in period h, P up,e,h is the online power in time period h, S i,le,dp is the deviation income of the i-th green power medium- and long-term transaction, C le,i,h,dp The i-th green power medium- and long-term transaction deviation penalty unit price in period h, P legm,i,h,dpis the deviation of the i-th green power medium- and long-term transaction caused by the power purchaser’s responsibility in period h, P lest,i,h The transaction volume of the i-th green power medium- and long-term trading contract in period h, P legm,i,h The i-th green power medium- and long-term trading power supply in period h, H is the total number of periods in a day, C wt,e,h , C pv,e,h are the electricity subsidies for wind power generation and photovoltaic power generation in time period h, P wt,e,h , P pv,e,h are the wind power and photovoltaic power consumed in time period h, respectively, c is the carbon trading price, E com is the carbon quota, E co2 is the carbon dioxide emissions, is the emission coefficient of carbon dioxide generated by the consumption of electricity, is the carbon dioxide emission coefficient of natural gas, C op is the energy purchase cost, C om The maintenance cost of energy equipment, The cost of purchasing thermal power from the public grid, is the mid- and long-term transaction cost of the i-th green electricity, The normal green electricity purchase cost is is the penalty cost for the deviation of the i-th green power medium- and long-term transaction, The cost of purchasing natural gas, is the cost of purchasing hydrogen, H is the total number of hours in a day, C e,h is the unit price of public grid electricity purchase in period h, P e,h is the amount of electricity purchased from the public grid during period h, C le,i,h is the purchase price of the i-th green power medium- and long-term transaction in period h, P le,i,h is the i-th green power medium- and long-term transaction purchase amount in period h, C ge,h is the normal green electricity purchase price in period h, P ge,h is the normal green electricity purchase amount in period h, C le,i,h,dp is the penalty unit price for the deviation of the i-th green power medium- and long-term transaction in period h, P le,i,h,dp is the deviation of the i-th green power medium- and long-term transaction caused by the power purchaser’s responsibility in period h, P lest,i,h The transaction volume of the i-th green power medium- and long-term trading contract in period h, P legm,i,h The amount of green electricity provided by the i-th medium- and long-term trading in period h, C g,h is the natural gas purchase price during period h, G g,h is the natural gas purchase amount in period h, is the unit price of hydrogen gas purchased during the h period, is the amount of hydrogen purchased in period h, i is the number of the energy supply production equipment, Ω 1A collection of production equipment for energy supply, The operation and maintenance cost per unit output energy of the i-th energy supply production equipment, is the output energy of the i-th energy supply production equipment in time period h, j is the number of the energy storage device, Ω 2 A collection of energy storage devices. is the operation and maintenance cost per unit charge and discharge power of the jth energy storage device, is the charging and discharging power of the jth energy storage device in time period h, is CO2 emissions, The emission coefficient of carbon dioxide generated by consuming electricity, is the emission coefficient of carbon dioxide produced by natural gas.

[0041] In one embodiment of the present invention, the delay constraint is

[0042]

[0043] The above formula describes that the time from when the task request arrives at the task entry router to when it is scheduled to the target data center should be less than the maximum tolerable delay of the task, and the task processing time should be after the task arrives. Indicates request r i processing time, Indicates that the task request

[0044] r i The total delay is composed of three parts: transmission delay, propagation delay and calculation delay, which are expressed as follows:

[0045] D total =(D tran +D pro +D com )Y m，l

[0046] Among them, the first item is the transmission delay, that is, the sum of the task passing through the servers on the transmission path, which is related to the bandwidth of the network path; the second item is the propagation delay (the actual physical distance divided by the speed of light); and the third item is the calculation delay, which is related to the task size and offloading decision.

[0047] Computing power scale constraints

[0048] The computing facilities on a single-node data center DC are further represented as:

[0049] Device = ((f, sum)

[0050] Among them, f represents the computing power of this type of device, sum represents the total number of devices of this type of computing devices in the data center, and let d vrepresents an instantiated computing facility object, then the computing facility set on the data center can be represented as Device = {d 1 , d 2 , …, d v Due to the computing power of the data center server, the computing power scale requested by the data center should be smaller than the maximum computing power that the data center can currently provide.

[0051]

[0052] in, Represents the computing resources required by the tasks accumulated in the data center.

[0053] Energy supply constraints

[0054] The energy consumption of the data center in time slot t is defined as the energy consumption generated by the data center processing tasks in the time slot. The energy consumption should be less than the total energy consumption resources that the energy system can provide in the time slot, that is:

[0055]

[0056] Where Q(t) represents the energy supply curve at time slot t.

[0057] Regarding step 104:

[0058] 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:

[0059] P e,h +∑P le,i,h +P ge,h +∑P e,m,h +ΣP e,j,ES-dis,h =ΣP e,k,h +P eL,h +P cpeL,h +ΣP e,j,ES-ch,h

[0060] ΣQ q,i,h +∑Q q,j,HS-dis,h =∑Q q,k,h +Q qL,h +ΣQ q,j,HS-ch,h

[0061] ΣC c,i,h +∑C c,j,HS-dis,h =C cL,h +∑C c,j,HS-ch,h

[0062] G g,h +∑G g,i,GS-dis,h=ΣG g,j,h +ΣG g,i,GS-ch,h

[0063]

[0064] 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,h P is the electric power generated by the mth energy supply production equipment that generates electric energy in time period h, e,j,ES-dis,h is the discharge power of the jth storage device in time period h, P e,k,h P is the power consumed by the kth energy supply device that consumes power in time period h, eL,h is the other electrical load during the h period, P e,j,ES-ch,h is the charging power of the jth storage device in period h, P cpeL,h is the power load consumed by the computing equipment in period h, 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,h The thermal power generated by the energy supply production equipment that generates heat energy in the i-th period h, Q q,j,HS-dis,h is the heat release power of the jth heat storage device in time period h, Q q,k,h The heat load of the energy supply production equipment for the kth energy consuming heat energy in time period h, Q qL,h is the heat load in period t, Q q,j,HS-ch,h is the charging power of the jth heat storage device in time period h, 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,h is the cooling power generated by the energy supply production equipment that generates cooling energy in the i-th period h, C c,j,HS-dis,h is the cooling power of the jth cold storage device in time period h, C cL,h is the cooling load in period h, C c,j,HS-ch,h is the cooling power of the jth cold storage device in time period h Ω 3 A collection of energy supply production equipment consuming natural gas, Ω 12 G is the gas storage equipment set. g,h The natural gas power purchased during the h period, G g,i,GS-dis,h is the gas discharge power of the jth gas storage device in period h, G g,j,h is the natural gas power of the energy supply production equipment consuming natural gas in the jth period h, G g,i,GS-ch,h The filling power of the jth gas storage device in period h Ω 14A collection of energy supply production equipment for consuming hydrogen, Ω 13 is the hydrogen storage equipment set, G H2,h is the amount of hydrogen purchased in period h, is the hydrogen discharge power of the jth hydrogen storage device in period h, The hydrogen power of the energy supply production equipment for the jth hydrogen-consuming energy source in period h, The hydrogen charging power of the jth hydrogen storage device in period h.

[0065] In one embodiment of the present invention, the power exchange constraint between the energy system and the upper network is determined by the following formula:

[0066]

[0067] In the formula, are the minimum and maximum power purchase power of the public grid thermal power, The minimum and maximum purchased natural gas power are respectively, The minimum and maximum purchased hydrogen power are respectively, are the minimum and maximum constant green electricity purchase power, P le,i,h is the i-th green power medium- and long-term transaction purchase amount in period h, P legm,i,h It is the amount of green electricity provided in the i-th medium- and long-term transaction in period h.

[0068] In one embodiment of the present invention, green power ratio constraint

[0069] GE rate ≥GE rate_nor

[0070] In the formula, GE rate_nor The lower limit of green electricity ratio.

[0071] Restrictions on Surplus Power Grid Access

[0072] Different regions have different requirements for the power of surplus power to be connected to the grid. Some regions guarantee full grid access, while some regions set limits on the power of surplus power to be connected to the grid for source-grid-load-storage integrated projects. Therefore, it is necessary to consider the constraints on the power of surplus power to be connected to the grid. The constraints on the power of surplus power to be connected to the grid need to meet two requirements: (1) it must not be greater than the maximum surplus power receiving power constraint of the upper-level network; (2) it must not be greater than a certain percentage of the installed capacity.

[0073]

[0074] P up,e,h ≤τ·S sum,ren

[0075] Where P up,e,h is the online power consumption in time period h (kW); is the maximum residual power received by the upper network (kW); S sum,ren is the sum of the installed capacity of renewable energy power generation (including wind turbines and photovoltaics, kW); τ is the maximum coefficient of surplus power access to the grid in the installed capacity of renewable energy power generation (%).

[0076] Regarding step 106:

[0077] In one embodiment of the present invention, step 106 may specifically include:

[0078] Determine economic and environmental goals based on the upper and lower models;

[0079] Normalize the economic goals and environmental protection goals to obtain normalized results;

[0080] Based on the normalized processing results, the optimal solution is determined.

[0081] In one embodiment of the present invention, the normalized processing result includes a normalized value of an environmental target and a normalized value of an economic target, and the normalized processing result is determined by the following formula:

[0082]

[0083] ω 1 +ω 2 =1

[0084] 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,c ) is the maximum value of the economic objective among all solutions in the current iteration, min i (f i,c ) 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.

[0085] In the embodiment of the present invention, a non-dominated sorting genetic algorithm is used for solving the problem. During the iteration, the economic goal and the environmental goal are added by weighted values. Since the economic goal and the environmental goal have different dimensions, they cannot be added directly. Therefore, the two goals need to be normalized.

[0086] In one embodiment of the present invention, the method adopted by the present invention is solved by the NSGA-II algorithm.

[0087] In this embodiment, in order to solve the multi-objective optimization problem, the present invention adopts the NSGA-II algorithm, which can effectively obtain the Pareto solution (optimal solution) by adding a fast non-dominated sorting algorithm, a crowding distance and an elite retention strategy. In the non-dominated sorting genetic algorithm that can be run collaboratively by the computing network, the crowding degree comparison method is used to replace the shared function method in NSGA, which eliminates the algorithm performance being too dependent on the shared parameter σ to a certain extent. share The new algorithm can maintain the diversity among the group members without the need for the algorithm user to define the parameters. In addition, the algorithm network can cooperate with the non-dominated sorting genetic algorithm, which has a lower computational complexity.

[0088] In summary, the multi-objective collaborative optimization operation method of computing network energy considering the timing scheduling of computing power tasks takes into account the mutual influence of the adjustability of computing power task timing and the supply of energy system, and takes the maximum benefit of data center and the lowest carbon emission as multiple objectives. The multi-objective collaborative optimization operation model of computing network energy considering the adjustable timing of computing power tasks is established. The optimization operation of the computing network system and the optimization operation of the energy system interact through computing power tasks and energy supply as coupling points and boundaries, and the global optimal Pareto solution set is achieved through computing power-energy collaborative iteration. It can overcome the inconsistency problem of operation schemes of traditional independent optimization operation, achieve a high coupling degree between computing power tasks and renewable energy power generation, improve the consumption rate of renewable energy, reduce the energy cost of data centers, and realize multi-level interaction of computing power resources-computing power tasks-load demand-energy supply to achieve the optimal overall operation scheme of computing network energy.

[0089] like Figure 2 , Figure 3 As shown, the embodiment of the present invention provides a computing network multi-objective collaborative optimization operation 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 multi-objective collaborative optimization operation device is located considering the timing scheduling of computing tasks provided by an embodiment of the present invention is provided. Figure 3 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.

[0090] like Figure 3 As shown, this embodiment provides a computing network multi-objective collaborative optimization operation device that considers the timing scheduling of computing tasks, and the device includes:

[0091] The model building module 300 is used to establish a multi-objective collaborative optimization model for computing network energy; wherein the multi-objective collaborative optimization model for computing network energy includes an upper model and a lower model;

[0092] The first data processing module 302 is used for the upper-layer model to determine the computing power task allocation plan with maximum benefit and minimum carbon emission as the objective function, delay constraint, computing power scale constraint and energy supply constraint as the constraint conditions, and task timing allocation as the decision variable, and send the computing power task allocation plan to the lower-layer model;

[0093] The second data processing module 304 is used for the lower-level model to take maximum benefit and minimum carbon emission as the objective function, power balance constraint, power exchange constraint between energy system and upper-level network, surplus power grid-connected constraint, green power proportion constraint as constraint conditions, and equipment operation strategy as decision variables, determine the energy supply plan, carbon emission and operation income, and send the energy supply plan, carbon emission and operation income to the upper-level model;

[0094] The third data processing module 306 is used to repeatedly iterate the upper model and the lower model to determine the optimal solution; wherein the optimal solution includes computing task timing optimization and energy supply curve.

[0095] 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, and the third data processing module 306 can be used to execute step 106 in the above method embodiment.

[0096] In one embodiment of the present invention, the maximum benefit and the minimum carbon emission are the objective functions, including the following formula:

[0097] max f i (y,e)

[0098] mix f e (y, e)

[0099] f i (y,e)=f in -f c

[0100] f in =f cp_profit +S up+S le,dp +S sub +S c

[0101]

[0102]

[0103] In the formula, max f i (y, e) is the objective function with the maximum benefit, mix f e (y, e) is the objective function with the minimum carbon emission, f in is the total benefit of the data center, f c is the total cost of the data center, f cp_profit is the computing power benefit, S up is the benefit of selling surplus electricity back to the grid, S le,dp is the benefit of the deviation of medium- and long-term green electricity trading, S sub is the power generation subsidy benefit, S c is the carbon trading benefit, where C rm represents the computing power demand of request r m represents the unit price of computing power resources of data center dc n represents the percentage of the computing power requests processed by data center dc n on the day in the total request volume, H is the total number of time periods in a day, C up,e,h is the unit price of selling surplus electricity back to the grid in the h-th time period, P up,e,h is the grid-connected power in the h-th time period, S i,le,dp is the i-th deviation benefit of medium- and long-term green electricity trading, C le,i,h,dp is the i-th deviation penalty unit price of medium- and long-term green electricity trading in the h-th time period, P legm,i,h,dp is the i-th deviation volume of medium- and long-term green electricity trading caused by the responsibility of the electricity purchaser in the h-th time period, P lest,i,h is the i-th contract trading volume of medium- and long-term green electricity trading in the h-th time period, P legm,i,h is the i-th electricity supply volume of medium- and long-term green electricity trading in the h-th time period, H is the total number of time periods in a day, C wt,e,h 、C pv,e,h are the per-kWh subsidies for wind power generation and photovoltaic power generation in the h-th time period respectively, P wt,e,h 、P pv,e,h are the wind power generation power and photovoltaic power generation power consumed in the h-th time period respectively, C c is the carbon trading price, E com is the carbon quota, E co2 is the carbon dioxide emission volume, is the emission coefficient of carbon dioxide generated by consuming electric energy, is the carbon dioxide emission coefficient of natural gas, C​​op is the energy acquisition cost, C om is the energy equipment maintenance cost is the cost of purchasing thermal power from the public grid is the medium- and long-term trading cost of the i-th green power is the cost of purchasing green power at all times is the deviation penalty cost of the i-th green power medium- and long-term trading is the cost of purchasing natural gas is the cost of purchasing hydrogen. H is the total number of time periods in a day, C e,h is the unit price of purchasing public grid power at time period h, P e,h is the quantity of public grid power purchased at time period h, C le,i,h is the unit price of purchasing the i-th green power medium- and long-term trading at time period h, P le,i,h is the quantity of the i-th green power medium- and long-term trading purchased at time period h, C ge,h is the unit price of purchasing green power at all times at time period h, P ge,h is the quantity of green power purchased at all times at time period h, C le,i,h,dp is the deviation penalty unit price of the i-th green power medium- and long-term trading at time period h, P le,i,h,dp is the deviation quantity of the i-th green power medium- and long-term trading caused by the responsibility of the power purchaser at time period h, P lest,i,h The contract trading volume of the i-th green power medium- and long-term trading at time period h, P legm,i,h The power supply quantity of the i-th green power medium- and long-term trading at time period h, C g,h is the unit price of purchasing natural gas at time period h, G g,h is the quantity of natural gas purchased at time period h is the unit price of purchasing hydrogen at time period h, G H2,h is the quantity of hydrogen purchased at time period h. i is the energy supply production equipment number, Ω 1 is the set of energy supply production equipment is the operation and maintenance cost per unit output energy of the i-th energy supply production equipment is the output energy of the i-th energy supply production equipment at time period h. j is the energy storage equipment number, Ω 2 is the set of energy storage equipment is the operation and maintenance cost per unit charge-discharge power of the j-th energy storage equipment is the charge-discharge power of the j-th energy storage equipment at time period h is the CO2 emission is the emission coefficient of carbon dioxide generated by consuming electric energy is the emission coefficient of carbon dioxide generated by natural gas

[0104] In one embodiment of the present invention, the third data processing unit repeatedly iterates when executing the upper model and the lower model to determine the optimal solution, and is used to perform the following operations:

[0105] Determine economic goals and environmental protection goals based on the upper model and the lower model;

[0106] Normalizing the economic target and the environmental target to obtain a normalized result;

[0107] Based on the normalization result, the optimal solution is determined.

[0108] In one embodiment of the present invention, the normalized processing result includes a normalized value of an environmental target and a normalized value of an economic target, and the normalized processing result is determined by the following formula:

[0109]

[0110] ω 1 +ω 2 =1

[0111] 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 current iteration, is the normalized value of the environmental protection target of the i-th solution in the current iteration, max i (f i,c ) is the maximum value of the economic objective among all solutions in the current iteration, min i (f i,c ) 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.

[0112] 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, and the power balance constraint is determined by the following formula:

[0113] P e,h +ΣP le,i,h +P ge,h +ΣP e,m,h +∑P e,j,ES-dis,h =∑P e,k,h +P eL,h +PcpeL,h +∑P e,j,ES-ch,h

[0114] ∑Q q,i,h +∑Q q,j,HS-dis,h =∑Q q,k,h +Q qL,h +∑Q q,j,HS-ch,h

[0115] ∑C c,i,h +∑C c,j,HS-dis,h =C cL,h +∑C c,j,HS-ch,h

[0116] G g,h +∑G g,i,GS-dis,h =∑G g,j,h +∑G g,i,GS-ch,h

[0117]

[0118] 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,h P is the electric power generated by the mth energy supply production equipment that generates electric energy in time period h, e,j,ES-dis,h is the discharge power of the jth storage device in time period h, P e,k,h P is the power consumed by the kth energy supply device that consumes power in time period h, eL,h is the other electrical load during the h period, P e,j,ES-ch,h is the charging power of the jth storage device in period h, P cpeL,h is the power load consumed by the computing equipment in period h, 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,h The thermal power generated by the energy supply production equipment that generates heat energy in the i-th period h, Q q,j,HS-dis,h is the heat release power of the jth heat storage device in time period h, Q q,k,h The heat load of the energy supply production equipment for the kth energy consuming heat energy in time period h, Q qL,h is the heat load in period t, Q q,j,HS-ch,h is the charging power of the jth heat storage device in time period h, 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,h is the cooling power generated by the energy supply production equipment that generates cooling energy in the i-th period h, Cc,j,HS-dis,h is the cooling power of the jth cold storage device in time period h, C cL,h is the cooling load in period h, C c,j,HS-ch,h is the cooling power of the jth cold storage device in time period h Ω 3 A collection of energy supply production equipment consuming natural gas, Ω 12 G is the gas storage equipment set. g,h The natural gas power purchased during the h period, G g,i,GS-dis,h is the gas discharge power of the jth gas storage device in period h, G g,j,h is the natural gas power of the energy supply production equipment of the jth natural gas consumption in period h, G g,i,GS-ch,h The filling power of the jth gas storage device in period h 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 period h, is the hydrogen discharge power of the jth hydrogen storage device in period h, The hydrogen power of the energy supply production equipment for the jth hydrogen-consuming energy source in period h, The hydrogen charging power of the jth hydrogen storage device in period h.

[0119] In one embodiment of the present invention, the power exchange constraint between the energy system and the upper network is determined by the following formula:

[0120]

[0121] 0≤P le,i,h ≤P legm,i,h

[0122] In the formula, are the minimum and maximum power purchase power of the public grid thermal power, The minimum and maximum purchased natural gas power are respectively, The minimum and maximum purchased hydrogen power are respectively, are the minimum and maximum long-term green electricity purchase power, P le,i,h is the i-th green power medium- and long-term transaction purchase amount in period h, P legm,i,h It is the amount of green electricity provided in the i-th medium- and long-term transaction in period h.

[0123] In one embodiment of the present invention, the method is solved by the NSGA-II algorithm.

[0124] It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a computing network multi-objective collaborative optimization operation device that considers the timing scheduling of computing tasks. In other embodiments of the present invention, a computing network multi-objective collaborative optimization operation device that considers the timing scheduling of computing tasks may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure can be implemented in hardware, software, or a combination of software and hardware.

[0125] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0126] An embodiment of the present invention also 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, a computing network multi-objective collaborative optimization operation method considering the timing scheduling of computing power tasks in any embodiment of the present invention is implemented.

[0127] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a computing network multi-objective collaborative optimization operation method that considers the timing scheduling of computing power tasks in any embodiment of the present invention.

[0128] Specifically, a system or device equipped with a storage medium can be provided, on which software program codes that implement the functions of any of the above-mentioned embodiments are stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program codes stored in the storage medium.

[0129] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0130] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0131] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0132] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0133] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0134] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-objective collaborative optimization operation method for computing network energy considering the timing scheduling of computing tasks, characterized in that: The method is applied to a single-node computing network, and includes: Establishing a multi-objective collaborative optimization model for computing network energy; wherein the multi-objective collaborative optimization model for computing network energy includes an upper layer model and a lower layer model; The upper-layer model takes maximum benefit and minimum carbon emission as objective functions, delay constraints, computing power scale constraints and energy supply constraints as constraints, and task timing allocation as decision variables to determine the computing power task allocation plan, and sends the computing power task allocation plan to the lower-layer model; The lower-level model takes maximum benefit and minimum carbon emission as the objective function, power balance constraint, power exchange constraint between energy system and upper-level network, surplus power access constraint, and green power ratio constraint as the constraint conditions, and equipment operation strategy as the decision variable to determine the energy supply plan, carbon emissions and operating income, and sends the energy supply plan, carbon emissions and operating income to the upper-level model; The upper model and the lower model are repeatedly iterated to determine the optimal solution; wherein the optimal solution includes computing task timing optimization and energy supply curve.

2. The method according to claim 1, characterized in that The objective function is to maximize the benefits and minimize the carbon emissions, including the following formula: max f i (y,e) mix f e (y,e) f i (y,e)=f in -f c f in =f cp_profit +S up +S le,dp +S sub +S c S c =C c ·(E nom -E co2 ) / 10000 f c =C op +C om Where max f i (y,e) is the objective function with the maximum benefit, mixf e (y, e) is the objective function of minimizing carbon emissions, f in is the total revenue of the data center, f c is the total cost of the data center, f cp_profit is the computing power income, S up S is the income from the surplus electricity grid connection, le,dp is the deviation income of green power medium and long term trading, S sub is the power generation subsidy income, S c Carbon trading benefits Representative request m The computing power required, stands for data center dc n The unit price of computing resources, Represents the data center dc of the day n The percentage of computing power requests processed in the total number of requests, H is the total number of time periods in a day, C up,e,h is the unit price of surplus electricity on-grid in period h, P up,e,h is the online power in time period h, S i,le,dp is the deviation income of the i-th green power medium- and long-term transaction, C le,i,h,dp The i-th green power medium- and long-term transaction deviation penalty unit price in period h, P legm,i,h,dp is the deviation of the i-th green power medium- and long-term transaction caused by the power purchaser’s responsibility in period h, P lest,i,h The transaction volume of the i-th green power medium- and long-term trading contract in period h, P legm,i,h The i-th green power medium- and long-term trading power supply in period h, H is the total number of periods in a day, C wt,e,h , C pv,e,h are the electricity subsidies for wind power generation and photovoltaic power generation in time period h, P wt,e,h , P pv,e,h are the wind power and photovoltaic power consumed in time period h, respectively, c is the carbon trading price, E com is the carbon quota, E co2 is the carbon dioxide emission, α e,CO2 is the emission coefficient of carbon dioxide generated by the consumption of electricity, is the carbon dioxide emission coefficient of natural gas, C op is the energy purchase cost, C om For energy equipment maintenance costs, The cost of purchasing thermal power from the public grid, is the mid- and long-term transaction cost of the i-th green electricity, The normal green electricity purchase cost, is the penalty cost for the deviation of the i-th green power medium- and long-term transaction, The cost of purchasing natural gas, is the cost of purchasing hydrogen, H is the total number of hours in a day, C e,h is the unit price of public grid electricity purchase in period h, P e,h is the amount of electricity purchased from the public grid during period h, C le,i,h is the purchase price of the i-th green power medium- and long-term transaction in period h, P le,i,h is the i-th green power medium- and long-term transaction purchase amount in period h, C ge,h is the normal green electricity purchase price in period h, P ge,h is the normal green electricity purchase amount in period h, C le,i,h,dp is the penalty unit price for the deviation of the i-th green power medium- and long-term transaction in period h, P le,i,h,dp is the deviation of the i-th green power medium- and long-term transaction caused by the power purchaser’s responsibility in period h, P lest,i,h The transaction volume of the i-th green power medium- and long-term trading contract in period h, P legm,i,h The amount of green electricity provided by the i-th medium- and long-term trading in period h, C g,h is the natural gas purchase price during period h, G g,h is the natural gas purchase amount in period h, is the unit price of hydrogen purchased during the h period, is the amount of hydrogen purchased in period h, i is the number of energy supply production equipment, Ω1 is the energy supply production equipment set, The operation and maintenance cost per unit output energy of the i-th energy supply production equipment, is the output energy of the i-th energy supply production equipment in time period h, j is the number of the energy storage device, Ω2 is the set of energy storage devices, is the operation and maintenance cost per unit charge and discharge power of the jth energy storage device, is the charging and discharging power of the jth energy storage device in time period h, is CO2 emissions, The emission coefficient of carbon dioxide generated by consuming electricity, is the emission coefficient of carbon dioxide produced by natural gas.

3. The method according to claim 2, characterized in that The upper model and the lower model are repeatedly iterated to determine the optimal solution, including: Determine economic goals and environmental protection goals based on the upper model and the lower model; Normalizing the economic target and the environmental target to obtain a normalized result; Based on the normalization result, the optimal solution is determined.

4. The method according to claim 3, characterized in that The normalized processing result includes the normalized value of the environmental protection target and the normalized value of the economic target, and the normalized processing result is determined by the following formula: ω1+ω2=1 In the formula, ω1 and ω2 are weight coefficients, is the normalized value of the economic objective of the i-th 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,c ) is the maximum value of the economic objective among all solutions in the current iteration, min i (f i,c ) 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.

5. The method according to claim 4, 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: P e,h +ΣP le,i,h +P ge,h +ΣP e,m,h +ΣP e,j,ES-dis,h =ΣP e,k,h +P eL,h +P cpeL,h +∑P e,j,ES-ch,h ∑Q q,i,h +ΣQ q,j,HS-dis,h =ΣQ q,k,h +Q qL,h +∑Q q,j,HS-ch,h ∑C c,i,h +∑C c,j,HS-dis,h =C cL,h +∑C c,j,HS-ch,h G g,h +∑G g,i,GS-dis,h =∑G g,j,h +∑G g,i,GS-ch,h 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,h P is the electric power generated by the mth energy supply production equipment that generates electric energy in time period h, e,j,ES-dis,h is the discharge power of the jth storage device in time period h, P e,k,h P is the power consumed by the kth energy supply device consuming electric energy in time period h, eL,h is the other electrical load during the h period, P e,j,ES-ch,h is the charging power of the jth storage device in period h, P cpeL,h is the power load consumed by the computing equipment in time period h, 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,h The thermal power generated by the energy supply production equipment that generates heat energy in the i-th period h, Q q,j,HS-dis,h is the heat release power of the jth heat storage device in time period h, Q q,k,h The heat load of the energy supply production equipment for the kth energy consuming heat energy in time period h, Q qL,h is the heat load in period t, Q q,j,HS-ch,h is the charging power of the jth heat storage device in time period h, 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,h is the cooling power generated by the energy supply production equipment that generates cooling energy in the i-th period h, C c,j,HS-dis,h is the cooling power of the jth cold storage device in time period h, C cL,h is the cooling load in period h, C c,j,HS-ch,h is the cooling power of the jth cold storage device in time period h, Ω3 is the set of energy supply production equipment consuming natural gas, Ω 12 G is the gas storage equipment set. g,h The natural gas power purchased during the h period, G g,i,GS-dis,h is the gas discharge power of the jth gas storage device in period h, G g,j,h is the natural gas power of the energy supply production equipment of the jth natural gas consumption in period h, G g,i,GS-ch,h The filling power of the jth gas storage device in period h 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 period h, is the hydrogen discharge power of the jth hydrogen storage device in period h, The hydrogen power of the energy supply production equipment for the jth hydrogen-consuming energy source in period h, The hydrogen charging power of the jth hydrogen storage device in period h.

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,i,h ≤P legm,i,h In the formula, are the minimum and maximum power purchase power of the public grid thermal power, The minimum and maximum purchased natural gas power are respectively, The minimum and maximum purchased hydrogen power are respectively, are the minimum and maximum long-term green electricity purchase power, P le,i,h is the i-th green power medium- and long-term transaction purchase amount in period h, P legm,i,h It is the amount of green electricity provided in the i-th medium- and long-term transaction in period h.

7. The method according to any one of claims 1 to 6, characterized in that The method is solved by NSGA-II algorithm.

8. A computing network multi-objective collaborative optimization operation device considering the timing scheduling of computing tasks, characterized in that: include: A model building module is used to establish a multi-objective collaborative optimization model for computing network energy; wherein the multi-objective collaborative optimization model for computing network energy includes an upper model and a lower model; The first data processing module is used for the upper-layer model to determine the computing power task allocation plan with maximum benefit and minimum carbon emission as the objective function, delay constraint, computing power scale constraint and energy supply constraint as the constraint conditions, and task timing allocation as the decision variable, and send the computing power task allocation plan to the lower-layer model; The second data processing module is used for the lower-level model to take maximum benefit and minimum carbon emission as the objective function, power balance constraint, power exchange constraint between energy system and upper-level network, surplus power access constraint, green power proportion constraint as constraint conditions, and equipment operation strategy as decision variables to determine the energy supply plan, carbon emissions and operating income, and send the energy supply plan, carbon emissions and operating income to the upper-level model; The third data processing module is used to repeatedly iterate the upper model and the lower model to determine the optimal solution; wherein the optimal solution includes computing task timing optimization and energy supply curve.

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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