Data center energy system scheduling method, device, equipment, medium and product

Abstract

The invention relates to a data center energy system scheduling method, device and equipment, a medium and a product in the technical field of intelligent management systems. The method comprises the following steps: constructing an economical objective function for minimizing the operation cost and an environmental objective function for minimizing the carbon emission; constructing a computing power side model and an energy supply side model; according to the supply data information and the demand data information in the data center energy system and constraint conditions, solving a supply and demand matching optimal solution of the data center energy system; and configuring the data center energy system according to a supply and demand matching optimal configuration scheme corresponding to the supply and demand matching optimal solution so as to realize an optimal operation target. According to the method, under the condition that the minimum operation cost and the minimum carbon emission serve as target functions, the operation models of the computing power side and the energy supply side and the constraint conditions are combined, and the targets of optimal energy system operation, optimal computing power resource allocation, the minimum carbon emission and the minimum energy operation cost of the data center are achieved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent management systems, and in particular to a data center energy system scheduling method, device, equipment, medium and product. Background Art

[0002] With the rapid development of artificial intelligence and big data technologies, the computing business undertaken by data centers has increased dramatically. Under the background of the dual carbon goals, data centers are large consumers of energy and carbon emissions. Under the condition of ensuring the computing services of data centers, it is particularly important to provide technical solutions for energy conservation and emission reduction. The current technical solutions for energy-computing-carbon emissions in data centers mainly focus on the research of computing-heat coordination and computing-electricity coordination. The technical solution for computing-heat coordination focuses on sensing the temperature of the server and regulating the optimal operation of the air-conditioning system. The technical solution for computing-electricity coordination focuses on improving the utilization efficiency of IT equipment and reducing the power consumption of equipment. The above technical solutions can improve the utilization efficiency of computing power and save energy and reduce emissions to a certain extent. However, the above technical solutions optimize and schedule from the single aspects of energy, computing power, and carbon emissions, and do not fully tap the optimization scheduling potential of all aspects and the relationship between the deep coupling of comprehensive energy systems, computing power resources, and carbon emissions. Summary of the invention

[0003] In order to solve the above technical problems, the embodiments of the present disclosure provide a data center energy system scheduling method, device, equipment, medium and product to achieve the goals of optimal energy system operation, optimal computing resource allocation, minimum carbon emissions and minimum energy operating costs in the data center.

[0004] A first aspect of an embodiment of the present disclosure provides a data center energy system scheduling method, including:

[0005] Construct an economic objective function to minimize operating costs and an environmental objective function to minimize carbon emissions;

[0006] Build computing power side model and energy supply side model;

[0007] According to the supply data information and demand data information and constraints in the data center energy system, solving the optimal solution for supply and demand matching of the data center energy system;

[0008] The data center energy system is configured according to the optimal supply and demand matching configuration plan corresponding to the optimal supply and demand matching solution to achieve the optimal operation goal.

[0009] A second aspect of the embodiments of the present disclosure provides a device, including:

[0010] An objective function module configured to construct an economic objective function for minimizing operating costs and an environmental objective function for minimizing carbon emissions;

[0011] A modeling module, configured to build a computing power side model and an energy supply side model;

[0012] A solution module, configured to solve the optimal solution for supply and demand matching of the data center energy system according to the supply data information and demand data information and constraint conditions in the data center energy system;

[0013] The configuration module is configured to configure the data center energy system according to the optimal supply and demand matching configuration scheme corresponding to the optimal supply and demand matching solution to achieve the optimal operation goal.

[0014] According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including:

[0015] at least one processor;

[0016] a memory for storing the at least one processor-executable instruction;

[0017] Wherein, the at least one processor is used to execute the instructions to implement the above method.

[0018] According to a fourth aspect of an embodiment of the present disclosure, a non-volatile computer-readable storage medium is provided. When instructions in the non-volatile computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the above-mentioned method.

[0019] According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, including a computer program, and the computer program implements the above method after being executed.

[0020] At least one of the above technical solutions adopted in the embodiments of the present disclosure can achieve the following beneficial effects: Under the condition of minimizing operating costs and carbon emissions as the objective function, the optimal solution for supply and demand matching of the data center energy system is solved by combining the operating models and constraints of the computing power side and the energy supply side; the data center energy system is configured according to the optimal supply and demand matching configuration scheme corresponding to the optimal supply and demand matching solution to achieve the optimal operating goal, and the comprehensive scheduling of the data center energy system with coordinated optimization of energy, computing power and carbon emissions is performed. The goals of optimal energy system operation, optimal computing power resource allocation, minimum carbon emissions and minimum energy operating costs of the data center are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0022] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 A flow chart of a data center energy system scheduling method provided by an embodiment of the present disclosure;

[0024] Figure 2 A schematic diagram of a process for solving a problem using a particle swarm optimization algorithm provided in an embodiment of the present disclosure;

[0025] Figure 3 A schematic diagram of the structure of a data center energy system scheduling device provided by an embodiment of the present disclosure;

[0026] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure;

[0027] Figure 5 A schematic diagram of the structure of an exemplary computer system provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0030] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0031] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0032] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0033] With the rapid development of artificial intelligence and big data technology, the computing business undertaken by data centers has increased dramatically. At the same time, data centers consume huge amounts of energy and produce a large amount of carbon dioxide. Against the background of the country's dual carbon goals, the coordinated optimization of computing, electricity, and carbon in data centers has become an important technical direction. In terms of computing power, the computing power of data centers can optimize resources according to changes in load and reduce electricity consumption. At the same time, it can participate in demand-side response, which is beneficial to the stability of the power grid. In terms of electricity, with the increase of distributed energy, the operation economy of data centers can be greatly improved by optimizing the operation of energy systems. In terms of carbon emissions, optimizing the energy structure of data centers and increasing the absorption rate of renewable energy can fundamentally reduce the carbon dioxide emissions of data centers. At present, more research or technical solutions focus on computing-heat coordination and computing-electricity coordination. The technical solution of computing-heat coordination focuses on the joint operation of servers and air-conditioning systems, measures the server temperature based on heat sensing technology, and then regulates the operation of the air-conditioning system to achieve the goal of energy saving. The technical solution of computing-electricity coordination focuses on the use of high-performance, low-energy consumption servers and storage devices to improve the efficiency of IT equipment in data centers, thereby reducing the electricity consumption of data centers. At the same time, an intelligent power management system is implemented to dynamically adjust power distribution according to workloads to ensure efficient use of resources. The above technical solutions can improve the efficiency of computing power utilization and save energy and reduce emissions to a certain extent. However, most technical solutions optimize scheduling from a single aspect of energy, computing power, and carbon emissions, without fully tapping the optimization scheduling potential of all aspects and the relationship between deeply coupling the integrated energy system, computing power resource adjustment, and carbon emissions.

[0034] Based on this, the present invention proposes a data center energy system scheduling method for coordinated optimization of energy, computing power and carbon emissions, fully considering the optimization and adjustment space of the three aspects of data center energy, computing power and carbon emissions, and achieving the goals of optimal energy system operation, optimal computing power resource allocation and lowest carbon dioxide emissions in the data center.

[0035] A feasible technical route for energy-computing-carbon coordinated optimization control is to give full play to the load flexibility of data centers and propose a method for data center energy system scheduling that takes into account load response characteristics. Specifically, by utilizing the load transferability of data centers, the transferable load of data centers is allocated to time periods with lower electricity prices and greater renewable energy output for processing while ensuring service quality. This achieves load optimization scheduling and improves the energy utilization of IT equipment, while achieving the goals of reducing data center energy costs, promoting renewable energy consumption, and reducing carbon emissions.

[0036] Exemplarily, the supply side of the energy system is photovoltaic, energy storage device, municipal power grid, and diesel generator. The demand side is the power equipment of the data center. The scheduling logic on both the supply and demand sides is: combining electricity price information, photovoltaic power generation information, and transferable load constraints, transfer the load to the period with lower electricity price and more photovoltaic power generation. The load of the data center first consumes the photovoltaic power generation, and the excess photovoltaic power generation is stored in the energy storage device. When the storage power of the energy storage device reaches the maximum limit, the photovoltaic power generation is connected to the grid. In this energy system, the energy storage device can store both excess photovoltaic power and power when the grid is low. Secondly, when the photovoltaic power generation is insufficient, the data center uses the energy storage device for power supply. Finally, when the photovoltaic power generation and the energy storage device cannot meet the power consumption of the data center, the data center uses the municipal power grid for power supply. When the municipal power is interrupted, the diesel generator starts to supply power to ensure the safety of power consumption in the data center.

[0037] Combine the following Figure 1-Figure 5 , the data center energy system scheduling method, device, equipment, medium and product provided in the embodiments of the present disclosure are described.

[0038] Figure 1 A flow chart of a data center energy system scheduling method provided by an embodiment of the present disclosure is shown as follows: Figure 1 As shown, a data center energy system scheduling method provided by an embodiment of the present disclosure includes:

[0039] S101. Constructing an economic objective function for minimizing operating costs and an environmental objective function for minimizing carbon emissions;

[0040] First, an economic objective function for minimizing operating costs and an environmental objective function for minimizing carbon emissions are constructed, wherein the economic objective function is the energy cost objective function; and the environmental objective function is the carbon dioxide emissions objective function.

[0041] That is, the system takes minimizing the operating cost and the carbon dioxide emission as the objective function. Minimizing the energy cost objective function and the carbon dioxide emission objective function can be used as the fusion target.

[0042] S102. Construct computing power side model and energy supply side model;

[0043] Construct the computing power side model and energy supply side model separately.

[0044] Among them, on the computing power side, the load types of computing services provided by the data center are classified and corresponding load models are established. The load types of computing services provided by the data center include: delay-tolerant loads and delay-sensitive loads. The delay-tolerant load model is built based on the delay-tolerant load; the delay-sensitive load model is built based on the delay-sensitive load.

[0045] In addition, on the computing power side, build an energy consumption model for IT equipment; build energy consumption models for air conditioners and other equipment.

[0046] On the energy supply side, an operation model of distributed energy in the energy system is constructed. For example, the energy supply side model includes: photovoltaic power generation model; energy storage device charging and discharging model; diesel generator fuel consumption model. When there are other energy supplies on the energy supply side, other energy supply models may also be included, such as wind power generation model.

[0047] S103, solving the optimal solution for supply and demand matching of the data center energy system according to the supply data information and demand data information and constraint conditions in the data center energy system;

[0048] According to the supply and demand data information and constraints in the integrated energy system, the algorithm is used to solve the photovoltaic power generation, the charging and discharging power of the energy storage device, the introduced power of municipal electricity, the power generation of diesel generators, the transfer amount and transfer time of transferable loads in each time period, and the best supply and demand matching information of the data center energy system in a day is given.

[0049] The above algorithm can be a particle swarm optimization algorithm or a genetic algorithm. The particle swarm optimization algorithm has the advantages of fast convergence speed, few parameters, and simple and easy algorithm implementation.

[0050] S104: configuring the data center energy system according to the optimal supply and demand matching configuration plan corresponding to the optimal supply and demand matching solution to achieve an optimal operation goal.

[0051] Based on the optimal supply and demand matching configuration plan corresponding to the optimal supply and demand matching solution of the data center energy system obtained in step S104, the data center energy system is configured to achieve the optimal operation goal.

[0052] In the embodiments of the present disclosure, under the condition of minimizing operating costs and carbon emissions as the objective function, combined with the operating models and constraints of the computing power side and the energy supply side, the optimal solution for supply and demand matching of the data center energy system is solved according to the supply data information and demand data information and constraints in the data center energy system; the data center energy system is configured according to the optimal supply and demand matching configuration plan corresponding to the optimal solution to supply and demand matching, so as to achieve the optimal operating goal, and to perform comprehensive scheduling of the data center energy system with coordinated optimization of energy, computing power and carbon emissions. The goals of optimal energy system operation, optimal computing power resource allocation, minimum carbon emissions and minimum energy operating costs for the data center are achieved.

[0053] In one embodiment of the present disclosure, building a computing power side model includes the following parts:

[0054] Data Center Load Type Analysis

[0055] The power demand of IT equipment depends mainly on the type of workload it handles, which can be divided into two categories: delay-tolerant and delay-sensitive. Delay-tolerant tasks (such as batch processing, data backup, and archiving) are not sensitive to time delays and can be flexibly scheduled for completion. Therefore, data centers can reduce resource usage through optimization measures (such as adjusting priorities and data compression). In contrast, delay-sensitive tasks (such as online games and real-time video) are extremely sensitive to time delays and require immediate responses. Data centers need to use efficient technologies (such as fast network equipment, reduced compression, and encryption) to ensure fast and reliable data transmission. It should be noted that the distinction between these two types of loads is not absolute, and the same application may have different latency requirements in different scenarios. Therefore, in the design and optimization of data centers, strategies need to be flexibly adjusted according to specific needs to maximize the requirements of various workloads.

[0056] Data center load operating characteristics

[0057] (1) Delay-tolerant load model

[0058]

[0059] in, Indicates the earliest and latest completion times for delay-tolerant workloads. is the load completion time; is the load arrival time; It is the basic processing time of the load; is responsible for the maximum delay time; μ is the user demand processing rate of the data center server, and is the operating parameter of the server.

[0060] (2) Latency-sensitive load model

[0061]

[0062] in, completion time for latency-sensitive workloads; is the load arrival time; It is the basic processing time of the load.

[0063] Data Center Energy Consumption Model

[0064] The energy consumption model of a data center can be composed of the energy consumption of IT equipment, the energy consumption of the air conditioning system, and the energy consumption of other equipment. Combined with the simplified model of the power usage efficiency (PUE) index of the data center, the energy consumption model of the data center is expressed as follows:

[0065]

[0066] in, is the total power consumption of the data center at time t; They are the power consumption of IT equipment and air conditioning system at time t respectively.

[0067] (1) IT equipment energy consumption model

[0068] Because it involves the problem of load demand response, a typical linear model considering server utilization is used to characterize the energy consumption of IT equipment. Assuming that the server energy consumption is linearly related to utilization, and the server is in any state between idle and full load according to the size of user demand, the energy consumption of IT equipment is:

[0069]

[0070] Among them, P idle The minimum power consumption of the server when it is powered on. peak is the power consumption of the server at full load; μ t is the average utilization of CPU (server); n t is the number of servers running at time t. t It is related to the number of load tasks processed at the current moment. The average CPU utilization at the current moment is equal to the average of the average utilization of the server processing delay-tolerant and delay-sensitive workload tasks at that moment. t The calculation method is as follows:

[0071]

[0072] in, is the number of latency-sensitive and latency-tolerant workloads processed at time t, Average utilization of a single server for latency-sensitive and latency-tolerant workloads, respectively.

[0073] (2) Other system energy consumption models

[0074]

[0075] In one embodiment of the present disclosure, building an energy supply side model includes the following parts:

[0076] (1) Photovoltaic power generation model

[0077] According to the open circuit voltage (Uoc), short circuit current (Isc), maximum power point voltage (Um), and maximum power point current (Im) of the photovoltaic cell under standard conditions, the photovoltaic cell is modeled. The photovoltaic power generation model is as follows:

[0078] ΔT(t)=T(t)-T ref (11)

[0079]

[0080] U ′ m (t) = U m (1+γΔT(t))(1+βΔS(t)) (15)

[0081] U ′ oc (t) = U oc (1-γΔT(t)))1+βΔS(t)) (16)

[0082]

[0083] Where T(t) is the temperature under normal working conditions at time t, T ref is the temperature under standard test conditions; ΔT(t) is the difference between the actual photovoltaic temperature and the standard temperature at time t; S(t) is the radiation intensity under general working conditions at time t, S ref is the radiation intensity under standard test conditions; ΔS(t) is the difference between the radiation intensity under standard conditions and the radiation intensity under normal conditions at time t. I' sc (t),U' oc (t),I' m (t), and U' m (t) are the short-circuit current, open-circuit voltage, maximum power point current, and maximum power point voltage under normal operating conditions at time t. C'1(t) and C'2(t) are two coefficients in the calculation process at time t; I(t) and U(t) are the current and voltage of the PV panel under normal operating conditions at time t; P t PV is the power generated by PV at time t (kW). Coefficients α, β and γ.

[0084] (2) Energy storage device charging and discharging model

[0085] The energy storage device can be charged when the grid electricity price is low or when there is surplus photovoltaic power generation, and discharged when the electricity price is high to meet the power demand of the data center during other periods. The charging and discharging model of the energy storage device is shown below.

[0086]

[0087] in, represents the state of charge of the energy storage device at time t; Δt is the time step (h); η ch and η dch They represent the charging and discharging efficiency of the energy storage device respectively; and Respectively represent the charging and discharging power of the energy storage device; C ESS is the capacity of the energy storage device.

[0088] (3) Diesel generator fuel consumption model

[0089]

[0090] in, It represents the fuel consumption of the diesel generator during period t; Indicates the rated power of the diesel generator; Represents the output power of the diesel generator during period t; A M , B M is the model coefficient.

[0091] In the disclosed embodiment, the dual constraints of demand-side response and carbon emissions can be considered to optimize scheduling, thereby constructing an economic objective function and an environmental objective function.

[0092] (1) Economic objective function

[0093] The economic objective function is the energy cost objective function, and the energy cost of the data center is the operating cost of the data center.

[0094] Data center energy costs:

[0095]

[0096] Among them, C DC Cost of running the data center; the cost of purchasing electricity for the grid; The cost of energy storage; Cost of electricity generation for diesel generators; is the cost of photovoltaic power generation; For demand response benefits.

[0097] 1) Cost of purchasing electricity from the power grid:

[0098]

[0099] in, is the power transmission of the power grid at time t; It is a time-of-use electricity price.

[0100] 2) Energy storage cost:

[0101]

[0102] in, is the energy storage charging and discharging power at time t; ESS is the maintenance cost per unit power of the energy storage device.

[0103] 3) Diesel generator power generation cost:

[0104]

[0105] in, is the diesel consumption at time t; π M For diesel prices.

[0106] 4) Photovoltaic power generation cost:

[0107]

[0108] in, is the photovoltaic power generation power at time t; π PV is the marginal cost of photovoltaic power generation per unit.

[0109] 5) Demand response benefits (distribution network dispatch incentives for shiftable loads to play the role of peak load shaving and valley filling, and corresponding economic incentives for load shifting during peak load periods):

[0110]

[0111] Among them, π tol is the unit shiftable load dispatching cost; The number of loads that can be shifted for time scheduling in period t.

[0112] (2) Environmental objective function (carbon emissions)

[0113] The environmental objective function is the carbon dioxide emission objective function:

[0114]

[0115] in, is the CO2 emission of the data center, σ and β are the carbon emission factors of grid power generation and diesel generator power generation, respectively.

[0116] (3) Constraints

[0117] 1) Network load task quantity constraints:

[0118]

[0119] in, is the number of network load tasks processed at time t before and after load scheduling.

[0120] 2) QoS (Quality of Service) constraints:

[0121] For delay-sensitive loads:

[0122]

[0123] 0≤n t ≤n max (33)

[0124] Among them, t max The maximum tolerable delay time for user data service requests; n max Indicates the upper limit of the number of servers in a data center.

[0125] For delay-tolerant loads:

[0126]

[0127] in, The delayed load of the data center for time transfer at time t; is the delay-tolerant load transmitted from server σ to the data center at time t; is the delayed load handled by the data center at time t.

[0128] To meet the requirements of the service level agreement, the total amount of delayed load at each moment satisfies the following conservation relationship:

[0129]

[0130] in, is the total amount of delayed load waiting to be processed at time t; T is the end time of the data load service.

[0131]

[0132] in, is the delay-tolerant load that should be processed at the end time t1; Q t、σ is the delayable load sent by server σ at time t.

[0133] 3) Energy balance constraints:

[0134]

[0135] in, is the power load demand of the data center at time t.

[0136] 4) Energy storage device constraints:

[0137]

[0138] Among them, P ch，max , P dis，max is the maximum charging power and the maximum discharging power; SoC min 、SoC max The minimum and maximum state of charge of the energy storage battery.

[0139] 5) Constraints on photovoltaic power generation:

[0140]

[0141] Among them, P pv，max It is the upper limit of the output of photovoltaic generator sets.

[0142] 6) Diesel generator constraints:

[0143]

[0144] Among them, P M,min , P M,max are the lower and upper limits of the diesel generator output power; ΔP M,min , ΔP M,max They are the maximum downward and upward climbing power of the diesel generator respectively.

[0145] In the embodiments of the present disclosure, during the operation of the data center energy system, the power purchase cost of the power grid and the cost of diesel generators are often higher than the cost of photovoltaic power generation. In addition, the peak electricity price of the power grid is higher than the valley electricity price. For the power consumption of data center loads, delay-tolerant loads and delay-sensitive loads are only transferred in time, and the total power consumption is not changed. Therefore, reducing energy costs, on the one hand, means increasing the use of photovoltaic power generation and reducing the purchase of electricity from the power grid and the power generation of diesel generators; on the other hand, it means that more valley power needs to be used when using power from the power grid. Therefore, the energy cost target and carbon dioxide emission target of the disclosed scheme can be converted into one target, and the energy cost target is consistent with the carbon dioxide emission target. The lower the energy cost, the more photovoltaic power is used, and the less power is purchased from the power grid and the less power is generated by diesel generators. The lower the carbon dioxide emission target function, the lower the CO2 emissions. Therefore, S103 of the embodiment of the present disclosure includes: based on the supply data information and demand data information and constraints in the data center system energy, with the goal of minimizing the energy cost objective function, a particle swarm optimization algorithm is used to solve the problem, and the photovoltaic power generation, the charging and discharging power of the energy storage device, the municipal power introduction power, the diesel generator power, the transfer amount and transfer time of the transferable load in each time period are obtained to obtain the optimal solution for the supply and demand matching of the data center energy system.

[0146] Figure 2 A schematic diagram of a process for solving a problem using a particle swarm optimization algorithm is provided in an embodiment of the present disclosure, such as Figure 2 As shown, the steps of solving the problem using the particle swarm optimization algorithm include:

[0147] S201, initializing the particle swarm, including the swarm size and the position and velocity of each particle;

[0148] Each particle in the particle swarm optimization algorithm has a velocity and a position. First, initialize the position and velocity of the particle.

[0149] S202, calculating the objective function value of each particle, determining the historical optimal value of the particle and the global optimal value in the population;

[0150] Calculate the objective function value and determine the historical optimal value of the particle and the global optimal value in the population.

[0151] S203, continuously updating the speed and position of particles;

[0152] The flying speed of particle i in N-dimensional space is represented by vector v i (v1, v2, ..., v N ), the position represents the vector x i =(x1, x2, ..., x N). The position and velocity models are shown in equations (41) and (42).

[0153]

[0154] in, is the velocity of the ith particle in the mth iteration; is the position of the ith particle in the mth iteration; ω is the inertia weight of the velocity; c1 is the individual learning factor; c2 is the social learning factor of the particle; r1 and r2 are random numbers; is the optimal position of the ith particle after m iterations; G m is the optimal position of all particles after at most m iterations.

[0155] S204, in each generation of evolution, the objective function value of each particle is calculated. If the current objective function value of the particle is better than its historical optimal value, the historical optimal value is replaced by the current objective function value; if the historical optimal value of the particle is better than the global optimal value in the population, the global optimal value in the population is replaced by the historical optimal value;

[0156] If the current objective function value of a particle is better than its historical optimal value, the latter is replaced by the former. Similarly, if the historical optimal value of a particle is better than the global optimal value in the population, the latter is replaced by the former.

[0157] S205, when the iteration reaches the set value or the accuracy reaches the convergence accuracy, the solution is completed and the global optimal solution is obtained.

[0158] In an embodiment of the present disclosure, the energy cost target and the carbon dioxide emission target are converted into one target, with minimizing the energy cost target function as the target, and a particle swarm optimization algorithm is used to solve the steps of scheduling the comprehensive energy system of a data center for the coordinated optimization of energy, computing power, and carbon emissions, so as to achieve the goals of optimal energy system operation, optimal computing power resource allocation, minimum carbon dioxide emissions, and minimum energy operating costs for the data center.

[0159] The above is only a preferred embodiment of the present disclosure. It should be pointed out that a person skilled in the art can make several improvements, optimizations and modifications without departing from the principle of the present disclosure, and these should also be considered within the scope of protection of the present disclosure.

[0160] Figure 3 A schematic diagram of a data center energy system scheduling device provided by an embodiment of the present disclosure, such as Figure 3 As shown, the device 300 includes:

[0161] Objective function module 301 is configured to construct an economic objective function for minimizing operating costs and an environmental objective function for minimizing carbon emissions;

[0162] A modeling module 302, configured to construct a computing power side model and an energy supply side model;

[0163] A solution module 303 is configured to solve the optimal solution for supply and demand matching of the data center energy system according to the supply data information and demand data information and constraint conditions in the data center energy system;

[0164] The configuration module 304 is configured to configure the data center energy system according to the optimal supply and demand matching configuration scheme corresponding to the optimal supply and demand matching solution to achieve the optimal operation goal.

[0165] In some embodiments, the economic objective function is an energy cost objective function; and the environmental objective function is a carbon dioxide emission objective function.

[0166] In some embodiments, the energy cost objective function is determined by the grid electricity purchase cost, energy storage cost, diesel generator power generation cost, photovoltaic power generation cost, and demand response benefit.

[0167] In some embodiments, the load types for which the data center provides computing services include: delay-tolerant loads and delay-sensitive loads, and the computing power side model includes: a delay-tolerant load model constructed based on the delay-tolerant load; and a delay-sensitive load model constructed based on the delay-sensitive load.

[0168] In some embodiments, the computing power side model also includes: IT equipment energy consumption model; air conditioning and other equipment energy consumption model.

[0169] In some embodiments, the energy supply side model includes: a photovoltaic power generation model; an energy storage device charging and discharging model; and a diesel generator fuel consumption model.

[0170] In some embodiments, the constraints include: network load task quantity constraint; service quality constraint; energy balance constraint; energy storage device constraint; photovoltaic power generation constraint; and diesel generator constraint.

[0171] In some embodiments, the solution module 303 is configured to use a particle swarm optimization algorithm to solve the supply and demand matching optimal solution of the data center energy system based on the supply data information and demand data information and constraints in the data center system energy, with the goal of minimizing the energy cost objective function.

[0172] In some embodiments, the solution module 303 includes:

[0173] The initialization submodule is configured to initialize the particle swarm, including the swarm size and the position and velocity of each particle;

[0174] A calculation submodule, configured to calculate the objective function value of each particle, determine the historical optimal value of the particle and the global optimal value in the population;

[0175] The update submodule is configured to continuously update the velocity and position of particles;

[0176] The replacement submodule is configured to calculate the objective function value of each particle in each generation of evolution. If the current objective function value of the particle is better than its historical optimal value, its historical optimal value is replaced by the current objective function value; if the historical optimal value of the particle is better than the global optimal value in the population, the historical optimal value is replaced by the global optimal value in the population;

[0177] The end submodule is configured to iterate until the set value is reached or the accuracy reaches the convergence accuracy, and the solution ends to obtain the global optimal solution.

[0178] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, an embodiment of the present disclosure also provides an electronic device, which includes at least one processor 401 and a memory 402 coupled to the processor 401, and the memory 402 is used to store executable instructions of at least one processor 401, wherein the at least one processor 401 is used to execute instructions to implement the steps of the above-mentioned method of the embodiment of the present disclosure.

[0179] The processor 401 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method of the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 401 or by instructions in the form of software. The processor 401 may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method in conjunction with the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a memory 402, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 401 reads the information in the memory 402 and completes the steps of the method in conjunction with its hardware.

[0180] Figure 5 The schematic diagram of the structure of an exemplary computer system provided by the embodiment of the present disclosure is as follows. According to the various operations / processing of the embodiment of the present disclosure, when implemented by software and / or firmware, the computer system having a dedicated hardware structure can be transmitted from a storage medium or a network, for example, Figure 5 The computer system 500 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can execute various functions, including the functions described above.

[0181] Computer system 500 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0182] like Figure 5 As shown, the computer system 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the computer system 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0183] A plurality of components in the computer system 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device capable of inputting information to the computer system 500, and the input unit 506 can receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 507 can be any type of device capable of presenting information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include but is not limited to a disk, an optical disk. The communication unit 509 allows the computer system 500 to exchange information / data with other devices over a network such as the Internet, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, for example, a Bluetooth™ device, a WI-FI device, a WiMax device, a cellular communication device, and / or the like.

[0184] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the above-mentioned method of the embodiment of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via a ROM 502 and / or a communication unit 509. In some embodiments, the computing unit 501 may be configured to perform the above-mentioned method of the embodiment of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0185] An embodiment of the present disclosure provides a non-volatile computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the above-mentioned method of the embodiment of the present disclosure.

[0186] The non-transitory computer-readable storage medium may be a volatile memory (volatile memory), such as a random-access memory (Random-Access Memory, RAM); or a non-volatile memory (non-volatile memory), such as a read-only memory (Read-Only Memory, ROM), a flash memory, a hard disk (Hard Disk Drive, HDD) or a solid-state drive (Solid-State Drive, SSD); or it may be a respective device including one or any combination of the above memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc.

[0187] It should be noted that the non-transient computer-readable storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0188] An embodiment of the present disclosure provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned uplink signal equalization method are implemented.

[0189] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the computer, partially on the computer, as an independent software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0190] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0191] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, exemplary hardware logic components that may be used include, without limitation, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0192] It should be noted that, in this article, terms such as "comprises", "includes" or any other variations 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 includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0193] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A data center energy system scheduling method, characterized in that: include: Construct an economic objective function to minimize operating costs and an environmental objective function to minimize carbon emissions; Build computing power side model and energy supply side model; According to the supply data information and demand data information and constraints in the data center energy system, solving the optimal solution for supply and demand matching of the data center energy system; The data center energy system is configured according to the optimal supply and demand matching configuration plan corresponding to the optimal supply and demand matching solution to achieve the optimal operation goal.

2. The method according to claim 1, characterized in that The economic objective function is an energy cost objective function; the environmental objective function is a carbon dioxide emission objective function.

3. The method according to claim 2, characterized in that The energy cost objective function is determined by the power purchase cost of the power grid, the energy storage cost, the power generation cost of the diesel generator, the photovoltaic power generation cost, and the demand response benefit.

4. The method according to claim 1, characterized in that: The load types for which the data center provides computing services include: delay-tolerant loads and delay-sensitive loads, and the computing power side model includes: a delay-tolerant load model constructed based on the delay-tolerant load; and a delay-sensitive load model constructed based on the delay-sensitive load.

5. The method according to claim 4, characterized in that The computing power side model also includes: IT equipment energy consumption model; air conditioning and other equipment energy consumption model.

6. The method according to claim 1, characterized in that The energy supply side model includes: a photovoltaic power generation model; an energy storage device charging and discharging model; and a diesel generator fuel consumption model.

7. The method according to any one of claims 1 to 6, characterized in that: The constraints include: network load task quantity constraint; service quality constraint; energy balance constraint; energy storage device constraint; photovoltaic power generation constraint; and diesel generator constraint.

8. The method according to claim 7, characterized in that Solving the optimal solution for supply and demand matching of the data center energy system according to the supply data information and demand data information and constraints in the data center energy system includes: According to the supply data information and demand data information in the data center system energy and the constraints, with the goal of minimizing the energy cost objective function, a particle swarm optimization algorithm is used to solve and obtain the optimal solution for the supply and demand matching of the data center energy system.

9. The method according to claim 8, characterized in that The particle swarm optimization algorithm is used to solve the problem, including: Initialize the particle swarm, including the swarm size and the position and velocity of each particle; Calculate the objective function value of each particle and determine the historical optimal value of the particle and the global optimal value in the population; Continuously update the particle's velocity and position; In each generation of evolution, the objective function value of each particle is calculated. If the current objective function value of the particle is better than its historical optimal value, its historical optimal value is replaced by the current objective function value; if the historical optimal value of the particle is better than the global optimal value in the population, the historical optimal value is replaced by the global optimal value in the population; When the iteration reaches the set value or the accuracy reaches the convergence accuracy, the solution ends and the global optimal solution is obtained.

10. A device, characterized in that: include: An objective function module configured to construct an economic objective function for minimizing operating costs and an environmental objective function for minimizing carbon emissions; A modeling module, configured to build a computing power side model and an energy supply side model; A solution module, configured to solve the optimal solution for supply and demand matching of the data center energy system according to the supply data information and demand data information and constraint conditions in the data center energy system; The configuration module is configured to configure the data center energy system according to the optimal supply and demand matching configuration scheme corresponding to the optimal supply and demand matching solution to achieve the optimal operation goal.

11. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 9.

12. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the non-transitory computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method according to any one of claims 1 to 9.

13. A computer program product, comprising a computer program, characterized in that After being executed, the computer program implements the method according to any one of claims 1 to 9.

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