CN117353290A - Combined scheduling method and computing equipment for data center and shared energy storage power station - Google Patents

Combined scheduling method and computing equipment for data center and shared energy storage power station Download PDF

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Publication number
CN117353290A
CN117353290A CN202311287780.8A CN202311287780A CN117353290A CN 117353290 A CN117353290 A CN 117353290A CN 202311287780 A CN202311287780 A CN 202311287780A CN 117353290 A CN117353290 A CN 117353290A
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data center
power
energy storage
electricity
shared energy
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Inventor
张硕
陈媛丽
李英姿
刘龙飞
吴诗琦
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University of Science and Technology Beijing USTB
North China Electric Power University
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University of Science and Technology Beijing USTB
North China Electric Power University
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Priority to CN202311287780.8A priority Critical patent/CN117353290A/en
Publication of CN117353290A publication Critical patent/CN117353290A/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The application discloses a joint scheduling method and computing equipment of a data center and a shared energy storage power station. The method comprises the following steps: determining a benefit value of a shared energy storage power station based on at least the electricity consumption condition of the data center, wherein the shared energy storage power station acquires renewable energy power for storage, and sells the stored power to the data center in a time-of-use electricity price mode; determining the energy consumption cost of the data center at least based on the electricity price of each time period of each electricity purchasing mode; respectively taking the maximum profit value of the shared energy storage power station as a first target and the minimum energy consumption cost of each period of the data center as a second target, correspondingly generating a shared energy storage optimization model and a data center optimization model, and constructing a joint scheduling model of the data center and the shared energy storage power station; and solving the joint scheduling model to obtain a scheduling result of the time-of-use electricity price of the shared energy storage power station and the electricity consumption power of the data center.

Description

Combined scheduling method and computing equipment for data center and shared energy storage power station
Technical Field
The application relates to the technical field of power systems, in particular to a joint scheduling method and computing equipment for a data center and a shared energy storage power station.
Background
Data centers (typically comprising multiple computers or clusters of computers) have become critical infrastructure for economic and social operations as a physical carrier for the operation of information systems in various industries, and efficient, clean, intensive, and cyclical green low-carbon development roads are the necessary direction of data center development.
As a demand response resource to be mined urgently, the data center load has the characteristics of high energy consumption, uncertainty and the like, and also has excellent scheduling potential. On one hand, the data center can increase the green energy consumption proportion by purchasing green electricity, self-building renewable energy sources, energy storage systems and the like, and is suitable for a novel power system with gradually increased new energy occupation ratio; on the other hand, the data center can mine the load release potential by combining the self electricity utilization property and bear the heavy duty of flexible regulation on the load side of the system. Diversified energy storage becomes a key support technology for meeting the continuous and stable power supply requirement of a data center and effectively aiming at the intermittence and fluctuation of renewable energy power generation.
However, the energy storage system has the problems of unsmooth cost dispersion, low effective utilization rate, low social initiative investment will and the like, and the investment construction of the data center on the energy storage system is limited. Shared energy storage (i.e., commonly shared energy storage power stations) provides a new idea to solve this problem by virtue of flexible resource allocation, and the shared energy storage power stations are introduced into the field of data centers to reduce the energy consumption cost of the data centers and realize the load balancing of the power grid.
Therefore, the exploration of a set of new modes capable of effectively pushing the linkage of the shared energy storage power station and the data center is significant for ensuring the maximum consumption of clean energy and improving the balance adjustment capability of the power system.
Disclosure of Invention
The present application provides a joint scheduling method and computing device for a data center and a shared energy storage power station in an effort to solve or at least alleviate at least one of the above-identified problems.
According to one aspect of the present application, there is provided a joint scheduling method for a data center and a shared energy storage power station, including: determining a profit value of a shared energy storage power station based at least on the electricity consumption condition of a data center, wherein the shared energy storage power station acquires renewable energy power storage and sells the stored power to the data center in a time-of-use electricity price form; determining energy consumption cost of the data center based on at least electricity prices of each time period of each electricity purchasing mode, wherein the electricity purchasing mode at least comprises: purchasing power via a power grid and the shared energy storage power station, wherein the power purchased via the power grid includes thermal power and renewable energy power; respectively taking the maximum profit value of the shared energy storage power station as a first target and the minimum energy consumption cost of each period of the data center as a second target, correspondingly generating a shared energy storage optimization model and a data center optimization model, and constructing a joint scheduling model of the data center and the shared energy storage power station; and solving the joint scheduling model to obtain a scheduling result comprising the time-of-use electricity price of the shared energy storage power station and the electricity power of the data center.
Optionally, in the method according to the present application, solving the joint scheduling model includes: solving the shared energy storage optimization model by utilizing a genetic algorithm, determining the time-of-use electricity price of the shared energy storage power station, and solving the data center optimization model by utilizing the determined time-of-use electricity price so as to determine the electricity power of the data center; solving the shared energy storage optimization model by utilizing the determined power consumption to update the time-of-use power price of the shared energy storage power station, and solving the data center optimization model by utilizing the updated time-of-use power price to update the power consumption of the data center; repeating the steps of updating the time-of-use electricity price and updating the electricity power until the preset condition is met, and obtaining the scheduling result.
Optionally, the method according to the present application further comprises: taking the maximum profit value of the shared energy storage power station as a first target, and generating a first objective function; taking charge and discharge power and electricity price of the shared energy storage power station in an operation period into consideration, and establishing a first constraint condition; generating the shared energy storage optimization model based on the first objective function and the first constraint condition; taking the minimum energy consumption cost of each period of the data center as a second target, and generating a second objective function; establishing a second constraint condition by combining the electric power of the data center with the green certificate transaction; the data center optimization model is generated based on the second objective function and the second constraint condition.
Optionally, in the method according to the present application, determining the benefit value of the shared energy storage power station based at least on the electricity usage of the data center includes: determining the income of the shared energy storage power station for selling electricity to the data center based on the electricity consumption condition of the data center; a revenue value for the shared energy storage power plant is determined based on the electricity sales revenue, the cost of purchasing renewable energy power, and the operating cost of the shared energy storage power plant.
Optionally, in the method according to the present application, determining the energy consumption cost of each period of the data center based at least on the electricity prices of each period includes: determining electricity purchasing cost of the data center based on time-of-use electricity prices of all electricity purchasing modes, wherein the electricity purchasing cost at least comprises cost of purchasing thermal power, cost of purchasing renewable energy power and cost of purchasing electricity through a shared energy storage power station; and taking the sum of the electricity purchase cost, the carbon emission cost and the green certificate cost as the energy consumption cost of the data center, wherein the carbon emission cost is determined by the price of the carbon emission quota and the hyperbranched carbon emission amount, and the green certificate cost is determined by the price of the green certificate transaction and the number of green certificates required to be purchased by the data center.
Optionally, in the method according to the present application, the first constraint includes: energy storage price constraint, charge-discharge power constraint, power variation constraint of a shared energy storage power station and state of charge constraint; the second constraint includes: the power balance constraint, the power variation constraint of the data center, the green evidence cost constraint and the carbon emission constraint.
Optionally, in the method according to the present application, balancing constraints with electric power includes: the data center purchases electric power in a power purchase mode to be balanced with the total electric power of the data center; and classifying the electric loads of the data center based on the demand response, wherein the electric loads of each class are balanced with the total electric power, and the electric loads of each class meet the demand response constraint condition.
Optionally, in the method according to the present application, the respective power of the electric loads is balanced with the total electric power, including:
wherein P is DC (t) represents the total power consumption of the data center at the time t; p (P) t base The power corresponding to the base load of the data center at the time t is obtained; p (P) t tran Representing the power corresponding to the transferable load of the data center at the time t; The power corresponding to the load can be reduced before the participation demand response scheduling corresponding to the time t; p (P) t cut And (5) cutting down the power corresponding to the load after the participation demand response corresponding to the time t is scheduled.
According to yet another aspect of the present application, there is provided a computing device comprising: one or more processor memories; one or more programs, wherein the one or more programs are stored in memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods described above.
According to yet another aspect of the present application, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described above.
In summary, according to the scheme of the application, firstly, the operation characteristics of the shared energy storage power station and the data center are considered, the linkage system of the shared energy storage power station and the data center is determined, the shared energy storage power station determines the charge and discharge strategy and the time-of-use electricity price according to the historical electricity consumption condition of the data center, and the data center determines the self electricity consumption power by combining the demand response characteristics of the electricity consumption load. Secondly, by constructing a joint scheduling model of the shared energy storage power station and the data center, the price guiding function of the shared energy storage power station is considered, the demand response potential of the data center is mined, the shared energy storage time-sharing electricity price and the charging and discharging strategy under the linkage of the two are adopted, and the electricity consumption power (namely, a load scheduling scheme) of the data center is adopted, so that the electricity consumption cost of the data center can be remarkably reduced, and the benefit value of the shared energy storage power station is improved. This has important significance for improving energy utilization efficiency, balancing power system loads and promoting sustainable development.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which set forth various ways in which the principles herein may be practiced, and all aspects and equivalents thereof are intended to fall within the scope of the claimed subject matter. The above, as well as additional purposes, features, and advantages of the present application will become more apparent from the following detailed description when read in conjunction with the accompanying drawings. Like reference numerals generally refer to like parts or elements throughout the present application.
FIG. 1 illustrates a frame structure diagram of a data center and shared energy storage power station linkage system according to some embodiments of the present application;
FIG. 2 illustrates a schematic diagram of a computing device 200 according to some embodiments of the present application;
FIG. 3 illustrates a flow diagram of a method 300 of joint scheduling of a data center and a shared energy storage power station, according to some embodiments of the present application;
FIG. 4 illustrates a flow chart for solving a joint scheduling model, according to some embodiments of the present application;
FIG. 5 illustrates a typical daily electrical load profile for a data center according to one embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
According to the method, the shared energy storage power station is used for cooperating with the power grid, under the condition of energy supply of the data center, carbon emission cost and green license transaction mechanisms are counted, the data center is encouraged to utilize renewable energy power more in a certain range, and the improvement of the shared energy storage income is achieved while the electricity consumption cost is reduced, and the improvement of energy conservation and emission reduction pressure is relieved. FIG. 1 illustrates a frame structure diagram of a data center and shared energy storage power station linkage system.
As shown in fig. 1, the linkage system at least comprises 4 main bodies of a shared energy storage power station, a data center, a power grid, a power generation enterprise for providing renewable energy power (such as hydroelectric power generation, wind power generation, solar power generation and the like). The shared energy storage power station comprises a plurality of energy storage units. The data center is in communication connection with the user clients and processes various user requests (e.g., interactive requirements, batch requirements, etc.). In this embodiment, the shared energy storage power station directly signs a long-term electricity purchasing protocol with the renewable energy power generation enterprise when charging, and the discharge amount is regarded as new energy electric quantity. And the shared energy storage power station establishes the selling price of electricity in each period according to the electricity consumption condition of the data center, so that the income of the shared energy storage power station is maximized. And the data center determines the power consumption of each period according to the power price of each period of the shared energy storage power station and the power grid power price, so that the self power consumption cost is minimized. Based on the data load type, the power consumption load of the data center is divided into a basic load, a transferable load and a load which can be reduced, and the fact that the data load requested by a user affects the optimization allocation of the data center server and causes different power consumption conditions of data center equipment is considered, so that power load demand response is realized.
Further description is made below in connection with fig. 1 from 2 aspects of the operational characteristics of the shared energy storage power station and the load characteristics of the data center.
The benefits of all parties are comprehensively considered in the shared energy storage power station, so that the energy storage market is more diversified to develop and has more economic benefits. The operation modes of the shared energy storage power station mainly can be divided into user investment, shared public energy storage, construction and operation of the shared energy storage power station borne by a third-party operator, existing distributed energy storage shared by users and the like, and the characteristics of different operation modes are shown in a table 1. The profit mode of the shared energy storage power station can be mainly classified into a capacity lease mode, a peak Gu Taoli mode, a participation auxiliary service mode and the like. And the third-party operator establishes a shared energy storage power station among the data center clusters and performs unified operation management on the energy storage power station, so that a plurality of data centers in the same power distribution network area can enjoy energy storage service, and the capacity of the whole network energy storage configuration is reduced. Meanwhile, the shared energy storage power station operators call the energy storage device to charge or discharge according to the electricity demand of each period of the data center, and the shared energy storage power station can effectively balance the load of the power grid by flexibly adjusting the energy storage and release, so that the stability and the power supply quality of the power system are improved. In addition, in order to promote the consumption and utilization of renewable energy sources and promote the consumption level of green power, the embodiment assumes that the shared energy storage power station only purchases green power from renewable energy source developers and sells the stored electric quantity to the data center in the form of time-of-use electricity price, and the data center does not need to pay service cost to the shared energy storage power station.
Table 1 shared energy storage power station operational characteristics analysis
The traditional energy supply of the data center mainly comes from a power grid, and the power cost of daily operation of the data center is influenced due to fluctuation of electricity consumption peak and valley periods at unit time of power grid electricity purchasing at the same time of high cost, high pollution and high energy consumption. The data center mainly includes three main load portions of an IT load, a cooling system, and a power conditioning system, wherein the largest power consumption portion in a typical data center is the IT load portion.
In the daily operation of a data center, three key parts of external energy supply, internal scheduling optimization and user requirements are mainly considered. The external energy supply comprises renewable energy sources (such as solar energy and wind energy) and traditional energy sources (such as coal electricity and diesel generators), and the output characteristics and the price of different energy sources can have different influences on the daily operation mode, the economy and the environmental influence of the data center; in the aspect of internal optimization of a data center, each task on a server is dynamically allocated mainly by comprehensively considering energy supply and user requirements; the user side typically includes multiple types of data service requirements, including interactive requirements that require real-time response, batch processing requirements that can tolerate delays, and so forth. Different types of user demands not only affect the space for optimal scheduling of data center servers, but also create differentiated demand response potential. Aiming at the equipment energy consumption characteristic and the data load space-time characteristic of the data center, according to the embodiment of the application, the power consumption load of the data center is classified based on the demand response, and the power consumption load is specifically classified into three types of basic load, transferable load and load reduction. Meanwhile, the electric loads of all the categories are required to meet the requirement response constraint condition. The description of these 3 types of electrical loads and their demand response constraints is as follows.
(1) Base load
The base load is mainly used for meeting the interaction requirement of the data center and ensuring the safe and stable operation of the data center, so that the data center does not have the time transferability characteristic and cannot change the time of the base load. In some embodiments, the base load is represented by the following equation (1):
P t base,after ＝P t base,before (1)
wherein P is t base,after The power corresponding to the base load of the data center at the moment t after the demand response is carried out; p (P) t base,before The power corresponding to the base load at the time t before the demand response.
(2) Load transferable
The batch type data load refers to a data load which can be processed in a batch type by delaying response. Because the batch load can be flexibly scheduled in different time periods, the energy consumption of the data center to meet the batch data load is referred to as a transferable load. On the premise of ensuring that the total load amount in the whole period is satisfied, the electricity consumption of each period can be flexibly regulated. Assume that the transferable period interval in the scheduling period is [ t ] tr- ,t tr+ ]Meanwhile, in order to prevent the problem that the data center server is frequently started and stopped in each period, the constraint of the minimum continuous running time is set for the transferable load, and the transfer power of the transferable load is constrained, as shown in the following formulas (2) - (3):
In the method, in the process of the invention,minimum continuous run time for transferable loads; />A transition state variable at the time t is a 0-1 variable, 1 indicates that transition occurs, and 0 indicates that no transition occurs; p (P) t tran The power corresponding to the transferable load at time t is represented,the upper and lower limits of the power corresponding to the transferable loads are respectively.
(3) Load can be reduced
The batch load processing of the data center has a time transfer characteristic, and part of batch loads also have a reducible characteristic, wherein the electric quantity consumed by the reducible batch loads is the reducible load of the corresponding data center. In view of satisfaction of data center users, it is necessary to constrain the upper and lower limits of the cut-down period and the cut-down number of times, as shown in formulas (4) - (6):
in the method, in the process of the invention,to cut down state variables; />Is at minimumContinuously reducing time; />Is the maximum reduction number; />The power corresponding to the load can be reduced before the participation demand response scheduling corresponding to the time t; p (P) t cut The power corresponding to the load is reduced after the participation demand response corresponding to the time t is scheduled; />The upper and lower limits of the load shedding coefficient; t represents a unit scheduling period, typically 1 day.
In an embodiment according to the present application, equations (1) - (6) above form a demand response constraint for the electrical load of the data center. For demand response constraints, it is necessary to ascertain the load parameters of the data center. In particular, the relevant load parameters for the transferable loads include: minimum continuous running time of transferable load, upper and lower limits of transferable load, transferable period interval; relevant parameters for the load that can be cut include: minimum continuous reduction time, maximum number of reductions.
The joint scheduling method of the data center and the shared energy storage power station can be realized in the computing equipment. Fig. 2 illustrates a block diagram of a computing device 200, according to some embodiments of the present application. It should be noted that the computing device 200 shown in fig. 2 is only an example, and in practice, the computing device used to implement the transportation scheduling method of the present application may be any type of device, and the hardware configuration of the computing device may be the same as the computing device 200 shown in fig. 2 or may be different from the computing device 200 shown in fig. 2. In practice, the computing device for implementing the transportation scheduling method of the present application may add or delete hardware components of the computing device 200 shown in fig. 2, and the specific hardware configuration of the computing device is not limited in the present application.
As shown in FIG. 2, in a basic configuration 202, a computing device 200 typically includes a system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between the processor 204 and the system memory 206.
Depending on the desired configuration, processor 204 may be any type of processor including, but not limited to: a microprocessor (μp), a microcontroller (μc), a digital information processor (DSP), or any combination thereof. Processor 204 may include one or more levels of cache, such as a first level cache 210 and a second level cache 212, a processor core 214, and registers 216. The example processor core 214 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a Digital Signal Processing (DSP) core, or any combination thereof. The example memory controller 218 may be used with the processor 204, or in some implementations, the memory controller 218 may be an internal part of the processor 204.
Depending on the desired configuration, system memory 206 may be any type of memory including, but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. Physical memory in a computing device is often referred to as volatile memory, RAM, and data in disk needs to be loaded into physical memory in order to be read by processor 204. The system memory 206 may include an operating system 220, one or more applications 222, and program data 224. In some implementations, the application 222 may be arranged to execute instructions on an operating system by the one or more processors 204 using the program data 224. The operating system 220 may be, for example, linux, windows or the like, which includes program instructions for handling basic system services and performing hardware-dependent tasks. The application 222 includes program instructions for implementing various user desired functions, and the application 222 may be, for example, a browser, instant messaging software, a software development tool (e.g., integrated development environment IDE, compiler, etc.), or the like, but is not limited thereto.
When the computing device 200 starts up running, the processor 204 reads the program instructions of the operating system 220 from the memory 206 and executes them. Applications 222 run on top of operating system 220, utilizing interfaces provided by operating system 220 and underlying hardware, to implement various user-desired functions. When the user launches the application 222, the application 222 is loaded into the memory 206, and the processor 204 reads and executes the program instructions of the application 222 from the memory 206.
Computing device 200 also includes storage device 232, storage device 232 including removable storage 236 (e.g., CD, DVD, U disk, removable hard disk, etc.) and non-removable storage 238 (e.g., hard disk drive HDD, etc.), both removable storage 236 and non-removable storage 238 being connected to storage interface bus 234.
Computing device 200 may also include a storage interface bus 234. Storage interface bus 234 enables communication from storage devices 232 (e.g., removable storage 236 and non-removable storage 238) to base configuration 202 via bus/interface controller 230. At least a portion of operating system 220, applications 222, and program data 224 may be stored on removable storage 236 and/or non-removable storage 238, and loaded into system memory 206 via storage interface bus 234 and executed by one or more processors 204 when computing device 200 is powered up or application 222 is to be executed.
Computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to basic configuration 202 via bus/interface controller 230. The example output device 242 includes a graphics processing unit 248 and an audio processing unit 250. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 252. The example peripheral interface 244 may include a serial interface controller 254 and a parallel interface controller 256, which may be configured to facilitate communication via one or more I/O ports 258 and external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.). The example communication device 246 may include a network controller 260 that may be arranged to facilitate communication with one or more other computing devices 262 over a network communication link via one or more communication ports 264.
The network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media in a modulated data signal, such as a carrier wave or other transport mechanism. A "modulated data signal" may be a signal that has one or more of its data set or changed in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or special purpose network, and wireless media such as acoustic, radio Frequency (RF), microwave, infrared (IR) or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 200 may be implemented as a personal computer including desktop and notebook computer configurations. Of course, computing device 200 may also be implemented as part of a small-form factor portable (or mobile) electronic device such as a cellular telephone, digital camera, personal Digital Assistant (PDA), personal media player device, wireless web-watch device, personal headset device, application specific device, or hybrid device that may include any of the above functions. And may even be implemented as servers, such as file servers, database servers, application servers, WEB servers, and the like. The embodiments of the present application are not limited in this regard.
In an embodiment according to the present application, the computing device 200 is configured to perform a joint scheduling method 300 of a data center and a shared energy storage power station according to the present application. Wherein the application 122 disposed on the operating system contains a plurality of program instructions for performing the method 300, which may instruct the processor 104 to perform the method 300 of the present application.
FIG. 3 illustrates a flow diagram of a method 300 of joint scheduling of a data center and a shared energy storage power station, according to some embodiments of the present application.
According to the embodiment of the application, based on the linkage system shown in fig. 1, the interaction condition of the shared energy storage power station and the data center accords with a master-slave game model architecture, so that a joint scheduling model related to the shared energy storage power station and the data center is established. The energy storage power station and the data center are shared as participants, and the respective strategies are time-of-use electricity price and electricity power consumption respectively. The shared energy storage power station of the game master takes the maximum value of electricity selling income as a target according to the price of purchasing green electricity per se, and takes corresponding constraint conditions into consideration to make a decision first; the data center of the follower makes the most favorable decision for the follower according to the pricing strategy of the shared energy storage power station and simultaneously considering the internal power consumption flexible load adjustment and the green power consumption policy under the corresponding constraint condition; and then the shared energy storage power station optimizes the pricing decision and the charge and discharge scheduling scheme of the shared energy storage power station according to the power utilization strategy of the data center, and the two parties influence each other until balance is realized.
In accordance with an embodiment of the present application, method 300 begins at 310. In 310, for a shared energy storage power plant, a revenue value for the shared energy storage power plant is determined based at least on the electricity usage of the data center. As described above, in the present embodiment, the shared energy storage power station obtains the renewable energy power to store, and sells the stored power to the data center in the form of time-of-use electricity prices.
In some embodiments, the benefit value is determined by the following two steps.
And the first step, based on the electricity consumption condition of the data center, determining the income of the shared energy storage power station for selling electricity to the data center. Specifically, the time-of-use electricity price and the charging power corresponding to the data center when the shared energy storage power station sells electricity to the data center are utilized to determine electricity selling income.
And a second step of determining a benefit value of the shared energy storage power station based on the electricity selling income, the cost of purchasing the renewable energy power, and the operating cost of the shared energy storage power station. Specifically, the electricity selling income minus the cost of purchasing renewable energy power and the running cost of the shared energy storage power station is the final difference value. Wherein, the cost of purchasing renewable energy power is determined according to the electricity price and the electricity purchasing power when purchasing renewable energy power; the operating costs of the shared energy storage power plant are determined from annual operating maintenance and depreciation costs. Specifically, the calculation method can refer to the following formula (12).
In 320, for the data center, an energy consumption cost of the data center is determined based at least on the electricity prices for each time period for each electricity purchase. As described above, the electricity purchasing mode at least includes: the power is purchased via a power grid and a shared energy storage power station, and in some embodiments, the power purchased via the power grid includes thermal power and renewable energy power.
According to the embodiment of the application, the power supply of the data center is mainly purchased from a large power grid through the power market, but due to the extremely high requirement on the power supply reliability and the desire to utilize green energy, the data center can purchase part of green power from renewable energy power generators through the power grid, and meanwhile, part of green power can be provided by the shared energy storage system, and green power transaction becomes an important means for the data center to use energy for greening. In some embodiments, the energy consumption cost of the data center is determined by the following two steps.
And firstly, determining the electricity purchasing cost of the data center based on the time-sharing electricity price of each electricity purchasing mode. The electricity purchasing cost of the data center at least comprises 3 parts of the cost of purchasing thermal power, the cost of purchasing renewable energy and electric power and the cost of purchasing electricity through the shared energy storage power station. The power purchase cost of each part in one scheduling period is determined by the real-time electricity price when power is purchased and the power purchased in the mode. The purchase costs of each part are expressed as follows:
Wherein C is fire Representing the cost of purchasing the thermal power; r is (r) fire (t) represents that the data center is electrically connected at time tReal-time electricity price of the network purchase thermal power; p (P) fire (t) represents the power of the data center purchasing thermal power via the power grid at time t; Δt represents a unit scheduling period duration, typically 1 hour; t represents a unit scheduling period, typically 1 day, i.e. 24 hours, i.e. T has a value of t=1, 2,3, …,24; c (C) res Representing the cost of purchasing renewable energy power; r is (r) res (t) represents a price at which the data center purchases renewable energy power at time t; p (P) res (t) represents the power of the data center purchasing renewable energy power at time t; c (C) ses Representing the purchase cost of the shared energy storage power station; r is (r) ses (t) represents the price of the data center purchasing electricity via the shared energy storage power station at time t;and (3) purchasing power (namely charging power of the data center) of the data center to the shared energy storage power station for a period t.
And a second step of determining the energy consumption cost of the data center based on the electricity purchase cost, the carbon emission cost, and the green evidence cost. In some embodiments, the sum of electricity purchase cost, carbon emission cost, and green evidence cost is taken as the energy consumption cost.
According to some embodiments, during the linking of the data center with the shared energy storage, the carbon emissions of the data center should comply with national carbon quota policy regulations, the carbon emissions exceeding the quota should purchase a corresponding carbon quota in the carbon trade market, the carbon emissions cost is determined by the price of the carbon emissions quota and the hyperbranched carbon emissions, optionally the carbon emissions cost C ce Is expressed by the following formula:
C ce ＝r ce ·ΔE CO2 (10)
wherein r is ce Representing prices at which data centers purchase carbon emission credits in a carbon trade market; ΔE CO2 Indicating that the quota is exceeded, the data center needs to purchase a hyperbranched carbon emission corresponding to the carbon quota in the carbon trade market.
In addition, the embodiment also introduces a green certificate transaction mechanism by comparing the actual consumption of renewable energy power by the data center with the regulation of a renewable energy consumption guarantee mechanismAnd calculating the difference of the reference consumption amount and the reference consumption amount for confirming the consumption ratio of the data center to obtain the number of green certificates required to be purchased by the data center, and further determining the green certificate cost of the data center. According to the present application, the green license cost is determined by the price of the green license transaction and the number of green licenses that the data center needs to purchase, optionally, the green license cost C gc Is expressed by the following formula:
C gc ＝r gc ·G GR (11)
wherein r is gc Representing the price of the green license transaction; g GR Indicating the number of green certificates that the data center needs to purchase.
And then in 330, respectively taking the maximum profit value of the shared energy storage power station as a first target and the minimum energy consumption cost of each period of the data center as a second target, correspondingly generating a shared energy storage optimization model and a data center optimization model, so as to construct a joint scheduling model of the data center and the shared energy storage power station.
According to an embodiment of the present application, the shared energy storage optimization model and the data center optimization model are generated separately in the following manner.
In one aspect, a maximum revenue value of the shared energy storage power station is used as a first target, and a first objective function is generated. Meanwhile, the charge and discharge power and electricity price of the shared energy storage power station in the operation period are considered, and a first constraint condition is established. Then, a shared energy storage optimization model is generated based on the first objective function and the first constraint condition.
For calculation of the benefit value reference may be made to the relevant description of the previous 310. According to some embodiments, the first objective function may be represented by the following equation (12):
wherein E is ses Representing a benefit value of sharing the energy storage power station in a scheduling period T; r is (r) ses (t) represents the price at which the data center purchases electricity from the shared energy storage power station (i.e., the selling price of the shared energy storage power station) for the period t;charging power for the data center of the t period; />Sharing electricity purchasing price of the energy storage power station for the period t; />Sharing the electricity purchasing power of the energy storage power station for the period t; c op The maintenance and depreciation cost for the annual operation of the energy storage equipment in unit capacity; q (Q) e To share the rated capacity of the stored energy.
In one operation period of the shared energy storage power station, the dispatching center of the shared energy storage power station can meet the charging and discharging requirements of the data center by regulating and controlling the energy storage device to charge or discharge. The first constraint mainly includes: energy storage price constraint, charge and discharge power constraint, power variation constraint of a shared energy storage power station and state of charge constraint. The first constraint is explained below. It should be noted that, in the present application, the parameters of each formula are consistent with each other, so the parameters of the formula that have been presented in the foregoing are not described herein again.
(1) Energy storage electricity price constraint
In the linkage process of the data center and the shared energy storage power station, a certain profit space is ensured to be possessed by the shared energy storage party, and the constraint of the energy storage electricity price is shown as a formula (13):
r min ≤r ses (t)≤r max (13)
wherein r is min And r max Respectively the lowest and the highest electricity prices of the shared energy storage power station.
(2) Sharing energy storage power station charge-discharge power constraints
Wherein mu is t1 Sum mu t2 The state of the energy storage power station shared in the t period is expressed by Boolean variable;respectively representing the maximum charge and discharge power of the shared energy storage power station.
(3) Shared energy storage power station power variation constraint
Similar to the data center power variation constraint, the short-term power variation of the shared energy storage power station due to charging and discharging should not exceed a maximum limit value, and the constraint is expressed as:
|P ses (t+1)-P ses (t)|≤ψ|P ses (t)| (15)
wherein P is ses (t) represents the power charged and discharged by the shared energy storage power station at the moment t (if the power is positive, the power is charged, and if the power is negative, the power is discharged); and psi represents the maximum difference in power variation at adjacent moments of the shared energy storage power station.
(4) State of charge constraints
The charge of the shared energy storage power station at each moment is between the upper limit and the lower limit, as shown in a formula (18):
wherein Q (t) is the electric quantity of the shared energy storage power station in the t period; u represents the self-discharge rate of the shared energy storage power station (which can be ignored in general); η (eta) + And eta - Respectively representing the charging and discharging efficiency of the energy storage power station; SOC (State of Charge) min 、SOC max For sharing the minimum value and the maximum value of the stored energy charge state.
Thus, the first objective function (equation (12)) and the first constraint (equations (13) - (18)) constitute a shared energy storage optimization model.
On the other hand, the minimum energy consumption cost of each period of the data center is taken as a second target, and a second objective function is generated. Meanwhile, a second constraint condition is established by combining the electric power of the data center with the green certificate transaction. And then, generating a data center optimization model based on the second objective function and the second constraint condition.
For calculation of energy consumption costs reference is made to the relevant description of the previous 320. According to some embodiments, the second objective function may be represented by the following equation (19):
min COST＝C fire +C res +C ses +C ce +C gc (19)
where COST represents the typical total daily operating energy COSTs of the data center; c (C) fire Representing the cost of purchasing thermal power by a data center through a power grid; c (C) res Representing the cost of purchasing renewable energy power by a data center; c (C) ses Representing the electricity purchasing cost of the data center through the shared energy storage power station; c (C) ce Carbon emission costs representing data center energy usage; c (C) gc Representing the green certification cost of the data center's energy usage.
For a second objective function, the second constraint conditions within the data center operating cycle mainly include: the power balance constraint, the power variation constraint of the data center, the green evidence cost constraint and the carbon emission constraint.
(1) Electrical power balance constraint for data center
The power balance constraint of the data center includes 2 parts. In one aspect, the data center purchases electricity via each purchase electricity mode to be balanced with the total electricity power of the data center, namely, the electricity power of the data center, the charge and discharge power of the shared stored energy, and the electricity power purchased by the power grid meet the electric power balance constraint at any moment, and the constraint is expressed as the following formula (20). On the other hand, the respective power of the electric loads of each class is balanced with the total electric power, and this constraint is expressed as the following formula (21). Furthermore, regarding the demand response constraints of the electrical loads of the respective categories, the relevant description of fig. 1 can be seen from the foregoing.
In the method, in the process of the invention,the power of the data center purchased electricity from the shared energy storage power station at the time t is represented; p (P) DC (t) represents the total power consumption of the data center at the time t; p (P) t base The power corresponding to the base load of the data center in period t.
(2) Data center power variation constraints
In a scheduling period, the power consumption of the data center should not change greatly in a short time (similar to the climbing rate limit of a generator set), and the difference value of the power at adjacent moments should not exceed the maximum value, wherein the constraint is expressed as:
|P DC (t+1)-P DC (t)|≤σ|P DC (t)| (22)
(3) Cost constraint for green evidence
In the formula, according to a renewable energy consumption guarantee mechanism, the purchasing of clean power by a data center meets the renewable energy consumption responsibility weight theta specified by the area GR ；W GR Representing the corresponding amount of power the data center purchased for the green license.
(4) Data center carbon emission constraints
Carbon emissions from data centers are mainly caused by power consumption of IT equipment and other infrastructure such as servers, while emergency back-up power supplies (e.g., diesel generators) are typically used only in sudden emergency situations. Therefore, the embodiment mainly considers carbon emission caused by electricity purchase of the data center from the power grid and carbon emission caused by the consumption of a small amount of renewable energy sources. This constraint is expressed as:
in the method, in the process of the invention,representing actual carbon emissions of the data center; x-shaped articles fire Representing a conversion coefficient of electric quantity from a power grid (conversion coefficient of electric quantity into corresponding carbon emission); x-shaped articles res And the power conversion coefficient of the renewable energy source power generation is represented.
Carbon emission allowance (i.e., hyperbranched carbon emission Δe) that data centers need to purchase CO2 ) I.e., the difference between the actual carbon emissions and the carbon allotment for the data center, is as follows:
in the method, in the process of the invention,representing the amount of carbon emissions quota evenly distributed by the data center to each scheduling period.
Thus, the second objective function (equation (19)) and the second constraint (equations (20) - (25), and equations (1) - (6)) constitute the data center optimization model.
Thus, the shared energy storage optimization model is used as an upper model, and the data center optimization model is used as a lower model, so that the joint scheduling model is formed. According to the embodiment of the application, the joint scheduling model is a master-slave game model. In summary, according to the transaction manner and rules of the novel market subject such as the shared energy storage power station and the data center, the joint scheduling model is assumed as follows:
(1) The shared energy storage power station and the data center are all completely rational decision makers;
(2) When the shared energy storage power station is charged, a long-term electricity purchasing protocol is directly signed with a renewable energy power generation enterprise, and green electricity is obtained at a lower price;
(3) The data center purchases power from the shared energy storage power station in a time-sharing electricity price mode, and capacity lease fees are not paid any more;
(4) The data center purchases the thermal power through the power grid, and also purchases part of green electricity through the power grid, and the green electricity price has certain premium compared with the thermal power price.
The joint scheduling model is then solved 340 to obtain scheduling results including the time-of-use price of the shared energy storage power station and the power usage of the data center.
According to some embodiments, the step of solving the joint scheduling model comprises: firstly, solving a shared energy storage optimization model by utilizing a genetic algorithm, determining the time-of-use electricity price of a shared energy storage power station, and solving a data center optimization model by utilizing the determined time-of-use electricity price so as to determine the electricity power of a data center; then, the shared energy storage optimization model is solved by utilizing the determined power consumption to update the time-sharing power price of the shared energy storage power station, and the data center optimization model is solved by utilizing the updated time-sharing power price to update the power consumption of the data center; and repeating the steps of iteratively updating the time-of-use electricity price and updating the electricity power on the basis until the preset condition is met, and obtaining a solution satisfying both the upper layer model and the lower layer model, namely a scheduling result.
FIG. 4 shows a flow chart for solving a joint scheduling model, and referring to FIG. 4, a specific solving flow is as follows:
(1) Reading daily electricity load data of a data center and shared energy storage related parameters to initialize parameters of a shared energy storage power station and the data center, wherein k=0, the number of iterations is set to be n, the number of iterations is m, the population variation rate is alpha, the crossover probability is beta, and the convergence error is epsilon;
(2) Generating n groups (as shown in fig. 4, n is 40) of electricity selling prices (namely time-sharing electricity prices) of the shared energy storage power stations at random initially by utilizing a genetic algorithm, and transmitting parameters to a lower layer data center optimization model;
(3) And the data center receives the electricity selling prices of the n groups of shared energy storage power stations, calculates electricity consumption conditions after the data center demand response, and calculates electricity consumption cost. For example, a YALMIP+CPLEX solver is called by MATLAB to solve the power consumption of each period of the data centerRate and purchase from grid and shared energy storage plant, current COST is calculated and maintained k The purchased electricity (i.e., the power used, as shown in fig. 4, power signal) is then returned to the shared energy storage power station;
(4) The shared energy storage power station calculates the individual fitness value of the shared energy storage power station according to the electricity purchasing strategy fed back by the data center, and retains the current optimal individual (namely, the income value)；
(5) Generating new electricity selling price of the shared energy storage power station by utilizing selection and variation of a genetic algorithm, wherein k=k+1, repeating the steps (3) - (5), and calculating electricity COST COST of the new obtained data center k Benefit of' and shared energy storage power stations
(6) If it isThen->COST k+1 ＝COST′ k The method comprises the steps of carrying out a first treatment on the surface of the Otherwise->COST k+1 ＝COST k ；
(7) If it isAnd |COST k+1 -COST k The method is finished, wherein the I is less than or equal to epsilon (namely, the preset condition is met, and the maximum iteration times are reached); otherwise, returning to the step (3).
According to the joint scheduling method 300 of the data center and the shared energy storage power station, firstly, the operation characteristics of the shared energy storage power station and the data center are considered, a linkage system of the shared energy storage power station and the data center is determined, the shared energy storage power station determines a charging and discharging strategy and a time-of-use electricity price according to historical electricity consumption conditions of the data center, and the data center determines self electricity consumption power by combining the demand response characteristics of an electricity consumption load. Secondly, by constructing a joint scheduling model of the shared energy storage power station and the data center, the price guiding function of the shared energy storage power station is considered, the demand response potential of the data center is mined, the shared energy storage time-sharing electricity price and the charging and discharging strategy under the linkage of the two are adopted, and the electricity consumption power (namely, a load scheduling scheme) of the data center is adopted, so that the electricity consumption cost of the data center can be remarkably reduced, and the benefit value of the shared energy storage power station is improved. This has important significance for improving energy utilization efficiency, balancing power system loads and promoting sustainable development.
To further illustrate the utility of the method 300 according to the present application, the cluster of data centers in the existing region a is targeted for electricity demand by grid purchasing (including thermal power and renewable energy power) and shared energy storage power station purchasing multiple source energy. The time length of the unit scheduling period is 1h, and the time length of the unit scheduling period is 1 day. The method 300 of the application is applied to jointly schedule the data center and the shared energy storage power station in the area A.
The relevant parameters are shown in Table 2, and if the profit model of the shared energy storage power station adopts capacity lease, the profit value is 200 yuan/kWh-year. The electrical load curves and flexible load parameters for a typical day of a data center cluster are shown in fig. 5 and table 3, respectively.
Table 2 example related parameters
Table 3 data center load parameters
The data center can refer to the sales electricity price of the power grid specified by the place in each period of electricity (thermal power) purchased from the power grid in one day. In general, the green electricity price is 0.03-0.05 yuan/kilowatt-hour based on the thermal power price, and the embodiment takes the thermal power price of 0.04 yuan/kilowatt-hour, so that the data center in the embodiment purchases the green electricity from the power grid at each time interval, namely, the electricity price is respectively as follows: peak period 1135.7 yuan/MWh, peak period 952.9 yuan/MWh, low valley period 330.1 yuan/MWh.
To more intuitively verify the effectiveness of the proposed data center and the linkage model of the shared energy storage, the following four scenarios are set forth herein for comparative analysis, as shown in table 4:
table 4 contrast scene settings
And then solving the joint scheduling model, and finally enabling the shared energy storage gain and the cost of the data center to gradually approach a relatively stable value when the iteration number reaches 30 times.
The benefits of sharing energy storage power stations in different scenarios and the electricity cost of the data center are shown in table 5. It can be seen that the total cost of daily operation of the data center is the lowest in scenario 4 (i.e., the scenario under method 300) and the benefit of sharing the energy storage power plant is the greatest. Comparing scenario 1 and scenario 3 (or scenario 2 and scenario 4) can see that when the shared energy storage power station is linked with the data center, the income ratio of the shared energy storage power station is increased when renting with the capacity, and the total cost of the data center is also obviously reduced, which means that the shared energy storage power station is linked with the data center, and the operation economy of the shared energy storage power station and the data center can be improved. Comparing scenario 3 and scenario 4 (or scenario 1 and scenario 2) it can be seen that the overall cost of the data center is greatly reduced when considering the data center participation in the demand response.
TABLE 5 comparison of economics under different scenarios
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In addition, in the background of the maximum income of the shared energy storage power station and the minimum cost of the data center, the price of the shared energy storage power station in the time-sharing electricity price of 2-6, 9-16, 19-21 and other time periods is lower than the price of the slave time-sharing electricity price, the shared energy storage power station tends to select discharge, and the data center is encouraged to purchase electricity from the shared energy storage power station preferentially in the electricity supply tension time period through the lower price, so that the method has a positive effect on promoting peak electricity conservation; the selling price of the shared energy storage power station is greater than the time-sharing electricity price in the period of 17-18 hours, which indicates that the shared energy storage power station tends to select charging and promotes new energy consumption in the period of low-ebb. Therefore, under the pricing strategy, the shared energy storage power station plays a role in peak clipping and valley filling while meeting the maximum benefit of the shared energy storage power station.
Meanwhile, the change condition of the charge quantity of the shared energy storage power station and the charge and discharge power of the shared energy storage power station at different moments are analyzed to obtain: the charging time of the shared energy storage power station is concentrated in a period with lower power prices of the power grids such as 1, 7-8, 11-12 and the like, the energy storage equipment is charged with the maximum charging power until the maximum storage limit of the system is reached, and the data center selects the power grid to use in the period to meet the self power consumption requirement; in the period of high electricity price of the power grid and electricity consumption peak of the data center, such as the period of 9-10, 15-16 and the like, the energy storage equipment discharges in the period, so that the method meets the normal principle.
And further analyzing the electricity load of each period of the data center after the shared energy storage power station is linked with the data center. Load shedding mainly occurs in 10, 13-17, 19-20, etc., and occurs in most electricity price peak periods. The transferable load is split from the original 13-16 time period to 4-8 five time periods, and the load duration is changed. After the data center participates in demand response, the flexible electric load generally shows a trend of transferring from the electricity consumption peak period to the valley period and the normal period, so that the new energy consumption is promoted; and the load is reduced to different degrees, the reduction time period is mostly the electricity consumption peak period, the power supply pressure in the power grid peak period is further relieved, and the dispatching economy is ensured.
According to the analysis, the data center is linked with the shared energy storage power station, so that the energy consumption cost of the data center can be effectively reduced on the premise of guaranteeing the reliability and stability of the data center, the benefit requirement of the data center is met, and the double-carbon target and the green low carbon are realized.
In summary, aiming at the problem of joint scheduling of the data center and the shared energy storage, the scheme of the application determines the selling price of the shared energy storage power station based on the time-of-use electricity price as a research basis, takes interaction of the shared energy storage power station and the data center as a research framework, and constructs a joint scheduling model of master-slave games of the data center and the shared energy storage power station on the basis of considering the demand response characteristics of the data center, the carbon emission cost, the green evidence cost and the renewable energy consumption guarantee mechanism, thereby obtaining the optimal strategy of the time-of-use electricity price of the shared energy storage power station and the electricity consumption of the data center. The main work and conclusions are as follows:
(1) The master-slave game linkage model taking the shared energy storage power station as a master and the data center as a follower is established, the benefit value of the shared energy storage power station is effectively improved by joint scheduling, and meanwhile, the total cost of the data center is reduced to a certain extent;
(2) After the data center is considered to participate in demand response, a time-sharing electricity price mechanism can be combined, so that the electric energy demand in the peak time is reduced, the peak clipping and valley filling effects are achieved, and win-win between shared energy storage and the data center is realized;
(3) The time-of-use electricity price strategy of the shared energy storage power station can adjust the energy consumption plan of the data center through price signals, so that the energy consumption cost of the data center is reduced while the income value of the shared energy storage power station is improved, the load fluctuation is stabilized, and the benefits of both game parties are considered.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present application, or certain aspects or portions of the methods and apparatus of the present application, may take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard drives, U-drives, floppy diskettes, CD-ROMs, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the application.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to execute the joint scheduling method of the data center and the shared energy storage power station of the present application according to the instructions in the program code stored in the memory.
By way of example, and not limitation, readable media comprise readable storage media and communication media. The readable storage medium stores information such as computer readable instructions, data structures, program modules, or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of readable media.
In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the examples herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present application is not directed to any particular programming language. It should be appreciated that the contents of the present application described herein can be implemented using a variety of programming languages, and that the above description of specific languages is provided for disclosure of preferred embodiments of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various application's aspects. However, the method of this application should not be interpreted as reflecting the intent: i.e., the claimed application requires more features than are expressly recited in each claim. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into a plurality of sub-modules.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Furthermore, some of the embodiments are described herein as methods or combinations of method elements that may be implemented by a processor of a computer system or by other means of performing the functions. Thus, a processor with the necessary instructions for implementing the described method or method element forms a means for implementing the method or method element. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is for performing functions performed by elements for purposes of this disclosure.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner. Furthermore, the number word "plurality" means "two" and/or "more than two".
While the application has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the foregoing description, will appreciate that other embodiments are contemplated within the scope of the application as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the subject matter of the application. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present application is illustrative, but not limiting, of the scope of the application, which is defined by the appended claims.

Claims (10)

1. A joint scheduling method of a data center and a shared energy storage power station comprises the following steps:
determining a profit value of a shared energy storage power station based at least on the electricity consumption condition of a data center, wherein the shared energy storage power station acquires renewable energy power storage and sells the stored power to the data center in a time-of-use electricity price form;
determining energy consumption cost of the data center based on at least electricity prices of each time period of each electricity purchasing mode, wherein the electricity purchasing mode at least comprises: purchasing power via a power grid and the shared energy storage power station, wherein the power purchased via the power grid includes thermal power and renewable energy power;
respectively taking the maximum profit value of the shared energy storage power station as a first target and the minimum energy consumption cost of each period of the data center as a second target, correspondingly generating a shared energy storage optimization model and a data center optimization model, and constructing a joint scheduling model of the data center and the shared energy storage power station;
and solving the joint scheduling model to obtain a scheduling result comprising the time-of-use electricity price of the shared energy storage power station and the electricity power of the data center.
2. The method of claim 1, wherein the solving the joint scheduling model comprises:
Solving the shared energy storage optimization model by utilizing a genetic algorithm, determining the time-of-use electricity price of the shared energy storage power station, and solving the data center optimization model by utilizing the determined time-of-use electricity price so as to determine the electricity power of the data center;
solving the shared energy storage optimization model by utilizing the determined power consumption to update the time-of-use power price of the shared energy storage power station, and solving the data center optimization model by utilizing the updated time-of-use power price to update the power consumption of the data center;
repeating the steps of updating the time-of-use electricity price and updating the electricity power until the preset condition is met, and obtaining the scheduling result.
3. The method of claim 1 or 2, wherein the generating the shared energy storage optimization model and the data center optimization model with the maximum profit value of the shared energy storage power station as a first target and the minimum energy consumption cost of each period of the data center as a second target respectively includes:
taking the maximum profit value of the shared energy storage power station as a first target, and generating a first objective function;
taking charge and discharge power and electricity price of the shared energy storage power station in an operation period into consideration, and establishing a first constraint condition;
Generating the shared energy storage optimization model based on the first objective function and the first constraint condition;
taking the minimum energy consumption cost of each period of the data center as a second target, and generating a second objective function;
establishing a second constraint condition by combining the electric power of the data center with the green certificate transaction;
the data center optimization model is generated based on the second objective function and the second constraint condition.
4. The method of any of claims 1-3, wherein the determining a revenue value for the shared energy storage power plant based at least on a power usage of the data center comprises:
determining the income of the shared energy storage power station for selling electricity to the data center based on the electricity consumption condition of the data center;
a revenue value for the shared energy storage power plant is determined based on the electricity sales revenue, the cost of purchasing renewable energy power, and the operating cost of the shared energy storage power plant.
5. The method of any of claims 1-4, wherein the determining the energy consumption cost for each time period of the data center based at least on the electricity prices for each time period comprises:
determining electricity purchasing cost of the data center based on time-of-use electricity prices of all electricity purchasing modes, wherein the electricity purchasing cost at least comprises cost of purchasing thermal power, cost of purchasing renewable energy power and cost of purchasing electricity through a shared energy storage power station;
And taking the sum of the electricity purchase cost, the carbon emission cost and the green certificate cost as the energy consumption cost of the data center, wherein the carbon emission cost is determined by the price of the carbon emission quota and the hyperbranched carbon emission amount, and the green certificate cost is determined by the price of the green certificate transaction and the number of green certificates required to be purchased by the data center.
6. The method of claim 3, wherein,
the first constraint includes: energy storage price constraint, charge-discharge power constraint, power variation constraint of a shared energy storage power station and state of charge constraint;
the second constraint includes: the power balance constraint, the power variation constraint of the data center, the green evidence cost constraint and the carbon emission constraint.
7. The method of claim 6, wherein the power balancing constraint comprises:
the data center purchases electric power in a power purchase mode to be balanced with the total electric power of the data center;
and classifying the electric loads of the data center based on the demand response, wherein the electric loads of each class are balanced with the total electric power, and the electric loads of each class meet the demand response constraint condition.
8. The method of claim 7, wherein the respective classes of electrical load corresponding power are balanced with total electrical power, comprising:
P DC (t)＝P t base +P t tran +P t cut* -P t cut ，
Wherein P is DC (t) represents the total power consumption of the data center at the time t; p (P) t base The power corresponding to the base load of the data center at the time t is obtained; p (P) t tran Representing the power corresponding to the transferable load of the data center at the time t; p (P) t cut* The power corresponding to the load can be reduced before the participation demand response scheduling corresponding to the time t; p (P) t cut And (5) cutting down the power corresponding to the load after the participation demand response corresponding to the time t is scheduled.
9. A computing device, comprising:
one or more processors;
a memory;
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing the method of any of claims 1-8.
10. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the method of any of claims 1-8.
CN202311287780.8A 2023-10-07 2023-10-07 Combined scheduling method and computing equipment for data center and shared energy storage power station Pending CN117353290A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117539726A (en) * 2024-01-09 2024-02-09 广东奥飞数据科技股份有限公司 Energy efficiency optimization method and system for green intelligent computing center
CN117539726B (en) * 2024-01-09 2024-04-26 广东奥飞数据科技股份有限公司 Energy efficiency optimization method and system for green intelligent computing center

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117539726A (en) * 2024-01-09 2024-02-09 广东奥飞数据科技股份有限公司 Energy efficiency optimization method and system for green intelligent computing center
CN117539726B (en) * 2024-01-09 2024-04-26 广东奥飞数据科技股份有限公司 Energy efficiency optimization method and system for green intelligent computing center

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