CN113285523A - Multi-station fusion data center optimization method and system based on data migration - Google Patents
Multi-station fusion data center optimization method and system based on data migration Download PDFInfo
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- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
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Abstract
The invention discloses a multi-station fusion data center optimization method and a system based on data migration, wherein the method comprises the following steps: step 1, establishing a data center load model based on operation parameters of a multi-station fusion data center of data migration, and acquiring a power balance constraint condition of the data center based on the data center load model; and 2, calculating the minimized comprehensive cost based on the energy balance constraint condition, and realizing the migration of the data in the data center based on the minimized comprehensive cost. The optimization method of the invention aims at minimizing the comprehensive cost and realizes the data migration in the data center through data processing delay constraint, and reduces the configuration capacity of the energy storage power station and saves the electricity consumption cost and the construction cost of the power grid on the premise of meeting the load requirement of the data center by utilizing the peak clipping and valley filling functions of the energy storage power station on the load. Meanwhile, theoretical basis and technical support are provided for the urban multi-station fusion construction and operation, and the method has good practical value.
Description
Technical Field
The invention relates to the field of data processing, in particular to a multi-station fusion data center optimization method and system based on data migration.
Background
In recent years, in order to solve the problems of unbalanced and insufficient development of the energy industry and the like, China puts forward the aim of building an energy internet, and requires to build a cleaner, low-carbon, safe and efficient energy system, and multi-station fusion is an important ring for the energy internet to be implemented to a terminal level. The multi-station integration is to integrate a transformer substation, a data center station, an energy storage station, a photovoltaic power station, a charging/converting station, a 5G communication base station and the like, and integrate multi-station resources by taking the transformer substation as a junction platform to form a multifunctional station mode of 'transformer substation + X'.
In the prior art, a plurality of substations and energy storage stations are analyzed in multi-station fusion, and the analysis on the influence of a data center station on a power grid is relatively less. With the rise of edge computing and the increase of the demand of customers on the quality of electric energy, data centers become more and more important, and energy conservation and consumption reduction as well as data migration strategies of the data centers are widely concerned. Generally speaking, the energy consumption of a data center mainly includes the energy consumption of data processing and the energy consumption of equipment operation, and optimization analysis is also performed in the prior art from both aspects. However, in the prior art, a single data center is mostly taken as a research object, and the cooperative operation of the data center and other functional stations is not involved. Therefore, a new optimization method for a multi-station fusion data center is needed to implement collaborative intersection and operation optimization of a data center station, a transformer substation, an energy storage station and other functional stations in a multi-station fusion operation mode.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a multi-station fusion data center optimization method based on data migration, which calculates the minimum comprehensive cost by establishing an energy balance constraint condition of a data center and realizes the migration of data in the data center based on the minimum comprehensive cost.
The invention adopts the following technical scheme.
The invention relates to a multi-station fusion data center optimization method based on data migration, which comprises the following steps: step 1, establishing a data center load model based on operation parameters of a multi-station fusion data center in data migration, and acquiring an energy balance constraint condition of the data center based on the data center load model; and 2, calculating the minimized comprehensive cost based on the energy balance constraint condition, and realizing the migration of the data in the data center based on the minimized comprehensive cost.
Preferably, step 1 further comprises: data center load model Ps＝Pi，t+Pk+Pz(ii) a Wherein, Pi，t、Pk、PzRespectively IT equipment energy consumption, refrigeration equipment energy consumption and other energy consumption.
Preferably, the IT equipment energy consumption comprises server energy consumption, and the server energy consumption is
Wherein, Ps，tFor the server power consumption at time t, PwAnd PslRespectively the operating power and the sleep power of the server, ntAnd mtThe number of the servers in the working state and the number of the servers in the dormant state at the moment t are respectively, and N is the total number of the servers in the data center.
Preferably, the energy balance constraint of the data center is Ps+Pc，t＝Pg，t+Pd，t+Pl，tWherein P issIs the power consumption of the data center at time t, Pc，tAnd Pd，tRespectively the charging quantity and the power consumption P of the energy storage power station where the data center is positioned at the moment tg，tFor the electricity purchasing quantity, P, of the energy storage power station in which the data center is located at time tl，tAnd the generated power of the photovoltaic power station at the moment t.
Preferably, the charging amount P of the energy storage power station where the data center is located at the moment tc，tAnd power consumption amount Pc，tThe method is obtained by calculation based on a battery charge-discharge model in data migration; and, the battery charging and discharging model in the energy storage power station is
Therein, SOCt-1And SOCtThe battery states at time t-1 and time t, respectively, C is the battery capacity, ηcAnd ηdCharge-discharge efficiency, P, of individual cellscAnd PdIs the charge and discharge power of the battery.
Preferably, the constraint condition of the battery state in the energy storage power station is SOCmin≤SOCt≤SOCmaxWherein, SOCminAnd SOCmaxMaximum and minimum states of charge of the battery, respectively; the constraint condition of the battery charge-discharge efficiency in the energy storage power station is
Wherein alpha isc，tAnd alphad，tThe charge-discharge state of the battery at time t, Pc，maxAnd Pd，maxThe maximum charge and discharge power of the battery, respectively.
Preferably, the generated power of the photovoltaic power station at the moment t is the output P of the photovoltaic power station at the moment tl，t＝ηle·pl，t·slWherein n isleFor the photoelectric conversion efficiency, p, of photovoltaic power stationsl，tIs the intensity of light per unit area at time t, slThe installation area of the photovoltaic power station.
Preferably, step 2 further comprises: the minimized comprehensive cost is min F ═ Fi+Fm+FgWherein F is the overall cost, FiFor investment costs, FmFor operating costs, FgThe cost is the electricity purchasing cost.
Preferably, the investment cost Fi＝Fi，c+Fi，jWherein F isi，cFor investment costs of energy-storage power stations, Fi，jThe investment cost for the photovoltaic power station; and the investment cost of the energy storage power station is
Wherein p isiThe unit capacity investment cost of the energy storage power station, C is the battery capacity, and f is an investment cost factor; the investment cost of the photovoltaic power station is
Wherein f iscoIs the coefficient of recovery of the photovoltaic power plant, CwpFor the installation capacity of photovoltaic systems，fevThe average investment cost per kilowatt of the photovoltaic power station.
Preferably, the recovery factor of the photovoltaic power plant is
Wherein r is the current rate, i.e. the ratio of the predicted value to the current value of the photovoltaic system, and n is the depreciation age of the photovoltaic system.
Preferably, the running cost is Fm＝Fm，s+Fm，lWherein F ism，sFor the operating costs of the data center, Fm，lThe operating cost of the energy storage power station; and the operating cost of the data center is
Wherein, Ps，tIs the power of the server at time t, pmCost per unit power; the operating cost of the energy storage power station is
Wherein k ismFor the operational maintenance factor, P, of a photovoltaic power plantl，tAnd the photovoltaic output power of the photovoltaic power station at the moment t is obtained.
Preferably, the electricity purchasing cost is
Wherein, Pg，tΔ t is the amount of electricity purchased at time t, ptThe unit electricity price of the electricity purchased from the power grid at the moment t.
Preferably, when migration of data in the data center is achieved with the aim of minimizing the integrated cost, the amount of data to be migrated is determined based on data processing delay constraints.
Preferably, the data processing latency is constrained to
Where μ is a performance index of the server, λtTo complete the total amount of data in the migrated data center, TdIs the maximum response time.
A second aspect of the present invention relates to a data migration-based multi-station fusion data center optimization system of the data migration-based multi-station fusion data center optimization method according to the first aspect of the present invention, which includes a plurality of photovoltaic power stations, a plurality of substations, a plurality of energy storage power stations, a plurality of data centers, and a data load migration server, wherein each of the plurality of photovoltaic power stations is connected to each of the plurality of substations, and transmits electric energy to each of the plurality of substations; each of the plurality of energy storage power stations corresponds to and is connected with each of the plurality of data centers one by one, and each of the plurality of transformer substations is accessed to charge and discharge the energy storage power stations; the multiple substations are connected with one another to transmit electric energy to a power grid; each of the data centers receives the electric energy information from the energy storage power stations and the transformer substations, and interacts the received electric energy information with other data centers based on the indication of the data load migration server. Compared with the prior art, the method and the system for optimizing the multi-station fusion data center based on data migration can obtain the minimum comprehensive cost of the multi-station fusion data center and realize the migration of data in the data center based on the minimum comprehensive cost and data processing delay constraint.
The beneficial effects of the invention also include:
1. data centers of different scales can be built based on transformer substations of different configurations, so that the service quality of the data centers is guaranteed, and meanwhile, the data processing cost is remarkably reduced by adopting a method of transferring data loads to the data centers with lower electricity prices.
2. The capacity of the energy storage system of the data center can be optimally configured, the energy storage can participate in the operation of a peak clipping and valley filling power grid by reserving spare power consumption in the data center, a power load curve is optimized, the operation electricity charge is reduced, and the operation stability of the system is improved. When the energy storage power station is in operation, the energy storage battery can store the power consumption to prevent power failure, thereby improving the stability and reliability of the operation of the data center.
3. The consumption level of new energy in the power grid can be improved by accessing the photovoltaic power station, and meanwhile, the service fusion is realized by optimizing resource allocation of the photovoltaic power station, the energy storage power station and the data center which are integrated, and the operation cost is reduced.
Drawings
FIG. 1 is a schematic diagram of a method flow in a multi-station fusion data center optimization method and system based on data migration according to the present invention;
fig. 2 is a system operation architecture diagram in a multi-station fusion data center optimization method and system based on data migration according to the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Fig. 1 is a schematic diagram of a method flow in a multi-station fusion data center optimization method and system based on data migration. As shown in fig. 1, a method for optimizing a multi-station converged data center based on data migration is characterized by comprising a step 1 and a step 2.
Step 1, establishing a data center load model based on operation parameters of a multi-station fusion data center in data migration, and acquiring an energy balance constraint condition of the data center based on the data center load model.
Preferably, the data center load model Ps＝Pi，t+Pk+Pz(ii) a Wherein, Pi，t、Pk、PzRespectively IT equipment energy consumption, refrigeration equipment energy consumption and other energy consumption. Specifically, the electrical loads in the data center mainly come from IT equipment, air conditioning systems, lighting and other systems, and the ratio of the energy consumption of the three systems is about 5: 4: 1.
Preferably, the IT equipment energy consumption comprises server energy consumption, and the server energy consumption is
Wherein, Ps，tFor the server power consumption at time t, PwAnd PslRespectively the operating power and the sleep power of the server, ntAnd mtThe number of the servers in the working state and the number of the servers in the dormant state at the moment t are respectively, and N is the total number of the servers in the data center.
Specifically, the IT device energy consumption mainly includes server energy consumption, communication device energy consumption, and storage device energy consumption. The server energy consumption is the main energy consumption of the IT equipment, and the server energy consumption is usually the energy consumed in data processing, so that the energy consumption usually occupies 80% of the energy consumption of all the IT equipment more stably. Therefore, the energy consumption of the IT equipment can be estimated only by establishing a server energy consumption model, and meanwhile, the total energy consumption of the data center can be estimated in proportion according to the energy consumption of the IT equipment.
In general, a server generally includes three states, an active state, a sleep state, and a power-off state. The server may be used to process received load data when in an active state, but the relative power consumption is higher when the server is in an active state. In addition, the server can be in a dormant state, in the dormant state, the server can be waken up at any time according to the received data exceeding the load, and the power consumption of the server in the dormant state is low.
According to the content, the load models of the data center at different time t moments can be accurately obtained. And according to the load model of the data center, the energy balance condition of the data center can be further obtained.
Preferably, the energy balance constraint of the data center is Ps+Pc，t＝Pg，t+Pd，t+Pl，tWherein P issIs the power consumption of the data center at time t, Pc，tAnd Pd，tRespectively the charging quantity and the power consumption P of the energy storage power station where the data center is positioned at the moment tg，tFor the electricity purchasing quantity, P, of the energy storage power station in which the data center is located at time tl，tAnd the generated power of the photovoltaic power station at the moment t.
It is worth noting that the energy balance constraint of the data center is that the power consumption of the data center is equal to the power obtained during the daily operation of the data center. Specifically, the power consumption of the data center can be obtained according to the sum of the load model of the data center and the charging amount of the energy storage power station, and the power quantity obtained by the data center is the sum of the power consumption of the energy storage power station where the data center is located, the purchasing power quantity and the power generation quantity of the photovoltaic power station. Therefore, in order to obtain the energy balance constraint condition of the data center, the value of the electric quantity needs to be calculated.
Preferably, the charging amount P of the energy storage power station where the data center is located at the moment tc，tAnd power consumption amount Pc，tThe method is obtained by calculation based on a battery charge-discharge model in data migration; and, the battery charging and discharging model in the energy storage power station is
Therein, SOCt-1And SOCtThe battery states at time t-1 and time t, respectively, C is the battery capacity, ηcAnd ηdCharge-discharge efficiency, P, of individual cellscAnd PdIs the charge and discharge power of the battery.
Preferably, the constraint condition of the battery state in the energy storage power station is SOCmin≤SOCt≤SOCmaxWherein, SOCminAnd SOCmaxMaximum and minimum states of charge of the battery, respectively; the constraint condition of the battery charge-discharge efficiency in the energy storage power station is
Wherein alpha isc，tAnd alphad，tThe charge-discharge state of the battery at time t, Pc，maxAnd Pd，maxThe maximum charge and discharge power of the battery, respectively.
Preferably, the generated power of the photovoltaic power station at the moment t is the output P of the photovoltaic power station at the moment tl，t＝ηle·pl，t·slWherein n isleFor the photoelectric conversion efficiency, p, of photovoltaic power stationsl，tIs the intensity of light per unit area at time t, slThe installation area of the photovoltaic power station.
And 2, calculating the minimized comprehensive cost based on the energy balance constraint condition, and realizing the migration of the data in the data center based on the minimized comprehensive cost.
It is worth to say that the constraint condition P of the energy balance can be calculated based on the physical characteristic index of the power grids+Pc，t＝Pg，t+Pd，t+Pl，tIn the formula, except the purchased electric quantity P at the time tg，tThe exact values of all variables except. Therefore, the power purchase amount P at the time t can be determined by the constraint conditiong，t. The electricity purchasing quantity at the time t is calculated, and the electricity price in the power grid at the time t can be acquired from the power grid electricity purchasing platform, so that the electricity purchasing cost at the time t can be acquired, and the minimized comprehensive cost is finally acquired.
Preferably, the minimum overall cost is min F ═ Fi+Fm+FgWherein F is the overall cost, FiFor investment costs, FmFor operating costs, FgThe cost is the electricity purchasing cost.
Preferably, the investment cost Fi＝Fi，c+Fi，jWherein F isi，cFor investment costs of energy-storage power stations, Fi，jThe investment cost for the photovoltaic power station; and the investment cost of the energy storage power station is
Wherein p isiThe unit capacity investment cost of the energy storage power station, C is the battery capacity, and f is an investment cost factor; the investment cost of the photovoltaic power station is
Wherein f iscoIs the coefficient of recovery of the photovoltaic power plant, CwpIs the installation capacity of the photovoltaic system, fevThe average investment cost per kilowatt of the photovoltaic power station.
Specifically, the investment cost is closely related to the construction scale of the station in the multi-station fusion process. For example, the number of devices required in the data center, the number and scale of energy storage battery modules, the number of charging piles, that is, the price, and the like. The investment costs typically include both the investment costs of the energy storage power station and the investment costs of the photovoltaic power station. In the process of calculating the investment cost of the energy storage power station, the depreciation rate of the storage battery needs to be considered, and the average storage battery cost in each day can be obtained by dividing the cost of one-time investment of the storage battery by the product of the recovery age and 365 days in one year.
In addition, the investment cost of the photovoltaic power station mainly comprises the cost of main equipment in the photovoltaic power station such as a battery panel, a photovoltaic inverter, an alternating current/direct current cable and a power distribution cabinet. The cost of the photovoltaic power station in each day can be calculated according to the recovery age of the photovoltaic power station.
Preferably, the running cost is Fm＝Fm，s+Fm，lWherein F ism，sFor the operating costs of the data center, Fm，lThe operating cost of the energy storage power station; and the operating cost of the data center is
Wherein, Ps，tIs the power of the server at time t, pmCost per unit power; the operating cost of the energy storage power station is
Wherein k ismFor the operational maintenance factor, P, of a photovoltaic power plantl，tAnd the photovoltaic output power of the photovoltaic power station at the moment t is obtained.
Specifically, the operation cost includes an operation cost of the data center device and an operation cost of the photovoltaic power station. For example, during the operation of a photovoltaic power station, safety monitoring and maintenance of the system are required, and generally speaking, the maintenance cost of the photovoltaic power station is proportional to the amount of power output by the system.
Preferably, the electricity purchasing cost is
Wherein, Pg，tAt time tPurchase amount of electricity, ptThe unit electricity price of the electricity purchased from the power grid at the moment t. Wherein, the electricity purchasing cost is calculated according to the energy balance constraint condition of the data center.
According to the invention, on the premise of ensuring the service quality of the data center, the data load can be transferred to the data center with lower electricity price, so that the data processing cost is obviously reduced. If the peak-valley difference between day and night is reduced for the energy storage power station, the impact and the influence of the load on the power grid can be balanced, the power supply and utilization cost is reduced, and the stability of system operation is improved.
In one embodiment, the energy storage power station may participate in the operation of the peak clipping and valley filling power grid after reserving enough spare power for the data center. For example, in the time period of 0:00-7:00, the price of electricity in the power grid is low, and the energy storage system can charge the storage battery in the time period; at times 8:00-11:00, the electricity prices in the grid are high, and in order to reduce operating costs and supply excess electricity to the grid, the energy storage system may discharge the storage battery during this time period. And in the time period of 11:00-18:00, the energy storage system is recharged, and in the time period of 18:00-23:00, the energy storage system is recharged, and the cycle of charging and discharging is carried out by taking the day as a unit.
Therefore, it is worth noting that the minimum electricity purchasing cost can be obtained by inquiring the electricity purchasing prices at different time under various voltage grade conditions in the local power grid, and further the minimum comprehensive cost can be obtained.
Preferably, when migration of data in the data center is achieved with the aim of minimizing the integrated cost, the amount of data to be migrated is determined based on data processing delay constraints.
Preferably, the data processing latency is constrained to
Where μ is a performance index of the server, λtTo complete the total amount of data in the migrated data center, TdIs the maximum response time.
Specifically, a data processing delay constraint formula
The total amount of data λ that can be processed by the data center within a fixed response time can be determinedt. The determined total amount of data lambdatThe excess data is migrated to other suitable data centers as compared to the existing data in the data center. Other suitable data centers can be data centers corresponding to energy storage power stations with lower electricity purchase prices. By the method, the operation cost of data can be further reduced, and the coordinated supply of the power grid energy storage is realized by purchasing power through the photovoltaic power station, the energy storage power station and the power grid.
The invention relates to a multi-station fusion data center optimization system based on data migration. Fig. 2 is a system operation architecture diagram in a multi-station fusion data center optimization method and system based on data migration according to the present invention. As shown in fig. 2, a multi-station converged data center optimization system based on data migration according to the multi-station converged data center optimization method based on data migration in the first aspect of the present invention includes a plurality of photovoltaic power stations, a plurality of substations, a plurality of energy storage power stations, a plurality of data centers, and a data load migration server.
Wherein each of the plurality of photovoltaic power stations is connected to each of the plurality of substations and delivers electrical energy to each of the plurality of substations; each of the plurality of energy storage power stations corresponds to and is connected with each of the plurality of data centers one by one, and each of the plurality of transformer substations is accessed to charge and discharge the energy storage power stations; the multiple substations are connected with one another to transmit electric energy to a power grid; each of the data centers receives the electric energy information from the energy storage power stations and the transformer substations, and interacts the received electric energy information with other data centers based on the indication of the data load migration server.
It is worth saying that the photovoltaic power station can be constructed on the vacant space on the roof of the data center, and the cost is saved through resource optimization configuration. In addition, the photovoltaic power station, the energy storage power station and the data center which are integrated can be built to multiplex power communication resources, so that the optimal configuration of the resources is effectively realized, the service fusion cross-border application is realized, and the operation cost of the data center is effectively reduced.
Compared with the prior art, the method and the system for optimizing the multi-station fusion data center based on data migration can obtain the minimum comprehensive cost of the multi-station fusion data center and realize the migration of data in the data center based on the minimum comprehensive cost and data processing delay constraint.
The beneficial effects of the invention also include:
1. data centers of different scales can be built based on transformer substations of different configurations, so that the service quality of the data centers is guaranteed, and meanwhile, the data processing cost is remarkably reduced by adopting a method of transferring data loads to the data centers with lower electricity prices.
2. The capacity of the energy storage system of the data center can be optimally configured, the energy storage can participate in the operation of a peak clipping and valley filling power grid by reserving spare power consumption in the data center, a power load curve is optimized, the operation electricity charge is reduced, and the operation stability of the system is improved. When the energy storage power station is in operation, the energy storage battery can store the power consumption to prevent power failure, thereby improving the stability and reliability of the operation of the data center.
3. The consumption level of new energy in the power grid can be improved by accessing the photovoltaic power station, and meanwhile, the service fusion is realized by optimizing resource allocation of the photovoltaic power station, the energy storage power station and the data center which are integrated, and the operation cost is reduced.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (15)
1. A multi-station fusion data center optimization method based on data migration is characterized by comprising the following steps:
step 1, establishing a data center load model based on operation parameters of a multi-station fusion data center of data migration, and acquiring an energy balance constraint condition of the data center based on the data center load model;
and 2, calculating the minimized comprehensive cost based on the energy balance constraint condition, and realizing the migration of the data in the data center based on the minimized comprehensive cost.
2. The method for optimizing the multi-station converged data center based on data migration according to claim 1, wherein the step 1 further comprises:
the data center load model Ps＝Pi,t+Pk+Pz；
Wherein, Pi,t、Pk、PzRespectively IT equipment energy consumption, refrigeration equipment energy consumption and other energy consumption.
3. The method for optimizing the multi-station converged data center based on data migration according to claim 2, wherein the method comprises the following steps:
the energy consumption of the IT equipment comprises the energy consumption of a server, and the energy consumption of the server is
Wherein, Ps,tFor the server power consumption at time t, PwAnd PslRespectively the operating power and the sleep power of the server, ntAnd mtThe number of the servers in the working state and the number of the servers in the dormant state at the moment t are respectively, and N is the total number of the servers in the data center.
4. The data migration-based multi-station fusion data center optimization method according to claim 1, wherein:
the power balance constraint of the data center is Ps+Pc,t＝Pg,t+Pd,t+Pl,tWherein P issPower consumption of data center at time t, Pc,tAnd Pd,tRespectively charging power and discharging power, P, of the energy storage power station at time tg,tFor the electricity purchasing quantity, P, of the energy storage power station in which the data center is located at time tl,tAnd the generated power of the photovoltaic power station at the moment t.
5. The method for optimizing the multi-station converged data center based on data migration according to claim 4, wherein:
charging power P of energy storage power station where data center is located at time tc,tAnd the consumed power Pc,tThe method is obtained by calculation based on a battery charge-discharge model in data migration; and the number of the first and second electrodes,
the battery charging and discharging model in the energy storage power station is SOCt·C＝SOCt-1·C+ηcPcΔt-Pd/ηdΔ t, wherein, SOCt-1And SOCtThe battery states at time t-1 and time t, respectively, C is the battery capacity, ηcAnd ηdCharge-discharge efficiency, P, of individual cellscAnd PdIs the charge and discharge power of the battery.
6. The method for optimizing the multi-station converged data center based on data migration according to claim 5, wherein:
the constraint condition of the battery state in the energy storage power station is SOCmin≤SOCt≤SOCmaxWherein, SOCminAnd SOCmaxMaximum and minimum states of charge of the battery, respectively;
the constraint condition of the charge and discharge efficiency of the battery in the energy storage power station is
Wherein alpha isc,tAnd alphad,tThe charge-discharge ratio of the battery at time t, Pc,maxAnd Pd,maxThe maximum charge and discharge power of the battery, respectively.
7. The method for optimizing the multi-station converged data center based on data migration according to claim 4, wherein:
the generated power of the photovoltaic power station at the t moment is the output P of the photovoltaic power station at the t momentl,t＝ηle·pl,t·slWherein n isleFor the photoelectric conversion efficiency, p, of photovoltaic power stationsl,tIs the intensity of light per unit area at time t, slThe installation area of the photovoltaic power station.
8. The method for optimizing the multi-station converged data center based on data migration according to claim 1, wherein the step 2 further comprises:
the minimized comprehensive cost is min F ═ Fi+Fm+FgWherein F is the overall cost, FiFor the total investment costs of energy-storage and photovoltaic power stations, FmFor the total operating costs of energy storage power stations, photovoltaic power stations and data centers, FgThe cost is the electricity purchasing cost.
9. The method for optimizing the multi-station converged data center based on data migration according to claim 8, wherein:
said investment cost Fi＝Fi,c+Fi,lWherein F isi,cFor investment costs of energy-storage power stations, Fi,lThe investment cost for the photovoltaic power station; and the number of the first and second electrodes,
the investment cost of the energy storage power station is
Wherein p isiThe unit capacity investment cost of the energy storage power station, C is the battery capacity, and f is an investment cost factor;
the investment cost of the photovoltaic power station is
Wherein f iscoIs the coefficient of recovery of the photovoltaic power plant, CwpIs the installation capacity of the photovoltaic system, fevThe average investment cost per kilowatt of the photovoltaic power station.
10. The method for optimizing the multi-station converged data center based on data migration according to claim 9, wherein:
the recovery coefficient of the photovoltaic power station is
Wherein r is the current rate, i.e. the ratio of the predicted value to the current value of the photovoltaic system, and n is the depreciation age of the photovoltaic system.
11. The method for optimizing the multi-station converged data center based on data migration according to claim 8, wherein:
the running cost is Fm＝Fm,s+Fm,l+Fm,cWherein F ism,sFor the operating costs of the data center, Fm,lFor the operating costs of photovoltaic power stations, Fm,cThe operating cost of the energy storage power station; and the number of the first and second electrodes,
the operating cost of the data center is
Wherein, Ps,tIs the power of the server at time t, pmCost per unit power;
the operating cost of the photovoltaic power station is
Wherein k ismFor the operational maintenance factor, P, of a photovoltaic power plantl,tAnd the photovoltaic output power of the photovoltaic power station at the moment t is obtained.
The operating cost of the energy storage power station is
Wherein k ism,c，km,cOperating maintenance factor, P, for charging and discharging respectively of energy storage power stationsc,t，Pd,tThe charging and discharging power of the energy storage power station at the moment t is obtained.
12. The method for optimizing the multi-station converged data center based on data migration according to claim 8, wherein:
the electricity purchasing cost is
Wherein, Pg,tΔ t is the amount of electricity purchased per unit time Δ t, ptThe unit electricity price of the electricity purchased from the power grid at the moment t.
13. The method for optimizing the multi-station converged data center based on data migration according to claim 1, wherein:
when data load migration among data centers is realized by taking the minimum comprehensive cost as a target, the data volume needing to be migrated is determined based on data processing delay constraint.
14. The method for optimizing the multi-station converged data center based on data migration according to claim 13, wherein:
the data processing delay constraint is
Where μ is a performance index of the server, λtTo complete the total amount of data in the migrated data center, TdIs the maximum response time.
15. A data migration-based multi-station converged data center optimization system of the data migration-based multi-station converged data center optimization method according to any one of claims 1 to 14, comprising a plurality of photovoltaic power stations, a plurality of energy storage power stations, a plurality of data centers and a data load migration server, wherein:
each of the photovoltaic power stations is connected with the data centers and the energy storage power stations in a one-to-one correspondence mode, and the photovoltaic power stations are used for charging and conveying electric energy for the data center operation and the energy storage power stations.
Each of the plurality of energy storage power stations corresponds to and is connected with each of the plurality of data centers one by one, and is connected into each of the plurality of substations to charge and discharge the energy storage power station;
each of the data centers receives electric energy information from the energy storage power station and the transformer substation, and interacts with other data centers on the basis of the indication of the data load migration server.
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