Load migration and balanced scheduling model construction method of multi-station fusion transformer substation, power supply optimization method and storage medium

Abstract

The invention provides a method for constructing a load migration and balanced scheduling model of a multi-station fusion substation, which comprises the following steps of: dividing loads into interactive and batch processing workloads according to load characteristics, establishing an interactive workload distribution model and a batch processing workload distribution model, and obtaining workloads processed by a data center at each moment; establishing a data center power model; batch processing workloads serve as migratable loads to participate in power system optimization scheduling, a load migration and balanced scheduling model is established, and the method comprises the steps that a multi-target objective function of the model is established with the highest utilization rate of renewable energy sources in a transformer substation and the minimum peak-valley difference of workloads processed by a data center in a day as targets; and establishing a data processing time delay constraint and a power balance constraint of the model. According to the invention, the migratable workload is migrated to the new energy large-power generation period by using the load migration and balance characteristics of the data center, so that the tension of power supply of the integrated energy system of the transformer substation is balanced, and the operation environmental protection property of the data center is improved.

Description

Technical Field

[0001] The present invention relates to a load migration and balanced dispatching model construction method of a multi-station fusion substation, a power supply optimization method and a storage medium, and belongs to the field of operation control. Background Art

[0002] The diverse application scenarios and business requirements of 5G networks have put forward more stringent requirements for wireless access networks, including flexible wireless resource management requirements, air interface coordination and site collaboration requirements, flexible functional deployment and edge computing requirements, and enhanced network automation management requirements. However, the traditional separate wireless access network D-RAN can no longer meet the diverse requirements of 5G, and the centralized wireless access network C-RAN has emerged under this drive.

[0003] According to the survey, only about 5% of C-RAN equipment rooms currently meet the future 5G evolution needs, and there is a large gap in equipment rooms. Therefore, it is possible to consider in-depth cooperation between power grid companies and telecom operators across the province. Power grid companies provide resources such as substations, data centers, and poles, and provide them with services such as 5G base station deployment and network aggregation node operating environment. Through co-construction and resource reuse, the existing pattern of "one power grid and multiple communication networks" can be broken, and the construction investment of both power grids and communication networks can be reduced.

[0004] In recent years, the integration model of data centers and substations has been developing. The main advantage of integration is that substations can provide power stability and security. The main modes of substation and data center integration currently include: data center and substation co-construction mode, data center adjacent to substation construction mode, multi-station integration mode, and substation-supported data center.

[0005] Regarding the power supply strategy of multi-station integrated substation, the high-power load of the data center in the existing technology is likely to cause the peak load of the multi-station integrated substation system to be too high. Therefore, it is urgent to propose a multi-station integrated substation power supply strategy that takes into account the high-power load of the data center. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for constructing a load migration and balanced scheduling model for a multi-station integrated substation, which utilizes the load migration and balancing characteristics of the data center to migrate the migratable workload to the renewable energy peak period, thereby balancing the power supply shortage of the substation integrated energy system.

[0007] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0008] In a first aspect, the present invention provides a method for constructing a load migration and balanced scheduling model for a multi-station fusion substation, comprising:

[0009] According to the characteristics of data center workloads, the load data is divided into interactive workloads that need to be processed immediately and batch workloads that can be migrated, and interactive workload allocation models and batch workload allocation models are established;

[0010] The outputs of the interactive workload distribution model and the batch workload distribution model are summed to obtain the workload processed by the data center at each moment;

[0011] Establish a data center power model based on the matching mechanism between various servers and workloads in the data center;

[0012] A multi-objective objective function of the load migration and balanced scheduling model is established with the goal of maximizing the utilization rate of renewable energy in the substation and minimizing the peak-to-valley difference of the workload processed by the data center within the forecast day;

[0013] Establish data processing delay constraints for load migration and balanced scheduling models based on the data processing speed of data center servers and the processing delay limit of interactive workloads;

[0014] The power balance constraints of the load migration and balanced scheduling model are established based on the data center power, user load, transmission power between the microgrid and the upper distribution network, and the power of new AC and DC loads in the substation.

[0015] Furthermore, the establishment of the interactive workload distribution model and the batch workload distribution model includes:

[0016] The interactive workload distribution model is:

[0017] ;

[0018] In the formula, for Time processing Interactive workloads, for Received at all times Interactive workloads, for Time allocated to tower Server-like Quasi-interactive workload, N represents the workload categories with different priorities; Is a server type variable;

[0019] The batch workload distribution model is established using a queuing model, expressed as:

[0020] ;

[0021] In the formula, for time The queue length for batch-like workloads, for time The queue length for batch-like workloads, for Received at all times Batch-like workloads, for Time processing Batch-like workloads.

[0022] Furthermore, the data center power model is established according to the matching mechanism between various servers and workloads in the data center, including:

[0023] A power model of a single type of server is established. For any type of server, the power model is:

[0024] ;

[0025] In the formula, for time Class server power, for The peak power of the server class, for Idle power of a server class, for Moment tower Server-like workloads; No. tower Data processing speed of class servers; It is the ratio of total energy consumption of data center to energy consumption of IT equipment; for Always at work The number of class servers;

[0026] Sum the power of all types of servers at each time to obtain the IT equipment power ;

[0027] According to the relationship between data center power and IT equipment power, a data center power model is established. The expression of the data center power model is:

[0028] ;

[0029] In the formula, For the efficiency of electric energy use, For data center power.

[0030] Furthermore, the multi-objective objective function of the load migration and balanced scheduling model is:

[0031] min F = [ 1 − P PV + P WT P PV + P WT + P gridbuy P peak − P valley ] ;

[0032] In the formula, is the total photovoltaic output, For the total wind power output, Transmit power to the microgrid from the upper distribution network. Deliver power to data centers during peak hours. Deliver power to data centers through the valley distribution network.

[0033] Furthermore, the data processing delay constraint of the load migration and balanced scheduling model is:

[0034] ;

[0035] In the formula, for Processing latency limits for interactive workloads; for time Interactive workloads handled by server-like for The data processing speed of the server.

[0036] Furthermore, the power balance constraint of the load migration and balanced scheduling model is:

[0037] ;

[0038] In the formula, Transmit power from the microgrid to the upper distribution network. Charging efficiency of charging stations, is the discharge efficiency of the charging station, Charging power for charging stations, Discharge power for the charging station, For user load; is the charging efficiency of the lth energy storage, is the discharge efficiency of the lth energy storage, is the charging power of the lth energy storage, is the discharge power of the lth energy storage.

[0039] Furthermore, the load migration and balanced scheduling model also includes a microgrid-upper distribution network interaction power constraint, and the microgrid-upper distribution network interaction power constraint is:

[0040] ;

[0041] In the formula, is the interaction power between the microgrid and the upper distribution network, It is the maximum value of the interaction power between the microgrid and the upper distribution network.

[0042] Furthermore, the load migration and balancing scheduling model also includes data center operation constraints, and the data center operation constraints include:

[0043] Processing interactive workloads must meet the maximum processing capacity constraint, maximum latency tolerance constraint, and satisfaction constraint, expressed as:

[0044] ;

[0045] in, ;

[0046] In the formula, is the preset minimum satisfaction level of the operator. for Always processing interactive workloads Number of class servers, for time The interactive workload processed by the server class; d is the transmission delay, D is the maximum tolerable delay, T is the data center working cycle, For the tower Class Server Processing The proportion of class workloads;

[0047] Processing batch workloads requires satisfying the maximum processing capacity constraint and the task completion constraint before the deadline, expressed as:

[0048] ;

[0049] Where M is the number of types of batch workloads; for Time The amount of batch processing workload of type m handled by the server, for Batch workloads are always being processed The number of class servers; is the deadline for processing the m-type batch workload; is the length of the batch workload queue at the deadline;

[0050] The number of servers handling interactive workloads and batch workloads needs to match the total number of servers in the data center, expressed as:

[0051] .

[0052] In a second aspect, the present invention provides a multi-station integrated substation power supply optimization method, comprising:

[0053] The day-ahead forecast data of the substation is collected at a preset time interval d to obtain the substation forecast data at each collection time; wherein the day-ahead forecast data of the substation includes: photovoltaic output data, wind power output data, interactive workload and batch workload within the forecast day;

[0054] Input batch workloads of all acquisition times into the pre-built load migration and balancing scheduling model to participate in the optimal scheduling of the power system;

[0055] The load migration and balanced scheduling model is solved by multi-objective method using normal boundary intersection method to obtain load data after migration of the data center at each collection time within the forecast day; the load data after migration of the data center at each collection time is composed of the load and interactive workload assigned by the data center at each collection time after the power system is optimized and scheduled;

[0056] According to the load data after the migration of the data center, the power of photovoltaic power output, wind power output, and new AC and DC loads in the substation at each collection time within the forecast day is optimized;

[0057] The load migration and balanced scheduling model is constructed and obtained by the construction method described in any one of items 1 to 8.

[0058] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in the first aspect or the second aspect is implemented.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] (1) The load migration and balanced scheduling model construction method of the multi-station integrated substation provided by the present invention separates the migratable batch processing workload according to the workload characteristics of the data center for load migration and balanced scheduling, which is conducive to alleviating the impact of the power-intensive data center on the operation stability of the substation integrated energy system; the quantities involved in the power model of the IT equipment are actually measurable, which is convenient for actual implementation; the load migration and balanced scheduling model takes the highest renewable energy utilization rate and the smallest peak-to-valley difference of the workload processed by the data center during the day as the objective function, takes the delay time of data processing that affects user satisfaction as a constraint for load scheduling, fully considers the user experience, and by solving the model, reasonably allocates the migratable batch processing workload to the period with high renewable energy output, thereby improving the utilization rate of renewable energy and the environmental protection of the data center operation.

[0061] (2) The multi-station integrated substation power supply optimization method provided by the present invention meets the needs of C-RAN construction, alleviates the power supply pressure of the multi-station integrated substation containing the data center, reduces the substation operation cost, and enhances the feasibility, economy, and environmental protection of the data center adjacent to the substation construction mode. It effectively solves the problems of decentralized management and unstable power supply in the D-RAN mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a scheduling flow chart of a multi-station integrated substation power supply strategy considering data center load migration as described in Example 2 of the present invention.

[0063] Figure 2 It is a topological diagram of the integrated energy system of the AC 380V substation in Example 3 of the present invention;

[0064] Figure 3 is the day-ahead prediction curve of the distributed photovoltaic output power in Example 3 of the present invention;

[0065] Figure 4 is a day-ahead prediction curve of the output power of the distributed wind turbine in Example 3 of the present invention;

[0066] Figure 5 is a data center workload and a processor work quantity curve in Example 3 of the present invention;

[0067] Figure 6 is the load curve of the data center load before and after participating in the coordinated scheduling in Example 3 of the present invention;

[0068] Figure 7 is a diagram showing the matching situation of the load and the output trend of new energy after the data center load participates in the coordinated scheduling in Example 3 of the present invention;

[0069] Figure 8is a voltage fluctuation comparison curve before and after the data center load participates in the coordinated scheduling in Example 3 of the present invention;

[0070] Fig. 9 This is a diagram showing the effect of data center load migration in the media application interactive load scenario in Example 4 of the present invention. DETAILED DESCRIPTION

[0071] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0072] Example 1

[0073] This embodiment provides a method for constructing a load migration and balanced scheduling model for a multi-station fusion substation, and the method includes the following steps:

[0074] Step 1: According to the characteristics of data center workloads, the load data is divided into interactive workloads that need to be processed immediately and batch workloads that can be migrated, and interactive workload allocation models and batch workload allocation models are established.

[0075] Specifically, electricity consumption by IT equipment and air-conditioning systems accounts for the main part of the total energy consumption of data centers, among which the energy consumption of IT equipment accounts for about 45% and the energy consumption of air-conditioning systems accounts for about 40%. Therefore, IT equipment is both the core load of the data center and directly reflects the energy efficiency of the data center.

[0076] The energy consumption of IT equipment is related to the amount of data tasks that the data center needs to process. Generally, it can be divided into two categories: interactive load and batch load. Interactive load is sensitive to delay and has no coordination ability in the time dimension. Batch load tolerates delay and the adjustment range ranges from a few hours to dozens of hours. The load characteristics of different workloads can be summarized as shown in the following table.

[0077]

[0078] For interactive loads, immediate allocation is required. If the current server is not idle, it needs to be allocated to a new server with the smallest difference in processing power from the original server. The allocation model is:

[0079] ; In the formula, for Time processing Interactive workloads, for Received at all times Interactive workloads, for Time allocated to tower Server-like Interactive workload, N represents the workload categories with different priorities, A server type variable.

[0080] The load distribution model of batch workload is established using the queuing model, and the expression is:

[0081] ; In the formula, for time The queue length for batch-like workloads, for time The queue length for batch-like workloads, for Received at all times Batch-like workloads, for Time processing Batch-like workloads.

[0082] Step 2: Sum the outputs of the interactive workload distribution model and the batch workload distribution model to obtain the workload processed by the data center at each moment.

[0083] Specifically, the workload processed by the data center at each moment is:

[0084] .

[0085] Step 3: Establish a data center power model based on the matching mechanism between various servers and workloads in the data center.

[0086] Specifically, establishing a data center power model includes:

[0087] Step 31: Establish a power model for a single type of server. For any type of server, the power model is:

[0088] ; In the formula, for time Class server power, for The peak power of the server class, for Idle power of a server class, for Moment tower Server-like workloads; No. tower Data processing speed of class servers; It is the ratio of total energy consumption of data center to energy consumption of IT equipment; for Always at work The number of class servers;

[0089] Step 32: Sum the power of all types of servers at each time to obtain the IT equipment power .

[0090] Step 33: According to the relationship between the data center power and the IT equipment power, a data center power model is established. The expression of the data center power model is:

[0091] ; In the formula, For the efficiency of electric energy use, For data center power.

[0092] Step 4: With the goal of maximizing the utilization rate of renewable energy in the substation and minimizing the peak-to-valley difference of the workload processed by the data center within the forecast day, a multi-objective objective function of the load migration and balanced scheduling model is established.

[0093] Specifically, the objective function of the load migration and balanced scheduling model is:

[0094] min F = [ 1 − P PV + P WT P PV + P WT + P gridbuy P peak − P valley ] ; In the formula, is the total photovoltaic output, For the total wind power output, Transmit power to the microgrid from the upper distribution network. Deliver power to data centers during peak hours. Deliver power to data centers through the valley distribution network.

[0095] Step 5: Establish the data processing delay constraint of the load migration and balanced scheduling model according to the data processing speed of the data center server and the processing delay limit of the interactive workload.

[0096] Specifically, the delay time of processing data is an important factor in measuring the user satisfaction of the data center. Therefore, the transfer scheduling of workloads must meet the data processing delay constraint, which is:

[0097] ; In the formula, for Processing latency limits for interactive workloads; for time Interactive workloads handled by server-like for The data processing speed of the server.

[0098] Step 6: Establish the power balance constraints of the load migration and balanced scheduling model based on the data center power, user load, transmission power between the microgrid and the upper distribution network, and the power of the new AC and DC loads in the substation.

[0099] It should be noted that multi-station integrated substations are often equipped with new AC / DC loads such as photovoltaic / wind power, energy storage systems, charging stations, and battery swap stations. When a data center is built in a substation, it is necessary to consider new AC / DC loads and formulate a coordinated operation strategy for the substation's integrated energy system and surrounding power resources.

[0100] Specifically, it is necessary to establish new AC and DC load models such as photovoltaics, wind power, energy storage systems, charging stations, and battery swap stations.

[0101] Optionally, the total photovoltaic output model is: P PV = P st G b [ 1 + k ( T et − T rt ) ] / G st ;

[0102] The total wind power output model is: ;

[0103] The electric energy model of the energy storage system is: E l , ESS t = ( 1 − u ) E l , ESS t − 1 + [ P l , on t ∗ η l , on − P l , off t / η l , off ] ;

[0104] The electric vehicle power model is: ; The power model of the battery swap station is:

[0105] In the formula, It is the maximum test power under standard conditions; , are the test light intensity under standard conditions and the light intensity during power generation; k is the power temperature coefficient; , are the reference temperature and the PV cell temperature, respectively; , , They are fan cut-in, cut-out and rated wind speed respectively; is the rated power of the fan; is the charging power of the first energy storage, is the discharge power of the lth energy storage; is the battery capacity of the lth electrical energy storage; , Electric vehicles in and The remaining power at the moment; , are the charging / discharging power of electric vehicles respectively; , are the charging / discharging efficiency of electric vehicles, respectively; is the DC charger power; is the discharge efficiency of the DC charger; , , , , They are the number of fully charged batteries, batteries to be charged, batteries to be discharged, empty batteries, and batteries that need to be replaced in time period t; , They are the total number of batteries and the maximum allowed charge or discharge number; is the total power of the battery swap station; , They are the battery charge / discharge 0-1 status, 0 means charging, 1 means discharging.

[0106] The power balance constraint of the load migration and balanced scheduling model established in this way is:

[0107] ; In the formula, Transmit power from the microgrid to the upper distribution network. Charging efficiency of charging stations, is the discharge efficiency of the charging station, Charging power for charging stations, Discharge power for the charging station, For user load.

[0108] Optionally, the load migration and balancing scheduling model also includes data center operation constraints.

[0109] Specifically, the data center operation constraints of the load migration and balanced scheduling model include:

[0110] The interactive load needs to be reasonably allocated to various types of servers according to the priority. The processing of interactive workloads needs to meet the maximum processing capacity constraint, the maximum delay tolerance constraint and the satisfaction constraint. The expression is:

[0111] ;

[0112] in, ; In the formula, is the preset minimum satisfaction level of the operator. for Always processing interactive workloads Number of class servers, for time The interactive workload processed by the server class; d is the transmission delay, D is the maximum tolerable delay, T is the data center working cycle, For the tower Class Server Processing The proportion of class workloads.

[0113] Processing batch workloads requires satisfying the maximum processing capacity constraint and the task completion constraint before the deadline, expressed as:

[0114] ; Where M is the number of types of batch workloads; for Time The amount of batch processing workload of type m handled by the server, for Batch workloads are always being processed The number of class servers; is the deadline for processing the m-type batch workload; The length of the batch workload queue at the deadline.

[0115] The number of servers handling interactive workloads and batch workloads needs to match the total number of servers in the data center, expressed as:

[0116] .

[0117] Optionally, the load migration and balanced scheduling model further includes a microgrid-upper distribution network interaction power constraint, and the microgrid-upper distribution network interaction power constraint of the load migration and balanced scheduling model is: ; In the formula, is the interaction power between the microgrid and the upper distribution network, It is the maximum value of the interaction power between the microgrid and the upper distribution network.

[0118] Example 2

[0119] This embodiment provides a multi-station fusion substation power supply optimization method, such as Figure 1 As shown, including:

[0120] The day-ahead forecast data of the substation is collected at preset time intervals d to obtain the substation forecast data at each collection time.

[0121] Specifically, the substation day-ahead forecast data includes: photovoltaic output data, wind power output data, interactive workload and batch workload within the forecast day.

[0122] It should be noted that d can be set to 30 minutes, 45 minutes or 60 minutes according to actual needs. In this embodiment, d is set to 60 minutes, and substation prediction data is collected at each hour.

[0123] The batch workloads of all acquisition times are input into the pre-built load migration and balancing scheduling model to participate in the power system optimization scheduling. The interactive workloads of all acquisition times are used as the real-time load of the data center at that moment, wherein the load migration and balancing scheduling model is constructed by the construction method described in Example 1.

[0124] The normal boundary intersection method is used to perform multi-objective solution to the load migration and balanced scheduling model to obtain the load data after the migration of the data center at each collection time within the forecast day; the load data after the migration of the data center at each collection time is composed of the load and interactive workload assigned by the data center at each collection time after the power system is optimized and scheduled.

[0125] The photovoltaic output, wind power output, and power of new AC and DC loads in the substation at each collection time within the forecast day are optimized according to the load data after the migration of the data center.

[0126] Example 3

[0127] Based on Example 2, this example is based on Figure 2 The AC 380V substation integrated energy system topology shown verifies the rationality of the multi-station integrated substation power supply optimization method provided by the present invention.

[0128] Specifically, the configuration of the photovoltaic / wind power system, energy storage station, charging station, data center, and battery swap station in the multi-station integrated substation is as follows:

[0129] 1) Photovoltaic power station: Considering the site area of ​​the integrated energy station, the rated power of the photovoltaic panels of this project is set to 1kW, with a total of 1,000 solar photovoltaic panels, the solar cell temperature is 25°C, the temperature condition coefficient is -0.0047, and the spectral irradiance is 1000W / m 2 The photovoltaic output curve is as follows: Figure 3 shown.

[0130] 2) Wind turbine power station: The rated power of a single wind turbine is 35kW, the cut-in wind speed is 3m / s, the cut-out wind speed is 25m / s, and the rated wind speed is 11m / s. The output curve of the distributed wind turbine is as follows: Figure 4 shown.

[0131] 3) Energy storage power station: The rated power of a single energy storage container of the energy storage system is 1MW and the capacity is 5kW·h. Due to site limitations, only one energy storage container is configured.

[0132] 4) Data Center: Configure 10 data center containers, set the server data processing speed to 5, set the server peak power to 10kW and idle power to 4kW respectively, set the ratio of total data center energy consumption to IT equipment energy consumption to 1.8, and set the power consumption ratio of IT equipment in the data center to 0.45. Workload at different times and the number of processor jobs like Figure 5 As shown in the figure, batch workloads are divided into two categories, with processing cycles of 8 hours and 16 hours respectively, and interactive workloads are classified into media data center types.

[0133] In order to verify the effectiveness of the method of combining the scheduling and coordinated operation scheme of the data center in the present invention, this section designs the following scheme for comparative analysis:

[0134] (1) In Scheme 1, the microgrid system is not connected to the data center load;

[0135] (2) In Scheme 2, the microgrid system is connected to the data center, but no load migration is performed;

[0136] (3) Scheme 3 is the optimization strategy proposed in this paper, which is to dynamically dispatch the data center microgrid.

[0137] After scheduling simulation, the load comparison before and after data center scheduling can be obtained. Figure 6 , Figure 7 As shown, the renewable energy utilization rate and peak-to-valley difference of the three schemes are shown in the following table.

[0138]

[0139] like Figure 6As shown in the figure, in scheme 2, the load of the data center and the power load are concentrated in the same period (0:00-8:00), resulting in high power supply demand for the station power system, and the distribution network needs to transmit more electricity to the microgrid. Scheme 3 takes into account that after load migration, the delayed load of the data center can be reasonably allocated according to the power load and the output of renewable energy. At the time when the output power of renewable energy is small (such as 4:00), the batch load cannot be fully covered. However, through energy storage discharge, the dependence on the distribution network power supply system is reduced, the utilization rate of renewable energy is improved, and the peak-to-valley difference is reduced. After 12:00, the output power of renewable energy increases, and the batch load can be processed centrally at this time. It can be seen that load migration can effectively improve the utilization rate of renewable energy and alleviate the pressure on the power grid.

[0140] Depend on Figure 6 , Figure 7 It can be seen that the power curves of the delayed load after the new energy output and dispatch are basically the same. When the output of renewable energy is large, such as 8:00-12:00, the microgrid has no electricity purchase behavior, so the load can be allocated to the period when the output of renewable energy is large; in the period when the output of renewable energy is insufficient, such as 15:00-24:00, the data center gives priority to the power generated by renewable energy, and the energy storage system discharges when it is insufficient. Table 3 shows that by reasonably distributing the load, the power demand curve of the data center can be matched with the output curve of renewable energy, thereby improving the utilization rate of renewable energy.

[0141] The integration of data centers and traditional substations will inevitably impact the stability of the substation's integrated energy system due to the power-intensive nature of the former. Figure 8 As shown in the figure: before load migration, the delayed load of the data center at 12 o'clock was large. After the delayed load participated in the scheduling, the voltage at 12 o'clock dropped significantly to outside the standard range. During the period of 17:00-24:00, the voltage rebounded because the delayed load power of the data center was low; after load migration, the delayed load of the data center at 12 o'clock decreased, and the voltage drop was alleviated. During the period of 17:00-24:00, the voltage was always within the standard range.

[0142] Example 4

[0143] This embodiment verifies the effect of load migration in an interactive load scenario dominated by media data center load. Fig. 9 As shown in the figure, for media loads, the data interaction volume of entertainment and media applications such as games reaches a peak in the evening; small-scale cloud computing and virtualization tasks such as user requests, data exchange, and applications are performed during the day, and non-critical tasks such as maintenance and backup can also be moved to the evening. Fig. 9As can be seen in the figure, the interactive load power peak occurs at 20:00-3:00 in the evening. After coordinated scheduling, the delayed load is moved to 8:00-16:00. While avoiding the load peak, the delayed load is also matched with the output of new energy, thereby improving the utilization rate of new energy.

[0144] Example 5

[0145] This embodiment provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the multi-station integrated substation power supply optimization method as described in Example 1 or Example 2 is implemented.

[0146] The storage medium may include, for example, a storage component of a tablet computer, a hard disk of a computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer storage medium may be any combination of one or more computer storage media.

[0147] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them. Those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for constructing a load migration and balanced scheduling model for a multi-station fusion substation, characterized in that: include: According to the characteristics of data center workloads, the load data is divided into interactive workloads that need to be processed immediately and batch workloads that can be migrated, and interactive workload allocation models and batch workload allocation models are established; The outputs of the interactive workload distribution model and the batch workload distribution model are summed to obtain the workload processed by the data center at each moment; Establish a data center power model based on the matching mechanism between various servers and workloads in the data center; A multi-objective objective function of the load migration and balanced scheduling model is established with the goal of maximizing the utilization rate of renewable energy in the substation and minimizing the peak-to-valley difference of the workload processed by the data center within the forecast day; Establish data processing delay constraints for load migration and balanced scheduling models based on the data processing speed of data center servers and the processing delay limit of interactive workloads; The power balance constraints of the load migration and balanced scheduling model are established based on the data center power, user load, transmission power between the microgrid and the upper distribution network, and the power of new AC and DC loads in the substation.

2. The method for constructing a load migration and balanced scheduling model for a multi-station integrated substation according to claim 1 is characterized in that: The establishing of the interactive workload distribution model and the batch workload distribution model comprises: The interactive workload distribution model is: ； In the formula, for Time processing Interactive workloads, for Received at all times Interactive workloads, for Time allocated to tower Server-like Quasi-interactive workload, N represents the workload categories with different priorities; Is a server type variable; The batch workload distribution model is established using a queuing model, expressed as: ； In the formula, for time The queue length for batch-like workloads, for time The queue length for batch-like workloads, for Received at all times Batch-like workloads, for Time processing Batch-like workloads.

3. The method for constructing a load migration and balanced scheduling model for a multi-station integrated substation according to claim 2 is characterized in that: The data center power model is established according to the matching mechanism between various servers and workloads in the data center, including: A power model of a single type of server is established. For any type of server, the power model is: ； In the formula, for time Class server power, for The peak power of the server class, for Idle power of a server class, for Moment tower Server-like workloads; No. tower Data processing speed of class servers; It is the ratio of total energy consumption of data center to energy consumption of IT equipment; for Always at work The number of class servers; Sum the power of all types of servers at each time to obtain the IT equipment power ; According to the relationship between data center power and IT equipment power, a data center power model is established. The expression of the data center power model is: ； In the formula, For the efficiency of electric energy use, For data center power.

4. The method for constructing a load migration and balanced scheduling model for a multi-station integrated substation according to claim 3 is characterized in that: The multi-objective objective function of the load migration and balanced scheduling model is: ； In the formula, is the total photovoltaic output, For the total wind power output, Transmit power to the microgrid from the upper distribution network. Deliver power to data centers during peak hours. Deliver power to data centers through the valley distribution network.

5. The method for constructing a load migration and balanced scheduling model for a multi-station integrated substation according to claim 4 is characterized in that: The data processing delay constraint of the load migration and balanced scheduling model is: ； In the formula, for Processing latency limits for interactive workloads; for time Interactive workloads handled by server-like for The data processing speed of the server.

6. The method for constructing a load migration and balanced scheduling model for a multi-station integrated substation according to claim 4 is characterized in that: The power balance constraint of the load migration and balanced scheduling model is: ； In the formula, Transmit power from the microgrid to the upper distribution network. Charging efficiency for charging stations, is the discharge efficiency of the charging station, Charging power for charging stations, Discharge power for the charging station, For user load; is the charging efficiency of the lth energy storage, is the discharge efficiency of the lth energy storage, is the charging power of the lth energy storage, is the discharge power of the lth energy storage.

7. The method for constructing a load migration and balanced scheduling model for a multi-station integrated substation according to claim 6 is characterized in that: The load migration and balanced scheduling model also includes a microgrid-upper distribution network interaction power constraint, and the microgrid-upper distribution network interaction power constraint is: ； In the formula, is the interaction power between the microgrid and the upper distribution network, It is the maximum value of the interaction power between the microgrid and the upper distribution network.

8. The method for constructing a load migration and balanced scheduling model for a multi-station integrated substation according to claim 4, characterized in that: The load migration and balancing scheduling model also includes data center operation constraints, and the data center operation constraints include: Processing interactive workloads must meet the maximum processing capacity constraint, maximum latency tolerance constraint, and satisfaction constraint, expressed as: ； in, ; In the formula, is the preset minimum satisfaction level of the operator. for Always processing interactive workloads Number of class servers, for time The interactive workload processed by the server class; d is the transmission delay, D is the maximum tolerable delay, T is the data center working cycle, For the tower Class Server Processing The proportion of class workloads; Processing batch workloads requires satisfying the maximum processing capacity constraint and the task completion constraint before the deadline, expressed as: ； Where M is the number of types of batch workloads; for Time The amount of batch processing workload of type m handled by the server, for Batch workloads are always being processed The number of class servers; is the deadline for processing the m-type batch workload; is the length of the batch workload queue at the deadline; The number of servers handling interactive workloads and batch workloads needs to match the total number of servers in the data center, expressed as: 。 9. A multi-station integrated substation power supply optimization method, characterized in that: include: The day-ahead forecast data of the substation is collected at a preset time interval d to obtain the substation forecast data at each collection time; wherein the day-ahead forecast data of the substation includes: photovoltaic output data, wind power output data, interactive workload and batch workload within the forecast day; Input batch workloads of all acquisition times into the pre-built load migration and balancing scheduling model to participate in the optimal scheduling of the power system; The load migration and balanced scheduling model is solved by multi-objective method using normal boundary intersection method to obtain load data after migration of the data center at each collection time within the forecast day; the load data after migration of the data center at each collection time is composed of the load and interactive workload assigned by the data center at each collection time after the power system is optimized and scheduled; Optimizing the photovoltaic power output, wind power output, and power of new AC and DC loads in the substation at each collection time within the forecast day according to the load data after the migration of the data center; The load migration and balanced scheduling model is constructed and obtained by the construction method described in any one of items 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 or claim 9 is implemented.

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