Data center optimal energy storage scheduling method and system considering demand response

Abstract

The invention discloses a data center optimal energy storage scheduling method considering demand response, and the method comprises the steps: obtaining real-time operation data of a data center and historical operation data in a day-ahead market and a frequency reserve market, and constructing a multi-level constraint condition according to the historical operation data in the day-ahead market and the frequency reserve market; according to the real-time operation data of the data center, by taking the maximum scheduling benefit of the data center in the day-ahead market and the frequency reserve market as a target, constructing a target function considering the maximum benefit of the day-ahead market and the frequency reserve market; combining the objective function and the multi-level constraint condition, and constructing a data center optimal energy storage scheduling model considering the demand response; and solving the optimal energy storage scheduling model of the data center considering the demand response to obtain an optimization strategy, so that the overall energy utilization efficiency is improved, and the optimal energy storage scheduling model has important significance on energy conservation, emission reduction and sustainable development.

Description

Technical Field

[0001] The present invention relates to the technical field of data center scheduling, and in particular to an optimal energy storage scheduling method and system for a data center taking demand response into consideration. Background Art

[0002] As an important entity participating in the day-ahead power market dispatch, the data center's ability to process large amounts of data provides favorable assistance to the data-intensive power grid dispatch. However, the power grid not only participates in daily dispatch, auxiliary frequency services are also one of the important links.

[0003] Existing research has taken into account the participation of data centers in the day-ahead market and the frequency service market, but the modeling is not sophisticated enough, resulting in insufficient coordination in the two markets, which is not conducive to the efficient allocation of energy. Therefore, it is urgent to study the optimal energy storage scheduling model for data centers that takes into account demand response, which is conducive to improving the efficiency of data centers in scheduling grid energy. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the present invention solves the problem that the existing modeling of data centers participating in the day-ahead market and the frequency service market is not sophisticated enough, resulting in insufficient coordination in participating in the two markets, which is not conducive to the efficient allocation of energy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an optimal energy storage scheduling method for a data center taking into account demand response, comprising:

[0008] Acquire real-time operation data of the data center and historical operation data in the day-ahead market and the frequency reserve market, and construct multi-level constraint conditions according to the historical operation data in the day-ahead market and the frequency reserve market;

[0009] According to the real-time operation data of the data center, taking the maximum dispatching benefit of the data center in the day-ahead market and the frequency reserve market as the goal, constructing an objective function that takes into account the maximum benefit of the day-ahead market and the frequency reserve market;

[0010] Combining the objective function and multi-level constraints to construct an optimal energy storage scheduling model for a data center taking demand response into account;

[0011] The optimal energy storage scheduling model for the data center taking demand response into account is solved to obtain an optimization strategy.

[0012] As a preferred solution of the optimal energy storage scheduling method for data centers taking demand response into account described in the present invention, the multi-level constraints include: battery operation boundary constraints, market operation constraints and external third-party operation constraints.

[0013] As a preferred solution of the optimal energy storage scheduling method for a data center taking into account demand response described in the present invention, the objective function includes:

[0014] Construct an objective function that includes the calculation of battery charge and discharge power, monitoring and management of battery status, and the impact of value fluctuations on battery scheduling;

[0015] The objective function is expressed as:

[0016]

[0017] Where t is the time step of the day-ahead market, Z is the time step of the frequency control reserve market, and P d is the discharge power bid in the day-ahead market, P c is the charging power bid in the day-ahead market, P fcr is the power provided to the frequency control reserve market, λ is the dispatch parameter of the day-ahead market, and λ fcr is the frequency control reserve market dispatch parameter, C r is the energy storage ramp cost, C fcr-r It is the ratio of the sum of the battery system cost and the power station balancing cost to the maximum operating hours in a year.

[0018] As a preferred solution of the optimal energy storage scheduling method for a data center taking into account demand response described in the present invention, the monitoring and management of the battery status includes:

[0019] Monitor the battery status in real time and ensure that the battery status is within a safe range according to the battery operation boundary constraints;

[0020] Sensors are used to detect battery parameters, calculate the current battery status, dynamically adjust the preset charge and discharge thresholds according to the battery status, keep the battery running in the best condition, and regularly evaluate the battery health status to take real-time preventive maintenance measures.

[0021] As a preferred solution of the optimal energy storage scheduling method for a data center taking into account demand response described in the present invention, the battery charging and discharging power includes:

[0022] Dynamically adjust charging current and voltage according to battery status and temperature;

[0023] When the battery is in low condition, a constant current charging mode is used to quickly charge the battery until the battery voltage approaches the rated value;

[0024] When the battery state is high, switch to constant voltage charging mode and charge slowly until the battery voltage stabilizes to the rated voltage;

[0025] Set the lower threshold of the battery operating status. When the battery operating status drops to the lower threshold, discharging stops.

[0026] As a preferred solution of the optimal energy storage scheduling method for a data center taking into account demand response described in the present invention, the battery operation boundary constraints include:

[0027] At all time steps, the battery's discharge power is between 0 and the discharge limit;

[0028] At all time steps, the battery’s charging power is between 0 and the charging limit;

[0029] The discharge power is less than or equal to the auxiliary variable multiplied by the discharge limit;

[0030] The charging power is less than or equal to 1 minus the auxiliary variable multiplied by the charging limit;

[0031] At the end of each time step, the battery state plus the charging power minus the discharging power divided by the discharge efficiency multiplied by the time step divided by the battery energy capacity gives the battery state for the next time step;

[0032] The battery status is between 0 and 1;

[0033] The battery is half-full at the beginning and end.

[0034] As a preferred solution of the optimal energy storage scheduling method for a data center taking into account demand response described in the present invention, solving the optimal energy storage scheduling model for a data center taking into account demand response includes:

[0035] Preprocess the sensor data and use the preprocessed data as input for the optimal energy storage scheduling model of the data center;

[0036] Set the parameters of the solver and use the script to perform optimization and solve the problem to get the optimal solution based on the objective function and constraints;

[0037] Extract the optimal solution from the solver and verify whether the optimization result meets all constraints. If so, convert the optimization result into a specific scheduling strategy.

[0038] In a second aspect, the present invention provides a system for optimal energy storage scheduling of a data center taking into account demand response, comprising:

[0039] A constraint condition construction module is obtained, which is used to obtain the real-time operation data of the data center and the historical operation data in the day-ahead market and the frequency reserve market, and construct a multi-level constraint condition according to the historical operation data in the day-ahead market and the frequency reserve market;

[0040] An objective function construction module is used to construct an objective function that takes into account the maximum benefits of the day-ahead market and the frequency reserve market based on the real-time operation data of the data center and taking the maximum scheduling benefit of the data center in the day-ahead market and the frequency reserve market as the goal;

[0041] Constructing a model module, for combining the objective function and multi-level constraints to construct an optimal energy storage scheduling model for a data center taking into account demand response;

[0042] The solution module is used to solve the optimal energy storage scheduling model of the data center taking into account demand response to obtain an optimization strategy.

[0043] In a third aspect, the present invention provides a computing device, comprising:

[0044] Memory and processor;

[0045] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the optimal energy storage scheduling method for a data center taking demand response into account are implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for optimal energy storage scheduling of a data center taking demand response into account.

[0047] Beneficial effects of the present invention: The present invention combines data center scheduling with the frequency service market with the goal of maximizing scheduling benefits, which can significantly improve the overall benefits of the data center. Through refined modeling and optimized scheduling, the energy cost of the data center can be effectively reduced. Reasonable scheduling of the battery energy storage system can reduce peak power consumption and reduce electricity bills. At the same time, it can use valley power for charging to further reduce electricity costs. Considering the rules and requirements of the frequency control reserve market, it can ensure that the data center participates in the frequency service market efficiently, improves response speed and stability, and provides reliable frequency regulation services for the power grid. Through optimized scheduling of the battery energy storage system, the battery utilization efficiency is maximized, the waste of electricity is reduced, and the overall energy utilization efficiency is improved, which is of great significance to energy conservation, emission reduction and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0049] Figure 1 An overall flow chart of an optimal energy storage scheduling method for a data center taking demand response into account provided by the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0053] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0054] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0055] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Example 1

[0057] Reference Figure 1 , which is an embodiment of the present invention, provides an optimal energy storage scheduling method for a data center taking demand response into account, comprising:

[0058] S100: acquiring real-time operation data of the data center and historical operation data in the day-ahead market and the frequency reserve market, and constructing multi-level constraint conditions according to the historical operation data in the day-ahead market and the frequency reserve market;

[0059] In the embodiment of the present application, the multi-level constraints include: battery operation boundary constraints, market operation constraints and external third-party operation constraints.

[0060] Battery operation boundary constraints include:

[0061] At all time steps, the battery's discharge power is between 0 and the discharge limit;

[0062] At all time steps, the battery’s charging power is between 0 and the charging limit;

[0063] The discharge power is less than or equal to the auxiliary variable multiplied by the discharge limit;

[0064] The charging power is less than or equal to 1 minus the auxiliary variable multiplied by the charging limit;

[0065] At the end of each time step, the battery state plus the charging power minus the discharging power divided by the discharge efficiency multiplied by the time step divided by the battery energy capacity gives the battery state for the next time step;

[0066] The battery status is between 0 and 1;

[0067] The battery is half-full at the beginning and end.

[0068] Specifically, the battery operation boundary constraint is expressed as:

[0069]

[0070] in, To charge the battery to the limit, is the battery discharge limit, δ(t) is the 0 / 1 auxiliary variable introduced in the linearization process, SoC is the battery state, η c is the battery charging efficiency, η d is the battery discharge efficiency, P d is the discharge power bid in the day-ahead market, P c is the charging power bid in the day-ahead market.

[0071] The market operation constraint is expressed as:

[0072]

[0073] Among them, β is the minimum dispatch requirement of the frequency market, γ fcr Is a 0 / 1 variable for whether frequency service is needed, E * is the total amount of battery, P fcr-u The bid price for the upward activation of the frequency containment reserve, P fcr-d is the bid for downward activation of the frequency suppression reserve, and Γ is the minimum charge acceptable to the frequency service market.

[0074] The third-party operation constraints are expressed as:

[0075]

[0076] P out (t)·η d =P d (t)+P fcr (z)

[0077] P in (t)=(P c (t)+P fcr (z))·η c

[0078] Among them, P bk is the reserve power reserved in the energy storage, P out is the energy flowing out of the battery, P in is the energy flowing into the battery.

[0079] Specifically, the battery operation restrictions, market operation rules and third-party operation rules are applied to construct constraints consisting of battery operation boundary constraints, market operation constraints and external third-party operation constraints. The battery operation boundary constraints are to establish the boundary conditions of battery status, charging and discharging power and efficiency based on the physical characteristics and operation restrictions of the battery; the market operation constraints are based on the market operation rules to set the dispatch requirements of the frequency market and the total battery quantity restrictions; the third-party operation constraints consider the third-party operation rules to set the reserve power and energy inflow and outflow restrictions of the energy storage system, and optimize the three constraints of battery operation boundary constraints, market operation constraints and external third-party operation constraints;

[0080] It should be noted that the battery charging and discharging characteristics of the battery operation boundary constraints are the basis of the operation of the energy storage system, which directly affects the energy storage efficiency and battery life. The battery charging and discharging power and efficiency limits are the key to ensuring the safe operation of the system; the dispatching requirements of the frequency market constrained by market operation are the basic conditions for the energy storage system to participate in market transactions, which directly affect the economic benefits of the energy storage system. The total battery quantity limit ensures the reasonable participation of the energy storage system in the market and avoids excessive competition; the setting of the backup power of the third-party operation constraint is to ensure that the energy storage system can provide necessary support in an emergency, and the energy inflow and outflow limit is to ensure the stable operation of the energy storage system under different market conditions. The optimization of these three constraints can improve the dispatching efficiency, reduce energy costs, and improve energy utilization.

[0081] S200: Based on the real-time operation data of the data center, with the goal of maximizing the dispatching efficiency of the data center in the day-ahead market and the frequency reserve market, an objective function that takes into account the maximum efficiency of the day-ahead market and the frequency reserve market is constructed;

[0082] In the embodiment of the present application, an objective function is constructed including the calculation of battery charge and discharge power, the monitoring and management of battery status, and the impact of value fluctuations on battery scheduling;

[0083] The objective function is expressed as:

[0084]

[0085] Where t is the time step of the day-ahead market, Z is the time step of the frequency control reserve market, and P d is the discharge power bid in the day-ahead market, P c is the charging power bid in the day-ahead market, P fcr is the power provided to the frequency control reserve market, λ is the dispatch parameter of the day-ahead market, and λ fcr is the frequency control reserve market dispatch parameter, C r is the energy storage ramp cost, C fcr-r It is the ratio of the sum of the battery system cost and the power station balancing cost to the maximum operating hours in a year.

[0086] Specifically, the goal is to maximize the dispatching benefits of data centers in the day-ahead market and frequency reserve market, taking into account the electricity prices, power, and cost of energy storage systems in each market; comprehensively consider the discharge and charging benefits of the day-ahead market, the benefits of the frequency market, the operating costs of the energy storage system, and the equipment costs, and construct a comprehensive objective function;

[0087] The objective function includes technical features such as: calculation of battery charge and discharge power, monitoring and management of battery status, and the impact of market value fluctuations on battery scheduling;

[0088] In the embodiment of the present application, the state of the battery is monitored in real time, and the battery state is made to operate within a safe range according to the battery operation boundary constraints;

[0089] Sensors are used to detect battery parameters, calculate the current battery status, dynamically adjust the preset charge and discharge thresholds according to the battery status, keep the battery running in the best condition, and regularly evaluate the battery health status to take real-time preventive maintenance measures.

[0090] Specifically, the battery status is monitored in real time to ensure that it operates within a safe range. Sensors are used to monitor battery parameters such as voltage, current, and temperature. The current battery status is determined through calculation, and the data is transmitted to the control system for dynamic monitoring and adjustment. The battery health status is regularly evaluated, including capacity attenuation and internal resistance changes. Preventive maintenance measures are implemented to extend battery life and ensure stable system operation.

[0091] It should be noted that the battery charging status is monitored to ensure that the battery operates within a safe range, avoid overcharging or over-discharging, consider the battery charging and discharging efficiency, optimize the charging and discharging strategy, and improve energy utilization efficiency.

[0092] In the embodiment of the present application, the charging current and voltage are dynamically adjusted according to the state and temperature of the battery;

[0093] When the battery is in low condition, a constant current charging mode is used to quickly charge the battery until the battery voltage approaches the rated value;

[0094] When the battery state is high, switch to constant voltage charging mode and charge slowly until the battery voltage stabilizes to the rated voltage;

[0095] Set the lower threshold of the battery operating status. When the battery operating status drops to the lower threshold, discharging stops.

[0096] Specifically, for charging control, a phased charging strategy is used; for discharging control, the discharge power is dynamically adjusted and the discharge strategy is optimized according to load demand and grid dispatch requirements to ensure efficient discharge while avoiding over-discharge; combined with the dispatch requirements of the day-ahead market and frequency market, the charging and discharging timing is optimized to reduce electricity consumption costs.

[0097] It should be noted that through staged charging, the battery can be prevented from running at a high charging rate for a long time, the thermal stress and chemical stress of the battery can be reduced, thereby extending the battery life, adjusting the charging current and voltage according to the battery status at different stages, improving the charging efficiency, reducing energy loss, improving system flexibility, and enhancing grid stability.

[0098] Specifically, system status monitoring is responsible for real-time monitoring of various operating parameters of the battery system, including voltage, current, temperature, battery status, etc., which is achieved through a sensor network. Data transmission is wireless or wired, and the system status is monitored in real time to detect abnormal conditions such as battery overheating, overcharging, over-discharging, etc. The system has a built-in fault diagnosis algorithm to identify potential faults based on historical data and real-time data, and provide timely warnings.

[0099] It should be noted that by optimizing the discharging and charging strategies, data centers can discharge when electricity prices are high and charge when electricity prices are low, thereby maximizing economic benefits, rationally scheduling battery charging and discharging, reducing energy waste, improving energy utilization efficiency, and contributing to energy conservation and emission reduction. By monitoring and managing battery status, it ensures that the battery operates within a safe range and enhances the stability and reliability of the system.

[0100] S300: Combine objective functions and multi-level constraints to build an optimal energy storage scheduling model for data centers taking into account demand response;

[0101] Specifically, the day-ahead market price of the electricity market and the price data of the frequency market are imported and converted into a sequence form, and the parameters of the battery energy storage system are input, such as battery capacity, charging efficiency, discharging efficiency, maximum charging / discharging power, etc.; the initial state of the battery is set, including the initial state of charge (SoC) and the initial charging and discharging power; the state of the battery management system and other related systems is initialized to ensure that all systems are in a known state before starting the optimization.

[0102] S400: solving the optimal energy storage scheduling model of the data center taking into account demand response to obtain an optimization strategy;

[0103] In an embodiment of the present application, the sensor data is preprocessed, and the preprocessed data is used as input to the optimal energy storage scheduling model of the data center;

[0104] Set the parameters of the solver and use the script to perform optimization and solve the problem to get the optimal solution based on the objective function and constraints;

[0105] Extract the optimal solution from the solver and verify whether the optimization result meets all constraints. If so, convert the optimization result into a specific scheduling strategy.

[0106] Specifically, the objective function and constraints are converted into mathematical expressions and parameterized in MATLAB, and the variables and parameters of the model are defined using MATLAB's symbolic or numerical calculation tools.

[0107] It should be noted that the optimization model can maximize the dispatching benefits of data centers in the day-ahead market and the frequency reserve market. The optimization objective function takes into account the benefits of both the day-ahead market and the frequency reserve market, enabling data centers to achieve the best benefits in both markets. Refined modeling and optimized scheduling effectively reduce the energy costs of data centers. Reasonable scheduling of battery energy storage systems can reduce peak power consumption and lower electricity bills, while utilizing off-peak power for charging to further reduce electricity costs and improve energy efficiency.

[0108] The above is a schematic scheme of a method for optimal energy storage scheduling of a data center taking into account demand response in this embodiment. It should be noted that the technical scheme of the device for optimal energy storage scheduling of a data center taking into account demand response and the technical scheme of the method for optimal energy storage scheduling of a data center taking into account demand response belong to the same concept. For details not described in detail in the technical scheme of the device for optimal energy storage scheduling of a data center taking into account demand response in this embodiment, please refer to the description of the technical scheme of the method for optimal energy storage scheduling of a data center taking into account demand response.

[0109] The device for optimal energy storage scheduling of a data center taking demand response into account in this embodiment includes:

[0110] A constraint condition construction module is obtained, which is used to obtain the real-time operation data of the data center and the historical operation data in the day-ahead market and the frequency reserve market, and construct a multi-level constraint condition according to the historical operation data in the day-ahead market and the frequency reserve market;

[0111] An objective function construction module is used to construct an objective function that takes into account the maximum benefits of the day-ahead market and the frequency reserve market based on the real-time operation data of the data center and taking the maximum scheduling benefit of the data center in the day-ahead market and the frequency reserve market as the goal;

[0112] Constructing a model module, for combining the objective function and multi-level constraints to construct an optimal energy storage scheduling model for a data center taking into account demand response;

[0113] The solution module is used to solve the optimal energy storage scheduling model of the data center taking into account demand response to obtain an optimization strategy.

[0114] This embodiment further provides a computing device, which is applicable to an optimal energy storage scheduling situation of a data center taking into account demand response, including:

[0115] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the optimal energy storage scheduling method for a data center taking demand response into account as proposed in the above embodiment.

[0116] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for realizing optimal energy storage scheduling of a data center taking demand response into account as proposed in the above embodiment.

[0117] The storage medium proposed in this embodiment and the method for realizing optimal energy storage scheduling of a data center taking into account demand response proposed in the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, reference can be made to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0118] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0119] Example 2

[0120] This embodiment is different from the first embodiment in that it provides a verification test of an optimal energy storage scheduling method for a data center taking demand response into account, in order to verify and illustrate the technical effects adopted in this method.

[0121] Suppose a large data center is equipped with a large-scale energy storage system and participates in the day-ahead market and frequency regulation market of the electricity market; the operating goal of the data center is to maximize its benefits in these two markets while complying with battery operation restrictions, market operation rules and third-party operation rules.

[0122] The goal is to maximize the dispatching benefits of the data center in the day-ahead market and the frequency regulation market. The objective function is expressed as:

[0123]

[0124] Among them, T1 is the time step set of the day-ahead market, T2 is the time step set of the frequency modulation market, is the discharge frequency at time t in the day-ahead market, is the charging power at time t in the day-ahead market, The power provided for the FM market, is the dispatch parameter of the day-ahead market, is the market dispatch parameter of the frequency modulation market, C ramp is the energy storage ramp cost, C battery It is the ratio of the sum of the battery system cost and the power station balancing cost to the maximum operating hours in a year.

[0125] Constructing constraints, the battery operation boundary constraints are expressed as:

[0126] SOC min ≤SOC t ≤SOC max

[0127]

[0128] Among them, SOC min and SOC max are the minimum and maximum charge states of the battery, P chargemax and P discharg emax are the maximum charge and discharge powers respectively.

[0129] The market operation constraint is expressed as:

[0130]

[0131] Among them, P minbid The minimum dispatch requirement of the frequency market, δ is a 0 / 1 variable for frequency service, indicating whether frequency service is needed, P total is the total battery volume, and Bids upward and downward for frequency suppression reserve, respectively.

[0132] The third-party operation constraints are expressed as:

[0133] P reserve =P out -P in

[0134] Among them, P reserve is the reserve power reserved in the energy storage, P out is the energy flowing out of the battery, P in is the energy flowing into the battery.

[0135] The above objective function and constraints are combined to construct an optimal energy storage scheduling model for data centers taking demand response into account.

[0136] Use MATLAB to solve and obtain the optimal energy storage scheduling strategy.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An optimal energy storage scheduling method for a data center taking demand response into account, characterized in that: include: Acquire real-time operation data of the data center and historical operation data in the day-ahead market and the frequency reserve market, and construct multi-level constraint conditions according to the historical operation data in the day-ahead market and the frequency reserve market; According to the real-time operation data of the data center, taking the maximum dispatching benefit of the data center in the day-ahead market and the frequency reserve market as the goal, constructing an objective function that takes into account the maximum benefit of the day-ahead market and the frequency reserve market; Combining the objective function and multi-level constraints to construct an optimal energy storage scheduling model for a data center taking demand response into account; The optimal energy storage scheduling model for the data center taking demand response into account is solved to obtain an optimization strategy.

2. The optimal energy storage scheduling method for a data center taking demand response into account as claimed in claim 1, characterized in that: Multi-level constraints include: battery operation boundary constraints, market operation constraints and external third-party operation constraints.

3. The optimal energy storage scheduling method for a data center taking into account demand response as claimed in claim 2, characterized in that: The objective function includes: Construct an objective function that includes the calculation of battery charge and discharge power, monitoring and management of battery status, and the impact of value fluctuations on battery scheduling; The objective function is expressed as: Where t is the time step of the day-ahead market, Z is the time step of the frequency control reserve market, and P d is the discharge power bid in the day-ahead market, P c is the charging power bid in the day-ahead market, P fcr is the power provided to the frequency control reserve market, λ is the dispatch parameter of the day-ahead market, and λ fcr is the frequency control reserve market dispatch parameter, C r is the energy storage ramp cost, C fcr-r It is the ratio of the sum of the battery system cost and the power station balancing cost to the maximum operating hours in a year.

4. The optimal energy storage scheduling method for a data center taking demand response into account as claimed in claim 3, characterized in that: Battery status monitoring and management include: Monitor the battery status in real time and ensure that the battery status is within a safe range according to the battery operation boundary constraints; Sensors are used to detect battery parameters, calculate the current battery status, dynamically adjust the preset charge and discharge thresholds according to the battery status, keep the battery running in the best condition, and regularly evaluate the battery health status to take real-time preventive maintenance measures.

5. The optimal energy storage scheduling method for a data center taking demand response into account as claimed in claim 4, characterized in that: Battery charge and discharge power includes: Dynamically adjust charging current and voltage according to battery status and temperature; When the battery is in low condition, a constant current charging mode is used to quickly charge the battery until the battery voltage approaches the rated value; When the battery state is high, switch to constant voltage charging mode and charge slowly until the battery voltage stabilizes to the rated voltage; Set the lower threshold of the battery operating status. When the battery operating status drops to the lower threshold, discharging stops.

6. The optimal energy storage scheduling method for a data center taking into account demand response as claimed in claim 2 or 5, characterized in that: Battery operation boundary constraints include: At all time steps, the battery's discharge power is between 0 and the discharge limit; At all time steps, the battery’s charging power is between 0 and the charging limit; The discharge power is less than or equal to the auxiliary variable multiplied by the discharge limit; The charging power is less than or equal to 1 minus the auxiliary variable multiplied by the charging limit; At the end of each time step, the battery state plus the charging power minus the discharging power divided by the discharge efficiency multiplied by the time step divided by the battery energy capacity gives the battery state for the next time step; The battery status is between 0 and 1; The battery is half-full at the beginning and end.

7. The optimal energy storage scheduling method for a data center taking demand response into account as claimed in claim 6, characterized in that: Solving the optimal energy storage scheduling model for the data center taking demand response into account includes: Preprocess the sensor data and use the preprocessed data as input for the optimal energy storage scheduling model of the data center; Set the parameters of the solver and use the script to perform optimization and solve the problem to get the optimal solution based on the objective function and constraints; Extract the optimal solution from the solver and verify whether the optimization result meets all constraints. If so, convert the optimization result into a specific scheduling strategy.

8. A system for an optimal energy storage scheduling method for a data center taking into account demand response, characterized in that: include: A constraint condition construction module is obtained, which is used to obtain the real-time operation data of the data center and the historical operation data in the day-ahead market and the frequency reserve market, and construct a multi-level constraint condition according to the historical operation data in the day-ahead market and the frequency reserve market; An objective function construction module is used to construct an objective function that takes into account the maximum benefits of the day-ahead market and the frequency reserve market based on the real-time operation data of the data center and taking the maximum scheduling benefit of the data center in the day-ahead market and the frequency reserve market as the goal; Constructing a model module, for combining the objective function and multi-level constraints to construct an optimal energy storage scheduling model for a data center taking into account demand response; The solution module is used to solve the optimal energy storage scheduling model of the data center taking into account demand response to obtain an optimization strategy.

9. An electronic device, characterized in that: The device comprises: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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