Energy storage cluster diversified modeling representation method, system, device and storage medium

Abstract

The application discloses a kind of energy storage cluster diversification modeling characterization method, system, equipment and storage medium, the method includes: obtaining the real-time data of each type of energy storage unit in energy storage cluster;Based on the real-time data obtained, different types of energy storage units are modeled using a diversified modeling method, and a diversified model library considering various types of energy storage units is constructed;According to the modeling results of the energy storage unit, the behavior of the energy storage cluster is characterized, the results of different diversified models are output, and the performance of the energy storage cluster is comprehensively evaluated;Using the diversified model constructed, an optimization algorithm is used to optimize the scheduling of the energy storage cluster;Monitor the operating state of the energy storage cluster, and adjust and update the diversified model in real time according to the operating state data.The application can effectively characterize the performance characteristics of various types of energy storage equipment and optimize the overall scheduling efficiency of the energy storage cluster.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of energy storage cluster diversification modeling characterization method, system, equipment and medium, belong to energy technology field. BACKGROUND

[0002] At present, the management and scheduling of energy storage cluster face many challenges, especially in the performance difference of different energy storage devices and complex environmental conditions. The existing technology is mostly focused on the modeling and scheduling of single energy storage device, and lacks comprehensive modeling and analysis method for multiple different types of energy storage devices. Traditional scheduling methods often ignore the heterogeneity between energy storage devices and the influence of different operating modes, resulting in low scheduling efficiency, and the response speed and stability of power system are not ideal.

[0003] The existing energy storage cluster scheduling method mostly relies on traditional linear modeling or empirical model, and fails to fully utilize diversified mathematical models to characterize the dynamic characteristics and interactive behavior of energy storage devices, which cannot adapt to more complex practical application scenarios.

[0004] Therefore, a system and method capable of diversified modeling and accurate characterization in energy storage cluster are needed to overcome the shortcomings of existing technology and improve scheduling accuracy and system response capability. SUMMARY

[0005] Therefore, the present application provides an energy storage cluster diversification modeling characterization method, system, computer device and storage medium, which can effectively characterize the performance characteristics of various energy storage devices and optimize the overall scheduling efficiency of energy storage cluster.

[0006] The first object of the present application is to provide an energy storage cluster diversification modeling characterization method.

[0007] The second object of the present application is to provide an energy storage cluster diversification modeling characterization system.

[0008] The third object of the present application is to provide a computer device.

[0009] The fourth object of the present application is to provide a storage medium.

[0010] The first object of the present application can be achieved by adopting the following technical solutions:

[0011] An energy storage cluster diversification modeling characterization method, the method comprises:

[0012] Obtaining real-time data of various energy storage units in energy storage cluster;

[0013] Based on the obtained real-time data, different types of energy storage units are modeled using diversified modeling methods, and a diversified model library considering various types of energy storage units is constructed.

[0014] According to the modeling results of the energy storage units, the behaviors of the energy storage cluster are diversifiedly characterized, the results of different diversified models are output, and the performance of the energy storage cluster is comprehensively evaluated;

[0015] Using the constructed diversified model, an optimization algorithm is used to optimize the scheduling of the energy storage cluster;

[0016] The running state of the energy storage cluster is monitored, and the diversified model is adjusted and updated in real time according to the running state data.

[0017] Further, the diversified modeling method is used to model different types of energy storage units, specifically including:

[0018] According to the physical characteristics and operating constraints of lithium iron phosphate batteries, lithium iron phosphate battery energy storage models are constructed from four dimensions of energy storage, power dynamic response, efficiency decay and economy;

[0019] According to the compression, storage and release process of air, a compressed air energy storage model is constructed through the isentropic process in thermodynamics;

[0020] According to the chemical reaction and electrolyte flow of the all-vanadium redox flow battery, an all-vanadium redox flow battery energy storage model is constructed;

[0021] According to the electric double layer between the two-layer electrode and the electrolyte, a supercapacitor energy storage model is constructed;

[0022] According to the 5G base station standby energy storage energy and the 5G base station standby energy storage dynamic response, a 5G base station standby energy storage model is constructed;

[0023] According to the aggregated flexibility of air conditioning load and the operating characteristics of air conditioning load, an air conditioning temperature control energy storage model is constructed;

[0024] According to the charging, discharging process of the electric vehicle battery, the energy management strategy and the load demand of the electric vehicle, an electric vehicle mobile energy storage model is constructed;

[0025] According to the computing resources, power consumption and auxiliary equipment of the data center, a data center virtual energy storage model is constructed.

[0026] Further, the lithium iron phosphate battery energy storage model includes an energy storage model, a power dynamic response model, an efficiency decay model and an economy model;

[0027] The energy storage model is as follows:

[0028] E LFPO =V LFPO ×Q LFPO

[0029]

[0030] wherein, E LFPO (t) is the energy of the lithium iron phosphate battery at time t, V LFPO is the voltage level accessed by the energy storage system, Q LFPO is the charge amount of the energy storage battery accessed by the energy storage system, is the charging power of the lithium iron phosphate battery at time t, is the discharging power of the lithium iron phosphate battery at time t, is the charging efficiency of the lithium iron phosphate battery, is the discharging efficiency of the lithium iron phosphate battery, is the lower limit of the energy of the lithium iron phosphate battery, is the upper limit of the energy of the lithium iron phosphate battery;

[0031] The power dynamic response model is as follows:

[0032]

[0033] wherein, is the maximum charging power, is the maximum discharging power, only one mode of charging or discharging is allowed at the same time, i.e.

[0034] The efficiency attenuation model includes a charging efficiency attenuation model and a discharging efficiency attenuation model, as follows:

[0035]

[0036] The economic model is as follows:

[0037]

[0038] wherein, C cap is the initial investment cost, C O&M is the operation and maintenance cost, C re is the residual value, i.e., the economic value of the lithium iron phosphate battery still has after the end of the designed life cycle, r is the discount rate, and T is the battery life.

[0039] Further, the 5G base station standby energy storage model includes a 5G base station standby energy storage energy storage model and a 5G base station standby energy storage dynamic response model;

[0040] The 5G base station standby energy storage energy storage model is as follows:

[0041] P load (t) = P load,avg + ΔP(t)

[0042]

[0043] wherein, E 5G is the 5G base station backup energy storage energy, P load (t) is the base station workload at time t, P load,avg is the average load of the base station, ΔP(t) is the fluctuation part of the load with communication traffic, D b is the minimum energy storage backup time, τ is the integral variable of time within the interval (t, t+D b );

[0044] The dynamic response model of the 5G base station backup energy storage is as follows:

[0045]

[0046] wherein, is the charging response of the 5G base station backup energy storage, is the discharging response of the 5G base station backup energy storage, is the discharging power of the 5G base station backup energy storage, is the charging power of the 5G base station backup energy storage, η ch is the charging efficiency of the energy storage unit, η dis is the discharging efficiency of the energy storage unit, is the maximum charging power of the 5G base station backup energy storage, is the maximum discharging power of the 5G base station backup energy storage.

[0047] Further, the electric vehicle mobile energy storage model includes the regulation time constraint of the electric vehicle, the charging and discharging power constraint of the electric vehicle, the state of charge constraint of the electric vehicle battery, and the travel demand constraint;

[0048] The regulation time constraint of the electric vehicle is as follows:

[0049]

[0050] wherein, t c is the regulation time of the electric vehicle, is the time when the electric vehicle accesses the power grid, is the time when the electric vehicle exits the power grid;

[0051] The charging and discharging power constraint of the electric vehicle is as follows:

[0052]

[0053] wherein, P EV is the charging and discharging power of the electric vehicle, is the minimum charging and discharging power of the electric vehicle, is the maximum charging and discharging power of the electric vehicle;

[0054] The electric vehicle battery state of charge constraint is as follows:

[0055]

[0056] where λ EV is the electric vehicle battery state of charge, is the minimum charge-discharge power of the electric vehicle battery, is the maximum charge-discharge power of the electric vehicle battery;

[0057] The travel demand constraint is as follows:

[0058]

[0059] where, is the state of charge of a single electric vehicle to meet user demand when leaving.

[0060] Further, the data center virtual energy storage model is as follows:

[0061] P CDC = P SER + P COOL + P ASS - P RES - P DIS + P CH

[0062] P SER = [(P SER_peak - P SER_idle ) · f CPU + P SER_idle ] · m SER_on

[0063] f CPU = r / (m SER_on μ SER )

[0064]

[0065] 0 ≤ m SER_on ≤ M k

[0066] where P CDC is the electric power consumed by the data center, P SER is the electric power consumed by the server, P COOL is the electric power consumed by the refrigeration equipment, P ASS is the electric power consumed by the auxiliary equipment, P RES is the renewable energy output, P DIS is the energy storage discharge power, P CH is the energy storage charging power, P SER_peakP represents the peak power of a data center server. SER_idle f is the idle power of the data center server. CPU m is the operating frequency of the data center server. SER_on Let r be the number of data center servers started, r be the data processing capacity of the data center servers, and μ be the data throughput. SER For the processing speed of data center servers, M represents the maximum operating frequency of the data center. k This refers to the number of servers in the data center.

[0067] Furthermore, based on the modeling results of the energy storage units, the behavior of the energy storage cluster is characterized in a diversified manner, specifically including:

[0068] During the grid connection of renewable energy, power output fluctuations are smoothed by adjusting the charging and discharging operation of the battery, as shown in the following formula:

[0069]

[0070] Among them, P wind (t) represents the wind power generation at time t, P solar (t) represents the photovoltaic power generation at time t, P grid (t) represents the power supplied by the grid at time t, P stored (t) represents the output power of the energy storage system at time t, P load (t) represents the load demand power at time t, and T is the optimization time period;

[0071] For battery energy storage participating in primary frequency regulation, a droop control strategy is adopted, as shown in the following formula:

[0072]

[0073] Among them, P stored Δf is the charging and discharging power of the battery energy storage, k is the gain coefficient of the battery energy storage, Δf is the frequency deviation of the system, f0 is the set dead zone value, and P0 is the charging power of the battery energy storage when the set adjustment dead zone is reached.

[0074] For battery energy storage participating in secondary frequency regulation, the charging and discharging power of the energy storage units under the energy storage station is allocated according to the optimized control method, as follows:

[0075] P ACE =ΔP tie +BΔf

[0076] Wherein, ΔP tie Here, B is the tie-line power deviation, B is the deviation coefficient of the regional frequency modulation, and Δf is the regional frequency deviation.

[0077] The minimum peak-valley difference of the energy storage system is taken as the target of the energy storage participating in peak regulation, and the optimization target is the maximum variance of the load curve, as follows:

[0078]

[0079] Wherein, P L (t) is the load of the energy storage system in the t period, P BESS (t) is the active power of the battery energy storage system in the t period, P av (t) is the equivalent average of the remaining load in the t period.

[0080] The maximum economic benefit of the charging and discharging of the energy storage system is taken as the target, and the adjustable energy storage capacity of the region is considered, and the charging and discharging power of each period is optimized, as follows:

[0081]

[0082] Wherein, Δt is a fixed time interval, C t is the time-of-use electricity price of the energy storage system.

[0083] The second object of the application can be achieved by adopting the following technical solutions:

[0084] A diversified modeling and characterization system for an energy storage cluster, the system comprising:

[0085] A data acquisition module for acquiring real-time data of various types of energy storage units in the energy storage cluster;

[0086] A model construction module for modeling different types of energy storage units using diversified modeling methods based on the acquired real-time data, and constructing a diversified model library considering various types of energy storage units;

[0087] A diversified characterization module for diversifying the behavior of the energy storage cluster according to the modeling results of the energy storage units, outputting the results of different diversified models, and comprehensively evaluating the performance of the energy storage cluster;

[0088] An optimization scheduling module for scheduling and optimizing the energy storage cluster using the diversified models constructed and an optimization algorithm;

[0089] A real-time monitoring module for monitoring the operating state of the energy storage cluster and adjusting and updating the diversified models in real time according to the operating state data.

[0090] The third object of the application can be achieved by adopting the following technical solutions:

[0091] A computer device comprising a processor and a memory for storing programs executable by the processor, wherein the processor implements the diversified modeling and characterization method of the energy storage cluster when executing the programs stored in the memory.

[0092] The fourth object of the present application can be achieved by adopting the following technical solution:

[0093] A storage medium stores a program, which is executed by a processor to implement the energy storage cluster diversification modeling characterization method described above.

[0094] The present application has the following beneficial effects relative to the prior art:

[0095] 1、The present application mostly relies on a single mathematical model, which cannot fully characterize the complex dynamic characteristics and interactive behavior of energy storage devices, especially in the face of complex and variable actual application scenarios, the expressiveness is limited, compared with the prior art, by introducing diversified mathematical models, including lithium iron phosphate battery, compressed air energy storage, all-vanadium redox flow battery, super capacitor and other energy storage technology models, the characteristics and behavior of different types of energy storage devices can be more accurately simulated, and the adaptability and accuracy of the model are improved.

[0096] 2、The present application covers a variety of energy storage unit types, including traditional battery energy storage, compressed air energy storage, liquid flow battery energy storage, super capacitor and other emerging energy storage technologies, and even 5G base station standby energy storage, electric vehicle mobile energy storage, air conditioning temperature control energy storage, data center virtual energy storage and other new application scenarios, this diversified modeling method can adapt to the needs of different types of energy storage units and various actual application environments, ensuring that the energy storage cluster scheduling can run in different scenarios.

[0097] 3、The present application can more accurately simulate the performance of energy storage devices under different working conditions, especially under dynamic change conditions such as load fluctuation and renewable energy output fluctuation, by diversifying the characterization of energy storage unit behavior, and combining the output results of different models for comprehensive evaluation, providing all-round performance analysis for the operation of energy storage cluster, this multi-dimensional evaluation method makes the scheduling more accurately adapt to different needs, for example, when considering energy cost, energy storage battery scheduling strategy, peak-valley difference rate optimization and other factors, economic and technical factors can be better considered, ensuring the efficiency of energy storage cluster in various application scenarios.

[0098] 4、The present application continuously monitors the running state of the energy storage cluster and dynamically adjusts and updates the model according to real-time data feedback. This real-time feedback mechanism ensures that the energy storage cluster can adapt to environmental changes such as load fluctuation and renewable energy output change, thereby maintaining efficiency and stability in different operating environments, avoiding the blindness and hysteresis of traditional scheduling methods in dynamic environments. BRIEF DESCRIPTION OF DRAWINGS

[0099] In order to make the technical solutions of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description only show some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of the shown structures.

[0100] Figure 1 The flow chart of the energy storage cluster diversification modeling characterization method of embodiment 1 of the present application.

[0101] Figure 2 The flow chart of the battery energy storage system participating in secondary frequency modulation of embodiment 1 of the present application.

[0102] Figure 3 The flow chart of the battery energy storage system participating in system peak shaving of embodiment 1 of the present application.

[0103] Figure 4 The structural block diagram of the energy storage cluster diversification modeling characterization system of embodiment 2 of the present application.

[0104] Figure 5 The structural block diagram of the computer device of embodiment 3 of the present application. DETAILED DESCRIPTION

[0105] In order to make the technical solutions of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description only show some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of the shown structures.

[0106] Embodiment 1:

[0107] As shown in the figure, the present embodiment provides an energy storage cluster diversification modeling characterization method, which comprises the following steps: Figure 1

[0108] S101, acquiring real-time data of various energy storage units in the energy storage cluster.

[0109] The step S101 of the present embodiment is a step of collecting basic data, wherein the collected real-time data includes voltage, current, temperature, SOC and other parameters.

[0110] S102, based on the acquired real-time data, using a diversified modeling method to model different types of energy storage units, and constructing a diversified model library considering various types of energy storage units.

[0111] ​The step S102 of the embodiment is a step of building a diversified energy storage model library. Energy storage technologies are various, and typical technologies include energy-type energy storage such as pumped storage and compressed air energy storage, and power-type energy storage such as flywheel energy storage and super capacitor. For the most widely used battery energy storage, there are many types of classification. A diversified model library is established for different types of energy storage units.

[0112] In the embodiment, a diversified modeling method is used to model different types of energy storage units, specifically including:

[0113] S1021, according to the physical properties and operating constraints of lithium iron phosphate batteries, a lithium iron phosphate battery energy storage model is constructed from four dimensions of energy storage, power dynamic response, efficiency decay and economy.

[0114] Lithium iron phosphate batteries (LiFePO4) are widely used in power systems due to their high safety, long cycle life and stable chemical properties. The lithium iron phosphate battery energy storage model of the embodiment is constructed based on its physical properties and operating constraints from four dimensions of energy storage, power dynamic response, efficiency decay and life management. The specific modeling is as follows:

[0115] 1) Constructing an energy storage model

[0116] The energy storage model of the lithium iron phosphate battery mainly includes energy storage and power output, mainly considering the relationship between battery energy storage and charging and discharging power, charging and discharging efficiency, and operating upper and lower limits.

[0117] E LFPO = V LFPO × Q LFPO

[0118]

[0119] Where E LFPO (t) is the energy of the lithium iron phosphate battery at time t, V LFPO is the voltage level of the energy storage system, Q LFPO is the charge of the energy storage battery connected to the energy storage system, is the charging power of the lithium iron phosphate battery at time t, is the discharging power of the lithium iron phosphate battery at time t, is the charging efficiency of the lithium iron phosphate battery, is the discharging efficiency of the lithium iron phosphate battery, is the lower limit of the energy of the lithium iron phosphate battery, is the upper limit of the energy of the lithium iron phosphate battery.

[0120] The state of charge of the battery is defined as the percentage of the current remaining capacity of the battery to the rated capacity, and the calculation formula is:

[0121]

[0122] SOC(t) is the state of charge of the lithium iron phosphate battery at time t (current time). LFPO (t) is the state of charge of the lithium iron phosphate battery at time t (current time).

[0123] 2) Constructing a power dynamic response model

[0124]

[0125] wherein, is the maximum charging power, is the maximum discharging power, and only one mode of charging or discharging is allowed at the same time, i.e.

[0126] 3) Efficiency attenuation model

[0127] The efficiency attenuation model includes a charging efficiency attenuation model and a discharging efficiency attenuation model. The charging and discharging efficiency changes nonlinearly with power, and a piecewise linearization approximation is adopted:

[0128]

[0129] The battery capacity decreases with the increase of the number of charging and discharging cycles, and the empirical formula is:

[0130]

[0131] In the formula, N is the cumulative equivalent cycle number, and α and β are attenuation coefficients. The typical values of the lithium iron phosphate battery are α = 3 × 10 -4 and β = 0.8.

[0132] 4) Economic model

[0133] The full life cycle cost includes initial investment cost, operation and maintenance cost, and residual value:

[0134]

[0135] wherein, C cap is the initial investment cost (yuan / kWh), C O&M is the operation and maintenance cost (yuan / kWh / year), C re is the residual value, i.e., the economic value of the lithium iron phosphate battery still possessed at the end of the designed life cycle, r is the discount rate, and T is the battery life (years).

[0136] S1022, according to the compression, storage and release process of air, a compressed air energy storage model is constructed through the isentropic process in thermodynamics.

[0137] The compressed air energy storage model of the embodiment involves the compression, storage and release process of air. Charging process: the compressor is driven by electric power to compress air into the storage container. Discharge process: the expander releases compressed air to generate mechanical work and convert it into electric energy. During the expansion process, the high-pressure gas of compressed air expands and becomes cold. Therefore, the expansion process requires a heat source to maintain the gas temperature to improve the expansion efficiency. In practice, compressed air energy storage usually also contains a thermal energy management system to improve the energy utilization efficiency of the system by recovering and storing the heat released during the compression process. Generally, the compressed air energy storage system can be simplified by the isentropic process in thermodynamics.

[0138]

[0139] wherein p1 is the initial pressure of compressed air, p2 is the final pressure of compressed air, V1 is the initial volume of gas, V2 is the final volume of gas, γ is the specific heat ratio of air, usually about 1.4; there are often heat loss, mechanical loss and the like in the compression and expansion process, so the compressed air energy storage system needs to consider the system loss, multiplied by the proportionality coefficient, is the charging efficiency, is the discharge efficiency; at the same time, the charging and discharging power should also meet the electrical performance requirements of the compressed air energy storage equipment, P CAES is the charging and discharging power of the compressed air energy storage, is the minimum value of the charging and discharging power of the compressed air energy storage, is the maximum value of the charging and discharging power of the compressed air energy storage.

[0140] S1023, according to the chemical reaction of the all-vanadium redox flow battery and the electrolyte flow, the all-vanadium redox flow battery energy storage model is constructed.

[0141] The all-vanadium redox flow battery energy storage model of the embodiment is based on the chemical reaction of the battery and the flow of the electrolyte, and the basic structure mainly includes electrolyte, two electrodes, ion exchange membrane and external circuit. Electric energy is converted into chemical energy by electrochemical reaction in the battery and stored, and the battery voltage is expressed by the following formula:

[0142]

[0143] V ohm =I dis ·R int

[0144]

[0145] P VRFB =V VRFB ·I VRFB =kV VRFB Q VRFB

[0146] Among them, V VRFB The voltage of the vanadium redox flow battery. V is the operating voltage. act To activate the polarization loss, we have the Ohmic polarization loss, V con The concentration polarization loss is α; the activation polarization loss is proportional to the current density and can be expressed by the Tafel equation. anode The Tafel slope of the anode, b anode I0 is the Tafel slope of the cathode, and I0 is the exchange current density of the cell. dis R is the discharge current of the battery; Ohmic polarization loss is mainly related to the conductivity of the electrolyte and electrode materials inside the battery, and can be described by Ohm's law. int Q represents the total internal resistance of the battery, including electrode resistance, electrolyte resistance, and ion exchange membrane resistance. Concentration polarization loss is related to the concentration distribution of ions in the electrolyte. B is a parameter related to the electrolyte properties, obtained through actual measurement. The output power of the flow battery is related to the voltage and discharge current. VRFB denoted as ν, where ν is the electrolyte flow rate of the vanadium redox flow battery, and k is a constant that depends on the battery design and electrolyte properties.

[0147] S1024. Construct a supercapacitor energy storage model based on the electrical double layer between the two electrodes and the electrolyte.

[0148] The supercapacitor energy storage model in this embodiment typically consists of an electrical double layer between two electrodes and an electrolyte. Energy storage is achieved through the distribution of charge at the electrode / electrolyte interface, mainly based on the charging and discharging process of the capacitor. Multiple capacitors working in parallel must be considered to handle instantaneous power fluctuations.

[0149]

[0150] Among them, C SPCP V is the capacitance value of the capacitor. SPCP Since supercapacitors have rapid charging and discharging characteristics, their charging and discharging currents are usually quite large. To more accurately simulate the performance of supercapacitors, the charging and discharging process can be approximated by a constant current model, assuming the current I... SPCP The voltage remains constant, and changes over time during the charging process as follows:

[0151]

[0152] in, The initial voltage for energy storage in the supercapacitor is given. Since the supercapacitor has high charging and discharging efficiency, energy loss can be neglected in the modeling.

[0153] S1025, according to the 5G base station standby energy storage energy and the 5G base station standby energy storage dynamic response, a 5G base station standby energy storage model is constructed.

[0154] 5G base station standby energy storage is mainly used to provide power support in the case of main power failure, load fluctuation or emergency. The standby energy storage system is usually composed of supercapacitors, lithium batteries and other devices, the purpose is to ensure that the base station can continue to work for a certain time in the case of power failure. Due to the flow-sensitive characteristics of 5G base station power load, the standby capacity of 5G base station will have a certain standby capacity over time. Therefore, the standby capacity is schedulable and can be used as a flexible resource of the power system; the 5G base station standby energy storage model of the embodiment includes a 5G base station standby energy storage energy storage model and a 5G base station standby energy storage dynamic response model.

[0155] 1) 5G base station standby energy storage energy storage model

[0156] P load (t) = P load,avg + ΔP(t)

[0157]

[0158] Wherein, E 5G is the 5G base station standby energy storage energy, P load (t) is the base station working load at time t, P load,avg is the average load of the base station, since the 5G base station is sensitive to flow, ΔP(t) is the fluctuation part of the load with communication flow, D b is the minimum energy storage standby time, τ is the integral variable of time in the interval (t, t+D b ), so the reserved capacity of the standby energy storage also changes with time.

[0159] 2) 5G base station standby energy storage dynamic response model

[0160]

[0161] Wherein, is the charging response of the 5G base station standby energy storage, is the discharge response of the 5G base station standby energy storage, is the discharge power of the 5G base station standby energy storage, is the charging power of the 5G base station standby energy storage, η ch is the charging efficiency of the energy storage unit, η dis is the discharge efficiency of the energy storage unit, the charging efficiency and the discharge efficiency of the energy storage unit are usually not equal, and change with temperature, load and use, is the maximum charging power of the 5G base station standby energy storage, The maximum discharging power of the standby energy storage for the 5G base station.

[0162] S1026, according to the aggregation flexibility of the air conditioning load and the operation characteristics of the air conditioning load, an air conditioning temperature control energy storage model is constructed.

[0163] The embodiment adopts a battery model to describe the aggregation flexibility of the air conditioning load, takes the cooling mode as an example, describes a first-order equivalent thermal parameter model of the air conditioning thermal dynamic characteristics, and uses a simple continuous power model to replace the discrete model to describe the operation characteristics of the air conditioning load.

[0164]

[0165] wherein, C TL is the equivalent heat capacity of the air conditioner; T in (t) is the indoor temperature; T out (t) is the outdoor temperature; R is the equivalent impedance of the air conditioner; C op is the coefficient of performance of the air conditioner; P TL (t) is the electric power of the air conditioning load at a certain moment; is the rated electric power of the air conditioner.

[0166] S1027, according to the charging and discharging process of the electric vehicle battery, the energy management strategy and the load demand of the electric vehicle, an electric vehicle mobile energy storage model is constructed.

[0167] The electric vehicle mobile energy storage model of the embodiment mainly involves the charging and discharging process of the battery, the energy management strategy and the load demand of the electric vehicle, and also equivalent the electric vehicle model to the battery model.

[0168] The electric vehicle mobile energy storage model includes the regulation time constraint of the electric vehicle, the charging and discharging power constraint of the electric vehicle, the state of charge constraint of the electric vehicle battery and the travel demand constraint.

[0169] The regulation time constraint of the electric vehicle is as follows:

[0170]

[0171] wherein, t c is the regulation time of the electric vehicle, is the time when the electric vehicle accesses the power grid, is the time when the electric vehicle accesses the power grid;

[0172] The charging and discharging power constraint of the electric vehicle is as follows:

[0173]

[0174] wherein, P EV is the charging and discharging power of the electric vehicle, The minimum charge-discharge power of the electric vehicle, The maximum charge-discharge power of the electric vehicle;

[0175] The state of charge of the electric vehicle battery is constrained as follows:

[0176]

[0177] Wherein, λ EV The state of charge of the electric vehicle battery, The minimum charge-discharge power of the electric vehicle battery, The maximum charge-discharge power of the electric vehicle battery;

[0178] The travel demand constraint is as follows:

[0179]

[0180] Wherein, The state of charge of a single electric vehicle when meeting user demand.

[0181] S1028, according to the computing resources, power consumption and auxiliary equipment of the data center, a data center virtual energy storage model is constructed.

[0182] The data center itself does not directly store power, but can optimize the scheduling of its computing resources, power consumption, auxiliary equipment to achieve load balancing and energy storage effect, the basic idea of the virtual energy storage model of the embodiment is to use the computing load of the data center to adjust the use and storage of energy, so that it plays a role similar to a battery, and a simplified mathematical model is used to describe the role of the data center as a virtual energy storage.

[0183] P CDC = P SER + P COOL + P ASS -P RES -P DIS + P CH

[0184] P SER = [(P SER_peak -P SER_idle )·f CPU + P SER_idle ]·m SER_on

[0185] f CPU = r / (m SER_on μ SER )

[0186]

[0187] 0≤m SER_on≤M k

[0188] wherein P CDC is the power consumed by the data center, P SER is the power consumed by the servers, P COOL is the power consumed by the cooling equipment, P ASS is the power consumed by the auxiliary equipment, P RES is the renewable energy output, P DIS is the power discharged by the energy storage, P CH is the power charged by the energy storage, P SER_peak is the peak power of the data center servers, P SER_idle is the idle time power of the data center servers, f CPU is the operating frequency of the data center servers, m SER_on is the number of data center servers, r is the data processing amount of the data center servers, μ SER is the processing rate of the data center servers, is the maximum operating frequency of the data center, M k is the number of data center servers.

[0189] S103, according to the modeling results of the energy storage unit, the behavior of the energy storage cluster is diversifiedly characterized, the results of different diversified models are output, and the performance of the energy storage cluster is comprehensively evaluated.

[0190] Step S103 of the embodiment is a step of diversifying the behavior of the energy storage. In order to realize the efficient operation and stability of the energy storage cluster under different loads and dynamic environments, the performance of the energy storage device under different working conditions is accurately simulated, so as to comprehensively evaluate the performance and response capability of the energy storage cluster. According to the modeling results of the energy storage unit, the behavior of the energy storage cluster is diversifiedly characterized.

[0191] S1031, smoothing the renewable energy output.

[0192] During the grid connection of renewable energy, the energy storage system can smooth the output fluctuation by adjusting the charging and discharging operation of the battery, reduce the phenomenon of wind and light abandonment, and enhance the stability of the power grid. Taking the general energy storage model as the object, the target is to minimize the volatility of the system and maximize the self-consumption of energy.

[0193]

[0194] wherein P wind (t) is the wind power generation power at time t, P solar (t) is the photovoltaic power generation power at time t, P grid (t) is the power provided by the power grid at time t, P stored (t) is the output power of the energy storage system at time t, Pload (t) is the load demand power at time t, and T is the optimization time period.

[0195] The constraints are as follows:

[0196] 1) Energy storage charging and discharging constraints:

[0197]

[0198] wherein, is the minimum limit of the energy storage power, is the maximum limit of the energy storage power.

[0199] 2) Energy storage state constraints:

[0200]

[0201] wherein, E stored (t) is the energy storage power of the energy storage system at time t, η charge is the charging efficiency, and η discharge is the discharging efficiency, and Δt is the time step.

[0202] 3) Upper and lower limits of the energy storage power:

[0203]

[0204] wherein, is the minimum limit of the energy storage power, is the maximum limit of the energy storage power.

[0205] S1032, participating in frequency regulation of the energy storage system.

[0206] For battery energy storage participating in primary frequency regulation, a droop control strategy is usually adopted.

[0207]

[0208] wherein, P stored is the charging and discharging power of the battery energy storage, k is the gain coefficient of the battery energy storage, Δf is the frequency deviation of the system, f0 is the set dead zone value, the battery energy storage does not work within the dead zone, and P0 is the charging power of the battery energy storage at the set adjustment dead zone.

[0209] For battery energy storage participating in secondary frequency regulation, first, the power shortage is calculated by the control center according to the system frequency deviation and tie-line power and other information, and dispatch instructions are sent to the battery energy storage according to the economic principle to make it cooperate with the AGC system to control the generator unit output. For the energy storage station, the energy storage units under its jurisdiction are distributed according to a certain optimization control method, as shown in Figure 2 .

[0210] PACE = ΔP tie + BΔf

[0211] wherein, ΔP tie is the tie-line power deviation, B is the deviation coefficient of regional frequency modulation, and Δf is the regional frequency deviation.

[0212] The up-regulation and down-regulation capacity of the energy storage cluster participating in frequency modulation is:

[0213]

[0214] wherein, P L is the discharge power of the mth energy storage, P BESS is the charging power of the mth energy storage, and N is the number of energy storage units in the energy storage cluster, to prevent the system power flow distribution from changing greatly due to frequency modulation, affecting the stable operation of the system, the up-regulation and down-regulation capacity of the energy storage cluster cannot exceed the safety threshold of the rated capacity;

[0215]

[0216] wherein, P av is the total up-regulation capacity, P BESS is the total down-regulation capacity; S c is the rated capacity of the energy storage cluster, and δ is the threshold value, and the up-regulation and down-regulation capacity of different energy storage units participating in frequency modulation is:

[0217]

[0218] S1033、participating energy storage system peak shaving.

[0219] In an actual power system, the new energy output has large time fluctuation, and there is a certain error in system load prediction, resulting in that the new power generation capacity is surplus in some time periods in the region and cannot be well accommodated, and the power generation capacity is small in some time periods, resulting in that the peak-valley difference of system load is large, so the energy storage needs to participate in the power system peak shaving mechanism, as shown in FIG. 1, to keep the relative stability of power in a fixed period. Figure 3 The general energy storage model is taken as the research object, and the operation target is to reduce the electricity cost and the peak-valley difference rate.

[0220] 1) Energy storage battery dispatching optimization strategy based on minimum peak-valley difference rate

[0221] Taking the minimum system peak-valley difference rate as the target of energy storage participating in peak shaving, the optimization target is to maximize the variance of the load curve, and the objective function is:

[0222]

[0223] wherein, P d (t) is the energy storage system load in the t period, and P d(t) is the active power of the battery energy storage system in the t period, P av (t) is the equivalent average value of the remaining load in the t period, and the calculation formula is as follows:

[0224] P BESS (t) = u c · η ch · P ch (t) - u d · P d (t) / η d

[0225]

[0226] Constraint:

[0227] S(t) = S(t-1) + u c · η ch · P ch (t) - u d · P dis (t) / η d

[0228] S min ≤ S(t) ≤ S max

[0229] 0 ≤ P ch (t) ≤ P max

[0230] 0 ≤ P dis (t) ≤ P max

[0231] Wherein, S(t) is the remaining capacity of the battery energy storage in the t period, S(t-1) is the remaining capacity of the battery energy storage in the t-1 period, η ch is the charging efficiency of the battery energy storage, η d is the discharging efficiency of the battery energy storage, P ch (t) is the battery charging power in the t period, P dis (t) represents the battery discharging power in the t period, u c is the state flag of charging in the t period, u d is the state flag of discharging in the t period, and the energy storage is limited to work in charging and discharging states at the same time.

[0232] 2) Energy storage battery dispatching optimization strategy based on minimum energy cost

[0233] Considering the electricity billing strategy of peak-valley electricity price in a certain region, the dispatching method takes the maximum economic benefit of the energy storage system charging and discharging as the target, and considers the adjustable energy storage capacity constraint of the region, and optimizes the energy storage charging and discharging power in each period.

[0234]

[0235] where T denotes the division of a day into T time periods, Δt is a fixed time interval, C t for the energy storage system.

[0236] S104, using the constructed diversified model, the optimization algorithm is used for scheduling optimization of the energy storage cluster.

[0237] The step S104 of the embodiment is the step of optimizing scheduling operation, using the constructed diversified model, the optimization algorithm (such as genetic algorithm, particle swarm algorithm, etc.) is used for scheduling optimization of the energy storage cluster, to ensure efficient operation under different load conditions.

[0238] S105, monitoring the running state of the energy storage cluster, and adjusting and updating the diversified model in real time according to the running state data.

[0239] The step S105 of the embodiment is the step of real-time monitoring and feedback adjustment, by monitoring the running state of the energy storage cluster, and adjusting and updating the model in real time according to the running data, to ensure the stability and efficiency of the energy storage cluster in dynamic environment.

[0240] In summary, the diversified modeling characterization method of the energy storage cluster of the embodiment has the following advantages:

[0241] 1) The flexibility and adaptability of the energy storage cluster scheduling system are improved, which can adapt to different types of energy storage devices and complex application scenarios.

[0242] 2) The behavior of the energy storage device is accurately simulated and characterized, thereby comprehensively improving the performance evaluation and scheduling strategy optimization of the energy storage cluster.

[0243] 3) The real-time monitoring and feedback mechanism is introduced, which improves the stability and response ability of the energy storage cluster.

[0244] It should be noted that although the method operations of the above embodiments are described in a particular order, this is not required or implied in any way as to the order of execution or that all operations be performed to achieve desirable results. Rather, the order of execution of the depicted steps can be changed, and / or certain steps can be omitted, combined with others, performed simultaneously, and / or performed in a different order.

[0245] Embodiment 2:

[0246] As Figure 4As shown in the figure, this embodiment provides a diversified modeling and characterization system for energy storage clusters. The system includes a data acquisition module 401, a model building module 402, a diversified characterization module 403, an optimization scheduling module 404, and a real-time monitoring module 405. The specific descriptions of each module are as follows:

[0247] The data acquisition module 401 is used to acquire real-time data of various energy storage units in the energy storage cluster;

[0248] The model building module 402 is used to model different types of energy storage units based on the acquired real-time data using diverse modeling methods, and to build a diversified model library that considers various types of energy storage units.

[0249] The diversified characterization module 403 is used to perform diversified characterization of the behavior of the energy storage cluster based on the modeling results of the energy storage unit, output the results of different diversified models, and comprehensively evaluate the performance of the energy storage cluster.

[0250] The optimization scheduling module 404 is used to optimize the scheduling of the energy storage cluster by employing optimization algorithms based on the constructed diversified model.

[0251] The real-time monitoring module 405 is used to monitor the operating status of the energy storage cluster and adjust and update the diversified model in real time based on the operating status data.

[0252] It should be noted that the system provided in this embodiment is only an example of the above-described division of functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0253] Example 3:

[0254] This embodiment provides a computer device, such as... Figure 5 As shown, it includes a processor 502, a memory, an input device 703, a display device 504, and a network interface 505 connected via a system bus 501. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 506 and internal memory 507. The non-volatile storage medium 506 stores an operating system, computer programs, and a database. The internal memory 507 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 502 executes the computer programs stored in the memory, it implements the diversified modeling and characterization method for energy storage clusters described in Embodiment 1 above, as follows:

[0255] The real-time data of various types of energy storage units in the energy storage cluster is acquired; based on the acquired real-time data, diversified modeling methods are used to model different types of energy storage units, and a diversified model library considering various types of energy storage units is constructed; the behavior of the energy storage cluster is diversifiedly characterized according to the modeling results of the energy storage units, the results of different diversified models are output, and the performance of the energy storage cluster is comprehensively evaluated; the diversified models constructed are used to optimize the scheduling of the energy storage cluster by using an optimization algorithm; the running state of the energy storage cluster is monitored, and the diversified models are adjusted and updated in real time according to the running state data.

[0256] Embodiment 4

[0257] The embodiment provides a storage medium, which is a computer-readable storage medium, and stores a computer program. When the computer program is executed by a processor, the energy storage cluster diversified modeling and characterization method in the above embodiment 1 is realized, as follows:

[0258] The real-time data of various types of energy storage units in the energy storage cluster is acquired; based on the acquired real-time data, diversified modeling methods are used to model different types of energy storage units, and a diversified model library considering various types of energy storage units is constructed; the behavior of the energy storage cluster is diversifiedly characterized according to the modeling results of the energy storage units, the results of different diversified models are output, and the performance of the energy storage cluster is comprehensively evaluated; the diversified models constructed are used to optimize the scheduling of the energy storage cluster by using an optimization algorithm; the running state of the energy storage cluster is monitored, and the diversified models are adjusted and updated in real time according to the running state data.

[0259] It should be noted that the computer-readable storage medium of the embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection with one or more conductive wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0260] In this embodiment, the computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this embodiment, the computer readable signal medium can include a computer readable program that is communicated, propagated, or transported, for example, in a baseband signal, or as part of a carrier wave, between a computing device and another device. For example, this computer readable signal medium can take the form of any appropriate medium including but not limited to electronic, magnetic, optical, or any suitable combination of the foregoing. The computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0261] The computer program product included in the computer readable storage medium can be implemented in any desirable programming language or combination thereof, including an object oriented programming language such as Java, Python, C++, and conventional procedural programming languages such as C or similar programming languages. The program can be executed in whole or in part on the user computer, as a stand-alone software package, partly on the user computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0262] The above description is only preferred embodiments of the present application, and it is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all aspects as illustrative and not restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended to encompass all changes falling within the meaning and range of equivalents of the claims.

Claims

1. A method of energy storage cluster diversification modeling characterization, comprising: The method comprises: acquiring real-time data of various energy storage units in the energy storage cluster; based on the acquired real-time data, using a diversified modeling method to model different types of energy storage units, and constructing a diversified model library considering various energy storage units; according to the modeling results of the energy storage units, the behavior of the energy storage cluster is diversifiedly characterized, the results of different diversified models are output, and the performance of the energy storage cluster is comprehensively evaluated; using the constructed diversified model, an optimization algorithm is used to optimize the scheduling of the energy storage cluster; monitoring the operating state of the energy storage cluster, and adjusting and updating the diversified model in real time according to the operating state data; the diversified modeling method for modeling different types of energy storage units comprises: according to the physical characteristics and operating constraints of lithium iron phosphate batteries, lithium iron phosphate battery energy storage models are constructed from four dimensions of energy storage, power dynamic response, efficiency decay and economy; according to the compression, storage and release process of air, a compressed air energy storage model is constructed through the isentropic process in thermodynamics; according to the chemical reaction and electrolyte flow of all-vanadium redox flow batteries, an all-vanadium redox flow battery energy storage model is constructed; according to the electric double layer between the two electrodes and the electrolyte, a supercapacitor energy storage model is constructed; according to the 5G base station standby energy storage energy and the 5G base station standby energy storage dynamic response, a 5G base station standby energy storage model is constructed; according to the aggregation flexibility of air conditioning load and the operating characteristics of air conditioning load, an air conditioning temperature control energy storage model is constructed; according to the charging, discharging process, energy management strategy and load demand of electric vehicle batteries, an electric vehicle mobile energy storage model is constructed; according to the computing resources, power consumption and auxiliary equipment of data centers, a data center virtual energy storage model is constructed; the diversified characterization of the behavior of the energy storage cluster according to the modeling results of the energy storage units comprises: in the process of renewable energy grid connection, the output fluctuation is smoothed by adjusting the charging and discharging operation of the battery, as follows: wherein P wind (t) is the wind power generation at time t, P solar (t) is the photovoltaic power generation at time t, P grid (t) is the power provided by the grid at time t, P stored (t) is the output power of the energy storage system at time t, P load (t) is the load demand power at time t, and T is the optimization time period. for battery energy storage participating in primary frequency modulation, a droop control strategy is adopted, as follows: wherein P stored is the charge-discharge power of the battery energy storage, k is the gain coefficient of the battery energy storage, Δf is the frequency deviation of the system, f0 is the set dead zone value, and P0 is the charge power of the battery energy storage at the set regulation dead zone. for battery energy storage participating in secondary frequency modulation, the charging and discharging power of the energy storage units under the jurisdiction of the energy storage station is distributed according to the optimization control method, as follows: P ACE = ΔP tie + BΔf where ΔP tie is the tie-line power deviation, B is the bias factor of regional frequency modulation, and Δf is the regional frequency deviation. taking the minimum peak-valley difference rate of the energy storage system as the target of energy storage participating in peak regulation, the optimization target is to maximize the variance of the load curve, as follows: Among them, P L (t) represents the load of the energy storage system during time period t, P BESS (t) represents the active power of the battery energy storage system during time period t, P av (t) represents the equivalent average value of the remaining loads during time period t; taking the maximum economic benefit of the charging and discharging of the energy storage system as the target, while considering the regional adjustable energy storage capacity constraint, the charging and discharging power of each period is optimized, as follows: where Δt is a fixed time interval, C t is the time-of-use price for the energy storage system.

2. The energy storage cluster diversification modeling characterization method of claim 1, wherein, the lithium iron phosphate battery energy storage model comprises an energy storage model, a power dynamic response model, an efficiency decay model and an economy model; the energy storage model is as follows: E LFPO = V LFPO x Q LFPO E LFPO (t) is the energy of the lithium iron phosphate battery at time t, V LFPO is the voltage level accessed by the energy storage system, Q LFPO is the charge amount of the energy storage battery accessed by the energy storage system, is the charging power of the lithium iron phosphate battery at time t, is the discharging power of the lithium iron phosphate battery at time t, is the charging efficiency of the lithium iron phosphate battery, is the discharging efficiency of the lithium iron phosphate battery, is the lower limit of the energy of the lithium iron phosphate battery, is the upper limit of the energy of the lithium iron phosphate battery; the power dynamic response model is as follows: wherein, is the maximum charging power, is the maximum discharging power, only one mode of charging or discharging is allowed at the same time, i.e. the efficiency decay model comprises a charging efficiency decay model and a discharging efficiency decay model, as follows: the economy model is as follows: Where C cap is the initial investment cost, C O&M is the operation and maintenance cost, C re is the residual value, r is the discount rate, and T is the battery life. Where C cap is the initial investment cost, C O&M is the operation and maintenance cost, C re is the residual value, r is the discount rate, and T is the battery life.

3. The energy storage cluster diversification modeling characterization method of claim 1, wherein, the 5G base station standby energy storage model comprises a 5G base station standby energy storage energy storage model and a 5G base station standby energy storage dynamic response model; the 5G base station standby energy storage energy storage model is as follows: P load (t) = P load,avg + ΔP(t) Wherein, E 5G is the 5G base station standby energy storage energy, P load (t) is the base station workload at time t, P load,avg is the average load of the base station, ΔP(t) is the fluctuation part of the load with communication traffic, D b is the minimum energy storage standby time, τ is the integral variable of time within the interval (t, t+D b ) the 5G base station standby energy storage dynamic response model is as follows: wherein, a charging response for a 5G base station backup energy storage, a discharging response for a 5G base station backup energy storage, a discharging power for a 5G base station backup energy storage, a charging power for a 5G base station backup energy storage, η ch a charging efficiency for an energy storage unit, η dis a discharging efficiency for an energy storage unit, a maximum charging power for a 5G base station backup energy storage, a maximum discharging power for a 5G base station backup energy storage.

4. The energy storage cluster diversification modeling characterization method of claim 1, wherein, The electric vehicle mobile energy storage model comprises a regulation time constraint of the electric vehicle, a charge-discharge power constraint of the electric vehicle, a battery state of charge constraint of the electric vehicle and a travel demand constraint; The regulation time constraint of the electric vehicle is as follows: Wherein, t c is the regulation time of the electric vehicle, is the time of the electric vehicle accessing the power grid, is the time of the electric vehicle accessing the power grid; The charge-discharge power constraint of the electric vehicle is as follows: wherein P EV is the charging and discharging power of the electric vehicle, is the minimum charging and discharging power of the electric vehicle, is the maximum charging and discharging power of the electric vehicle; The battery state of charge constraint of the electric vehicle is as follows: where λ EV is the state of charge of the electric vehicle battery, is the minimum charge-discharge power of the electric vehicle battery, is the maximum charge-discharge power of the electric vehicle battery; The travel demand constraint is as follows: wherein, State of charge to meet user demand for a single electric vehicle at the time of connection.

5. The energy storage cluster diversification modeling characterization method of claim 1, wherein, The data center virtual energy storage model is as follows: P CDC = P SER + P COOL + P ASS - P RES - P DIS + P CH P SER = [(P SER_peak -P SER_idle ) · f CPU + P SER_idle ] · m SER_on f CPU = r / (m SER_on μ SER ) 0 < m SER_on ≤ M k where P CDC is the power consumed by the data center, P SER is the power consumed by the servers, P COOL is the power consumed by the cooling equipment, P ASS is the power consumed by the auxiliary equipment, P RES is the renewable energy output, P DIS is the energy storage discharge power, P CH is the energy storage charge power, P SER_peak is the peak power of the data center servers, P SER_idle is the idle time power of the data center servers, f CPU is the operating frequency of the data center servers, m SER_on is the number of data center servers in operation, r is the data processing volume of the data center servers, μ SER is the processing rate of the data center servers, is the maximum operating frequency of the data center, M k is the number of data center servers.

6. A diversified modeling representation system for energy storage clusters, comprising: The system comprises: A data acquisition module configured to acquire real-time data of various energy storage units in an energy storage cluster; A model construction module configured to construct a diversified model library considering various energy storage units by using diversified modeling methods based on the acquired real-time data; A diversified representation module configured to represent behaviors of the energy storage cluster in a diversified manner according to modeling results of the energy storage units, output results of different diversified models, and comprehensively evaluate performance of the energy storage cluster; An optimal scheduling module configured to utilize the constructed diversified models to perform scheduling optimization on the energy storage cluster by using an optimization algorithm; A real-time monitoring module configured to monitor a running state of the energy storage cluster and perform real-time adjustment and update on the diversified models according to the running state data; The diversified modeling methods for modeling different types of energy storage units specifically comprise: According to physical characteristics and operation constraints of the lithium iron phosphate battery, a lithium iron phosphate battery energy storage model is constructed from four dimensions of energy storage, power dynamic response, efficiency attenuation and economy; According to compression, storage and release processes of air, a compressed air energy storage model is constructed through an isentropic process in thermodynamics; According to chemical reactions and electrolyte flow of the all-vanadium redox flow battery, an all-vanadium redox flow battery energy storage model is constructed; According to an electric double layer between a two-layer electrode and an electrolyte, a supercapacitor energy storage model is constructed; According to energy and dynamic response of 5G base station standby energy storage, a 5G base station standby energy storage model is constructed; According to aggregation flexibility of air conditioning load and operation characteristics of the air conditioning load, an air conditioner temperature control energy storage model is constructed; According to charging, discharging processes, energy management strategies and load demand of the electric vehicle, an electric vehicle mobile energy storage model is constructed; According to computing resources, power consumption and auxiliary equipment of the data center, a data center virtual energy storage model is constructed; The diversified representation of behaviors of the energy storage cluster according to the modeling results of the energy storage units specifically comprises: In the process of renewable energy grid connection, output fluctuation is smoothed by adjusting charge-discharge operations of the battery, as follows: wherein P wind (t) is the wind power generation power at time t, P solar (t) is the photovoltaic power generation power at time t, P grid (t) is the power provided by the grid at time t, P stored (t) is the output power of the energy storage system at time t, P load (t) is the load demand power at time t, and T is the optimization time period. For battery energy storage participating in primary frequency modulation, a droop control strategy is adopted, as follows: wherein P stored is the charge-discharge power of the battery storage, k is the gain coefficient of the battery storage, Δf is the frequency deviation of the system, f0 is the set dead zone value, and P0 is the charge power of the battery storage at the set adjustment dead zone. For battery energy storage participating in secondary frequency modulation, charge-discharge power distribution of energy storage units under jurisdiction of the energy storage station is performed according to an optimal control method, as follows: P ACE = ΔP tie + BΔf where ΔP tie is the tie-line power deviation, B is the bias factor of regional frequency modulation, and Δf is the regional frequency deviation. Taking minimum peak-valley difference rate of the energy storage system as a target of energy storage participating in peak shaving, an optimal target is maximum variance of the load curve, as follows: Among them, P L (t) represents the load of the energy storage system during time period t, P BESS (t) represents the active power of the battery energy storage system during time period t, P av (t) represents the equivalent average value of the remaining loads during time period t; Taking maximum economic benefit of charge-discharge of the energy storage system as a target, while considering regional adjustable energy storage capacity constraints, charge-discharge power of each period is optimized, as follows: where Δt is a fixed time interval, C t is the time-of-use price for the energy storage system.

7. A computer device comprising a processor and a memory for storing a processor executable program, characterized in that, The processor implements the energy storage cluster diversification modeling characterization method in any of claims 1-5 when executing the program stored in the memory.

8. A storage medium storing a program, characterized by comprising: The program is executed by the processor to implement the energy storage cluster diversification modeling characterization method in any of claims 1-5.

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