New energy station internal energy storage power distribution optimization method and system, terminal and medium

Abstract

The invention relates to the technical field of power grid control, and particularly discloses an energy storage power distribution optimization method and system in a new energy station, a terminal and a medium, and the system comprises an energy storage management module, a power estimation module, a power control module and an energy prediction module, the system operation efficiency is improved, the equipment safety is guaranteed, the power estimation module predicts and calculates the power demand of the energy storage unit in the next preset period in real time, the power control module distributes the power demand for maintaining the system stability among different energy storage units, and the energy prediction module is used for predicting the future energy demand and supply and optimizing the scheduling strategy; according to the method, energy resources can be effectively utilized, the operation cost is reduced, the strategy is dynamically adjusted according to real-time data and changing environmental conditions, a decision maker is helped to make a more scientific and accurate decision, the fluctuation of wind power integration power is reduced, and the task completion time is shortened.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid control, and particularly relates to an optimization method, system, terminal and medium for energy storage power distribution in a new energy station. Background Art

[0002] With the continuous increase in the penetration rate of new energy in the distribution network, due to the strong randomness and volatility of new energy power generation, the traditional new energy grid-connected optimal dispatching method can no longer meet the actual needs of large-scale distributed power source access. Configuring an energy storage system for new energy power generation can improve the output characteristics of new energy power generation, and then alleviate the problem of uneven distribution of electric power and electricity in the power system in terms of time and space, which is conducive to ensuring stable power supply and improving the utilization rate of new energy power generation.

[0003] With the economic development, the traditional single energy storage system can no longer meet the growing energy and power demands of the microgrid. Therefore, the hybrid energy storage system (HESS) composed of multiple energy storage media has increasingly become a research hotspot. At present, the hybrid energy storage system composed of a battery and a supercapacitor is one of the more widely used HESSs. When using the hybrid energy storage system to improve power fluctuations, the traditional power distribution method simply separates the high and low frequencies of power fluctuations and respectively suppresses them by the supercapacitor and the battery. Such a distribution method is very likely to cause excessive charge and discharge action times of the battery or overcharge and over-discharge of the supercapacitor, thereby increasing the operating cost of the battery and reducing its service life. At the same time, the power equalization strategy is often adopted for monomer power distribution inside the energy storage medium, without considering the state of the monomers inside the energy storage medium.

[0004] Therefore, it is necessary to propose an optimization method, system, terminal and medium for energy storage power distribution in a new energy station to at least partially solve the problems existing in the prior art. Summary of the Invention

[0005] The purpose of the present invention is to provide an optimization method, system, terminal and medium for energy storage power distribution in a new energy station to at least partially solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An energy storage power distribution optimization system in a new energy station, comprising: an energy storage management module, a power estimation module, a power control module, an energy prediction module, a data management module, and a security communication module. The energy storage management module is responsible for charge and discharge control, capacity scheduling, fault detection, and maintenance, improving the operation efficiency of the system and ensuring the safety of equipment. The power estimation module predicts and calculates the power demand of the energy storage unit in the next preset period in real time. The power control module distributes the power demand for maintaining the system stability among different energy storage units. The energy prediction module is used to predict future energy demand and supply and optimize the scheduling strategy. The data management module is responsible for collecting various data in the station in real time and processing, analyzing, and modeling the collected data. The security communication module is responsible for realizing data exchange and coordination among different modules, preventing unauthorized access and operations, and ensuring the stability of the system and the security of data.

[0008] Preferably, the energy storage management module uses model predictive control (MPC) to analyze the influence of the current output power of the energy storage system on its future smoothing ability, optimize the charge and discharge strategy of the energy storage system, and uses the LOF algorithm to detect abnormal states and perform fault diagnosis in real time in combination with historical data, and is integrated with the power system part through RESTful.

[0009] Define the MPC objective function considering the smoothing ability of the energy storage system as:

[0010]

[0011] In the formula, j is the time period when the energy storage system operates, j = 0, 1, 2, …, M, t is the starting moment, SOC HESS_ref is the reference value of the SOC of the energy storage single system, SOC HESS is the state of charge of the energy storage system, and P e is the output power of the energy storage system.

[0012] The equality constraint conditions and inequality constraint conditions are as follows:

[0013] P g (t + 1) = SOC HESS (t) + P w (t)

[0014] |P g (t + j) - P g (t + j - 1)| ≤ δ

[0015] |P e (t + j - 1)| ≤ P e.rat.

[0016] SOC HESS_min ≤ SOC HESS (t + j) ≤ SOCHESS_max

[0017] In the formula, P g is the wind power grid-connected power, and P w is the wind power output power. P e.rat. represents the rated charge and discharge power of the energy storage system. SOC HESS_min and SOC HESS_max respectively represent the upper and lower limits of the SOC of the set energy storage system.

[0018] Preferably, the power estimation module uses the K-means algorithm to cluster and identify different load patterns and typical situations for load data, uses time series data to establish an LSTM model to predict future power demands, and dynamically adjusts the power through reinforcement learning to adapt to load fluctuations.

[0019] Preferably, the power control module uses fuzzy logic control according to the prediction results to dynamically adjust the power distribution with the goal of improving system stability, introduces actual environmental parameters for analysis and verification through simulation verification, and ensures the feasibility and stability of system operation.

[0020] Preferably, the energy prediction module uses the LSTM model to predict future demands based on actual operation data, combines the prediction results with the genetic algorithm to achieve automated scheduling, and uses the prediction results for risk assessment and management to formulate emergency plans.

[0021] An energy storage power distribution optimization method in a new energy station includes:

[0022] Step 1: Analyze the structure of the wind power microgrid with a hybrid energy storage system, and establish a simulation model in Matlab / Simulink to analyze the charging characteristics of lead-acid batteries and supercapacitors;

[0023] Step 2: In the upper-layer power distribution, dynamically adjust the overall charge and discharge instructions of the energy storage system through fuzzy control to prevent overcharging and over-discharging of the energy storage system;

[0024] Step 3: In the lower-layer power distribution, distribute the power demand for maintaining system stability among different energy storage units, and correct the low-pass filter coefficient in real time according to the SOC of the supercapacitor;

[0025] Step 4: In the energy storage unit distribution, use the rotation charge and discharge control strategy to reduce the overall charge and discharge switching times of the battery pack and improve the life of the battery pack.

[0026] Preferably, in Step 3, the Kalman filter is used to estimate the state of the supercapacitor in real time, the utilization potential of the supercapacitor is preferentially exploited, and the output times of the battery are reduced; in Step 4, the rotation charge and discharge control strategy is determined by comprehensively considering the state of charge of single lead-acid batteries and the charge and discharge switching times.

[0027] In Step 2, the fuzzy control adopted is a double-input single-output fuzzy controller. The inputs are the state of charge of the energy storage system and the power difference after the initial distribution, and the output is the grid-connected power correction amount. The calculation formula for the state of charge of the energy storage system is as follows:

[0028]

[0029] In the formula, Q BAT and Q SC are the capacities of the battery and the supercapacitor respectively, and SOC BAT and SOC SC are the states of charge of the battery and the supercapacitor respectively;

[0030] The centroid method is used for defuzzification operation, and its calculation formula is as follows:

[0031]

[0032] In the formula, μ 1i (t) represents the input membership function value of the state of charge of the energy storage system at time t, μ 2j (t) represents the power difference after the initial distribution at time t, and P cor_ij is the corresponding output quantity.

[0033] In Step 3, in order to keep the supercapacitor working in the normal state for as long as possible, it is necessary to adjust the filtering coefficient τ, and its formula is as follows:

[0034] τ = τ s +Δτ

[0035] In the formula, Δτ is the filtering coefficient adjustment value, and τ s is the initial value of the filtering coefficient.

[0036] Table 1 Filtering Coefficient Adjustment Table

[0037]

[0038]

[0039] The reference power P BAT_ref of the battery and the reference power P SC_ref of the supercapacitor in the corrected s domain are shown as follows:

[0040]

[0041] P HESS_ref is the power reference value of the energy storage single system.

[0042] In step four, the specific steps of the rotation charge and discharge control strategy and the battery selection rules are as follows:

[0043] (1) Initialize parameters such as the state of charge, charge and discharge times, and charge and discharge depth of the batteries in the control center;

[0044] (2) Divide all batteries into a charging group A and a discharging group B, sort them according to the charge and discharge times within the group, and number them. Judge the magnitude and nature of the battery pack power command. If it is a charging command, go to step (3); if it is a discharging command, go to step (4);

[0045] (3) When the power command is for charging, select the corresponding number of batteries within group A according to its magnitude to form the corresponding power. The selection rule is that the batteries with fewer charge and discharge times are preferred. If the charge and discharge times are the same, select the batteries with a lower SOC. If the SOC and the charge and discharge times are both the same, select them in ascending order of the serial number within a group. If the remaining capacity within the group cannot meet the power requirement, temporarily obtain batteries from group B for charging, and the selection priority is the same as that within group A;

[0046] (4) When the power command is for discharging, select the corresponding number of batteries within group B according to its magnitude to form the corresponding power. The selection rule is that the batteries with fewer charge and discharge times are preferred. If the charge and discharge times are the same, select the batteries with a higher SOC. If the SOC and the charge and discharge times are both the same, select them in ascending order of the serial number within a group. If the remaining capacity within the group cannot meet the power requirement, if the remaining capacity within the group cannot meet the power requirement, temporarily obtain batteries from group A for charging, and the selection priority is the same as that within group B;

[0047] (5) The control center calculates the SOC of each battery and observes whether there is a battery whose SOC reaches the charge / discharge conversion threshold. If so, reallocate the charge and discharge groups. If not, output the power distribution value;

[0048] (6) Whether the scheduling task is completed. If it is completed, end this scheduling. If it is not completed, wait for the next charge and discharge command to arrive.

[0049] A computer-readable storage medium stores multiple instructions, and the instructions are adapted to be loaded and executed by a processor of a terminal device for an energy storage power distribution optimization system in a new energy station as described in any one of the above.

[0050] A terminal device includes a processor and a computer-readable storage medium. The processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are adapted to be loaded and executed by the processor for an energy storage power distribution optimization system in a new energy station as described in any one of the above.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] The present invention utilizes model predictive control to analyze the influence of the current output power of the energy storage system on its future power smoothing ability, dynamically adjusts the overall charge-discharge strategy of the energy storage system through fuzzy control, completes power smoothing while ensuring the reasonable distribution of the power of the energy storage system, uses a combination of a battery pack and a super capacitor, and gives priority to tapping the utilization potential of the super capacitor, reduces the overall charge-discharge switching times of the battery pack, improves the service life of the battery pack, and ensures the overall service life of the energy storage system. In this way, energy resources can be effectively utilized, operating costs can be reduced, the strategy can be dynamically adjusted according to real-time data and changing environmental conditions, helping decision-makers make more scientific and accurate decisions, reducing the volatility of the wind power grid-connected power, and reducing the task completion time.

[0053] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further directions, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a framework diagram of an energy storage power distribution optimization system in a new energy station of the present invention;

[0055] Figure 2 is a flowchart of an energy storage power distribution optimization method in a new energy station of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] It should be understood that the term "and / or" used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] Embodiment 1:

[0059] Please refer to Figure 1As shown in the figure, an energy storage power distribution optimization system in a new energy station includes: an energy storage management module, a power estimation module, a power control module, an energy prediction module, a data management module, and a security communication module. The energy storage management module is responsible for charge and discharge control, energy scheduling, fault detection, and maintenance, improving the system operation efficiency and ensuring equipment safety. The power estimation module predicts and calculates the power demand of the energy storage unit in the next preset cycle in real time. The power control module distributes the power demand for maintaining system stability among different energy storage units. The energy prediction module is used to predict future energy demand and supply and optimize the scheduling strategy. The data management module is responsible for collecting various data in the station in real time, and processing, analyzing, and modeling the collected data. The security communication module is responsible for realizing data exchange and coordination among different modules, preventing unauthorized access and operations, and ensuring system stability and data security.

[0060] The energy storage management module uses model predictive control (MPC) to analyze the impact of the current output power of the energy storage system on the future smoothing ability, optimize the charge and discharge strategy of the energy storage system, and uses the LOF algorithm to detect abnormal states and perform fault diagnosis in real time in combination with historical data, and is integrated with the power system part through RESTful.

[0061] The power estimation module uses the K-means algorithm to cluster the load data to identify different load patterns and typical situations, uses time series data to establish an LSTM model to predict future power demand, and dynamically adjusts the power through reinforcement learning to adapt to load fluctuations.

[0062] The power control module uses fuzzy logic control according to the prediction results to dynamically adjust the power distribution with the goal of improving system stability, introduces actual environmental parameters for analysis and verification through simulation verification, and ensures the feasibility and stability of system operation.

[0063] The energy prediction module uses the LSTM model to predict future demand based on actual operation data, combines the prediction results with the genetic algorithm to achieve automated scheduling, and uses the prediction results for risk assessment and management to formulate emergency plans.

[0064] Embodiment 2:

[0065] Please refer to Figure 2 As shown in the figure, a method for optimizing energy storage power distribution in a new energy station includes:

[0066] Step 1: Analyze the structure of a wind power microgrid with a hybrid energy storage system, and establish a simulation model in Matlab / Simulink to analyze the charging characteristics of lead-acid batteries and supercapacitors;

[0067] Step 2: In the upper-layer power distribution, the overall charge-discharge command of the energy storage system is dynamically adjusted through fuzzy control to prevent overcharging and over-discharging of the energy storage system;

[0068] Step 3: In the lower-layer power distribution, the power demand for maintaining system stability is distributed among different energy storage units, and the low-pass filter coefficient is corrected in real time according to the SOC of the supercapacitor;

[0069] Step 4: In the energy storage unit distribution, the rotation charge-discharge control strategy is used to reduce the overall charge-discharge switching times of the battery pack and improve the life of the battery pack.

[0070] In Step 3, the Kalman filter is used to estimate the state of the supercapacitor in real time, giving priority to tapping the utilization potential of the supercapacitor and reducing the output times of the battery; in Step 4, the rotation charge-discharge control strategy is determined by comprehensively considering the state of charge of individual lead-acid batteries and the charge-discharge switching times.

[0071] Example of the rotation charge-discharge control strategy: Taking charging as an example only, assume that the battery pack has 4 individual batteries, numbered 1, 2, 3, and 4 respectively, with different SOC values, 2>4>3>1, and the number of charge-discharge times carried out is sorted from largest to smallest, in turn 1>2 = 3 = 4. In the initial state, batteries 1 and 2 are the charging group, and batteries 3 and 4 are the discharging group; when starting to charge, according to the battery selection principle, since the number of charge-discharge times of battery 1 is higher than that of battery 2, even though the SOC of battery 1 is lower, battery 2 is still selected to be charged first; after battery 2 is fully charged and there is still a charging demand, at this time battery 2 cuts into the discharging group and battery 1 is charged; after battery 1 is fully charged and there is still a charging demand, at this time, since the number of charge-discharge times of battery 3 and battery 4 is equal, but the SOC of battery 3 is lower than that of battery 4, battery 3 is selected to cut into the charging group. The selection rule for the discharging state is the same as that for the charging state.

[0072] As can be seen from the above, the present invention uses model predictive control to analyze the influence of the current output power of the energy storage system on the future power smoothing ability, dynamically adjusts the overall charge-discharge strategy of the energy storage system through fuzzy control, completes power smoothing while ensuring the reasonable distribution of the power of the energy storage system, uses the combination of the battery pack and the supercapacitor, gives priority to tapping the utilization potential of the supercapacitor, reduces the overall charge-discharge switching times of the battery pack, improves the life of the battery pack, ensures the overall life of the energy storage system, can effectively utilize energy resources, reduce the operating cost, dynamically adjusts the strategy according to real-time data and changing environmental conditions, helps decision-makers make more scientific and accurate decisions, reduces the volatility of the wind power grid-connected power, and reduces the task completion time.

[0073] Example 3:

[0074] An embodiment of the present invention further provides a terminal device, including a processor and a computer-readable storage medium. The processor is configured to implement each instruction; the computer-readable storage medium is configured to store multiple instructions, and the instructions are adapted to be loaded and executed by the processor to perform each process of the energy storage power distribution optimization system embodiment as described in any one of the above.

[0075] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements each process of the energy storage power distribution optimization system embodiment as described above, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0076] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0077] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a program, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a method for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0078] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the processes in Figure 1One or more processes and / or blocks Figure 1 The functions specified in one or more blocks.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 One or more processes and / or blocks Figure 1 The steps of the functions specified in one or more blocks.

[0080] In the drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments of the present invention are involved. For other structures, reference can be made to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0081] The flowcharts shown in the drawings are only illustrative examples, and do not necessarily include all contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined or partially merged. Therefore, the actual execution order may be changed according to the actual situation.

[0082] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for optimizing energy storage power distribution in a new energy station, comprising: Energy storage management module, power estimation module, power control module, energy prediction module, characterized in that the energy storage management module is responsible for charge and discharge control, capacity scheduling, fault detection and maintenance, the power estimation module predicts and calculates the power demand of the energy storage unit in the next preset cycle in real time, the power control module distributes the power demand to maintain the stability of the system among different energy storage units, and the energy prediction module is used to predict future energy demand and supply and optimize the scheduling strategy.

2. The energy storage power distribution optimization system in a new energy station according to claim 1 is characterized in that: The energy storage management module uses model predictive control to analyze the impact of the current output power of the energy storage system on the future smoothing capacity, optimizes the charging and discharging strategy of the energy storage system, uses the LOF algorithm in combination with historical data to detect abnormal conditions in real time and perform fault diagnosis, and integrates with the power system through RESTful implementation.

3. The energy storage power distribution optimization system in a new energy station according to claim 2 is characterized in that: The MPC objective function considering the smoothing capability of the energy storage system is defined as: Where j is the time period of the energy storage system operation, j = 0, 1, 2, ..., M, t is the starting time, SOC HESS_ref is the SOC reference value of the energy storage system, SOC HESS is the SOC value of the energy storage system at the corresponding time, P e is the output power of the energy storage system. The equality and inequality constraints are as follows: P g (t+1)=SOC HESS (t)+P w (t) |P g (t+j)-P g (t+j-1)|≤δ |P e (t+j-1)|≤P e.rat. SOC HESS_min ≤SOC HESS (t+j)≤SOC HESS_max Where P g is the wind power grid-connected power, P w is the wind power output power, P e.rat. Indicates the rated power of energy storage system charging and discharging, SOC HESS_min and SOC HESS_max They respectively represent the upper and lower limits of the energy storage system SOC.

4. The energy storage power distribution optimization system in a new energy station according to claim 2 is characterized in that: The power estimation module uses the K-means algorithm to cluster load data to identify different load patterns and typical situations, uses time series data to establish an LSTM model to predict future power demand, and dynamically adjusts power through reinforcement learning to adapt to load fluctuations.

5. The energy storage power distribution optimization system in a new energy station according to claim 4 is characterized in that: The power control module uses fuzzy logic control according to the prediction results to dynamically adjust power distribution with the goal of improving system stability, and introduces actual environmental parameters for analysis and verification through simulation verification.

6. The energy storage power distribution optimization system in a new energy station according to claim 5 is characterized in that: The energy prediction module uses the LSTM model to predict future demand based on actual operation data, combines the prediction results with the genetic algorithm to achieve automated scheduling, and uses the prediction results to conduct risk assessment and management and formulate emergency plans.

7. The energy storage power distribution optimization system in a new energy station according to claim 6 is characterized in that: The system also includes a data management module and a safety communication module. The data management module collects various types of data in the station in real time and processes, analyzes and models the collected data. The safety communication module is used for data exchange and coordination between different modules.

8. A method for optimizing energy storage power distribution in a new energy station, based on the energy storage power distribution optimization system in a new energy station according to any one of claims 1 to 7, characterized in that: include: Step 1: Analyze the wind power microgrid structure with hybrid energy storage system, and establish a simulation model in Matlab / Simulink to analyze the charging characteristics of batteries and supercapacitors; Step 2: In the upper power allocation, the overall charge and discharge instructions of the energy storage system are dynamically adjusted through fuzzy control in order to prevent the energy storage system from being overcharged or over-discharged; Step 3: In the lower-level power allocation, the power demand for maintaining system stability is allocated among different energy storage units, and the low-pass filter coefficient is corrected in real time according to the supercapacitor SOC; Step 4: In the allocation of energy storage units, a rotation charge and discharge control strategy is used to control the overall charge and discharge switching times of the battery pack.

9. The method for optimizing energy storage power distribution in a new energy station according to claim 8, characterized in that: In step 2, the fuzzy control used is a dual-input-single-output fuzzy controller, the input is the state of charge of the energy storage system and the power difference after the initial distribution, and the output is the grid-connected power correction. The calculation formula for the state of charge of the energy storage system is as follows: In the formula, Q BAT and Q SC are the capacity of the battery and supercapacitor, SOC BAT and SOC SC are the charge states of the battery and supercapacitor respectively; The centroid method is used for defuzzification operation, and its calculation formula is as follows: In the formula, μ 1i (t) represents the input membership function value of the energy storage system charge state at time t, μ 2j (t) represents the power difference after the initial allocation at time t, P cor_ij is the corresponding output.

10. A method for optimizing energy storage power distribution in a new energy station according to claim 9, characterized in that: In step three, the filter coefficient τ is adjusted, and the formula is as follows: τ=τ s +Δt Where Δτ is the filter coefficient adjustment value, τ s is the initial value of the filter coefficient; When the supercapacitor is being charged and the SOC is in a critical overcharge state, Δτ is reduced; When the supercapacitor is in discharge and the SOC is in a critical overcharge state, increase Δτ; When the supercapacitor is being charged and the SOC is in a critical over-discharge state, increase Δτ; When the supercapacitor is in discharge and the SOC is in a critical over-discharge state, Δτ is reduced; When the supercapacitor is charging or discharging and the SOC is in a normal state, Δτ = 0; Corrected battery reference power P in s domain BAT_ref And supercapacitor reference power P SC_ref They are shown as follows: P HESS_ref It is the reference value of single energy storage system power.

11. The method for optimizing energy storage power distribution in a new energy station according to claim 8, characterized in that: In step three, Kalman filtering is used to estimate the state of the supercapacitor in real time, so as to exploit the utilization potential of the supercapacitor as a priority goal.

12. The method for optimizing energy storage power distribution in a new energy station according to claim 8, characterized in that: In step 4, the alternating charge and discharge control strategy is determined according to the charge state of the single battery and the number of charge and discharge switching times.

13. A method for optimizing energy storage power distribution in a new energy station according to claim 12, characterized in that: In step 4, the specific steps of the alternating charge and discharge control strategy and the battery selection rules are as follows: (1) Initialize the battery's state of charge, charge and discharge times, and charge and discharge depth in the control center; (2) All batteries are divided into charging group A and discharging group B, and the batteries are sorted within the group according to the number of times they are charged and discharged, and are numbered, and the size and nature of the battery group power instruction are determined. If it is a charging instruction, go to step (3); if it is a discharging instruction, go to step (4); (3) When the power instruction is charging, the corresponding number of batteries are selected from group A according to their size to form the corresponding power. The selection rule is that the battery with fewer charge and discharge times is given priority. If the charge and discharge times are the same, the battery with a lower SOC is selected. If the SOC and the charge and discharge times are the same, the battery is selected from the smallest to the largest number in a group. If the remaining capacity in the group cannot meet the power requirement, the battery is temporarily obtained from group B for charging. The selection priority is the same as that in group A. (4) When the power instruction is to discharge, a corresponding number of batteries are selected from group B according to their size to form a corresponding power. The selection rule is that the battery with fewer charge and discharge times is given priority. If the charge and discharge times are the same, the battery with a higher SOC is selected. If the SOC and the charge and discharge times are the same, the battery is selected from the smallest to the largest number in a group. If the remaining capacity in the group cannot meet the power requirement, the battery is temporarily obtained from group A for charging. The selection priority is consistent with that in group B. (5) The control center counts the SOC of each battery and observes whether the battery SOC reaches the charge / discharge conversion threshold. If so, the charge / discharge group is reallocated. If not, the power allocation value is output; (6) Determine whether the scheduling task is completed. If it is completed, end the current scheduling. If it is not completed, wait for the next charge and discharge instruction to arrive.

14. A computer-readable storage medium, characterized in that: There are multiple instructions, which are suitable for being loaded and executed by a processor of a terminal device, according to any one of claims 1-7, in a new energy station energy storage power distribution optimization system.

15. A terminal device, characterized in that: A system for optimizing energy storage power distribution in a new energy station comprising a processor and a computer-readable storage medium, the processor being used to implement various instructions; the computer-readable storage medium being used to store multiple instructions, the instructions being suitable for being loaded and executed by the processor, a system for optimizing energy storage power distribution in a new energy station according to any one of claims 1-7.

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