Cloud-based hybrid energy storage power smoothing method and system, and storage medium

Abstract

The invention discloses a cloud-based hybrid energy storage power smoothing method and system and a storage medium. The method comprises the following steps: determining a power allocation reference value sequence of a link power control target; calculating the link pulsation power, and determining the power fluctuation compensated by the SC system; at least one value in the power distribution reference value sequence serves as output and is transmitted to a power distribution controller, the SC system or the LIB system is determined to be started or stopped according to the hybrid energy storage controller, and smooth adjustment is carried out; calculating residual pulsation power according to the link pulsation power and the hybrid energy storage controller; and smoothing the SC system power based on the residual pulsation power, and determining an SC system power regulation smoothing component. By reasonably distributing the workload of the LIB system and the SC system in the hybrid energy storage system, the stress borne by the LIB system is effectively reduced, the aging speed is delayed, the overall service life is prolonged, and the stability and reliability of energy storage in an optical storage system and an optical storage and charging combined system are remarkably improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of energy storage technology, and in particular to a cloud-based hybrid energy storage power smoothing method, system and storage medium. Background Art

[0002] At present, LIB systems are usually used as energy storage devices to release or absorb short-term peak power of photovoltaic grid-connected power generation systems to ensure the stability of the system output power. However, the LIB system is realized through chemical reactions, and its aging speed and charge and discharge sensitivity are directly related to its service life and the number of charge and discharge times. Frequent charging and discharging will lead to problems such as accelerated aging of the LIB system, shortened service life and increased system maintenance costs. In contrast, the SC system, as a high-power energy storage device, can quickly release or absorb short-term peak power when large signal disturbances occur in the power generation system to ensure the stability of the system output power. In the case of heat, the actual internal situation of the distribution network is complex, the existing energy storage configuration is insufficient, and the short-term output of the SC system is difficult to control, resulting in unreasonable power distribution, and the traditional virtual power plant frequency regulation strategy is difficult to apply.

[0003] In view of the problems of accelerated aging, shortened service life and increased maintenance costs faced when LIB systems are applied to photovoltaic grid-connected power generation systems and photovoltaic storage and charging combined systems for excess power consumption and fluctuation relief, it is urgent to develop a hybrid energy storage control solution. At present, relevant studies have proposed LIB systems to smooth the power fluctuations of microgrids. They use hybrid energy storage composed of power storage and energy storage as the control object, and achieve output optimization through different control methods. However, the existing solutions do not fully utilize the long-term energy storage characteristics of the LIB system and the short-term high-power charging and discharging capabilities of the SC system. By reasonably allocating the workload of the two in the system, the stress on the LIB system can be effectively reduced, thereby delaying its aging speed, improving the overall service life, and reducing the operation and maintenance costs of energy storage batteries, significantly improving the stability and reliability of energy storage in photovoltaic storage and photovoltaic storage and charging combined systems.

[0004] The above problems need to be solved urgently. Summary of the invention

[0005] In order to solve the related technical problems, the present invention provides a cloud-based hybrid energy storage power smoothing method, system and storage medium to solve the problems mentioned in the above background technology part.

[0006] To achieve the above purpose, the embodiment of the present invention adopts the following technical solution:

[0007] In a first aspect, an embodiment of the present invention provides a cloud-based hybrid energy storage power smoothing method, the method comprising the following steps:

[0008] At least collect data including the original link power and the last energy storage state point of the LIB system and SC system;

[0009] Determine the power allocation reference value sequence of the link power control target by predicting the power demand of the adaptive model;

[0010] Calculating link pulsation power according to the original link power and the link power control target, and determining power fluctuations compensated by the SC system;

[0011] At least one value in the power distribution reference value sequence is transmitted as an output to a power distribution controller, and the hybrid energy storage controller determines to enable or stop the SC system or the LIB system and performs smooth adjustment;

[0012] According to the link pulsating power and the hybrid energy storage controller, the start-stop smoothing component is subtracted from the total pulsating power to calculate the remaining pulsating power;

[0013] The SC system power is smoothed based on the residual pulsating power to determine a SC system power regulation smoothing component.

[0014] As an optional implementation, the cloud-based hybrid energy storage power smoothing method further includes:

[0015] The SC system start-stop smoothing component and the SC system power regulation smoothing component are combined to output the SC system smoothing component.

[0016] As an optional implementation manner, the SC system start-stop smoothing component is combined with the SC system power regulation smoothing component to output the SC system smoothing component, and then further includes:

[0017] The LIB system energy storage takes on the task of smoothing the remaining pulsating power, combines the SC system start-stop smoothing component and the SC system power regulation smoothing component with the link, outputs the LIB system smoothing component, and completes the smoothing of the pulsating power in the control cycle.

[0018] As an optional implementation manner, the power demand prediction of the adaptive model is used to determine the power allocation reference value sequence of the link power control target, specifically including:

[0019] Through the power demand prediction of the adaptive model, the power allocation reference value sequence of the current link power control target is determined by SOC based on the original link power and the stored data of the SC system.

[0020] As an optional implementation manner, smoothing the SC system power based on the residual pulsating power to determine the SC system power regulation smoothing component includes:

[0021] The SC system power is smoothed based on the residual pulsating power, a power regulation smoothing component of the SC system is determined, and a dynamic programming strategy is used to optimize the power output allocation of the SC system and the LIB system.

[0022] In a second aspect, an embodiment of the present invention provides a cloud-based hybrid energy storage power smoothing system, which adopts the cloud-based hybrid energy storage power smoothing method proposed in the first aspect, including:

[0023] An information collection module, used to collect data including at least the original link power and the last energy storage state point of the LIB system and the SC system;

[0024] A power control target determination module, used to determine a power allocation reference value sequence of a link power control target by predicting power demand of an adaptive model;

[0025] A pulsation power calculation module, used to calculate the link pulsation power according to the original link power and the link power control target, and determine the power fluctuation compensated by the SC system;

[0026] A hybrid energy storage start-stop control strategy module is used to output at least one value in the power distribution reference value sequence to the power distribution controller, determine whether to start or stop the SC system or the LIB system according to the hybrid energy storage controller, and perform smooth adjustment;

[0027] The hybrid energy storage power regulation strategy module is used to smooth the SC system power based on the residual pulsating power and determine the SC system power regulation smoothing component.

[0028] As an optional implementation, the cloud-based hybrid energy storage power smoothing system further includes: an energy storage system smoothing processing module, which is used to combine the SC system start-stop smoothing component with the SC system power regulation smoothing component and output the SC system smoothing component.

[0029] As an optional implementation manner, the power control target determination module is specifically used to:

[0030] Through the power demand prediction of the adaptive model, the power allocation reference value sequence of the current link power control target is determined by SOC based on the original link power and the stored data of the SC system.

[0031] As an optional implementation, the hybrid energy storage power regulation strategy module is specifically used for:

[0032] The SC system power is smoothed based on the residual pulsating power, a power regulation smoothing component of the SC system is determined, and a dynamic programming strategy is used to optimize the power output allocation of the SC system and the LIB system.

[0033] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the cloud-based hybrid energy storage power smoothing method provided in the first aspect above.

[0034] The technical solution proposed in the embodiment of the present invention reasonably distributes the workload of the LIB system and the SC system in the hybrid energy storage system, optimizes the charging and discharging strategy, dynamically allocates energy storage and release tasks, and gives full play to the "peak shaving and valley filling" function of the SC system, thereby reducing the impact of the charging and discharging peak power on the LIB system, effectively alleviating the stress on the LIB system, delaying the aging speed, and increasing the overall service life, thereby significantly improving the stability and reliability of energy storage in photovoltaic storage and photovoltaic storage and charging combined systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate and understand the technical solutions in the embodiments of the present invention, the following briefly introduces the background technology of the present invention and the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the contents of the embodiments of the present invention and these drawings without paying any creative work.

[0036] Figure 1 A schematic diagram of a cloud-based hybrid energy storage power smoothing method provided in an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of the principle of a primary system provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of power allocation calculation for a LIB system and a SC system provided in an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of a specific application of a cloud-based hybrid energy storage power smoothing method provided by an embodiment of the present invention;

[0040] Figure 5 A structural block diagram of a cloud-based hybrid energy storage power smoothing system provided by an embodiment of the present invention;

[0041] Figure 6 A schematic diagram of the specific application principle of the cloud-based hybrid energy storage power smoothing system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the technical problems solved by the present invention, the technical solutions adopted and the technical effects achieved clearer, the technical solutions of the embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1

[0044] Please refer to Figure 1 As shown in the figure, the cloud-based hybrid energy storage power smoothing method 100 in this embodiment includes the following steps:

[0045] S101. Collect at least the original link power and the data including the energy storage state point of the last LIB system and the SC system;

[0046] S102. Determine the power allocation reference value sequence of the link power control target through the power demand prediction of the adaptive model;

[0047] S103. Calculate the link pulsation power according to the original link power and the link power control target, and determine the power fluctuation compensated by the SC system;

[0048] S104. At least one value in the power distribution reference value sequence is output to the power distribution controller, and the hybrid energy storage controller determines whether to enable or stop the SC system or the LIB system, and performs smooth adjustment;

[0049] S105. According to the link pulsating power and the hybrid energy storage controller, the start-stop smoothing component is subtracted from the total pulsating power to calculate the remaining pulsating power;

[0050] S106. Smooth the SC system power based on the residual pulsating power to determine a SC system power regulation smoothing component.

[0051] In this embodiment, the hybrid energy storage includes LIB system and SC system energy storage. The hybrid energy storage system participates in the control of the power system frequency. Figure 2 As shown, in this embodiment, the local primary system includes a power grid, a bidirectional interface converter, a LIB system, an SC system energy storage device, a load device and a distributed power supply. In the figure, the lithium battery represents the LIB system, the supercapacitor represents the SC system energy storage device, and the load end represents the load device.

[0052] In this embodiment, the energy storage status point data of the last LIB system and SC system include but are not limited to the charging state, discharging state, battery state of charge (State of Charge, SOC), state of health (State of Health, SOH), etc. of each energy storage device.

[0053] Exemplarily, the method of determining a power allocation reference value sequence of a link power control target through power demand prediction of an adaptive model specifically includes: determining a power allocation reference value sequence of a current link power control target through SOC based on the original link power and stored data of the SC system through power demand prediction of an adaptive model.

[0054] Specifically, in this embodiment, the SC system and the LIB system are modeled, including their state variables, decision variables and state transition equations. The state variables include SOC, temperature, and SOH. t ; Decision variables include charge and discharge power P SC And the charge and discharge power P LIB The state transition equation is to define the transition relationship of each system from one state to another, including the following:

[0055] SC System:

[0056] Among them, η SC is the efficiency, Δt is the time step, E SC is the total energy of the SC system.

[0057] LIB system:

[0058] Among them, η SC is the efficiency, Δt is the time step, E LIB is the total energy of the LIB system.

[0059] Set the objective function and constraints;

[0060] The objective functions include minimizing the total cost and maximizing the system performance;

[0061] The formula for minimizing the total cost is as follows:

[0062]

[0063] Among them, c SC and c LIB is the unit power cost of the SC and LIB systems, and loss(t) is the energy loss.

[0064] The formula for maximizing system performance is as follows:

[0065]

[0066] Where efficiency(t) is the overall efficiency of the system, P total (t) is the total power output.

[0067] Constraints include power balance, SOC range, power limit, and temperature.

[0068] Power balance constraint: P SC (t)+P LIB (t) = P load (t)-P gen (t)

[0069] SOC range: SOC LIB (t)&SOC LIB (t)∈(min,max)

[0070] Power limit: P SC (t)≤P SC,max ;P LIB (t)≤P LIB,max

[0071] Among them, P load (t) is the load demand, P gen (t) is the power generation.

[0072] Temperature constraint: TempS C,tmin ≤Temp SC ,t≤Temp SC,tmax ;

[0073] Temp LIB,tmin ≤Temp LIB,t ≤Temp LIB,tmax ;

[0074] SOH Constraints: SOH SC,tmin ≤SOH SC,t ≤SOH SC,tmax ;

[0075] SOH LIB,tmin ≤SOH LIB,t ≤SOH LIB,tmax ;

[0076] Based on the above objective functions and constraints, the objective function algorithm of the optimization solver is as follows:

[0077]

[0078] Among them, α is the trade-off coefficient.

[0079] According to the previous n states x t-1 ,xt-2 ,…,x t-n and the transition matrix P, predicting the next state x t , determine the power allocation reference value sequence of the link power control target. In this embodiment, the steps are as follows:

[0080] Sensors collect the state parameters of the hybrid energy storage system. The controller sends information such as load demand power and hybrid energy storage system state parameters to the prediction model. The prediction model predicts the system reference value in the future based on the current value and the prediction value sequence. The optimization solver calculates the power distribution reference value sequence. The first value in the reference value sequence is sent to the controller, and the controller performs power distribution between the LIB system and the SC system. At the same time, the reference value sequence is sent to the prediction model to provide data for the prediction value sequence. Figure 3 shown.

[0081] At the same time, the current original link power and the state parameters of LIB and SC are updated at the next moment, and the above steps are executed again.

[0082] A value in the solved power distribution reference value sequence is used as an output and transmitted to the power distribution controller, and the SC system or LIB system is enabled or disabled according to the hybrid energy storage start-stop smoothing component, and smoothing adjustment is performed;

[0083] The optimization result output is as follows:

[0084] Decision variable sequence:

[0085]

[0086] State variable sequence:

[0087]

[0088] Objective function value:

[0089] Constraint satisfaction:

[0090]

[0091]

[0092] Optimization results:

[0093]

[0094]

[0095] In this implementation, the current link power control target is determined by the battery state of charge (SOC) based on the original link power and the hybrid energy storage data including the last energy storage state point data of the LIB system and the SC system.

[0096] Exemplarily, calculating the link pulsation power according to the original link power and the link power control target, and determining the power fluctuation compensated by the SC system includes:

[0097] The cloud receives data, and preferably, the link power control target is set at the last time point; the link pulsating power is obtained based on the link power control target and the original link power. In this embodiment, the charging state of the hybrid energy storage system is monitored in real time: the charging state of each energy storage device (not limited to LIB system, SC system, etc.) in the hybrid energy storage system is monitored, and the monitoring content includes but is not limited to the battery state of charge (State of Charge, SOC), load stability, seasonal changes, intraday changes, etc., and the current load size, that is, the amount of electrical energy required to be supplied. Analyze the load demand of the current system and the demand for power smoothing based on the monitored data.

[0098] In this embodiment, the link pulsation power of each time period is determined according to the real-time monitored LIB system status and system load demand: the link pulsation power is calculated according to the original link power and the link power control target to serve as input data of the hybrid energy storage system.

[0099] In this embodiment, the link pulse power is the next state of the link power control target, and the iterative formula code is as follows:

[0100] V§=\min_{P"}\left{C(P,P")+\gammaV(P")\right}

[0101] Where P refers to the link power control target, and P" refers to the link pulse power. Under the current link power control target P, by analyzing all possible next link pulse powers P", the link pulse power P" that minimizes the total cost or maximizes the total reward is obtained.

[0102] In this embodiment, the controller adopts a dual-input single-output structure, wherein the first input is the SOC of the LIB system and the SC system, ranging between [0, 1], and the system state parameters include {NB, NM, NS, ZO, PS, PM, PB}, which respectively represent the SOC as {very low, low, relatively low, moderate, relatively high, high, very high}; the second input is the normalized Psc, and P LIB, the range is also between [0, 1], the system state parameters are {D, C}, which represent the LIB system or SC system {discharge, charge} respectively; the output is the corrected power Psc and P LIB , and the threshold ranges are [-0.25, 0.25] and [-0.05, 0.05] respectively. P_c is the grid-connected power that meets the grid-connected standard; Phess is the charging and discharging power of the hybrid energy storage system; P LIB is the charging and discharging power of the LIB system; Psc is the SC system power.

[0103] Exemplarily, at least one value in the power distribution reference value sequence is transmitted as an output to the power distribution controller, and the hybrid energy storage controller determines whether to enable or disable the SC system or the LIB system, and performs smooth adjustment.

[0104] In this embodiment, the start-stop control layer of the cloud formulates a hybrid energy storage start-stop plan, and the hybrid energy storage start-stop state and the hybrid energy storage start-stop smoothing component are determined according to the following conditions: 1. The power limit specified by the power grid and the maximum charge and discharge power of the LIB system; 2. The power supply conditions of the power grid and the power supply conditions of distributed energy (such as solar energy and wind energy); 3. The charging state, discharging state, SOC, SOH, etc. of energy storage devices such as batteries and SC systems in the LIB system.

[0105] Specifically, the start-stop control layer in the cloud monitors the status of the hybrid energy storage system and determines whether the SC system or LIB system needs to be enabled or stopped, and determines the start-stop control strategy. Monitor the start-stop smoothing component of the SC system, determine whether the SC system or LIB system needs to be enabled or stopped, and make start-stop decisions based on the monitoring results.

[0106] In this embodiment, the SC system and the LIB system are managed by partitions. The SC system has a top zone, a bottom zone, and a normal zone. The LIB system has an overcharge limit zone, a high voltage zone, a normal zone, a low voltage zone, and an over-discharge limit zone. The SC system is used as the main control voltage source and adopts a V / f strategy. The SOC terminal voltage state of the SC system and the SOC secondary calculation of the SC system are as follows:

[0107] SOC=(U-Umin) / (Umax-Umin)

[0108] Wherein, U is the terminal voltage of the SC system, Umax and Umin are the rated maximum and minimum voltages of the SC system;

[0109] SOCs=(U-Usmin) / (Usmax-Usmin)

[0110] Among them, Usmax and Usmin are obtained by enlarging Umax and reducing Umin respectively, and the enlargement and reduction interval is P_a

[0111] When SOCs≥SOC constraint value, the start-stop control layer instructs the SC system to allow discharge coordination; when SOCs≤SOC constraint value, the start-stop control layer instructs the SC system to allow charging coordination;

[0112] The LIB system acts as a slave controller and adopts direct power control. In this embodiment, it is necessary to limit the maximum charge and discharge power of the battery under different SOCs, P_b = maximum coordination coefficient * rated maximum power; maximum coordination coefficient ∈ [0,1]. When SOCs are in the normal range, the LIB system enters the normal mode, and the power command is P; when SOCs deviate from the normal range, the start-stop module instructs the LIB system to enter the coordination mode, and the size of P depends on the depth of SOCs deviation; when SOCs enter the extreme range, the power P is used as the coordination power command. At this time, the response power P_r of the SC system allows SOCs to return to the normal range at the fastest speed and complete smooth adjustment.

[0113] Exemplarily, according to the link pulsating power and the hybrid energy storage controller, the start-stop smoothing component is subtracted from the total pulsating power to calculate the remaining pulsating power. In this embodiment, the hybrid energy storage power adjustment layer in the cloud performs smoothing based on the remaining pulsating power, determines the SC system power adjustment smoothing component for the LIB system output optimization target, and the SC system start-stop smoothing component is combined with the SC system power regulation smoothing component and enters the hybrid energy storage output hybrid energy storage smoothing component.

[0114] Exemplarily, the SC system start-stop smoothing component and the SC system power regulation smoothing component are combined to output the SC system smoothing component.

[0115] In this embodiment, the LIB system energy storage takes on the task of smoothing the remaining pulsating power, combines the SC system start-stop smoothing component and the SC system power regulation smoothing component with the link, outputs the LIB system smoothing component, and completes the smoothing of the pulsating power in the control cycle.

[0116] In this embodiment, the remaining pulsating power is calculated according to the hybrid energy storage start-stop control layer and the hybrid energy storage power regulation layer, that is, the difference between the link pulsating power and the hybrid energy storage start-stop smoothing component:

[0117] Remaining pulsating power = link pulsating power - hybrid energy storage start-stop smoothing component P_r Pmax or Pmin, determines the amount of electric energy that the hybrid energy storage system needs to charge and discharge.

[0118] In this embodiment, the LIB system also undertakes the task of smoothing the remaining fluctuating power. The SC system start-stop smoothing component and the SC system power regulation smoothing component are combined with the link. The line pulsating power enters the LIB system and outputs the LIB system smoothing component, completing the smoothing of the pulsating power in the control cycle.

[0119] Exemplarily, the smoothing of the SC system power based on the residual pulsating power to determine the SC system power regulation smoothing component includes:

[0120] The SC system power is smoothed based on the residual pulsating power, a power regulation smoothing component of the SC system is determined, and a dynamic programming strategy is used to optimize the power output allocation of the SC system and the LIB system.

[0121] In this embodiment, the optimal value function J*(t, SOCSC, SOCLIB) at time t is defined; starting from the last time step T-1, recursively forward; for each time step t, all possible decisions P are calculated. SC (t) and P LIB (t); Select the decision that minimizes the indicator function at the current stage and update the optimal value function: J * (t,SOC SC ,SOC LIB )=P SC ,P LIB min(c(t,P SC ,P LIB )+J * (t+1,SOC SC (t+1),SOC LIB (t+1)

[0122] The first value in the reference value sequence is fed back to the controller to form a closed-loop control.

[0123] The above-mentioned optimization solver and operation module are deployed in the cloud and interact with the equipment data acquisition module on a regular basis to realize online energy management.

[0124] The cloud-based hybrid energy storage power smoothing method 100 proposed in this embodiment is aimed at the existing technology: most microgrid studies only simulate and analyze power-type energy storage such as SC system as a power-type device, and only theoretically believe that the capacity configuration is sufficient, without considering the scenario of insufficient capacity of the SC system, resulting in the problem of reduced availability of the SC system when the capacity is insufficient in actual applications; the substitutability of power-type energy storage such as SC system for energy-type energy storage such as LIB system (not limited to lithium battery) in low-power applications is not considered. The existing scheme mainly splits the respective control instructions through low-pass filtering, but in fact, under low power, since the cycle period of power-type energy storage is higher than that of energy-type energy storage, completely replacing energy-type energy storage with power-type energy storage can effectively reduce the energy storage loss cost, which is not involved in the existing scheme; the constraints on charging of SC system and LIB system such as grid electricity price and grid backflow prevention are not considered. When peak electricity price or peak electricity price occurs, the scheme of the present invention can avoid charging of SC system at this time. When the grid backflow is prevented, the power fluctuation should be solved by the power limitation of LIB system, SC system and power generation system.

[0125] In specific applications, the preferred implementation scheme of the cloud-based hybrid energy storage power smoothing method 100 proposed in this embodiment is as follows: Figure 4 As shown, the lithium battery in the figure represents the LIB system. It should be noted that the specific implementation method of the cloud-based hybrid energy storage power smoothing method 100 proposed in this embodiment is not limited to this.

[0126] The cloud-based hybrid energy storage power smoothing method 100 proposed in this embodiment reasonably distributes the workload of the LIB system and the SC system in the hybrid energy storage system, optimizes the charging and discharging strategy, dynamically allocates energy storage and release tasks, and gives full play to the "peak shaving and valley filling" role of the SC system, thereby reducing the impact of the charging and discharging peak power on the LIB system, effectively alleviating the stress on the LIB system, delaying the aging speed, and increasing the overall service life, significantly improving the stability and reliability of energy storage in the photovoltaic storage and photovoltaic storage and charging combined systems.

[0127] Embodiment 2

[0128] Please refer to Figure 5 As shown in the figure, the cloud-based hybrid energy storage power smoothing system 500 in this embodiment adopts the cloud-based hybrid energy storage power smoothing method 100 proposed in the above-mentioned embodiment 1, including:

[0129] The information collection module 501 is used to collect data including at least the original link power and the last energy storage state point of the LIB system and the SC system;

[0130] A power control target determination module 502 is used to determine a power allocation reference value sequence of a link power control target by predicting power demand of an adaptive model;

[0131] A pulsation power calculation module 503, configured to calculate the link pulsation power according to the original link power and the link power control target, and determine the power fluctuation compensated by the SC system;

[0132] The hybrid energy storage start-stop control strategy module 504 is used to output at least one value in the power distribution reference value sequence to the power distribution controller, determine whether to start or stop the SC system or the LIB system according to the hybrid energy storage controller, and perform smooth adjustment;

[0133] The hybrid energy storage power regulation strategy module 505 is used to smooth the SC system power based on the residual pulsating power and determine the SC system power regulation smoothing component.

[0134] Exemplarily, the cloud-based hybrid energy storage power smoothing system further includes: an energy storage system smoothing processing module 506, which is used to combine the SC system start-stop smoothing component with the SC system power regulation smoothing component, and output the SC system smoothing component.

[0135] Exemplarily, the power control target determination module 502 is specifically used to: determine the power allocation reference value sequence of the current link power control target through SOC based on the original link power and the stored data of the SC system through the power demand prediction of the adaptive model.

[0136] Exemplarily, the hybrid energy storage power regulation strategy module 505 is specifically used to: smooth the SC system power based on the residual pulsating power, determine the SC system power regulation smoothing component, and optimize the power output allocation of the SC system and the LIB system using a dynamic programming strategy.

[0137] It is worth mentioning that the specific functions of each functional module in the cloud-based hybrid energy storage power smoothing system 500 proposed in this embodiment have been specifically described in the above-mentioned embodiment 1, and will not be repeated in this embodiment.

[0138] When applying specific Figure 6 As shown, Figure 6 This is a schematic diagram of the specific application principle of the cloud-based hybrid energy storage power smoothing system 500 proposed in this embodiment. The cloud data calculation module in the figure refers to the cloud-based hybrid energy storage power smoothing system proposed in this embodiment. However, the specific implementation plan is not limited to this and can be adjusted according to the actual application scenario.

[0139] The cloud-based hybrid energy storage power smoothing system 500 proposed in this embodiment reasonably distributes the workload of the LIB system and the SC system in the SC system, optimizes the charging and discharging strategy, and dynamically allocates energy storage and release tasks, giving full play to the "peak shaving and valley filling" function of the SC system, thereby reducing the impact of the charging and discharging peak power on the LIB system, effectively alleviating the stress on the LIB system, delaying the aging speed, and increasing the overall service life, thereby significantly improving the stability and reliability of energy storage in the photovoltaic storage and photovoltaic storage and charging combined systems.

[0140] Embodiment 3

[0141] This embodiment proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the cloud-based hybrid energy storage power smoothing method 100 as described in the first embodiment.

[0142] It should be noted that the above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0143] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A cloud-based hybrid energy storage power smoothing method, characterized in that: include: At least collect data including the original link power and the last energy storage state point of the LIB system and SC system; Determine the power allocation reference value sequence of the link power control target by predicting the power demand of the adaptive model; Calculating link pulsation power according to the original link power and the link power control target, and determining power fluctuations compensated by the SC system; At least one value in the power distribution reference value sequence is transmitted as an output to a power distribution controller, and the hybrid energy storage controller determines to enable or stop the SC system or the LIB system and performs smooth adjustment; According to the link pulsating power and the hybrid energy storage controller, the start-stop smoothing component is subtracted from the total pulsating power to calculate the remaining pulsating power; The SC system power is smoothed based on the residual pulsating power to determine a SC system power regulation smoothing component.

2. The cloud-based hybrid energy storage power smoothing method according to claim 1 is characterized in that: The cloud-based hybrid energy storage power smoothing method also includes: The SC system start-stop smoothing component and the SC system power regulation smoothing component are combined to output the SC system smoothing component.

3. The cloud-based hybrid energy storage power smoothing method according to claim 2 is characterized in that: The step of combining the SC system start-stop smoothing component with the SC system power regulation smoothing component to output the SC system smoothing component further includes: The LIB system energy storage takes on the task of smoothing the remaining pulsating power, combines the SC system start-stop smoothing component and the SC system power regulation smoothing component with the link, outputs the LIB system smoothing component, and completes the smoothing of the pulsating power in the control cycle.

4. The cloud-based hybrid energy storage power smoothing method according to claim 1 is characterized in that: The power demand prediction of the adaptive model is used to determine the power allocation reference value sequence of the link power control target, specifically including: Through the power demand prediction of the adaptive model, the power allocation reference value sequence of the current link power control target is determined by SOC based on the original link power and the stored data of the SC system.

5. The cloud-based hybrid energy storage power smoothing method according to any one of claims 1 to 4, characterized in that: The step of smoothing the SC system power based on the residual pulsating power to determine the SC system power regulation smoothing component includes: The SC system power is smoothed based on the residual pulsating power, a power regulation smoothing component of the SC system is determined, and a dynamic programming strategy is used to optimize the power output allocation of the SC system and the LIB system.

6. A cloud-based hybrid energy storage power smoothing system, characterized in that: The system adopts the cloud-based hybrid energy storage power smoothing method described in claim 1, comprising: An information collection module, used to collect data including at least the original link power and the last energy storage state point of the LIB system and the SC system; A power control target determination module, used to determine a power allocation reference value sequence of a link power control target by predicting power demand of an adaptive model; A pulsation power calculation module, used to calculate the link pulsation power according to the original link power and the link power control target, and determine the power fluctuation compensated by the SC system; A hybrid energy storage start-stop control strategy module is used to output at least one value in the power distribution reference value sequence to the power distribution controller, determine whether to start or stop the SC system or the LIB system according to the hybrid energy storage controller, and perform smooth adjustment; The hybrid energy storage power regulation strategy module is used to smooth the SC system power based on the residual pulsating power and determine the SC system power regulation smoothing component.

7. The cloud-based hybrid energy storage power smoothing system according to claim 6 is characterized in that: Also includes: The energy storage system smoothing processing module is used to combine the SC system start-stop smoothing component with the SC system power regulation smoothing component and output the SC system smoothing component.

8. The cloud-based hybrid energy storage power smoothing system according to claim 7 is characterized in that: The power control target determination module is specifically used for: Through the power demand prediction of the adaptive model, the power allocation reference value sequence of the current link power control target is determined by SOC based on the original link power and the stored data of the SC system.

9. The cloud-based hybrid energy storage power smoothing system according to any one of claims 6 to 8, characterized in that: The hybrid energy storage power regulation strategy module is specifically used for: The SC system power is smoothed based on the residual pulsating power, a power regulation smoothing component of the SC system is determined, and a dynamic programming strategy is used to optimize the power output allocation of the SC system and the LIB system.

10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the cloud-based hybrid energy storage power smoothing method as claimed in claim 1.

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