Distributed energy storage multi-target power control method and device

Abstract

The invention provides a distributed energy storage multi-target power control method and device. The method comprises the following steps: acquiring real-time power, wherein the real-time power comprises power grid side power at the current moment, energy storage power at the current moment and scheduling plan power at the current moment; based on the real-time power, the current power grid side target power is adjusted by executing a control strategy; based on the current power grid side target power and the real-time power, the energy storage power at the next moment is calculated in combination with a closed-loop increment damping adjustment algorithm; and correcting the energy storage power at the next moment based on constraint conditions to obtain final energy storage power, wherein the constraint conditions comprise a one-way threshold logic constraint, a physical boundary constraint and a battery electric quantity protection constraint. According to the distributed energy storage multi-target power control method and device provided by the invention, the problem of power oscillation caused by sampling time delay in an existing energy management system and the problem of strategy conflict in a multifunctional scene can be solved.

Description

Technical Field

[0001] This invention relates to the field of smart grid and distributed energy management technology, and in particular to a method and apparatus for multi-objective power control of distributed energy storage. Background Technology

[0002] With the increasing popularity of industrial and commercial energy storage, the tasks that energy management systems (EMS) need to handle are becoming increasingly complex. A standard energy storage system usually needs to simultaneously meet multiple requirements such as peak shaving and valley filling (operating according to plan), preventing transformer overload, controlling maximum demand costs, preventing photovoltaic backfeeding to the grid (anti-reverse current), and maximizing the absorption of surplus photovoltaic power.

[0003] The existing EMS control strategies have the following significant drawbacks in practical engineering applications: 1. Communication delay easily causes system oscillations: Due to the current use of traditional open-loop regulation algorithms for calculation, there is a 1-3 second communication delay between the smart meter and the PCS (energy storage converter) for data sampling. Therefore, the calculated intermediate variable (net load) often contains false deviations, causing large jumps in control commands and forming a "dead loop oscillation". 2. Strategy logic conflicts: When multiple functions coexist, strategy logic conflicts are prone to occur. For example, when a user issues an "active discharge" command, the grid power consumption decreases or even reverses. Traditional logic may misjudge this as "photovoltaic excess", thus incorrectly triggering the "photovoltaic consumption mode", causing the grid power to be stuck at a high consumption threshold, unable to execute the predetermined discharge plan. 3. Heavy dependence on input parameters: Existing algorithms often need to collect independent data such as photovoltaic inverter power and load power separately, increasing sensor costs and system complexity. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a distributed energy storage multi-objective power control method and device, which can solve the power oscillation problem caused by sampling delay and the strategy conflict problem in multi-functional scenarios in the existing energy management system.

[0005] In a first aspect, the present invention provides a distributed energy storage multi-objective power control method, comprising:

[0006] S1. Obtain real-time power, which includes the current grid-side power, the current energy storage power, and the current dispatch plan power.

[0007] S2. Based on real-time power, adjust the current target power on the grid side by executing control strategies;

[0008] S3. Based on the current grid-side target power and real-time power, calculate the energy storage power at the next moment using the closed-loop incremental damping regulation algorithm;

[0009] S4. Based on the constraints, the energy storage power at the next moment is corrected to obtain the final energy storage power. The constraints include unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints.

[0010] Furthermore, the control strategies include grid security mandatory takeover strategy, active discharge awareness recognition strategy, passive photovoltaic absorption strategy, and default plan follow strategy. When executing the control strategies, the execution priority of the grid security mandatory takeover strategy, active discharge awareness recognition strategy, passive photovoltaic absorption strategy, and default plan follow strategy decreases in that order.

[0011] Furthermore, adjusting the current grid-side target power by implementing control strategies includes:

[0012] S201. Determine whether the current power on the grid side exceeds the upper limit of transformer capacity and / or the upper limit of demand declaration. If yes, execute the grid safety forced takeover strategy and adjust the current target power on the grid side to reduce it to the upper limit of transformer capacity and / or the upper limit of demand declaration. If no, execute step S202.

[0013] S202. Determine whether the energy management system is in a user-defined planned discharge state based on the current scheduled power. If yes, execute the active discharge intention recognition strategy and adjust the current grid-side target power to ensure it is not lower than the anti-reverse current threshold. If not, execute step S203.

[0014] S203. Determine whether the energy management system is in standby or charging state based on the current scheduled power, and determine whether the energy management system is in reverse feed state based on the current grid power and whether the net observed power is less than 0. If yes, execute the passive photovoltaic absorption strategy and adjust the current grid target power to reduce it to the absorption threshold; otherwise, execute step S204.

[0015] S204. Execute the default plan follow strategy and adjust the current grid-side target power to be equal to the current scheduling plan power.

[0016] Furthermore, based on the current grid-side target power and real-time power, the calculation of the energy storage power at the next moment, combined with the closed-loop incremental damping adjustment algorithm, includes: calculating the grid-side power deviation at the current moment based on the current grid-side target power and the grid-side power at the current moment; and calculating the energy storage power at the next moment by introducing an incremental damping coefficient based on the grid power deviation at the current moment and the energy storage power at the current moment.

[0017] Furthermore, the energy storage power is subsequently corrected based on unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints to obtain the final energy storage power, including:

[0018] S401. Based on the one-way threshold logic constraint, the energy storage power at the next moment is corrected to obtain the first corrected energy storage power.

[0019] S402. Based on physical boundary constraints, the first modified energy storage power is successively modified by lower limit and upper limit to obtain the third modified energy storage power;

[0020] S403. Based on the battery power protection constraint, the third modified energy storage power is modified to obtain the final energy storage power.

[0021] Secondly, an embodiment of the present invention provides a distributed energy storage multi-objective power control device, comprising:

[0022] Data acquisition module: Acquires real-time power, which includes the current grid-side power, the current energy storage power, and the current dispatch plan power.

[0023] Decision-making and regulation module: Based on real-time power, it regulates the current target power on the grid side by executing control strategies;

[0024] Incremental damping calculation module: Based on the current grid-side target power and real-time power, it calculates the energy storage power at the next moment using a closed-loop incremental damping adjustment algorithm;

[0025] Constraint Correction Module: Based on the constraints, the energy storage power at the next moment is corrected to obtain the final energy storage power. The constraints include unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints.

[0026] This application provides a distributed energy storage multi-objective power control method and device, which brings the following beneficial effects:

[0027] 1. This application does not require the installation of photovoltaic side meters or load side meters. It only requires inputting three core data points: the current grid-side power, the current energy storage power, and the current scheduling plan power, to achieve full-function control, which greatly reduces hardware deployment costs and communication failure rate.

[0028] 2. This application can effectively suppress sawtooth wave oscillation caused by communication delay through incremental damping adjustment algorithm, and solve the problem of sampling asynchrony from a mathematical principle. The actual test shows that when the load changes from 0 to rated power, the adjustment process of the energy management system is smooth and without overshoot, and the steady-state error is controlled within ±2% of the target value. It eliminates the "sawtooth wave" current impact commonly found in traditional algorithms and effectively protects battery life.

[0029] 3. This application proposes a priority-based control strategy, which automatically identifies "active discharge" and "photovoltaic consumption" scenarios and dynamically switches control targets by executing the control strategy;

[0030] 4. This application establishes constraints that integrate transformer overload protection and demand control functions, enabling millisecond-level response to grid impacts, prioritizing transformer safety, and achieving comprehensive coverage from economic optimization to equipment safety.

[0031] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0033] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating a distributed energy storage multi-objective power control method provided in an embodiment of the present invention.

[0035] Figure 2 A comparison diagram of the step response of a closed-loop incremental damping regulation algorithm and a traditional open-loop regulation algorithm for a distributed energy storage multi-objective power control method provided in an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1

[0038] To facilitate understanding of this embodiment, in conjunction with Figure 1 This invention provides a detailed description of a distributed energy storage multi-objective power control method disclosed in an embodiment of the invention.

[0039] S1. Obtain real-time power, which includes the current grid-side power, the current energy storage power, and the current dispatch plan power.

[0040] In S1, the power on the grid side at the current time is the measured power P on the grid side at time t. grid (t), the energy storage power at the current time is the measured energy storage power P at time t. bess (t), the current scheduling plan power is the scheduling plan power P at time t. plan (t), the current grid power, current energy storage power, and current scheduling plan power are obtained periodically through the energy management system, with an acquisition period of 200ms.

[0041] In this embodiment, when the grid-side power is positive at the current moment, it indicates that the energy management system is in a power purchase state; when the grid-side power is negative at the current moment, it indicates that the energy management system is in a reverse power supply state. When the energy storage power is positive at the current moment, it indicates that the energy management system is in a charging state; when the energy storage power is negative at the current moment, it indicates that the energy management system is in a discharging state. When the scheduled power is negative at the current moment, it indicates that the energy management system is in a user-planned discharge state; when the scheduled power is positive at the current moment, it indicates that the energy management system is in a user-planned charging state or a standby state.

[0042] S2. Based on real-time power, adjust the current grid-side target power by executing control strategies.

[0043] In S2, the control strategies include a grid safety mandatory takeover strategy, an active discharge awareness recognition strategy, a passive photovoltaic absorption strategy, and a default plan follow strategy. When executing these control strategies, the execution priority of the grid safety mandatory takeover strategy, the active discharge awareness recognition strategy, the passive photovoltaic absorption strategy, and the default plan follow strategy decreases sequentially. The current grid-side target power is the grid-side target power at time t. The current grid-side power target is obtained based on a finite state machine (FSM), and its expression is P. target_grid (t).

[0044] Specifically, adjusting the current target power on the grid side by implementing control strategies includes:

[0045] S201. Determine whether the current power on the grid side exceeds the upper limit of transformer capacity and / or the upper limit of demand declaration. If yes, execute the grid safety forced takeover strategy and adjust the current target power on the grid side to reduce it to the upper limit of transformer capacity and / or the upper limit of demand declaration. If no, execute step S202.

[0046] In this embodiment, the grid security mandatory takeover strategy can reduce the current grid-side target power to the upper limit of transformer capacity and / or the upper limit of demand declaration.

[0047] Furthermore, the expression for the current grid-side power exceeding the upper limit of transformer capacity is as follows:

[0048] P grid (t)>P limit_trans

[0049] In the formula, P limit_trans This represents the upper limit of transformer capacity.

[0050] The expression for the current grid-side power exceeding the upper limit of demand declaration is:

[0051] P grid (t)>P limit_demand

[0052] In the formula, P limit_demand This is the upper limit for demand declaration.

[0053] The expression for reducing the current grid-side target power to the upper limit of transformer capacity is:

[0054] P target_grid (t)=P limit_trans

[0055] In the formula, P target_grid (t) represents the current target power on the grid side.

[0056] The expression for reducing the current grid-side target power to the upper limit of demand declaration is as follows:

[0057] P target_grid (t)=P limit_demand

[0058] S202. Determine whether the energy management system is in a user-defined planned discharge state based on the current scheduled power. If yes, execute the active discharge intention identification strategy and adjust the current grid-side target power to ensure it is not lower than the anti-reverse current threshold. If not, execute step S203.

[0059] In this embodiment, the active discharge intent recognition strategy can ignore the current net load status, forcibly shield the photovoltaic absorption logic, and enable the energy management system to allow energy storage discharge. The current grid-side target power is not lower than the anti-reverse current threshold, which is 30W.

[0060] Furthermore, when the energy management system is in a user-defined planned discharge state, the current scheduled power value is less than 0, and its expression is:

[0061] P plan (t)<0

[0062] The current expression for ensuring that the target power on the grid side is not lower than the anti-reverse current threshold is:

[0063] P target_grid (t)≥T anti

[0064] In the formula, T anti To prevent backflow threshold.

[0065] S203. Determine whether the energy management system is in standby or charging state based on the current scheduled power, and determine whether the energy management system is in reverse feed state based on the current grid power and whether the net observed power is less than 0. If yes, execute the passive photovoltaic absorption strategy and adjust the current grid target power to reduce it to the absorption threshold; otherwise, execute step S204.

[0066] In this embodiment, the passive photovoltaic absorption strategy enables the energy management system to charge and absorb excess power, keeping the current grid-side target power at the absorption threshold, which is 100W. The net observed power is used to represent the actual power surplus / deficit status on the user side without considering the current power output of the energy storage system (battery) (i.e., removing the impact of the current energy storage operation), and can reflect the original supply and demand difference between the user load and photovoltaic power generation.

[0067] Furthermore, when the energy management system is in standby or charging mode, the current planned power value is greater than or equal to 0, and its expression is:

[0068] P plan (t)≥0

[0069] When the energy management system is in reverse feed mode, the current power value on the grid side is less than 0, and its expression is:

[0070] P grid (t)<0

[0071] The expression for the current grid-side target power being reduced to the absorption threshold is:

[0072] P target_grid (t)=T pv

[0073] In the formula, T pv This is the absorption threshold.

[0074] S204. Execute the default plan follow strategy and adjust the current grid-side target power to be equal to the current scheduling plan power.

[0075] In this embodiment, the planned follow strategy can make the current grid-side target power equal to the current scheduling planned power by not setting a closed-loop grid adjustment target.

[0076] Furthermore, the expression for the current grid-side target power equaling the current time-based scheduling plan power is:

[0077] P target_grid (t)=Pplan (t)

[0078] S3. Based on the current grid-side target power and real-time power, calculate the energy storage power at the next moment using a closed-loop incremental damping adjustment algorithm.

[0079] Specifically, the calculation of the energy storage power at the next moment based on the current grid-side target power and real-time power, combined with the closed-loop incremental damping adjustment algorithm, includes: calculating the grid-side power deviation at the current moment based on the current grid-side target power and the current grid-side power; and calculating the energy storage power at the next moment by introducing an incremental damping coefficient based on the current grid power deviation and the current energy storage power.

[0080] Wherein, the power deviation on the grid side at the current moment is the power deviation on the grid side at time t, ΔE(t), and its expression is:

[0081] ΔE(t)=P target_grid (t)-P grid (t)

[0082] The expression for the energy storage power at the next moment is:

[0083] P cmd (t+1)=P bess (t)+K·ΔE(t)

[0084] In the formula, K is the incremental damping coefficient, which ranges from (0,1). In this embodiment, the incremental damping coefficient is 0.3, indicating that the energy management system corrects only 30% of the detection error each time.

[0085] In this embodiment, since the current grid-side power P grid (t) may lag behind the current energy storage power P. bess Direct full correction (t) would lead to excessive adjustment, while the incremental damping coefficient introduced in this application can achieve a low-pass filtering effect, making the power regulation curve converge exponentially rather than oscillating and diverging.

[0086] Figure 2 This is a comparison chart of the step response between the closed-loop incremental damping adjustment algorithm provided in this embodiment and the traditional open-loop adjustment algorithm. Combined with... Figure 2 As can be seen, the closed-loop incremental damping adjustment algorithm provided in this embodiment has the following advantages over the traditional open-loop adjustment algorithm:

[0087] 1. The closed-loop incremental damping regulation algorithm provided in this embodiment can solve the "sawtooth wave oscillation" caused by communication delay. Due to the 1-3 second data sampling communication delay between the smart meter and the energy storage converter (PCS), the intermediate variable (net load) calculated by the traditional open-loop regulation algorithm contains spurious deviations. This can cause large jumps in control commands, resulting in "dead loop oscillation" or current surges.

[0088] 2. The closed-loop incremental damping regulation algorithm provided in this embodiment can reduce hardware deployment costs and system complexity. In order to calculate the net load, traditional open-loop regulation algorithms often need to collect independent data such as photovoltaic inverter power and load power separately, which increases sensor costs and system complexity.

[0089] 3. The closed-loop incremental damping adjustment algorithm provided in this embodiment can improve control accuracy and steady-state performance, while the traditional open-loop adjustment algorithm is prone to generating large steady-state errors due to time delay and spurious deviation.

[0090] 4. The closed-loop incremental damping regulation algorithm provided in this embodiment can protect battery life. In contrast, the frequent power oscillations and large current jumps of traditional open-loop regulation algorithms can impact the battery and shorten its lifespan.

[0091] S4. Based on the constraints, the energy storage power at the next moment is corrected to obtain the final energy storage power. The constraints include unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints.

[0092] In S4, since the energy storage power may exceed the physical capacity of the energy storage in the next moment, it is necessary to correct the energy storage power in the next moment through constraints so that the final energy storage power can integrate transformer protection, physical boundary limitation and battery life protection. For scenarios with anti-reverse current requirements, it is ensured that the system will not send illegal power back to the grid in order to meet the grid connection regulations.

[0093] Furthermore, the energy storage power is subsequently corrected based on unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints to obtain the final energy storage power, including:

[0094] S401. The energy storage power at the next moment is corrected based on the unidirectional threshold logic constraint to obtain the first corrected energy storage power.

[0095] Among them, the first corrected energy storage power obtained by correcting the energy storage power at the next moment based on unidirectional threshold logic constraints includes:

[0096] If the energy storage power limits the discharge of the energy management system in the next moment (i.e., the value of the energy storage power in the next moment is closer to 0 than the value of the current scheduling plan power), the energy storage power in the next moment is used as the first corrected energy storage power; otherwise, the initial next moment is corrected to the current scheduling plan power, and the current scheduling plan power is the first corrected energy storage power.

[0097] If the power on the grid side exceeds the upper limit of transformer capacity and / or the upper limit of demand declaration at the current moment, the energy storage power at the next moment will be used as the first corrected energy storage power; otherwise, the initial next moment will be corrected to the current moment's scheduled power, and the current moment's scheduled power will be the first corrected energy storage power.

[0098] Furthermore, based on the unidirectional threshold logic constraint, the energy storage power at the next moment is corrected, and the expression for the first corrected energy storage power is obtained as follows:

[0099] P1 = max(P plan (t),P cmd (t+1))

[0100] S402. Based on physical boundary constraints, the first modified energy storage power is successively modified by lower limit and upper limit to obtain the third modified energy storage power.

[0101] The process of lower-limiting the first corrected energy storage power to obtain the second corrected energy storage power based on physical boundary constraints includes: comparing the first corrected energy storage power with the negative value of the maximum discharge power according to the maximum discharge power, and taking the larger value of the two as the second corrected energy storage power.

[0102] The method for obtaining the third corrected energy storage power by adjusting the upper limit of the second corrected energy storage power based on the physical boundary bundle includes: comparing the second corrected energy storage power with the negative value of the maximum charging power according to the maximum charging power, and taking the smaller value of the two as the third corrected energy storage power.

[0103] Furthermore, based on physical boundary constraints, the lower and upper limits of the first modified energy storage power are sequentially modified to obtain the expression for the third modified energy storage power:

[0104] P3=min(max(P1,-P max_dis ),P max_chg

[0105] In the formula, P max_dis For the maximum discharge power, P max_chg This represents the maximum charging power.

[0106] S403. Based on the battery power protection constraint, the third modified energy storage power is modified to obtain the final energy storage power.

[0107] The final energy storage power obtained by correcting the third modified energy storage power based on battery power protection constraints includes:

[0108] When the battery capacity is greater than or equal to the charging protection limit and the third corrected energy storage power is greater than 0, the final energy storage power is set to 0; otherwise, the third corrected energy storage power is used as the final energy storage power.

[0109] When the battery charge is less than the discharge protection lower limit and the third corrected energy storage power is less than 0, the final energy storage power is set to 0; otherwise, the third corrected energy storage power is used as the final energy storage power.

[0110] Furthermore, the expression for the final energy storage power obtained by correcting the third modified energy storage power based on battery power protection constraints includes:

[0111] When SOC≥SOC high And P final When P > 0, then P final =0, otherwise P final =P3;

[0112] When SOC≤SOC low And P final When <0, then P final =0, otherwise P final =P3.

[0113] In the above expression, SOC represents the battery capacity. high As the upper limit of charging protection, SOC low This is the lower limit of discharge protection.

[0114] Example 2

[0115] This embodiment provides a distributed energy storage multi-objective power control device, including:

[0116] Data acquisition module: Acquires real-time power, which includes the current grid-side power, the current energy storage power, and the current dispatch plan power.

[0117] Decision-making and regulation module: Based on real-time power, it regulates the current target power on the grid side by executing control strategies;

[0118] Incremental damping calculation module: Based on the current grid-side target power and real-time power, it calculates the energy storage power at the next moment using a closed-loop incremental damping adjustment algorithm;

[0119] Constraint Correction Module: Based on the constraints, the energy storage power at the next moment is corrected to obtain the final energy storage power. The constraints include unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints.

[0120] It should be noted that the distributed energy storage multi-objective power control device provided in this embodiment can realize all the contents of the distributed energy storage multi-objective power control method provided in Embodiment 1, and will not be repeated here.

[0121] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0122] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-objective power control method for distributed energy storage, characterized in that, include: S1. Obtain real-time power, which includes the current grid-side power, the current energy storage power, and the current dispatch plan power. S2. Based on real-time power, adjust the current target power on the grid side by executing control strategies; S3. Based on the current grid-side target power and real-time power, calculate the energy storage power at the next moment using the closed-loop incremental damping regulation algorithm; S4. Based on the constraints, the energy storage power at the next moment is corrected to obtain the final energy storage power. The constraints include unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints.

2. The distributed energy storage multi-objective power control method according to claim 1, characterized in that, The control strategies include grid security mandatory takeover strategy, active discharge awareness recognition strategy, passive photovoltaic absorption strategy, and default plan follow strategy. When executing the control strategies, the execution priority of the grid security mandatory takeover strategy, active discharge awareness recognition strategy, passive photovoltaic absorption strategy, and default plan follow strategy decreases in that order.

3. The distributed energy storage multi-objective power control method according to claim 2, characterized in that, Adjusting the current grid-side target power by implementing control strategies includes: S201. Determine whether the current power on the grid side exceeds the upper limit of transformer capacity and / or the upper limit of demand declaration. If yes, execute the grid safety forced takeover strategy and adjust the current target power on the grid side to reduce it to the upper limit of transformer capacity and / or the upper limit of demand declaration. If no, execute step S202. S202. Determine whether the energy management system is in a user-defined planned discharge state based on the current scheduled power. If yes, execute the active discharge intention recognition strategy and adjust the current grid-side target power to ensure it is not lower than the anti-reverse current threshold. If not, execute step S203. S203. Determine whether the energy management system is in standby or charging state based on the current scheduled power, and determine whether the energy management system is in reverse feed state based on the current grid power and whether the net observed power is less than 0. If yes, execute the passive photovoltaic absorption strategy and adjust the current grid target power to reduce it to the absorption threshold; otherwise, execute step S204. S204. Execute the default plan follow strategy and adjust the current grid-side target power to be equal to the current scheduling plan power.

4. The distributed energy storage multi-objective power control method according to claim 1, characterized in that, The calculation of the next moment's energy storage power, based on the current grid-side target power and real-time power, combined with the closed-loop incremental damping adjustment algorithm, includes: calculating the grid-side power deviation at the current moment based on the current grid-side target power and the current grid-side power; and calculating the next moment's energy storage power by introducing an incremental damping coefficient, based on the current grid power deviation and the current energy storage power.

5. The distributed energy storage multi-objective power control method according to claim 1, characterized in that, The energy storage power at the next moment is corrected sequentially based on unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints to obtain the final energy storage power, including: S401. Based on the one-way threshold logic constraint, the energy storage power at the next moment is corrected to obtain the first corrected energy storage power. S402. Based on physical boundary constraints, the first modified energy storage power is successively modified by lower limit and upper limit to obtain the third modified energy storage power; S403. Based on the battery power protection constraint, the third modified energy storage power is modified to obtain the final energy storage power.

6. A distributed energy storage multi-objective power control device, characterized in that, include: Data acquisition module: Acquires real-time power, which includes the current grid-side power, the current energy storage power, and the current dispatch plan power. Decision-making and regulation module: Based on real-time power, it regulates the current target power on the grid side by executing control strategies; Incremental damping calculation module: Based on the current grid-side target power and real-time power, it calculates the energy storage power at the next moment using a closed-loop incremental damping adjustment algorithm; Constraint Correction Module: Based on the constraints, the energy storage power at the next moment is corrected to obtain the final energy storage power. The constraints include unidirectional threshold logic constraints, physical boundary constraints, and battery power protection constraints.

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