Micro-grid source and grid load cooperative scheduling method and system

Abstract

The invention relates to the technical field of micro-grids, in particular to a micro-grid source and grid load cooperative scheduling method and system. The method comprises the following steps: firstly, constructing a micro-grid energy storage scheduling model according to collected operation state data, and establishing an energy storage cost optimization objective function so as to optimize and solve to obtain an energy storage collaborative scheduling strategy; and calculating residual fluctuation between the operation state data and the target operation state data by using a one-dimensional residual network, and carrying out dynamic collaborative optimization on an energy storage collaborative scheduling strategy. According to the scheme, the scheduling simulation result of the microgrid energy storage scheduling model is converted into the energy storage cost optimization objective function representing the power balance degree of different equipment in the microgrid, so that the optimal operation strategy of the power generation equipment and the load equipment with the lowest energy storage mutual aid cost and the lowest comprehensive penalty is obtained as the energy storage cooperative scheduling strategy; and cooperative scheduling of the equipment in the micro-grid is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and particularly to a method and system for collaborative scheduling of the source-network-load in a microgrid. Background Art

[0002] With the increase in the proportion of new energy in the power system, the system's demand for flexible resources has further increased. Various types of energy storage, represented by battery energy storage and pumped-storage energy storage, are widely used. Their flexible output regulation ability provides a solution for the collaborative optimization of the source-network-load-storage, but at the same time, it poses higher requirements for the efficient utilization of energy storage resources.

[0003] In a microgrid, there are generally power generation units or power consumption units such as distributed power sources, energy storage devices, and local loads. The coordinated and optimized operation of the source-storage-load is an important guarantee for the optimization process of microgrid energy management. As a carrier for distributed power sources to access the distribution network, there is currently a lack of technical solutions for collaborative scheduling of various devices in the microgrid. Summary of the Invention

[0004] The main object of the present invention is to provide a method and system for collaborative scheduling of the source-network-load in a microgrid, aiming to solve the problem that there is currently a lack of technical solutions for collaborative scheduling of various devices in the microgrid.

[0005] The technical solution proposed by the present invention is as follows:

[0006] A method for collaborative scheduling of the source-network-load in a microgrid, comprising:

[0007] Collecting the operation status data of each energy storage device in the microgrid;

[0008] Constructing a microgrid energy storage scheduling model based on the collected operation status data, wherein the microgrid energy storage scheduling model takes the operation strategies of the power generation devices and load devices in the microgrid as input parameters, and takes the operation status data of the energy storage devices after adopting the operation strategies as output parameters;

[0009] Establishing an energy storage cost optimization objective function based on the microgrid energy storage scheduling model, and optimizing and solving the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation devices and load devices;

[0010] Inputting the optimal operation strategies of the power generation devices and load devices into the microgrid energy storage scheduling model to obtain the output real-time operation status data;

[0011] Dynamically optimizing the energy storage collaborative scheduling strategy based on the real-time operation status data.

[0012] Preferably, it further comprises:

[0013] Taking the reduction of new - energy curtailment as the goal, a new - energy curtailment objective function is established. Since the new - energy curtailment objective function is affected by the randomness of wind and light, a target confidence level τ1 is given to describe the new - energy curtailment objective function:

[0014]

[0015] In the formula, f aw is the wind curtailment volume, and f av is the light curtailment volume; is the number of wind turbines, is the number of photovoltaic units; is the wind - curtailment cost function of the i - th wind turbine, is the light - curtailment cost function of the i - th photovoltaic unit; is the planned output of the i - th wind turbine at time t, is the planned output of the i - th photovoltaic unit at time t; Due to the uncertainty of wind and solar power itself, when the actual maximum output cannot meet the planned output, the wind turbines and photovoltaic units output at the actual maximum value.

[0016] Preferably, the dynamic collaborative optimization of the energy - storage collaborative scheduling strategy based on the real - time operation - state data includes:

[0017] Obtain the target operation - state data;

[0018] Calculate the residual fluctuation between the real - time operation - state data and the target operation - state data based on a one - dimensional residual network;

[0019] Dynamically and collaboratively optimize the energy - storage collaborative scheduling strategy based on the residual fluctuation to obtain the optimal energy - storage collaborative scheduling strategy, and use the optimal energy - storage collaborative scheduling strategy to perform collaborative scheduling on the equipment in the micro - grid.

[0020] Preferably, the operation - state data of the energy - storage device is the current stored power of the energy - storage device, and the representation form of the operation - state data is:

[0021] {X n |n ∈ [1, N]},

[0022] In the formula, X n represents the operation - state data of the n - th energy - storage device in the micro - grid, and N represents the total number of energy - storage devices in the micro - grid.

[0023] Preferably, the power - generation equipment includes generators and wind turbines. Among them, the set of power - generation equipment and load equipment associated with the n - th energy - storage device is:

[0024]

[0025] In the formula, Indicates the m-th n wind turbine associated with the n-th energy storage device, where M n represents the total number of wind turbines associated with the n-th energy storage device; Indicates the r-th n generator associated with the n-th energy storage device, where R n represents the total number of generators associated with the n-th energy storage device; Indicates the h-th n load device associated with the n-th energy storage device, where H n represents the total number of load devices associated with the n-th energy storage device; The wind turbines and generators associated with the energy storage device are used to supply power to the energy storage device, and the energy storage device is used to supply power to the associated load devices;

[0026] The method further includes:

[0027] Taking the power delivered by the power generation device to the energy storage device at a future moment as the operation strategy of the power generation device, and taking the power required by the load device at a future moment as the operation strategy of the load device.

[0028] Preferably, the energy storage cost optimization objective function takes the operation strategies of the power generation device and the load device as input parameters, and takes the energy storage mutual assistance cost of the microgrid under this operation strategy as the objective function value; The expression formula of the energy storage cost optimization objective function is:

[0029]

[0030] In the formula, F(θ) represents the energy storage cost optimization objective function, and θ represents the operation strategies of the power generation device and the load device; Indicates the power delivered by the m-th n wind turbine associated with the n-th energy storage device to the n-th energy storage device, Indicates the power delivered by the r-th n generator associated with the n-th energy storage device to the n-th energy storage device; Indicates the power delivered by the n-th energy storage device to the h-th n load device associated therewith; β and γ represent cost coefficients.

[0031] Preferably, establishing an energy storage cost optimization objective function based on the microgrid energy storage scheduling model, and optimizing and solving the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation device and the load device, including:

[0032] Initializing a set of operation strategies α_0 for the power generation device and the load device:

[0033]

[0034] In the formula, represents the power transmitted from the m-th wind turbine associated with the n-th energy storage device in the operation strategy α_0 to the n-th energy storage device; n The power transmitted from the m-th wind turbine associated with the n-th energy storage device in the operation strategy α_0 to the n-th energy storage device; represents the power transmitted from the r-th generator associated with the n-th energy storage device in the operation strategy α_0 to the n-th energy storage device; n The power transmitted from the r-th generator associated with the n-th energy storage device in the operation strategy α_0 to the n-th energy storage device; represents the power transmitted from the n-th energy storage device to the h-th load device associated therewith in the operation strategy α_0; n The power transmitted from the n-th energy storage device to the h-th load device associated therewith in the operation strategy α_0;

[0035] Set the current iteration number of the operation strategy to t and the maximum iteration number to Max, then the t-th iteration result of the operation strategy is α_t.

[0036] Preferably, after setting the current iteration number of the operation strategy to t and the maximum iteration number to Max, and the t-th iteration result of the operation strategy is α_t, it further includes:

[0037] Calculate the penalty value of the operation strategy α_t:

[0038] G(α_t) = β1G1(α_t) + β2G2(α_t) + β3G3(α_t),

[0039]

[0040] In the formula, G(α_t) represents the penalty value of the operation strategy α_t; β1, β2, and β3 represent penalty coefficients; represents the average power transmitted from the m-th wind turbine associated with the n-th energy storage device to the n-th energy storage device; n The average power transmitted from the m-th wind turbine associated with the n-th energy storage device to the n-th energy storage device; represents the average power transmitted from the r-th generator associated with the n-th energy storage device to the n-th energy storage device; G1(α_t) represents the fluctuation penalty of the operation strategy α_t; n The average power transmitted from the r-th generator associated with the n-th energy storage device to the n-th energy storage device; G1(α_t) represents the fluctuation penalty of the operation strategy α_t; represents the required power of the hn-th load device associated with the n-th energy storage device; G2(α_t) represents the demand power consumption penalty of the operation strategy α_t; represents the discarded wind energy of the m-th wind turbine associated with the n-th energy storage device; n The discarded wind energy of the m-th wind turbine associated with the n-th energy storage device; represents the wind energy generated by the m-th wind turbine associated with the n-th energy storage device; G3(α_t) represents the natural energy loss penalty of the operation strategy α_t. n The wind energy generated by the m-th wind turbine associated with the n-th energy storage device; G3(α_t) represents the natural energy loss penalty of the operation strategy α_t.

[0041] Preferably, after calculating the penalty value of the operation strategy α_t, it further includes:

[0042] Iterate the operation strategy, where the iteration formula is:

[0043]

[0044] In the formula, exp(·) represents the exponential function with the natural constant as the base; represents the gradient operator of the optimal energy storage mutual assistance cost optimization objective function, represents the gradient of the operation strategy α_t;

[0045] Let t = t + 1, and then execute the above calculation to obtain the penalty value of the operation strategy α_t, and subsequent steps until the maximum number of iterations is reached. Take the operation strategy α_Max at this time as the energy storage collaborative scheduling strategy.

[0046] The present invention also proposes a microgrid source-network-load collaborative scheduling system, which applies the microgrid source-network-load collaborative scheduling method; the system includes:

[0047] A collection module, configured to: collect the operation status data of each energy storage device in the microgrid;

[0048] A processing module, configured to: construct a microgrid energy storage scheduling model according to the collected operation status data; establish an energy storage cost optimization objective function based on the microgrid energy storage scheduling model, and optimize and solve the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation equipment and the load equipment; input the optimal operation strategies of the power generation equipment and the load equipment into the microgrid energy storage scheduling model to obtain the output real-time operation status data;

[0049] An execution module, configured to: perform dynamic collaborative optimization on the energy storage collaborative scheduling strategy based on the real-time operation status data.

[0050] Through the above technical solutions, the following beneficial effects can be achieved:

[0051] The microgrid source-network-load collaborative scheduling method proposed by the present invention can perform collaborative scheduling on various devices in the microgrid. First, a microgrid energy storage scheduling model is constructed based on the collected operation status data, and an energy storage cost optimization objective function is established for optimization and solution to obtain the energy storage collaborative scheduling strategy. Then, a one-dimensional residual network is used to calculate the residual fluctuation between the operation status data and the target operation status data, and the energy storage collaborative scheduling strategy is dynamically collaboratively optimized. The solution of the present invention converts the scheduling simulation result of the microgrid energy storage scheduling model into an energy storage cost optimization objective function representing the power balance degree of different devices in the microgrid, so as to obtain the optimal operation strategies of the power generation devices and load devices with the lowest energy storage mutual assistance cost and the smallest comprehensive penalty as the energy storage collaborative scheduling strategy, and dynamically collaboratively optimize the energy storage collaborative scheduling strategy based on the extracted residual fluctuation according to the device operation status, thereby realizing the collaborative scheduling of devices in the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the structures shown in these drawings without creative efforts.

[0053] Figure 1 It is a flowchart of the steps of the first embodiment of a microgrid source-network-load collaborative scheduling method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The present invention proposes a microgrid source-network-load collaborative scheduling method and system.

[0056] As shown in the Figure 1 accompanying drawings, in the first embodiment of a microgrid source-network-load collaborative scheduling method proposed by the present invention, this embodiment includes the following steps:

[0057] Step S110: Collect the operation status data of each energy storage device in the microgrid.

[0058] Step S120: Construct a microgrid energy storage scheduling model according to the collected operation status data, where the microgrid energy storage scheduling model takes the operation strategies of the power generation devices and load devices in the microgrid as input parameters and the operation status data of the energy storage devices after adopting the operation strategies as output parameters.

[0059] Step S130: Establish an energy storage cost optimization objective function based on the microgrid energy storage scheduling model, and optimize and solve the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation equipment and the load equipment.

[0060] Step S140: Input the optimal operation strategies of the power generation equipment and the load equipment into the microgrid energy storage scheduling model to obtain the output real-time operation status data.

[0061] Step S150: Dynamically co-optimize the energy storage collaborative scheduling strategy based on the real-time operation status data.

[0062] The microgrid source-network-load collaborative scheduling method proposed by the present invention can perform collaborative scheduling on various devices in the microgrid; firstly, a microgrid energy storage scheduling model is constructed according to the collected operation status data, and an energy storage cost optimization objective function is established and then optimized and solved to obtain an energy storage collaborative scheduling strategy; then, a one-dimensional residual network is used to calculate the residual fluctuation between the operation status data and the target operation status data, and the energy storage collaborative scheduling strategy is dynamically co-optimized. The solution of the present invention converts the scheduling simulation result of the microgrid energy storage scheduling model into an energy storage cost optimization objective function representing the power balance degree of different devices in the microgrid, so as to obtain the optimal operation strategies of the power generation equipment and the load equipment with the lowest energy storage mutual assistance cost and the smallest comprehensive penalty as the energy storage collaborative scheduling strategy, and dynamically co-optimize the energy storage collaborative scheduling strategy based on the extracted residual fluctuation according to the device operation status, thereby realizing the collaborative scheduling of the devices in the microgrid.

[0063] In the second embodiment of a microgrid source-network-load collaborative scheduling method proposed by the present invention, based on the first embodiment, this embodiment further includes the following steps:

[0064] Step S210: Establish a new energy curtailment objective function with the goal of reducing new energy curtailment. Since the new energy curtailment objective function is affected by the randomness of wind and light, a target confidence level τ1 is given to describe the new energy curtailment objective function:

[0065]

[0066] In the formula, f aw is the wind curtailment volume, and f av is the light curtailment volume; is the number of wind turbines, is the number of photovoltaic units; is the wind curtailment cost function of the i-th wind turbine, is the wind curtailment cost function of the i-th photovoltaic unit; is the planned output of the i-th wind turbine at time t, $P_{i,t}$ is the scheduled output of the $i$-th photovoltaic unit in the $t$-th period; due to the uncertainty of wind and photovoltaic power itself, when the actual maximum output cannot meet the scheduled output, the wind turbine and the photovoltaic unit output at the actual maximum value;

[0067] Step S220: Determine the wind curtailment volume of the wind turbine and the light curtailment volume of the photovoltaic unit based on the new energy curtailment objective function, so as to perform coordinated scheduling on the microgrid.

[0068] Specifically, in this embodiment, a new energy curtailment objective function is established to determine the wind curtailment volume of the wind turbine and the light curtailment volume of the photovoltaic unit, so as to perform coordinated scheduling on the microgrid; thereby improving the energy utilization efficiency and management level of the microgrid.

[0069] In the third embodiment of a microgrid source-network-load coordinated scheduling method proposed by the present invention, based on the first embodiment, step S150 includes the following steps:

[0070] Step S310: Obtain the target operating state data.

[0071] Step S320: Calculate the residual fluctuation between the real-time operating state data and the target operating state data based on the one-dimensional residual network.

[0072] Step S330: Dynamically optimize the energy storage coordinated scheduling strategy based on the residual fluctuation to obtain the optimal energy storage coordinated scheduling strategy, and use the optimal energy storage coordinated scheduling strategy to perform coordinated scheduling on the equipment in the microgrid.

[0073] Specifically, this embodiment gives a solution on how to dynamically optimize the energy storage coordinated scheduling strategy based on the real-time operating state data.

[0074] In the fourth embodiment of a microgrid source-network-load coordinated scheduling method proposed by the present invention, based on the first embodiment, the operating state data of the energy storage device is the current stored power of the energy storage device, and the representation form of the operating state data is:

[0075] $\{X_{n}$ n $|n\in[1,N]\}$,

[0076] where $X_{n}$ n represents the operating state data of the $n$-th energy storage device in the microgrid, and $N$ represents the total number of energy storage devices in the microgrid.

[0077] In the fifth embodiment of a microgrid source-network-load coordinated scheduling method proposed by the present invention, based on the fourth embodiment, the power generation equipment includes a generator and a wind turbine. Among them, the power generation equipment and the load equipment set associated with the $n$-th energy storage device are:

[0078]

[0079] In the formula, represents the m n th wind turbine associated with the nth energy storage device, and M n represents the total number of wind turbines associated with the nth energy storage device; represents the r n th generator associated with the nth energy storage device, and R n represents the total number of generators associated with the nth energy storage device; represents the h n th load device associated with the nth energy storage device, and H n represents the total number of load devices associated with the nth energy storage device; the wind turbines and generators associated with the energy storage device are used to supply power to the energy storage device, and the energy storage device is used to supply power to the associated load devices.

[0080] This embodiment further includes the following steps:

[0081] Step S510: Take the power that the power generation device delivers to the energy storage device at a future moment as the operation strategy of the power generation device, and take the power required by the load device at a future moment as the operation strategy of the load device.

[0082] Specifically, this embodiment gives the operation strategy of the power generation device and the operation strategy of the load device.

[0083] In the sixth embodiment of a microgrid source-network-load collaborative scheduling method proposed by the present invention, based on the fifth embodiment, the energy storage cost optimization objective function takes the operation strategies of the power generation device and the load device as input parameters, and takes the energy storage mutual assistance cost of the microgrid under this operation strategy as the objective function value; the expression formula of the energy storage cost optimization objective function is:

[0084]

[0085] In the formula, F(θ) represents the energy storage cost optimization objective function, and θ represents the operation strategies of the power generation device and the load device; represents the power that the m n th wind turbine associated with the nth energy storage device delivers to the nth energy storage device, represents the power that the r n th generator associated with the nth energy storage device delivers to the nth energy storage device; represents the power that the nth energy storage device delivers to the h n th load device associated with it; β and γ represent cost coefficients.

[0086] In the seventh embodiment of a microgrid source-network-load collaborative scheduling method proposed by the present invention, based on the sixth embodiment, step S130 includes the following steps:

[0087] Step S710: Initialize and generate an operation strategy α_0 for a set of power generation equipment and load equipment:

[0088]

[0089] Wherein, represents the power transmitted from the m-th wind turbine associated with the n-th energy storage device in the operation strategy α_0 to the n-th energy storage device; n The power transmitted to the n-th energy storage device; represents the power transmitted from the r-th generator associated with the n-th energy storage device in the operation strategy α_0 to the n-th energy storage device; n The power transmitted to the n-th energy storage device; represents the power transmitted from the n-th energy storage device to the h-th load device associated therewith in the operation strategy α_0; n The power transmitted to the h-th load device.

[0090] Step S720: Set the current iteration number of the operation strategy to t and the maximum iteration number to Max, then the t-th iteration result of the operation strategy is α_t.

[0091] Specifically, this embodiment gives a solution for optimizing and solving the established energy storage cost optimization objective function.

[0092] In the eighth embodiment of a microgrid source-network-load collaborative scheduling method proposed by the present invention, based on the seventh embodiment, after step S720, the following steps are further included:

[0093] Step S810: Calculate the penalty value of the operation strategy α_t:

[0094] G(α_t) = β1G1(α_t) + β2G2(α_t) + β3G3(α_t),

[0095]

[0096] Wherein, G(α_t) represents the penalty value of the operation strategy α_t; β1, β2, and β3 represent penalty coefficients; represents the average power transmitted from the m-th wind turbine associated with the n-th energy storage device to the n-th energy storage device; n The average power transmitted to the n-th energy storage device; represents the average power transmitted from the r-th generator associated with the n-th energy storage device to the n-th energy storage device; G1(α_t) represents the fluctuation penalty of the operation strategy α_t; n The average power transmitted to the n-th energy storage device; denotes the demand power of the nth energy storage device for the associated hnth load device; G2(α_t) represents the demand power consumption penalty of the operation strategy α_t; denotes the discarded wind energy of the mth n wind turbine associated with the nth energy storage device, denotes the wind energy generated by the mth n wind turbine associated with the nth energy storage device; G3(α_t) represents the natural energy loss penalty of the operation strategy α_t.

[0097] In the ninth embodiment of a microgrid source-network-load collaborative scheduling method proposed by the present invention, based on the eighth embodiment, in step S810, the following steps are further included:

[0098] Step S910: Iterate the operation strategy, where the iteration formula is:

[0099]

[0100] In the formula, exp(·) represents the exponential function with the natural constant as the base; denotes the gradient operator of the optimal energy storage mutual assistance cost optimization objective function, denotes the gradient of the operation strategy α_t.

[0101] Step S920: Let t = t + 1, then execute step S810 and the subsequent steps until the maximum number of iterations is reached, and use the operation strategy α_Max at this time as the energy storage collaborative scheduling strategy.

[0102] The present invention also proposes a microgrid source-network-load collaborative scheduling system, and this system applies the microgrid source-network-load collaborative scheduling method; the system includes:

[0103] A collection module, used for: collecting the operation status data of each energy storage device in the microgrid.

[0104] A processing module, used for: constructing a microgrid energy storage scheduling model according to the collected operation status data; establishing an energy storage cost optimization objective function based on the microgrid energy storage scheduling model, and optimizing and solving the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation devices and load devices; inputting the optimal operation strategies of the power generation devices and load devices into the microgrid energy storage scheduling model to obtain the output real-time operation status data.

[0105] An execution module, used for: dynamically collaborating and optimizing the energy storage collaborative scheduling strategy based on the real-time operation status data.

[0106] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0107] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A microgrid source-network-load collaborative scheduling method, characterized in that Including: Collecting the operation status data of each energy storage device in the microgrid; Constructing a microgrid energy storage scheduling model based on the collected operation status data, where the microgrid energy storage scheduling model takes the operation strategies of the power generation devices and load devices in the microgrid as input parameters and the operation status data of the energy storage devices after adopting the operation strategies as output parameters; Establishing an energy storage cost optimization objective function based on the microgrid energy storage scheduling model and optimizing and solving the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation devices and load devices; Inputting the optimal operation strategies of the power generation devices and load devices into the microgrid energy storage scheduling model to obtain the output real-time operation status data; Dynamically co-optimizing the energy storage collaborative scheduling strategy based on the real-time operation status data.

2. The microgrid source-network-load collaborative scheduling method according to claim 1, characterized in that, Also including: Establishing a new energy curtailment objective function with the goal of reducing new energy curtailment. Since the new energy curtailment objective function is affected by the randomness of wind and light, a target confidence level τ1 is given to describe the new energy curtailment objective function: where, f aw is the wind rejection amount, and f av is the photovoltaic rejection amount; is the number of wind turbines, is the number of photovoltaic units; is the wind rejection cost function of the i-th wind turbine, is the wind rejection cost function of the i-th photovoltaic unit; is the scheduled output of the i-th wind turbine at time t, is the scheduled output of the i-th photovoltaic unit at time t; due to the uncertainty of wind and photovoltaic power itself, when the actual maximum output cannot meet the scheduled output, the wind turbine and the photovoltaic unit output at the actual maximum value.

3. A microgrid source-network-load collaborative scheduling method according to claim 1, characterized in that, The dynamically co-optimizing the energy storage collaborative scheduling strategy based on the real-time operation status data includes: Obtaining the target operation status data; Calculating the residual fluctuation between the real-time operation status data and the target operation status data based on a one-dimensional residual network; Dynamically co-optimizing the energy storage collaborative scheduling strategy based on the residual fluctuation to obtain the optimal energy storage collaborative scheduling strategy, and using the optimal energy storage collaborative scheduling strategy to co-schedule the devices in the microgrid.

4. A microgrid source-network-load collaborative scheduling method according to claim 1, characterized in that The operation status data of the energy storage device is the current stored power of the energy storage device, and the representation form of the operation status data is: {X n | n ∈ [1, N]}, Wherein, X n represents the operation status data of the nth energy storage device in the microgrid, and N represents the total number of energy storage devices in the microgrid.

5. A microgrid source-network-load collaborative scheduling method according to claim 4, characterized in that, The power generation devices include generators and wind turbines. Among them, the set of power generation devices and load devices associated with the nth energy storage device is: In the formula, represents the m n th wind turbine associated with the nth energy storage device, and M n represents the total number of wind turbines associated with the nth energy storage device; represents the r n th generator associated with the nth energy storage device, and R n represents the total number of generators associated with the nth energy storage device; represents the h n th load device associated with the nth energy storage device, and H n represents the total number of load devices associated with the nth energy storage device; the wind turbines and generators associated with the energy storage device are used to supply power to the energy storage device, and the energy storage device is used to supply power to the associated load devices; The method also includes: Taking the power delivered by the power generation device to the energy storage device at a future moment as the operation strategy of the power generation device and the power required by the load device at a future moment as the operation strategy of the load device.

6. A microgrid source-network-load collaborative scheduling method according to claim 5, characterized in that, The energy storage cost optimization objective function takes the operation strategies of the power generation device and the load device as input parameters and the energy storage mutual assistance cost of the microgrid under this operation strategy as the objective function value; the expression formula of the energy storage cost optimization objective function is: In the formula, F(θ) represents the optimization objective function of energy storage cost, and θ represents the operation strategies of power generation equipment and load equipment; represents the power transmitted from the m n th wind turbine associated with the nth energy storage device to the nth energy storage device, represents the power transmitted from the r n th generator associated with the nth energy storage device to the nth energy storage device; represents the power transmitted from the nth energy storage device to the h n th load device associated therewith; β and γ represent cost coefficients.

7. A microgrid source-network-load collaborative scheduling method according to claim 6, characterized in that, The establishing an energy storage cost optimization objective function based on the microgrid energy storage scheduling model and optimizing and solving the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation devices and load devices includes: Initializing and generating a set of operation strategies α_0 of the power generation devices and load devices: In the formula, represents the power transmitted from the m n th wind turbine associated with the nth energy storage device in the operation strategy α_0 to the nth energy storage device; represents the power transmitted from the r n th generator associated with the nth energy storage device in the operation strategy α_0 to the nth energy storage device; represents the power transmitted from the nth energy storage device to the h n th load device associated therewith in the operation strategy α_0; Setting the current iteration number of the operation strategy as t and the maximum iteration number as Max, then the tth iteration result of the operation strategy is α_t.

8. A method for coordinated scheduling of a microgrid's power source, grid, and load according to claim 7, characterized in that, After setting the current iteration number of the operation strategy as t and the maximum iteration number as Max, and the tth iteration result of the operation strategy is α_t, it also includes: Calculating the penalty value of the operation strategy α_t: G(α_t) = β1G1(α_t) + β2G2(α_t) + β3G3(α_t), Wherein, G(α_t) represents the penalty value of the operation strategy α_t; β1, β2, and β3 represent penalty coefficients; represents the average power delivered by the mth n wind turbine associated with the nth energy storage device to the nth energy storage device; represents the average power delivered by the rth n generator associated with the nth energy storage device to the nth energy storage device; G1(α_t) represents the fluctuation penalty of the operation strategy α_t; represents the required power of the hth n load device associated with the nth energy storage device; G2(α_t) represents the demand power consumption penalty of the operation strategy α_t; represents the discarded wind energy of the mth n wind turbine associated with the nth energy storage device, represents the wind energy generated by the mth n wind turbine associated with the nth energy storage device; G3(α_t) represents the natural energy loss penalty of the operation strategy α_t.

9. A microgrid source-network-load collaborative scheduling method according to claim 8, characterized in that After calculating the penalty value of the operation strategy α_t, it also includes: Iterating the operation strategy, where the iteration formula is: In the formula, exp(·) represents the exponential function with the natural constant as the base; represents the gradient operator of the optimal energy storage mutual assistance cost optimization objective function, represents the gradient of the operation strategy α_t; Let \(t = t + 1\), then execute the above calculation to obtain the penalty value of the operation strategy \(\alpha_t\), and subsequent steps until the maximum number of iterations is reached. At this time, the operation strategy \(\alpha_{Max}\) is used as the energy storage collaborative scheduling strategy.

10. A microgrid source-network-load collaborative scheduling system, characterized in that, Apply the microgrid source-network-load collaborative scheduling method according to any one of claims 1-9; the system includes: A collection module, configured to: collect the operation status data of each energy storage device in the microgrid; A processing module, configured to: construct a microgrid energy storage scheduling model based on the collected operation status data; establish an energy storage cost optimization objective function based on the microgrid energy storage scheduling model, and optimize and solve the established energy storage cost optimization objective function to obtain the optimal operation strategies of the power generation device and the load device; input the optimal operation strategies of the power generation device and the load device into the microgrid energy storage scheduling model to obtain the output real-time operation status data; An execution module, configured to: perform dynamic collaborative optimization on the energy storage collaborative scheduling strategy based on the real-time operation status data.

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