A method and device for energy storage scheduling based on zoutendijk method

Abstract

The application relates to the technical field of power system optimization calculation, in particular to a kind of energy storage scheduling method and equipment based on Zoutendijk method, the method comprises the following steps: constructing the objective function and constraint condition of energy storage peak clipping and valley filling scheduling model;The load prediction value of power grid, the output prediction value of wind farm and photovoltaic power station are input into the energy storage peak clipping and valley filling scheduling model, an initial iteration point is selected and an iteration termination condition is set;The constraint condition of current iteration point is classified;Determine whether the current iteration point is a feasible region point;Calculate the feasible descent direction and determine whether the iteration termination condition is met;Calculate the maximum value and optimal search step of search step;Output optimal solution vector.The energy storage scheduling method disclosed by the application does not need to calculate and store large matrix, has fast calculation speed and is easy to realize, can guarantee to obtain optimal solution in the calculation process, and realizes the calculation of energy storage system to carry out peak clipping and valley filling optimal scheduling output.

Description

Technical Field

[0001] This invention relates to the field of power system optimization calculation technology, and in particular to an energy storage dispatching method and device based on the Zoutendijk method. Background Technology

[0002] Vigorously developing renewable energy power generation is an inevitable path. Due to the volatility and anti-peak-shaving characteristics of wind power and photovoltaics, the grid connection of large-scale renewable energy sources increases the difficulty of grid peak shaving, system dispatching costs, and operational risks.

[0003] Energy storage systems can store excess electricity during low-load periods and participate in peak shaving during peak periods, exhibiting excellent energy time-shifting characteristics. Especially for power systems with a high proportion of renewable energy, configuring a certain capacity of energy storage can, while maximizing overall economic benefits, improve the system's ability to absorb renewable energy and reduce the volatility of the net load curve and the difficulty of peak shaving for the grid, resulting in more stable output from thermal power units. Therefore, researching scheduling methods that incorporate energy storage for peak shaving and valley filling is of great significance for achieving the most economical absorption of wind and solar power.

[0004] Currently, the main methods for solving the peak-shaving and valley-filling scheduling output of energy storage are intelligent algorithms, including genetic algorithms and particle swarm optimization. However, these methods cannot guarantee convergence to the global optimum. In addition, dynamic programming is also a commonly used algorithm for solving the peak-shaving and valley-filling scheduling output of energy storage, but this type of method is prone to the curse of dimensionality. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the prior art cannot guarantee convergence to the global optimal solution.

[0006] To address the aforementioned technical problems, this invention provides an energy storage scheduling method based on the Zoutendijk method, comprising the following steps:

[0007] S1. Based on the power grid load forecast, the wind farm and photovoltaic power station output forecast, and the maximum charging and discharging power of the energy storage power station, construct the objective function and constraints of the energy storage peak shaving and valley filling scheduling model; the objective function of the energy storage peak shaving and valley filling scheduling model is the minimum variance of the net load.

[0008] S2. Input the power grid load forecast, wind farm and photovoltaic power station output forecast into the energy storage peak shaving and valley filling scheduling model, and randomly select the initial iteration point. Perform iterations, where And set the iteration termination condition. , ;

[0009] S3, Set the current iteration point The constraints are divided into valid constraints and invalid constraints, resulting in a set of valid constraints. Ineffective constraint set ;

[0010] S4. Determine the current iteration point. Is it an interior point of the feasible region? If the current iteration point is... If a point is an interior point of the feasible region, determine whether the point satisfies the iteration termination condition. If the iteration termination condition is satisfied, stop the iteration and output the current iteration point. If the optimal solution vector is not satisfied, calculate the current iteration point. Search direction Proceed to S6; if the current iteration point If a point is not an interior point of the feasible region, proceed to S5;

[0011] S5. Construct and solve the relationship between the search direction and feasible descent directions to obtain the optimal search direction. and its corresponding optimal feasible descent direction value If the optimal feasible descent direction value The termination condition is met If the iteration stops, output the current iteration point. The optimal solution vector; if the optimal feasible descent direction value Termination conditions not met Then proceed to S6;

[0012] S6. Based on the current iteration point Search direction Set of Invalid Constraints Calculate the maximum search step size, and then solve for the optimal search step size. ;make , Switch to S2;

[0013] S7. Based on the wind curtailment rate of wind farms, the solar curtailment rate of photovoltaic power plants, the charging power of energy storage system during the charging period, and the discharge power of energy storage system during the discharging period in the optimal solution vector output by the energy storage peak shaving and valley filling scheduling model, control the energy storage system to participate in the grid peak shaving and valley filling scheduling.

[0014] In one embodiment of the present invention, the objective function of the energy storage peak shaving and valley filling scheduling model is the minimum variance of the net load, expressed as:

[0015]

[0016] in, For net load variance, For the total scheduling cycle, for Net load for the time period This represents the average net load over the entire scheduling cycle.

[0017] In one embodiment of the present invention, the Net load for the time period for:

[0018]

[0019] The average net load over the entire scheduling period for:

[0020]

[0021] in, for The initial load of the power grid during the time period, for The output power of the wind farm during the period for The wind curtailment rate of wind farms during certain periods has ; for The output power of the photovoltaic power station during the period for The curtailment rate of solar power plants during certain periods has ; for The charging power of the time-of-use energy storage system The charging efficiency of the energy storage system; for Discharge power of the time-limited energy storage system This refers to the discharge efficiency of the energy storage system.

[0022] In one embodiment of the present invention, the constraints of the energy storage peak shaving and valley filling scheduling model include:

[0023] Output constraints for wind farms and solar power plants:

[0024]

[0025]

[0026] in, for The maximum output of the wind farm during the specified time period. for The maximum output of the photovoltaic power station during the specified time period;

[0027] Wind curtailment rate of wind farms and solar curtailment rate of photovoltaic power plants:

[0028]

[0029]

[0030] in, This represents the maximum allowable wind curtailment rate for a wind farm. This represents the maximum allowable curtailment rate for photovoltaic power plants.

[0031] Energy storage system charging and discharging power constraints:

[0032]

[0033]

[0034] in, This represents the maximum charging power of the energy storage system. This represents the maximum discharge power of the energy storage system.

[0035] Since the energy storage system cannot charge and discharge simultaneously, it also needs to meet certain requirements. ;

[0036] Energy storage system charge ratio constraints:

[0037]

[0038]

[0039] in, for The charge ratio of the time-of-use energy storage system for The remaining power of the time-limited energy storage system This represents the maximum amount of electricity that the energy storage system can store. This represents the lower limit of the charge ratio for energy storage systems. This represents the upper limit of the charge ratio of the energy storage system.

[0040] In one embodiment of the present invention, in S3, the current iteration point Valid and invalid constraints are determined by substituting the current iteration point into the constraint conditions. If the constraint condition achieves an equality, it is a valid constraint; otherwise, it is an invalid constraint. The expression is:

[0041]

[0042]

[0043] in, For a valid set of constraints, For the specific set of values ​​of the effective constraints, This is a set of ineffective constraints. This is the set of specific values ​​for ineffective constraints.

[0044] In one embodiment of the present invention, in S4, if the current iteration point If a point is an interior point of the feasible region, then find the objective function. At the current iteration point gradient at If the iteration termination condition is met. If the iteration stops, output the current iteration point. The optimal solution vector is given; if the iteration termination condition is not met... Then calculate the current iteration point. Search direction Then, move to S6.

[0045] In one embodiment of the present invention, in step S5, the relationship between the search direction and the feasible descent direction is constructed and solved to obtain the optimal search direction. and its corresponding optimal feasible descent direction value ,include:

[0046] The relationship between the search direction and the feasible descent direction is:

[0047]

[0048] The relationship between the search direction and feasible descent direction is solved using linear programming algorithms such as the simplex method to obtain the optimal search direction. The optimal solution and its corresponding optimal feasible descent direction value .

[0049] In one embodiment of the present invention, in S6, according to the current iteration point Search direction, set of ineffective constraints Calculate the maximum value of the search step size, including:

[0050] Based on the current iteration point Search direction, set of ineffective constraints Construct about search step size System of equations:

[0051]

[0052] Solving the system of equations yields the maximum value of the search step size. for:

[0053] .

[0054] In one embodiment of the invention, the maximum value of the search step size is used. Calculate the optimal search step size include:

[0055] Based on the current iteration point The maximum value of the search direction and search step size Construct a system of equations:

[0056]

[0057] Solving the system of equations yields the optimal search step size. .

[0058] This invention also provides an energy storage scheduling device based on the Zoutendijk method, comprising:

[0059] Memory, used to store computer programs;

[0060] A processor is used to implement the steps of the above-described energy storage scheduling method based on the Zoutendijk method when executing the computer program.

[0061] The technical solution of the present invention has the following advantages compared with the prior art:

[0062] The energy storage scheduling method described in this invention constructs an energy storage peak-shaving and valley-filling scheduling model and uses the Zoutendijk algorithm to optimize the energy storage peak-shaving and valley-filling scheduling output. The search direction is determined by calculating the gradient of the objective function of the energy storage peak-shaving and valley-filling scheduling model at the iteration point, and the optimal solution is gradually approximated. This yields the wind curtailment rate of the electric field, the solar curtailment rate of the photovoltaic power station, the charging power during the charging period of the energy storage system, and the discharging power during the discharging period of the energy storage system. This energy storage scheduling method based on the Zoutendijk method does not require the calculation and storage of large matrices, has a fast calculation speed, and is easy to implement. It guarantees the finding of the optimal solution during the calculation process, realizing the calculation of the optimal scheduling output of the energy storage system for peak-shaving and valley-filling. Attached Figure Description

[0063] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein...

[0064] Figure 1 This is a flowchart of an energy storage scheduling method based on the Zoutendijk method according to the present invention;

[0065] Figure 2 These are wind power forecast output, photovoltaic forecast output, and typical daily load curves provided in this embodiment of the invention.

[0066] Figure 3 This is a comparison chart of the net load curve before optimization and the net load curve after optimization using the method of this invention, provided in an embodiment of the invention.

[0067] Figure 4 This is a diagram showing the charging and discharging power of the energy storage system at various times after optimization using the energy storage scheduling method of the present invention, provided in an embodiment of the present invention. Detailed Implementation

[0068] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0069] Example 1

[0070] Reference Figure 1 As shown, this invention provides an energy storage scheduling method based on the Zoutendijk method, the steps of which include:

[0071] S1. Based on the power grid load forecast, the wind farm and photovoltaic power station output forecast, and the maximum charging and discharging power of the energy storage power station, construct the objective function and constraints of the energy storage peak shaving and valley filling scheduling model.

[0072] The objective function of the energy storage peak shaving and valley filling scheduling model is to minimize the variance of the net load.

[0073]

[0074] in, For net load variance, For the total scheduling cycle, for Net load for the time period This represents the average net load over the entire scheduling cycle.

[0075] The Net load for the time period The expression is:

[0076]

[0077] The average net load over the entire scheduling period The expression is:

[0078]

[0079] in, for The initial load of the power grid during the time period, for The output power of the wind farm during the period for The wind curtailment rate of wind farms during certain periods has ; for The output power of the photovoltaic power station during the period for The curtailment rate of solar power plants during certain periods has ; for The charging power of the time-of-use energy storage system The charging efficiency of the energy storage system; for Discharge power of the time-limited energy storage system This refers to the discharge efficiency of the energy storage system.

[0080] The constraints of the energy storage peak shaving and valley filling scheduling model include:

[0081] Output constraints for wind farms and solar power plants:

[0082]

[0083]

[0084] in, for The maximum output of the wind farm during the specified time period. for The maximum output of the photovoltaic power station during the specified time period;

[0085] Wind curtailment rate of wind farms and solar curtailment rate of photovoltaic power plants:

[0086]

[0087]

[0088] in, This represents the maximum allowable wind curtailment rate for a wind farm. This represents the maximum allowable curtailment rate for photovoltaic power plants.

[0089] Energy storage system charging and discharging power constraints:

[0090]

[0091]

[0092] in, This represents the maximum charging power of the energy storage system. This represents the maximum discharge power of the energy storage system.

[0093] Since the energy storage system cannot charge and discharge simultaneously, it also needs to meet certain requirements. ;

[0094] Energy storage system charge ratio constraints:

[0095]

[0096]

[0097] in, for The charge ratio of the time-of-use energy storage system for The remaining power of the time-limited energy storage system This represents the maximum amount of electricity that the energy storage system can store. This represents the lower limit of the charge ratio for energy storage systems. This represents the upper limit of the charge ratio of the energy storage system.

[0098] S2. Input the power grid load forecast, wind farm and photovoltaic power station output forecast into the energy storage peak shaving and valley filling scheduling model, and randomly select the initial iteration point. Perform iterations, where The iteration termination condition is set according to the solution accuracy. , .

[0099] S3, Set the current iteration point The constraints are divided into valid constraints and invalid constraints, resulting in a set of valid constraints. Ineffective constraint set .

[0100] Current iteration point Valid and invalid constraints are determined by substituting the current iteration point into the constraint conditions. If the constraint condition achieves an equality, it is a valid constraint; otherwise, it is an invalid constraint. The expression is:

[0101]

[0102]

[0103] in, For a valid set of constraints, For the specific set of values ​​of the effective constraints, This is a set of ineffective constraints. This is the set of specific values ​​for ineffective constraints.

[0104] S4. Determine the current iteration point. Whether a point is an interior point of the feasible region is used to determine the current iteration point. Does it meet all the constraints of the energy storage peak shaving and valley filling scheduling model?

[0105] If the current iteration point If a point is an interior point of the feasible region, then find the objective function. At the current iteration point gradient at If the iteration termination condition is met. If the iteration stops, output the current iteration point. The optimal solution vector is given; if the iteration termination condition is not met... Then calculate the current iteration point. Search direction Then, move to S6.

[0106] If the current iteration point If a point is not an interior point of the feasible region, proceed to S5.

[0107] S5. The relationship between the search direction and feasible descent directions is as follows:

[0108]

[0109] The relationship between the search direction and feasible descent direction is solved using linear programming algorithms such as the simplex method to obtain the optimal search direction. The optimal solution and its corresponding optimal feasible descent direction value .

[0110] If the optimal feasible descent direction value The termination condition is met If the iteration stops, output the current iteration point. The optimal solution vector; if the optimal feasible descent direction value Termination conditions not met Then proceed to S6.

[0111] S6. Based on the current iteration point Search direction Set of Invalid Constraints Construct about search step size System of equations:

[0112]

[0113] Solving the system of equations yields the maximum value of the search step size. for:

[0114]

[0115] Based on the current iteration point The maximum value of the search direction and search step size Construct a system of equations:

[0116]

[0117] Solving the system of equations yields the optimal search step size. .

[0118] make , Switch to S2;

[0119] S7. Based on the wind curtailment rate of wind farms, the solar curtailment rate of photovoltaic power plants, the charging power of energy storage system during the charging period, and the discharge power of energy storage system during the discharging period in the optimal solution vector output by the energy storage peak shaving and valley filling scheduling model, control the energy storage system to participate in the grid peak shaving and valley filling scheduling.

[0120] The energy storage scheduling method described in this invention constructs an energy storage peak-shaving and valley-filling scheduling model and uses the Zoutendijk algorithm to optimize the energy storage peak-shaving and valley-filling scheduling output. The search direction is determined by calculating the gradient of the objective function of the energy storage peak-shaving and valley-filling scheduling model at iteration points, and the optimal solution is gradually approximated. This yields the wind curtailment rate of the electric field, the solar curtailment rate of the photovoltaic power station, the charging power during the charging period of the energy storage system, and the discharging power during the discharging period of the energy storage system. The energy storage scheduling method based on the Zoutendijk method described in this invention does not require the calculation and storage of large matrices, has a fast calculation speed, and is easy to implement. It guarantees the finding of the optimal solution during the calculation process, realizing the calculation of the optimal scheduling output of the energy storage system for peak-shaving and valley-filling.

[0121] Example 2

[0122] In this embodiment, a wind-solar-thermal-storage energy base is used as a case study to input the above-mentioned energy storage peak shaving and valley filling scheduling model. Its peak load is 1442MW, the total installed capacity of the photovoltaic power station is 150MW, the rated capacity of the wind farm is 400MW, the maximum energy storage capacity is 400MWh, the maximum charge and discharge power is 100MW, and the upper limit of the charge ratio is 0.9 and the lower limit is 0.1. Figure 2 This includes forecasted wind power output, forecasted photovoltaic power output, and typical daily load curves. Figure 3 The graph shows a comparison of the net load curves before and after optimization using the Zoutendijk-based energy storage scheduling method. Figure 4 The above-mentioned energy storage scheduling method based on the Zoutendijk method is used to optimize the energy storage system, and the charging and discharging power diagrams at various times are shown.

[0123] from Figure 3 It can be seen that the energy storage dispatch method based on the Zoutendijk method effectively reduces the peak load of the power system by storing electrical energy during peak load periods. Simultaneously, during off-peak periods, the energy stored in the energy storage system is released to supplement the system load, achieving load balance and thus alleviating the pressure on the power system and improving supply-demand balance efficiency.

[0124] Example 3

[0125] This embodiment provides an energy storage scheduling device based on the Zoutendijk method, including:

[0126] Memory, used to store computer programs;

[0127] A processor is used to implement the steps of the above-described energy storage scheduling method based on the Zoutendijk method when executing the computer program.

[0128] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0132] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An energy storage scheduling method based on the Zoutendijk method, characterized in that the steps are as follows: include: S1. Based on the power grid load forecast, the wind farm and photovoltaic power station output forecast, and the maximum charging and discharging power of the energy storage power station, construct the objective function and constraints of the energy storage peak shaving and valley filling scheduling model; the objective function of the energy storage peak shaving and valley filling scheduling model is the minimum variance of the net load. S2. Input the power grid load forecast, wind farm and photovoltaic power station output forecast into the energy storage peak shaving and valley filling scheduling model, and randomly select the initial iteration point. Perform iterations, where And set the iteration termination condition. , ; S3, Set the current iteration point The constraints are divided into valid constraints and invalid constraints, resulting in a set of valid constraints. Ineffective constraint set ; S4. Determine the current iteration point. Is it an interior point of the feasible region? If the current iteration point is... If a point is an interior point of the feasible region, determine whether the point satisfies the iteration termination condition. If the iteration termination condition is satisfied, stop the iteration and output the current iteration point. If the optimal solution vector is not satisfied, calculate the current iteration point. Search direction Proceed to S6; if the current iteration point If a point is not an interior point of the feasible region, proceed to S5; S5. Construct and solve the relationship between the search direction and feasible descent directions to obtain the optimal search direction. and its corresponding optimal feasible descent direction value If the optimal feasible descent direction value The termination condition is met If the iteration stops, output the current iteration point. The optimal solution vector; if the optimal feasible descent direction value Termination conditions not met Then proceed to S6; S6. Based on the current iteration point Search direction Set of Invalid Constraints Calculate the maximum search step size, and then solve for the optimal search step size. ;make , Switch to S2; S7. Based on the wind curtailment rate of wind farms, the solar curtailment rate of photovoltaic power plants, the charging power of energy storage system during the charging period, and the discharge power of energy storage system during the discharging period in the optimal solution vector output by the energy storage peak shaving and valley filling scheduling model, control the energy storage system to participate in the grid peak shaving and valley filling scheduling.

2. The energy storage scheduling method based on the Zoutendijk method according to claim 1, characterized in that, The objective function of the energy storage peak shaving and valley filling scheduling model is the minimum variance of the net load, expressed as: ； in, For net load variance, For the total scheduling cycle, for Net load for the time period This represents the average net load over the entire scheduling cycle.

3. The energy storage scheduling method based on the Zoutendijk method according to claim 2, characterized in that, The Net load for the time period for: ； The average net load over the entire scheduling period for: ； in, for The initial load of the power grid during the time period, for The output power of the wind farm during the period for The wind curtailment rate of wind farms during certain periods has ; for The output power of the photovoltaic power station during the period for The curtailment rate of solar power plants during certain periods has ; for The charging power of the time-of-use energy storage system The charging efficiency of the energy storage system; for Discharge power of the time-limited energy storage system This refers to the discharge efficiency of the energy storage system.

4. The energy storage scheduling method based on the Zoutendijk method according to claim 3, characterized in that, The constraints of the energy storage peak shaving and valley filling scheduling model include: Output constraints for wind farms and solar power plants: ； ； in, for The maximum output of the wind farm during the specified time period. for The maximum output of the photovoltaic power station during the specified time period; Wind curtailment rate of wind farms and solar curtailment rate of photovoltaic power plants: ； ； in, This represents the maximum allowable wind curtailment rate for a wind farm. This represents the maximum allowable curtailment rate for photovoltaic power plants. Energy storage system charging and discharging power constraints: ； ； in, This represents the maximum charging power of the energy storage system. This represents the maximum discharge power of the energy storage system. Since the energy storage system cannot charge and discharge simultaneously, it also needs to meet certain requirements. ; Energy storage system charge ratio constraints: ； ； in, for The charge ratio of the time-of-use energy storage system for The remaining power of the time-limited energy storage system This represents the maximum amount of electricity that the energy storage system can store. This represents the lower limit of the charge ratio for energy storage systems. This represents the upper limit of the charge ratio of the energy storage system.

5. The energy storage scheduling method based on the Zoutendijk method according to claim 4, characterized in that, In S3, the current iteration point Valid and invalid constraints are determined by substituting the current iteration point into the constraint conditions. If the constraint condition achieves an equality, it is a valid constraint; otherwise, it is an invalid constraint. The expression is: ； ； in, For a valid set of constraints, For the specific set of values ​​of the effective constraints, This is a set of ineffective constraints. This is the set of specific values ​​for ineffective constraints.

6. The energy storage scheduling method based on the Zoutendijk method according to claim 5, characterized in that, In S4, if the current iteration point If a point is an interior point of the feasible region, then find the objective function. At the current iteration point gradient at If the iteration termination condition is met. If the iteration stops, output the current iteration point. The optimal solution vector is given; if the iteration termination condition is not met... Then calculate the current iteration point. Search direction Then, move to S6.

7. The energy storage scheduling method based on the Zoutendijk method according to claim 6, characterized in that, In step S5, the relationship between the search direction and feasible descent directions is constructed and solved to obtain the optimal search direction. and its corresponding optimal feasible descent direction value ,include: The relationship between the search direction and the feasible descent direction is: ； The relationship between the search direction and feasible descent direction is solved using linear programming algorithms such as the simplex method to obtain the optimal search direction. The optimal solution and its corresponding optimal feasible descent direction value .

8. The energy storage scheduling method based on the Zoutendijk method according to claim 7, characterized in that, In S6, based on the current iteration point Search direction, set of ineffective constraints Calculate the maximum value of the search step size, including: Based on the current iteration point Search direction, set of ineffective constraints Construct about search step size System of equations: ； Solving the system of equations yields the maximum value of the search step size. for: 。 9. The energy storage scheduling method based on the Zoutendijk method according to claim 8, characterized in that, Use the maximum value of the search step size Calculate the optimal search step size include: Based on the current iteration point The maximum value of the search direction and search step size Construct a system of equations: ； Solving the system of equations yields the optimal search step size. .

10. An energy storage scheduling device based on the Zoutendijk method, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the energy storage scheduling method based on the Zoutendijk method as described in any one of claims 1 to 9 when executing the computer program.

Images