Energy storage peak load shifting optimization scheduling method based on sensitivity analysis

Abstract

The invention discloses an energy storage peak load shifting optimization scheduling method based on sensitivity analysis, and belongs to the technical field of energy storage optimization scheduling, and the method comprises the steps: 1, obtaining the power load demand, the carbon emission coefficient of a power grid g, the power of an energy storage system s, the capacity of the energy storage system s, and the maximum demand; 2, constructing an energy storage peak load shifting model of the criterion of maximum contribution of daily net carbon emission reduction; and 3, solving the energy storage peak load shifting model to obtain an optimal charging and discharging scheme of the energy storage system. According to the method, the optimal energy storage charging and discharging strategy can be obtained, so that the net carbon emission reduction contribution of the energy storage system can be maximized, and an effective solution is provided for solving the problem of energy storage peak load shifting large-scale optimization engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization, and particularly to an optimized scheduling method for energy storage peak shaving and valley filling based on a sensitivity analysis algorithm. Background Art

[0002] Energy storage systems play a crucial role in power systems. They can effectively balance the supply and demand relationship and improve the efficiency and stability of power systems. Energy storage systems charge during the low-carbon emission period of the power grid and discharge during the high-carbon emission period of the power grid, and utilize the difference in the carbon emission coefficients of the power grid to achieve peak shaving and valley filling, thereby achieving a net carbon emission reduction contribution.

[0003] The problem of peak shaving and valley filling optimization is to formulate an optimal charge-discharge strategy for 24 hours, that is, to determine the charging and discharging power of the energy storage system in each time period, with the goal of maximizing the net carbon emission reduction contribution, based on the daily carbon emission coefficient curve and load prediction curve of the power grid. Currently, the widely adopted solutions are the constant power strategy or the variable power strategy. The constant power or variable power strategy has a simple idea, but in the face of frequent changes in the carbon emission coefficients of the power grid, the complexity of determining the energy storage charge-discharge time periods increases rapidly, it is impossible to reasonably determine the energy storage power, and there may be problems of power waste or insufficient demand, reducing the net carbon emission reduction benefit. In addition, the constant power or variable power strategy cannot handle non-continuous complex constraint conditions, and the search space for the peak shaving and valley filling problem is often very large. Since the charging and discharging power in each time period changes continuously, the value ranges of multiple variables need to be considered simultaneously, which leads to a high-dimensional search space. Traditional algorithms face the dual computational pressures of high-dimensional search and mixed constraints (such as SOC limitations and charge-discharge times), resulting in often being simplified to a sub-optimal single-objective model in engineering, sacrificing the carbon emission reduction potential. Summary of the Invention

[0004] The present invention is proposed to solve the above-mentioned deficiencies in the prior art, and provides an optimized scheduling method for energy storage peak shaving and valley filling based on sensitivity analysis, in order to obtain the optimal energy storage charge-discharge strategy, thereby maximizing the net carbon emission reduction contribution of the energy storage system, and providing an effective solution for solving the large-scale optimization engineering problem of energy storage peak shaving and valley filling.

[0005] The present invention realizes the invention purpose through the following technical solutions.

[0006] The characteristics of an optimized scheduling method for energy storage peak shaving and valley filling based on sensitivity analysis according to the present invention include the following steps:

[0007] Step 1, obtain the power load demand, the carbon emission coefficient of the power grid, the power of the energy storage system, the capacity of the energy storage system, and the maximum demand;

[0008] Step 2: Based on the data in Step 1, construct an energy storage peak shaving and valley filling model;

[0009] Step 3: Solve the energy storage peak shaving and valley filling model to obtain the optimal charge and discharge scheme of the energy storage system.

[0010] The characteristics of an optimal scheduling method for energy storage peak shaving and valley filling based on sensitivity analysis described in the present invention also lie in that in Step 2, the objective function of the energy storage peak shaving and valley filling model is constructed using Equation (1):

[0011] (1)

[0012] In Equation (1), is the net carbon emission reduction contribution of the energy storage over the total time period ; represents any time period; is the carbon emission coefficient of the power grid at time period ; is the charging amount of the energy storage system at time period ; is the discharging amount of the energy storage system at time period ; is the implicit carbon emission coefficient per unit discharging amount of the energy storage system ;

[0013] The constraint conditions of the energy storage peak shaving and valley filling model are constructed using Equations (2) - (10):

[0014] (2)

[0015] (3)

[0016] (4)

[0017] (5)

[0018] (6)

[0019] (7) (8)

[0020] (9)

[0021] (10)

[0022] In Equations (2) - (10), is the energy storage system Rated power; is the total period under the sampling interval, is the total period under the total number of samples; 、 are the energy storage system the minimum and maximum values of the remaining battery level; is during the energy storage system the remaining battery level; is during the energy storage system the remaining battery level, 、 are the energy storage system charge and discharge conversion coefficient; is the initial period of the energy storage system the remaining battery level, is the end period of the energy storage system the remaining battery level; is the power grid at during the power supply, is the user at during the power consumption; 、 are the power grid at during the minimum and maximum power supply, is the user the maximum demand.

[0023] Further, step 3 includes:

[0024] Step 3.1, define the number of cycles , initialize ; Define the remaining battery level vector of the energy storage system at the th cycle , define the upper bound vector and the lower bound vector , where represents the remaining battery level of the energy storage system at the th cycle under the period of the energy storage system are respectively the upper and lower bounds; And initialize ;

[0025] Define the remaining battery level vector of the energy storage system s at the th cycle Length of the continuous subsequence , initialize ;

[0026] Step 3.2, calculate and , and construct using the lower bound vector ;

[0027] Step 3.3, calculate the adjustment interval of the continuous subsequence of ;

[0028] Step 3.4, based on the adjustment interval, calculate the left deviation vector for and the right deviation vector ; ;

[0029] Step 3.5, based on and , formulate the adjustment strategy for the minimum index and the maximum index ;

[0030] Step 3.6, based on and , adjust to obtain the remaining power vector of the energy storage system s in the +1-th loop;

[0031] Step 3.7, if , let ;

[0032] If , assign to ;

[0033] Step 3.8, after assigning to , if , end the iteration and execute Step 3.9; otherwise, return to Step 3.3 and execute sequentially;

[0034] Step 3.9, according to , calculate the optimal charge-discharge plan, including: the optimal charging amount and the optimal discharging amount for each period; where represents the optimal charging amount at time t, represents the optimal discharging amount at time t;

[0035] Step 3.10, calculate the optimal net carbon emission reduction contribution using Equation (15) :

[0036] (15).

[0037] Furthermore, step 3.2 includes:

[0038] Step 3.2.1, calculate the maximum number of consecutive charging periods ;

[0039] When and , update the upper bound of the remaining power of the period energy storage system in the forward direction of time ; where the upper bound of, represents the remaining power of the th cycle of the period energy storage system ;

[0040] Calculate the maximum number of consecutive discharging periods ;

[0041] When and , update the upper bound of the remaining power of the period energy storage system in the reverse direction of time ; where, the upper bound of, represents the remaining power of the th cycle of the period energy storage system ;

[0042] When and , update the upper bound of the remaining power of the period energy storage system in the forward direction of time ;

[0043] Step 3.2.2, for and , update the lower bound of in the reverse direction of time :

[0044] If , then let ;

[0045] If , then let ; where, Lower bound;

[0046] Step 3.2.3, when and , let .

[0047] Furthermore, the said Step 3.3 includes:

[0048] For the starting index of the continuous subsequence, determine the index set of the continuous subsequence with the starting index ;

[0049] If , then the left end of the continuous subsequence with the starting index increases by the adjustment amount , and the left end of the continuous subsequence with the starting index decreases by the adjustment amount ; where represents the power consumption of user l during the period; represents the remaining power of the energy storage system in the period under the th cycle; represents the remaining power of the energy storage system in the period under the th cycle, represents the upper bound of the discharge amount of the energy storage system during the period, and

[0050] If , then the increasing adjustment amount of the left end of the continuous subsequence with the starting index , and the decreasing adjustment amount of the left end of the continuous subsequence with the starting index ; where represents the upper bound of the charging amount of the energy storage system during the period, and

[0051] If , then the right end of the continuous subsequence with the starting index increases by the adjustment amount , and the right end of the continuous subsequence with the starting index decreases by the adjustment amount ; where represents the power consumption of user l during the period; Indicates the remaining power of the time - period energy storage system in the Indicates the remaining power of the time - period energy storage system in the Indicates the upper bound of the discharge amount of the time - period energy storage system, and

[0052] If , then the right end of the continuous subsequence with the starting index of is increased by the adjustment amount , and the right end of the continuous subsequence with the starting index of is decreased by the adjustment amount ; where Indicates the upper bound of the charge amount of the time - period energy storage system, and

[0053] Determine the maximum index of the continuous subsequence with the starting index of , and the minimum index of the continuous subsequence with the starting index of ;

[0054] The adjustment interval of the continuous subsequence with the starting index of is ; where Indicates the remaining power of the time - period energy storage system in the Indicates the remaining power of the time - period energy storage system in the Indicates the lower bound of Indicates the upper bound of

[0055] Furthermore, step 3.4 includes:

[0056] Step 3.4.1, set the left - biased vector in the th cycle as ; where Indicates the left - biased vector in the th cycle for The left partial derivative, denotes the subsequence under the index set;

[0057] Let the right partial derivative vector at the th iteration be ; where denotes the th iteration of the right partial derivative of with respect to

[0058] Initialize ;

[0059] Step 3.4.2, According to , if , then let , otherwise, obtain using Equation (11):

[0060] (11)

[0061] In Equation (11), denotes and;

[0062] Step 3.4.3, According to , if , then let , otherwise, obtain using Equation (12):

[0063] (12)

[0064] Step 3.4.4, After assigning to , return to Step 3.4.2 and execute sequentially until is reached, thereby obtaining the left partial derivative vector of and the right partial derivative vector .

[0065] Furthermore, the said Step 3.5 includes:

[0066] When , calculate the minimum index to be adjusted, calculate the maximum index to be adjusted;

[0067] When , calculate the maximum index to be adjusted, calculate the minimum index to be adjusted;

[0068] When calculate the minimum index to be adjusted and calculate the maximum index to be adjusted ;

[0069] When calculate the minimum index to be adjusted and calculate the maximum index to be adjusted .

[0070] Furthermore, the step 3.6 includes:

[0071] If , the increased adjustment amount is obtained from Equation (13) as , so as to obtain ;

[0072] (13)

[0073] In Equation (13), represents the remaining power of the energy storage system during the th cycle , represents the remaining power of the energy storage system during the th cycle , represents the remaining power of the energy storage system during the th cycle , represents the remaining power of the energy storage system during the th cycle ;

[0074] If , the decreased adjustment amount is obtained from Equation (14) as , so as to obtain ;

[0075] (14)

[0076] In Equation (14), represents the remaining power of the energy storage system during the th cycle , represents the remaining power of the energy storage system during the th cycle , represents the remaining power of the energy storage system during the th cycle remaining power indicating the remaining power of the energy storage system during the period of the n-th cycle;

[0077] If , let .

[0078] Furthermore, step 3.9 includes:

[0079] For , if , then let the optimal charging amount in the t period be , and let the optimal discharging amount in the t period be ;

[0080] If , then let , and let ;

[0081] If , then let , and let .

[0082] An electronic device according to the present invention includes a memory and a processor, characterized in that the memory is used to store a program for supporting the processor to execute the energy storage peak shaving and valley filling optimization scheduling method, and the processor is configured to execute the program stored in the memory.

[0083] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0084] 1. The present invention establishes a mathematical model for the energy storage peak shaving and valley filling optimization problem. Different from the traditional peak shaving and valley filling optimization problem, this model also adds a maximum demand constraint, has a wider application scenario, and designs a solution method based on the sensitivity analysis method to quickly output the best energy storage charge and discharge scheme;

[0085] 2. The sensitivity analysis method designed by the present invention finds the optimization direction and adjusts and optimizes the current feasible solution to the greatest extent. Such a method design improves the optimization efficiency and cleverly avoids the influence of difficult-to-solve non-linear constraints. This method is an exact optimization method. Compared with the constant power and variable power strategies, the sensitivity analysis method can consider the constraint conditions more comprehensively, avoid sacrificing the carbon emission reduction potential, and at the same time solve the problems of power waste or insufficient demand. Compared with the heuristic method, facing the double computational pressure of high-dimensional search and mixed constraints, the sensitivity analysis method has a faster optimization speed and solves the problem that it is difficult for the heuristic method to obtain the optimal charge and discharge strategy. Description of the Drawings

[0086] Figure 1 is the overall flowchart of the present invention;

[0087] Figure 2 is an example diagram for determining the adjustment range of continuous subsequences of the present invention;

[0088] Figure 3 is the specific flowchart of the sensitivity analysis algorithm of the present invention. Specific Embodiments

[0089] In this embodiment, as Figure 1 shown, a method for peak shaving and valley filling of overseas energy storage based on a sensitivity analysis algorithm comprehensively considers power load demand, time-of-use power grid carbon emission coefficient, energy storage system power, energy storage system capacity, and maximum demand. By establishing a mathematical model and applying a method based on sensitivity analysis for solution, the optimal daily charge and discharge plan of the energy storage system is obtained. Specifically, the method includes the following steps:

[0090] Step 1: Obtain the power load demand, carbon emission coefficient of the power grid , power of the energy storage system , capacity of the energy storage system , and maximum demand;

[0091] Step 2: Based on the data in Step 1, construct an energy storage peak shaving and valley filling model;

[0092] Step 2.1: Use Equation (1) to construct the objective function of the energy storage peak shaving and valley filling model:

[0093] (1)

[0094] In Equation (1), is the net carbon emission reduction contribution of the energy storage over the total time period ; represents any time period; is the carbon emission coefficient of the power grid in the time period, is the charging amount of the energy storage system in the time period; is the discharging amount of the energy storage system in the time period, is the implicit carbon emission coefficient per unit discharging amount of the energy storage system ;

[0095] Step 2.2: Use Equations (2)-(10) to construct the constraint conditions of the energy storage peak shaving and valley filling model:

[0096] (2)

[0097] (3)

[0098] (4)

[0099] (5)

[0100] (6)

[0101] (7) (8)

[0102] (9)

[0103] (10)

[0104] In formulas (2) to (10), is the rated power of the energy storage system ; is the sampling interval under the total time period , is the total number of samplings under the total time period ; , are respectively the minimum and maximum values of the remaining power of the energy storage system ; is the remaining power of the energy storage system during the time period ; is the remaining power of the energy storage system during the time period , , are the charge and discharge conversion coefficients of the energy storage system ; is the remaining power of the energy storage system at the initial time period , is the remaining power of the energy storage system at the end time period ; is the power supply of the power grid during the time period, is the power consumption of the user during the time period; , are the minimum and maximum power supplies of the power grid during the time period, is the maximum demand of the user .

[0105] Step 3. Solve the energy storage peak shaving and valley filling model to obtain the optimal charge and discharge scheme of the energy storage system. As Figure 3 shown, the specific process of the peak shaving and valley filling problem solved by the sensitivity analysis algorithm of the present invention is given in the form of pseudo code. Specifically, traditional methods usually adopt predefined step sizes or step size selection that only relies on gradient information, while this method constructs a gradient vector through sensitivity analysis and adaptively determines the step size using the backtracking line search technique, which not only avoids iterative oscillations but also achieves the maximum optimization of feasible solutions. This step size adjustment strategy based on sensitivity drive enables the algorithm to monotonically approach the optimal solution during the parameter adjustment process, reflecting a substantial innovation over traditional step size control methods.

[0106] Step 3.1. Define the number of loops , and initialize ; Define the remaining power vector of the energy storage system at the -th loop, define the upper bound vector and the lower bound vector , where represents the remaining power of the energy storage system at the time period, and are the upper and lower bounds of respectively; and initialize ;

[0107] Define the length of the continuous subsequence of the remaining power vector of the energy storage system s at the -th loop, and initialize ;

[0108] Step 3.2. Calculate and , and construct with the lower bound vector ;

[0109] Step 3.2.1. Calculate the maximum number of consecutive charging time periods ;

[0110] When and , update the upper bound of the remaining power of the energy storage system at the time period in the forward direction of time;

[0111] Calculate the maximum number of consecutive discharging time periods ;

[0112] When and when, update in reverse time the remaining electricity of the time period energy storage system the upper bound of ; ;

[0113] Step 3.2.2. For and when, update in reverse time the lower bound of :

[0114] If , then let ;

[0115] If , then let ;

[0116] Step 3.2.3. When and when, let ;

[0117] Step 3.3. Calculate the adjustment interval of the continuous subsequence of

[0118] For the starting index of the continuous subsequence, determine the index set of the continuous subsequence with the starting index being ; In this embodiment, the adjustment interval determination process of the continuous subsequence process of Figure 2 is as shown in . In the figure, the horizontal axis represents the time series, and the vertical axis represents the magnitude of the energy storage system's electricity. The three solid lines (labeled ) respectively correspond to the upper limits of the energy storage system's electricity in different time periods, and the three dashed lines (labeled ) correspond to the lower limits of the remaining energy storage. Determining the adjustment interval of the continuous subsequence is essentially: The overall adjustment interval of the continuous subsequence of

[0119] If , then the left end of the continuous subsequence with the starting index increases by the adjustment amount , and the left end of the continuous subsequence with the starting index decreases by the adjustment amount ; Among them, represents the electricity consumption of user l in the time period; represents the th cycle Time storage system The remaining power; Indicates Next cycle Time storage system The remaining power, express Time storage system The upper limit of the discharge capacity, and ;

[0120] if , then the starting index is The amount of increase in the left end of the continuous subsequence , the starting index is The left end of the consecutive subsequence is reduced by the adjustment amount ;in, express Time storage system The upper limit of the charge capacity, and ;

[0121] if , then the starting point index is The right end of the continuous subsequence increases by the adjustment amount , the starting index is The right end of the consecutive subsequence is reduced by the adjustment amount ;in, Indicates that user l is Electricity consumption during the time period; Indicates Next cycle Time storage system The remaining power; Indicates Next cycle Time storage system The remaining power; express Time storage system The upper limit of the discharge capacity, and ;

[0122] if , then the starting index is The right end of the continuous subsequence increases by the adjustment amount , the starting index is The right end of the consecutive subsequence is reduced by the adjustment amount ;in, express Time storage system The upper limit of the charge capacity, and ;

[0123] Determine the maximum index of the continuous subsequence with the starting index being , and the minimum index of the continuous subsequence with the starting index being ; ; ;

[0124] The adjustment interval of the continuous subsequence with the starting index being is ; where, ; represents the remaining power of the energy storage system at the time period in the th cycle, represents the remaining power of the energy storage system at the time period in the th cycle, represents the lower bound of , represents the upper bound of ;

[0125] Step 3.4, calculate the left and right partial derivatives of ;

[0126] Step 3.4.1, assume that the left partial derivative vector at the th cycle is ; where, represents the left partial derivative of at the th cycle with respect to , represents the subsequence of in the index set ;

[0127] Assume that the right partial derivative vector at the th cycle is ; where, represents the right partial derivative of at the th cycle with respect to ;

[0128] Initialize ;

[0129] Step 3.4.2, according to , if , then let , otherwise, obtain using Equation (11):

[0130]

[0131] In formula (11), represents AND;

[0132] Step 3.4.3, according to if then let otherwise, obtain using formula (12):

[0133]

[0134] Step 3.4.4, after assigning to , return to Step 3.4.2 and execute sequentially until to obtain the left partial derivative vector of and the right partial derivative vector ;

[0135] Step 3.5, formulate the adjustment strategy for the index according to the following different situations:

[0136] When calculate the minimum index to be adjusted, and calculate the maximum index to be adjusted;

[0137] When calculate the maximum index to be adjusted, and calculate the minimum index to be adjusted;

[0138] When calculate the minimum index to be adjusted, and calculate the maximum index to be adjusted;

[0139] When calculate the minimum index to be adjusted, and calculate the maximum index to be adjusted;

[0140] Step 3.6, based on and adjust to obtain ;

[0141] Calculate the increased adjustment amount using formula (13) as :

[0142]

[0143] Calculate the decreased adjustment amount using formula (14) as :

[0144]

[0145] If , the increased adjustment amount obtained from Equation (13) is ;

[0146] If , the decreased adjustment amount obtained from Equation (14) is , ;

[0147] If , ;

[0148] Step 3.7, if , let ;

[0149] If , assign to ;

[0150] Step 3.8, after assigning to , if , end the iteration and execute Step 3.9; otherwise, return to Step 3.3 and execute sequentially;

[0151] Step 3.9, calculate the optimal charging and discharging scheme according to :

[0152] Calculate the optimal charging amount and the optimal discharging amount for each period;

[0153] For , if , let the optimal charging amount in period t be , and let the optimal discharging amount in period t be ;

[0154] If , let , and let ;

[0155] If , let , and let ;

[0156] Step 3.10, calculate the optimal net carbon emission reduction contribution using Equation (15) :

[0157] (15).

[0158] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0159] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is run by a processor, it executes the steps of the above method.

[0160] In summary, the method of the present invention finds the adjustment direction by introducing a method based on sensitivity analysis and using the partial derivative of the objective function with respect to the decision variable, and then optimizes the energy storage charge and discharge strategy according to the adjustment direction until the charge and discharge strategy reaches the optimum. This mathematical model precise optimization algorithm can obtain the optimal energy storage charge and discharge strategy, and has high computational efficiency, which can maximize the net carbon emission reduction contribution.

Claims

1. An optimal scheduling method for energy storage peak shaving and valley filling based on sensitivity analysis, characterized in that Including the following steps: Step 1, obtain the electricity load demand, the carbon emission factor of the power grid , the power of the energy storage system , the capacity of the energy storage system , and the maximum demand; Step 2: Based on the data in Step 1, construct a peak shaving and valley filling model for energy storage; Step 3: Solve the peak shaving and valley filling model for energy storage to obtain the optimal charge and discharge scheme for the energy storage system.

2. The energy storage peak shaving and valley filling optimization scheduling method based on sensitivity analysis according to claim 1, characterized in that In Step 2, the objective function of the peak shaving and valley filling model for energy storage is constructed using Equation (1): (1) In formula (1), is the total time period and represents the net carbon emission reduction contribution of energy storage; represents any time period; is the carbon emission coefficient of the power grid during the time period, is the charging amount of the energy storage system during the time period; is the discharging amount of the energy storage system during the time period, is the implicit carbon emission coefficient per unit discharging amount of the energy storage system; ​ The constraint conditions of the peak shaving and valley filling model for energy storage are constructed using Equations (2)-(10): (2) (3) (4) (5) (6) (7) (8) (9) (10) In Formula (2) - Formula (10), is the rated power of the energy storage system ; is the sampling interval under the total time period , is the total number of samplings under the total time period ; , are respectively the minimum and maximum values of the remaining power of the energy storage system ; is the remaining power of the energy storage system during the time period ; is the remaining power of the energy storage system during the time period, , are the charge and discharge conversion coefficients of the energy storage system ; is the remaining power of the energy storage system at the initial time period ; is the remaining power of the energy storage system at the end time period ; is the power supply of the power grid during the time period, is the power consumption of the user during the time period, ; is the minimum and maximum power supplies of the power grid during the time period, is the maximum demand of the user 3. The optimized scheduling method for energy storage peak shaving and valley filling based on sensitivity analysis according to claim 2, wherein, Step 3 includes: Step 3.1: Define the number of loops , initialize ; Define the remaining power vector of the energy storage system at the th loop, define the upper bound vector and the lower bound vector , where represents the remaining power of the energy storage system at the th loop during the period, and are respectively the upper and lower bounds; and initialize ; Define the remaining power vector of the energy storage system s in the length of the continuous subsequence of , initialize ; Step 3.2, calculate and , and construct with the lower bound vector ; Step 3.3, calculate the adjustment interval of the continuous subsequence; Step 3.4, calculate based on the adjustment interval for left deviation vector of and right deviation vector ; Step 3.5, based on and , formulate an adjustment strategy for the minimum index and the maximum index ; Step 3.6, based on and , adjust to obtain the remaining power vector of the energy storage system s in the ; Step 3.7, if , let ; If , assign to ; Step 3.8, after assigning to , if , end the iteration and execute Step 3.9; otherwise, return to Step 3.3 and execute sequentially; Step 3.9, according to , calculate the optimal charge-discharge plan, including: the optimal charge amount and the optimal discharge amount in each period; where represents the optimal charge amount in period t, represents the optimal discharge amount in period t; Step 3.10, calculate the optimal net carbon emission reduction contribution using Equation (15) :[[-END]] (15)。 4. A peak shaving and valley filling optimization scheduling method for energy storage based on sensitivity analysis according to claim 3, characterized in that Step 3.2 includes: Step 3.2.1, calculate the maximum number of consecutive charging periods ; When and at that time, update the remaining power of the time-of-use energy storage system in the forward direction of time to its upper bound ; where the upper bound of represents the remaining power of the time-of-use energy storage system in the th cycle time-of-use energy storage system ; Calculate the maximum number of consecutive discharge periods ; When and at this time, update the upper bound of the remaining power of the time-of-use energy storage system time-of-use energy storage system in reverse order according to time; among them, the upper bound of ; where the upper bound of represents the remaining power of the time-of-use energy storage system in the time-of-use energy storage system in the nth cycle; When and at this time, update the energy storage system in the positive time direction time-of-use energy storage system remaining power upper bound ; Step 3.2.

2. For and at that time, update the lower bound of in reverse time : If , then let ; If , then let ; where the lower bound of Step 3.2.3, when and at that time, let .

5. A method for optimizing the scheduling of energy storage for peak shaving and valley filling based on sensitivity analysis according to claim 4, characterized in that, Step 3.3 includes: For the starting index of a continuous subsequence , determine the index set of the continuous subsequence with the starting index ; ; If , the left end of the continuous subsequence with the starting index of is increased by the adjustment amount , and the left end of the continuous subsequence with the starting index of is decreased by the adjustment amount ; where represents the electricity consumption of user l during the period; represents the remaining electricity of the energy storage system at the period in the th cycle; represents the remaining electricity of the energy storage system at the period in the th cycle, represents the upper bound of the discharge amount of the energy storage system during the period, and ; If , the increase adjustment amount at the left end of the continuous subsequence with the starting index of , and the decrease adjustment amount at the left end of the continuous subsequence with the starting index of ; where represents the upper bound of the charging amount of the time interval energy storage system , and ; ; If , the right end of the continuous subsequence with the starting index of is increased by the adjustment amount , and the right end of the continuous subsequence with the starting index of is decreased by the adjustment amount ; where represents the power consumption of user l during the time period; represents the remaining power of the energy storage system in the time period under the th cycle; represents the remaining power of the energy storage system in the time period under the th cycle; represents the upper bound of the discharge amount of the energy storage system in the time period, and ; If , the right end of the continuous subsequence with the starting index is increased by the adjustment amount , and the right end of the continuous subsequence with the starting index is decreased by the adjustment amount ; where represents the upper bound of the charging amount of the time-of-use energy storage system, and ; Determine the maximum index of the consecutive subsequence with the starting index as and the minimum index of the consecutive subsequence with the starting index as ; ;​ The starting index is the adjustment interval of the continuous subsequence is ; where represents the remaining power of the energy storage system during the th cycle , represents the remaining power of the energy storage system during the th cycle , represents the lower bound of and represents the upper bound of 6. The optimized scheduling method for energy storage peak shaving and valley filling based on sensitivity analysis according to claim 5, characterized in that, Step 3.4 includes: Step 3.4.1: Let the left partial derivative vector at the -th iteration be ; where represents the left partial derivative of at the -th iteration, and represents the subsequence of under the index set . Let the right partial derivative vector under the -th cycle be ; where represents the -th cycle of the right partial derivative of . Initialization ; Step 3.4.

2. According to , if , then let , otherwise, obtain using Equation (11): (11) In formula (11), represents AND; Step 3.4.3, according to , if , then let , otherwise, obtain using Equation (12): (12) Step 3.4.4: After assigning to , return to Step 3.4.2 and execute sequentially until is reached, thereby obtaining the left deviation vector of and the right deviation vector and .

7. A peak shaving and valley filling optimal scheduling method for energy storage based on sensitivity analysis according to claim 6, characterized in that, Step 3.5 includes: When calculate the minimum index to be adjusted calculate the maximum index to be adjusted ; When calculate the maximum index to be adjusted calculate the minimum index to be adjusted ; When calculate the minimum index to be adjusted calculate the maximum index to be adjusted ; When calculate the minimum index to be adjusted calculate the maximum index to be adjusted .

8. A method for optimizing the energy storage peak shaving and valley filling scheduling based on sensitivity analysis according to claim 7, characterized in that, Step 3.6 includes: If , the increased adjustment amount obtained from Equation (13) is , thus obtaining ; (13) In Equation (13), represents the remaining power of the time-of-use energy storage system in the th cycle, represents the remaining power of the time-of-use energy storage system in the th cycle, represents the remaining power of the time-of-use energy storage system in the th cycle, and represents the remaining power of the time-of-use energy storage system in the If , the reduction adjustment amount obtained from Equation (14) is , thus obtaining ; (14) In formula (14), represents the remaining power of the energy storage system during the th cycle, represents the remaining power of the energy storage system during the th cycle, represents the remaining power of the energy storage system during the th cycle, represents the remaining power of the energy storage system during the th cycle; If , let .

9. The optimal scheduling method for energy storage peak shaving and valley filling based on sensitivity analysis according to claim 8, characterized in that, Step 3.9 includes: For if then let the optimal charging amount in period t be and let the optimal discharging amount in period t be ; If , then let , and let ; If , then let , and let .

10. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program for supporting the processor to execute any one of the peak shaving and valley filling optimization scheduling methods for energy storage as recited in Claims 1-9, and the processor is configured to execute the program stored in the memory.

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