Hybrid energy storage continuous time polymerization and dynamic regulation and control method

Abstract

The invention discloses a hybrid energy storage continuous time aggregation and dynamic regulation and control method, which relates to the field of electrical engineering, and comprises the following steps: carrying out continuous time modeling on a power density type energy storage unit and an energy density type energy storage unit in a hybrid energy storage device to obtain power output constraints and energy storage constraints of the two energy storage units; arranging to obtain a compact form # imgabs0 # of the operation constraint of the energy storage units, calculating an average operation domain # imgabs1 # of all the energy storage units, and adopting affine transformation # imgabs2 # to ensure the feasibility of the operation of the energy storage units. Affine transformation # imgabs3 # is adopted to approximate the operation domain of the aggregation energy storage unit, so that the operation domains of the power density type aggregation energy storage unit and the energy density type aggregation energy storage unit are solved respectively, and then the aggregation regulation and control interval of the hybrid energy storage device is obtained. And regulating and controlling the hybrid energy storage device to charge and discharge the hybrid energy storage device during net load climbing, load peak and low ebb. The method has the advantages of being high in solving speed, good in regulation and control effect and suitable for participating in operation of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering technology, and more specifically, relates to a hybrid energy storage continuous time aggregation and dynamic control method. Background Art

[0002] With the advancement of the "dual carbon" strategy, the penetration of distributed renewable energy sources such as photovoltaic and wind power in distribution networks has increased significantly. The intermittent and volatile nature of their output has led to dramatic fluctuations in the net load of the distribution network (the difference between the load power and the output of distributed energy resources). This is typically manifested by the "duck curve" phenomenon: the net load plummets in the early morning when photovoltaic output rises sharply, and then rises sharply again in the evening when photovoltaic output declines, creating a significant "climbing" event. Such fluctuations increase the pressure on the distribution network to regulate peak loads and absorb new energy. Therefore, the use of energy storage for regulation is an appropriate method to enhance the distribution network's ability to buffer net load fluctuations.

[0003] Existing methods for regulating net load ramping and peak-shaving using energy storage present numerous difficulties. First, the large number of energy storage units involved in regulation necessitates efficient aggregation to determine the feasible domain of aggregated energy storage units and implement unified regulation. Second, load ramping requires rapid response, while load shaving requires large-capacity energy storage units. Existing methods struggle to achieve long-term energy management while maintaining responsiveness. Therefore, energy storage regulation requires a hybrid energy storage system that combines power-density and energy-density storage. Third, existing load and energy storage modeling utilizes discrete-time modeling, making it difficult to accurately characterize the continuous dynamic characteristics of energy storage units and load ramping, resulting in a loss of flexibility and operational risks. Therefore, a continuous-time modeling method that can incorporate aggregated energy storage unit regulation is needed. Fourth, load ramping is short-lived, while load peaks and valleys can last for several hours. Therefore, the operating states of the two aggregated energy storage units in the hybrid energy storage system should be dynamically adjusted to accommodate varying load conditions. Summary of the Invention

[0004] The purpose of the present invention is to provide a hybrid energy storage continuous time aggregation and dynamic control method to solve the difficulties raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A hybrid energy storage continuous time aggregation and dynamic control method, comprising:

[0007] S1. Conduct continuous-time modeling of the power-density and energy-density energy storage units in the hybrid energy storage device to obtain the power output constraints and energy storage constraints of the two energy storage units;

[0008] S2. Arrange the compact form of the energy storage unit operation constraints Among them, w i is a high-dimensional polyhedron, F is the coefficient matrix, f i is the coefficient vector, is the interpolation vector of the energy storage unit power, and the average operating range of all energy storage units is calculated in, is the energy storage corresponding to the average operating domain, is the coefficient vector;

[0009] S3. Using affine transformation To ensure the feasibility of the energy storage unit operation, and φ i are the translation vector and transformation matrix in the i-th affine transformation, is the operating domain corresponding to the average value of the energy storage unit operating constraints, is the operating domain obtained by the i-th affine transformation;

[0010] S4. Using affine transformation To approximate the operating domain of the aggregated energy storage unit, and φ are respectively To W app The translation vector and transformation matrix in the affine transformation, W app is the approximate operating domain of the aggregated energy storage unit, and the operating domains of the power density type aggregated energy storage unit and the energy density type aggregated energy storage unit are solved respectively, and then the aggregated control range of the hybrid energy storage device is obtained;

[0011] S5. Regulate the hybrid energy storage device so that it charges and discharges when the net load is ramping, during load peaks and valleys.

[0012] Preferably, the hybrid energy storage includes power density type energy storage and energy density type energy storage; and the continuous time modeling includes performing continuous time modeling on all power curves and energy storage curves.

[0013] Preferably, all powers are modeled as continuous time curves P D (t); The interpolation vector of the power authority in each time period is based on cubic Bernstein interpolation and is in is interpolation.

[0014] Specifically, each interpolation can be obtained by the curve P in the time period t∈[0,1] D The values ​​P at the beginning and end of (t) D (0),P D (1) and slope P' D (0), P' D (1) Calculation:

[0015]

[0016] Specifically, is the number of permutations, and the analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector Determine uniquely according to the following formula:

[0017]

[0018] Preferably, P D (t) has the following integral, differential, equality and inequality properties:

[0019]

[0020]

[0021] Specifically, all power curves are modeled according to equations (32)-(34) to obtain their corresponding interpolation vectors, and the interpolation of all power curve modeling complies with the properties of equations (35)-(38).

[0022] Preferably, all stored energy is modeled as a continuous time curve E D (t); The interpolation vector of the energy storage in each time period is based on the fourth Bernstein interpolation and is in is interpolation.

[0023] Specifically, each interpolation can be obtained by the curve E in the time period t∈[0,1] D The values ​​E at the beginning and end of (t) D (0),E D (1) and slope E' D (0), E' D (1) Calculation:

[0024]

[0025] Specifically, is the number of permutations, and the analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector Determine uniquely according to the following formula:

[0026]

[0027] Preferably, E D (t) has the following differential, equality, and inequality properties:

[0028]

[0029] Specifically, all energy storage curves are modeled according to equations (39)-(41) to obtain their corresponding interpolation vectors, and the interpolation of all energy storage curve modeling complies with the properties of equations (42)-(44).

[0030] Preferably, both the single power density type energy storage unit and the energy density type energy storage unit are modeled according to equations (14)-(16), where equation (45) is the energy storage constraint, and (46)-(47) are the energy storage charge and discharge constraints.

[0031]

[0032] Specifically, i (i = 1, 2, ..., k) is the number, τ is the total number of time periods, is the interpolation vector of the energy storage unit power, E i is the vector corresponding to the upper and lower limits of energy storage, P i , is the vector corresponding to the upper and lower limits of energy storage power, and W1 is the parameter matrix.

[0033] Specifically, the constraints of power-density energy storage and energy-density energy storage are the same, and the aggregation methods are also the same, with only the parameter values ​​being different.

[0034] Preferably, the compact form of the operational constraints of a single energy storage unit geometrically corresponds to a high-dimensional polyhedron w i :

[0035]

[0036] Specifically, F is the coefficient matrix, f i is the coefficient vector, and the operating domain corresponding to the average value of the energy storage unit operation constraints is The expression is as follows:

[0037]

[0038] Specifically, is the energy storage corresponding to the average operating domain, is the coefficient vector; w i and The affine transformation relationship and corresponding constraints between are as follows:

[0039]

[0040] Specifically, and φ i are the translation vector and transformation matrix in the i-th affine transformation, M iis a coefficient matrix; Equation (52) is the constraint to ensure that Equation (51) holds.

[0041] Specifically, the formula can ensure that after the affine transformation, is internal to ensure the feasibility of the operation of the energy storage unit.

[0042] Preferably, the operating domain W of the aggregated energy storage unit as a whole is the Minkowski sum of the operating domains of the individual energy storage units, and φ are respectively To W app The translation vector and transformation matrix in the affine transformation have the following relationship:

[0043]

[0044] Specifically, the formula can ensure that after the affine transformation, the internal structure is maintained to ensure the feasibility of the operation of the aggregated energy storage unit. The operation domain of the aggregated energy storage obtained by aggregation is maximized, and the objective function is set to solve the trace of the largest matrix φ:

[0045] max trace(φ) (57)

[0046] st(52),(55)-(56) (58)

[0047] Specifically, solving equation (57) yields and φ, and then calculate

[0048] Preferably, the polymerized W app ={E B |FE B ≤f}, f is the controllable capacity range of the aggregate energy storage unit.

[0049] Specifically, E B is the interpolation vector corresponding to the energy storage of the aggregated energy storage unit, They are respectively the upper bound of continuous time energy storage, the lower bound of continuous time energy storage, the maximum continuous time charging power, and the maximum continuous time discharging power obtained by aggregation.

[0050] Preferably, the method for controlling the aggregation of hybrid energy storage is to dynamically adjust the operating states of the two aggregated energy storage units in the hybrid energy storage at different times of the day. f ,I s , to achieve buffering of net load climbing and peak shaving and valley filling.

[0051] FE Bf ≤f f ,FE Bs ≤f s (59)

[0052] W1E Bf =P f ,W1E Bs =P s (60)

[0053] P B =I f P Bf +I s P Bs (61)

[0054] I f +I s =1 (62)

[0055] Specifically, let E Bf ,E Bs Corresponding to the storage capacity of power density type aggregated energy storage unit and energy density type aggregated energy storage unit respectively, let f f ,f s Corresponding to the control capability range of power density type aggregated energy storage unit and energy density type aggregated energy storage unit respectively, let P f ,P s They correspond to the charging and discharging power of the power density type aggregated energy storage unit and the energy density type aggregated energy storage unit respectively.

[0056] The implementation of the hybrid energy storage continuous time aggregation and dynamic control method of the present invention has the following beneficial effects:

[0057] 1. The present invention provides a continuous-time modeling method for an energy storage unit and an aggregated control capability range for a hybrid energy storage unit, which improves control accuracy and real-time performance, has fast response and small error.

[0058] 2. The present invention provides a method for utilizing hybrid energy storage units to participate in distribution network regulation, utilizing the synergy of power density and energy density energy storage to suppress bidirectional ramping, reduce abandoned solar power, and lower grid operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of a hybrid energy storage continuous time aggregation and dynamic control method;

[0060] Figure 2 This is a conceptual diagram of hybrid energy storage participating in distribution network regulation;

[0061] Figure 3 This is the action state diagram of hybrid energy storage according to the changes of net load in different periods;

[0062] Figure 4 This is the topology diagram of the distribution network based on the IEEE-13 node system;

[0063] Figure 5This is the daily distributed photovoltaic power generation diagram;

[0064] Figure 6 The net load of the distribution network during the day with and without hybrid energy storage deployment;

[0065] Figure 7 It is the charging and discharging situation of the two aggregated energy storage units in the hybrid energy storage; DETAILED DESCRIPTION

[0066] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0067] The present invention discloses a hybrid energy storage continuous time aggregation and dynamic control method, which relates to the field of electrical engineering and includes the following steps: continuous time modeling of the power density type and energy density type energy storage units in the hybrid energy storage device to obtain the power output constraints and storage capacity constraints of the two energy storage units. The compact form of the energy storage unit operation constraints is obtained. And calculate the average operating range of all energy storage units Using affine transformation To ensure the feasibility of the energy storage unit operation. Using affine transformation By approximating the operating domain of an aggregated energy storage unit, the operating domains of power-density aggregated energy storage units and energy-density aggregated energy storage units are solved, respectively, to obtain the aggregate control range of the hybrid energy storage device. The hybrid energy storage device is controlled to charge and discharge during net load ramping, peak load, and valley load periods. Experimental results demonstrate that the method described in this invention offers fast solution speed, excellent control effectiveness, and is suitable for distribution network operation.

[0068] To achieve the above objectives, the present invention provides a hybrid energy storage continuous time aggregation and dynamic control method, comprising:

[0069] S1. Conduct continuous-time modeling of the power-density and energy-density energy storage units in the hybrid energy storage device to obtain the power output constraints and energy storage constraints of the two energy storage units;

[0070] S2. Arrange the compact form of the energy storage unit operation constraints Among them, w i is a high-dimensional polyhedron, F is the coefficient matrix, f i is the coefficient vector, is the interpolation vector of the energy storage unit power, and the average operating range of all energy storage units is calculated in, is the energy storage corresponding to the average operating domain, is the coefficient vector;

[0071] S3. Using affine transformation To ensure the feasibility of the energy storage unit operation, and φ i are the translation vector and transformation matrix in the i-th affine transformation, is the operating domain corresponding to the average value of the energy storage unit operating constraints, is the operating domain obtained by the i-th affine transformation;

[0072] S4. Using affine transformation To approximate the operating domain of the aggregated energy storage unit, and φ are respectively To W app The translation vector and transformation matrix in the affine transformation, W app is the approximate operating domain of the aggregated energy storage unit, and the operating domains of the power density type aggregated energy storage unit and the energy density type aggregated energy storage unit are solved respectively, and then the aggregated control range of the hybrid energy storage device is obtained;

[0073] S5. Regulate the hybrid energy storage device so that it charges and discharges when the net load is ramping, during load peaks and valleys.

[0074] Preferably, the hybrid energy storage includes power density type energy storage and energy density type energy storage; and the continuous time modeling includes performing continuous time modeling on all power curves and energy storage curves.

[0075] Preferably, all powers are modeled as continuous time curves P D (t); The interpolation vector of the power authority in each time period is based on cubic Bernstein interpolation and is in is interpolation.

[0076] Specifically, each interpolation can be obtained by the curve P in the time period t∈[0,1] D The values ​​P at the beginning and end of (t) D (0),P D (1) and slope P' D (0), P' D (1) Calculation:

[0077]

[0078] Specifically, is the number of permutations, and the analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector Determine uniquely according to the following formula:

[0079]

[0080] Preferably, P D (t) has the following integral, differential, equality and inequality properties:

[0081]

[0082] Specifically, all power curves are modeled according to equations (63)-(65) to obtain their corresponding interpolation vectors, and the interpolation of all power curve modeling complies with the properties of equations (66)-(69).

[0083] Preferably, all stored energy is modeled as a continuous time curve E D (t); The interpolation vector of the energy storage in each time period is based on the fourth Bernstein interpolation and is in is interpolation.

[0084] Specifically, each interpolation can be obtained by the curve E in the time period t∈[0,1] D The values ​​E at the beginning and end of (t) D (0),E D (1) and slope E' D (0), E' D (1) Calculation:

[0085]

[0086] Specifically, is the number of permutations, and the analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector Determine uniquely according to the following formula:

[0087]

[0088] Preferably, E D (t) has the following differential, equality, and inequality properties:

[0089]

[0090]

[0091] Specifically, all energy storage curves are modeled according to equations (70)-(72) to obtain their corresponding interpolation vectors, and the interpolation of all energy storage curve modeling complies with the properties of equations (73)-(75).

[0092] Preferably, both a single power density type energy storage unit and an energy density type energy storage unit are modeled according to equations (76)-(78), where equation (76) is the energy storage constraint and (77)-(78) are the energy storage charge and discharge constraints.

[0093]

[0094] Specifically, i (i = 1, 2, ..., k) is the number, τ is the total number of time periods, is the interpolation vector of the energy storage unit power, E i is the vector corresponding to the upper and lower limits of energy storage, P i , is the vector corresponding to the upper and lower limits of energy storage power, and W1 is the parameter matrix.

[0095] Specifically, the constraints of power-density energy storage and energy-density energy storage are the same, and the aggregation methods are also the same, with only the parameter values ​​being different.

[0096] Preferably, the compact form of the operational constraints of a single energy storage unit geometrically corresponds to a high-dimensional polyhedron w i :

[0097]

[0098] Specifically, F is the coefficient matrix, f i is the coefficient vector, and the operating domain corresponding to the average value of the energy storage unit operation constraints is The expression is as follows:

[0099]

[0100] Specifically, is the energy storage corresponding to the average operating domain, is the coefficient vector; w i and The affine transformation relationship and corresponding constraints between are as follows:

[0101]

[0102] Specifically, and φ i are the translation vector and transformation matrix in the i-th affine transformation, M i is a coefficient matrix; Equation (83) is the constraint to ensure that Equation (82) holds.

[0103] Specifically, the formula can ensure that after the affine transformation, is internal to ensure the feasibility of the operation of the energy storage unit.

[0104] Preferably, the operating domain W of the aggregated energy storage unit is the Minkowski sum of the operating domains of the individual energy storage units, and φ are respectively To W appThe translation vector and transformation matrix in the affine transformation have the following relationship:

[0105]

[0106] Specifically, the formula can ensure that after the affine transformation, the internal structure is maintained to ensure the feasibility of the operation of the aggregated energy storage unit. The operation domain of the aggregated energy storage obtained by aggregation is maximized, and the objective function is set to solve the trace of the largest matrix φ:

[0107] max trace(φ) (88)

[0108] st(83),(86)-(87) (89)

[0109] Specifically, solving equation (88) yields and φ, and then calculate

[0110] Preferably, the polymerized W app ={E B |FE B ≤f}, f is the controllable capacity range of the aggregate energy storage unit.

[0111] Specifically, E B is the interpolation vector corresponding to the energy storage of the aggregated energy storage unit, They are respectively the upper bound of continuous time energy storage, the lower bound of continuous time energy storage, the maximum continuous time charging power, and the maximum continuous time discharging power obtained by aggregation.

[0112] Preferably, the method for controlling the aggregation of hybrid energy storage is to dynamically adjust the operating states of the two aggregated energy storage units in the hybrid energy storage at different times of the day. f ,I s , to achieve buffering of net load climbing and peak shaving and valley filling.

[0113] FE Bf ≤f f ,FE Bs ≤f s (90)

[0114] W1E Bf =P f ,W1E Bs =P s (91)

[0115] P B =I f P Bf +I s P Bs (92)

[0116] I f +I s =1 (93)

[0117] Specifically, let E Bf ,E Bs Corresponding to the storage capacity of power density type aggregated energy storage unit and energy density type aggregated energy storage unit respectively, let f f ,f s Corresponding to the control capability range of power density type aggregated energy storage unit and energy density type aggregated energy storage unit respectively, let P f ,P s They correspond to the charging and discharging power of the power density type aggregated energy storage unit and the energy density type aggregated energy storage unit respectively.

[0118] The following is a further description of the beneficial effects that can be achieved by this embodiment in conjunction with a specific application scenario. The corresponding program is written on the computing software MATLAB R2024a, and the Yalmip solver equipped with Gurobi is called to solve the problem. To solve the optimization problem, the computing device used is: a Legion Savior laptop equipped with an Intel Core i9-10510U processor, 32GB of running memory, and running the Windows 11 Professional operating system. The adjustment capability range obtained by aggregation is as follows Figure 5 shown.

[0119] For a distribution network based on the IEEE-13 node system, there are five distributed photovoltaics and one hybrid energy storage unit deployed. The specific topology is as follows: Figure 3 As shown in the figure, the operation status of the hybrid energy storage unit is compared with that of the hybrid energy storage unit. The power density type energy storage unit in the hybrid energy storage unit is flywheel energy storage, totaling four; the energy density type energy storage unit is all-vanadium liquid flow battery, totaling four. The daily output of distributed photovoltaic is shown in the figure. Figure 4 As shown, the penalty for curtailing solar power is 200￥ / MWh. The parameters of the all-vanadium flow battery and flywheel energy storage are shown in Table 1 and Table 2:

[0120] Table 1 Flywheel energy storage parameters

[0121] serial number Maximum discharge power Maximum charging power Maximum energy storage Minimum energy storage 1 2MW 2MW 8MWh 0MWh 2 0.8MW 0.8MW 4MWh 0MWh 3 0.8MW 0.8MW 5MWh 0MWh 4 1.1MW 1.1MW 6MWh 0MWh

[0122] Table 2 Parameters of all-vanadium redox flow battery

[0123] serial number Maximum discharge power Maximum charging power Maximum energy storage Minimum energy storage 1 1.3MW 1.3MW 0.3MWh 0MWh 2 0.85MW 0.85MW 0.2MWh 0MWh 3 1MW 1MW 0.25MWh 0MWh 4 1MW 1MW 0.28MWh 0MWh

[0124] Aggregating power density type and energy density type energy storage units, the parameters of the aggregated energy storage unit are shown in Table 3:

[0125] Table 3 Aggregation results

[0126]

[0127] Calling the hybrid energy storage unit to participate in the operation of the distribution network, solving the total distribution network power purchase cost and the distributed photovoltaic abandonment penalty, and obtaining the net load curve of the distribution network in two cases: Figure 5 As shown, the total operating costs are as follows:

[0128] Table 4 Solution results

[0129]

[0130]

[0131] It can be seen that compared with the operation of the distribution network without hybrid energy storage, the configuration of hybrid energy storage can reduce the operating costs of the distribution network, while also achieving peak shaving and valley filling of the net load and smooth net load climbing.

[0132] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0133] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A hybrid energy storage continuous time aggregation and dynamic control method, characterized in that: The steps include: S1. Conduct continuous-time modeling of the power-density and energy-density energy storage units in the hybrid energy storage device to obtain the power output constraints and energy storage constraints of the two energy storage units; S2. Arrange the compact form of the energy storage unit operation constraints Among them, w i is a high-dimensional polyhedron, F is the coefficient matrix, f i is the coefficient vector, is the interpolation vector of the energy storage unit power, and the average operating range of all energy storage units is calculated in, is the energy storage corresponding to the average operating domain, is the coefficient vector; S3. Using affine transformation To ensure the feasibility of the energy storage unit operation, and φ i are the translation vector and transformation matrix in the i-th affine transformation, is the operating domain corresponding to the average value of the energy storage unit operating constraints, is the operating domain obtained by the i-th affine transformation; S4. Using affine transformation To approximate the operating domain of the aggregated energy storage unit, and φ are respectively To W app The translation vector and transformation matrix in the affine transformation, W app is the approximate operating domain of the aggregated energy storage unit, and the operating domains of the power density type aggregated energy storage unit and the energy density type aggregated energy storage unit are solved respectively, and then the aggregated control range of the hybrid energy storage device is obtained; S5. Regulate the hybrid energy storage device so that it charges and discharges when the net load is ramping, during load peaks and valleys.

2. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 1, characterized in that: Hybrid energy storage includes power density energy storage and energy density energy storage; continuous time modeling includes continuous time modeling of all power curves and energy storage curves; All powers are modeled as continuous time curves P D (t); The interpolation vector of the power authority in each time period is based on cubic Bernstein interpolation and is in is the interpolation; each interpolation is determined by the curve P in the time period t∈[0,1] D The values ​​P at the beginning and end of (t) D (0),P D (1) and slope P' D (0), P' D (1) Calculation: The analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector Determine uniquely according to the following formula: in, is the number of permutations.

3. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 2, characterized in that: P D (t) has the following integral, differential, equality and inequality properties: Among them, all power curves are modeled according to formulas (1)-(3) to obtain their corresponding interpolation vectors, and the interpolation of all power curve modeling complies with the properties of formulas (4)-(7).

4. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 3, characterized in that: All stored energy is modeled as E D (t); The interpolation vector of the energy storage in each time period is based on the fourth Bernstein interpolation and is in is the interpolation; each interpolation is determined by the curve E in the time period t∈[0,1] D The values ​​E at the beginning and end of (t) D (0),E D (1) and slope E' D (0), E' D (1) Calculation: The analytical expression of the curve in the time period t∈[0,1] is given by its interpolation vector Determine uniquely according to the following formula: in, is the number of permutations.

5. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 4, characterized in that: Energy storage curve E D (t) has the following differential, equality, and inequality properties: Among them, all energy storage curves are modeled according to formulas (8)-(10) to obtain their corresponding interpolation vectors, and the interpolation of all energy storage curve modeling complies with the properties of formulas (11)-(13).

6. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 5, characterized in that: Both single power density type energy storage unit and energy density type energy storage unit are modeled according to the formula (14)-(16); where i (i = 1, 2, ..., k) is the number, τ is the total number of time periods, is the interpolation vector of the energy storage unit power, E i is the vector corresponding to the upper and lower limits of energy storage, P i , is the vector corresponding to the upper and lower limits of energy storage power, and W1 is the parameter matrix, as follows: Among them, equation (14) is the energy storage constraint, and (15)-(16) are the energy storage charge and discharge constraints.

7. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 6, characterized in that: The compact form of the operating constraints of a single energy storage unit corresponds geometrically to a high-dimensional polyhedron w i , as shown below: Among them, F is the coefficient matrix, f i is the coefficient vector, and the operating domain corresponding to the average value of the energy storage unit operation constraints is The expression is as follows: in, is the energy storage corresponding to the average operating domain, is the coefficient vector; w i and The affine transformation relationship and corresponding constraints between are as follows: in, and φ i are the translation vector and transformation matrix in the i-th affine transformation, M i is a coefficient matrix; Formula (21) is the constraint to ensure the validity of Formula (20).

8. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 7, characterized in that: The operating domain W of the aggregated energy storage unit is the Minkowski sum of the operating domains of the individual energy storage units, and the following relationship exists: in, and φ are respectively To W app The translation vector and transformation matrix in the affine transformation of ; solving φ is as follows: max trace(φ) (26) st(21),(24)-(25) (27) Where trace(φ) is the trace of matrix φ; solving equation (26) yields and φ, and then calculate 9. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 8, characterized in that: The aggregated W app ={E B |FE B ≤f}, Among them, E B is the interpolation vector corresponding to the energy storage of the aggregated energy storage unit, are the upper bound of the continuous time energy storage obtained by aggregation, the lower bound of the continuous time energy storage, the maximum continuous time charging power, and the maximum continuous time discharging power; f is the controllable capacity range of the aggregated energy storage unit.

10. A hybrid energy storage continuous time aggregation and dynamic control method according to claim 9, characterized in that: Let E Bf ,E Bs Corresponding to the storage capacity of power density type aggregated energy storage unit and energy density type aggregated energy storage unit respectively, let f f ,f s Corresponding to the control capability range of power density type aggregated energy storage unit and energy density type aggregated energy storage unit respectively, let P f ,P s Corresponding to the charging and discharging power of the power density type aggregated energy storage unit and the energy density type aggregated energy storage unit, the designed hybrid energy storage aggregation control model is as follows: FE Bf ≤f f ,FE Bs ≤f s (28) W1E Bf =P f ,W1E Bs =P s (29) P B =I f P Bf +I s P Bs (30) I f +I s =1 (31) Among them, by dynamically adjusting the operating status of the two aggregated energy storage units in the hybrid energy storage at different times of the day f ,I s , to achieve buffering of net load climbing and peak shaving and valley filling.

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