A method and terminal for optimizing the configuration of a microgrid energy storage

Abstract

The present invention discloses a method and a terminal for optimizing the configuration of a microgrid energy storage. First, a target function for restricting the capacity of the microgrid energy storage is established, and the target function is transformed into an augmented target function in combination with the frequency safety constraint conditions. Then, the virtual inertia constraint conditions, virtual damping constraint conditions, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equations of the generator sets in the microgrid system are established. Then, in combination with the augmented target function, virtual inertia constraint conditions, virtual damping constraint conditions, energy storage capacity optimization interval, and differential-algebraic equations, an energy storage capacity optimization model is established. Finally, the energy storage capacity optimization model is used to optimize the configuration of the microgrid energy storage. The present invention enables the microgrid system to maintain sufficient inertia and damping support, avoids frequency instability during the operation of the microgrid, and at the same time realizes the optimal configuration of the energy storage capacity, enabling the system to operate safely and stably.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimal configuration, and particularly relates to a method and a terminal for optimizing the configuration of a microgrid energy storage. Background Art

[0002] The large-scale access of new energy in the power grid has led to an increase in the energy storage capacity configuration of the microgrid system, resulting in a decline in the dynamic response performance of the system and affecting the stable operation of the system. Moreover, the large-scale access of new energy has also led to a lack of sufficient inertia and damping support in the microgrid system, which is prone to frequency instability problems. That is, the output ratios of wind power, DC, distributed power sources, etc. exceed 47%, the rotational inertia of the power system is insufficient, the frequency regulation ability of the power grid continues to decline, and the system frequency drops to 48.8 Hz at the lowest.

[0003] Therefore, in the face of the large-scale access of new energy, how to optimize the microgrid energy storage configuration while avoiding frequency instability during the operation of the microgrid and enabling the system to operate safely and stably has become an urgent problem to be solved. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to provide a method and a terminal for optimizing the configuration of a microgrid energy storage, which can help the microgrid system maintain sufficient inertia and damping support, avoid frequency instability during the operation of the microgrid while optimizing the microgrid energy storage configuration, and enable the system to operate safely and stably.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for optimizing the configuration of a microgrid energy storage includes the steps of:

[0007] S1. Establish an objective function for restricting the size of the microgrid energy storage capacity, and transform the objective function into an augmented objective function in combination with the frequency safety constraint conditions;

[0008] S2. Establish the virtual inertia constraint conditions, virtual damping constraint conditions, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equations of the microgrid system generator sets;

[0009] S3. Combine the augmented objective function, the virtual inertia constraint conditions, the virtual damping constraint conditions, the energy storage capacity optimization interval, and the differential-algebraic equations to establish an energy storage capacity optimization model;

[0010] S4. Use the energy storage capacity optimization model to optimize the configuration of the microgrid energy storage.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is as follows:

[0012] A microgrid energy storage optimization configuration terminal includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0013] S1. Establish an objective function that constrains the size of the microgrid energy storage capacity, and transform the objective function into an augmented objective function in combination with the frequency security constraint conditions;

[0014] S2. Establish the virtual inertia constraint conditions, virtual damping constraint conditions, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equations of the microgrid system generator set;

[0015] S3. Combine the augmented objective function, the virtual inertia constraint conditions, the virtual damping constraint conditions, the energy storage capacity optimization interval, and the differential-algebraic equations to establish an energy storage capacity optimization model;

[0016] S4. Use the energy storage capacity optimization model to optimize the configuration of the microgrid energy storage.

[0017] The beneficial effects of the present invention are as follows: A microgrid energy storage optimization configuration method and terminal are provided. An energy storage capacity optimization model is established based on the objective function that constrains the size of the microgrid energy storage capacity. Under the constraint conditions, parameters such as virtual inertia, virtual damping, and energy storage capacity between different virtual synchronous generators are matched, so that the microgrid system maintains sufficient inertia and damping support, avoids frequency instability during the operation of the microgrid, and at the same time realizes the optimized configuration of the energy storage capacity, enabling the system to operate safely and stably. Description of the Drawings

[0018] Figure 1 It is a step schematic diagram of a microgrid energy storage optimization configuration method according to an embodiment of the present invention;

[0019] Figure 2 It is a flow schematic diagram of a microgrid energy storage optimization configuration method according to an embodiment of the present invention;

[0020] Figure 3 It is a flow schematic diagram of a solution optimization algorithm of a microgrid energy storage optimization configuration method according to an embodiment of the present invention;

[0021] Figure 4 It is a three-machine nine-node simulation system diagram of a comparison experiment of a microgrid energy storage optimization configuration method according to an embodiment of the present invention;

[0022] Figure 5 It is a waveform diagram of the frequency of Model 1 before and after optimization in a comparison experiment of a microgrid energy storage optimization configuration method according to an embodiment of the present invention;

[0023] Figure 6Waveform diagram of the rate of change of frequency of Model 1 in the comparison experiment of a microgrid energy storage optimal configuration method according to an embodiment of the present invention before and after optimization;

[0024] Figure 7 Waveform diagram of the voltage of Node 1 of Model 1 in the comparison experiment of a microgrid energy storage optimal configuration method according to an embodiment of the present invention before and after optimization;

[0025] Figure 8 Waveform diagram of the frequency of Model 2 in the comparison experiment of a microgrid energy storage optimal configuration method according to an embodiment of the present invention before and after optimization;

[0026] Figure 9 Waveform diagram of the rate of change of frequency of Model 2 in the comparison experiment of a microgrid energy storage optimal configuration method according to an embodiment of the present invention before and after optimization;

[0027] Figure 10 Structural schematic diagram of a microgrid energy storage optimal configuration terminal according to an embodiment of the present invention.

[0028] Label description:

[0029] 1. A microgrid energy storage optimal configuration terminal; 2. A processor; 3. A memory. Specific embodiments

[0030] To describe in detail the technical content, achieved objectives and effects of the present invention, the following is described in conjunction with the embodiments and with reference to the accompanying drawings.

[0031] Please refer to Figures 1 to 9 , a microgrid energy storage optimal configuration method, comprising the steps of:

[0032] S1. Establish an objective function that restricts the size of the microgrid energy storage capacity, and transform the objective function into an augmented objective function in combination with the frequency security constraint conditions;

[0033] S2. Establish the virtual inertia constraint conditions, virtual damping constraint conditions, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equations of the microgrid system generator sets;

[0034] S3. Combine the augmented objective function, the virtual inertia constraint conditions, the virtual damping constraint conditions, the energy storage capacity optimization interval, and the differential-algebraic equations to establish an energy storage capacity optimization model;

[0035] S4. Use the energy storage capacity optimization model to perform energy storage optimal configuration for the microgrid.

[0036] As can be seen from the above description, the beneficial effects of the present invention are as follows: Based on the objective function that restricts the energy storage capacity of the microgrid, an energy storage capacity optimization model is established. Under the constraint conditions, parameters such as virtual inertia, virtual damping, and energy storage capacity between different virtual synchronous generators are matched, so that the microgrid system maintains sufficient inertia and damping support, avoids frequency instability during the operation of the microgrid, and at the same time realizes the optimal allocation of the energy storage capacity, enabling the system to operate safely and stably.

[0037] Further, the specific process of transforming the objective function into an augmented objective function by combining frequency security constraint conditions includes:

[0038] Set a frequency security index that includes the maximum frequency change rate and the frequency deviation at the lowest frequency point;

[0039] Establish a penalty factor representing the frequency security constraint conditions according to the frequency security index;

[0040] Substitute the penalty factor into the objective function to obtain the augmented objective function.

[0041] As can be seen from the above description, through the penalty function, the objective function with constraints is transformed into an unconstrained augmented objective function for easy solution, thus ensuring that the lowest frequency point and the maximum frequency change rate of the microgrid are within a reasonable range, and improving the frequency security of the microgrid while optimizing the energy storage capacity configuration.

[0042] Further, the virtual inertia constraint conditions are specifically:

[0043] Set the upper limit and lower limit of the virtual inertia of the virtual synchronous generator of the microgrid;

[0044] The virtual damping constraint conditions are specifically:

[0045] Set the upper limit and lower limit of the virtual damping of the virtual synchronous generator of the microgrid.

[0046] As can be seen from the above description, by setting the upper limit and lower limit to constrain the virtual inertia and virtual damping, the virtual inertia and virtual damping between different virtual synchronous machines are optimized and configured to ensure the frequency security of the system.

[0047] Further, the specific process of step S4 is as follows:

[0048] S41. Input the system parameters of the microgrid into the energy storage capacity optimization model;

[0049] S42. Use the solution optimization algorithm to find the optimal solution of the energy storage capacity optimization model;

[0050] S43. Use the optimal solution to optimize the energy storage configuration of the microgrid.

[0051] As can be seen from the above description, when using the energy storage capacity optimization model to optimize the energy storage configuration of the microgrid, a solution optimization algorithm is introduced, so that the output of the energy storage capacity optimization model obtains the optimal solution, and the frequency safety stability that the system can achieve and the energy storage capacity configuration reach the optimal.

[0052] Further, the specific step S1 is as follows:

[0053] The objective function is established with the minimum energy storage capacity of the microgrid as the goal.

[0054] As can be seen from the above description, the objective function is established with the minimum energy storage capacity of the microgrid as the goal, so as to minimize the energy storage capacity while ensuring the frequency safety of the microgrid, and make the dynamic response performance of the system the best.

[0055] Please refer to Figure 10 , a microgrid energy storage optimization configuration terminal 1, including a memory 3, a processor 2, and a computer program stored on the memory 3 and operable on the processor 2. When the processor 2 executes the computer program, the following steps are implemented:

[0056] S1. Establish an objective function that restricts the size of the microgrid energy storage capacity, and transform the objective function into an augmented objective function by combining frequency safety constraint conditions;

[0057] S2. Establish the virtual inertia constraint condition, virtual damping constraint condition, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equation of the microgrid system generator set;

[0058] S3. Combine the augmented objective function, the virtual inertia constraint condition, the virtual damping constraint condition, the energy storage capacity optimization interval, and the differential-algebraic equation to establish an energy storage capacity optimization model;

[0059] S4. Use the energy storage capacity optimization model to optimize the energy storage configuration of the microgrid.

[0060] As can be seen from the above description, the beneficial effects of the present invention are as follows: An energy storage capacity optimization model is established based on the objective function that restricts the size of the microgrid energy storage capacity. Under the constraint conditions, parameters such as virtual inertia, virtual damping, and energy storage capacity between different virtual synchronous generators are matched, so that the microgrid system maintains sufficient inertia and damping support, avoids frequency instability during the operation of the microgrid, and at the same time realizes the optimization configuration of the energy storage capacity, enabling the system to operate safely and stably.

[0061] Further, the specific process of transforming the objective function into an augmented objective function by combining frequency safety constraint conditions includes:

[0062] Set a frequency safety index that includes the maximum frequency change rate and the frequency deviation at the lowest frequency point;

[0063] Establish a penalty factor representing the frequency safety constraint conditions according to the frequency safety index;

[0064] Substitute the penalty factor into the objective function to obtain the augmented objective function.

[0065] As can be seen from the above description, by using the penalty function, the objective function with constraints is transformed into an unconstrained augmented objective function for easy solution, thus ensuring that the lowest frequency point and the maximum frequency change rate of the microgrid are within a reasonable range, and improving the frequency safety of the microgrid while optimizing the energy storage capacity configuration.

[0066] Furthermore, the virtual inertia constraint condition is specifically:

[0067] Set the upper and lower limit values of the virtual inertia of the virtual synchronous generator of the microgrid;

[0068] The virtual damping constraint condition is specifically:

[0069] Set the upper and lower limit values of the virtual damping of the virtual synchronous generator of the microgrid.

[0070] As can be seen from the above description, by setting the upper and lower limit values to constrain the virtual inertia and virtual damping, the virtual inertia and virtual damping between different virtual synchronous machines are optimized and configured to ensure the frequency safety of the system.

[0071] Furthermore, the specific steps of step S4 are as follows:

[0072] S41. Input the system parameters of the microgrid into the energy storage capacity optimization model;

[0073] S42. Use the solution optimization algorithm to find the optimal solution of the energy storage capacity optimization model;

[0074] S43. Use the optimal solution to optimize the energy storage configuration of the microgrid.

[0075] As can be seen from the above description, when using the energy storage capacity optimization model to optimize the energy storage configuration of the microgrid, a solution optimization algorithm is introduced, so that the output of the energy storage capacity optimization model obtains the optimal solution, and the frequency safety stability and energy storage capacity configuration that the system can achieve reach the optimal.

[0076] Furthermore, the specific steps of step S1 are as follows:

[0077] Establish the objective function with the minimum energy storage capacity of the microgrid as the goal.

[0078] As can be seen from the above description, an objective function is established with the minimum microgrid energy storage capacity as the goal, so as to minimize the energy storage capacity while ensuring the frequency security of the microgrid and achieving the best dynamic response performance of the system.

[0079] A microgrid energy storage optimization configuration method and terminal of the present invention can be applied to the scenario of microgrid energy storage configuration, which will be described below through specific embodiments:

[0080] Please refer to Figures 1 to 9 , the first embodiment of the present invention is:

[0081] A microgrid energy storage optimization configuration method, in combination with Figure 1 and Figure 2 shown, includes the steps:

[0082] S1. Establish an objective function that restricts the size of the microgrid energy storage capacity, and transform the objective function into an augmented objective function in combination with the frequency security constraint conditions;

[0083] In this embodiment, with the minimum microgrid energy storage capacity as the goal, the expression of the constructed objective function is as follows:

[0084]

[0085] In the formula, H is the objective function, n represents that there are n virtual synchronous generators in the system; Cap j represents the size of the energy storage capacity of the jth virtual synchronous generator.

[0086] In this embodiment, transforming the objective function into an augmented objective function in combination with the frequency security constraint conditions specifically includes: setting a frequency security index including the maximum frequency change rate and the frequency deviation at the lowest frequency point; establishing a penalty factor representing the frequency security constraint conditions according to the frequency security index; substituting the penalty factor into the objective function to obtain the augmented objective function.

[0087] Among them, the frequency security index is set as the lowest frequency point and the maximum frequency change rate. Therefore, the lowest frequency point index is transformed into the frequency deviation at the lowest frequency point, and the maximum frequency change rate remains unchanged; introducing a penalty function K, the expression of the total penalty factor is:

[0088] K = K 11 + K 22 (2)

[0089] In the formula, K 11 and K 22 satisfy the following relationship:

[0090]

[0091] In the formula, K 11The penalty factor representing the lowest frequency point constraint, ΔF max The maximum value of the deviation of the lowest frequency point, in Hz; K 22 The penalty factor representing the maximum frequency change rate constraint The maximum value of the maximum frequency change rate, in Hz / s.

[0092] Thus, the objective function can be transformed into an augmented objective function considering frequency security constraints:

[0093]

[0094] S2. Establish the virtual inertia constraint condition, virtual damping constraint condition, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equations of the generator set of the microgrid system;

[0095] In this embodiment, the virtual inertia and virtual damping of the virtual synchronous generator need to meet certain constraint conditions, and neither of them can be too large or too small. If it is too small, it cannot provide sufficient inertia and damping levels for the system; if it is too large, it will affect the adjustment response speed of the generator and the system stability will deteriorate.

[0096] To ensure both the generator response performance and the frequency security of the microgrid, establish the virtual inertia constraint condition and the virtual damping constraint condition:

[0097]

[0098] In the formula, J min , J max respectively represent the lower limit and upper limit of the virtual inertia of the virtual synchronous generator, J j represents the virtual inertia of the jth virtual synchronous generator, and the unit is kg·m2; D pmin , D pmax respectively represent the lower limit and upper limit of the virtual damping, D pj represents the virtual damping of the pjth virtual synchronous generator.

[0099] In this embodiment, the energy storage capacity optimization interval is set as:

[0100] Cap min ≤Cap j ≤Cap max (7)

[0101] In the formula, Cap j represents the size of the energy storage capacity of the jth virtual synchronous generator, in Ah, Cap min , Cap max respectively represent the lower limit and upper limit of the VSG energy storage capacity.

[0102] In this embodiment, the process of establishing the differential-algebraic equations of the generator sets in the microgrid system includes:

[0103] For a microgrid system that simultaneously includes multiple synchronous generators and multiple virtual synchronous generators, it is necessary to obtain the differential-algebraic equations of the generator sets, and the speed control system, excitation system, etc. of the synchronous generators need to be considered. The differential equation sets of the i-th synchronous generator and the j-th virtual synchronous generator can be expressed as:

[0104]

[0105] In the formula, x represents the state variable matrix of the generator, z represents the real and imaginary part matrix of the node voltage, the subscript dg,i represents the i-th synchronous generator, and the subscript vsg,j represents the j-th virtual synchronous generator.

[0106] Thus, the differential equation set of the generator can be expressed as:

[0107]

[0108] In the formula, x and z respectively contain the following variables:

[0109] x = [θ dg,i , ω dg,i , I 2d,i , I 2q,i , V t2,i , V f,i , E fd,i , V A,i ,

[0110] φ q,i , φ d,i , φ fd,i , φ kd,i , φ kq,i ,

[0111] x 1,i , x 2,i , x 3,i , x 4,i ,

[0112] θ vsg,j , ω vsg,j , M f1,j T

[0113] z = [U 1λ_real , U 1λ_imag , …] T

[0114] In the formula, θ dg,i , ω dg,i respectively represent the power angle and angular frequency of the synchronous generator of the [the blank here might be a typo, it should probably be "i-th" instead of "the"]th, I​2d,i , I 2q,i is the state variable during the d - q transformation of the i - th synchronous generator, V t2,i , V f,i , E fd,i , V A,i is the state variable in the exciter of the i - th synchronous generator, φ q,i , φ d,i , φ fd,i , φ kd,i , φ kq,i is the state variable of the synchronous generator flux linkage equation, x 1,i , x 2,i , x 3,i , x 4,i is the state variable introduced in the governor and PSS (Power System Stabilizer) links of the i - th synchronous generator; θ vsg,j , ω vsg,j represents the virtual power angle and virtual angular frequency of the - th VSG, M f1,j is the state variable in the voltage control link of the j - th virtual synchronous generator; U 1λ_real , U 1λ_imag The subscript represents three phases A, B, and C, and respectively represents the real part and imaginary part of the voltage of the λ - phase at node 1.

[0115] The algebraic equation system is the three - phase power flow equation of the system, denoted as:

[0116] 0 = g(x, z) (10)

[0117] S3. Combine the augmented objective function, virtual inertia constraint conditions, virtual damping constraint conditions, energy storage capacity optimization interval, and differential - algebraic equations to establish an energy storage capacity optimization model;

[0118] In this embodiment, combining equations (1) to (9), the energy storage capacity optimization model of the micro - grid system under small disturbances is obtained:

[0119]

[0120] S4. Use the energy storage capacity optimization model to perform energy storage optimization configuration for the micro - grid.

[0121] In this embodiment, step S4 is specifically:

[0122] S41. Input the system parameters of the micro - grid into the energy storage capacity optimization model;

[0123] S42. Use the solution optimization algorithm to find the optimal solution of the energy storage capacity optimization model;

[0124] S43. Use the optimal solution to perform energy storage optimization configuration for the micro - grid.

[0125] In the embodiment, the solution optimization algorithm adopts an optimization strategy of MFO (MothFlame Optimization, MFO) algorithm. MFO algorithm has strong parallel optimization capability and can search for the global optimal solution to the maximum extent. Its algorithm flow is as follows: Figure 3 shown.

[0126] Among them, the MFO algorithm first obtains the optimization problem, including the objective function, constraints, optimization model, etc., and then sets the parameters required by the MFO algorithm. Specifically, the maximum number of optimization iterations can be set to 10 times and the proxy parameter to 10.

[0127] The system parameters input into the model include grid conductor impedance, capacitance, damping constraints, voltage constraints, transmission power limits, etc. The optimal solution includes the upstream power supply damping, microgrid distributed power supply damping and energy storage configuration capacity.

[0128] In this embodiment, the comparison experiment of the energy storage capacity optimization model is as follows:

[0129] like Figure 4 As shown, based on the MATLAB / Simulink simulation platform, a three-machine nine-node simulation system is built, where B is the bus, the subscript indicates the bus number, C indicates the capacitor, the subscript indicates the bus number, T indicates the transformer, RL indicates the line impedance, and the subscript 94 indicates a line with B9 at the head end and B4 at the end; Load indicates the load; SG indicates the upper power supply, and VSG indicates the power supply in the microgrid. In this embodiment, VSG1 is a wind turbine and VSG2 is a photovoltaic.

[0130] Model 1 includes the following:

[0131] When the frequency safety constraint is not considered, the MFO optimization algorithm is used to obtain the optimization results of virtual inertia, virtual damping and energy storage capacity, as shown in Table 1. In Table 1, J1, Dp1, and Cap1 represent the virtual inertia, virtual damping, and energy storage capacity of VSG1, respectively; J2, Dp2, and Cap2 represent the virtual inertia, virtual damping, and energy storage capacity of VSG2, respectively.

[0132] Table 1 Optimization configuration results without considering frequency safety constraints

[0133] Variable name Optimal value Initial value Minimum value Maximum value <![CDATA[J1[kg·m 2 > 1984.4713 405.2847 0 2000 <![CDATA[J2[kg·m 2 > 376.232137 405.2847 0 2000 Dp1[Nms] 16278.0975 20264.2367 16264 25264 Dp2[Nms] 21671.7163 20264.2367 16264 25264 Cap1[Ah] 4068.5916 5000 4000 5000 Cap2[Ah] 4158.2155 5000 4000 5000

[0134] After the MFO algorithm, the optimal capacity of the microgrid system is 8226.8071Ah, and the two virtual synchronous machines are configured with 4068.5916Ah and 4158.2155Ah respectively.

[0135] After that, the optimized results are transmitted to the underlying devices of the microgrid through control, and the microgrid systems before and after the optimized configuration are obtained. Then, the changes in the center of inertia frequency of the system and the voltage at Node 1 before and after the energy storage optimization configuration are compared. The results are shown respectively as Figures 5 to 7 follows.

[0136] By analyzing the lowest point of the system frequency and the maximum frequency change rate, it can be found that although the energy storage capacity of the system reaches the optimum under the constraint conditions, compared with that before optimization, the maximum frequency change rate does not change significantly. However, the lowest point of the frequency is lower and the frequency security is reduced. The MFO algorithm has no obvious impact on the voltage. That is, the following conclusion can be drawn: when the frequency security constraint is not considered, the MFO algorithm sacrifices a certain frequency security to make the energy storage capacity configuration of the microgrid system optimal.

[0137] Model 2 includes the following contents:

[0138] When considering the frequency security constraint on the basis of Model 1, Model 2 takes the frequency stability constraint into account on the basis of Model 1.

[0139] Moreover, in order to effectively constrain the frequency within a reasonable range, the upper and lower limits of the frequency constraint are set respectively as:

[0140]

[0141] On this basis, when considering the frequency security constraint, the virtual inertia, virtual damping and the optimized results of the energy storage capacity are obtained by using the MFO optimization algorithm, as shown in Table 2.

[0142] Table 2 Optimized configuration results after considering the frequency security constraint

[0143] Variable name Optimal value Initial value Minimum value Maximum value <![CDATA[J1[kg·m 2 > 1946.11751 405.2847 0 2000 <![CDATA[J2[kg·m 2 > 489.350376 405.2847 0 2000 Dp1[Nms] 23346.201 20264.2367 16264 25264 Dp2[Nms] 18152.7409 20264.2367 16264 25264 Cap1[Ah] 4577.6324 5000 4000 5000 Cap2[Ah] 4030.4350 5000 4000 5000

[0144] It can be seen that when considering the frequency security constraint, the optimal energy storage capacity of the microgrid system obtained by optimizing with the MFO algorithm is 8708.0674 Ah, and the two virtual synchronous machines are respectively configured with 4577.6324 Ah and 4030.4350 Ah. Compared with the case of not considering the frequency security constraint, the energy storage capacity increases.

[0145] See Figure 8 and Figure 9 , Figure 8 On the basis of Figure 5 , the frequency security constraint is added. By comparing Figure 8 the frequency security indicators with and without considering the frequency security constraint, it is found that when considering the frequency security constraint, the lowest point of the system frequency is significantly increased, the maximum frequency change rate at the fault moment is reduced, and the frequency security of the system is improved.

[0146] By comparing Model 1 and Model 2, it can be seen that the microgrid energy storage capacity optimization configuration algorithm considering frequency security constraints proposed in this embodiment can not only optimize the microgrid energy storage capacity, but also ensure that the lowest point of the microgrid frequency and the maximum frequency change rate are within a reasonable range.

[0147] Please refer to Figure 10 , Embodiment 2 of the present invention is as follows:

[0148] A microgrid energy storage optimization configuration 1 includes a memory 3, a processor 2, and a computer program stored on the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps of Embodiment 1 described above are implemented.

[0149] In summary, the present invention provides a microgrid energy storage optimization configuration method and terminal. Based on the objective function that restricts the size of the microgrid energy storage capacity, an energy storage capacity optimization model is established. Under the constraint conditions, parameters such as virtual inertia, virtual damping, and energy storage capacity between different virtual synchronous generators are matched, so that the microgrid system maintains sufficient inertia and damping support, avoids frequency instability during the operation of the microgrid, and at the same time realizes the optimization configuration of the energy storage capacity, enabling the system to operate safely and stably.

[0150] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent transformation made using the content of the specification and drawings of the present invention, or directly or indirectly applied in the relevant technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A method for optimizing the configuration of a microgrid energy storage system, characterized in that, it includes the following steps: S1. Establish an objective function that constrains the size of the microgrid energy storage capacity, and transform the objective function into an augmented objective function by combining frequency security constraint conditions; S2. Establish the virtual inertia constraint condition, virtual damping constraint condition, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equations of the microgrid system generator set; S3. Combine the augmented objective function, the virtual inertia constraint condition, the virtual damping constraint condition, the energy storage capacity optimization interval, and the differential-algebraic equations to establish an energy storage capacity optimization model; S4. Use the energy storage capacity optimization model to optimize the configuration of the microgrid energy storage system; The specific process of transforming the objective function into an augmented objective function by combining frequency security constraint conditions includes: Set a frequency security index that includes the maximum frequency change rate and the frequency deviation at the lowest frequency point; Establish a penalty factor representing the frequency security constraint condition according to the frequency security index; Substitute the penalty factor into the objective function to obtain the augmented objective function; The virtual inertia constraint condition is specifically: Set the upper and lower limits of the virtual inertia of the microgrid virtual synchronous generator; The virtual damping constraint condition is specifically: Set the upper and lower limits of the virtual damping of the microgrid virtual synchronous generator; The specific content of step S4 is: S41. Input the system parameters of the microgrid into the energy storage capacity optimization model; S42. Use an optimization algorithm to solve the optimal solution of the energy storage capacity optimization model; S43. Use the optimal solution to optimize the configuration of the microgrid energy storage system.

2. A method for optimizing the configuration of a microgrid energy storage system according to claim 1, characterized in that, the specific content of step S1 is: Establish the objective function with the minimum microgrid energy storage capacity as the goal.

3. A microgrid energy storage optimization configuration terminal, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, characterized in that, when the processor executes the computer program, the following steps are implemented: S1. Establish an objective function that constrains the size of the microgrid energy storage capacity, and transform the objective function into an augmented objective function by combining frequency security constraint conditions; S2. Establish the virtual inertia constraint condition, virtual damping constraint condition, energy storage capacity optimization interval of the microgrid virtual synchronous generator, and the differential-algebraic equations of the microgrid system generator set; S3. Combine the augmented objective function, the virtual inertia constraint condition, the virtual damping constraint condition, the energy storage capacity optimization interval, and the differential-algebraic equations to establish an energy storage capacity optimization model; S4. Use the energy storage capacity optimization model to optimize the configuration of the microgrid energy storage system; The specific process of transforming the objective function into an augmented objective function by combining frequency security constraint conditions includes: Set a frequency security index that includes the maximum frequency change rate and the frequency deviation at the lowest frequency point; Establish a penalty factor representing the frequency security constraint condition according to the frequency security index; Substitute the penalty factor into the objective function to obtain the augmented objective function; The specific virtual inertia constraint condition is as follows: Set the upper limit value and the lower limit value of the virtual inertia of the virtual synchronous generator of the microgrid; The specific virtual damping constraint condition is as follows: Set the upper limit value and the lower limit value of the virtual damping of the virtual synchronous generator of the microgrid; The specific step S4 is as follows: S41. Input the system parameters of the microgrid into the energy storage capacity optimization model; S42. Use the solution optimization algorithm to find the optimal solution of the energy storage capacity optimization model; S43. Use the optimal solution to perform energy storage optimization configuration for the microgrid.

4. A microgrid energy storage optimization configuration terminal according to claim 3, characterized in that the specific step S1 is as follows: Establish the objective function with the minimum energy storage capacity of the microgrid as the goal.

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