Optimal configuration method for hybrid energy storage capacity of port micro-grid

Abstract

The invention discloses a port micro-grid hybrid energy storage capacity optimal configuration method, which comprises the following steps of: firstly, acquiring power generation data, and filtering wave energy and wind energy data by adopting a sliding filtering algorithm according to a grid power fluctuation standard to reduce power fluctuation; decomposing the power demand of the hybrid energy storage system through a variational mode decomposition technology, and separating power components in different modes; carrying out feature extraction and analysis on the power component of each mode by utilizing Hilbert transform, and optimizing a power distribution strategy; and finally, combining economic evaluation models of the battery and the super capacitor to realize capacity optimization configuration of the hybrid energy storage system. According to the method, the capacity minimization configuration of the battery and the super capacitor can be realized, and the operation efficiency and the economical efficiency of the port micro-grid hybrid energy storage system are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy in power systems, and mainly relates to a method for optimizing configuration of hybrid energy storage capacity in a port microgrid. Background Art

[0002] In today's power system, renewable energy generation is characterized by volatility and intermittency, posing significant challenges to the stable operation of power systems. With the development of energy storage technology, effectively controlling the charging and discharging of energy storage and configuring its capacity to achieve optimal economic performance have become technical bottlenecks that the power industry urgently needs to overcome.

[0003] While traditional filtering algorithms can meet filtering requirements in a timely manner, they cannot effectively achieve moderate smoothing. Excessive smoothing can lead to excessive energy storage activity, placing a significant burden on the storage system. How to combine the characteristics of renewable energy generation to achieve efficient configuration of hybrid energy storage capacity remains a pressing technical challenge in the power system sector. Summary of the Invention

[0004] The present invention addresses the problem that the energy storage capacity of the existing technology cannot be effectively configured, and proposes a method for optimizing the configuration of hybrid energy storage capacity in a port microgrid. First, power generation data is obtained, and according to the power fluctuation standard of the power grid, a sliding filter algorithm is used to filter the wave energy and wind energy data to reduce power fluctuations; then, the power demand of the hybrid energy storage system is decomposed through variational modal decomposition technology to separate the power components under different modes; then, the Hilbert transform is used to extract and analyze the features of the power components of each mode to further optimize the power allocation strategy; finally, the economic evaluation model of batteries and supercapacitors is combined to achieve the optimal configuration of the capacity of the hybrid energy storage system. The method of the present invention can achieve the minimum configuration of the capacity of batteries and supercapacitors, effectively improving the operating efficiency and economy of the hybrid energy storage system of the port microgrid.

[0005] In order to achieve the above-mentioned object, the technical solution adopted by the present invention is: a method for optimizing the configuration of hybrid energy storage capacity of a port microgrid, comprising the following steps:

[0006] S1. Power generation data acquisition: The power generation data includes at least wave energy and wind energy power input. The wave energy power requires obtaining wave energy period and significant wave height data measured by the buoy, inputting the wave energy period and significant wave height into a wave energy power conversion matrix, and outputting wave energy power generation data. The wind energy power requires obtaining wind speed data measured by the buoy, inputting the wind speed data into a wind power conversion model, and outputting wind energy power generation data.

[0007] S2. Filtering: The power generation data obtained in step S1 is processed using a sliding filtering algorithm to obtain smoothed filtered wave energy and wind energy grid-connected power and energy storage power;

[0008] S3. Hybrid energy storage power allocation: The power of the hybrid energy storage system is decomposed using variational mode decomposition (VMD) to obtain power components under different modes. The Hilbert transform is used to extract and analyze the power components under each mode, reconstruct the modal components, and allocate low-frequency components to the battery, while high-frequency components and residual power are allocated to the supercapacitor.

[0009] S4. Capacity optimization configuration: Based on the economic evaluation model of batteries and supercapacitors and the hybrid energy storage constraints, the capacity optimization configuration of the hybrid energy storage system is achieved. The objective function of the economic evaluation model based on batteries and supercapacitors is:

[0010] C=C BO +C BM +C CO +C CM

[0011] Where C BO is the annual comprehensive cost of the battery, C BM is the annual maintenance cost of the battery; C CO is the annual comprehensive cost of the supercapacitor, C CM is the annual maintenance cost of the supercapacitor.

[0012] The constraints of the economic evaluation model based on batteries and supercapacitors include at least hybrid energy storage SOC (state of charge, SOC) constraints and hybrid energy storage rated capacity constraints.

[0013] As an improvement of the present invention, the grid-connected power based on the sliding filter algorithm in step S2 is expressed as:

[0014]

[0015] Where, P(t) is the power at time t; L is the width of the sliding window; P G (t) is the smoothed grid-connected power at time t.

[0016] As an improvement of the present invention, in the variational modal decomposition process of step S3, the variational problem expression of the constraint problem is constructed based on the constraint condition that the sum of the modal components is equal to the original signal:

[0017]

[0018] Where, f(t) represents the original data at time t; represents the partial derivative with respect to time t; δ(t) is the Dirac function; {u k} represents the set of modal components; {w k} is the corresponding center frequency set; K is the number of modal decompositions;

[0019] By introducing the quadratic penalty factor α and the Lagrange multiplier operator λ, the problem is transformed into an unconstrained variational problem, and its augmented Lagrangian expression is as follows:

[0020]

[0021] The alternating direction multiplier method is used to solve the above variational problem. The algorithm simplifies the original problem by decomposing the optimization process and updates the modal components through alternating iterations. Center frequency and Lagrange multipliers Finally, the optimal solution is obtained;

[0022] The modal component update method is as follows:

[0023]

[0024] Where, f(w),λ(w),u i (w) are f(w),λ(w),u i (w) the corresponding Fourier transform; Equivalent to the current remaining amount Wiener filtering;

[0025] The center frequency is updated as follows:

[0026]

[0027] The Lagrange multiplier update method is as follows:

[0028]

[0029] Where τ is the Lagrange multiplier update parameter; is the result of Fourier transform of λ(t);

[0030] Continuously update the parameters until the iteration termination condition is met:

[0031]

[0032] Where ε is the preset judgment accuracy threshold.

[0033] As another improvement of the present invention, in the Hilbert transform of step S3, the instantaneous amplitude, frequency phase and Hilbert marginal spectrum of each modal component are obtained by Hilbert transform;

[0034] Perform Hilbert transform on each modal component:

[0035]

[0036] The analytical signal of each modal component is constructed and converted into polar form as follows:

[0037]

[0038] Where, α k (t) is the amplitude function, ψ k (t) is the phase function;

[0039] The instantaneous amplitude of each modal component is specifically:

[0040]

[0041] The frequency phase of each modal component is specifically:

[0042]

[0043] The instantaneous frequency expression of each modal component is:

[0044]

[0045] Solve for the Hilbert amplitude spectrum:

[0046]

[0047] The Hilbert marginal spectrum of each mode is specifically:

[0048]

[0049] As another improvement of the present invention, in step S3, based on the Hilbert transform, the low-frequency component is allocated to the battery, the high-frequency component and the residual power are allocated to the supercapacitor, the Hilbert transform transforms the power into the frequency domain, the high-frequency and low-frequency modal components have a clear dividing point, the low-frequency modal component is reconstructed into a low-frequency component, and the high-frequency modal component is reconstructed into a high-frequency component, specifically:

[0050]

[0051] Where, P bat Indicates the power of the battery; P cap Indicates the power smoothed by the supercapacitor; IMF r represents the rth intrinsic mode function; res represents the residual component after decomposition; m represents the number of modes used to distinguish high-frequency and low-frequency components.

[0052] As another improvement of the present invention, in the objective function of step S4:

[0053]

[0054] Where, P brate Indicates the rated power of the battery to be configured; E B is the rated capacity of the battery; P crate Indicates the rated power of the battery to be configured; E C is the rated capacity of the supercapacitor; λ is the discount rate; Y is the operating cycle of the system; B BP 、B BE and B BY They are the unit power cost, unit capacity cost and operation and maintenance cost of the battery; C CP 、C CE and C CY They are the unit power cost, unit capacity cost and operation and maintenance cost of supercapacitors.

[0055] As another improvement of the present invention, in the constraint conditions of step S4, the hybrid energy storage SOC constraint and the hybrid energy storage rated capacity constraint both include battery constraint and supercapacitor constraint respectively.

[0056] The SOC constraint of the battery is specifically:

[0057] SOC bmin ≤SOC b ≤SOC bmax

[0058] Where, SOC bmax and SOC bmin Respectively represent the maximum and minimum state of charge of the battery;

[0059] The SOC constraint of the supercapacitor is specifically:

[0060] SOC cmin ≤SOC c ≤SOC cmax

[0061] Where, SOC cmax and SOC cmin are the maximum and minimum state of charge values of the supercapacitor respectively;

[0062] The rated capacity constraints of the battery are specifically:

[0063]

[0064] Where ΔT is the power command interval of the energy storage system; SOC b0 Indicates the initial state of charge of the battery; T is the scheduling period;

[0065] The rated capacity constraints of the supercapacitor are specifically as follows:

[0066]

[0067] Where, SOC c0 Indicates the initial state of charge of the supercapacitor.

[0068] Compared with the existing technology, the present invention has the following beneficial effects: the present invention discloses a method for optimizing the configuration of hybrid energy storage capacity of a port microgrid, which adopts a sliding filter algorithm to smooth power fluctuations, thereby obtaining smooth and stable grid-connected power and hybrid energy storage power, effectively suppressing random power fluctuations; decomposing the hybrid energy storage power through a variational mode decomposition algorithm to achieve precise separation of power signals; on this basis, using the Hilbert transform to achieve frequency domain conversion, integrating the dual advantages of time domain smoothing and frequency domain decomposition. According to the decomposition results, high-frequency power is allocated to supercapacitors and low-frequency power is allocated to batteries, realizing precise coordinated control of hybrid energy storage. The present invention significantly improves the response characteristics of the energy storage system by optimizing the energy storage configuration strategy, provides a theoretical basis and practical guidance for the grid connection of renewable power generation equipment, and has both significant environmental benefits and engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0070] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0071] Example 1

[0072] A method for optimizing the configuration of hybrid energy storage capacity in port microgrids, such as Figure 1 As shown, the following steps are included:

[0073] Step S1: Obtain power generation data, which includes at least actual wave energy and wind energy power input. The wave energy power requires obtaining the wave energy period and effective wave height data measured by the buoy, inputting the wave energy period and effective wave height into the wave energy power conversion matrix, and outputting wave energy power generation data; the wind energy power requires obtaining the wind speed data measured by the buoy, inputting the wind speed data into the wind power conversion model, and outputting wind energy power generation data.

[0074] Step S2: Filtering is performed using a sliding filtering algorithm to obtain the smoothed wave energy and wind energy grid-connected power and energy storage power.

[0075] The original wind power of wave energy and wind energy does not necessarily meet the grid connection requirements. A filtering algorithm is used to smooth out power fluctuations, and the power that meets the grid connection requirements is connected to the grid, which is the grid-connected power. The remaining power is smoothed by energy storage, which is the energy storage power.

[0076] The sliding average filter algorithm is a classic algorithm for smoothing power fluctuations. It uses the average power value within the time interval before and after the current sampling point to achieve a smoothing filter effect. The implementation process is as follows: each new power signal sampled is stored in a window array. At the same time, the data at the end of the array is removed, and the arithmetic mean of the array is calculated as the smoothed grid-connected power at the current moment. The grid-connected power based on the sliding filter algorithm is expressed as:

[0077]

[0078] Where, P(t) is the power at time t; L is the width of the sliding window; P G (t) is the smoothed grid-connected power at time t.

[0079] Step S3: Decompose the power of the hybrid energy storage system using variational modal decomposition technology to obtain power components under different modes. Use Hilbert transform to extract and analyze the features of the power components under each mode, assign low-frequency components to batteries, and high-frequency components and residual power to supercapacitors.

[0080] The Hilbert transform transforms the power into the frequency domain. There is a clear dividing point between high-frequency and low-frequency modal components. The low-frequency modal components are reconstructed into low-frequency components, and the high-frequency modal components are reconstructed into high-frequency components.

[0081] Variational mode decomposition adaptively decomposes the signal through optimization methods. Its core is to solve the variational problem with constraints. The variational problem of variational mode decomposition can be defined as finding K modal components. It is required that each modal component u k (t) has a specific center frequency and minimum bandwidth, while satisfying the constraint that the sum of the modal components is equal to the original input signal f. After the decomposition is completed, the Hilbert transform is performed on each mode to obtain its unilateral spectrum characteristics:

[0082]

[0083] Where * is the convolution operator; δ(t) is the Dirac function, which is calculated as follows:

[0084]

[0085] By adapting the original signal and adjusting the corresponding center frequency, and adding the exponential term Modulate the narrowband spectrum corresponding to each center frequency.

[0086]

[0087] Complete the construction of the variational problem expression for the constrained problem:

[0088]

[0089] In the formula, {u k}={u1,u2,…,u k} is the set of all modal components decomposed from the original signal; in addition, the set of center frequencies corresponding to all eigenmodal components is {w k}={w1,w2,…,w k}.

[0090] In the process of solving the variational problem, we first introduce a quadratic penalty factor α. The Lagrangian multiplication operator λ is introduced to transform the equation into an unconstrained variational problem. The augmented Lagrangian expression is as follows:

[0091]

[0092] Then, the alternating direction multiplier method is used to iteratively solve the above variational problem, transforming the optimization problem into a series of sub-optimization problems that are easy to solve. To find the extreme point of the Lagrangian function, that is, to find the optimal solution. The value of is:

[0093]

[0094] Where n is the number of iterations; X is u k The complete set of possible values of (t) is transformed into the frequency domain using Fourier transform:

[0095]

[0096] Where, is the Fourier transform of f(w); w is a random frequency. Use ww k Substituting w for the first term, we get:

[0097]

[0098] Transformed into the form of non-negative frequency interval integral:

[0099]

[0100] Similarly, the problem of determining the center frequency can be converted to the frequency domain:

[0101]

[0102] Update method for solving the center frequency of the earth:

[0103]

[0104] Where, Equivalent to the current remaining amount Wiener filtering; is the center frequency of the current submodal component, Perform Fourier transform to get u k (t).

[0105] The steps of the variational mode decomposition algorithm are as follows:

[0106] 1) Initialization λ 1 , and let n = 0;

[0107] 2) Yes Make updates;

[0108] 3) Update the Lagrangian operator λ:

[0109]

[0110] Where τ is the Lagrange multiplier update parameter; is the result of Fourier transform of λ(t);

[0111] 4) For a given error precision ε, if If it is satisfied, the iteration ends and all modal components are obtained, otherwise set n=n+1 and return to step 2).

[0112] Through Hilbert transform, the instantaneous amplitude, frequency phase and Hilbert marginal spectrum of each modal component obtained by variational modal decomposition can be obtained.

[0113] Perform Hilbert transform on each modal component:

[0114]

[0115] The analytical signal of each modal component is constructed and converted into polar form as follows:

[0116]

[0117] Where, α k (t) is the amplitude function, ψ k (t) is the phase function.

[0118] The instantaneous amplitude of each modal component is specifically:

[0119]

[0120] The frequency phase of each modal component is specifically:

[0121]

[0122] The instantaneous frequency expression of each modal component is:

[0123]

[0124] Solve for the Hilbert amplitude spectrum:

[0125]

[0126] The Hilbert marginal spectrum of each mode is specifically:

[0127]

[0128] Based on the Hilbert algorithm, the low-frequency component is assigned to the battery, and the high-frequency component and residual power are assigned to the supercapacitor. The allocation process is as follows:

[0129]

[0130] Where, IMF r represents the rth intrinsic mode function; res represents the residual component after decomposition; m represents the number of modes used to distinguish high-frequency and low-frequency components.

[0131] Step S4: Combine the economic evaluation model of batteries and supercapacitors, comprehensively consider factors such as cost, life and efficiency, and achieve capacity optimization configuration of the hybrid energy storage system.

[0132] The Hilbert transform converts modal components in the time domain to the frequency domain, which represents the relationship between frequency and amplitude, with a clear demarcation point between high- and low-frequency modal components. Low-frequency modal components are summed to form low-frequency components, while high-frequency components are summed to form high-frequency components. The high-frequency components and residual components are assigned to the supercapacitor, while the low-frequency components are assigned to the supercapacitor.

[0133] In a smooth power scenario, the energy storage battery needs to complete the charge or discharge command within the specified time. The minimum rated power of the energy storage battery should be greater than the maximum value of the assigned command. If the working efficiency of the energy storage battery is taken into account, its actual charge and discharge power is:

[0134]

[0135] Where, P bat (t) represents the charge and discharge power of the battery after taking the charge and discharge efficiency into consideration. and They represent the charging efficiency and discharging efficiency at time t, respectively.

[0136] The minimum rated power P of the battery that meets the power directive requirementsbrate It can be configured as follows:

[0137]

[0138] Where, P brate Indicates the rated power of the battery to be configured. T represents the scheduling period. and Represent the charging and discharging power instructions at time t respectively.

[0139] Similarly, the capacity of the supercapacitor can be determined:

[0140]

[0141] Where, P cap (t) represents the battery charge and discharge power after taking into account the charge and discharge efficiency; and Respectively represent the charging efficiency and discharging efficiency at time t; P crate Indicates the rated power of the battery to be configured; and They represent the charging power command and discharging power command at time t respectively.

[0142] The rated capacity of the battery is E B , its initial state of charge (SOC) is SOC b0 , the state of charge (SOC) at time i is as follows:

[0143]

[0144] Where ΔT is the power command interval of the energy storage system.

[0145] The battery's state of charge (SOC) must meet certain constraints:

[0146] SOC bmin ≤SOC b ≤SOC bmax

[0147] Where, SOC bmax and SOC bmin Represents the maximum and minimum state of charge of the battery respectively.

[0148] The rated capacity of the battery can be obtained:

[0149]

[0150] The rated capacity of the supercapacitor is E C , its initial state of charge (SOC) is SOC c0, the state of charge (SOC) at time i is as follows:

[0151]

[0152] The state of charge (SOC) of the supercapacitor must meet certain constraints:

[0153] SOC cmin ≤SOC c ≤SOC cmax

[0154] Where, SOC cmax and SOC cmin are the maximum and minimum state of charge values of the supercapacitor, respectively.

[0155] Rated capacity of supercapacitor:

[0156]

[0157] After analyzing the hybrid energy storage system and considering its cost, the economic evaluation model of batteries and ultracapacitors aims to optimize the annual comprehensive cost and maintenance cost of the hybrid energy storage system.

[0158] C=C BO +C BM +C CO +C CM

[0159] Where C BO is the annual comprehensive cost of the battery, C BM is the annual maintenance cost of the battery; C CO is the annual comprehensive cost of the supercapacitor, C CM is the annual maintenance cost of the supercapacitor.

[0160]

[0161]

[0162] Where λ is the discount rate; Y is the operating period of the system; B BP 、B BE and B BY They are the unit power cost, unit capacity cost and operation and maintenance cost of the battery; C CP 、C CE and C CY They are the unit power cost, unit capacity cost and operation and maintenance cost of supercapacitors.

[0163] It should be noted that the above content merely illustrates the technical idea of the present invention and cannot be used to limit the scope of protection of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications all fall within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the configuration of hybrid energy storage capacity in a port microgrid, characterized in that: The steps include: S1. Power generation data acquisition: The power generation data includes at least wave energy and wind energy power inputs, wherein the wave energy power is obtained by inputting the wave energy period and effective wave height into a wave energy power conversion matrix to output wave energy power generation data; the wind energy power is obtained by inputting wind speed data into a wind power conversion model to output wind energy power generation data; S2, filtering: The power generation data obtained in step S1 is processed using a sliding filtering algorithm to obtain smoothed filtered wave energy and wind energy grid-connected power and energy storage power; S3. Hybrid energy storage power allocation: Decompose the power of the hybrid energy storage system through variational modal decomposition to obtain power components under different modes. Use Hilbert transform to extract and analyze the features of the power components under each mode, reconstruct the modal components, allocate low-frequency components to the battery, and high-frequency components and residual power to the supercapacitor; S4. Capacity optimization configuration: Based on the economic evaluation model of batteries and supercapacitors and the hybrid energy storage constraints, the capacity optimization configuration of the hybrid energy storage system is achieved. The objective function of the economic evaluation model based on batteries and supercapacitors is: C=C BO +C BM +C CO +C CM Where C BO is the annual comprehensive cost of the battery, C BM is the annual maintenance cost of the battery; C CO is the annual comprehensive cost of the supercapacitor, C CM is the annual maintenance cost of the supercapacitor; The constraints of the economic evaluation model based on batteries and supercapacitors include at least a hybrid energy storage SOC constraint and a hybrid energy storage rated capacity constraint.

2. The method for optimizing the configuration of hybrid energy storage capacity of a port microgrid according to claim 1, characterized in that: The grid-connected power based on the sliding filter algorithm in step S2 is expressed as: Where, P(t) is the power at time t; L is the width of the sliding window; P G (t) is the smoothed grid-connected power at time t.

3. The method for optimizing the configuration of hybrid energy storage capacity of a port microgrid according to claim 1, characterized in that: In the variational modal decomposition process of step S3, the variational problem expression of the constraint problem is constructed based on the constraint condition that the sum of the modal components is equal to the original signal: Where, f(t) represents the original data at time t; represents the partial derivative with respect to time t; δ(t) is the Dirac function; {u k } represents the set of modal components; {w k } corresponds to the set of central frequencies; K is the number of modal decompositions; By introducing the quadratic penalty factor α and the Lagrange multiplier operator λ, the problem is transformed into an unconstrained variational problem, and its augmented Lagrangian expression is as follows: The alternating direction multiplier method is used to solve the above variational problem. Specifically, the modal components are updated by alternating iterations. Center frequency and Lagrange multipliers Continuously update the parameters until the iteration termination condition is met and the optimal solution is obtained: Where ε is the preset judgment accuracy threshold.

4. The method for optimizing the configuration of hybrid energy storage capacity of a port microgrid according to claim 3, characterized in that: During the variational modal decomposition process of step S3, the modal components are updated by alternating iterations. Center frequency and Lagrange multipliers Finally, the optimal solution is obtained, where The modal component update method is as follows: Where, f(w),λ(w),u i (w) are f(w),λ(w),u i (w) the corresponding Fourier transform; Equivalent to the current remaining amount Wiener filtering; The center frequency is updated as follows: The Lagrange multiplier update method is as follows: Where τ is the Lagrange multiplier update parameter; is the result of Fourier transform of λ(t).

5. The method for optimizing the configuration of hybrid energy storage capacity of a port microgrid according to claim 1, characterized in that: In the Hilbert transform of step S3, the instantaneous amplitude, frequency phase and Hilbert marginal spectrum of each modal component are obtained by Hilbert transform; Perform Hilbert transform on each modal component: The analytical signal of each modal component is constructed and converted into polar form as follows: Where, α k (t) is the amplitude function, ψ k (t) is the phase function; The instantaneous amplitude of each modal component is specifically: The frequency phase of each modal component is specifically: The instantaneous frequency expression of each modal component is: Solve for the Hilbert amplitude spectrum: The Hilbert marginal spectrum of each mode is specifically:

6. A method for optimizing configuration of hybrid energy storage capacity of a port microgrid according to claim 4 or 5, characterized in that: In step S3, based on the Hilbert transform, the low-frequency component is allocated to the battery, and the high-frequency component and the residual power are allocated to the supercapacitor, specifically: Where, P bat Indicates the power of the battery; P cap Indicates the power smoothed by the supercapacitor; IMF r represents the rth intrinsic mode function; res represents the residual component after decomposition; m represents the number of modes used to distinguish high-frequency and low-frequency components.

7. The method for optimizing the configuration of hybrid energy storage capacity of a port microgrid according to claim 1, characterized in that: In the objective function of step S4: Where, P brate Indicates the rated power of the battery to be configured; E B is the rated capacity of the battery; P crate Indicates the rated power of the battery to be configured; E C is the rated capacity of the supercapacitor; λ is the discount rate; Y is the operating period of the system; B BP 、B BE and B BY They are the unit power cost, unit capacity cost and operation and maintenance cost of the battery; C CP 、C CE and C CY They are the unit power cost, unit capacity cost and operation and maintenance cost of supercapacitors.

8. The method for optimizing the configuration of hybrid energy storage capacity of a port microgrid according to claim 7, characterized in that: In the constraint conditions of step S4, the hybrid energy storage SOC constraint and the hybrid energy storage rated capacity constraint both include battery constraint and supercapacitor constraint respectively. The SOC constraint of the battery is specifically: SOC bmin ≤SOC b ≤SOC bmax Where, SOC bmax and SOC bmin Respectively represent the maximum and minimum state of charge of the battery; The SOC constraint of the supercapacitor is specifically: SOC cmin ≤SOC c ≤SOC cmax Where, SOC cmax and SOC cmin are the maximum and minimum state of charge values of the supercapacitor respectively; The rated capacity constraints of the battery are specifically: Where ΔT is the power command interval of the energy storage system; SOC b0 Indicates the initial state of charge of the battery; T is the scheduling period; The rated capacity constraints of the supercapacitor are specifically as follows: Where, SOC c0 Indicates the initial state of charge of the supercapacitor.

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