Intelligent micro-grid power supply and load balance control method based on integration of light, storage and charging

Abstract

The invention relates to the technical field of light storage and charging integration, in particular to an intelligent micro-grid power supply and load balance control method based on light storage and charging integration, which comprises the following steps of: synchronously acquiring a photovoltaic string output current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance curve, and outputting a mismatch characteristic frequency spectrum; the mismatch characteristic frequency spectrum marks a resonance risk frequency band; and based on the mismatched characteristic frequency spectrum, generating a light-storage-charging power coordination instruction set through a harmonic suppression collaborator, including instructing a photovoltaic string to start an active damping mode in a resonant frequency band and dynamically distributing differential switching frequency of a charging pile. According to the method, a mismatch characteristic frequency spectrum model fusing a photovoltaic current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance characteristic is constructed for the first time, a resonance risk frequency band is analyzed and judged through frequency domain coupling, and feedforward identification of inter-harmonic excitation conditions of multi-source high-frequency conversion equipment is realized; the method has higher system level adaptability and harmonic risk positioning precision.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic storage and charging integration, and in particular to a smart microgrid power supply and load balancing control method based on photovoltaic storage and charging integration. Background Art

[0002] Against the backdrop of the deep integration of photovoltaic, energy storage, and electric vehicle charging systems, integrated photovoltaic-storage-charging microgrid architectures are being widely deployed in industrial parks, transportation hubs, and edge grids. In this type of architecture, photovoltaic string inverters, energy storage converters, and charging piles all use high-frequency PWM control. A large number of high-order harmonics and switching excitation frequencies exist in the 2-5kHz frequency band. Due to the complex electromagnetic coupling relationship between different devices, when the photovoltaic harmonic components, grid impedance sag frequencies, and charging pile switching frequencies are superimposed, frequency domain resonance can easily occur, leading to problems such as bus voltage distortion, power quality degradation, and converter failure. Existing methods mostly rely on static filter design or independent device suppression methods, making it difficult to uniformly coordinate the harmonic behavior of each module.

[0003] In addition, with the continuous expansion of the number of charging piles and the trend of dense distribution of switching frequency configurations, traditional single-frequency avoidance and fixed scheduling strategies can no longer meet the needs of multi-device coordination in dynamic environments; especially under system startup or load fluctuation conditions, the distribution of resonant frequency bands is uncertain, and the existing technology lacks a mechanism that can jointly analyze the mismatch characteristic spectrum based on the photovoltaic current spectrum, energy storage end impedance characteristics and charging pile frequency configuration, and then form coordinated control instructions for the photovoltaic-storage-charging equipment; therefore, there is an urgent need for an intelligent control method that supports spectrum feature recognition and coordinated control instruction generation to achieve feedforward harmonic coordinated suppression and improve system stability. Summary of the Invention

[0004] The present invention provides an intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging to solve the problems of poor resonance suppression capability and power quality control effect of the microgrid system in an environment with multiple devices in parallel and high-frequency disturbance.

[0005] The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging includes the following steps:

[0006] S1: Synchronously collect the output current spectrum of the photovoltaic string, the switching frequency set of the charging pile, and the impedance curve of the energy storage converter, and output a mismatch characteristic spectrum. The mismatch characteristic spectrum marks the resonance risk frequency band;

[0007] S2: Based on the mismatch characteristic spectrum, a light-storage-charging power coordination instruction set is generated through a harmonic suppression coordinator, including instructing the photovoltaic string to enable active damping mode in the resonant frequency band and dynamically allocate differentiated switching frequencies of charging piles.

[0008] Optionally, the S1 includes:

[0009] S11: synchronously collect the current time domain signal of the photovoltaic string output, the switch frequency set configured by the charging pile, and the voltage and current time domain signals of the energy storage converter port;

[0010] S12: convert the photovoltaic current signal into a frequency spectrum distribution, and calculate the frequency domain impedance curve of the energy storage converter using the voltage and current signals of the energy storage converter;

[0011] S13: combine the photovoltaic spectrum, the charging pile frequency, and the energy storage impedance to construct a resonance risk coefficient model;

[0012] S14: compare the resonance risk coefficient with the adaptively calculated frequency threshold value, and identify and label the frequency band with resonance risk.

[0013] Optionally, the S11 comprises:

[0014] S111: acquire the output current time domain signal of the photovoltaic string through a multi-channel synchronous acquisition device;

[0015] S112: read the switch frequency setting parameters of all grid-connected charging piles, and construct a charging pile switch frequency set;

[0016] S113: synchronously acquire the voltage and current signals of the energy storage converter port.

[0017] Optionally, the S12 comprises:

[0018] S121: perform fast Fourier transform on the acquired photovoltaic string output current time domain signal to obtain the amplitude distribution characteristics of the photovoltaic current in the frequency domain;

[0019] S122: respectively perform frequency domain transformation on the voltage and current signals of the energy storage converter port, and construct the impedance curve under the frequency response based on the ratio of the two.

[0020] Optionally, the S121 comprises:

[0021] S1211: perform denoising, truncation, and window function processing on the acquired photovoltaic string output current signal to improve the accuracy and stability of frequency domain analysis;

[0022] S1212: input the preprocessed photovoltaic current time domain signal into a fast Fourier transform algorithm, extract the frequency domain amplitude distribution, and obtain the harmonic frequency components and their corresponding intensities.

[0023] Optionally, the S2 comprises:

[0024] S21: for the identified resonance risk frequency band, extract the corresponding harmonic component, inject a same-frequency reverse current into the photovoltaic string, and realize dynamic damping, with the greater the risk, the stronger the damping;

[0025] S22: Optimize the switching frequency distribution of each charging pile while meeting the minimum frequency interval and avoiding risky frequency bands, balancing adjustment costs and system resonance risks;

[0026] S23: Based on the impedance characteristics of the resonance risk frequency band, the virtual negative impedance compensation amount of the energy storage converter is calculated, and a frequency domain control instruction is generated and issued as part of the power coordination instruction set.

[0027] Optionally, the S21 includes:

[0028] S211: For each identified resonance risk frequency band, extract the harmonic component corresponding to the frequency band from the PV string output current spectrum;

[0029] S212: Based on the extracted resonant component, a current control instruction with the same frequency and opposite direction is calculated, and the damping coefficient is dynamically adjusted in combination with the risk intensity to generate an active damping current instruction and send it to the PV string.

[0030] Optionally, the S212 includes:

[0031] S2121: Based on the extracted resonant component, construct a current waveform with the same frequency and opposite phase, and dynamically calculate the damping coefficient according to the ratio of the resonance risk coefficient to the adaptive risk threshold;

[0032] S2122: Send the calculated damping current as a control instruction to the photovoltaic string inverter to perform an active injection operation.

[0033] Optionally, the S22 includes:

[0034] S221: Comprehensively consider the equipment switching cost and system resonance risk level caused by the charging pile frequency adjustment, and construct a joint optimization objective function including the frequency offset cost term and the resonance risk integral term;

[0035] S222: Set the minimum frequency spacing constraint between charging piles to ensure frequency dispersion. At the same time, each frequency is required to avoid the resonance risk band marked in the early stage to form a frequency feasible domain.

[0036] S223: Use the simulated annealing algorithm to perform a global search on the objective function, solve the optimal frequency allocation solution that meets all constraints, and output a new charging pile switching frequency configuration set.

[0037] Optionally, the S23 includes:

[0038] S231: Analyze the impedance characteristics of the energy storage converter at the corresponding frequency based on the identified resonance risk frequency band, and calculate the amplitude and phase compensation parameters of the virtual negative impedance;

[0039] S232: Construct the calculated compensation parameters into frequency domain control instructions, and send them to the energy storage converter as part of the power coordination instruction set.

[0040] Beneficial effects of the present invention:

[0041] This invention is the first to construct a mismatch characteristic spectrum model that integrates the photovoltaic current spectrum, the charging pile switching frequency set and the impedance characteristics of the energy storage converter. It determines the resonance risk frequency band through frequency domain coupling analysis, and realizes the feedforward identification of the harmonic excitation conditions between multi-source high-frequency conversion equipment. This method breaks through the traditional passive identification mode based only on the source-grid impedance ratio or a single harmonic threshold judgment, and has stronger system-level adaptability and harmonic risk positioning accuracy.

[0042] This invention proposes a collaborative power control instruction generation mechanism for the three types of "photovoltaic-storage-charging" equipment, innovatively designs a photovoltaic active damping injection strategy, a differentiated frequency optimization algorithm for charging piles, and a virtual negative impedance compensation method on the energy storage side, and constructs a feedback-free feedforward multi-terminal harmonic suppression system. This solution not only has the engineering advantages of parallel deployment and no need for real-time feedback modeling, but can also significantly improve the resonance suppression capability and power quality control effect of the microgrid system in multiple devices in parallel and high-frequency disturbance environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0045] Figure 2 This is a characteristic spectrum diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0047] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0048] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0049] like Figure 1-Figure 2 As shown, the intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging includes the following steps:

[0050] S1: Synchronously collect the PV string output current spectrum, charging pile switching frequency set and energy storage converter impedance curve, output mismatch characteristic spectrum, and mark the resonance risk frequency band of the mismatch characteristic spectrum;

[0051] S1 specifically includes:

[0052] S11: By deploying a multi-channel synchronous acquisition unit, three types of key data are collected: the current time-domain signal output by the photovoltaic string, which is used to reflect the characteristics of the harmonic source; the switching frequency set configured by the charging pile, which is used to identify the excitation frequency points that may stimulate resonance; and the voltage and current time-domain signals at the energy storage converter port, which are used for subsequent impedance characteristic modeling. Specifically, the following are included:

[0053] (1) PV string output current time domain signal i pv (t);

[0054] (2) Charging pile switching frequency configuration set F ev ={f ev1 ,f ev2 ,...,f evn};

[0055] (3) Energy storage converter port voltage v ess (t) and current i ess (t);

[0056] S12: Perform frequency domain transformation on the photovoltaic current signal to obtain its spectrum distribution. Simultaneously, the voltage and current signals of the energy storage converter are used to calculate its frequency domain impedance curve to form a frequency response representation of the power supply side and the energy storage side, providing input for subsequent coupling analysis. Specifically, this includes:

[0057] (1) Perform fast Fourier transform on the photovoltaic output current to obtain the spectrum, which is expressed as:

[0058]

[0059] Among them, I pv (f) is the photovoltaic current spectrum;

[0060] (2) Calculate the frequency domain impedance curve of the energy storage converter, which is expressed as:

[0061]

[0062] Among them, Z ess (f) is the frequency domain impedance of the energy storage converter, f is the frequency variable, f min is the minimum value of the frequency variable, f max Maximum value of frequency variable;

[0063] S13: Combining the above spectrum data and frequency configuration, a resonance risk coefficient model is constructed. This model couples the PV harmonic amplitude, the charging pile switching frequency position, and the impedance sag of the energy storage converter to identify the frequency points that may cause resonance excitation. It is expressed as:

[0064]

[0065] Among them, R(f) is the resonance risk coefficient, f is the frequency variable, the scanning range is 2-5, f evk The main switching frequency or its equivalent excitation frequency configured for the kth charging pile is in the range of 3-6, and δ(·) is the Dirac function that satisfies the condition f=f evk When δ(ff evk )=1;

[0066] S14: Compare the calculated resonance risk coefficient with the dynamically calculated adaptive frequency threshold. If the risk coefficient exceeds the threshold, the frequency point is marked as a resonance risk frequency band, providing a reference for subsequent power coordination control strategies, which is expressed as:

[0067] R(f)>γ(f);

[0068] Where γ(f) is the adaptive risk threshold, γ(f) = K·f -αK is the system safety factor, with a default value of 0.25, taking into account both sensitivity and stability. The safety margin, measurement accuracy, and equipment redundancy of station-level designs are different. A smaller value will increase sensitivity, make reporting easier, and be safer. A larger value can reduce false alarms but may result in missed detections. α is the frequency attenuation exponent, with a default value of 0.8. The microgrid bus and lines exhibit frequency-dependent damping, and high-frequency harmonic energy naturally decays. The threshold needs to decrease with frequency to reflect this trend. The larger the exponent, the faster the threshold decreases. A too large exponent will easily cause false alarms at high frequencies, while a too small exponent will be insensitive to medium frequencies.

[0069] S2: Based on the mismatch characteristic spectrum, a harmonic suppression coordinator is used to generate a set of solar-storage-charging power coordination instructions, including instructing the photovoltaic strings to enable active damping mode in the resonant frequency band and dynamically allocating differentiated switching frequencies of charging piles.

[0070] S2 specifically includes:

[0071] S21: For each identified resonance risk frequency band, extract the photovoltaic output harmonic components in that frequency band and generate a set of current instructions with the same frequency and opposite direction to form the active damping control amount of the photovoltaic string. The intensity of the damping control amount is determined by the risk level. The higher the risk, the larger the damping coefficient, but it does not exceed the set maximum limit value, which is expressed as:

[0072]

[0073] Among them, i damp (t) is the active damping current injected by the photovoltaic string, β is the active damping coefficient, which is used to adjust the amplitude of the injected current and has a value range of 0-0.15. max is the maximum limit of the damping coefficient, β max =0.15. Since the frequency compensation behavior will affect the power output and stability of the photovoltaic system itself, the active damping control should set an injection ratio upper limit, I pv (f r ) is the PV string output current at the resonant frequency f r The complex spectrum value at f r It is the frequency point identified as the resonance risk, with a value range of 2-5. R(f r ) is at frequency f r The resonance risk coefficient at , ranging from 0 to 1, γ is the adaptive risk threshold, which is dynamically calculated with frequency, and Re{·} refers to the complex real part extraction operation, which is used to extract the current waveform that can be directly used for time domain injection from the spectral components to ensure that the generated damping current has the same frequency and direction as the target harmonic in the time domain. is a complex exponential function, which generates a sinusoidal signal with the same frequency and phase as the harmonic component, used to accurately synthesize the injection waveform. 0.1 is the risk adjustment coefficient. In order to make R(f r) = γ, β is 0.15, so that the adjustment range covers from mild to high risk situations;

[0074] S22: Construct a set of optimization objective functions that comprehensively consider the equipment cost of frequency adjustment and the overall system resonance risk. Constrain the new frequency of each charging pile to be separated by a certain frequency width and avoid risky frequency bands. Based on this model, a simulated annealing algorithm is used to solve the optimal charging pile frequency allocation scheme, thereby reducing the overall harmonic coupling risk. Specifically, it includes:

[0075] (1) Taking into account the equipment switching cost and system resonance risk level caused by the frequency adjustment of the charging pile, a joint optimization objective function including the frequency offset cost term and the resonance risk integral term is constructed, which is expressed as:

[0076]

[0077] in, For the optimized charging pile switching frequency configuration set, each frequency point is in the range of 3.5–5.5. is the new switching frequency of the kth charging pile, ranging from 3 to 5. is the original switching frequency of the kth charging pile, ω k is the frequency adjustment cost weight of the kth charging pile, d k is the distance from the kth charging pile to the busbar, ω k Set to the inverse of the distance between the charging pile and the busbar, reflecting the principle of "the closer the distance, the lower the frequency regulation cost". Charging piles close to the busbar have a greater impact on system coupling and should therefore be allocated to safe frequencies first. λ is the system resonance risk suppression weight coefficient, ranging from 2.5 to 5, ensuring that the risk item weight is stronger when the risk control priority is higher.

[0078] (2) Set the minimum frequency interval constraint between charging piles to ensure the frequency dispersion. At the same time, each frequency is required to avoid the resonance risk frequency band marked in the early stage to form a frequency feasible domain. The constraint condition is expressed as:

[0079]

[0080] Where Δf min is the minimum interval constraint between the switching frequencies of any two charging piles, Δf min ≥200, in order to avoid superposition resonance caused by the frequencies of different piles being too close, setting the minimum interval constraint can improve the frequency dispersion and reduce the coupling between systems, f r is the identified resonance risk frequency point, with a value range of 2-5, Δf b is the resonant frequency band avoidance bandwidth, with a fixed value of 300. Frequency avoidance constraints prevent new frequencies from falling into high-risk frequency bands;

[0081] (3) A simulated annealing algorithm is used to perform a global search on the objective function to find the optimal frequency allocation solution that satisfies all constraints and output a new charging pile switching frequency configuration set, which is expressed as:

[0082]

[0083] in, is the optimized charging pile switching frequency configuration set, where each frequency point is in the range of 3.5-5.5, and n is the number of grid-connected charging piles (i.e., the total number of charging piles participating in the optimization), ranging from 5-50, which determines the dimension of the frequency offset term and the number of constraints in the objective function;

[0084] S23: Based on the frequency-domain impedance dip characteristics within the resonance risk frequency band, the virtual negative impedance compensation parameters required to be injected into the energy storage converter are calculated, forming corresponding frequency-domain control instructions. These instructions are then sent to the energy storage-side control unit as part of the photovoltaic-storage-charging power coordination instruction set to improve the system's harmonic suppression capability in the target frequency band. This is expressed as:

[0085] Z comp (f) = -K z ·e jθ ·|Z ess (f)|;

[0086]

[0087] θ=∠Z ess (f) +180°;

[0088] Among them, Z comp (f) is the virtual negative impedance injected by the energy storage converter at frequency f, which is related to Z ess (f) Same magnitude but opposite direction, Z ess (f) is the equivalent output impedance injected by the energy storage converter at frequency f, with a value range of 0.01-10, K z is the virtual negative impedance gain coefficient, ranging from 0 to 0.5, e jθ It is a complex exponential function that defines the rotation direction (phase) of the injected impedance and is used to generate the complex impedance in the corresponding direction to ensure the phase accuracy of the frequency domain compensation. ess (f) is the phase angle of the energy storage converter impedance, ranging from [-180, 180], and θ is the phase angle of the virtual impedance injection.

[0089] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0090] The above is only a preferred embodiment of the present invention. It should be pointed out that 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 should also be regarded as within the scope of protection of the present invention.

Claims

1. An intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging is characterized by: The following steps are involved: S1: Synchronously collect the output current spectrum of the photovoltaic string, the switching frequency set of the charging pile, and the impedance curve of the energy storage converter, and output a mismatch characteristic spectrum. The mismatch characteristic spectrum marks the resonance risk frequency band; S2: Based on the mismatch characteristic spectrum, a light-storage-charging power coordination instruction set is generated through a harmonic suppression coordinator, including instructing the photovoltaic string to enable active damping mode in the resonant frequency band and dynamically allocate differentiated switching frequencies of charging piles.

2. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 1 is characterized in that: Said S1 comprises: S11: Synchronously collect the current time domain signal output by the photovoltaic string, the switching frequency set configured by the charging pile, and the voltage and current time domain signals of the energy storage converter port; S12: converting the photovoltaic current signal into a frequency spectrum distribution, and calculating the frequency domain impedance curve of the energy storage converter using the voltage and current signals of the energy storage converter; S13: Construct a resonance risk coefficient model by combining the photovoltaic spectrum, charging pile frequency, and energy storage impedance; S14: Compare the resonance risk coefficient with the dynamically calculated adaptive frequency threshold to identify and mark frequency segments with resonance risks.

3. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 2 is characterized in that: The S11 includes: S111: Acquire the output current time domain signal of the photovoltaic string through a multi-channel synchronous acquisition device; S112: Read the switching frequency setting parameters of all grid-connected charging piles and construct a charging pile switching frequency set; S113: Synchronously collect voltage and current signals of the energy storage converter port.

4. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 2 is characterized in that: The S12 includes: S121: performing a fast Fourier transform on the collected photovoltaic string output current time domain signal to obtain the amplitude distribution characteristics of the photovoltaic current in the frequency domain; S122: Perform frequency domain transformation on the voltage and current signals at the energy storage converter port respectively, and construct an impedance curve under its frequency response based on the ratio of the two.

5. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 4 is characterized in that: The S121 includes: S1211: De-noises, truncates, and performs window function processing on the collected PV string output current signal to improve the accuracy and stability of frequency domain analysis; S1212: Input the preprocessed photovoltaic current time domain signal into the fast Fourier transform algorithm, extract the frequency domain amplitude distribution, and obtain the harmonic frequency components and their corresponding intensities.

6. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 5 is characterized in that: The S2 includes: S21: For the identified resonance risk frequency band, the corresponding harmonic components are extracted and a reverse current with the same frequency is injected into the PV strings to achieve dynamic damping. The greater the risk, the stronger the damping. S22: Optimize the switching frequency distribution of each charging pile while meeting the minimum frequency interval and avoiding risky frequency bands, balancing adjustment costs and system resonance risks; S23: Based on the impedance characteristics of the resonance risk frequency band, the virtual negative impedance compensation amount of the energy storage converter is calculated, and a frequency domain control instruction is generated and issued as part of the power coordination instruction set.

7. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 6 is characterized in that: The S21 includes: S211: For each identified resonance risk frequency band, extract the harmonic component corresponding to the frequency band from the PV string output current spectrum; S212: Based on the extracted resonant component, a current control instruction with the same frequency and opposite direction is calculated, and the damping coefficient is dynamically adjusted in combination with the risk intensity to generate an active damping current instruction and send it to the PV string.

8. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 6 is characterized in that: The S212 includes: S2121: Based on the extracted resonant component, construct a current waveform with the same frequency and opposite phase, and dynamically calculate the damping coefficient according to the ratio of the resonance risk coefficient to the adaptive risk threshold; S2122: Send the calculated damping current as a control instruction to the photovoltaic string inverter to perform an active injection operation.

9. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 6 is characterized in that: The S22 includes: S221: Comprehensively consider the equipment switching cost and system resonance risk level caused by the charging pile frequency adjustment, and construct a joint optimization objective function including the frequency offset cost term and the resonance risk integral term; S222: Set the minimum frequency spacing constraint between charging piles to ensure frequency dispersion. At the same time, each frequency is required to avoid the resonance risk band marked in the early stage to form a frequency feasible domain. S223: Use the simulated annealing algorithm to perform a global search on the objective function, solve the optimal frequency allocation solution that meets all constraints, and output a new charging pile switching frequency configuration set.

10. The intelligent microgrid power supply and load balancing control method based on integrated photovoltaic storage and charging according to claim 6 is characterized in that: The S23 includes: S231: Analyze the impedance characteristics of the energy storage converter at the corresponding frequency based on the identified resonance risk frequency band, and calculate the amplitude and phase compensation parameters of the virtual negative impedance; S232: Construct the calculated compensation parameters into frequency domain control instructions, and send them to the energy storage converter as part of the power coordination instruction set.

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