WO2022165914A1

WO2022165914A1 - Cooperative control method for distributed voltage source converter, and alternating-current/direct-current hybrid microgrid

Info

Publication number: WO2022165914A1
Application number: PCT/CN2021/080170
Authority: WO
Inventors: 张祯滨; 巴巴悠米·欧路利可; 李�真; 王瑞琪; 胡存刚
Original assignee: 山东大学
Priority date: 2021-02-05
Filing date: 2021-03-11
Publication date: 2022-08-11
Also published as: CN112803505A; CN112803505B

Abstract

Disclosed in the present invention are a cooperative control method for a distributed voltage source converter, and an alternating-current/direct-current hybrid microgrid. The method comprises: adjusting a power flow between an alternating-current sub-grid and a direct-current sub-grid by means of a bidirectional interlinking converter; when a load of the alternating-current sub-grid suddenly changes, calculating the total optimal power input of the alternating-current sub-grid, and determining whether the DER total power in the current alternating-current sub-grid reaches the total optimal power input of the alternating-current sub-grid; if so, calculating an input power contribution of each DER in the alternating-current sub-grid by means of a virtual inertia algorithm; and if the DER total power in the current alternating-current sub-grid does not reach the total optimal power input of the alternating-current sub-grid, and the DER in the direct-current sub-grid has redundant power, the direct-current sub-grid participating in cooperative control. The present invention remarkably improves the inertia of a microgrid, reduces a communication bandwidth, prolongs the service life of a storage device, and has the characteristics of fast and stable control.

Description

Coordinated control method of distributed voltage source converter and AC-DC hybrid microgrid

technical field

The invention relates to the technical field of distributed voltage source converter coordinated control, in particular to a distributed voltage source converter coordinated control method and an AC-DC hybrid microgrid.

Background technique

The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

In order to reduce the environmental impact of the greenhouse gases that cause global warming, the demand for electricity from renewable sources is increasing. 40% of the total carbon dioxide emissions in the atmosphere are generated by electricity generation worldwide. Therefore, energy for the power industry needs to be shifted from non-renewable energy sources (such as coal and natural gas) to more sustainable energy sources (such as solar, wind, hydro, hydrogen, etc.). With existing technology, however, this change in energy form comes at the cost of lower power quality and a weaker grid. For example, large-scale rotary high-inertia centralized power generation systems are used in traditional power systems, while high-level renewable energy power generation systems are associated with distributed power sources (mainly static).

For the prominent problem of frequency control in low-inertia AC microgrids, the existing solutions can be divided into two types, namely: traditional droop-based methods and synchronous motor inertial simulation. The droop method uses battery energy storage control to improve the frequency response of the power system. Synchronous machine inertia simulation, also known as virtual synchronous generator (VSG), utilizes adaptive inertia and damping coefficients to improve the frequency performance of a microgrid or power system during sudden load changes.

The weak inertia problem of DC microgrids can usually be solved by three methods, namely: large energy storage system, large DC bus capacitance value and virtual impedance (virtual inductance and capacitance). The first method is the oldest, the other two are relatively new. The cost of using large energy storage and DC bus capacitors is very high, so the virtual impedance method and its improvement and application have attracted more and more attention.

In conventional droop-based methods, the rate of frequency change is high during abrupt load changes, a problem that may lead to power system instability. And these methods have a constant droop factor, which cannot be modified during the dynamic frequency process, resulting in a slower return of the frequency to the rated value after a deviation occurs.

Compared with the traditional droop method, the synchronous motor inertia simulation shows better performance. However, for the multiple VSG power converters distributed on various wind farms/solar photovoltaic generators within a microgrid, how to coordinate them to effectively provide enough inertia to reduce frequency disturbances, there is currently no solution.

In addition, although the virtual impedance method has more advantages than the other two DC microgrid inertial control methods, there is still a lack of suitable methods for the situation where there are multiple voltage source converters distributed in different locations of the microgrid. That said, there is currently no technical solution that can combine the virtual inertia created by all converters to improve the inertia of the microgrid system and stabilize the DC bus voltage changes caused by sudden changes in load or generation.

SUMMARY OF THE INVENTION

In order to solve the above problems, the present invention proposes a distributed voltage source converter cooperative control method and an AC-DC hybrid microgrid, multi-agent cooperative control, so that multiple converters in the microgrid work together, and the inertia of the microgrid is improved. response.

In some embodiments, the following technical solutions are adopted:

A distributed voltage source converter cooperative control method, comprising:

Regulate the power flow between the AC sub-grid and the DC sub-grid through the bidirectional interconnected converter;

When the load of the AC sub-grid changes suddenly, calculate the total optimal power input of the AC sub-grid, and judge whether the total DER power in the current AC sub-grid reaches the total optimal power input of the AC sub-grid; if so, calculate the AC sub-grid through the virtual inertia algorithm The input power contribution of each DER in the sub-grid; if it is not reached, and the DER in the DC sub-grid has excess power, the DC sub-grid participates in the cooperative control;

When the load of the DC sub-grid suddenly changes, calculate the total optimal power input of DC, and judge whether the total DER power in the current DC sub-grid reaches the total optimal power input of DC; Input power contribution; if not reached, and the DER in the AC sub-grid has excess power, the AC sub-grid participates in the cooperative control.

Further, the DC sub-grid participates in the coordinated control, which specifically includes:

The AC sub-grid provides all the available power in the energy storage system and obtains the power balance from the DC sub-grid; the DC sub-grid provides the balanced power required for the optimal control of the AC sub-grid through the virtual inertia method.

Further, the AC sub-grid participates in the collaborative control, which specifically includes:

The DC sub-grid provides all the available power in the energy storage system and obtains the power balance from the AC sub-grid; the AC sub-grid provides the balanced power required for the optimal control of the AC sub-grid through the virtual inertia method.

Further, if the total power of the DER in the AC sub-grid does not reach its corresponding total optimal power input, and the DER in the DC sub-grid has no excess power, the virtual inertia algorithm is used to calculate the power of each DER in the AC sub-grid. input power contribution;

If the total power of the DER in the DC sub-grid does not reach its corresponding total optimal power input, and the DER in the AC sub-grid has no excess power, the input power contribution of each DER in the DC sub-grid is calculated by the virtual inertia algorithm .

Further, the input power contribution of each DER in the AC sub-grid or DC sub-grid is calculated by the virtual inertia algorithm, and the contribution of each DER follows two rules:

(i) Each DER will allocate power at its maximum rating;

(ii) Each DER will distribute power according to its current state of charge.

Further, each DER will allocate power according to its maximum rated value, which is embodied through capacity coordination control, including:

in,

P _{i_max} and P _{j_max} are the maximum rated power of the energy storage system at DER _i and DER _j , respectively,

are the contribution power input of DER _i at time steps k and k+1, respectively,

is the contributed power input of DER _j at time step k, x∈[ac,dc].

Further, each DER will allocate power according to its current state of charge, which is embodied by coordinated control of the state of charge, including:

in,

are the dynamic average state of charge of the energy storage system in DER _i and DER _j , respectively,

is the change function of the average state of charge of the system with time, SoC _i (t) is the change function of the state of charge of the energy storage system in DER _i with time, SoC _i is the state of charge of the energy storage system in DER _i , and SoC _min is the energy storage system The minimum state of charge of the system, τ denotes the integral parameter, Yi is the set of all neighbors of node _i , and a _ij is the (i,j)th element of the adjacency matrix.

Further, the input power contribution of each DER in the AC sub-grid or the DC sub-grid is calculated by the virtual inertia algorithm, specifically:

where _ηxi = is the charging efficiency/discharging efficiency of battery and supercapacitor energy storage, _ΔPxi is the power input contribution of each _DERi , βxi is the energy storage charging coefficient at DERi, and x represents the AC sub-grid or DC sub-grid .

In other embodiments, the following technical solutions are adopted:

An AC-DC hybrid microgrid is characterized in that the above-mentioned distributed voltage source converter collaborative control method is used to realize the AC-DC power grid collaborative control.

In other embodiments, the following technical solutions are adopted:

A terminal device, comprising a processor and a memory, the processor is used to implement each instruction; the memory is used to store a plurality of instructions, the instructions are suitable for being loaded by the processor and executing the above-mentioned distributed voltage source converter cooperative control method .

Compared with the prior art, the beneficial effects of the present invention are:

1. The present invention significantly improves the inertia of a renewable energy AC-DC hybrid microgrid with multiple distributed converter interfaces. Reduces the frequency of the system or changes in the DC bus voltage when there is a sudden change in the power supplied by the source or the power drawn by the load.

2. The present invention utilizes the energy distributed in the whole microgrid to the greatest extent, and makes it work together to ensure the frequency or DC bus voltage control of the microgrid. Therefore, as the number of DERs increases, they can also be accommodated to continue operating. The prior art does not incorporate the capability of multiple DERs into the control of frequency and DC microgrid bus voltage.

3. The cooperative control method of the present invention only uses the communication of each DER and its immediate neighbors. Uses less communication bandwidth and works well in the presence of communication channel delays and interference.

4. The present invention provides power input by using supercapacitor to cover, adjusts the larger frequency deviation from the rated value, and prolongs the battery life of the energy storage system.

5. The present invention safely maintains the frequency or the DC bus voltage change rate within a range that ensures the stability of the microgrid system.

6. The present invention significantly improves the inertia of the microgrid, reduces the communication bandwidth, prolongs the service life of the storage device, and has the characteristics of fast and stable control.

Other features and advantages of additional aspects of the invention will be set forth in part from the description that follows, and in part will become apparent from the description below, or will be learned by practice of the present aspects.

Description of drawings

1 is a schematic diagram of an AC-DC hybrid microgrid with distributed energy sources (DER) in an embodiment of the present invention;

2 is an internal view of each distributed energy source (DER) in the DC sub-grid according to an embodiment of the present invention;

3 is an internal view of each distributed energy source (DER) in an AC sub-grid in an embodiment of the present invention;

4(a)-(b) are the network physical layouts of the DERs in the embodiment of the present invention;

5 is a schematic diagram of a collaborative virtual inertial algorithm in an embodiment of the present invention;

FIG. 6 is a flow chart of improving the collaborative virtual inertia of AC sub-grids in an embodiment of the present invention;

7 is a flow chart of improving the collaborative virtual inertia of DC sub-grids in an embodiment of the present invention;

FIG. 8 is the VSG-droop control of the inverter in the embodiment of the present invention;

FIG. 9 is a schematic diagram of a control scheme of an inverter in an embodiment of the present invention;

FIG. 10 is a schematic diagram of predictive control of virtual inertia of a DC-DC bidirectional boost converter.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

Example 1

In one or more embodiments, a distributed voltage source converter coordinated control method is disclosed, which is used for an AC/DC hybrid microgrid powered by renewable energy to realize distributed coordinated control.

Figure 1 presents an overview of an AC-DC hybrid microgrid powered by renewable energy sources (only solar and wind energy are shown, but also for tidal, wave, geothermal, hydro, bioenergy, etc.). The microgrid consists of a DC sub-grid and an AC sub-grid, which are connected together by bidirectional interconnected converters. Each sub-grid has distributed energy sources (DERs) and connected loads. The DC sub-grid has M DERs and the AC sub-grid has N DERs.

Figure 2 presents an internal view of each distributed energy source (DER) in the DC sub-grid. The solar and wind power sources are connected to the DC bus through a step-up DC-DC converter, and the generated renewable energy is stored in an energy storage system (ESS).

The ESS consists of a battery (B1) and a supercapacitor (C1). The controller _Gess (s) ensures that only when the load power ΔPL and the DC bus voltage v _dc change slightly, it is regulated by the energy provided by the battery; when the load power _ΔPL and the DC bus voltage v _dc. Capacitors provide energy to regulate. This extends battery life and reduces the cost of microgrid systems. Both fuel cells and flywheel energy storage systems have high battery-like energy densities and can be used to achieve the same purpose as the battery shown in the figure.

Similarly, Figure 3 presents an internal view of each DER, interface power converter, and energy storage system of the AC grid, while showing how they connect to the inverter and provide Vdc across the capacitors.

In the AC-DC hybrid microgrid, the turn-on of large electric pumps and air conditioners causes a sudden change in the load of the microgrid, resulting in a decrease in the frequency of the AC sub-grid and a change in the voltage in the DC sub-grid. Therefore, we need to use a power electronic converter to adjust the frequency back to the rated value. In the present invention, we will control multiple distributed energy sources in the microgrid, namely: DER _dc1 , DER _dc2 ,…DER _dcM in the DC sub-grid, and DER _ac1 , DER _ac2 ,… DER _acN in the AC sub-grid ,As shown in Figure 1.

As shown in Fig. 4(a)-(b), each DER is represented as a

node

1,2,...i,...M or N.

The interconnected converter operates like a switch to regulate the power flow between the two sub-grids. For example, during the operation of the AC sub-grid, if the DER in the AC sub-grid does not have enough power to adjust the frequency deviation caused by sudden load changes, the virtual inertia cooperative control scheme will be applied in the present invention. If the DER in the DC sub-grid has excess power, the DER in the AC sub-grid will get power from the DER in the DC sub-grid. As shown in Fig. 6, this process is completed according to the two steps of the aforementioned virtual inertial cooperative control. Figure 7 illustrates how the inertial operation of the DC sub-grid can be achieved with the additional support of the AC sub-grid DER when the energy in the DC sub-grid is insufficient. Therefore, power can flow through the interconnected converters in both directions, enhancing the inertia of the system.

The coordinated control method for distributed voltage source converters in this embodiment is specifically:

Referring to Fig. 6, for the AC sub-grid, when the load changes suddenly, calculate the total optimal power input of the AC, and judge whether the total DER power in the current AC sub-grid reaches the total optimal power input of the AC; if so, through the virtual inertia The algorithm calculates the input power contribution of each DER; if not reached, judges whether the DER in the DC sub-grid has excess power:

If the DER in the DC sub-grid has no excess power, the DC sub-grid will not participate in the cooperative control. In this case, only the AC sub-grid participates in the cooperative control.

If the DER in the DC sub-grid has excess power, the AC sub-grid and the DC sub-grid together provide power. The AC sub-grid provides all the available power in the energy storage system and obtains the power balance from the DC sub-grid.

For example, if the total required power is 50 kW, and only 40 kW of the AC sub-grid is available, 10 kW can be obtained from the DC sub-grid at this time to achieve power balance. The DC sub-grid provides the shortfall/balance required for optimal control of the AC sub-grid.

For the same reason, referring to Fig. 7, for the DC sub-grid, when the load changes suddenly, calculate the total optimal power input of DC, and judge whether the total DER power in the current DC sub-grid reaches the total optimal power input of DC; , calculate the input power contribution of each DER through the virtual inertia algorithm; if it is not reached, judge whether the DER in the AC sub-grid has excess power:

If the DER in the AC sub-grid has no excess power, the AC sub-grid will not participate in the cooperative control. In this case, only the DC sub-grid participates in the cooperative control.

If the DER in the AC sub-grid has excess power, the AC sub-grid and the DC sub-grid together provide power. The DC sub-grid provides all the available power in the energy storage system and gets the power balance from the AC sub-grid.

Both sub-grids apply the cooperative virtual inertia algorithm in Fig. 5.

Referring to Fig. 5, it is performed in two steps to realize the virtual inertia cooperative control in each sub-grid;

(1) In all energy storage systems that need to improve inertia, use model predictive control (MPC) to calculate the total optimal power input ΔP _ct to control the frequency and frequency change rate in the AC sub-grid and the voltage deviation and the DC sub-grid. The rate of change of the voltage deviation.

Specifically, model predictive control is used to calculate the optimal required total power control input for the objective functions G _ac and G _dc :

a) G _ac is the AC sub-grid cost function, denoted as

Among them, G _ac is the objective function to be minimized, the inertia of the microgrid system

Δf is the frequency deviation,

The rate of change of frequency (ROCOF), ΔP acT is the total optimal power required by all _DERs in the microgrid to tune the frequency, and μ _f , μ _df and μ _u are the tuning weights for frequency, ROCOF and input power, respectively.

Δf,

and ΔP _acT satisfy:

AC frequency deviation (Δf _min ≤Δf≤Δf _max ), rate of change of frequency (ROCOF) limit

and power limit constraints (P _{i_min} ≤ΔP acT _≤P _{i_max} ).

The instantaneous control input needs to satisfy both the regulation requirements and the physical limits of the DER (rated power, P _{i_max} ).

b) G _dc is the DC sub-grid cost function, denoted as

Among them, G _dc is the objective function to be minimized, the virtual capacitance of the DC sub-grid

ΔP _dc =v _dc i _cdc , C _v is the virtual capacitance, and i _cdc represents the desired DC control input current (see Figure 10).

Δv _dc ,

and ΔP _dcT satisfy:

DC voltage deviation limit (Δv _dcmin ≤Δv _dc ≤Δv _dcmax ), DC bus change rate limit

Power limit constraints (P _{i_min} ≤ΔP _{dcT ≤P} _{i_max} ).

The solutions of equations (1a) and (1b) give the optimal sum of all power converters in the microgrid to maintain frequency deviation, frequency change rate, DC bus voltage deviation, and DC bus voltage change rate within the upper and lower limits. power value.

The output of this step is the optimal required total power control input ΔP _xT for each sub-grid, where x∈[ac,dc], (ie ΔP _acT , ΔP _dcT are the total power control inputs for the AC and DC sub-grids, respectively).

(2) The total required power input is distributed to M DERs in the DC sub-grid and N DERs in the AC sub-grid according to the principle of the maximum rated power and the state of charge at a given time.

make

Each DERi contribution follows two rules:

(i) Each DER will allocate power at its maximum rating;

(ii) Each DER will distribute power according to its current state of charge.

These two rules are embodied by the capacity cooperative control (Equations 2a and 2b) and the state of charge cooperative control (

Equations

3a and 3b), respectively. The capacity is cooperatively controlled at the next time step k, providing each DER with

The power input contribution of the state-of-charge cooperative control provides the energy storage coefficient

The following will respectively describe the results obtained by the capacity cooperative control and the charge level cooperative control.

and

the process of.

①Coordinated capacity control

Assume that the common number of DERs in the AC or DC sub-grid of the microgrid is Y. For DERi and its neighbors DERj, the following capacity control equation ensures that the input power contribution of each _DERi is proportional to its rated capacity. Therefore, a DER with a larger power rating can provide more power for adjusting the frequency/voltage offset.

in,

and _γxi , _γxj are weighting coefficients proportional to the installed maximum energy storage capacity, x∈[ac,dc], subject to

That is, the sum of the input power for all sampling times is equal to the total initial input power.

②Coordinated control of charging state

The state-of-charge cooperative control is described by

Equations

3a and 3b. Each DER _i only needs the state-of-charge information of neighboring DERs to obtain the average charge level of all Y-DERs in the microgrid system-wide (where Y is the general number of DERs in the AC or DC sub-grid). This allows it to require lower bandwidth and work well even in the event of communication failures or delays.

The system average state of charge (SoC) is based on an observer design:

where τ represents the integral parameter, Y _i is the set of all neighbors of node i, and a _ij is the (i,j)th element of the adjacency matrix. P _{i_max} and P _{j_max} are the maximum rated power of the energy storage system at DER _i and the maximum rated power of DER _j , respectively;

are the contribution power input of DER _i at time step k+1 and time step k, respectively; x={ac,dc}.

The cost-based energy storage participation coefficient is defined as:

in,

is the change function of the average state of charge of the system with time, SoC _i (t) is the change function of the state of charge of the energy storage system in DER _i with time, SoC _i is the state of charge of the energy storage system in DER _i , and SoC _min is the energy storage system The minimum state of charge of the system.

That is, when the state of charge of DER _i (SoC _i ) is greater than the average state of charge observed at node i, DER _i provides power input, otherwise it provides no power. Furthermore, the higher the charge level of DER _i , the greater its contribution to the power input required to achieve the control objective.

Considering the charging efficiency of batteries and supercapacitors, the final contribution of each DERi is given by Equation (4)

where _ηxi = is the charging efficiency/discharging efficiency of the battery and supercapacitor energy storage, _ΔPxi is the power input contribution of each _DERi , and βxi is the energy storage charging coefficient at DERi for the x-subgrid. β _xi energy storage coefficient, DER _i is the i-th DER,

is the contributed power input of DER _i at time step k.

As shown in Figure 8, in the AC sub-grid, the power input at time k

Applied to VSG-MPC inverter control, to control the inverter shown in Figure 9. As shown in Figure 10, in the DC sub-grid, the power input

According to relationship

Provides the current reference _icdc for virtual inertial control.

Embodiment 2

In one or more embodiments, an AC-DC hybrid microgrid is disclosed, which adopts the distributed voltage source converter coordinated control method in the first embodiment to realize the coordinated control of the AC-DC power grid.

Embodiment 3

In one or more embodiments, a terminal device is disclosed, including a server, the server including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the The distributed voltage source converter cooperative control method in the first embodiment is implemented in the program. For brevity, details are not repeated here.

It should be understood that, in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may include read-only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

In the implementation process, each step of the above-mentioned method can be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.

The distributed voltage source-converter cooperative control method in the first embodiment can be directly embodied as executed by a hardware processor, or executed by a combination of hardware and software modules in the processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, detailed description is omitted here.

Those of ordinary skill in the art can realize that the unit, that is, the algorithm step of each example described in conjunction with this embodiment, can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, they do not limit the scope of protection of the present invention. Those skilled in the art should understand that on the basis of the technical solutions of the present invention, those skilled in the art do not need to pay creative work. Various modifications or variations that can be made are still within the protection scope of the present invention.

Claims

A distributed voltage source converter cooperative control method, characterized in that it includes:

Regulate the power flow between the AC sub-grid and the DC sub-grid through the bidirectional interconnected converter;

When the load of the AC sub-grid changes suddenly, calculate the total optimal power input of the AC sub-grid, and judge whether the total DER power in the current AC sub-grid reaches the total optimal power input of the AC sub-grid; if so, calculate the AC sub-grid through the virtual inertia algorithm The input power contribution of each DER in the sub-grid; if it is not reached, and the DER in the DC sub-grid has excess power, the DC sub-grid participates in the cooperative control;

When the load of the DC sub-grid changes suddenly, calculate the total optimal power input of DC, and judge whether the total DER power in the current DC sub-grid reaches the total optimal power input of DC; Input power contribution; if not reached, and the DER in the AC sub-grid has excess power, the AC sub-grid participates in the cooperative control.
The method for coordinated control of distributed voltage source converters according to claim 1, wherein the DC sub-grid participates in the coordinated control, and specifically includes:

The AC sub-grid provides all the available power in the energy storage system and obtains the power balance from the DC sub-grid; the DC sub-grid provides the balanced power required for the optimal control of the AC sub-grid through the virtual inertia method.
The method for coordinated control of distributed voltage source converters according to claim 1, wherein the AC sub-grid participates in the coordinated control, and specifically includes:

The DC sub-grid provides all the available power in the energy storage system and obtains the power balance from the AC sub-grid; the AC sub-grid provides the balanced power required for the optimal control of the AC sub-grid through the virtual inertia method.
The method for coordinated control of distributed voltage source converters according to claim 1, wherein if the total DER power in the AC sub-grid does not reach its corresponding total optimal power input, and the DC sub-grid If the DER has no excess power, the input power contribution of each DER in the AC sub-grid is calculated by the virtual inertia algorithm;

If the total power of the DER in the DC sub-grid does not reach its corresponding total optimal power input, and the DER in the AC sub-grid has no excess power, the input power contribution of each DER in the DC sub-grid is calculated by the virtual inertia algorithm .
A distributed voltage source converter cooperative control method according to claim 1, wherein the input power contribution of each DER in the AC sub-grid or the DC sub-grid is calculated by a virtual inertia algorithm, and the contribution of each DER is Follow two rules:

(i) Each DER will allocate power at its maximum rating;

(ii) Each DER will distribute power according to its current state of charge.
The method for coordinated control of distributed voltage source converters according to claim 5, wherein each DER will allocate power according to its maximum rated value, which is embodied by capacity coordinated control, and specifically includes:

in,
P i_max and P j_max are the maximum rated power of the energy storage system at DER i and DER j , respectively,

are the contribution power input of DER i at time steps k and k+1, respectively,
is the contributed power input of DER j at time step k, x∈[ac,dc].
The method for coordinated control of distributed voltage source converters according to claim 5, wherein each DER will allocate power according to its current state of charge, which is embodied by coordinated control of the state of charge, and specifically includes:

in,
are the dynamic average state of charge of the energy storage system in DER i and DER j , respectively,
is the change function of the average state of charge of the system with time, SoC i (t) is the change function of the state of charge of the energy storage system in DER i with time, SoC i is the state of charge of the energy storage system in DER i , and SoC min is the energy storage system The minimum state of charge of the system, τ denotes the integral parameter, Yi is the set of all neighbors of node i , and a ij is the (i,j)th element of the adjacency matrix.
A distributed voltage source converter cooperative control method according to claim 1, wherein the input power contribution of each DER in the AC sub-grid or the DC sub-grid is calculated by a virtual inertia algorithm, which is specifically:

where ηxi = is the charging efficiency/discharging efficiency of battery and supercapacitor energy storage, ΔPxi is the power input contribution of each DERi , βxi is the energy storage charging coefficient at DERi, and x represents the AC sub-grid or DC sub-grid .
An AC-DC hybrid microgrid is characterized in that the coordinated control method of the distributed voltage source converter according to any one of claims 1-8 is adopted to realize the coordinated control of the AC-DC power grid.
A terminal device, comprising a processor and a memory, the processor is used to implement each instruction; the memory is used to store a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing any one of claims 1-8 The described distributed voltage source converter cooperative control method.