A method and system for source-load coordinated control of a virtual power plant for power electronic transformers

Abstract

The present invention discloses a method and system for coordinated source-load control of a power electronic transformer virtual power plant. The method comprises: constructing a load compensation cost model and establishing an economic dispatch model with the goal of maximizing the total revenue of the virtual power plant; iteratively solving local subproblems and global coordination problems based on the economic dispatch model to obtain optimized PET port interactive active power and photovoltaic output power, updating the energy storage battery state of charge so that the energy storage battery state of charge meets a limited range; aggregating photovoltaic, energy storage, and PET loads into a virtual group that can be uniformly dispatched, obtaining the virtual group state of charge, and performing dynamic coordinated control of energy storage, photovoltaic, and loads to achieve system power balance. The present invention can achieve coordinated control and rapid response of source and load resources, improving the stability, security, and economy of the power grid in high-penetration renewable energy scenarios.

Description

Technical Field

[0001] The present invention belongs to the field of source-load coordinated control of distribution networks, and relates to a source-load coordinated control method and system for a power electronic transformer (PET) virtual power plant. Background Art

[0002] As the energy structure shifts toward a high proportion of renewable energy, the large-scale integration of distributed energy, energy storage systems, and flexible loads places higher demands on the flexibility and stability of the power grid. However, the small capacity, large scale, and wide variety of distributed energy resources require flexible resources to match random fluctuations in source and load through demand response mechanisms. Therefore, the concept of virtual power plants was proposed: aggregating the various distributed resources in the power grid into a whole, which is uniformly controlled and managed by a virtual power plant. As a new resource aggregation technology, virtual power plants have gradually become an important means of improving the system's regulatory capabilities by coordinating decentralized source, load, and storage resources to participate in power market and grid regulation. However, existing virtual power plant technology still has limitations.

[0003] Traditional virtual power plants (VPPs) focus on simple resource aggregation and scheduling, but fail to fully exploit the dynamic control potential of key power electronic equipment (PETs), failing to fully integrate the collaborative optimization of VPPs with the flexible power regulation capabilities of PETs. Furthermore, the increasing proportion of intermittent power sources has exacerbated grid volatility and uncertainty, placing higher demands on the coordination capabilities of VPPs. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method for collaborative source-load control of a power electronic transformer virtual power plant. By deeply integrating the PET multi-port control characteristics with the collaborative optimization technology of the virtual power plant, the collaborative control and rapid response of the source-load resources of the PET virtual power plant are realized, thereby improving the stability, reliability and economy of the power grid system in high-penetration renewable energy scenarios.

[0005] The present invention adopts the following technical solutions.

[0006] A first aspect of the present invention provides a source-load coordinated control method for a power electronic transformer virtual power plant, the method comprising:

[0007] Step 1: Construct a load compensation cost model based on the PET operating parameters, and then establish an economic dispatch model based on the load compensation cost model with the goal of maximizing the total revenue of the virtual power plant;

[0008] Step 2: Based on the economic dispatch model, iteratively solve the local sub-problems and the global coordination problem to obtain the optimized PET port interactive active power and photovoltaic output power. Combined with the characteristic parameters of the distributed power supply, the energy storage battery state of charge is updated to ensure that the energy storage battery state of charge meets the specified range.

[0009] Step 3: Aggregate PV, energy storage, and PET loads into a uniformly schedulable virtual machine group. Calculate the virtual machine group state of charge (SOC) based on the optimized PET port interactive active power, PV output power, and the updated energy storage battery SOC. Coordinated control of PV, energy storage, and PET loads is performed based on the SOC of the virtual machine group to achieve system power balance.

[0010] Preferably, in step 1, according to the PET operating parameters Construct the following load compensation cost model:

[0011]

[0012] The constraints are:

[0013]

[0014]

[0015] Where, is the load compensation cost, R L is the equivalent resistance of the circuit, The reactive power provided to the PET reactive compensation equipment, is the loss factor of PET reactive compensation equipment, is the interactive active power of the PET port, is the interactive reactive power of the PET port, For PET i The active power interacting between each port and the bus, For PET i The reactive power of the interaction between each port and the busbar, V i For PET i The effective value of the port voltage.

[0016] Preferably, in step 1, the following economic dispatch model is established based on the load compensation cost model with the goal of maximizing the total revenue of the virtual power plant:

[0017]

[0018]

[0019] Where, is the objective function,B is the total revenue of the virtual power plant, is the electricity selling price, z is the global power plan value, C is the marginal cost of electricity generation, is the energy storage charging and discharging power; is the photovoltaic output power; Compensate for the load.

[0020] Preferably, in step 2, iteratively solving local subproblems and global coordination problems based on the economic dispatch model to obtain optimized PET port interactive active power and photovoltaic output power includes:

[0021] (1) The objective function expression of the local subproblem is as follows: , used in the k In the iterative process, the k Suboptimal photovoltaic output power and energy storage charging and discharging power , and then calculate the ideal interactive active power of the PET port and PET AC port coupling variables :

[0022]

[0023]

[0024] Where, is the penalty coefficient, For the k The global power plan value of the iteration, is the objective function of the economic dispatch model;

[0025] (2) The global coordination problem is used to determine the active power of the PET ports under ideal conditions. and PET AC port coupling variables The global power plan value is iterated and fed back to the local subproblem until , output the current 、 , as the optimized PET port interactive active power and photovoltaic output power ; The formula for iterating the global power plan value is:

[0026]

[0027] Where, For the k The global power plan value at +1 iteration, is the global power planning error threshold, is the penalty coefficient.

[0028] Preferably, in step 2, the optimized PET port interactive active power and photovoltaic output power Combined with the characteristic parameters of distributed power supply Update the state of charge of the energy storage battery so that the updated state of charge of the energy storage battery meets To avoid overcharge or overdischarge, the update formula is as follows:

[0029]

[0030] Where, is the updated state of charge of the energy storage battery, The interval between each update; is the charge and discharge efficiency, is the energy storage battery capacity, is the energy storage charging and discharging power.

[0031] Preferably, the calculation formula for the energy storage charging and discharging power is as follows:

[0032]

[0033] Where, The charging and discharging power of the energy storage battery when the state of charge of the energy storage battery is equal to 20%, It is the charging and discharging power of the energy storage battery when the state of charge of the energy storage battery is equal to 80%.

[0034] Preferably, in step 3, photovoltaic, energy storage and PET loads are aggregated into a virtual machine group that can be uniformly scheduled, and the active power of the optimized PET port interaction is , photovoltaic output power P PV and updated energy storage battery state of charge The charge status of the virtual machine group is as follows:

[0035]

[0036] Where, is the charge state of the virtual machine group, is the updated state of charge of the energy storage battery, For energy storage charging efficiency, is the photovoltaic efficiency coefficient, is the energy storage discharge efficiency, is the energy storage battery capacity.

[0037] Preferably, in step 3, the actions to be taken by the energy storage, photovoltaics, and loads in the case of power shortage or power surplus are determined according to the charge state of the virtual machine group, including:

[0038] (1) System power situation is , and the virtual machine group's charge state When it is not close to the upper limit, control the energy storage charging to make ;

[0039] (2) System power situation is , and the virtual machine group's charge state When it approaches the upper limit, if the PET is not in full load operation state, the PET load will be increased; if the PET is in full load operation state, the photovoltaic output will be reduced. ;

[0040] (3) System power situation is , and the virtual machine group's charge state When it is not close to the lower limit, control the energy storage discharge to make ;

[0041] (4) System power situation is , and the virtual machine group's charge state When approaching the lower limit, if PET is not in no-load operation state, then reduce PET load; if PET is in no-load operation state, then increase photovoltaic output to make ;

[0042] Among them, when When, think Close to the upper limit, otherwise not close to the upper limit;

[0043] when When, think Close to the lower limit, otherwise not close to the lower limit, is the judgment threshold.

[0044] A second aspect of the present invention provides a power electronic transformer virtual power plant source-load coordinated control system, comprising:

[0045] A model building module is used to build a load compensation cost model based on the PET operating parameters, and to establish an economic dispatch model based on the load compensation cost model with the goal of maximizing the total revenue of the virtual power plant;

[0046] The iterative optimization module is used to iteratively solve local sub-problems and global coordination problems based on the economic dispatch model to obtain the optimized PET port interactive active power and photovoltaic output power. The state of charge of the energy storage battery is updated based on the characteristic parameters of the distributed power supply to ensure that the state of charge of the energy storage battery meets the specified range.

[0047] The collaborative control module is used to aggregate photovoltaic, energy storage and PET loads into a virtual machine group that can be uniformly scheduled. The virtual machine group charge state is obtained based on the optimized PET port interactive active power and photovoltaic output power and the updated energy storage battery charge state. The photovoltaic, energy storage and PET loads are collaboratively controlled according to the virtual machine group charge state to achieve system power balance.

[0048] A third aspect of the present invention provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; and the processor is used to operate according to the instructions to execute the steps of the method.

[0049] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0050] The beneficial effects of the present invention are as follows:

[0051] The present invention constructs a load compensation cost model according to the PET operating parameters, and establishes an economic dispatch model based on the load compensation cost model with the goal of maximizing the total profit of the virtual power plant. It fully considers the port parameters and adjustment capabilities of the PET, combines the characteristic parameters of the distributed power supply and the photovoltaic and energy storage adjustment capabilities, realizes the optimization of the interactive active power and photovoltaic output power of the PET port, takes into account the SOC limit problem, updates the charge state of the energy storage battery, avoids overcharging or over-discharging of the energy storage battery, and improves practicality and safety.

[0052] The present invention decomposes the complex global optimization problem of maximizing the total revenue of a virtual power plant into local sub-problems and a global coordination problem and solves them iteratively, thereby improving the optimization efficiency. By introducing coupling variables, the deviation between the local solution and the global objective is corrected, which can improve the solution accuracy and convergence.

[0053] The present invention aggregates photovoltaic, energy storage and PET loads into a virtual machine group that can be uniformly scheduled. The state of charge of the virtual machine group is obtained by comprehensively considering the optimized PET port interactive active power, photovoltaic output power and updated energy storage battery state of charge to determine the behavior of energy storage, photovoltaic and loads in the case of power shortage or power surplus, thereby realizing optimized coordinated control of PET virtual power plant source and load. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a source-load coordinated control method for a power electronic transformer virtual power plant proposed by the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Embodiment 1 of the present invention provides a method for source-load coordinated control of a power electronic transformer virtual power plant, such as Figure 1 As shown, including:

[0057] Step 1: Construct a load compensation cost model based on the PET operating parameters, and then establish an economic dispatch model based on the load compensation cost model with the goal of maximizing the total revenue of the virtual power plant;

[0058] Step 101: Constructing a load compensation cost model based on PET operating parameters, specifically including:

[0059] (1) The port parameters of PET, including the interaction power between the AC and DC ports and the bus, are shown in the following formula.

[0060]

[0061] Where, is the acquired PET port parameter, For PET i The active power interacting between each port and the busbar is specified to flow in the positive direction from the AC side to the DC side. For PET i The reactive power interacting between each port and the busbar.

[0062] (2) Construction of load compensation cost model:

[0063]

[0064] The constraints are the bus power balance equation:

[0065]

[0066]

[0067] Where, Compensate for the load.

[0068] R L is the equivalent resistance of the circuit, The reactive power provided to the PET reactive compensation equipment, It is the loss factor of PET reactive power compensation equipment.

[0069] For PET i The active power interacting between each port and the busbar is specified to flow in the positive direction from the AC side to the DC side. For PET i The reactive power interacting between each port and the busbar.

[0070] is the interactive active power of the PET port, The interactive reactive power of the PET port.

[0071] V i For PET i The effective value of the port voltage.

[0072] Step 102: Based on the load compensation cost model, an economic dispatch model is established with the goal of maximizing the total revenue of the virtual power plant.

[0073] Further preferably, an economic dispatch model is established based on the load compensation cost model. The objective function of the economic dispatch model is set to maximize the virtual power plant revenue, which is calculated as follows:

[0074]

[0075]

[0076] Where, is the objective function, B is the total revenue of the virtual power plant, Electricity sales price, z is the global target variable, C is the marginal cost of electricity generation, is the energy storage charging and discharging power; is the photovoltaic output power; Compensate for the load.

[0077] Step 2: Based on the economic dispatch model, iteratively solve the local sub-problems and the global coordination problem to obtain the optimized PET port interactive active power and photovoltaic output power. Combined with the characteristic parameters of the distributed power supply, the energy storage battery state of charge is updated to ensure that the energy storage battery state of charge meets the specified range, including:

[0078] Step 201: Based on the economic dispatch model of step 1, iteratively solve the local sub-problems and the global coordination problem to obtain the optimized PET port interactive active power and photovoltaic output power Specifically, the ADMM optimization algorithm is used to decompose the global problem into local sub-problems and global coordination problems, which are solved alternately to coordinate the local decisions of multiple PETs:

[0079] (1) The optimization objective of the local subproblem is to minimize the negative return. The objective function expression is as follows:

[0080]

[0081] The physical meaning of solving the local subproblem is to minimize its own cost while satisfying the local constraints and approaching the global target variable. , Defined as k The global power plan value at the iteration Before the iteration starts z Initial value Artificially given.

[0082] Local sub-problem k The photovoltaic output power is obtained after iterations and energy storage charging and discharging power , and then calculate the ideal interactive active power of the PET port and PET AC port coupling variables .

[0083]

[0084]

[0085] Where, is the penalty coefficient.

[0086] (2) The purpose of global coordination is to iterate the global target variable. The iteration formula is as follows.

[0087]

[0088] Where, For the k +Global power plan value at iteration 1.

[0089] The global coordination problem is solved Substitute into the local subproblem solution, and we can solve and , and then substitute it into the global coordination problem to solve it. Stop when the current 、 , as the final optimized PET port interactive active power , photovoltaic output power ,in is the global power planning error threshold.

[0090] Step 202: The optimized PET port interactive active power and photovoltaic output power are combined with characteristic parameters of the distributed power supply to update the state of charge of the energy storage battery so that the state of charge of the energy storage battery meets the limited range;

[0091] (1) Obtain the characteristic parameters of the distributed power supply in the system, as shown in the following formula.

[0092]

[0093] Where, To obtain the characteristic parameters of the distributed power supply in the system, is the charge and discharge efficiency, is the energy storage battery capacity.

[0094] (2) Dynamically update the energy storage battery SOC so that the SOC meets the constraints (battery SOC boundary ), to avoid overcharging or over-discharging of the battery. The SOC update formula is as follows:

[0095]

[0096] Where, for The updated energy storage battery SOC, The interval between each update, is the charge and discharge efficiency, is the energy storage battery capacity.

[0097] Due to the change in the system's flexible load regulation, the total system load L Also changed, usually, the system base load is set in advance L base , in the above formula The calculation formula is as follows.

[0098]

[0099] Where, is the charging and discharging power of the energy storage battery when the SOC is equal to 20%, It is the charging and discharging power of the energy storage battery when the SOC is equal to 80%.

[0100] Step 3: Aggregate the PV, energy storage, and PET loads into a virtual machine group that can be uniformly scheduled. The virtual machine group state of charge is obtained based on the interactive active power of the PET port, the PV output power, and the energy storage battery state of charge in step 2. Based on the virtual machine group state of charge, the PV, energy storage, and PET loads are coordinated and controlled to achieve system power balance. This includes:

[0101] Step 301: Aggregate the optimization results of step 2, including PPV 、 The three are dynamically combined to form a "virtual machine group" that can be uniformly scheduled. The state of charge of the virtual machine group is shown in the following formula:

[0102]

[0103] Where, is the charge state of the virtual machine group, Energy storage charging efficiency (80%~85%), is the photovoltaic efficiency coefficient (0~1), which is related to real-time light and temperature. It is the energy storage discharge efficiency (80%~85%).

[0104] Step 302: According to step 301 , determine the actions of energy storage, photovoltaics and loads in the case of power shortage or power surplus, and conduct coordinated control of virtual power plant sources and loads.

[0105] Further preferably, the virtual power plant needs to flexibly control the behavior of each component in the system according to different scenarios, as shown in Table 1.

[0106] Table 1

[0107]

[0108] Among them, when When, think Close to the upper limit, otherwise not close to the upper limit;

[0109] when When, think Close to the lower limit, otherwise not close to the lower limit, is the judgment threshold.

[0110] Embodiment 2 of the present invention provides a power electronic transformer virtual power plant source-load coordinated control system, including:

[0111] A model building module is used to build a load compensation cost model based on the PET operating parameters, and to establish an economic dispatch model based on the load compensation cost model with the goal of maximizing the total revenue of the virtual power plant;

[0112] The iterative optimization module is used to iteratively solve local sub-problems and global coordination problems based on the economic dispatch model to obtain the optimized PET port interactive active power and photovoltaic output power. The state of charge of the energy storage battery is updated based on the characteristic parameters of the distributed power supply to ensure that the state of charge of the energy storage battery meets the specified range.

[0113] The collaborative control module is used to aggregate photovoltaic, energy storage and PET loads into a virtual machine group that can be uniformly scheduled. The virtual machine group charge state is obtained based on the optimized PET port interactive active power and photovoltaic output power and the updated energy storage battery charge state. The photovoltaic, energy storage and PET loads are collaboratively controlled according to the virtual machine group charge state to achieve system power balance.

[0114] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0115] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0116] The present invention constructs a load compensation cost model according to the PET operating parameters, and establishes an economic dispatch model based on the load compensation cost model with the goal of maximizing the total profit of the virtual power plant. It fully considers the port parameters and adjustment capabilities of the PET, combines the characteristic parameters of the distributed power supply and the photovoltaic and energy storage adjustment capabilities, realizes the optimization of the interactive active power and photovoltaic output power of the PET port, takes into account the SOC limit problem, updates the charge state of the energy storage battery, avoids overcharging or over-discharging of the energy storage battery, and improves practicality and safety.

[0117] The present invention decomposes the complex global optimization problem of maximizing the total revenue of a virtual power plant into local sub-problems and a global coordination problem and solves them iteratively, thereby improving the optimization efficiency. By introducing coupling variables, the deviation between the local solution and the global objective is corrected, which can improve the solution accuracy and convergence.

[0118] The present invention aggregates photovoltaic, energy storage and PET loads into a virtual machine group that can be uniformly scheduled. The state of charge of the virtual machine group is obtained by comprehensively considering the optimized PET port interactive active power, photovoltaic output power and updated energy storage battery state of charge to determine the behavior of energy storage, photovoltaic and load in the case of power shortage or power surplus. It can realize the optimized collaborative control of PET virtual power plant source and load, and has scalability, supports distributed expansion of equipment, and is more conducive to active aggregation and collaborative control of source and load.

[0119] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0120] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0121] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0122] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, the state information of the computer-readable program instructions is used to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), so that the electronic circuit can execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for source-load coordinated control of a power electronic transformer virtual power plant, characterized in that: The method comprises: Step 1: Construct a load compensation cost model based on the PET operating parameters. On the basis of the load compensation cost model, establish the following economic dispatch model with the goal of maximizing the total revenue of the virtual power plant: Where, is the objective function, B is the total revenue of the virtual power plant, is the electricity selling price, z is the global power plan value, C is the marginal cost of electricity generation, is the energy storage charging and discharging power; is the photovoltaic output power; Compensate for load costs; Step 2: Based on the economic dispatch model, iteratively solve the local sub-problems and the global coordination problem to obtain the optimized PET port interactive active power and photovoltaic output power. Combined with the characteristic parameters of the distributed power supply, the energy storage battery state of charge is updated to ensure that the energy storage battery state of charge meets the specified range. Step 3: Aggregate photovoltaic, energy storage and PET loads into a virtual machine group that can be uniformly scheduled, obtain the virtual machine group charge state based on the optimized PET port interactive active power and photovoltaic output power and the updated energy storage battery charge state, and coordinately control photovoltaic, energy storage and PET loads according to the virtual machine group charge state to achieve system power balance, wherein photovoltaic, energy storage and PET loads are aggregated into a virtual machine group that can be uniformly scheduled, and the optimized PET port interactive active power is obtained. , photovoltaic output power P PV and updated energy storage battery state of charge The charge status of the virtual machine group is as follows: Where, is the charge state of the virtual machine group, is the updated state of charge of the energy storage battery, For energy storage charging efficiency, is the photovoltaic efficiency coefficient, is the energy storage discharge efficiency, is the energy storage battery capacity.

2. The method for source-load coordinated control of a power electronic transformer virtual power plant according to claim 1, characterized in that: In step 1, according to the PET operating parameters Construct the following load compensation cost model: The constraints are: Where, is the load compensation cost, R L is the equivalent resistance of the circuit, The reactive power provided to the PET reactive compensation equipment, is the loss factor of PET reactive compensation equipment, is the interactive active power of the PET port, is the interactive reactive power of the PET port, For PET i The active power interacting between each port and the bus, For PET i The reactive power of the interaction between each port and the busbar, V i For PET i The effective value of the port voltage.

3. The method for source-load coordinated control of a power electronic transformer virtual power plant according to claim 1, characterized in that: In step 2, the local sub-problems and the global coordination problem are iteratively solved based on the economic dispatch model to obtain the optimized PET port interactive active power and PV output power, including: (1) The objective function expression of the local subproblem is as follows: , used in the k In the iterative process, the k Suboptimal photovoltaic output power and energy storage charging and discharging power , and then calculate the ideal interactive active power of the PET port and PET AC port coupling variables : Where, is the penalty coefficient, For the k The global power plan value of the iteration, is the objective function of the economic dispatch model; (2) The global coordination problem is used to determine the active power of the PET ports under ideal conditions. and PET AC port coupling variables The global power plan value is iterated and fed back to the local subproblem until , output the current 、 , as the optimized PET port interactive active power and photovoltaic output power ; The formula for iterating the global power plan value is: Where, For the k The global power plan value at +1 iteration, is the global power planning error threshold, is the penalty coefficient.

4. The method for source-load coordinated control of a power electronic transformer virtual power plant according to claim 1, characterized in that: In step 2, the optimized PET port interactive active power and photovoltaic output power Combined with the characteristic parameters of distributed power supply Update the state of charge of the energy storage battery so that the updated state of charge of the energy storage battery meets To avoid overcharging or over-discharging of the battery, the update formula is as follows: Where, is the updated state of charge of the energy storage battery, The interval between each update; is the charge and discharge efficiency, is the energy storage battery capacity, is the energy storage charging and discharging power, It is the state of charge of the energy storage battery.

5. The method for source-load coordinated control of a power electronic transformer virtual power plant according to claim 4, characterized in that: The calculation formula for the energy storage charging and discharging power is as follows: Where, The charging and discharging power of the energy storage battery when the state of charge of the energy storage battery is equal to 20%, The charging and discharging power of the energy storage battery when the state of charge of the energy storage battery is equal to 80%, L is the total system load, L base The default system baseline load.

6. The method for source-load coordinated control of a power electronic transformer virtual power plant according to claim 1, characterized in that: In step 3, the photovoltaic, energy storage, and PET loads are coordinated and controlled according to the state of charge of the virtual machine group to achieve system power balance, including: (1) System power situation is , and the virtual machine group's charge state When it is not close to the upper limit, control the energy storage charging to make ,in is the photovoltaic efficiency coefficient, 、 P PV To optimize the interactive active power and photovoltaic output power of the PET port; (2) System power situation is , and the virtual machine group's charge state When it approaches the upper limit, if the PET is not in full load operation state, the PET load will be increased; if the PET is in full load operation state, the photovoltaic output will be reduced. ; (3) System power situation is , and the virtual machine group's charge state When it is not close to the lower limit, control the energy storage discharge to make ; (4) System power situation is , and the virtual machine group's charge state When approaching the lower limit, if PET is not in no-load operation state, then reduce PET load; if PET is in no-load operation state, then increase photovoltaic output to make ; Among them, when When, think Close to the upper limit, otherwise not close to the upper limit; when When, think Close to the lower limit, otherwise not close to the lower limit, is the judgment threshold.

7. A power electronic transformer virtual power plant source-load coordinated control system, running the method according to any one of claims 1 to 6, characterized in that: The system comprises: A model building module is used to build a load compensation cost model based on the PET operating parameters, and to establish an economic dispatch model based on the load compensation cost model with the goal of maximizing the total revenue of the virtual power plant; The iterative optimization module is used to iteratively solve local sub-problems and global coordination problems based on the economic dispatch model to obtain the optimized PET port interactive active power and photovoltaic output power. The state of charge of the energy storage battery is updated based on the characteristic parameters of the distributed power supply to ensure that the state of charge of the energy storage battery meets the specified range. The collaborative control module is used to aggregate photovoltaic, energy storage and PET loads into a virtual machine group that can be uniformly scheduled. The virtual machine group charge state is obtained based on the optimized PET port interactive active power and photovoltaic output power and the updated energy storage battery charge state. The photovoltaic, energy storage and PET loads are collaboratively controlled according to the virtual machine group charge state to achieve system power balance.

8. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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