A metrology-driven micro-assembly collaborative power command tracking method and device

Abstract

The specification provides one or more embodiments of a measurement-driven micro-grid collaborative power instruction tracking method and device, the method comprising: in response to the power scheduling instruction issued by the power distribution network, the micro-grid cluster control center acquires PCC power measurement data from the public connection point connected to it; inputting the PCC power measurement data into the pre-established hierarchical instruction tracking model, and determining whether the actual output power of the micro-grid cluster deviates from the scheduling instruction, whether the operation cost is acceptable, and whether the related constraints in the hierarchical instruction tracking model are met through the hierarchical instruction tracking model; the micro-grid cluster control center updates the coordination signal in real time according to the DG power measurement data in the micro-grid through a non-iterative calculation method; the internal control center of the micro-grid calculates the optimal output ratio of the power supply in the network according to the received PCC power measurement data and the coordination signal, and each distributed power source tracks the above optimal output ratio according to its own rated power.

Description

Technical Field

[0001] One or more embodiments in this specification relate to the field of power grids, and more particularly to a method and apparatus for distribution micro-cooperative power command tracking based on measurement-driven methods. Background Technology

[0002] When a microgrid cluster connects to and participates in distribution network dispatch, the upstream distribution network issues dispatch instructions to the point of common coupling (PCC) of the microgrid cluster, specifying whether the microgrid cluster's PCC injects or absorbs active power into the distribution network. The time required to calculate the optimal output power of distributed power sources within the microgrid cluster is one of the main factors affecting the microgrid cluster's ability to track distribution network instructions.

[0003] Generally, centralized and distributed methods are the two main approaches to command tracing for controllable distributed power sources within a microgrid cluster. Centralized optimization methods involve the dispatch center collecting and processing global information before issuing control commands to each controller, leading to increased processing time and greater difficulty in achieving rapid tracing. Distributed power sources are characterized by their numerous points and wide distribution. Distributed methods involve distributed collaborative optimization by the local controllers of the distributed power sources. Each controller is responsible for a smaller problem scale, resulting in reduced computational difficulty and time. However, traditional distributed methods often require multiple iterations of boundary information exchange between the local controllers and the microgrid control center when calculating the optimal power setpoint for the distributed power sources. This iterative process is time-consuming, leading to slow tracing speeds.

[0004] With technological advancements, some researchers have adopted the non-optimal result of a single iteration in distributed tracking methods as the current setpoint for the output power of distributed energy sources, referring to this calculation method as the non-iterative mode. However, the aforementioned non-iterative calculation method inherently contains systemic and model errors, which accumulate over time and ultimately affect the accuracy of tracking. Furthermore, while the above method considers the tracking of distributed energy sources on distribution network dispatch, it does not account for the multi-level tracking of dispatch commands issued by the distribution network when multiple microgrids are connected to the distribution network. Summary of the Invention

[0005] In view of this, this specification provides a measurement-driven micro-cooperative power command tracking method and apparatus for one or more embodiments, which can solve the shortcomings existing in related technologies.

[0006] To achieve the above objectives, this specification provides the following technical solutions for one or more embodiments:

[0007] According to a first aspect of one or more embodiments of this specification, a measurement-driven distribution-microgrid coordinated power command tracking method is proposed, applied to a multi-level power system model formed after a microgrid cluster is integrated into a higher-level distribution network. The method includes:

[0008] In response to the power dispatch command issued by the distribution network, the microgrid cluster control center obtains PCC power measurement data from its grid-connected point of common coupling and distributes it to each microgrid.

[0009] The PCC power measurement data is input into a pre-established hierarchical command tracing model. The hierarchical command tracing model is used to determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data meets the relevant constraints in the hierarchical command tracing model. The hierarchical command tracing model includes an inner model and an outer model. The inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the goal of minimizing the charging and discharging cost of the microgrid cluster and the distributed power sources within the microgrid cluster. The scheduling command is a time-varying parameter in the inner and outer objective functions. The linear constraints corresponding to the inner and outer objective functions are transformed based on the error constraints of the scheduling command, the adjustable power output constraints of the distributed energy sources, the node voltage amplitude constraints, and the power flow constraints.

[0010] Under the conditions that the actual power output of the microgrid cluster deviates from the dispatch command, the operating cost is acceptable, and the relevant constraints in the hierarchical command tracking model are met, the microgrid cluster control center updates the coordination signal in real time based on the DG power measurement data in the microgrid using the non-iterative calculation method in the upper-level command tracking model to compensate for the power deviation caused by the non-iterative optimization method, and then sends it to each microgrid.

[0011] The microgrid's internal control center calculates the optimal power output ratio of the power sources within the grid based on the received PCC power measurement data and coordination signals. Each distributed power source tracks the optimal power output ratio in a distributed and collaborative manner according to its own rated power to achieve power sharing among the power sources. The real-time DG power measurement data is then fed back to the microgrid cluster control center through the hierarchical command tracking model.

[0012] As a preferred embodiment, the real-time updating of the coordination signal based on the DG power measurement data within the microgrid includes:

[0013] The coordination signal is updated using the original-dual decomposition algorithm and the distributed projection subgradient gradient algorithm.

[0014] As a preferred embodiment, updating the coordinated signal using the primal-dual decomposition algorithm and distributed projection subgradient includes:

[0015] The dual problem of the tracking model constructed based on the original-dual decomposition algorithm and distributed projective subgradient is calculated using the Lagrangian function.

[0016] Through consensus algorithms and power sharing strategies, each distributed power source can output power in its optimal state.

[0017] As a preferred option,

[0018] The inner objective function is: ;

[0019] in, for Internal communication side section, for Internal DC side section, Let represent the objective function of distributed generation within a microgrid. Indicated middle exist The active power output at all times;

[0020] The outer objective function is: ;

[0021] in, This represents the total number of microgrids contained in the microgrid cluster. Indicating the microgrid cluster Inside Quantity, The objective function for operating the microgrid cluster is... and They are respectively exist The active power output and line losses at all times.

[0022] As a preferred option,

[0023] The instruction tracking error constraint included in the outer constraint corresponding to the outer objective function is as follows: ;

[0024] in, The actual active power injected into the point of common connection for the microgrid cluster. This indicates the scheduling instruction. The maximum allowable tracking error;

[0025] The outer constraint includes the output power constraints of each microgrid within the microgrid cluster as follows:

[0026] ;

[0027] in, , , , These respectively represent the microgrid clusters Adjustable upper and lower limits for active and reactive power;

[0028] The voltage amplitude constraint included in the outer constraint is: ;

[0029] in, express The allowable range of the output voltage amplitude at PCC at any given time;

[0030] The power flow equations included in the outer constraint are: ;

[0031] in, ( ), ( ), ( The vectors representing the total voltage amplitude, injected active power, and reactive power of the microgrid cluster are respectively. This represents a nonlinear function relating the injected power and voltage amplitude of a microgrid cluster.

[0032] The instruction tracking error constraint included in the inner constraint corresponding to the inner objective function is as follows: ;

[0033] in, for time Power scheduling commands issued to internal distributed power sources. For the present All power supplies are Output power at any moment for Maximum allowable power tracking error;

[0034] The distributed energy output constraints included in the inner layer constraints are:

[0035] ;

[0036] in, , , , They represent Output active power and reactive power (when The rated maximum and minimum values ​​(at time);

[0037] The voltage amplitude constraint included in the inner layer constraint is: ;

[0038] in, express time Inside The permissible range of output voltage amplitude.

[0039] According to a second aspect of one or more embodiments of this specification, a measurement-driven distribution-microgrid collaborative power command tracking device is proposed, applied to a multi-level power system model formed after a microgrid cluster is integrated into a higher-level distribution network. The device includes:

[0040] Acquisition Unit: In response to the power dispatch command issued by the distribution network, the microgrid cluster control center acquires PCC power measurement data from its grid-connected common connection point and distributes it to each microgrid;

[0041] Input Unit: Inputs the PCC power measurement data into a pre-established hierarchical command tracing model. The hierarchical command tracing model determines whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data meets the relevant constraints in the hierarchical command tracing model. The hierarchical command tracing model includes an inner model and an outer model. The inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the goal of minimizing the charging and discharging cost of the microgrid cluster and the distributed power sources within the microgrid cluster. The scheduling command is a time-varying parameter in the inner and outer objective functions. The linear constraints corresponding to the inner and outer objective functions are transformed based on the error constraints of the scheduling command, the adjustable constraints of the distributed power output, the node voltage amplitude constraints, and the power flow constraints.

[0042] Update Unit: Under the conditions that the actual power output of the microgrid cluster deviates from the dispatch command, the operating cost is acceptable, and the relevant constraints in the hierarchical command tracing model are met, the microgrid cluster control center updates the coordination signal in real time based on the DG power measurement data in the microgrid using the non-iterative calculation method in the upper-level command tracing model to compensate for the power deviation caused by the non-iterative optimization method, and then sends it to each microgrid.

[0043] Feedback Unit: The microgrid internal control center calculates the optimal power output ratio of the power sources within the grid based on the received PCC power measurement data and coordination signals. Each distributed power source tracks the optimal power output ratio in a distributed and collaborative manner according to its own rated power to achieve power sharing among the power sources. The real-time DG power measurement data is then fed back to the microgrid cluster control center through the hierarchical command tracking model.

[0044] According to a third aspect of one or more embodiments of this specification, an electronic device is provided, comprising:

[0045] processor;

[0046] Memory used to store processor-executable instructions;

[0047] The processor implements the steps of the method as described in the first aspect by running the executable instructions.

[0048] According to a fourth aspect of one or more embodiments of this specification, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0049] As can be seen from the above technical solutions, the technical effects of the measurement-driven distributed projection subgradient instruction tracing method provided in one or more embodiments of this specification are as follows:

[0050] (1) Compared with the traditional centralized method, which is more difficult and time-consuming to collect information and perform centralized calculations, making it difficult to achieve fast tracking, this manual adopts a distributed collaborative optimization method. The problem scale of each controller is smaller, and the calculation difficulty and time are reduced.

[0051] (2) Compared with the traditional non-iterative mode, which introduces systematic errors, model errors and systematic errors accumulate over time and eventually affect the accuracy of tracking, the real-time measurement feedback introduced in the optimization decision-making stage of this manual makes the tracking process form a closed loop. Moreover, it adopts a hierarchical form, dividing the model into inner and outer layers, which can dynamically and quickly correct tracking errors and reduce the impact of model errors and systematic errors on tracking accuracy.

[0052] (3) This specification considers the tracking of instructions issued by the distribution network by each microgrid and the distributed power sources within the microgrid when multiple microgrids are connected to the distribution network. Attached Figure Description

[0053] Figure 1 This is an exemplary embodiment of an instruction tracing system architecture diagram.

[0054] Figure 2 This is a flowchart of a measurement-driven micro-cooperative power command tracking method provided in an exemplary embodiment.

[0055] Figure 3 This is a schematic diagram of a specific instruction tracing method provided in an exemplary embodiment.

[0056] Figure 4 This is a schematic structural diagram of a device provided in an exemplary embodiment.

[0057] Figure 5This is a block diagram of a measurement-driven micro-cooperative power command tracking device provided in an exemplary embodiment. Detailed Implementation

[0058] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0059] It should be noted that in other embodiments, the corresponding methods are not necessarily performed in the order shown and described in this specification.

[0060] The steps of the method. In some other embodiments, the method may include more or fewer steps than those described herein. Furthermore, a single step described herein may be broken down into multiple steps in other embodiments; and multiple steps described herein may be combined into a single step in other embodiments.

[0061] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0062] To further illustrate one or more embodiments of this specification, the following embodiments are provided:

[0063] When a microgrid cluster connects to and participates in distribution network dispatch, the upstream distribution network issues dispatch instructions to the point of common coupling (PCC) of the microgrid cluster, specifying that the microgrid cluster injects or absorbs active power from the PCC into the distribution network. The time required to calculate the output power of distributed generation sources within the microgrid cluster is one of the main factors affecting the microgrid cluster's ability to track distribution network instructions.

[0064] Figure 1 This is an exemplary embodiment of an instruction tracing system architecture diagram. (See diagram for example.) Figure 1As shown, the command tracking system includes a distribution network 11, a microgrid cluster 12, and a control center. The microgrid cluster 12 comprises a PCC121 and multiple microgrids (a first microgrid 122, a second microgrid 123, and a third microgrid 124). The distribution network 11 can issue dispatch commands to the control center 13, instructing the first microgrid 122, the second microgrid 123, and the third microgrid 124 to inject or absorb active power from the PCC121 into the distribution network 11.

[0065] Generally, centralized and distributed methods are the two main approaches to command tracing for controllable distributed power sources within a microgrid cluster. Centralized optimization methods involve the dispatch center collecting and processing global information before issuing control commands to each controller, leading to increased processing time and greater difficulty in achieving rapid tracing. Distributed power sources are characterized by their numerous points and wide distribution. Distributed methods involve distributed collaborative optimization by the local controllers of the distributed power sources. Each controller is responsible for a smaller problem scale, resulting in reduced computational difficulty and time. However, traditional distributed methods often require multiple iterations of boundary information exchange between the local controllers and the microgrid control center when calculating the optimal power setpoint for the distributed power sources. This iterative process is time-consuming, leading to slow tracing speeds.

[0066] With technological advancements, some researchers have adopted the non-optimal result of a single iteration in distributed tracking methods as the current setpoint for the output power of distributed energy sources, referring to this calculation method as the non-iterative mode. However, the aforementioned non-iterative calculation method inherently contains systemic and model errors, which accumulate over time and ultimately affect the accuracy of tracking. Furthermore, while the above method considers the tracking of distributed energy sources on distribution network dispatch, it does not account for the multi-level tracking of dispatch commands issued by the distribution network when multiple microgrids are connected to the distribution network.

[0067] To address the shortcomings of related technologies, this specification proposes an instruction tracing method based on measurement-driven and consistency algorithms.

[0068] Figure 2 This is a flowchart illustrating a measurement-driven, micro-cooperative power command tracking method provided in an exemplary embodiment. (See attached diagram.) Figure 2 As shown, this method is applied to a power grid device, the power grid including the device, a wind power generation device, an energy storage system including two sets of energy storage devices, and a load. The device is used to coordinate the output of the wind power generation device and the energy storage system according to the load demand and the power generation of the wind power generation device. The method may include the following steps:

[0069] Step 202: In response to the power dispatch command issued by the distribution network, the microgrid cluster control center obtains PCC power measurement data from its grid-connected common connection point and sends it to each microgrid.

[0070] This dispatch command can be applied only to the coordination signal set of each microgrid in the microgrid cluster. The microgrid cluster can obtain power measurement data from the PCC through measurement devices; this specification does not limit the specific measurement devices.

[0071] Step 204: Input the PCC power measurement data into a pre-established hierarchical command tracing model. Use the hierarchical command tracing model to determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data meets the relevant constraints in the hierarchical command tracing model. The hierarchical command tracing model includes an inner model and an outer model. The inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the goal of minimizing the charging and discharging cost of the microgrid cluster and the distributed power sources within the microgrid cluster. The scheduling command is a time-varying parameter in the inner and outer objective functions. The linear constraints corresponding to the inner and outer objective functions are transformed based on the error constraints of the scheduling command, the adjustable power output constraints of the distributed energy sources, the node voltage amplitude constraints, and the power flow constraints.

[0072] In one embodiment, the inner objective function is: ;

[0073] in, for Internal communication side section, for Internal DC side section, Let represent the objective function of distributed generation within a microgrid. Indicated middle exist The active power output at all times;

[0074] The outer objective function is: ;

[0075] in, This represents the total number of microgrids contained in the microgrid cluster. Indicating the microgrid cluster Inside Quantity, The objective function for operating the microgrid cluster is... and They are respectively exist The active power output and line losses at all times.

[0076] Furthermore, the instruction tracking error constraint included in the outer constraint corresponding to the outer objective function is as follows: ;

[0077] in, The actual active power injected into the point of common connection for the microgrid cluster. This indicates the scheduling instruction. The maximum allowable tracking error;

[0078] The outer constraint includes the output power constraints of each microgrid within the microgrid cluster as follows:

[0079] ;

[0080] in, , , , These respectively represent the microgrid clusters Adjustable upper and lower limits for active and reactive power;

[0081] The voltage amplitude constraint included in the outer constraint is: ;

[0082] in, express The allowable range of the output voltage amplitude at PCC at any given time;

[0083] The power flow equations included in the outer constraint are: ;

[0084] in, ( ), ( ), ( The vectors representing the total voltage amplitude, injected active power, and reactive power of the microgrid cluster are respectively. This represents a nonlinear function relating the injected power and voltage amplitude of a microgrid cluster.

[0085] The instruction tracking error constraint included in the inner constraint corresponding to the inner objective function is as follows: ;

[0086] in, for time Power scheduling commands issued to internal distributed power sources. For the present All power supplies are Output power at any moment for Maximum allowable power tracking error;

[0087] The distributed energy output constraints included in the inner layer constraints are:

[0088] ;

[0089] in, , , , They represent Output active power and reactive power (when The rated maximum and minimum values ​​(at time);

[0090] The voltage amplitude constraint included in the inner layer constraint is: ;

[0091] in, express time Inside The permissible range of output voltage amplitude.

[0092] Step 206: Under the conditions that the actual power output of the microgrid cluster deviates from the dispatch command, the operating cost is acceptable, and the relevant constraints in the hierarchical command tracking model are met, the microgrid cluster control center updates the coordination signal in real time based on the DG power measurement data in the microgrid using the non-iterative calculation method in the upper-level command tracking model to compensate for the power deviation caused by the non-iterative optimization method, and then sends it to each microgrid.

[0093] Step 208: The microgrid internal control center calculates the optimal power output ratio of the power sources within the grid based on the received PCC power measurement data and coordination signals. Each distributed power source tracks the optimal power output ratio in a distributed and collaborative manner according to its own rated power to achieve power sharing among the power sources. The real-time DG power measurement data is then fed back to the microgrid cluster control center through the hierarchical command tracking model.

[0094] In this embodiment, on the one hand, compared to the traditional centralized method which is difficult and time-consuming to collect information and perform centralized calculations, making it difficult to achieve fast tracking, this embodiment adopts a distributed collaborative optimization method based on consensus algorithms. The problem scale of each controller is smaller, reducing the computational difficulty and time complexity. On the other hand, compared to the traditional non-iterative mode which introduces system errors, and the accumulation of model errors and system errors over time, ultimately affecting the accuracy of tracking, this embodiment introduces real-time measurement feedback in the optimization decision-making stage to form a closed loop in the tracking process. Furthermore, it adopts a hierarchical approach, dividing the model into inner and outer layers, which can dynamically and quickly correct tracking errors, reducing the impact of model errors and system errors on tracking accuracy. In addition, this embodiment considers the tracking of commands issued by the distribution network by each microgrid and the distributed power sources within the microgrid when multiple microgrids are connected to the distribution network, while also realizing power sharing among the various power sources.

[0095] In one embodiment, when the deviation between the actual power output of the microgrid cluster and the dispatch command is acceptable, the operating cost is acceptable, and the relevant constraints in the hierarchical command tracking model are met, the coordination signal issued to each microgrid in the microgrid cluster is updated through the hierarchical command tracking model, and the coordination signal is sent to the corresponding microgrid so that each microgrid updates its output power and enables the corresponding power source to output power based on the updated output power. This includes: when the deviation between the actual power output of the microgrid cluster and the dispatch command is acceptable, the operating cost is acceptable, and the relevant outer constraints in the hierarchical command tracking model are met, the microgrid cluster control center updates the coordination signal issued to the microgrid in real time based on the DG power measurement data in the microgrid, and determines each microgrid in the microgrid cluster as a microgrid cluster one by one; when the deviation between the actual power output of the microgrid and the dispatch command is acceptable, the operating cost is acceptable, and the relevant inner constraints in the hierarchical command tracking model are met, the microgrid control center calculates the optimal power output ratio of each power source in real time based on the DG power measurement data, and enables the output power of each power source to track the optimal power output ratio in a distributed manner to achieve power sharing.

[0096] Figure 3 This is a flowchart illustrating a specific instruction tracing method as provided in an exemplary embodiment. For example... Figure 3 As shown, the method may include the following steps:

[0097] Step 302: The microgrid cluster receives the command issued by the distribution network at time t. Step 304: The microgrid cluster control center obtains the PCC power measurement data from the PCC. Step 306: Determine whether the deviation between the actual power output of the microgrid cluster and the dispatch command, the operating cost are acceptable, and whether the outer constraints are met. If not, proceed to step 308a: the microgrid cluster control center updates the coordination signal. , and based on Update the power command for each microgrid and simultaneously send the power command to each microgrid; if so, proceed to step 308b, do not update the command, and proceed to the next time step.

[0098] Step 310: Identify each microgrid in the microgrid cluster as a microgrid cluster. Step 312: Determine whether the operating cost of the microgrid cluster is acceptable and whether it meets inner-layer constraints and power sharing. If not, proceed to step 314a, where the microgrid control center calculates the optimal output ratio of each power source in real time based on the DG power measurement data, and enables the output power of each power source to track this optimal output ratio in a distributed manner to achieve power sharing; if yes, proceed to step 314b, without updating the power command, and proceed to the next power scheduling window. .

[0099] Step 316: The microgrid cluster receives power dispatch instructions and coordination signals from the microgrid cluster control center. During the power scheduling window (in Real-time calculation and updates within ) Timing coordination signal (index satisfy It updates its optimal power command in real time. The microgrid control center, based on the optimal power command... Calculate the optimal power output ratio of each power source in the network. Each power supply By distributing the tracking to the optimal power output ratio, the final result is achieved. During the power dispatch window Power point tracking. Step 318, the microgrid cluster completes... The tracking of one moment shifts to the next.

[0100] In one embodiment, the coordination signal is updated in real time based on the DG power measurement data within the microgrid. This includes updating the coordinated signal using a primal-dual decomposition algorithm and a distributed projection subgradient.

[0101] Furthermore, updating the coordination signal through the primal-dual decomposition algorithm and distributed projection subgradient includes: calculating the dual problem of the tracking model constructed based on the primal-dual decomposition algorithm and distributed projection subgradient using the Lagrangian function; and achieving optimal power output for each distributed power source through a consensus algorithm and a power sharing strategy.

[0102] The coordination signal is calculated using primal-dual decomposition and distributed projective subgradient method. And related parameters.

[0103] First, the outer tracking model is designed as follows:

[0104] ;

[0105] Construct the following Lagrange function:

[0106] ;

[0107] Where g represents the inequality and equality constraints in the tracking model, and the coordination signal It consists of dual multipliers constrained in the tracking model. and They form a regular coefficient relationship.

[0108] Based on the above analysis, the original tracking model can be described in the form of a dual problem as follows:

[0109] ;

[0110] In the case of only the outer model (i.e., centralized), the projection gradient algorithm is used to derive the control signal from the dual problem described above. And the calculation formula for the required scheduling scheme, finally obtained Real-time distributed instruction tracing algorithm.

[0111] The algorithm flow steps are as follows:

[0112] Step 1: Use measuring equipment to collect the actual active power of the PCC. Feedback is sent to the microgrid cluster control center;

[0113] Step two, the microgrid cluster control center in Update coordination signals within time as well as Power command It is then broadcast and distributed to the local controllers of each microgrid. The specific update formula for the coordination signal is as follows:

[0114] ;

[0115] in, , For the dual scalar multipliers corresponding to the constraints, its dynamic equation is:

[0116] ;

[0117] Step 3: The microgrid control center receives the coordination signal. , Power command and DG power measurement data fed back from the measurement equipment within the microgrid. Update the current power setting value. The update method is as follows:

[0118] ;

[0119] in, ; Indicates and and Connected Number.

[0120] Step four, the microgrid cluster completes the process within the window period. Power command at PCC point at all times The distribution and coordination of signals will enter the next window period. .

[0121] In non-iterative mode, the algorithm steps are executed, which is equivalent to performing only one iteration of the common primal-dual decomposition, and then the power command setpoint is issued to the microgrid cluster, effectively reducing the computation time. Furthermore, when When the tracking of a time step meets the judgment requirements, the control signal does not need to be updated, and the process can proceed directly to the next time step.

[0122] Step 5: The inner layer adopts a distributed cooperative control method to achieve power sharing.

[0123] First, the microgrid's internal control center is in Within a time period (of which) ) Calculate the optimal power output ratio of the power sources within the network based on the received PCC power measurement data and coordination signals. ,in, The following system of equations can be used to solve the problem:

[0124] ;

[0125] in, for The droop coefficient.

[0126] Secondly, within a given time period, each power source... The active power is output according to the following formula:

[0127] .

[0128] When the microgrid is inside After tracking the power dispatch commands from the microgrid control center once, the system updates the output of each distributed power source proportionally through bidirectional information transmission using a power-sharing coordination and consistency method, and then calculates the optimal power output ratio. , so that each By outputting power according to the above formula, the optimal power output can be achieved through distributed tracking, so that each distributed power source operates in the optimal power output state.

[0129] The measurement-driven, micro-cooperative power command tracing method mentioned in this specification employs a multi-level, non-iterative distributed command tracing strategy, and its tracing method is as follows:

[0130] The model for connecting microgrid clusters to the county distribution network is divided into inner and outer layers. A non-iterative distributed method is used to track the power dispatch commands from the upper-level distribution network and update the coordination signal through a consensus-based distributed projection subgradient algorithm. as well as Setting power The inner layer employs a distributed power-sharing method to further improve tracking performance, as shown below:

[0131] The microgrid's internal control center calculates the optimal power output ratio of the power sources within the grid based on the received PCC power measurement data and coordination signals. Each distributed power source then collaboratively tracks the optimal power output ratio according to its own rated power to achieve power sharing among the power sources. Real-time DG power measurement data is then fed back to the microgrid cluster control center through a hierarchical command tracking model.

[0132] In this embodiment, a distributed approach is used to decompose large-scale complex problems into smaller problems that can be computed locally by each distributed power controller, effectively improving the solution speed. A non-iterative nested rolling optimization mode is adopted to calculate the output power setpoints of each microgrid and each distributed power source within the microgrid level by level, enabling each distributed power source to quickly track its own transient power commands. A measurement feedback method is used to realize the real-time and dynamic correction of the accumulated tracking error of transient non-optimal power commands, ultimately achieving optimal tracking of power commands issued by the distribution network by the microgrid cluster.

[0133] Figure 4 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 4 At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, memory 408, and non-volatile memory 410, and may also include other hardware required for its functions. One or more embodiments of this specification can be implemented in software, for example, the processor 402 reads the corresponding computer program from the non-volatile memory 410 into memory 408 and then runs it. Of course, in addition to software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0134] Please refer to Figure 5 A measurement-driven micro-cooperative power command tracking device can be applied to the device shown in Figure 8 to implement the technical solution of this specification. The device may include:

[0135] The acquisition unit 502 is used to respond to the power dispatch command issued by the distribution network, and the microgrid cluster control center acquires PCC power measurement data from its grid-connected common connection point and sends it to each microgrid.

[0136] Input unit 504 is used to input the PCC power measurement data into a pre-established hierarchical command tracing model. The hierarchical command tracing model is used to determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data meets the relevant constraints in the hierarchical command tracing model. The hierarchical command tracing model includes an inner model and an outer model. The inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the goal of minimizing the charging and discharging cost of the microgrid cluster and the distributed power sources within the microgrid cluster. The scheduling command is a time-varying parameter in the inner objective function and the outer objective function. The linear constraints corresponding to the inner objective function and the outer objective function are transformed based on the error constraints of the scheduling command, the adjustable constraints of the distributed power output, the node voltage amplitude constraints, and the power flow constraints.

[0137] The update unit 506 is used to update the coordination signal in real time based on the DG power measurement data in the microgrid, in order to make up for the power deviation caused by the non-iterative optimization method, and to send it to each microgrid, under the conditions that the actual power output of the microgrid cluster deviates from the dispatch command, the operating cost is acceptable, and the relevant constraints in the hierarchical command tracking model are met.

[0138] Feedback unit 508 is used by the microgrid internal control center to calculate the optimal power output ratio of the power sources in the grid based on the received PCC power measurement data and coordination signals. Each distributed power source tracks the optimal power output ratio in a distributed and collaborative manner according to its own rated power to achieve power sharing among the power sources. The real-time DG power measurement data is fed back to the microgrid cluster control center through the hierarchical instruction tracking model.

[0139] Optionally, the update unit 506 is specifically used for:

[0140] The coordination signal is updated using the original-dual decomposition algorithm and the distributed projection subgradient algorithm.

[0141] Optionally, the update unit 506 is specifically used for:

[0142] The dual problem of the tracking model constructed based on the original-dual decomposition algorithm and distributed projective subgradient is calculated using the Lagrangian function.

[0143] Through consensus algorithms and power sharing strategies, each distributed power source can output power in its optimal state.

[0144] Optional,

[0145] The inner objective function is: ;

[0146] in, for Internal communication side section, for Internal DC side section, Let represent the objective function of distributed generation within a microgrid. Indicated middle exist The active power output at all times;

[0147] The outer objective function is: ;

[0148] in, This represents the total number of microgrids contained in the microgrid cluster. Indicating the microgrid cluster Inside Quantity, The objective function for operating the microgrid cluster is... and They are respectively exist The active power output and line losses at all times.

[0149] Optional,

[0150] The instruction tracking error constraint included in the outer constraint corresponding to the outer objective function is as follows: ;

[0151] in, The actual active power injected into the point of common connection for the microgrid cluster. This indicates the scheduling instruction. The maximum allowable tracking error;

[0152] The outer constraint includes the output power constraints of each microgrid within the microgrid cluster as follows:

[0153] ;

[0154] in, , , , These respectively represent the microgrid clusters Adjustable upper and lower limits for active and reactive power;

[0155] The voltage amplitude constraint included in the outer constraint is: ;

[0156] in, express The allowable range of the output voltage amplitude at PCC at any given time;

[0157] The power flow equations included in the outer constraint are: ;

[0158] in, ( ), ( ), ( ( ) represent vectors consisting of the total voltage amplitude of the microgrid cluster, the injected active power, and the reactive power, respectively. This represents a nonlinear function relating the injected power and voltage amplitude of a microgrid cluster.

[0159] The instruction tracking error constraint included in the inner constraint corresponding to the inner objective function is as follows: ;

[0160] in, for time Power scheduling commands issued to internal distributed power sources. For the present All power supplies are Output power at any moment for Maximum allowable power tracking error;

[0161] The distributed energy output constraints included in the inner layer constraints are:

[0162] ;

[0163] in, , , , They represent Output active power and reactive power (when The rated maximum and minimum values ​​(at time);

[0164] The voltage amplitude constraint included in the inner layer constraint is: ;

[0165] in, express time Inside The permissible range of output voltage amplitude.

[0166] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0167] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0168] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0169] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0170] The computer-readable medium (or computer-readable storage medium) described above or in any other form may store computer instructions that, when executed by a processor, implement one or more of the embodiments described above, thereby realizing the technical solutions of this specification.

[0171] This specification also provides a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby achieving the technical solutions of this specification. This computer program may be specifically recorded on the computer-readable medium described above or in any other form, and this specification does not impose any limitations on this.

[0172] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0173] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0174] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this specification. The singular forms “a,” “described,” and “the” used in one or more embodiments of this specification and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0175] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of this specification, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "in response to a determination," or "when," or "in the event of a determination."

[0176] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the scope of protection of one or more embodiments of this specification.

Claims

1. A measurement-driven, micro-cooperative power command tracking method, characterized in that, The method, applied to a multi-level power system model formed after a microgrid cluster is integrated into a higher-level distribution network, includes: In response to the power dispatch command issued by the distribution network, the microgrid cluster control center obtains PCC power measurement data from its grid-connected point of common coupling and distributes it to each microgrid. The PCC power measurement data is input into a pre-established hierarchical command tracing model. The hierarchical command tracing model is used to determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data meets the relevant constraints in the hierarchical command tracing model. The hierarchical command tracing model includes an inner model and an outer model. The inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the goal of minimizing the charging and discharging cost of the microgrid cluster and the distributed power sources within the microgrid cluster. The scheduling command is a time-varying parameter in the inner and outer objective functions. The linear constraints corresponding to the inner and outer objective functions are transformed based on the error constraints of the scheduling command, the adjustable power output constraints of the distributed energy sources, the node voltage amplitude constraints, and the power flow constraints. Under the conditions that the actual power output of the microgrid cluster deviates from the dispatch command, the operating cost is acceptable, and the relevant constraints in the hierarchical command tracking model are met, the microgrid cluster control center updates the coordination signal in real time based on the DG power measurement data in the microgrid using the non-iterative calculation method in the upper-level command tracking model to compensate for the power deviation caused by the non-iterative optimization method, and then sends it to each microgrid. The microgrid's internal control center calculates the optimal power output ratio of the power sources within the grid based on the received PCC power measurement data and coordination signals. Each distributed power source tracks the optimal power output ratio in a distributed and collaborative manner according to its own rated power to achieve power sharing among the power sources. The real-time DG power measurement data is then fed back to the microgrid cluster control center through the hierarchical command tracking model.

2. The method according to claim 1, characterized in that, The real-time updating of the coordination signal based on the DG power measurement data within the microgrid includes: The coordination signal is updated using the original-dual decomposition algorithm and the distributed projection subgradient algorithm.

3. The method according to claim 2, characterized in that, The step of updating the coordinated signal using the original-dual decomposition algorithm and distributed projection subgradient includes: The dual problem of the tracking model constructed based on the original-dual decomposition algorithm and distributed projective subgradient is calculated using the Lagrangian function. Through consensus algorithms and power sharing strategies, each distributed power source can output power in its optimal state.

4. The method according to claim 1, characterized in that, The inner objective function is: ; in, for Internal communication side section, for Internal DC side section, Let represent the objective function of distributed generation within a microgrid. Indicated middle exist The active power output at all times; The outer objective function is: ; in, This represents the total number of microgrids contained in the microgrid cluster. Indicating the microgrid cluster Inside Quantity, The objective function for operating the microgrid cluster is... and They are respectively exist The active power output and line losses at all times.

5. The method according to claim 4, characterized in that, The instruction tracking error constraint included in the outer constraint corresponding to the outer objective function is as follows: ; in, The actual active power injected into the point of common connection for the microgrid cluster. This indicates the scheduling instruction. The maximum allowable tracking error; The outer constraint includes the output power constraints of each microgrid within the microgrid cluster as follows: ； in, , , , These respectively represent the microgrid clusters Adjustable upper and lower limits for active and reactive power; The voltage amplitude constraint included in the outer constraint is: ; in, express The allowable range of the output voltage amplitude at PCC at any given time; The power flow equations included in the outer constraint are: ; in, ( ), ( ), ( The vectors representing the total voltage amplitude, injected active power, and reactive power of the microgrid cluster are respectively. This represents a nonlinear function relating the injected power and voltage amplitude of a microgrid cluster. The instruction tracking error constraint included in the inner constraint corresponding to the inner objective function is as follows: ; in, for time Power scheduling commands issued to internal distributed power sources. For the present All power supplies are Output power at any moment for Maximum allowable power tracking error; The distributed energy output constraints included in the inner layer constraints are: ； in, , , , They represent Output active power and reactive power (when The rated maximum and minimum values ​​(at time); The voltage amplitude constraint included in the inner layer constraint is: ; in, express time Inside The permissible range of output voltage amplitude.

6. A measurement-driven, micro-cooperative power command tracking device, characterized in that, The device, applicable to a multi-level power system model formed after a microgrid cluster is integrated into a higher-level distribution network, comprises: Acquisition Unit: In response to the power dispatch command issued by the distribution network, the microgrid cluster control center acquires PCC power measurement data from its grid-connected common connection point and distributes it to each microgrid; Input Unit: Inputs the PCC power measurement data into a pre-established hierarchical command tracing model. The hierarchical command tracing model determines whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data meets the relevant constraints in the hierarchical command tracing model. The hierarchical command tracing model includes an inner model and an outer model. The inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the goal of minimizing the charging and discharging cost of the microgrid cluster and the distributed power sources within the microgrid cluster. The scheduling command is a time-varying parameter in the inner and outer objective functions. The linear constraints corresponding to the inner and outer objective functions are transformed based on the error constraints of the scheduling command, the adjustable constraints of the distributed power output, the node voltage amplitude constraints, and the power flow constraints. Update Unit: Under the conditions that the actual power output of the microgrid cluster deviates from the dispatch command, the operating cost is acceptable, and the relevant constraints in the hierarchical command tracing model are met, the microgrid cluster control center updates the coordination signal in real time based on the DG power measurement data in the microgrid using the non-iterative calculation method in the upper-level command tracing model to compensate for the power deviation caused by the non-iterative optimization method, and then sends it to each microgrid. Feedback Unit: The microgrid internal control center calculates the optimal power output ratio of the power sources within the grid based on the received PCC power measurement data and coordination signals. Each distributed power source tracks the optimal power output ratio in a distributed and collaborative manner according to its own rated power to achieve power sharing among the power sources. The real-time DG power measurement data is then fed back to the microgrid cluster control center through the hierarchical command tracking model.

7. The apparatus according to claim 6, characterized in that, The update unit is specifically used for: The coordinated signal is updated using the original-dual decomposition algorithm and distributed projection subgradient.

8. The apparatus according to claim 7, characterized in that, The update unit is specifically used for: The dual problem of the tracking model constructed based on the original-dual decomposition algorithm and distributed projective subgradient is calculated using the Lagrangian function. Through consensus algorithms and power sharing strategies, each distributed power source can output power in its optimal state.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-5 by running the executable instructions.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-5.

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