Measurement driving-based micro-power distribution cooperative power instruction tracking method and device

Abstract

One or more embodiments of the invention provide a distribution and micro-grid cooperative power instruction tracking method and device based on measurement driving, and the method comprises the steps: a micro-grid cluster control center obtains PCC power measurement data from a grid-connected common connection point in response to a power scheduling instruction issued by a distribution network; inputting the PCC power measurement data into a pre-established hierarchical instruction tracking model, and judging whether the actual power output of the microgrid cluster, the scheduling instruction deviation and the operation cost are accepted or not through the hierarchical instruction tracking model, and judging whether related constraints in the hierarchical instruction tracking model are met or not; the micro-grid cluster control center updates a coordination signal in real time according to DG power measurement data in the micro-grid through a non-iterative calculation method; and the micro-grid internal control center calculates the optimal power output ratio of the power supply in the grid according to the received PCC power measurement data and the coordination signal, and each distributed power supply tracks the optimal power output ratio in a distributed and cooperative manner according to the own rated power.

Description

Technical Field

[0001] One or more embodiments in this specification relate to the field of power grids, and in particular, to a method and device for tracking power instructions of a distribution micro-cooperative system based on measurement drive. Background Art

[0002] When a microgrid cluster is connected to and participates in the distribution network dispatch, the upper distribution network sends dispatch instructions to the point of common coupling (PCC) of the microgrid cluster, specifying the microgrid cluster PCC to inject or absorb active power into the distribution network. The time spent on calculating the optimal output power of distributed generation in the microgrid cluster is one of the main factors affecting the tracking effect of the microgrid cluster on the distribution network instructions.

[0003] Generally speaking, centralized and distributed are the two main methods for implementing command tracking for controllable distributed power sources within a microgrid cluster. The centralized optimization method is that the dispatch center collects and processes global information before optimizing the solution, and then issues control instructions to each controller, which increases the time consumption and difficulty of fast tracking. The characteristics of distributed power sources are "many points and wide areas". The distributed method uses the local controllers of the distributed power sources to perform distributed collaborative optimization. The scale of the problem that each controller is responsible for is small, and the calculation difficulty and time are correspondingly reduced. However, when calculating the optimal power setting value of the distributed power source, the traditional distributed method often requires the local controller and the microgrid control center to interact with the boundary information multiple times for iterative calculation. This iterative process is time-consuming, resulting in a slower tracking speed.

[0004] With the development of technology, some researchers use the single iteration and non-optimal result in the distributed tracking method as the output power setting value of the distributed energy at the current moment, and call this calculation method the non-iterative mode. However, the non-iterative calculation method proposed above itself has system errors and model errors, which will eventually affect the accuracy of tracking as time accumulates. In addition, although the above method takes into account the tracking of the distribution network dispatch by distributed energy, it does not take into account the multi-level situation of tracking the dispatch instructions issued by the distribution network when multiple microgrids are connected to the distribution network. Summary of the invention

[0005] In view of this, the present specification provides a measurement-driven based power instruction tracking method and device for micro-cooperation for one or more embodiments, which can solve the deficiencies in the related art.

[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 power instruction tracking method for a micro-grid cluster is proposed, which is applied to a multi-level power system model formed after a micro-grid cluster is incorporated into an upper-level distribution network. The method includes:

[0008] In response to the power dispatching instruction 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;

[0009] The PCC power measurement data is input into a pre-established hierarchical instruction tracking model, and the hierarchical instruction tracking model is used to determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data satisfies the relevant constraints in the hierarchical instruction tracking model; wherein the hierarchical instruction tracking model includes an inner model and an outer model, and the inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the minimum cost of charging and discharging of the microgrid cluster and the distributed power sources in the microgrid cluster as the goal, and the scheduling instruction is a time-varying parameter in the inner objective function and the outer objective function, and the linear constraints corresponding to the inner objective function and the outer objective function are converted based on the error constraint of the scheduling instruction, the adjustable constraint of the distributed energy output power, the node voltage amplitude constraint, and the power flow constraint;

[0010] When the actual power output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant constraints in the hierarchical instruction tracking model are met, the microgrid cluster control center uses the non-iterative calculation method in the superior instruction tracking model to update the coordination signal in real time according to the DG power measurement data in the microgrid to compensate for the power deviation generated by the non-iterative optimization method, and sends it to each microgrid;

[0011] The internal control center of the microgrid calculates the optimal power output ratio of the power supply in the grid according to the received PCC power measurement data and coordination signal. Each distributed power supply collaboratively tracks the optimal power output ratio according to its own rated power to achieve power sharing among the power supplies, and feeds back the real-time DG power measurement data to the microgrid cluster control center through the hierarchical instruction tracking model.

[0012] As a preferred solution, the real-time updating of the coordination signal according to the DG power measurement data in the microgrid includes:

[0013] The coordination signal is updated by a primal-dual decomposition algorithm and a distributed projected sub-gradient gradient algorithm.

[0014] As a preferred solution, the updating of the coordination signal by the primal-dual decomposition algorithm and the distributed projection subgradient includes:

[0015] The dual problem of the tracking model constructed based on the primal-dual decomposition algorithm and the distributed projected subgradient is calculated by Lagrangian function;

[0016] Through the consistency algorithm and power sharing strategy, each distributed power source can output power in the best state.

[0017] As a preferred solution,

[0018] The inner objective function is:

[0019] Among them, 1~m k For MG k Internal AC side, m k +1~n k For MG k Inner DC side part, C k [P k,i (t)] represents the objective function of the distributed generation in the microgrid, P k,i (t) represents MG k Medium DG k,i The active power output at time t;

[0020] The outer objective function is:

[0021] Wherein, N represents the total number of microgrids contained in the microgrid cluster, n k Indicates the MG in the microgrid cluster k Inner DG k,i The number of k (t)] is the objective function of the microgrid cluster operation, P k (t) and MG k Active power output and line loss at time t.

[0022] As a preferred solution,

[0023] The outer constraints corresponding to the outer objective function include the instruction tracking error constraints: 0,set (t)-P0(t)|≤E;

[0024] Wherein, P0(t) is the active power actually injected into the common connection point of the microgrid cluster, P 0,set (t) represents the scheduling instruction, and E is the maximum tracking error allowed;

[0025] The output power constraints of each microgrid in the microgrid cluster included in the outer constraint are:

[0026]

[0027] in, P k , Q k Respectively represent the MGs in the microgrid cluster k Adjustable upper and lower limits for active and reactive power;

[0028] The voltage amplitude constraints included in the outer constraints are:

[0029] in, Indicates the permissible range of the output voltage amplitude at PCC at time t;

[0030] The power flow equation included in the outer constraint is: V = h(P, Q);

[0031] in, They represent the total voltage amplitude of the microgrid cluster, the vector composed of the injected active power and reactive power, respectively, and h(P,Q) represents the nonlinear function between the injected power and voltage amplitude of the microgrid cluster;

[0032] The instruction tracking error constraint contained in the inner constraints corresponding to the inner objective function is:

[0033] in, t s Moment MG k The power dispatch instruction issued to the internal distributed power source, P k (t s ) is the current MG k All power supplies are at t s Output power at the moment, E k For MG k Maximum power tracking error allowed;

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

[0035]

[0036] in, Respectively represent DG k,i The output active power and reactive power (when i∈{m k +1,m k +2,...n k}) rated maximum and minimum values;

[0037] The voltage amplitude constraints included in the inner layer constraints are:

[0038] in, Indicates t s Moment MG k Inner DG k,i The allowable range of output voltage amplitude.

[0039] According to a second aspect of one or more embodiments of this specification, a measurement-driven power instruction tracking device for micro-grid coordination is proposed, which is applied to a multi-level power system model formed after a microgrid cluster is incorporated into an upper-level distribution network. The device includes:

[0040] Acquisition unit: In response to the power dispatching instruction issued by the distribution network, the microgrid cluster control center acquires the PCC power measurement data from its grid-connected public connection point and sends it to each microgrid;

[0041] Input unit: input the PCC power measurement data into a pre-established hierarchical instruction tracking model, and judge whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data satisfies the relevant constraints in the hierarchical instruction tracking model through the hierarchical instruction tracking model; wherein the hierarchical instruction tracking model includes an inner model and an outer model, and the inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the minimum cost of charging and discharging of the microgrid cluster and the distributed power sources in the microgrid cluster as the goal, and the scheduling instruction is a time-varying parameter in the inner objective function and the outer objective function, and the linear constraints corresponding to the inner objective function and the outer objective function are converted based on the error constraint of the scheduling instruction, the adjustable constraint of the distributed energy output power, the node voltage amplitude constraint, and the power flow constraint;

[0042] Update unit: When the actual power output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant constraints in the hierarchical instruction tracking model are met, the microgrid cluster control center uses the non-iterative calculation method in the superior instruction tracking model to update the coordination signal in real time according to the DG power measurement data in the microgrid to compensate for the power deviation generated by the non-iterative optimization method, and sends it to each microgrid;

[0043] Feedback unit: The internal control center of the microgrid calculates the optimal power output ratio of the power supply in the grid according to the received PCC power measurement data and coordination signal. Each distributed power source collaboratively tracks the optimal power output ratio according to its own rated power to achieve power sharing among the power sources, and feeds back the real-time DG power measurement data to the microgrid cluster control center through the hierarchical instruction tracking model.

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

[0045] processor;

[0046] a memory for storing processor-executable instructions;

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

[0048] According to a fourth aspect of one or more embodiments of the present specification, a computer-readable storage medium is provided, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0049] It can be seen from the above technical solutions that the technical effects of the measurement-driven distributed projection subgradient instruction tracking method provided by 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, and difficult to achieve fast tracking, this specification adopts a distributed collaborative optimization method, in which the scale of the problem each controller is responsible for is smaller, and the difficulty and time of calculation are reduced;

[0051] (2) Compared with the traditional non-iterative mode, which will introduce system errors, model errors and system errors will accumulate over time and eventually affect the accuracy of tracking. The real-time measurement feedback introduced in the optimization decision link of this specification makes the tracking process form a closed loop, and adopts a hierarchical form to divide the model into two layers, inner and outer layers, which can dynamically and quickly correct the tracking error, reducing the impact of model errors and system errors on tracking accuracy;

[0052] (3) This specification takes into account the situation where multiple microgrids are connected to the distribution network, and the tracking of instructions issued by the distribution network by each microgrid and the distributed power sources within the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is an architectural diagram of an instruction tracing system provided by an exemplary embodiment.

[0054] Figure 2 It is a flowchart of a power instruction tracking method for micro-coordination based on measurement drive provided by an exemplary embodiment.

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

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

[0057] Figure 5It is a block diagram of a power instruction tracking device for micro-coordination based on measurement drive provided by an exemplary embodiment. DETAILED DESCRIPTION

[0058] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices 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 steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0060] 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0061] To further illustrate one or more embodiments of the present invention, the following embodiments are provided:

[0062] When a microgrid cluster is connected to and participates in the distribution network dispatch, the upper distribution network sends dispatch instructions to the point of common coupling (PCC) of the microgrid cluster, specifying the microgrid cluster to inject or absorb active power from the PCC to the distribution network. The time consumption of calculating the output power of distributed power sources in the microgrid cluster is one of the main factors affecting the tracking effect of the microgrid cluster on the distribution network instructions.

[0063] Figure 1 FIG. 1 is an architecture diagram of an instruction tracking system provided by an exemplary embodiment. Figure 1As shown, the instruction tracking system includes a distribution network 11, a microgrid cluster 12, and a control center. Among them, the microgrid cluster 12 includes a PCC 121 and multiple microgrids (a first microgrid 122, a second microgrid 123, and a third microgrid 124). The distribution network 11 can issue a dispatch instruction to the control center 13 to instruct the first microgrid 122, the second microgrid 123, and the third microgrid 124 to inject or absorb active power from the PCC 121 to the distribution network 11.

[0064] Generally speaking, centralized and distributed are the two main methods for implementing command tracking for controllable distributed power sources within a microgrid cluster. The centralized optimization method is that the dispatch center collects and processes global information before optimizing the solution, and then issues control instructions to each controller, which increases the time consumption and difficulty of fast tracking. The characteristics of distributed power sources are "many points and wide areas". The distributed method uses the local controllers of the distributed power sources to perform distributed collaborative optimization. The scale of the problem that each controller is responsible for is small, and the calculation difficulty and time are correspondingly reduced. However, when calculating the optimal power setting value of the distributed power source, the traditional distributed method often requires the local controller and the microgrid control center to interact with the boundary information multiple times for iterative calculation. This iterative process is time-consuming, resulting in a slower tracking speed.

[0065] With the development of technology, some researchers use the single iteration and non-optimal result in the distributed tracking method as the output power setting value of the distributed energy at the current moment, and call this calculation method the non-iterative mode. However, the non-iterative calculation method proposed above itself has system errors and model errors, which will eventually affect the accuracy of tracking as time accumulates. In addition, although the above method takes into account the tracking of the distribution network dispatch by distributed energy, it does not take into account the multi-level situation of tracking the dispatch instructions issued by the distribution network when multiple microgrids are connected to the distribution network.

[0066] In order to solve the deficiencies in the related art, this specification proposes an instruction tracing method based on measurement drive and consistency algorithm.

[0067] Figure 2 FIG. 1 is a flow chart of a power instruction tracking method for a micro-distribution system based on measurement and drive provided by an exemplary embodiment. Figure 2 As shown, the method is applied to a device of a power grid, the power grid includes the device, a wind power generation device, an energy storage system including two groups 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 situation of the wind power generation device; the method may include the following steps:

[0068] Step 202: In response to the power dispatching instruction 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.

[0069] The dispatch instruction may only be directed to the coordination signal set of each microgrid in the microgrid cluster. The microgrid cluster may obtain power measurement data from the PCC through a measurement device, and this specification does not limit the specific measurement device.

[0070] Step 204, input the PCC power measurement data into a pre-established hierarchical instruction tracking model, and determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data satisfies the relevant constraints in the hierarchical instruction tracking model through the hierarchical instruction tracking model; wherein the hierarchical instruction tracking model includes an inner model and an outer model, and the inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the minimum cost of charging and discharging of the microgrid cluster and the distributed power sources in the microgrid cluster as the goal, and the scheduling instruction is a time-varying parameter in the inner objective function and the outer objective function, and the linear constraints corresponding to the inner objective function and the outer objective function are converted based on the error constraint of the scheduling instruction, the adjustable constraint of the distributed energy output power, the node voltage amplitude constraint, and the power flow constraint.

[0071] In one embodiment, the inner objective function is: Among them, 1~m k For MG k Internal AC side, m k +1~n k For MG k Inner DC side part, C k [P k,i (t)] represents the objective function of the distributed generation in the microgrid, P k,i (t) represents MG k Medium DG k,i The active power output at time t;

[0072] The outer objective function is:

[0073] Wherein, N represents the total number of microgrids contained in the microgrid cluster, n k Indicates the MG in the microgrid cluster k Inner DG k,i The number of k (t)] is the objective function of the microgrid cluster operation, P k (t) and MG kActive power output and line loss at time t.

[0074] Furthermore, the instruction tracking error constraint included in the outer constraint corresponding to the outer objective function is: |P 0,set (t)-P0(t)|≤E;

[0075] Wherein, P0(t) is the active power actually injected into the common connection point of the microgrid cluster, P 0,set (t) represents the scheduling instruction, and E is the maximum tracking error allowed;

[0076] The output power constraints of each microgrid in the microgrid cluster included in the outer constraint are:

[0077]

[0078] in, P k , Q k Respectively represent the MGs in the microgrid cluster k Adjustable upper and lower limits for active and reactive power;

[0079] The voltage amplitude constraints included in the outer constraints are:

[0080] in, Indicates the permissible range of the output voltage amplitude at PCC at time t;

[0081] The power flow equation included in the outer constraint is: V = h(P, Q);

[0082] in, They represent the total voltage amplitude of the microgrid cluster, the vector composed of the injected active power and reactive power, respectively, and h(P,Q) represents the nonlinear function between the injected power and voltage amplitude of the microgrid cluster;

[0083] The instruction tracking error constraint contained in the inner constraints corresponding to the inner objective function is:

[0084] in, t s Moment MG k The power dispatch instruction issued to the internal distributed power source, P k (t s ) is the current MG k All power supplies are at t s Output power at the moment, E k For MG k Maximum power tracking error allowed;

[0085] The distributed energy output constraints contained in the inner constraints are:

[0086]

[0087] in, Respectively represent DG k,i The output active power and reactive power (when i∈{m k +1,m k +2,...n k}) rated maximum and minimum values;

[0088] The voltage amplitude constraints included in the inner layer constraints are:

[0089] in, Indicates t s Moment MG k Inner DG k,i The allowable range of output voltage amplitude.

[0090] Step 206, when the actual power output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant constraints in the hierarchical instruction tracking model are met, the microgrid cluster control center uses the non-iterative calculation method in the upper-level instruction tracking model to update the coordination signal in real time according to the DG power measurement data in the microgrid to compensate for the power deviation caused by the non-iterative optimization method, and sends it to each microgrid.

[0091] In step 208, the internal control center of the microgrid calculates the optimal power output ratio of the power supply in the grid according to the received PCC power measurement data and the coordination signal, and each distributed power supply collaboratively tracks the optimal power output ratio according to its own rated power to achieve power sharing among the power supplies, and feeds back the real-time DG power measurement data to the microgrid cluster control center through the hierarchical instruction tracking model.

[0092] In this embodiment, on the one hand, compared with the traditional centralized method, which is more difficult and time-consuming to collect information and calculate centrally, and it is difficult to achieve fast tracking, this embodiment adopts a distributed collaborative optimization method based on a consistency algorithm, and the scale of the problem that each controller is responsible for is smaller, and the difficulty and time of calculation are reduced; on the other hand, compared with the traditional non-iterative mode itself, which will introduce system errors, model errors and system errors accumulate over time, and will eventually affect the accuracy of tracking. The real-time measurement feedback introduced in the optimization decision link of this embodiment makes the tracking process form a closed loop, and adopts a hierarchical form to divide the model into two layers, inner and outer layers, which can dynamically and quickly correct the tracking error and reduce the impact of model errors and system errors on tracking accuracy; in addition, this embodiment takes into account the case where multiple microgrids are connected to the distribution network, and each microgrid and the distributed power sources within the microgrid track the instructions issued by the distribution network, while realizing power sharing among the power sources.

[0093] In one embodiment, when the actual work output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant constraints in the hierarchical instruction tracking model are met, the coordination signal sent to each microgrid in the microgrid cluster is updated through the hierarchical instruction tracking model, and the coordination signal is sent to the corresponding microgrid, so that each microgrid updates the output power and makes the corresponding power supply work based on the updated output power, including: when the actual work output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant outer constraints in the hierarchical instruction tracking model are met, the microgrid cluster control center updates the coordination signal sent to the microgrid in real time according to 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 actual work output of the microgrid deviates from the dispatching instruction, the operating cost is acceptable, and the relevant inner constraints in the hierarchical instruction tracking model are met, the microgrid control center calculates the optimal work output ratio of each power supply in real time according to the DG power measurement data, and makes the output power of each power supply track the optimal work output ratio in a distributed manner to achieve power sharing.

[0094] Figure 3 FIG. 1 is a flowchart of a specific instruction tracing method provided by an exemplary embodiment. Figure 3 As shown, the method may include the following steps:

[0095] 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 actual power output of the microgrid cluster deviates from the dispatching command, whether the operating cost is acceptable, and whether the outer constraints are met. If not, proceed to step 308a, and the microgrid cluster control center updates the coordination signal u k (t), and according to uk (t) Update the power instructions of each microgrid and send the power instructions to each microgrid at the same time; if yes, go to step 308b, do not update the instructions, and go to the next moment.

[0096] Step 310, determine each microgrid in the microgrid cluster as a microgrid cluster one by one. Step 312, determine whether the operating cost of the microgrid cluster is acceptable and whether the inner constraints and power sharing are met. If not, proceed to step 314a, 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 makes the output power of each power source track the optimal power output ratio in a distributed manner to achieve power sharing; if yes, proceed to step 314b, and do not update the power command to enter the next power scheduling window period [t+1, t+2).

[0097] Step 316: The microgrid cluster receives the power dispatch instruction and coordination signal u from the microgrid cluster control center. k (t), during the power scheduling window [t, t+1) (where ) to calculate and update t in real time s The coordination signal u at time k (t s )(Indicator s satisfies ), update its own optimal power instruction in real time The microgrid control center uses the optimal power command Calculate the optimal power output ratio of each power source in the network Each power supply DG k,i According to the distributed tracking of the optimal power ratio, the MG k Power tracking in the power dispatch window period [t, t+1). Step 318: The microgrid cluster completes the tracking at time t and moves to the next time.

[0098] In one embodiment, the coordination signal u is updated in real time based on the DG power measurement data in the microgrid. k (t), comprising: updating the coordination signal by a primal-dual decomposition algorithm and a distributed projection subgradient.

[0099] Furthermore, the updating of the coordination signal through the primal-dual decomposition algorithm and the distributed projection subgradient includes: calculating the dual problem of the tracking model constructed based on the primal-dual decomposition algorithm and the distributed projection subgradient through the Lagrangian function; and achieving the optimal power output of each distributed power source through the consistency algorithm and the power sharing strategy.

[0100] The coordination signal u is calculated using the primal-dual decomposition and distributed projected subgradient method. k (t) and related parameters.

[0101] First, the outer tracking model is designed as follows:

[0102]

[0103] Construct the following Lagrangian function:

[0104]

[0105] Where g represents the inequality and equality constraints in the tracking model, and the coordination signal u k (t) consists of the dual multipliers of the constraints in the tracking model, and v and ρ are in a canonical coefficient relationship.

[0106] Based on the above analysis, the original tracking model can be described as a dual problem, which can be written as:

[0107]

[0108] In the case of only the outer model (i.e., centralized), the projected gradient algorithm is used to derive the control signal u from the above dual problem. k (t) and the calculation formula of the required scheduling scheme, and finally the distributed instruction tracking algorithm at time t is obtained.

[0109] The algorithm flow steps are:

[0110] Step 1: Use measurement equipment to collect the actual active power P0(t) of PCC and feed it back to the microgrid cluster control center;

[0111] Step 2: The microgrid cluster control center is in [t s ,t s+1 ) time to update the coordination signal u k (t s ) and MG k Power command And broadcast it to each microgrid local controller. The specific update formula of the coordination signal is as follows:

[0112]

[0113] in, λ k , γ k is the dual scalar multiplier of the corresponding constraint, and its dynamic equation is

[0114]

[0115] Step 3: The microgrid control center receives the coordination signal u k (t s), MG k Power command And the DG power measurement data fed back by the measurement equipment in the microgrid Update the current power setting value The update method is as follows

[0116]

[0117] in, b k ,b l Indicates that MG k and MG l Number of connected MGs.

[0118] Step 4: The microgrid cluster completes the PCC power command P at the window period [t, t+1). 0,set (t) distribution and coordination signal generation, entering the next window period [t+1, t+2).

[0119] In the non-iterative mode, the algorithm steps are executed, which is equivalent to executing only one iteration of the common primal-dual decomposition, and then the power command setting value is issued to the microgrid cluster, which effectively reduces the calculation time. In addition, when the tracking at time t meets the judgment requirements, the control signal does not need to be updated and goes directly to the next moment.

[0120] Step 5: The inner layer uses a distributed collaborative control method to achieve power sharing.

[0121] First, the microgrid internal control center During the time (including ) Calculate the optimal power output ratio of the power supply in the network based on the received PCC power measurement data and coordination signal in, It can be solved by the following system of equations:

[0122]

[0123] Among them, m k,i For DG k,i The droop coefficient.

[0124] Secondly, within the time, each power source DG k,i The output active power is as follows:

[0125]

[0126] When the DG in the microgrid k,iAfter completing the tracking of the power dispatching instructions of the microgrid control center, each distributed power source is updated proportionally through the coordinated consistency method of power sharing through two-way information transmission, and the optimal power output ratio α is obtained. * k , so that each DG k,i The power output is carried out according to the above formula, and finally the distributed tracking of the optimal power output is realized, so that each distributed power source can operate in the optimal power output state.

[0127] The measurement-driven power instruction tracking method for micro-cooperative power control mentioned in this specification adopts a multi-level non-iterative distributed instruction tracking strategy, and its tracking method is as follows:

[0128] The model corresponding to the microgrid cluster connected to the county distribution network is divided into two layers, inner and outer layers, and a non-iterative distributed method is used to track the power dispatch instructions of the upper distribution network, and the coordination signal u is updated through a distributed projection subgradient algorithm based on consistency. k (t) and MG k Set power The inner layer uses a distributed power sharing method to further improve the tracking effect, as shown below:

[0129] The internal control center of the microgrid calculates the optimal power output ratio of the power supply in the grid based on the received PCC power measurement data and coordination signals. Each distributed power source collaboratively tracks the above optimal power output ratio based on its own rated power to achieve power sharing among the power sources, and feeds back the real-time DG power measurement data to the microgrid cluster control center through a hierarchical command tracking model.

[0130] In this embodiment, a large-scale complex problem is decomposed into small problems that can be calculated locally by each distributed power supply controller through a distributed method, which effectively improves the solution speed; a non-iterative nested rolling optimization mode is adopted to calculate the output power set value of each microgrid and each distributed power supply in the microgrid step by step, thereby completing the rapid tracking of each distributed power supply to its own transient power instruction; a measurement feedback method is adopted to realize real-time and dynamic correction of the cumulative tracking error of transient non-optimal power instructions, and finally realize the optimal tracking of the power instructions issued by the distribution network by the microgrid cluster.

[0131] Figure 4 is a schematic structural diagram of a device provided by an exemplary embodiment. Figure 4At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, a memory 408, and a non-volatile memory 410, and may also include hardware required for other functions. One or more embodiments of this specification may be implemented based on software, such as the processor 402 reading the corresponding computer program from the non-volatile memory 410 into the memory 408 and then running it. Of course, in addition to the software implementation, one or more embodiments of this specification do not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0132] Please refer to Figure 5 A power instruction tracking device based on measurement drive and micro-cooperation can be applied to Figure 5 In the device shown in the figure, to implement the technical solution of this specification, the device may include:

[0133] The acquisition unit 502 is used for responding to the power dispatch instruction issued by the distribution network, and the microgrid cluster control center obtains the PCC power measurement data from the public connection point of the grid and sends it to each microgrid;

[0134] An input unit 504 is used to input the PCC power measurement data into a pre-established hierarchical instruction tracking model, and determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data satisfies the relevant constraints in the hierarchical instruction tracking model through the hierarchical instruction tracking model; wherein the hierarchical instruction tracking model includes an inner model and an outer model, and the inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the minimum cost of charging and discharging of the microgrid cluster and the distributed power sources in the microgrid cluster as the goal, and the scheduling instruction is a time-varying parameter in the inner objective function and the outer objective function, and the linear constraints corresponding to the inner objective function and the outer objective function are converted based on the error constraint of the scheduling instruction, the adjustable constraint of the distributed energy output power, the node voltage amplitude constraint, and the power flow constraint;

[0135] The updating unit 506 is used for updating the coordination signal in real time according to the DG power measurement data in the microgrid by the non-iterative calculation method in the upper-level instruction tracking model to compensate for the power deviation generated by the non-iterative optimization method, and sending it to each microgrid when the actual power output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant constraints in the hierarchical instruction tracking model are met;

[0136] Feedback unit 508 is used for the microgrid internal control center to calculate the optimal power output ratio of the power supply in the network according to the received PCC power measurement data and coordination signal. Each distributed power supply tracks the optimal power output ratio according to its own rated power to achieve power sharing among the power supplies, and feeds back the real-time DG power measurement data to the microgrid cluster control center through the hierarchical instruction tracking model.

[0137] Optionally, the updating unit 506 is specifically configured to:

[0138] The coordination signal is updated by a primal-dual decomposition algorithm and a distributed projected subgradient algorithm.

[0139] Optionally, the updating unit 506 is specifically configured to:

[0140] The dual problem of the tracking model constructed based on the primal-dual decomposition algorithm and the distributed projected subgradient is calculated by Lagrangian function;

[0141] Through the consistency algorithm and power sharing strategy, each distributed power source can output power in the best state.

[0142] Optional,

[0143] The inner objective function is: Among them, 1~m k For MG k Internal AC side, m k +1~n k For MG k Inner DC side part, C k [P k,i (t)] represents the objective function of the distributed generation in the microgrid, P k,i (t) represents MG k The active power output by DGk,i at time t;

[0144] The outer objective function is: Wherein, N represents the total number of microgrids contained in the microgrid cluster, n k Indicates the MG in the microgrid cluster k Inner DG k,i The number of k (t)] is the objective function of the microgrid cluster operation, P k (t) and MG k Active power output and line loss at time t.

[0145] Optional,

[0146] The outer constraints corresponding to the outer objective function include the instruction tracking error constraints: 0,set (t)-P0(t)|≤E;

[0147] Wherein, P0(t) is the active power actually injected into the common connection point of the microgrid cluster, P 0,set (t) represents the scheduling instruction, and E is the maximum tracking error allowed;

[0148] The output power constraints of each microgrid in the microgrid cluster included in the outer constraint are:

[0149]

[0150] in, P k , Q k Respectively represent the MGs in the microgrid cluster k Adjustable upper and lower limits for active and reactive power;

[0151] The voltage amplitude constraints included in the outer constraints are:

[0152] in, Indicates the permissible range of the output voltage amplitude at PCC at time t;

[0153] The power flow equation included in the outer constraint is: V = h(P, Q);

[0154] in, They represent the total voltage amplitude of the microgrid cluster, the vector composed of the injected active power and reactive power, respectively, and h(P,Q) represents the nonlinear function between the injected power and voltage amplitude of the microgrid cluster;

[0155] The instruction tracking error constraint contained in the inner constraints corresponding to the inner objective function is:

[0156] in, t s Moment MG k The power dispatch instruction issued to the internal distributed power source, P k (t s ) is the current MG k All power supplies are at t s Output power at the moment, E k For MG k Maximum power tracking error allowed;

[0157] The distributed energy output constraints contained in the inner constraints are:

[0158]

[0159] in, Respectively represent DG k,i The output active power and reactive power (when i∈{m k +1,m k +2,...n k}) rated maximum and minimum values;

[0160] The voltage amplitude constraints included in the inner layer constraints are:

[0161] in, Indicates t s Moment MG k Inner DG k,i The allowable range of output voltage amplitude.

[0162] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver, a game console, a tablet computer, a wearable device or a combination of any of these devices.

[0163] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0164] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0165] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0166] For the computer-readable medium (or computer-readable storage medium) as described above or in any other form, computer instructions may be stored thereon, and when the instructions are executed by the processor, one or more of the above-mentioned embodiments are implemented, thereby realizing the technical solution of this specification.

[0167] This specification also proposes a computer program, which, when executed by a processor, implements one or more of the above embodiments, thereby realizing the technical solution of this specification. The computer program can be specifically recorded in the above or any other form of computer-readable medium, and this specification does not limit this.

[0168] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0169] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0170] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0171] 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, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0172] The above description is merely a preferred embodiment of one or more embodiments of the present specification and is not intended to limit one or more embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included in the scope of protection of one or more embodiments of the present specification.

Claims

1. A power instruction tracking method for micro-coordination based on measurement drive, characterized in that: Applied to a multi-level power system model formed after a microgrid cluster is incorporated into a superior distribution network, the method comprises: In response to the power dispatching instruction 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; The PCC power measurement data is input into a pre-established hierarchical instruction tracking model, and the hierarchical instruction tracking model is used to determine whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data satisfies the relevant constraints in the hierarchical instruction tracking model; wherein the hierarchical instruction tracking model includes an inner model and an outer model, and the inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the minimum cost of charging and discharging of the microgrid cluster and the distributed power sources in the microgrid cluster as the goal, and the scheduling instruction is a time-varying parameter in the inner objective function and the outer objective function, and the linear constraints corresponding to the inner objective function and the outer objective function are converted based on the error constraint of the scheduling instruction, the adjustable constraint of the distributed energy output power, the node voltage amplitude constraint, and the power flow constraint; When the actual power output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant constraints in the hierarchical instruction tracking model are met, the microgrid cluster control center uses the non-iterative calculation method in the superior instruction tracking model to update the coordination signal in real time according to the DG power measurement data in the microgrid to compensate for the power deviation generated by the non-iterative optimization method, and sends it to each microgrid; The internal control center of the microgrid calculates the optimal power output ratio of the power supply in the grid according to the received PCC power measurement data and coordination signal. Each distributed power supply collaboratively tracks the optimal power output ratio according to its own rated power to achieve power sharing among the power supplies, and feeds back the real-time DG power measurement data to the microgrid cluster control center through the hierarchical instruction tracking model.

2. The method according to claim 1, characterized in that The real-time updating of the coordination signal according to the DG power measurement data in the microgrid includes: The coordination signal is updated by a primal-dual decomposition algorithm and a distributed projected subgradient algorithm.

3. The method according to claim 2, characterized in that The updating of the coordination signal by the primal-dual decomposition algorithm and the distributed projection subgradient comprises: The dual problem of the tracking model constructed based on the primal-dual decomposition algorithm and the distributed projected subgradient is calculated by Lagrangian function; Through the consistency algorithm and power sharing strategy, each distributed power source can output power in the best state.

4. The method according to claim 1, characterized in that: The inner objective function is: Among them, 1~m k For MG k Internal AC side, m k +1~n k For MG k Inner DC side part, C k [P k,i (t)] represents the objective function of the distributed generation in the microgrid, P k,i (t) represents MG k The active power output by DGk,i at time t; The outer objective function is: Wherein, N represents the total number of microgrids contained in the microgrid cluster, n k Indicates the MG in the microgrid cluster k Inner DG k,i The number of k (t)] is the objective function of the microgrid cluster operation, P k (t) and MG k Active power output and line loss at time t.

5. The method according to claim 4, characterized in that The outer constraints corresponding to the outer objective function include the instruction tracking error constraints: 0,set (t)-P0(t)|≤E; Wherein, P0(t) is the active power actually injected into the common connection point of the microgrid cluster, P 0,set (t) represents the scheduling instruction, and E is the maximum tracking error allowed; The output power constraints of each microgrid in the microgrid cluster included in the outer constraint are: in, P k , Q k They represent the MGs in the microgrid cluster respectively. k Adjustable upper and lower limits for active and reactive power; The voltage amplitude constraints included in the outer constraints are: in, Indicates the permissible range of the output voltage amplitude at PCC at time t; The power flow equation included in the outer constraint is: V = h(P, Q); in, They represent the total voltage amplitude of the microgrid cluster, the vector composed of the injected active power and reactive power, respectively, and h(P,Q) represents the nonlinear function between the injected power and voltage amplitude of the microgrid cluster; The instruction tracking error constraint contained in the inner constraints corresponding to the inner objective function is: in, t s Moment MG k The power dispatch instruction issued to the internal distributed power source, P k (t s ) is the current MG k All power supplies are at t s Output power at the moment, E k For MG k Maximum power tracking error allowed; The distributed energy output constraints contained in the inner constraints are: in, Respectively represent DG k,i The output active power and reactive power (when i∈{m k +1,m k +2,...n k }) rated maximum and minimum values; The voltage amplitude constraints included in the inner layer constraints are: in, Indicates t s Moment MG k Inner DG k,i The allowable range of output voltage amplitude.

6. A power instruction tracking device for micro-coordination based on measurement drive, characterized in that: The device is applied to a multi-level power system model formed after a microgrid cluster is incorporated into a superior distribution network, and includes: Acquisition unit: In response to the power dispatching instruction issued by the distribution network, the microgrid cluster control center acquires the PCC power measurement data from its grid-connected public connection point and sends it to each microgrid; Input unit: input the PCC power measurement data into a pre-established hierarchical instruction tracking model, and judge whether the operating cost of the microgrid cluster is acceptable and whether the PCC power measurement data satisfies the relevant constraints in the hierarchical instruction tracking model through the hierarchical instruction tracking model; wherein the hierarchical instruction tracking model includes an inner model and an outer model, and the inner objective function corresponding to the inner model and the outer objective function corresponding to the outer model are established with the minimum cost of charging and discharging of the microgrid cluster and the distributed power sources in the microgrid cluster as the goal, and the scheduling instruction is a time-varying parameter in the inner objective function and the outer objective function, and the linear constraints corresponding to the inner objective function and the outer objective function are converted based on the error constraint of the scheduling instruction, the adjustable constraint of the distributed energy output power, the node voltage amplitude constraint, and the power flow constraint; Update unit: When the actual power output of the microgrid cluster deviates from the dispatching instruction, the operating cost is acceptable, and the relevant constraints in the hierarchical instruction tracking model are met, the microgrid cluster control center uses the non-iterative calculation method in the superior instruction tracking model to update the coordination signal in real time according to the DG power measurement data in the microgrid to compensate for the power deviation generated by the non-iterative optimization method, and sends it to each microgrid; Feedback unit: The internal control center of the microgrid calculates the optimal power output ratio of the power supply in the grid according to the received PCC power measurement data and coordination signal. Each distributed power source collaboratively tracks the optimal power output ratio according to its own rated power to achieve power sharing among the power sources, and feeds back the real-time DG power measurement data to the microgrid cluster control center through the hierarchical instruction tracking model.

7. The device according to claim 1, characterized in that The updating unit is specifically used for: The coordination signal is updated via a primal-dual decomposition algorithm and distributed projected subgradients.

8. The device according to claim 7, characterized in that The updating unit is specifically used for: The dual problem of the tracking model constructed based on the primal-dual decomposition algorithm and the distributed projected subgradient is calculated by Lagrangian function; Through the consistency algorithm and power sharing strategy, each distributed power source can output power in the best state.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor implements the steps of the method according to any one of claims 1 to 5 by running the executable instructions.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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