ADMM-Based Cooperative Distributed Optimal Scheduling Method and System for Distribution Network-Microgrid Cluster

Abstract

The present invention provides a collaborative distributed optimal scheduling method and system for a distribution network - microgrid cluster based on ADMM, including: dividing virtual regions based on the different interests of the source - load - storage entities in the distribution network - microgrid cluster, and constructing a distributed optimization overall framework; decomposing the total optimal scheduling objective function and constraint conditions through the alternating direction method of multipliers, and constructing a distributed optimal scheduling model for the distribution network - microgrid cluster considering multiple interest entities; and solving the distributed optimal scheduling model based on the alternating direction method of multipliers. Aiming at the problems existing in the centralized scheduling method of the distribution network - microgrid cluster, such as large computational amount, difficult communication, and difficult coordination of interest entities, the present invention proposes a distributed optimal scheduling method for the distribution network sub - regions and the controllable resource sub - regions of the microgrid cluster based on different interest entities, which can maintain good economy, and at the same time has feasibility and comprehensive advantages.

Description

Technical Field

[0001] The invention belongs to the field of coordinated optimal dispatch of distribution network - microgrid cluster, and particularly relates to a coordinated distributed optimal dispatch method and system for distribution network - microgrid cluster based on ADMM. Background Art

[0002] In recent years, with the gradual aggravation of energy and environmental problems, vigorously developing clean energy has become the consensus of the international community and an important energy strategy in China. Renewable energy and distributed generation have received increasing attention. Although distributed power sources have prominent advantages, they have many problems themselves. For example, the single - unit access cost of distributed power sources is high and the control is difficult. As an effective way to absorb and manage distributed power sources, the microgrid, as a micro - energy system integrating "source - load - storage", can, through internal regulation, cope with the safety and stability problems brought to the distribution system after a large number of distributed power sources are connected, and then improve the acceptance capacity and utilization efficiency of the distribution system for distributed renewable energy. It has been more and more widely applied to the distribution system.

[0003] A microgrid is a small power generation and distribution system aggregated by distributed power sources, energy storage systems, energy conversion devices, loads, monitoring and protection devices, etc. It can operate in parallel with the upper - level power grid or operate in island mode. However, the traditional single microgrid has limited capacity to absorb renewable energy and limited ability to actively support the voltage / frequency of the distribution network. With the increase in the number of microgrids in the distribution network, if multiple geographically adjacent microgrids have different investment entities, different operation goals or different renewable energy conditions; have the willingness to interact in terms of electricity, control, information and funds, etc., and the need to achieve common goals through cooperation, then multiple microgrids can be connected via medium - and low - voltage distribution lines to form an interconnected and mutually - supplied integrated network, that is, a microgrid cluster.

[0004] At present, the centralized dispatch method of distribution network - microgrid cluster has problems such as large computational load, difficult communication, and difficult coordination of interest subjects. The rapidly increasing number of distributed resources will increase the computational pressure on the centralized computing cloud, making higher hardware requirements for the optimization center's computer, storage devices and other hardware devices; a large number of distributed resources need to establish a reliable communication path with the centralized optimization center to realize the interaction of massive data and information between the cloud and the terminal, which will lead to higher requirements for the communication bandwidth of the distribution system. In addition, the large - scale transmission of data also increases the risk of errors; the distribution network - microgrid cluster involving source - load - storage resources often has multiple interest subjects. Each interest subject has goals for its own scheduling requirements, and due to privacy considerations, the information of each subject is not transparent. The traditional centralized control method is difficult to meet the requirements. In view of the problems existing in the above - mentioned centralized dispatch, it is necessary to adopt a distributed algorithm to realize the optimal dispatch of the distribution network - microgrid cluster. Summary of the Invention

[0005] The present invention aims to overcome the defects existing in the collaborative optimal scheduling of microgrid clusters, and provides a collaborative distributed optimal scheduling method for distribution network - microgrid clusters based on ADMM. To achieve the above object, the present invention patent adopts the following technical solutions:

[0006] The collaborative distributed optimal scheduling method for distribution network - microgrid clusters based on ADMM includes:

[0007] Based on the different interests of the source - load - storage entities in the distribution network - microgrid clusters, virtual regions are divided to obtain the overall framework of distributed optimization;

[0008] Based on the overall framework of distributed optimization, the alternating direction method of multipliers is used to decompose the objective function and constraint conditions of the total optimal scheduling, and a distributed optimal scheduling model for the distribution network - microgrid clusters is constructed based on the decomposition results;

[0009] Based on the alternating direction method of multipliers, the distributed optimal scheduling model is solved, and the scheduling is optimized according to the solution results.

[0010] In the above method, the overall framework of distributed optimization includes a distribution network region and a controllable resource region of the microgrid cluster. The steps of using the alternating direction method of multipliers to decompose the total optimal scheduling objective function based on the overall framework of distributed optimization include:

[0011] Based on the overall framework of distributed optimization, the total optimal scheduling objective function is solved distributively through the ADMM algorithm to obtain the objective function of the distribution network region and the objective functions of each interest entity in the controllable resource region of the microgrid;

[0012] Among them, the objective function of the distribution network region is:

[0013]

[0014] In the formula, C net is the sum of the network loss cost and the main - grid power purchase cost on the distribution network region side, is the Lagrange multiplier, P i t is the original injection power of the coupling boundary variable between the distribution network region and the controllable resource region of the microgrid cluster, is the injected virtual power belonging to the controllable resource region of the microgrid cluster, and ρ is the penalty parameter corresponding to the alternating direction method of multipliers;

[0015] The objective functions of each interest entity in the controllable resource region of the microgrid are:

[0016]

[0017] In the formula, C res,iis the sum of the dispatching costs of the gas turbines, photovoltaics, wind turbines, energy storage, and flexible loads on the regional side of the controllable resources in the microgrid cluster. is the Lagrange multiplier, and P i t is the original injection power of the coupling boundary variables between the distribution network area and the controllable resource area of the microgrid cluster. is the injected virtual power belonging to the controllable resource area of the microgrid cluster, and ρ is the penalty parameter corresponding to the alternating direction multiplier method.

[0018] In the above method, the step of decomposing the constraint conditions of the total optimal dispatch by using the alternating direction multiplier method based on the distributed optimization overall framework includes:

[0019] Based on the distributed optimization overall framework, the constraint conditions of the total optimal dispatch are decomposed by using the alternating direction multiplier method to obtain the constraint conditions of the distribution network area, the constraint conditions of the controllable resource area of the microgrid cluster, and the coupling constraints.

[0020] In the above method, the constraint conditions of the distribution network area are:

[0021] The input active power of any node in the network is equal to the sum of the transmission active powers of all the connecting lines of the node; the input reactive power of any node in the network is equal to the sum of the transmission reactive powers of all the connecting lines of the node; the branch power flow between nodes i and j is equal to the difference between the product of the branch conductance and the square of the voltage of node i and the product of the voltage of node i, the voltage of node j, and the cosine of the phase angle difference between the voltages of nodes i and j, and then subtracting the product of the voltages of nodes i and j and the sine of the phase angle difference between the branch susceptance and nodes i and j; the sum of the active powers flowing into and out of node i is 0;

[0022] The branch power flow between nodes i and j is between the upper and lower limits of the branch power flow, and the voltage amplitude of node i is between the upper and lower limits of the voltage;

[0023]

[0024]

[0025] In the formula, respectively represent the active and reactive powers flowing into node i at time t, represents the power consumption load of node i at time t, and V i t 、 represent the voltage of node i at time t, G ij 、B ij are the conductance and susceptance between nodes i and j at time t, and θ t ij is the phase angle difference between the voltages of nodes i and j at time t, Represents the branch power flow between nodes i and j at time t, node respectively represent the upper and lower limits of the voltage of node i at time t, P L,max,i-j 、P L,min,i-j respectively represent the upper and lower limits of the branch power flow between nodes i and j at time t.

[0026] In the above method, the constraint conditions of the controllable resource area of the microgrid group are as follows:

[0027] The active power output of the microgrid group m is between its maximum and minimum active power outputs. The actual output of the microgrid group m with controllable resources at time t minus the output of the microgrid group m at time t-1 does not exceed the maximum and minimum ramp constraints;

[0028]

[0029] In the formula, respectively represent the maximum and minimum outputs of the microgrid group m with controllable resources at time t, represents the actual output of the microgrid group m with controllable resources at time t, r t m,up 、r t m,down respectively represent the maximum ramp rate and the landslide rate of the microgrid group m with active power ramp constraints at time t, and Δt is the ramp calculation interval time;

[0030] Among the adjustable resources, the operation constraints of the energy storage unit are as follows:

[0031] The active power output of the energy storage unit k is between its maximum and minimum active power outputs. The stored energy of the energy storage unit k at time t-1 minus the energy charged and discharged within Δt time cannot exceed the upper and lower limits of the stored energy of the energy storage unit k;

[0032]

[0033] In the formula, k is the number of energy storage units, respectively represent the maximum and minimum outputs of the energy storage unit k at time t, respectively represent the upper and lower limits of the stored energy of the energy storage unit k at time t, represents the actual power of the energy storage unit k at time t, represents the actual stored energy of the energy storage unit k at time t, Δt is the charge and discharge calculation interval time, and η is its charge and discharge efficiency;

[0034] The coupling constraint is:

[0035] The coupling boundary variable on the distribution network side of node i is equal to the coupling boundary variable on the controllable resource side of the microgrid group. The coupling boundary variable on the controllable resource side of the microgrid group is equal to the sum of the active power variables of each controllable resource passing through node i sum;

[0036]

[0037] Wherein, P i t is the coupling boundary variable on the distribution network side, and K i is the number of controllable resources of the microgrid cluster under node i, is the coupling boundary variable on the controllable resource side of the microgrid cluster, is the active variable of each controllable resource at node i.

[0038] In the above method, solving the distributed optimization scheduling model based on the alternating direction multiplier method includes:

[0039] Independently solving the optimization models of the distribution network area and the controllable resource area of the microgrid cluster based on the alternating direction multiplier method;

[0040] Exchanging coupling variables;

[0041] Until the iterative convergence condition is satisfied:

[0042]

[0043] Wherein, ε is the convergence accuracy, and k is the number of iterations.

[0044] In the above method, the specific process of solving includes:

[0045] Step 1: Initialize the variable P i t (k) in the optimization model, and initially set its Lagrange multiplier and penalty function ρ, and the number of iterations k = 1;

[0046] Step 2: Independently solve the optimization models of the controllable resources of each microgrid cluster to obtain the optimization variables that meet their own objectives, and transmit the key coupling boundary variables to the distribution network through the connection point;

[0047] Step 3: The distribution network uses the obtained boundary coupling data, combines its own optimization model, and solves the non-linear optimization problem of the distribution network through the particle swarm optimization algorithm, and then transmits the optimized P i t (k) to the controllable resource area of the microgrid cluster to complete the coupling information interaction;

[0048] Step 4: Check the convergence condition; if it is satisfied, the iteration ends and the scheduling result of the controllable resources of the microgrid cluster is output; if the iteration does not reach convergence, return to Step 2, the number of iterations k = k + 1, repeat the above solution process, and update the Lagrange multiplier according to the following formula:

[0049]

[0050] A scheduling system, comprising:

[0051] The first module: configured to construct a distributed optimization overall framework, specifically, virtual area division is performed based on the different interests of the source-load-storage entities in the distribution network - microgrid cluster. The distribution network area consists of all nodes of the distribution network, and the controllable resource area of the microgrid cluster includes four microgrid clusters;

[0052] The second module: configured to decompose the total optimization scheduling objective function and constraint conditions by the alternating direction method of multipliers, and construct a distributed optimization scheduling model for the distribution network - microgrid cluster considering multiple interest entities;

[0053] The third module: configured to solve the distributed optimization scheduling model based on the alternating direction method of multipliers, and optimize the scheduling according to the solution structure.

[0054] An electronic device, a computer-readable storage medium storing computer-executable instructions; and one or more processors, the one or more processors being coupled to the computer-readable storage medium and configured to execute the computer-executable instructions so that the device executes the above method.

[0055] A readable storage medium storing computer-executable instructions, the computer-executable instructions, when executed by a processor, configuring the processor to execute the above method.

[0056] Compared with the closest prior art, the beneficial effects of the present invention are as follows:

[0057] The present invention provides a comprehensive evaluation method and system for an energy storage power station for power grid peak regulation and frequency modulation, including: performing virtual area division based on the different interests of the source-load-storage entities in the distribution network - microgrid cluster to construct a distributed optimization overall framework; decomposing the total optimization scheduling objective function and constraint conditions by the alternating direction method of multipliers to construct a distributed optimization scheduling model for the distribution network - microgrid cluster considering multiple interest entities; and solving the distributed optimization scheduling model based on the alternating direction method of multipliers. In view of the problems existing in the centralized scheduling method of the distribution network - microgrid cluster, such as large computational amount, communication difficulties, and difficulty in coordinating interest entities, the present invention proposes a distributed optimization scheduling method for the distribution network sub-region and the controllable resource sub-region of the microgrid cluster based on different interest entities, further improving the absorption capacity of the distribution system for distributed energy and the power supply reliability, realizing the coordinated complementarity of power generation resources, enhancing the operation stability and reliability, reducing the operation cost of the system, improving the energy utilization efficiency, being able to maintain good economy, and having feasibility and comprehensive advantages at the same time. Description of the Drawings

[0058] Figure 1 Schematic flow chart of a collaborative distributed optimal scheduling method for a distribution network - microgrid cluster based on ADMM provided by the present invention;

[0059] Figure 2 Schematic diagram of the virtual area decomposition framework of the distribution network - microgrid cluster provided by the present invention;

[0060] Figure 3 Schematic flow chart of solving the distributed optimal scheduling model provided by the present invention;

[0061] Figure 4 Schematic diagram of the network structure of the embodiment provided by the present invention;

[0062] Figure 5 Output result graph of the controllable resources of the distribution network - microgrid cluster for 96 scheduling cycles within a day in the embodiment provided by the present invention. Specific implementation manners

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] Embodiment 1:

[0065] As Figure 1 shown, the present invention provides a collaborative distributed optimal scheduling method for a distribution network - microgrid cluster based on ADMM, specifically including:

[0066] 1. Virtual area division is carried out based on the different interests of the source - load - storage entities in the distribution network - microgrid cluster, and a distributed optimization overall framework is constructed, specifically including:

[0067] The distribution network area consists of all the nodes of the distribution network. The controllable resource area of the microgrid cluster includes four microgrid clusters, and each microgrid contains different types of controllable resources, including gas turbines, wind turbines, photovoltaics, energy storage, flexible loads, etc. Different from the traditional optimization method, each virtual area can flexibly set its own independent objective function and perform optimal scheduling and solution within the sub - area respectively to meet the needs of each interest subject. The specific virtual area decomposition framework is as Figure 2 shown.

[0068] Each virtual area needs to achieve collaborative optimization through the exchange of coupling boundary variables. Since all the controllable resources of the microgrid clusters are directly connected to the distribution network, the injection power at the resource connection point can be used as the coupling variable. At the original injection power Pi On the basis of this, the injected virtual power X belonging to the controllable resource area of the microgrid group is added. i The decoupling between the controllable resource side and the distribution network side of the microgrid group is realized.

[0069] 2. Decompose the total optimal scheduling objective function and constraint conditions by the alternating direction method of multipliers, and construct a distributed optimal scheduling model for the microgrid group considering multiple interest subjects.

[0070] Decompose the total optimal scheduling objective function and constraint conditions by the alternating direction method of multipliers, and construct a distributed optimal scheduling model for the microgrid group considering multiple interest subjects, including the definition of the distributed optimal scheduling objective function and the definition of the distributed optimal scheduling constraint conditions.

[0071] According to the above virtual partition results, the entire power grid is divided into a distribution network area and a controllable resource area of the microgrid group (including multiple interest subjects such as photovoltaic, wind turbine, gas turbine, energy storage, and flexible load). Mathematically, the total objective function also needs to be decomposed into two parts: the distribution network area and the controllable resource area of the microgrid group.

[0072] Specifically, the distribution network area mainly includes the network loss cost C loss and the power purchase cost C Grid from the main grid. The controllable resource area of the microgrid group mainly includes the gas turbine cost C MT , the photovoltaic cluster scheduling cost C PV , the wind turbine scheduling cost C WT , the energy storage scheduling cost C ESS , and the flexible load scheduling cost C DR , as follows:

[0073]

[0074] C net = C Grid + C loss

[0075] C res = C MT + C ESS + C DR + C PV + C WT

[0076] Specifically, the cost functions of each controllable resource in the microgrid group area are defined as follows:

[0077] Gas turbine power generation cost model:

[0078]

[0079] C MTis the real-time power generation cost of the gas turbine, P t MT,j is the active power output of the j-th gas turbine at time t, N MT is the total number of gas turbines, η t MT,j is its power generation efficiency value.

[0080] Energy storage scheduling cost model:

[0081]

[0082] In the formula, C ESS is the real-time scheduling cost of the electrical energy storage, c ESS is the unit regulation cost of the electrical energy storage, N ESS is the total number of electrical energy storages, and represent the charging and discharging powers of the electrical energy storage at time t, respectively.

[0083] Flexible load scheduling cost model:

[0084]

[0085] In the formula, C DR is the real-time scheduling cost of the flexible load, c DR represents the unit scheduling cost of the flexible load, represents the active power regulation amount of the flexible load at time t.

[0086] Main grid power purchase cost model:

[0087]

[0088] In the formula, C Grid is the real-time power purchase cost of the active distribution network, represents the time-of-use power purchase unit price, P t Grid represents the power quantity purchased from the main grid at time t.

[0089] Photovoltaic scheduling cost model:

[0090]

[0091] Wind turbine scheduling cost model:

[0092]

[0093] In the formula, C PV is the real-time scheduling cost of the photovoltaic, C WT is the real-time scheduling cost of the photovoltaic, c PV and c WT are the unit scheduling costs of the photovoltaic power station and the wind turbine, respectively.

[0094] Network loss cost model:

[0095]

[0096] Wherein, N node is the total number of nodes, V t i and V t j are the voltages of nodes i and j at time t, is the sum of the head and tail nodes with node i as the head, G ij is the conductance between nodes i and j, θ t ij is the voltage phase angle difference between nodes i and j at time t.

[0097] Specifically, according to the above cost model, a Lagrange multiplier is added to the separable objective function F to construct the objective function in its Lagrangian form:

[0098]

[0099] Secondly, the separable objective function is solved distributively through the ADMM algorithm, and the objective function of the distribution network area:

[0100]

[0101] The objective functions of each stakeholder within the controllable resource area of the microgrid cluster:

[0102]

[0103] Specifically, the distributed optimal scheduling constraint conditions include:

[0104] 1) Constraint conditions of the distribution network area:

[0105]

[0106]

[0107] Wherein, respectively represent the active and reactive powers flowing into node i at time t, represents the power consumption load of node i at time t, G ij and B ij are the conductance and susceptance between nodes i and j at time t, θ t ij is the voltage phase angle difference between nodes i and j at time t, respectively represent the upper and lower limits of the voltage of node i at time t, P L,max,i-j and P L,min,i-j respectively represent the upper and lower limits of the branch power flow between nodes i and j at time t.

[0108] 2) Constraints in the controllable resource area of the microgrid cluster:

[0109]

[0110] In the formula, respectively represent the maximum and minimum power outputs of the microgrid cluster m with controllable resources at time t, and r t m,up , r t m,down respectively represent the maximum climbing and sliding rates of the microgrid cluster m with active power climbing constraints at time t.

[0111] In addition, among the controllable resources, the operating constraints of the energy storage unit are as follows:

[0112]

[0113] In the formula, respectively represent the maximum and minimum power outputs of the energy storage unit k at time t, respectively represent the upper and lower limits of the stored energy of the energy storage unit k at time t, and η is its charge-discharge efficiency.

[0114] 3) Coupling constraints:

[0115]

[0116] In the formula, P i t is the coupling boundary variable on the distribution network side, is the coupling boundary variable on the controllable resource side of the microgrid cluster, and is calculated by summing the active power variables of the controllable resources of each microgrid cluster at node i.

[0117] 3. Solve the distributed optimization scheduling model based on the alternating direction multiplier method, specifically including:

[0118] In the above optimization model, the optimization models of the distribution network area and the controllable resource area of the microgrid cluster not only need to be solved independently, but also need to exchange coupling variables until the iterative convergence condition is met:

[0119]

[0120] In the formula, ε is the convergence accuracy, and k is the number of iterations.

[0121] The flow chart for solving the distributed optimization scheduling model based on the alternating direction multiplier method is as Figure 3 shown, and the specific process of its solution includes:

[0122] Step 1: For the variable P in the optimization model it (k), Initialize it. In addition, it is necessary to initialize its Lagrange multipliers and penalty function ρ, and set the iteration number k = 1.

[0123] Step 2: Solve the optimization models of the controllable resources in each microgrid group separately to obtain the optimization variables that meet their own objectives, and transmit the key coupled boundary variables to the distribution network through the grid connection point.

[0124] Step 3: The distribution network uses the obtained boundary coupling data, combines its own optimization model, and solves the non-linear optimization problem of the distribution network through the particle swarm optimization algorithm. Then, transmit the optimized P i t (k) to the controllable resource area of the microgrid group to complete the coupling information interaction.

[0125] Step 4: Check the convergence condition. If it is satisfied, the iteration ends and the scheduling results of the controllable resources in the microgrid group are output; if the iteration does not reach convergence, return to Step 2, the iteration number k = k + 1, repeat the above solution process, and update the Lagrange multiplier according to the following formula:

[0126]

[0127] Preferably, in this section, the IEEE-33 node distribution system is selected as the research object. Among them, nodes 3, 7, 20, and 29 are connected to the microgrids respectively to form a microgrid group, node 12 is connected to a centralized energy storage, and node 28 is connected to an interruptible flexible load. The specific network structure is as Figure 4 shown. Among them, the types of controllable resources in the microgrid and the range of the grid connection parameters of the distribution network - microgrid group in 96 dispatch cycles within a day are shown in Table 1:

[0128] Table 1 Range of grid connection parameters of microgrid group resources in 96 dispatch cycles

[0129]

[0130] The output of the controllable resources of the distribution network - microgrid group in 96 dispatch cycles within a day is as Figure 5 shown.

[0131] Compare the distributed optimization based on the ADMM method with the traditional centralized optimization scheduling results. Table 2 shows the scheduling cost results of comparing the distributed optimization results with the traditional centralized optimization results:

[0132] Table 2 Comparison of scheduling costs between distributed optimization results and traditional centralized optimization results

[0133]

[0134] As can be seen from Table 2, the total dispatching cost under the distributed regulation mode is 12,195 yuan, which is comparable to the dispatching cost of 12,148 yuan under the centralized regulation mode, and it has better achieved the interest coordination between the distribution network area and the controllable resource area of the microgrid cluster. Compared with the traditional centralized regulation mode, the distributed regulation mode can solve the problems existing in centralized regulation, such as heavy computational burden, communication difficulties, opaque information between regions, and conflicting goals of interest subjects, while maintaining comparable economy, and has feasibility and comprehensive advantages in the optimization process of the distribution network - microgrid cluster involving multiple interest subjects.

[0135] Embodiment 2

[0136] Based on the same inventive concept, the present application also provides a dispatching system, including:

[0137] The first module: configured to construct a distributed optimization overall framework, specifically, virtual area division is carried out based on the different interests of the source - load - storage entities in the distribution network - microgrid cluster. The distribution network area consists of all nodes of the distribution network, and the controllable resource area of the microgrid cluster includes four microgrid clusters;

[0138] The second module: configured to decompose the total optimization dispatching objective function and constraint conditions by the alternating direction method of multipliers, and construct a distributed optimization dispatching model for the distribution network - microgrid cluster considering multiple interest subjects;

[0139] The third module: configured to solve the distributed optimization dispatching model based on the alternating direction method of multipliers, and optimize the dispatching according to the solution structure.

[0140] Embodiment 3

[0141] Based on the same inventive concept, the present application also provides an electronic device, a computer - readable storage medium storing computer - executable instructions; and one or more processors, the one or more processors being coupled to the computer - readable storage medium and configured to execute the computer - executable instructions so that the device executes the above - mentioned method.

[0142] Embodiment 4

[0143] Based on the same inventive concept, the present application also provides a readable storage medium storing computer - executable instructions, and when the computer - executable instructions are executed by a processor, the processor is configured to execute the above - mentioned method.

[0144] It should be understood that the parts not elaborated in detail in this specification all belong to the prior art.

[0145] It should be understood that the above description of the preferred embodiment is relatively detailed, and it should not be considered as a limitation on the protection scope of the present invention patent. Under the inspiration of the present invention, those of ordinary skill in the art can also make substitutions or deformations without departing from the protection scope defined by the claims of the present invention, and all of them fall within the protection scope of the present invention. The scope of protection claimed by the present invention shall be subject to the appended claims.

Claims

1. An ADMM-based collaborative distributed optimal scheduling method for distribution network - microgrid cluster, characterized in that, it includes: Based on the different interests of the source-load-storage entities in the distribution network - microgrid cluster, virtual regions are divided to obtain a distributed optimization overall framework; Based on the distributed optimization overall framework, the alternating direction method of multipliers is used to decompose the objective function and constraints of the total optimal scheduling, and a distributed optimal scheduling model for the distribution network - microgrid cluster is constructed based on the decomposition results; Based on the alternating direction method of multipliers, the distributed optimal scheduling model is solved, and the scheduling is optimized according to the solution results; The distributed optimization overall framework includes a distribution network region and a controllable resource region of the microgrid cluster. The step of decomposing the total optimal scheduling objective function by using the alternating direction method of multipliers based on the distributed optimization overall framework includes: Based on the distributed optimization overall framework, the total optimal scheduling objective function is solved distributively through the ADMM algorithm to obtain the objective function of the distribution network region and the objective functions of each interest entity in the controllable resource region of the microgrid cluster; Among them, the objective function of the distribution network region is: Where C net is the sum of the network loss cost on the regional side of the distribution network and the main grid power purchase cost, is the Lagrange multiplier, is the original injection power of the coupling boundary variable between the distribution network area and the controllable resource area of the microgrid cluster, is the injected virtual power belonging to the controllable resource area of the microgrid cluster, and ρ is the penalty parameter corresponding to the alternating direction multiplier method; The objective functions of each interest entity in the controllable resource region of the microgrid cluster are: Where, C res,i is the sum of the scheduling costs of gas turbines, photovoltaics, wind turbines, energy storage, and flexible loads on the regional side of the controllable resources in the microgrid cluster, is the Lagrange multiplier, is the original injection power of the coupling boundary variables between the distribution network area and the controllable resource area of the microgrid cluster, is the injected virtual power belonging to the controllable resource area of the microgrid cluster, and ρ is the penalty parameter corresponding to the alternating direction multiplier method; The step of decomposing the constraints of the total optimal scheduling by using the alternating direction method of multipliers based on the distributed optimization overall framework includes: Based on the distributed optimization overall framework, the alternating direction method of multipliers is used to decompose the constraints of the total optimal scheduling to obtain the constraints of the distribution network region, the constraints of the controllable resource region of the microgrid cluster, and the coupling constraints; The constraints of the distribution network region are: The input active power of any node in the network is equal to the sum of the transmitted active powers of all the connecting lines of the node; the input reactive power of any node in the network is equal to the sum of the transmitted reactive powers of all the connecting lines of the node; the branch power flow between nodes i and j is equal to the difference between the product of the branch conductance and the square of the voltage of node i and the product of the voltage of node i, the voltage of node j, and the cosine value of the voltage phase angle difference between i and j nodes, and then subtract the product of the voltage of node i, the voltage of node j, the branch susceptance, and the sine value of the voltage phase angle difference between i and j nodes; the sum of the active powers flowing into and out of node i is 0; The branch power flow between nodes i and j is between the upper and lower limits of the branch power flow, and the voltage amplitude of node i is between the upper and lower limits of the voltage; In the formula, respectively represent the active and reactive power flowing into node i at time t, represents the power consumption load of node i at time t, represents the voltage of node i at time t, G ij and B ij are the conductance and susceptance between nodes i and j at time t, θ t ij is the phase angle difference between the voltages of nodes i and j at time t, represents the branch power flow between nodes i and j at time t, and the nodes respectively represent the upper and lower limits of the voltage of node i at time t, P L,max,i-j and P L,min,i-j respectively represent the upper and lower limits of the branch power flow between nodes i and j at time t; The constraints of the controllable resource region of the microgrid cluster are: The active power output of microgrid cluster m is between its maximum and minimum active power outputs, and the actual output of microgrid cluster m with controllable resources at time t minus the output of microgrid cluster m at time t-1 does not exceed the maximum and minimum ramping constraints; Wherein, respectively represent the maximum and minimum power outputs of the microgrid group m containing controllable resources at time t, represents the actual power output of the microgrid group m containing controllable resources at time t, r t m,up and r t m,down respectively represent the maximum climbing and sliding rates of the microgrid group m with active power climbing constraints at time t, and Δt is the climbing calculation interval time; Among the controllable resources, the operating constraints of the energy storage unit are as follows: The active power output of energy storage unit k is between its maximum and minimum active power outputs, and the stored energy of energy storage unit k at time t-1 minus the energy charged and discharged within Δt time does not exceed the upper and lower limits of the stored energy of energy storage unit k; Where k is the number of energy storage units, respectively represent the maximum and minimum outputs of the energy storage unit k at time t, respectively represent the upper and lower limits of the stored energy of the energy storage unit k at time t, represents the actual power of the energy storage unit k at time t, represents the actual stored energy of the energy storage unit k at time t, Δt is the charge and discharge calculation interval time, and η is its charge and discharge efficiency; The coupling constraints are: The coupling boundary variables on the distribution network side of node i are equal to the coupling boundary variables on the controllable resource side of the microgrid cluster, and the coupling boundary variables on the controllable resource side of the microgrid cluster are equal to the sum of the active power variables of each controllable resource passing through node i. The sum; In the formula, is the coupling boundary variable on the distribution network side, and K i is the number of controllable resources of the microgrid cluster under node i, is the coupling boundary variable on the controllable resource side of the microgrid cluster, is the active power variable of each controllable resource at node i; The step of solving the distributed optimal scheduling model by using the alternating direction method of multipliers includes: Based on the alternating direction method of multipliers, independent solutions are carried out for the optimization models of the distribution network region and the controllable resource region of the microgrid cluster; Exchange the coupling variables; until the iterative convergence condition is satisfied: where ε is the convergence accuracy and k is the number of iterations.

2. The method according to claim 1, characterized in that, the specific process of the solution includes: Step 1: Initialize the variables in the optimization model and perform initial settings for its Lagrange multipliers and the penalty function ρ, and set the iteration number k = 1; Step 2: Solve the optimization models of the controllable resources of each microgrid group separately to obtain the optimization variables that meet their own objectives, and transmit the key coupled boundary variables to the distribution network through the connection point; Step 3: The distribution network uses the obtained boundary coupling data, combines its own optimization model, and solves the non-linear optimization problem of the distribution network through the particle swarm optimization algorithm. Then, the optimized result is transmitted to the controllable resource area of the microgrid cluster to complete the coupling information interaction; Step 4: Check the convergence condition; if satisfied, the iteration ends and the controllable resource scheduling result of the microgrid cluster is output; if the iteration does not reach convergence, return to Step 2, the number of iterations k = k + 1, repeat the above solution process, and update the Lagrange multiplier according to the following formula:

3. A scheduling system, characterized in that, for executing the ADMM-based collaborative distributed optimal scheduling method for the distribution network - microgrid cluster according to claim 1 or 2, the scheduling system includes: The first module: configured to construct a distributed optimization overall framework, specifically by dividing virtual regions based on the different interests of the source-load-storage entities in the distribution network - microgrid cluster. The distribution network region consists of all the nodes of the distribution network, and the controllable resource region of the microgrid cluster contains four microgrid clusters; The second module: configured to decompose the total optimization scheduling objective function and constraint conditions by the alternating direction method of multipliers, and construct a distributed optimization scheduling model for the distribution network - microgrid cluster considering multiple interest entities; The third module: configured to solve the distributed optimization scheduling model based on the alternating direction method of multipliers and optimize the scheduling according to the solution structure.

4. An electronic device, characterized in that, a computer-readable storage medium storing computer-executable instructions; and one or more processors, the one or more processors being coupled to the computer-readable storage medium and configured to execute the computer-executable instructions so that the device executes the method according to any one of claims 1-2.

5. A readable storage medium, characterized in that, stores computer-executable instructions that, when executed by a processor, configure the processor to execute the method according to any one of claims 1-2.

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