Scheduling method and device of power distribution network and electronic equipment

Abstract

The invention provides a dispatching method and device of a power distribution network and electronic equipment. The method comprises the following steps: constructing an optimal scheduling model of a power distribution network to a micro-grid, and constructing an objective function of the optimal scheduling model; constructing constraint conditions of the optimal scheduling model; the ADMM distributed optimization algorithm is improved according to the optimization scheduling model containing the constraint conditions, and an improved ADMM distributed optimization algorithm is obtained; solving the objective function by using an improved ADMM distributed optimization algorithm to obtain the dispatching condition of the power distribution network; according to the dispatching condition, the power distribution network dispatches the micro-grid, and the method solves the problem that in the prior art, the dispatching efficiency of the power distribution network is low.

Description

Technical Field

[0001] The present application relates to power grid dispatching, and in particular, to a dispatching method, a dispatching device and an electronic device for a distribution network. Background Art

[0002] Distributed energy, as a novel energy supply model, has attracted much attention from enterprises in recent years. It makes full use of renewable energy for power generation, which can not only reduce environmental pollution, but also improve the utilization rate of new energy to create good economic benefits, save investment in power transmission and grid capacity, and reduce network losses on power transmission lines; it can also play a supplementary supporting role for large power grids and improve the safety and reliability of power systems.

[0003] In the existing technology, most of the methods for solving the optimization scheduling problem of microgrids incorporating into the main distribution network system are based on a two-level decision model, solved by genetic algorithms, and using two-level planning theory to solve the cooperative game between microgrids and the power grid; there are also methods that build a distribution network multi-objective planning model based on the microgrid topology and use an improved particle swarm algorithm to solve it. However, these technologies are less efficient in the process of dispatching the distribution network.

[0004] Therefore, there is an urgent need for a highly efficient distribution network scheduling method. Summary of the invention

[0005] The main purpose of the present application is to provide a dispatching method, a dispatching device and an electronic device for a distribution network, so as to at least solve the problem of low dispatching efficiency of the distribution network in the prior art.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a distribution network dispatching method is provided, comprising: constructing an optimal dispatching model of the distribution network for a microgrid, and constructing an objective function of the optimal dispatching model, wherein the microgrid at least includes energy storage equipment, wind turbines and photovoltaic units, and the objective function takes the minimum total operating cost as the optimization goal; constructing constraints of the optimal dispatching model, wherein the constraints include a first safety constraint, an equipment constraint, and a second safety constraint, wherein the first safety constraint includes a high-voltage system power constraint, a first flow constraint and a voltage and current constraint, and the equipment constraint includes at least an energy storage system operation constraint. The second safety constraint condition includes a second power flow constraint condition and an exchange power constraint condition of the microgrid; according to the optimization scheduling model including the constraint conditions, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, and the improved ADMM distributed optimization algorithm at least converts the original nonlinear programming model into a second-order cone programming through relaxation; the improved ADMM distributed optimization algorithm is used to solve the objective function to obtain the scheduling status of the distribution network; according to the scheduling status, the distribution network schedules the microgrid.

[0007] Optionally, constructing the objective function of the optimization scheduling model includes: constructing a first optimization objective minC of the distribution network DS (x DS ),in, j represents a node of the distribution network, DS represents the distribution network, HV represents a high voltage power transmission system, represents the amount of electricity that the distribution network plans to purchase from the high-voltage power transmission system within Δt, represents the purchase price of the electric quantity of the high-voltage electric power transmission system, represents the expected power generation of the wind turbines in the distribution network, represents the expected photovoltaic power generation of the distribution network, represent the decision variables of the wind turbine power generation and the photovoltaic power generation respectively, Respectively represent the abandoned power costs of the wind turbine power generation and the photovoltaic power generation; construct the second optimization target minC of the microgrid e (x e ),in,

[0008] represents the exchange power from the microgrid to the distribution network per unit time of node j, represents the purchase price of the electricity of the microgrid, represents the expected power generation of the wind turbine of the microgrid, represents the expected power generation of photovoltaic power generation of the microgrid; constructing the objective function of the optimization scheduling model, the objective function is i is the number of the module of the distribution network or the module of the microgrid in the set M, and M is the total number of the distribution networks and the microgrids in the entire system.

[0009] Optionally, constructing the constraint conditions of the optimization scheduling model includes: constructing the first safety constraint conditions, constructing the high-voltage system constraint conditions and The first power flow constraint

[0010] Item and And the voltage and current constraints and Among them, δ(j) is the set of branch end nodes with j as the head node, π(j) is the set of branch head node with j as the end node, P jk,t , Q jk,t are the active and reactive power injected into node j at time t, ij is the branch between node i and node j, r i,j 、x i,j are the resistance and reactance values ​​of branch ij, I ij,t is the current amplitude flowing from node i to node j at time t, P ij,t , Q ij,t are the active power and reactive power flowing from node i to node j at time t, are the wind turbine power and photovoltaic power of the distribution network at time period t and node j respectively, are the active power generated by the energy storage device and the active power generated by the high-voltage power transmission system at time period t and node j, respectively. are the reactive power generated by the energy storage device and the reactive power generated by the high-voltage power transmission system at time period t and node j, respectively, are the reactive power of the wind turbine power generation and the reactive power of the photovoltaic power generation of the distribution network at time period t and node j respectively, V j , are the lower and upper limits of the voltage amplitude, is the upper limit of the current amplitude, are the squares of node voltage and branch current amplitude respectively; construct the device constraint condition and in, represents the energy storage power of node j at time t, represents the energy storage discharge power at time t, represents the energy storage charging power at time t, represents the capacity of the energy storage system at t+1, They represent the charge and discharge efficiency, are 0-1 variables representing the charging and discharging state of the energy storage system; constructing the second safety constraint condition, the second safety constraint condition includes constructing the second power flow constraint condition and the exchange power constraint condition of the microgrid, the second power flow constraint condition is

[0011] and The switching power constraint of the microgrid is: and Where V j,t 、V i,t are the voltages of node j and node t at time t, represents the active power generated by the microgrid at time period t and node i, are the wind turbine power and photovoltaic power of the microgrid at time period t and node j respectively, are the reactive power generated by the microgrid at time period t and node j, is the reactive power of the wind turbine power generation and the reactive power of the photovoltaic power generation of the microgrid at time period t and node j, are the lower limit and upper limit of the power generation of the microgrid respectively, are respectively the lower limit and upper limit of the reactive power generated by the microgrid.

[0012] Optionally, according to the optimization scheduling model including the constraint condition, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, including: an initialization step, according to the constraint condition, initializing the parameters of the improved ADMM distributed optimization algorithm, the parameters at least including an iteration index, a convergence criterion, a step size, an auxiliary variable and a dual variable; a setting step, setting the auxiliary variable to a first value, setting the dual variable to a second value, and obtaining a first formula according to the first value and the second value In the formula, e i is the constraint value of the auxiliary variable, is the value of the constraint value of the auxiliary variable from node i to node j at time t, is the auxiliary variable, is the value of the auxiliary variable from node i to node j at time t, N is the set of all nodes, T is the set of all times, ρ1 is the iteration parameter, is the dual variable, C i (x i ) is the first update step of the objective function at time i, according to the second formula; Update the first value to obtain a third value, is the value of the constraint value of the auxiliary variable in the k+1 state, is the value of the dual variable in the k state; the second updating step, according to the third formula Update the second value to obtain a fourth value, where: is the fourth value, is the value of the auxiliary variable in the k+1 state; a judging step, judging whether the improved ADMM distributed optimization algorithm converges at least according to the size of the difference between the third value and the fourth value; when the ADMM distributed optimization algorithm converges, the ADMM distributed optimization algorithm is the improved ADMM distributed optimization algorithm.

[0013] Optionally, the initialization step includes: an initial step of initializing the parameters; and repeating the initial step at least once until the distribution network and the microgrid meet preset conditions.

[0014] Optionally, the setting step includes: setting a first constraint according to the energy exchange between the distribution network and the microgrid Said is the energy exchanged from node i to node j in the microgrid, is the energy exchanged from node j to node i in the microgrid; the first constraint is improved to obtain the second constraint Where i is the set of the distribution network and the microgrid participating in energy trading, They are respectively the values ​​of the constraint values ​​of the auxiliary variable from node i to node j and from node j to node i at time t; the first value and the second value are input into the second constraint to obtain the first formula.

[0015] Optionally, the first updating step includes: setting a fourth formula According to the fourth formula, a fifth value is obtained; and the fifth value is input into the second formula to obtain the third value.

[0016] Optionally, the judging step includes: when the absolute value of the difference between the third value and the fourth value is not greater than a preset threshold, the ADMM distributed optimization algorithm converges; when the absolute value of the difference between the third value and the fourth value is greater than the preset threshold, the number of iterations is increased by one, and the initialization step, the setting step, the first update step, the second update step and the judging step are repeated at least once in sequence until the absolute value of the difference between the third value and the fourth value is not greater than the preset threshold, and the ADMM distributed optimization algorithm converges.

[0017] According to another aspect of the present application, a dispatching device for a distribution network is provided, comprising:

[0018] The first construction unit constructs an optimization dispatching model of the distribution network to the microgrid, and constructs an objective function of the optimization dispatching model, wherein the microgrid includes at least energy storage equipment, a wind turbine generator set and a photovoltaic generator set, and the objective function takes minimizing the total operating cost as an optimization goal;

[0019] A second construction unit constructs the constraint conditions of the optimization scheduling model, wherein the constraint conditions include a first safety constraint condition, an equipment constraint condition, and a second safety constraint condition, wherein the first safety constraint condition includes a high-voltage system power constraint condition, a first power flow constraint condition, and a voltage and current constraint condition, wherein the equipment constraint condition includes at least an energy storage system operation constraint condition, a constraint condition of the wind turbine generator set, and a constraint condition of the photovoltaic generator set, and wherein the second safety constraint condition includes a second power flow constraint condition and an exchange power constraint condition of the microgrid;

[0020] An improvement unit improves the ADMM distributed optimization algorithm according to the optimization scheduling model including the constraint conditions to obtain an improved ADMM distributed optimization algorithm, wherein the improved ADMM distributed optimization algorithm at least converts the original nonlinear programming model into a second-order cone programming through relaxation;

[0021] A solving unit, using the improved ADMM distributed optimization algorithm to solve the objective function to obtain the dispatching status of the distribution network;

[0022] A dispatching unit, wherein the distribution network dispatches the microgrid according to the dispatching situation.

[0023] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the methods described above.

[0024] Applying the technical solution of the present application, first, construct an optimization dispatching model of the distribution network to the microgrid, and construct the objective function of the optimization dispatching model; secondly, construct the constraint conditions of the optimization dispatching model; then, according to the optimization dispatching model including the constraint conditions, improve the ADMM distributed optimization algorithm to obtain an improved ADMM distributed optimization algorithm; thirdly, use the improved ADMM distributed optimization algorithm to solve the objective function to obtain the dispatching situation of the distribution network; finally, according to the dispatching situation, the distribution network dispatches the microgrid. Compared with the prior art, the solution of the present application constructs an optimization dispatching model by setting the objective function and constraint conditions, and improves the ADMM distributed optimization algorithm according to the optimization dispatching model. The improved ADMM distributed optimization algorithm converts the original nonlinear programming model into the SOCP form for solution, which can more conveniently utilize the properties of the convex optimization problem, improve the solution efficiency, and thus obtain the optimal solution faster. The distribution network can dispatch the microgrid faster according to the dispatching situation of the distribution network obtained by the optimal solution, thereby solving the problem of low dispatching efficiency of the distribution network in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for scheduling a distribution network provided in an embodiment of the present application is shown;

[0027] Figure 2 A schematic diagram of a flow chart of a method for dispatching a distribution network provided according to an embodiment of the present application is shown;

[0028] Figure 3 A schematic diagram of an improved process flow of an ADMM distributed optimization algorithm of a distribution network scheduling method provided according to an embodiment of the present application is shown;

[0029] Figure 4 A structural block diagram of a dispatching device for a distribution network provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] As introduced in the background technology, the prior art has the problem of low dispatching efficiency of distribution networks. To solve the above problem, the embodiments of the present application provide a dispatching method, a dispatching device and an electronic device for a distribution network.

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0035] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for dispatching a distribution network according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0036] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] In this embodiment, a dispatching method for a distribution network running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] Figure 2 is a flow chart of a method for dispatching a distribution network according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0039] Step S201, constructing an optimal dispatching model of the distribution network to the microgrid, and constructing an objective function of the above-mentioned optimal dispatching model, wherein the above-mentioned microgrid at least includes energy storage equipment, wind turbines and photovoltaic units, and the above-mentioned objective function takes minimizing the total operating cost as the optimization goal.

[0040] Specifically, a distribution-multi-microgrid optimization scheduling model including energy storage equipment (ESS), wind turbines (WTG), photovoltaic units (PVG) and other power equipment is constructed, and the objective function is established with the minimum total operating cost as the optimization goal. The present invention takes the IEEE33 node system connected to three microgrids as an example to verify the effectiveness of the proposed method. All algorithms are programmed in MATLAB R2019a, and Gurobi 9.5.1 is used to solve the optimization calculation. The system voltage level is 12.66kV, the voltage safety adjustment range is [0.95, 1.05], and WTG, PVG, ESS and other equipment are installed at the same time. The equipment parameters are shown in Table 1.

[0041] Table 1

[0042]

[0043] Step S202, constructing the constraints of the above-mentioned optimization scheduling model, the above-mentioned constraints include a first safety constraint, an equipment constraint, and a second safety constraint. The above-mentioned first safety constraint includes a high-voltage system power constraint, a first flow constraint, and a voltage and current constraint. The above-mentioned equipment constraint includes at least an energy storage system operation constraint, a constraint of the above-mentioned wind turbine set, and a constraint of the above-mentioned photovoltaic set. The above-mentioned second safety constraint includes a second flow constraint and an exchange power constraint of the above-mentioned microgrid.

[0044] Specifically, the power constraints of the high-voltage system mainly include the safe operating conditions of the system equipment, power quality, user needs and the power flow algorithm of the power system; the power flow constraints refer to a series of conditions and restrictions that the power flow (i.e. the flow of electricity and energy) must meet when the power system is operating; the voltage and current constraints refer to a series of conditions and restrictions that the voltage and current of the power grid must meet when the power grid system is operating.

[0045] Step S203, according to the above-mentioned optimization scheduling model including the above-mentioned constraints, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, and the above-mentioned improved ADMM distributed optimization algorithm at least relaxes the original nonlinear programming model into a second-order cone programming.

[0046] Specifically, the exchange power constraint of the microgrid refers to a series of conditions and restrictions that need to be met when exchanging electric energy between the microgrid and the external power grid (such as the distribution network).

[0047] Specifically, ADMM is an optimization algorithm that minimizes the objective function of the original problem by decomposing it into several sub-problems and then solving these sub-problems alternately. Its advantages include: convergence, ADMM can quickly converge to the optimal solution in many cases, especially for structured optimization problems; distributed computing, ADMM is suitable for distributed computing environments, and can decompose the original problem into multiple sub-problems, which are then solved in parallel on different computing nodes; robustness, ADMM also shows good robustness for some non-smooth and non-convex optimization problems.

[0048] Specifically, ADMM is widely used in image processing, machine learning, signal processing, statistics and other fields, for example: Image restoration: ADMM can be used to solve problems such as image denoising and image restoration; Sparse representation learning: ADMM can be used to solve sparse representation learning problems, such as Lasso regression, sparse coding, etc.; Non-convex optimization: ADMM shows good results in solving non-convex optimization problems, especially for non-convex problems with special structures. In general, ADMM is a flexible and efficient optimization algorithm that is suitable for a variety of optimization problems, especially for large-scale and structured optimization problems. It has good application prospects.

[0049] Step S204: using the improved ADMM distributed optimization algorithm to solve the objective function, to obtain the dispatching status of the distribution network.

[0050] Specifically, the optimization result obtained by solving the ADMM algorithm is the dispatching status of the distribution network. By adjusting the design of the objective function and the constraints, different dispatching optimizations can be performed on the distribution network, such as minimizing the total cost, minimizing the total power consumption, etc.

[0051] Step S205: According to the above scheduling situation, the above distribution network schedules the above microgrid.

[0052] In the above embodiment, first, an optimization dispatching model of the distribution network to the microgrid is constructed, and the objective function of the above optimization dispatching model is constructed; secondly, the constraint conditions of the above optimization dispatching model are constructed; then, according to the above optimization dispatching model including the above constraint conditions, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm; thirdly, the above objective function is solved by using the above improved ADMM distributed optimization algorithm to obtain the dispatching situation of the above distribution network; finally, according to the above dispatching situation, the above distribution network dispatches the above microgrid. Compared with the prior art, the scheme of the present application constructs an optimization dispatching model by setting the objective function and constraint conditions, and improves the ADMM distributed optimization algorithm according to the optimization dispatching model. The improved ADMM distributed optimization algorithm converts the original nonlinear programming model into the SOCP form for solution, which can more conveniently utilize the properties of the convex optimization problem, improve the solution efficiency, and thus obtain the optimal solution faster. The distribution network can dispatch the microgrid faster according to the dispatching situation of the distribution network obtained by the optimal solution, thereby solving the problem of low dispatching efficiency of the distribution network in the prior art.

[0053] In one embodiment of the present application, constructing the objective function of the above-mentioned optimization scheduling model includes: constructing the first optimization objective minC of the above-mentioned distribution network DS (x DS ),in, j represents a node of the above distribution network, DS represents the above distribution network, HV represents the high voltage power transmission system, represents the amount of electricity that the above distribution network plans to purchase from the above high-voltage power transmission system within △t, represents the purchase price of the electricity of the above-mentioned high-voltage power transmission system, represents the expected power generation of wind turbines in the above distribution network, represents the expected power generation of photovoltaic power generation in the above distribution network, They represent the decision variables of the wind turbine power generation and the photovoltaic power generation, respectively. They represent the abandoned power costs of the wind turbine power generation and the photovoltaic power generation respectively; the second optimization target minC of the microgrid is constructed. e (x e ),in, represents the exchange power from the microgrid to the distribution network per unit time of node j, represents the purchase price of the electricity of the above microgrid, represents the expected power generation of the wind turbine in the above microgrid, represents the expected power generation of photovoltaic power generation of the above microgrid; construct the above objective function of the above optimization scheduling model, and the above objective function is i is the number of the module of the distribution network or the module of the microgrid in the set M, and M is the total number of the distribution network and the microgrid in the whole system. In this method, by constructing the objective function of the optimization scheduling model, the energy exchange and power generation behavior of the distribution network and the microgrid can be effectively scheduled. The first optimization goal is to maximize the benefit of the distribution network purchasing electricity from the high-voltage power transmission system, taking into account the expected power generation of wind turbines and photovoltaic power generation and the cost of abandoned power, so as to achieve the optimal utilization of resources. The second optimization goal is to maximize the energy exchange benefit between the microgrid and the distribution network, taking into account the purchase price of electricity and the expected power generation of wind turbines and photovoltaic power generation. By comprehensively considering the energy exchange and power generation of the distribution network and the microgrid, the objective function can more effectively schedule the behavior of each module in the system, thereby maximizing the overall energy utilization efficiency of the system.

[0054] Specifically, considering absorbing more renewable energy and meeting load requirements, the electricity import is at a positive cost, and the distribution network (DS) optimization target is established. It is assumed that only the high-voltage system purchase power and the microgrid exchange power are involved in the adjustment, that is, the ESS maintains its dispatch power in real-time operation.

[0055] In order to introduce various constraints to ensure that the power system meets various safety and stability requirements during operation, and further maximize the efficiency and reliability of the system, in one embodiment of the present application, the constraints of the above-mentioned optimization scheduling model are constructed, including: constructing the above-mentioned first safety constraint condition, constructing the above-mentioned high-voltage system constraint condition and The first power flow constraint condition and And the above voltage and current constraints and Among them, δ(j) is the set of branch end nodes with j as the head node, π(j) is the set of branch head node with j as the end node, P jk,t , Q jk,t are the active and reactive power injected into node j at time t, ij is the branch between node i and node j, r i,j 、x i,j are the resistance and reactance values ​​of branch ij respectively, I ij,t is the current amplitude flowing from node i to node j at time t, P ij,t , Q ij,t are the active power and reactive power flowing from node i to node j at time t, are the wind turbine power and photovoltaic power of the above distribution network at time period t and node j respectively, are the active power generated by the energy storage device and the active power generated by the high-voltage power transmission system at time period t and node j, respectively. are the reactive power generated by the energy storage device and the reactive power generated by the high-voltage power transmission system at time period t and node j, respectively. are the reactive power of the wind turbine and the reactive power of the photovoltaic power generation in the distribution network at time period t and node j, V j , are the lower and upper limits of the voltage amplitude, is the upper limit of the current amplitude, are the squares of node voltage and branch current amplitude respectively; construct the above equipment constraints and in, represents the energy storage power of node j at time t, represents the energy storage discharge power at time t, represents the energy storage charging power at time t, represents the capacity of the above energy storage system at t+1, They represent the charge and discharge efficiency, are 0-1 variables representing the charging and discharging state of the energy storage system; construct the second safety constraint condition, which includes constructing the second power flow constraint condition and the exchange power constraint condition of the microgrid. The second power flow constraint condition is

[0056] and The exchange power constraint of the above microgrid is: and Where V j,t 、V i,t are the voltages of node j and node t at time t, represents the active power generated by the above microgrid at time period t and node i, are the wind turbine power and photovoltaic power of the microgrid at time period t and node j respectively, are the reactive power generated by the microgrid at time period t and node j, is the reactive power of the wind turbine generator and the reactive power of the photovoltaic generator of the microgrid at time period t and node j, are the lower and upper limits of the power generation of the microgrid, respectively. are respectively the lower limit and upper limit of the reactive power generated by the above microgrid.

[0057] Specifically, It is equal to the energy of the energy storage system at time t plus the energy charged at time t minus the energy released at that time. From the equipment constraints, it can be seen that ESS injection is the positive direction of optimization, and it is ensured that each ESS cannot be charged and discharged at the same time.

[0058] In another embodiment, according to the above-mentioned optimization scheduling model including the above-mentioned constraints, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, including: an initialization step, according to the above-mentioned constraints, initializing the parameters of the above-mentioned ADMM distributed optimization algorithm, the above-mentioned parameters at least include an iteration index, a convergence criterion, a step size, an auxiliary variable and a dual variable; a setting step, setting the above-mentioned auxiliary variable to a first value, setting the above-mentioned dual variable to a second value, and obtaining a first formula according to the above-mentioned first value and the above-mentioned second value. In the formula, e i is the constraint value of the auxiliary variable mentioned above, is the value of the constraint value of the auxiliary variable from node i to node j at time t, are the auxiliary variables mentioned above, is the value of the auxiliary variable from node i to node j at time t, N is the set of all nodes, T is the set of all times, ρ1 is the iteration parameter, is the above dual variable, C i (x i ) is the objective function at time i; the first update step, according to the second formula Update the first value to obtain a third value. is the value of the constraint value of the auxiliary variable in the k+1 state, is the value of the above dual variable in state k; the second update step, according to the third formula Update the second value to obtain the fourth value, where: is the fourth value mentioned above, The step of determining the value of the auxiliary variable in the k+1 state is to determine whether the ADMM distributed optimization algorithm has converged at least according to the difference between the third value and the fourth value; when the ADMM distributed optimization algorithm has converged, the ADMM distributed optimization algorithm is the improved ADMM distributed optimization algorithm. In this method, the improved ADMM distributed optimization algorithm considers factors such as iteration index, convergence criterion, step size, etc. when initializing parameters, making the algorithm more stable and efficient. In the setting step, by setting the auxiliary variables and the dual variables, the constraint values ​​and the objective function can be calculated more accurately. In the updating step, by updating the auxiliary variables and the dual variables, the optimal solution can be converged faster. The conditions for whether the algorithm has converged are considered in the judgment step, unnecessary calculations are avoided, and the efficiency of the algorithm is improved, thereby further improving the convergence speed and computational efficiency of the improved ADMM distributed optimization algorithm.

[0059] In order to ensure that the parameters of the distribution network and the microgrid are correctly initialized before starting operation, thereby further improving the reliability and stability of the system, in an embodiment of the present application, the initialization step includes: an initial step to initialize the above parameters; repeating the initial step at least once until the distribution network and the microgrid meet the preset conditions.

[0060] Specifically, set the iteration index k = 1, set the convergence standard δ = 0.0001, set the step size ρ1 = 1, set the auxiliary

[0061] Auxiliary variables Set the dual variable λ(0) = 0 and repeat the initialization for each entity in turn.

[0062] In another embodiment, the setting step includes: setting a first constraint according to the energy exchange between the distribution network and the microgrid Above is the energy exchanged from node i to node j in the above microgrid, is the energy exchanged from node j to node i in the microgrid; the first constraint is improved to obtain the second constraint Where i is the set of the above distribution network and the above microgrid participating in energy trading, are the values ​​of the constraint values ​​of the auxiliary variables from node i to node j and from node j to node i at time t; the first value and the second value are input into the second constraint to obtain the first formula. In this method, by setting the first constraint, the energy exchange between microgrids can be limited to further prevent excessive energy flow or imbalance. Then, the second constraint is obtained by improving the constraint condition to further optimize the direction and amount of energy exchange, and to further ensure the efficiency and balance of energy exchange. The first formula finally obtained can help optimize the energy exchange scheme and further improve the overall energy utilization efficiency and system stability. In this way, the energy exchange between the distribution network and the microgrid can be more effectively managed, making the energy exchange more reasonable and controllable.

[0063] Specifically, set λ=λ(k), the first constraint must be satisfied between the distribution network and the microgrid where energy exchange occurs For convenience, the variable representation of the first constraint is modified as follows Assume that the distribution network and multi-microgrids participating in the transaction belong to the set M. Let the set after removing the module i from M be N. Introduce auxiliary variables Replace the first and second constraints with definition is the dual variable associated with the second constraint. Then the augmented Lagrangian function of P is: Where ρ1>0 is the quadratic penalty parameter of the second constraint.

[0064] In order to further update the auxiliary variables, in one embodiment of the present application, the first updating step includes: setting the fourth formula According to the fourth formula, a fifth value is obtained; the fifth value is input into the second formula to obtain the third value.

[0065] Specifically, variable update: update the auxiliary variables and dual variables through the subproblem results.

[0066] Specifically, given the parameter ρ1(k) in iteration k, the dual variable and auxiliary variables Each module i solves its local optimization problem: the subproblem (P1) of P is

[0067] Solution based on P1 Update auxiliary variables and the dual variable λ. Specifically, it is updated by the following formula:

[0068] Since the auxiliary variables are only coupled between each pair of information-interacting subregions, they can be solved by only considering the optimization of a pair of subregions i and j:

[0069]

[0070] Get updates from this The optimal closed-form solution of :

[0071] In another embodiment, the above-mentioned judgment step includes: when the absolute value of the difference between the above-mentioned third value and the above-mentioned fourth value is not greater than the preset threshold, the above-mentioned ADMM distributed optimization algorithm converges; when the absolute value of the difference between the above-mentioned third value and the above-mentioned fourth value is greater than the above-mentioned preset threshold, the number of iterations is increased once, and the above-mentioned initialization step, the above-mentioned setting step, the above-mentioned first update step, the above-mentioned second update step and the above-mentioned judgment step are repeated at least once in sequence, until the absolute value of the difference between the above-mentioned third value and the above-mentioned fourth value is not greater than the preset threshold, and the above-mentioned ADMM distributed optimization algorithm converges. In this method, by checking the difference between the third and fourth values, it is possible to timely find out whether the algorithm has converged. If the difference is small, it means that the algorithm has stabilized and the iteration can be stopped. If the difference is large, it is necessary to increase the number of iterations to further optimize the solution until the convergence condition is reached, thereby further improving the convergence speed and stability of the algorithm, and further ensuring that the algorithm converges to the optimal solution within a limited number of iterations.

[0072] Specifically, we only discuss the transactions between all microgrid devices and the distribution network, and do not consider the transactions between all microgrid devices. Therefore, when i=1 in the above formula, it is the sequence number of the distribution network, and the other sequences represent all microgrid devices. Therefore, the algorithm convergence criterion is

[0073] Specifically, Figure 3 The figure shows the improvement process of the ADMM distributed optimization algorithm. The process is as follows: start the improvement algorithm; perform entity initialization, where the entity includes iteration index, convergence criterion, step size, auxiliary variables and dual variables; local optimization, including solving the model and obtaining the optimal solution; auxiliary variable update; dual variable update; convergence judgment, when the algorithm is judged to converge, the algorithm improvement ends, when the algorithm is judged not to converge, the number of iterations increases by 1, and the local optimization and subsequent steps are repeated until the algorithm converges and the algorithm improvement ends.

[0074] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the distribution network dispatching method of the present application will be described in detail below in combination with specific embodiments.

[0075] This embodiment relates to a specific distribution network dispatching method, which includes the following steps:

[0076] Step 1: Construct an optimal dispatching model of the distribution network for the microgrid;

[0077] Step 2: construct the objective function and constraint conditions of the optimization scheduling model, wherein the constraint conditions include the first safety constraint condition, the equipment constraint condition and the second safety constraint condition;

[0078] Step 3: Improve the ADMM distributed optimization algorithm according to the optimization scheduling model to obtain an improved ADMM distributed optimization algorithm. The improvement process includes entity initialization, local optimization, auxiliary variable update, dual variable update and algorithm convergence judgment. When the algorithm does not converge, repeat the steps in the improvement process until the algorithm converges.

[0079] Step 4: Use the improved ADMM distributed optimization algorithm to solve the objective function and obtain the dispatching status of the distribution network;

[0080] Step 5: The distribution network dispatches the microgrid according to the dispatch situation.

[0081] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0082] The present application also provides a dispatching device for a distribution network. It should be noted that the dispatching device for a distribution network in the present application can be used to execute the dispatching method for a distribution network provided in the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0083] The following is an introduction to the dispatching device for the distribution network provided in an embodiment of the present application.

[0084] Figure 4 Schematic diagram of a dispatching device for a distribution network according to an embodiment of the present application. Figure 4 As shown, the device comprises:

[0085] The first construction unit 10 constructs an optimization dispatching model of the distribution network to the microgrid, and constructs an objective function of the optimization dispatching model. The microgrid at least includes energy storage equipment, wind turbines and photovoltaic units. The objective function takes the minimum total operating cost as the optimization goal.

[0086] Specifically, a distribution-multi-microgrid optimization scheduling model including energy storage equipment (ESS), wind turbines (WTG), photovoltaic units (PVG) and other power equipment is constructed, and the objective function is established with the minimum total operating cost as the optimization goal. The present invention takes the IEEE33 node system connected to 3 microgrids as an example to verify the effectiveness of the proposed method. All algorithms are programmed in MATLAB R2019a, and Gurobi 9.5.1 is used to solve the optimization calculation. The system voltage level is 12.66kV, the voltage safety adjustment range is [0.95, 1.05], and WTG, PVG, ESS and other equipment are installed at the same time.

[0087] The second construction unit 20 constructs the constraints of the above-mentioned optimization scheduling model, wherein the above-mentioned constraints include a first safety constraint, an equipment constraint, and a second safety constraint. The above-mentioned first safety constraint includes a high-voltage system power constraint, a first flow constraint, and a voltage and current constraint. The above-mentioned equipment constraint includes at least an energy storage system operation constraint, a constraint of the above-mentioned wind turbine set, and a constraint of the above-mentioned photovoltaic set. The above-mentioned second safety constraint includes a second flow constraint and an exchange power constraint of the above-mentioned microgrid.

[0088] The improvement unit 30 improves the ADMM distributed optimization algorithm according to the above-mentioned optimization scheduling model including the above-mentioned constraints to obtain an improved ADMM distributed optimization algorithm. The above-mentioned improved ADMM distributed optimization algorithm at least relaxes the original nonlinear programming model into a second-order cone programming.

[0089] Specifically, ADMM is an optimization algorithm that minimizes the objective function of the original problem by decomposing it into several sub-problems and then solving these sub-problems alternately. Its advantages include: convergence, ADMM can quickly converge to the optimal solution in many cases, especially for structured optimization problems; distributed computing, ADMM is suitable for distributed computing environments, and can decompose the original problem into multiple sub-problems, which are then solved in parallel on different computing nodes; robustness, ADMM also shows good robustness for some non-smooth and non-convex optimization problems.

[0090] Specifically, ADMM is widely used in image processing, machine learning, signal processing, statistics and other fields, for example: Image restoration: ADMM can be used to solve problems such as image denoising and image restoration; Sparse representation learning: ADMM can be used to solve sparse representation learning problems, such as Lasso regression, sparse coding, etc.; Non-convex optimization: ADMM shows good results in solving non-convex optimization problems, especially for non-convex problems with special structures. In general, ADMM is a flexible and efficient optimization algorithm that is suitable for a variety of optimization problems, especially for large-scale and structured optimization problems. It has good application prospects.

[0091] The solving unit 40 uses the improved ADMM distributed optimization algorithm to solve the objective function to obtain the dispatching status of the distribution network.

[0092] Specifically, the optimization result obtained by solving the ADMM algorithm is the dispatching status of the distribution network. By adjusting the design of the objective function and the constraints, different dispatching optimizations can be performed on the distribution network, such as minimizing the total cost, minimizing the total power consumption, etc.

[0093] The dispatching unit 50 dispatches the microgrid based on the above-mentioned dispatching situation.

[0094] In the above embodiment, first, an optimization dispatching model of the distribution network to the microgrid is constructed, and the objective function of the above optimization dispatching model is constructed; secondly, the constraint conditions of the above optimization dispatching model are constructed; then, according to the above optimization dispatching model including the above constraint conditions, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm; thirdly, the above objective function is solved by using the above improved ADMM distributed optimization algorithm to obtain the dispatching situation of the above distribution network; finally, according to the above dispatching situation, the above distribution network dispatches the above microgrid. Compared with the prior art, the scheme of the present application constructs an optimization dispatching model by setting the objective function and constraint conditions, and improves the ADMM distributed optimization algorithm according to the optimization dispatching model. The improved ADMM distributed optimization algorithm converts the original nonlinear programming model into the SOCP form for solution, which can more conveniently utilize the properties of the convex optimization problem, improve the solution efficiency, and thus obtain the optimal solution faster. The distribution network can dispatch the microgrid faster according to the dispatching situation of the distribution network obtained by the optimal solution, thereby solving the problem of low dispatching efficiency of the distribution network in the prior art.

[0095] In one embodiment of the present application, the first construction unit includes a first construction subunit, a second construction subunit and a third construction subunit, wherein the first construction subunit is used to construct the first optimization target minC of the above-mentioned distribution network. DS (x DS ),in, j represents a node of the above distribution network, DS represents the above distribution network, HV represents the high voltage power transmission system, represents the amount of electricity that the above distribution network plans to purchase from the above high-voltage power transmission system within △t, represents the purchase price of the electricity of the above-mentioned high-voltage power transmission system, represents the expected power generation of wind turbines in the above distribution network, represents the expected power generation of photovoltaic power generation in the above distribution network, They represent the decision variables of the wind turbine power generation and the photovoltaic power generation, respectively. They represent the abandoned power costs of the wind turbine power generation and the photovoltaic power generation respectively; the second construction subunit is used to construct the second optimization target minC of the microgrid. e (x e ),in,

[0096] represents the exchange power from the microgrid to the distribution network per unit time of node j, represents the purchase price of the electricity of the above microgrid, represents the expected power generation of the wind turbine in the above microgrid, represents the expected power generation of photovoltaic power generation of the above microgrid; the third construction subunit is used to construct the above objective function of the above optimization scheduling model, and the above objective function is i is the number of the module of the distribution network or the module of the microgrid in the set M, and M is the total number of the distribution network and the microgrid in the whole system. In this scheme, by constructing the objective function of the optimization scheduling model, the energy exchange and power generation behavior of the distribution network and the microgrid can be effectively scheduled. The first optimization goal is to maximize the benefit of the distribution network purchasing electricity from the high-voltage power transmission system, taking into account the expected power generation of wind turbines and photovoltaic power generation and the cost of abandoned power, so as to achieve the optimal utilization of resources. The second optimization goal is to maximize the energy exchange benefit between the microgrid and the distribution network, taking into account the purchase price of electricity and the expected power generation of wind turbines and photovoltaic power generation. By comprehensively considering the energy exchange and power generation of the distribution network and the microgrid, the objective function can more effectively schedule the behavior of each module in the system, thereby maximizing the overall energy utilization efficiency of the system.

[0097] Specifically, considering absorbing more renewable energy and meeting load requirements, the electricity import is at a positive cost, and the distribution network (DS) optimization target is established. It is assumed that only the high-voltage system purchase power and the microgrid exchange power are involved in the adjustment, that is, the ESS maintains its dispatch power in real-time operation.

[0098] In order to introduce various constraints, thereby ensuring that the power system meets various safety and stability requirements during operation, and further maximizing the efficiency and reliability of the system, in one embodiment of the present application, the second construction unit includes a fourth construction subunit, a fifth construction subunit and a sixth construction subunit, wherein the fourth construction subunit is used to construct the above-mentioned first safety constraint condition, construct the above-mentioned high-voltage system constraint condition and The first power flow constraint condition and And the above voltage and current constraints and Among them, δ(j) is the set of branch end nodes with j as the head node, π(j) is the set of branch head node with j as the end node, P jk,t , Q jk,t are the active and reactive power injected into node j at time t, ij is the branch between node i and node j, r i,j 、x i,j are the resistance and reactance values ​​of branch ij respectively, I ij,t is the current amplitude flowing from node i to node j at time t, P ij,t , Q ij,t are the active power and reactive power flowing from node i to node j at time t, are the wind turbine power and photovoltaic power of the above distribution network at time period t and node j respectively, are the active power generated by the energy storage device and the active power generated by the high-voltage power transmission system at time period t and node j, respectively. are the reactive power generated by the energy storage device and the reactive power generated by the high-voltage power transmission system at time period t and node j, respectively. are the reactive power of the wind turbine and the reactive power of the photovoltaic power generation in the distribution network at time period t and node j, V j , are the lower and upper limits of the voltage amplitude, is the upper limit of the current amplitude, are the squares of the node voltage and branch current amplitude respectively; the fifth construction subunit is used to construct the above device constraint conditions and in, represents the energy storage power of node j at time t, represents the energy storage discharge power at time t, represents the energy storage charging power at time t, represents the capacity of the above energy storage system at t+1, They represent the charge and discharge efficiency, are 0-1 variables representing the charging and discharging state of the energy storage system; the sixth construction subunit is used to construct the second safety constraint condition, which includes constructing the second power flow constraint condition and the exchange power constraint condition of the microgrid. The second power flow constraint condition is

[0099] and The exchange power constraint of the above microgrid is: and Where V j,t 、V i,t are the voltages of node j and node t at time t, represents the active power generated by the above microgrid at time period t and node i, are the wind turbine power and photovoltaic power of the microgrid at time period t and node j respectively, are the reactive power generated by the microgrid at time period t and node j, is the reactive power of the wind turbine generator and the reactive power of the photovoltaic generator of the microgrid at time period t and node j, are the lower and upper limits of the power generation of the microgrid, respectively. are respectively the lower limit and upper limit of the reactive power generated by the above microgrid.

[0100] Specifically, It is equal to the energy of the energy storage system at time t plus the energy charged at time t minus the energy released at that time. From the equipment constraints, it can be seen that ESS injection is the positive direction of optimization, and it is ensured that each ESS cannot be charged and discharged at the same time.

[0101] In another embodiment, the improvement unit includes an initialization subunit, a setting subunit, a first update subunit, a second update subunit, a judgment subunit and a determination subunit, wherein the initialization subunit is used to perform an initialization step, and initialize the parameters of the above-mentioned ADMM distributed optimization algorithm according to the above-mentioned constraints, and the above-mentioned parameters at least include an iteration index, a convergence criterion, a step size, an auxiliary variable and a dual variable; the setting subunit is used to perform a setting step, set the above-mentioned auxiliary variable to a first value, set the above-mentioned dual variable to a second value, and obtain the first formula according to the above-mentioned first value and the above-mentioned second value. In the formula, e i is the constraint value of the auxiliary variable mentioned above, is the value of the constraint value of the auxiliary variable from node i to node j at time t, are the auxiliary variables mentioned above, is the value of the auxiliary variable from node i to node j at time t, N is the set of all nodes, T is the set of all times, ρ1 is the iteration parameter, is the above dual variable, C i (x i ) is the objective function at time i; the first updating subunit is used to perform the first updating step, according to the second formula Update the first value to obtain a third value. is the value of the constraint value of the auxiliary variable in the k+1 state, is the value of the dual variable in the k state; the second updating subunit is used to perform the second updating step according to the third formula Update the second value to obtain the fourth value, where: is the fourth value mentioned above, is the value of the auxiliary variable in the k+1 state; the judging subunit is used to perform the judging step, and at least judge whether the ADMM distributed optimization algorithm converges according to the size of the difference between the third value and the fourth value; the determining subunit is used to determine that the ADMM distributed optimization algorithm is the improved ADMM distributed optimization algorithm when the ADMM distributed optimization algorithm converges. In this scheme, the improved ADMM distributed optimization algorithm considers factors such as iteration index, convergence criterion, step size, etc. when initializing parameters, making the algorithm more stable and efficient. In the setting step, by setting the auxiliary variables and dual variables, the constraint values ​​and objective functions can be calculated more accurately. In the updating step, by updating the auxiliary variables and dual variables, the optimal solution can be converged faster. The conditions for whether the algorithm converges are considered in the judging step, unnecessary calculations are avoided, and the efficiency of the algorithm is improved, thereby further improving the convergence speed and computational efficiency of the improved ADMM distributed optimization algorithm.

[0102] In order to better ensure that the parameters of the distribution network and the microgrid are correctly initialized before starting operation, thereby further improving the reliability and stability of the system, in one embodiment of the present application, the initialization subunit includes an initial module and a repetition module, wherein the initial module is used to execute the initial steps to initialize the above parameters; the repetition module is used to repeat the above initial steps at least once until the above distribution network and the above microgrid meet the preset conditions.

[0103] Specifically, set the iteration index k = 1, set the convergence standard δ = 0.0001, set the step size ρ1 = 1, and set the auxiliary variable Set the dual variable λ(0) = 0 and repeat the initialization for each entity in turn.

[0104] In another embodiment, the setting subunit includes a setting module, an improvement module and a first input module, wherein the setting module is used to set the first constraint according to the energy exchange between the above-mentioned distribution network and the above-mentioned microgrid. Above is the energy exchanged from node i to node j in the above microgrid, is the energy exchanged from node j to node i in the microgrid; the improvement module is used to improve the first constraint to obtain the second constraint Where i is the set of the above distribution network and the above microgrid participating in energy trading, are the values ​​of the constraint values ​​of the auxiliary variables from node i to node j and from node j to node i at time t; the first input module is used to input the first value and the second value into the second constraint to obtain the first formula. In this scheme, by setting the first constraint, the energy exchange between microgrids can be limited to further prevent excessive energy flow or imbalance. Then, the second constraint is obtained by improving the constraint condition to further optimize the direction and amount of energy exchange, and to further ensure the efficiency and balance of energy exchange. The first formula finally obtained can help optimize the energy exchange scheme and further improve the overall energy utilization efficiency and system stability. Thereby, the energy exchange between the distribution network and the microgrid can be managed more effectively, making the energy exchange more reasonable and controllable.

[0105] Specifically, set λ=λ(k), the first constraint must be satisfied between the distribution network and the microgrid where energy exchange occurs For convenience, the variable representation of the first constraint is modified as follows Assume that the distribution network and multi-microgrids participating in the transaction belong to the set M. Let the set after removing the module i from M be N. Introduce auxiliary variables Replace the first and second constraints with definition is the dual variable associated with the second constraint. Then the augmented Lagrangian function of P is: Where ρ1>0 is the quadratic penalty parameter of the second constraint.

[0106] In order to further update the auxiliary variable, in one embodiment of the present application, the first updating subunit includes a setting module, a generating module and a second input module, wherein the setting module is used to set the fourth formula The generating module is used to obtain the fifth value according to the fourth formula; the second input module is used to input the fifth value into the second formula to obtain the third value.

[0107] Specifically, variable update: update the auxiliary variables and dual variables through the subproblem results.

[0108] Specifically, given the parameter ρ1(k) in iteration k, the dual variable and auxiliary variables Each module i solves its local optimization problem: the subproblem (P1) of P is

[0109] Solution based on P1 Update auxiliary variables and the dual variable λ. Specifically, it is updated by the following formula:

[0110] Since the auxiliary variables are only coupled between each pair of information-interacting subregions, they can be solved by only considering the optimization of a pair of subregions i and j:

[0111]

[0112] Get updates from this The optimal closed-form solution of :

[0113] In another embodiment, the judgment subunit includes a first determination module and a second determination module, wherein the first determination module is used for the above-mentioned ADMM distributed optimization algorithm to converge when the absolute value of the difference between the above-mentioned third value and the above-mentioned fourth value is not greater than the preset threshold; the second determination module is used for increasing the number of iterations once when the absolute value of the difference between the above-mentioned third value and the above-mentioned fourth value is greater than the above-mentioned preset threshold, and repeating the above-mentioned initialization step, the above-mentioned setting step, the above-mentioned first update step, the above-mentioned second update step and the above-mentioned judgment step at least once in sequence, until the absolute value of the difference between the above-mentioned third value and the above-mentioned fourth value is not greater than the preset threshold, and the above-mentioned ADMM distributed optimization algorithm converges. In this scheme, by checking the difference between the third and fourth values, it is possible to timely find out whether the algorithm has converged. If the difference is small, it means that the algorithm has become stable and the iteration can be stopped. If the difference is large, it is necessary to increase the number of iterations to further optimize the solution until the convergence condition is reached, thereby further improving the convergence speed and stability of the algorithm, and further ensuring that the algorithm converges to the optimal solution within a limited number of iterations.

[0114] Specifically, we only discuss the transactions between all microgrid devices and the distribution network, and do not consider the transactions between all microgrid devices. Therefore, when i=1 in the above formula, it is the sequence number of the distribution network, and the other sequences represent all microgrid devices. Therefore, the algorithm convergence criterion is

[0115] The above-mentioned dispatching device for the distribution network includes a processor and a memory, and the above-mentioned first construction unit, second construction unit, improvement unit, solution unit and dispatching unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[0116] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem of low dispatching efficiency of the distribution network in the prior art can be solved by adjusting kernel parameters.

[0117] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0118] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the distribution network scheduling method, the method comprising:

[0119] Step S201, constructing an optimal dispatching model of the distribution network to the microgrid, and constructing an objective function of the above-mentioned optimal dispatching model, wherein the above-mentioned microgrid at least includes energy storage equipment, wind turbines and photovoltaic units, and the above-mentioned objective function takes minimizing the total operating cost as the optimization goal.

[0120] Step S202, constructing the constraints of the above-mentioned optimization scheduling model, the above-mentioned constraints include a first safety constraint, an equipment constraint, and a second safety constraint. The above-mentioned first safety constraint includes a high-voltage system power constraint, a first flow constraint, and a voltage and current constraint. The above-mentioned equipment constraint includes at least an energy storage system operation constraint, a constraint of the above-mentioned wind turbine set, and a constraint of the above-mentioned photovoltaic set. The above-mentioned second safety constraint includes a second flow constraint and an exchange power constraint of the above-mentioned microgrid.

[0121] Step S203, according to the above-mentioned optimization scheduling model including the above-mentioned constraints, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, and the above-mentioned improved ADMM distributed optimization algorithm at least relaxes the original nonlinear programming model into a second-order cone programming.

[0122] Step S204: using the improved ADMM distributed optimization algorithm to solve the objective function, to obtain the dispatching status of the distribution network.

[0123] Step S205: According to the above scheduling situation, the above distribution network schedules the above microgrid.

[0124] An embodiment of the present invention provides a processor, the processor is used to run a program, wherein when the program is run, the scheduling method of the distribution network is executed, the method comprising:

[0125] Step S201, constructing an optimal dispatching model of the distribution network to the microgrid, and constructing an objective function of the above-mentioned optimal dispatching model, wherein the above-mentioned microgrid at least includes energy storage equipment, wind turbines and photovoltaic units, and the above-mentioned objective function takes minimizing the total operating cost as the optimization goal.

[0126] Step S202, constructing the constraints of the above-mentioned optimization scheduling model, the above-mentioned constraints include a first safety constraint, an equipment constraint, and a second safety constraint. The above-mentioned first safety constraint includes a high-voltage system power constraint, a first flow constraint, and a voltage and current constraint. The above-mentioned equipment constraint includes at least an energy storage system operation constraint, a constraint of the above-mentioned wind turbine set, and a constraint of the above-mentioned photovoltaic set. The above-mentioned second safety constraint includes a second flow constraint and an exchange power constraint of the above-mentioned microgrid.

[0127] Step S203, according to the above-mentioned optimization scheduling model including the above-mentioned constraints, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, and the above-mentioned improved ADMM distributed optimization algorithm at least relaxes the original nonlinear programming model into a second-order cone programming.

[0128] Step S204: using the improved ADMM distributed optimization algorithm to solve the objective function, to obtain the dispatching status of the distribution network.

[0129] Step S205: According to the above scheduling situation, the above distribution network schedules the above microgrid.

[0130] An embodiment of the present invention provides a device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0131] Step S201, constructing an optimal dispatching model of the distribution network to the microgrid, and constructing an objective function of the above-mentioned optimal dispatching model, wherein the above-mentioned microgrid at least includes energy storage equipment, wind turbines and photovoltaic units, and the above-mentioned objective function takes minimizing the total operating cost as the optimization goal.

[0132] Step S202, constructing the constraints of the above-mentioned optimization scheduling model, the above-mentioned constraints include a first safety constraint, an equipment constraint, and a second safety constraint. The above-mentioned first safety constraint includes a high-voltage system power constraint, a first flow constraint, and a voltage and current constraint. The above-mentioned equipment constraint includes at least an energy storage system operation constraint, a constraint of the above-mentioned wind turbine set, and a constraint of the above-mentioned photovoltaic set. The above-mentioned second safety constraint includes a second flow constraint and an exchange power constraint of the above-mentioned microgrid.

[0133] Step S203, according to the above-mentioned optimization scheduling model including the above-mentioned constraints, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, and the above-mentioned improved ADMM distributed optimization algorithm at least relaxes the original nonlinear programming model into a second-order cone programming.

[0134] Step S204: using the improved ADMM distributed optimization algorithm to solve the objective function, to obtain the dispatching status of the distribution network.

[0135] Step S205: According to the above scheduling situation, the above distribution network schedules the above microgrid.

[0136] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0137] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0138] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0139] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0140] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0142] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0143] The memory may include non-permanent memory 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.

[0144] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes 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.

[0145] 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.

[0146] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0147] In the above embodiment, first, an optimization dispatching model of the distribution network to the microgrid is constructed, and the objective function of the above optimization dispatching model is constructed; secondly, the constraint conditions of the above optimization dispatching model are constructed; then, according to the above optimization dispatching model including the above constraint conditions, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm; thirdly, the above objective function is solved by using the above improved ADMM distributed optimization algorithm to obtain the dispatching situation of the above distribution network; finally, according to the above dispatching situation, the above distribution network dispatches the above microgrid. Compared with the prior art, the scheme of the present application constructs an optimization dispatching model by setting the objective function and constraint conditions, and improves the ADMM distributed optimization algorithm according to the optimization dispatching model. The improved ADMM distributed optimization algorithm converts the original nonlinear programming model into the SOCP form for solution, which can more conveniently utilize the properties of the convex optimization problem, improve the solution efficiency, and thus obtain the optimal solution faster. The distribution network can dispatch the microgrid faster according to the dispatching situation of the distribution network obtained by the optimal solution, thereby solving the problem of low dispatching efficiency of the distribution network in the prior art.

[0148] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dispatching a distribution network, characterized in that: include: Constructing an optimal dispatching model of the distribution network to the microgrid, and constructing an objective function of the optimal dispatching model, wherein the microgrid includes at least energy storage equipment, a wind turbine and a photovoltaic unit, and the objective function takes minimizing the total operating cost as an optimization goal; Constructing the constraints of the optimization scheduling model, the constraints include a first safety constraint, an equipment constraint, and a second safety constraint, the first safety constraint includes a high-voltage system power constraint, a first power flow constraint, and a voltage and current constraint, the equipment constraint includes at least an energy storage system operation constraint, a wind turbine constraint, and a photovoltaic unit constraint, the second safety constraint includes a second power flow constraint and an exchange power constraint of the microgrid; According to the optimization scheduling model including the constraint conditions, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, wherein the improved ADMM distributed optimization algorithm at least converts the original nonlinear programming model into a second-order cone programming through relaxation; Solving the objective function using the improved ADMM distributed optimization algorithm to obtain the dispatching status of the distribution network; According to the scheduling situation, the distribution network schedules the microgrid.

2. The method for dispatching a distribution network according to claim 1, characterized in that: Constructing the objective function of the optimization scheduling model includes: Construct the first optimization target min C of the distribution network DS (x DS ),in, j represents a node of the distribution network, DS represents the distribution network, HV represents a high voltage power transmission system, represents the amount of electricity that the distribution network plans to purchase from the high-voltage power transmission system within Δt, represents the purchase price of the electric quantity of the high-voltage electric power transmission system, represents the expected power generation of the wind turbines in the distribution network, represents the expected photovoltaic power generation of the distribution network, represent the decision variables of the wind turbine power generation and the photovoltaic power generation respectively, Respectively represent the abandoned power costs of the wind turbine power generation and the photovoltaic power generation; Construct the second optimization target minC of the microgrid e (x e ),in, represents the exchange power from the microgrid to the distribution network per unit time of node j, represents the purchase price of the electricity of the microgrid, represents the expected power generation of the wind turbine of the microgrid, represents the expected power generation of photovoltaic power generation of the microgrid; Construct the objective function of the optimization scheduling model, the objective function is i is the number of the module of the distribution network or the module of the microgrid in the set M, and M is the total number of the distribution networks and the microgrids in the entire system.

3. The method for dispatching a distribution network according to claim 1, characterized in that: The constraints for constructing the optimization scheduling model include: Construct the first safety constraint condition and construct the high voltage system constraint condition and The first power flow constraint and And the voltage and current constraints and Among them, δ(j) is the set of branch end nodes with j as the head node, π(j) is the set of branch head node with j as the end node, P jk,t , Q jk,t are the active and reactive power injected into node j at time t, ij is the branch between node i and node j, r i,j 、x i,j are the resistance and reactance values ​​of branch ij respectively, I ij,t is the current amplitude flowing from node i to node j at time t, P ij,t , Q ij,t are the active power and reactive power flowing from node i to node j at time t, are the wind turbine power and photovoltaic power of the distribution network at time period t and node j respectively, are the active power generated by the energy storage device and the active power generated by the high-voltage power transmission system at time period t and node j, respectively. are the reactive power generated by the energy storage device and the reactive power generated by the high-voltage power transmission system at time period t and node j, respectively, are the reactive power of the wind turbine power generation and the reactive power of the photovoltaic power generation of the distribution network at time period t and node j respectively, V j , are the lower and upper limits of the voltage amplitude, is the upper limit of the current amplitude, are the squares of node voltage and branch current amplitudes respectively; Constructing the device constraints and in, represents the energy storage power of node j at time t, represents the energy storage discharge power at time t, represents the energy storage charging power at time t, represents the capacity of the energy storage system at t+1, They represent the charge and discharge efficiency, are 0-1 variables respectively representing the charging and discharging status of the energy storage system; Constructing the second safety constraint condition, the second safety constraint condition includes constructing the second power flow constraint condition and the switching power constraint condition of the microgrid, the second power flow constraint condition is and The switching power constraint of the microgrid is: and Where V j,t 、V i,t are the voltages of node j and node t at time t, represents the active power generated by the microgrid at time period t and node i, are the wind turbine power and photovoltaic power of the microgrid at time period t and node j respectively, are the reactive power generated by the microgrid at time period t and node j, is the reactive power of the wind turbine power generation and the reactive power of the photovoltaic power generation of the microgrid at time period t and node j, are the lower limit and upper limit of the power generation of the microgrid respectively, are respectively the lower limit and upper limit of the reactive power generated by the microgrid.

4. The method for dispatching a distribution network according to claim 1, characterized in that: According to the optimization scheduling model including the constraint conditions, the ADMM distributed optimization algorithm is improved to obtain an improved ADMM distributed optimization algorithm, including: An initialization step, initializing the parameters of the ADMM distributed optimization algorithm according to the constraint conditions, wherein the parameters at least include an iteration index, a convergence criterion, a step size, an auxiliary variable, and a dual variable; The setting step is to set the auxiliary variable to a first value and the dual variable to a second value, and obtain a first formula according to the first value and the second value. In the formula, e i is the constraint value of the auxiliary variable, is the value of the constraint value of the auxiliary variable from node i to node j at time t, is the auxiliary variable, is the value of the auxiliary variable from node i to node j at time t, N is the set of all nodes, T is the set of all times, ρ1 is the iteration parameter, is the dual variable, C i (x i ) is the objective function at time i; In the first update step, according to the second formula Update the first value to obtain a third value, is the value of the constraint value of the auxiliary variable in the k+1 state, is the value of the dual variable in the k state; The second update step, according to the third formula Update the second value to obtain a fourth value, where: is the fourth value, is the value of the auxiliary variable in the k+1 state; A judging step, judging whether the ADMM distributed optimization algorithm has converged at least according to the difference between the third value and the fourth value; When the ADMM distributed optimization algorithm converges, the ADMM distributed optimization algorithm is determined to be the improved ADMM distributed optimization algorithm.

5. The method for dispatching a distribution network according to claim 4, characterized in that: The initialization step comprises: An initial step is to initialize the parameters; The initial step is repeated at least once until the distribution network and the microgrid meet preset conditions.

6. The method for dispatching a distribution network according to claim 4, characterized in that: The setting steps include: A first constraint is set according to energy exchange between the distribution network and the microgrid Said is the energy exchanged from node i to node j in the microgrid, is the energy exchanged from node j to node i in the microgrid; The first constraint is improved to obtain the second constraint Where i is the set of the distribution network and the microgrid participating in energy trading, are respectively the values ​​of the constraint values ​​of the auxiliary variable from node i to node j and from node j to node i at time t; The first value and the second value are input into the second constraint to obtain the first formula.

7. The method for dispatching a distribution network according to claim 4, characterized in that: The first updating step comprises: Set the fourth formula According to the fourth formula, a fifth value is obtained; The fifth value is input into the second formula to obtain the third value.

8. The method for dispatching a distribution network according to claim 4, characterized in that: The judging step comprises: When the absolute value of the difference between the third value and the fourth value is not greater than a preset threshold, the ADMM distributed optimization algorithm converges; When the absolute value of the difference between the third value and the fourth value is greater than the preset threshold, the number of iterations is increased by one, and the initialization step, the setting step, the first update step, the second update step and the judgment step are repeated at least once in sequence until the absolute value of the difference between the third value and the fourth value is not greater than the preset threshold, and the ADMM distributed optimization algorithm converges.

9. A dispatching device for a distribution network, characterized in that: include: The first construction unit constructs an optimization dispatching model of the distribution network to the microgrid, and constructs an objective function of the optimization dispatching model, wherein the microgrid includes at least energy storage equipment, a wind turbine generator set and a photovoltaic generator set, and the objective function takes minimizing the total operating cost as an optimization target; A second construction unit constructs the constraint conditions of the optimization scheduling model, wherein the constraint conditions include a first safety constraint condition, an equipment constraint condition, and a second safety constraint condition, wherein the first safety constraint condition includes a high-voltage system power constraint condition, a first power flow constraint condition, and a voltage and current constraint condition, wherein the equipment constraint condition includes at least an energy storage system operation constraint condition, a constraint condition of the wind turbine generator set, and a constraint condition of the photovoltaic generator set, and wherein the second safety constraint condition includes a second power flow constraint condition and an exchange power constraint condition of the microgrid; An improvement unit improves the ADMM distributed optimization algorithm according to the optimization scheduling model including the constraint conditions to obtain an improved ADMM distributed optimization algorithm, wherein the improved ADMM distributed optimization algorithm at least converts the original nonlinear programming model into a second-order cone programming through relaxation; A solving unit, using the improved ADMM distributed optimization algorithm to solve the objective function to obtain the dispatching status of the distribution network; A dispatching unit, wherein the distribution network dispatches the microgrid according to the dispatching situation.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing a dispatching method for a distribution network as described in any one of claims 1 to 8.

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