Power distribution network dispatching method and device

Abstract

The invention provides a power distribution network dispatching method and device, and relates to the field of electric power. A scheduling optimization model with the total operation cost as a target function and with the network power flow constraint, the new energy power generation constraint, the energy storage system constraint, the overcurrent and overvoltage constraint and the demand side response constraint as constraint conditions is constructed, then based on the scheduling optimization model, data analysis processing is performed on the operation data, and a scheduling optimization model is established. And determining a scheduling optimal solution for the power distribution network equipment, and further generating a scheduling instruction for the power distribution network equipment, thereby realizing scheduling control on the power distribution network equipment. Conditions such as access of the distributed power supply, network power flow, new energy and demand response are fully considered, a more reasonable power distribution network dispatching instruction can be obtained through analysis on the premise that the total operation cost is saved, the electric energy quality problem caused by overload of a power distribution network line and violation of voltage amplitude limit is avoided, and the dispatching demand of the power distribution network is met.

Description

Technical Field

[0001] The present application relates to the field of electric power, and in particular to a distribution network dispatching method and device. Background Art

[0002] The distribution network is the intermediate link between the high-voltage transmission network of the power system and the users. The distribution network has multiple voltage levels, complex network structure, and various equipment types. In order to ensure the reliable and efficient operation of the distribution network, it is necessary to carry out scheduling planning.

[0003] At present, the large-scale access of distributed power sources in the distribution network has caused many new technical problems, such as violation of the operating restrictions of the distribution network, resulting in violation of voltage amplitude restrictions and overload of distribution network lines. On the other hand, due to the increase in current and flow of other loads in the distribution network, such as electric vehicles, electric heat pumps and demand response projects, the line may be overloaded, leading to power quality problems. It can be seen that traditional dispatching methods can no longer meet the dispatching needs. Summary of the invention

[0004] The embodiments of the present application provide a distribution network scheduling method and device to solve the problem that traditional scheduling methods cannot meet scheduling requirements.

[0005] In a first aspect, an embodiment of the present application provides a distribution network scheduling method, including:

[0006] Acquire operation data of the distribution network; wherein the operation data includes output data, cost data and load data of distribution network equipment;

[0007] Based on the dispatch optimization model, the operation data is analyzed and processed to generate dispatch instructions for the distribution network equipment; wherein the dispatch optimization model is used to generate the dispatch optimization solution for the distribution network equipment; the dispatch optimization model takes the total operating cost as the objective function, and takes the network flow constraint, the new energy power generation constraint, the energy storage system constraint, the overcurrent and overvoltage constraint and the demand side response constraint as the constraint conditions;

[0008] The distribution network equipment is dispatched and controlled according to the dispatch instruction.

[0009] In a possible implementation manner, before performing data analysis processing on the operation data based on the scheduling optimization model, the method further includes:

[0010] Performing second-order cone relaxation processing on the nonlinear AC power flow equation constraints corresponding to the distribution network to obtain the network power flow constraints;

[0011] The network power flow constraint is composed of a node power balance constraint, a branch voltage drop constraint, and a second-order cone relaxation relation between power loss and electrical quantity.

[0012] In a possible implementation, the performing data parsing processing on the operation data based on the dispatch optimization model to generate a dispatch instruction for the distribution network equipment includes:

[0013] Determining an optimization solution task based on the operating data, the objective function, and constraints corresponding to the objective function;

[0014] Solving the optimization task to determine the optimal solution of the objective function; wherein the optimal solution is the output data of the distribution network equipment that makes the objective function obtain the minimum value;

[0015] According to the preferred solution, a dispatch instruction for the distribution network equipment is generated; wherein the dispatch instruction is used to dispatch the operating output of the distribution network equipment.

[0016] In a possible implementation, solving the optimization task to determine the optimal solution of the objective function includes:

[0017] The optimization solution task is split to obtain subtasks corresponding to the optimization solution task, so that each agent unit iteratively solves the corresponding subtask based on the gradient descent method to determine the solution result of each subtask after convergence;

[0018] The optimization solution task is updated and iterated according to the solution results of each subtask until the optimization solution task converges and the optimal solution of the objective function is obtained; wherein the solution result of each subtask is a local solution of the optimization solution task.

[0019] In a possible implementation, updating and iterating the optimization solution task according to the solution results of each subtask until the optimization solution task converges to obtain the optimal solution of the objective function includes:

[0020] Based on the multi-agent consensus update strategy, the solution results of each subtask are integrated to obtain the global solution of the optimization solution task;

[0021] Based on the global solution, updating the optimization solution task;

[0022] Repeat the steps of splitting the optimization solution task to obtain subtasks corresponding to the optimization solution task until the optimization solution task converges and the optimal solution of the objective function is obtained.

[0023] In one possible implementation, the distribution network equipment includes a distributed power source, an energy storage system, and new energy equipment; the proportion of the distributed power source output in the operating data in the output of the distribution network equipment is higher than a first preset threshold, and the proportion of the adjustable load in the load data is lower than a second preset threshold.

[0024] In a possible implementation, the objective function is composed of the main grid electricity purchase cost, distributed power generation cost, energy storage operation cost, demand side response cost, demand side response benefit and network loss cost.

[0025] In a possible implementation, the performing dispatch control on the distribution network equipment according to the dispatch instruction includes:

[0026] The dispatching instruction is sent to a carrier controller, and the dispatching instruction is executed based on the carrier controller to realize dispatching control of the distribution network equipment.

[0027] In a second aspect, an embodiment of the present application provides a distribution network dispatching device, including:

[0028] An acquisition unit, used to acquire operation data of the distribution network; wherein the operation data includes output data, cost data and load data of distribution network equipment;

[0029] A first processing unit is used to perform data analysis and processing on the operation data based on a scheduling optimization model to generate a scheduling instruction for the distribution network equipment; wherein the scheduling optimization model is used to generate a scheduling optimization solution for the distribution network equipment; the scheduling optimization model takes the total operating cost as the objective function, and takes the network flow constraint, the new energy power generation constraint, the energy storage system constraint, the overcurrent and overvoltage constraint, and the demand side response constraint as the constraint conditions;

[0030] A control unit is used to perform dispatch control on the distribution network equipment according to the dispatch instruction.

[0031] In a possible implementation manner, before the first processing unit, the device further includes:

[0032] A second processing unit is used to perform second-order cone relaxation processing on the nonlinear AC power flow equation constraint corresponding to the distribution network to obtain the network power flow constraint;

[0033] The network power flow constraint is composed of a node power balance constraint, a branch voltage drop constraint, and a second-order cone relaxation relation between power loss and electrical quantity.

[0034] The embodiment of the present application provides a distribution network dispatching method and device, constructs a dispatching optimization model with total operating cost as the objective function, and with network flow constraints, new energy generation constraints, energy storage system constraints, overcurrent and overvoltage constraints, and demand-side response constraints as constraints. Then, based on the dispatching optimization model, the operation data is parsed and processed to determine the dispatching optimal solution for the distribution network equipment, and then the dispatching instructions for the distribution network equipment are generated to achieve dispatching control of the distribution network equipment. The dispatching optimization model constructed in the embodiment of the present application takes the total operating cost as the objective function, and fully considers the access of distributed power sources, network flow, new energy, and demand response in the process of parsing and processing the operation data. It can be analyzed to obtain more reasonable distribution network dispatching instructions on the premise of saving the total operating cost, avoid power quality problems caused by overloading of distribution network lines and violation of voltage amplitude limits, and meet the dispatching needs of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 is a flow chart for implementing the distribution network dispatching method provided in the embodiment of the present application;

[0037] Figure 2 is a flowchart of another distribution network dispatching method provided in an embodiment of the present application;

[0038] Figure 3 is a schematic diagram of the topology of the power distribution network system provided in an embodiment of the present application;

[0039] Figure 4 It is a schematic diagram of the convergence speed of the centralized optimization algorithm provided in the embodiment of the present application;

[0040] Figure 5 It is a schematic diagram of the convergence speed of the distributed optimization algorithm provided in the embodiment of the present application;

[0041] Figure 6 is a schematic diagram of voltage amplitude provided in an embodiment of the present application;

[0042] Figure 7 is a schematic diagram of a node voltage phase angle provided in an embodiment of the present application;

[0043] Figure 8 is a schematic diagram of network loss provided in an embodiment of the present application;

[0044] Fig. 9It is a schematic diagram of the overall operating cost of the power distribution network provided in an embodiment of the present application;

[0045] Fig.10 It is a structural diagram of the distribution network dispatching device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application. SUMMARY OF THE INVENTION

[0048] After research, the applicant found that at present, the large-scale access of distributed power sources in the distribution network has caused many new technical problems, such as violation of the operating restrictions of the distribution network, resulting in violation of the voltage amplitude limit and overload of the distribution network lines, etc. On the other hand, due to the increase in current and flow of other loads in the distribution network, such as electric vehicles, electric heat pumps and demand response projects, the line may be overloaded, resulting in power quality problems. It can be seen that the traditional scheduling method can no longer meet the scheduling needs.

[0049] In order to meet the distribution network dispatching needs under the large-scale integration of distributed power sources, in the implementation mode of the present application, the operation data is parsed and processed based on the constructed dispatching optimization model to determine the dispatching instructions for the distribution network. In the process of parsing and processing the operation data, the access of distributed power sources, network trends, new energy sources and demand response and other conditions are fully considered. Under the premise of saving the total operating cost, more reasonable distribution network dispatching instructions can be parsed to avoid power quality problems caused by overloading of distribution network lines and violation of voltage amplitude limits, thereby meeting the distribution network dispatching needs and improving the solution efficiency by utilizing distributed solution.

[0050] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0051] Figure 1 The implementation flow chart of the distribution network dispatching method provided in the embodiment of the present application is detailed as follows:

[0052] Step 101, obtaining operation data of the distribution network; wherein the operation data includes output data, cost data and load data of the distribution network equipment.

[0053] Exemplarily, this embodiment obtains operating data of the distribution network, including output data, cost data and load data of distribution network equipment, for example, power data, cost coefficient, cost function, electricity purchase price and other data of each distribution network equipment, as well as load data and other data.

[0054] Step 102, based on the dispatch optimization model, the operation data is analyzed and processed to generate dispatch instructions for the distribution network equipment; wherein the dispatch optimization model is used to generate the dispatch optimization solution for the distribution network equipment; the dispatch optimization model takes the total operating cost as the objective function, and takes the network flow constraint, the renewable energy power generation constraint, the energy storage system constraint, the overcurrent and overvoltage constraint and the demand side response constraint as the constraint conditions.

[0055] In a feasible implementation manner, this embodiment pre-builds a scheduling optimization model, which takes the total operating cost as the objective function and takes network flow constraints, new energy power generation constraints, energy storage system constraints, overcurrent and overvoltage constraints, and demand-side response constraints as constraints.

[0056] Among them, the objective function is composed of the main grid electricity purchase cost, distributed power generation cost, energy storage operation cost, demand side response cost, demand side response benefit and network loss cost.

[0057] Exemplarily, this embodiment analyzes the acquired operating data based on a pre-built scheduling optimization model. For example, planning and solving are performed under the constraints of constraints with the total system operating cost as the objective function, and the optimal scheduling solution for the distribution network equipment is determined under the premise of low operating costs, thereby obtaining scheduling instructions for the distribution network equipment.

[0058] Step 103: Dispatch and control the distribution network equipment according to the dispatch instruction.

[0059] Exemplarily, after determining a dispatch instruction for a distribution network device, this embodiment performs dispatch control on the distribution network device based on the dispatch instruction. For example, based on the dispatch instruction, the embodiment performs power control on the distribution network device.

[0060] In summary, the embodiment of the present application is based on a pre-built scheduling optimization model, with the total operating cost as the objective function. In the process of analyzing and processing the operating data, it fully considers the conditions such as the access of distributed power sources, network trends, new energy and demand response. It can analyze and obtain more reasonable distribution network scheduling instructions while saving the total operating cost, avoid power quality problems caused by overloading of distribution network lines and violation of voltage amplitude limits, and meet the distribution network scheduling needs.

[0061] This embodiment aims to minimize the total operating cost of the system operator in the distribution network scheduling optimization problem.

[0062] In some embodiments, the objective function of the dispatch optimization model is composed of the main grid electricity purchase cost, distributed power generation cost, energy storage operating cost, demand side response cost, demand side response revenue and network loss cost.

[0063] In an example, this embodiment considers the main grid electricity purchase cost, the operating costs of distributed power sources and energy storage, the benefits and costs of implementing demand-side response, and the costs caused by network losses. The objective function is defined as follows:

[0064]

[0065] Where T is the total control interval considered in the optimization problem, C grid The cost of purchasing electricity from the main grid is usually different at different times of the day. grid,t The power purchased by the main network, C g (·) is the power generation cost function of distributed generation, and are the cost functions for charging and discharging the energy storage unit s, P g,t is the active power output of distributed power source g at time t, is the charging power of energy storage unit s at time t, is the discharge power of energy storage unit s at time t, D r,t is the load transferred (reduced) by user r through demand-side management at time t, C r,t is the compensation or cost coefficient related to the demand-side response at time t. G is the set of all adjustable distributed power sources within the optimization control interval, S is the set of all adjustable energy storage units within the optimization control interval, and R is the set of all users participating in the demand response within the control interval. α is an artificially determined penalty coefficient used to control the weight of network loss in the objective function.

[0066] This embodiment comprehensively considers the impact of various new sources and loads and demand-side response mechanisms on the output of distributed power sources, reduces the operating costs of the distribution network, and controls network losses and voltage over-limit problems.

[0067] The dispatching problem of the distribution network is subject to a variety of physical electrical constraints, including non-convex network power flow constraints that are difficult to solve. The embodiment of the present application performs second-order cone relaxation on the nonlinear AC power flow equation constraints corresponding to the distribution network to improve the computational efficiency.

[0068] In some embodiments, before performing data analysis on the operation data based on the scheduling optimization model, the method further includes:

[0069] Perform second-order cone relaxation on the nonlinear AC power flow equation constraints corresponding to the distribution network to obtain the network power flow constraints;

[0070] The network power flow constraints are composed of node power balance constraints, branch voltage drop constraints, and the second-order cone relaxation relationship between power loss and electrical quantity.

[0071] For example, in the distribution network, the branch power flow model is usually used to describe the network operation status. Since the distribution network is usually radial and the power flow flows radially, in order to improve the computational efficiency, the nonlinear non-convex network power flow constraints can be relaxed to second-order cone constraints, thereby converting the optimal power flow problem from a difficult non-convex optimization problem to a convex optimization problem of second-order cone programming.

[0072] In one example, the converted network power flow constraints include:

[0073]

[0074] In the formula, it is assumed that the power flow flows from node i to node j and then to node group k, P i , P j Inject active power into nodes i and j, Q i , Q j Inject reactive power into nodes i and j, r ij +jx ij is the line impedance connecting nodes i and j, V i 、V j are the voltage amplitudes of nodes i and j respectively, P ij and Q ij are the active and reactive power flows of the line flowing through branch ij, P jk and Q jk are the active and reactive power flows flowing through branch jk respectively.

[0075] Formula (1) represents the node power balance, formula (2) represents the branch voltage drop, and formula (3) is the relationship between power loss and electrical quantity. The second-order cone relaxation of .

[0076] This embodiment adopts a second-order cone programming branch power flow model of the distribution network, so that the model is converted into a convex optimization problem, which facilitates the rapid solution of the model.

[0077] In addition to network flow constraints, the constraints of the dispatch optimization model include renewable energy generation constraints, energy storage system constraints, overcurrent and overvoltage constraints, and demand-side response constraints.

[0078] In a feasible implementation method, renewable energy power generation constraints: Taking photovoltaic as an example, the maximum and minimum active output, reactive output constraints and apparent power capacity constraints are considered respectively:

[0079]

[0080] (P g,t )2 +(Q g,t ) 2 ≤(S g ) 2 #(6)

[0081] -P g,t tanθ≤Q g,e ≤P g,e tanθ#(7)

[0082] In the formula, is the predicted maximum active output of the distributed power source (photovoltaic is used as an example here), Q g,t is the reactive power output of distributed power source, S g is the capacity of distributed power source, and θ is the power factor angle of distributed power source.

[0083] Energy storage system constraints: The charging and discharging state of the energy storage system includes the following constraints:

[0084]

[0085] SoC min ≤SoC t ≤SoC max #(11)

[0086] Where η charge and η discharge They are the charging and discharging efficiency of the energy storage unit, SoC t is the state of charge of the energy storage unit at time t. Here we only consider constraints (7)-(11). From the perspective of long-term operating benefits, we can also consider the potential cost of energy storage battery degradation, and then constrain its charge and discharge cycle times and state switching frequency to reduce long-term operating costs.

[0087] Overcurrent and overvoltage constraints: In order to prevent the access of distributed power sources from causing power flow backflow, thereby causing the voltage at the end of the distribution network to increase and cause power quality problems such as voltage exceeding the limit, the node voltage amplitude constraint is set:

[0088]

[0089] In the formula, and are the minimum and maximum values ​​allowed for the voltage amplitude of node i, respectively.

[0090] In order to prevent the line from being over-currented for a long time and destroying the thermal stability of the line, the following line capacity constraints are considered:

[0091]

[0092] Demand-side response constraints:

[0093]

[0094] Where D r,t If it is a positive value, it means that user r reduces its load at time t, and if it is a negative value, it means that it increases its load at that time. represents the maximum load that can be increased or transferred to this moment if user r commits to participate in demand-side response at moment t, is the maximum load that user r can reduce at time t.

[0095] The distribution network dispatching method provided in this embodiment is applicable to different optimization scenarios. This embodiment takes the worst optimization scenario as an example to perform optimization and solution to ensure the feasibility of optimization for any scenario.

[0096] In this embodiment, the predicted maximum output of the distributed power source is considered. Adjustable load according to user's needs is an uncertainty parameter. Without loss of generality, the constraints in the optimization problem of this embodiment can be characterized as f(x)≤b. It is observed that the above parameters exist in part b. Then, considering the objective function, when the maximum output of the distributed power source decreases, the cost of purchasing electricity from the main grid increases, and its cost item C grid It is usually much greater than the cost of distributed power generation; at the same time, when the adjustable load decreases, the system flexibility decreases, causing the cost of the objective function to increase. In summary, the worst optimization scenario for the optimization problem is the scenario where the distributed power generation predicts the maximum output and the adjustable load is the minimum.

[0097] In a feasible implementation, this embodiment can represent the worst scenario as the proportion of distributed power source output in the distribution network equipment output in the operating data is higher than the first preset threshold, and the proportion of adjustable load in the load data is less than the second preset threshold.

[0098] This embodiment considers the uncertainty of distributed power sources and user loads at the same time, forming a robust optimization problem, so that the solution of the problem is suitable for various types of complex scenarios.

[0099] The following further introduces how to solve the operation data based on the scheduling optimization model.

[0100] In some embodiments, based on the dispatch optimization model, the operation data is parsed and processed to generate dispatch instructions for the distribution network equipment, including:

[0101] Based on the operating data, the objective function and the constraints corresponding to the objective function, the optimization solution task is determined.

[0102] The optimization task is solved to determine the optimal solution of the objective function; wherein the optimal solution is the output data of the distribution network equipment that makes the objective function obtain the minimum value.

[0103] According to the optimal solution, a dispatch instruction for the distribution network equipment is generated; wherein the dispatch instruction is used to dispatch the operating output of the distribution network equipment.

[0104] Exemplarily, this embodiment determines the optimization solution task based on the operating data under the worst scenario, the objective function in the aforementioned embodiment, and the constraints corresponding to the objective function; then solves the optimization solution task to determine the output data of the distribution network equipment that makes the objective function achieve the minimum value, that is, the optimal solution; then, based on the optimal solution, generates a dispatch instruction for the distribution network equipment to dispatch the distribution network equipment to operate the output.

[0105] In this embodiment, considering that there are many nodes in the distribution network and the amount of calculation is huge, the optimization problem is decomposed into subtasks and solved through a distributed algorithm to achieve a fast solution.

[0106] In some embodiments, solving the optimization solution task to determine the optimal solution of the objective function includes:

[0107] The optimization solution task is split and processed to obtain subtasks corresponding to the optimization solution task.

[0108] So that each agent unit iteratively solves each corresponding subtask based on the gradient descent method, determines the solution result of each subtask after convergence, and updates and iterates the optimization solution task according to the solution result of each subtask, until the optimization solution task converges and obtains the optimal solution of the objective function; wherein the solution result of each subtask is the local solution of the optimization solution task.

[0109] Exemplarily, this embodiment splits the optimization solution task to obtain subtasks corresponding to the optimization solution task. Each agent unit then iteratively solves each subtask based on the gradient descent method, determines the solution result of each subtask after convergence, and updates and iterates the optimization solution task according to the solution results of each subtask until the optimization solution task converges and obtains the optimal solution of the objective function.

[0110] In an example, the optimization problem in this embodiment can be rewritten as follows:

[0111]

[0112] stD(X)=0

[0113] C(X)≤0

[0114] In the formula, X is all decision variables of the optimization problem, M is the dimension of X, and Ω is the feasible domain of X. D(X) is the set of all equality constraints, and C(X) is the set of all inequality constraints. Since the objective function and D(X) and C(X) are convex functions under given conditions, we can further obtain the Lagrange dual function of the above formula:

[0115]

[0116] Therefore, L can be decomposed into M proxy subtasks to be solved in the following form:

[0117]

[0118] For each agent, X i =[x 1i ,x 2i ,…,x Mi ] T and the corresponding Lagrange multipliers and A copy of the corresponding part of the main problem X,λ,μ.

[0119] Each agent updates its decision according to the local optimization iteration, communicates with neighboring agents and adjusts the dual variables. For each subtask, the gradient descent method is used to solve the minimization problem L i , and its corresponding iteration formula is as follows:

[0120]

[0121] In the formula, is the decision variable of the subtask at the k+1th iteration, α i It is the iteration step size, which is flexibly selected according to the convergence speed of the subtask. and is the value of the Lagrange multiplier for the k+1th iteration, and β is its adjustment step size. Stop the iteration when , and get the optimal solution of the subtask.

[0122] In some embodiments, the optimization solution task is updated and iterated according to the solution results of each subtask until the optimization solution task converges and the optimal solution of the objective function is obtained, including:

[0123] According to the solution results of each subtask, the optimization solution task is updated; the updated optimization solution task is sent to each subtask, and the steps of iterative solution of each subtask based on the gradient descent method are repeated until the optimization solution task converges and the optimal solution of the objective function is obtained.

[0124] In some embodiments, the optimization solution task is updated according to the solution results of each subtask, including:

[0125] Based on the multi-agent consensus update strategy, the solution results of each subtask are integrated to obtain the global solution of the optimization solution task; based on the global solution, the optimization solution task is updated.

[0126] For example, after all subtasks converge, a multi-agent consensus update strategy is adopted to update the global optimization problem solution:

[0127] X k+1 =WX k+1

[0128] λ k+1 =Wλ k+1

[0129] μ k+1 =Wμ k+1

[0130] In the formula, X is the decision variable matrix, which consists of X of each subtask i The vectors are composed of λ and μ, respectively, which are Lagrange multiplier matrices, which are composed of copies of the global multipliers for the corresponding subtasks. We assume that for each agent, there is a point-to-point communication network with any other agent in the network, and W is the information weight matrix of the communication network.

[0131] During each global iteration, the decision variables and global multipliers are updated, and the updated results are sent to the subtasks for a new step of subtask solution - main problem update - iteration. The iterative convergence criterion is ∥∥L k -L k-1 ∥∥2<δ. When the main problem converges, the decision variables of each subtask satisfy X1=X2=…=X M ,λ1=λ2=…=λ M , and μ1=μ2=…=μ M .

[0132] This embodiment uses a distributed algorithm to solve the optimization problem, which protects user privacy while enhancing the security, elasticity and real-time response capability of the system, and can support real-time optimization scheduling and fault response of the power system.

[0133] The above-mentioned embodiment introduces how to solve the optimization problem. The following further introduces how to dispatch and control the distribution network equipment according to the dispatch instructions obtained by the solution.

[0134] In some embodiments, according to the dispatch instruction, the distribution network equipment is dispatched and controlled, including:

[0135] Send dispatching instructions to the carrier controller, and execute the dispatching instructions based on the carrier controller to realize dispatching control of distribution network equipment.

[0136] Exemplarily, dispatching instructions are executed based on the carrier controller to implement dispatching control of distribution network equipment.

[0137] The carrier controller includes any one or more of DSP (TI C2000 series), MSP430, STM32, FPGA and various programmable PWM digital controllers.

[0138] Figure 2 Another implementation flow chart of a distribution network dispatching method provided in an embodiment of the present application is described in detail as follows:

[0139] Step 201, obtaining the operation data of the distribution network; wherein the operation data includes output data, cost data and load data of the distribution network equipment.

[0140] Among them, the distribution network equipment includes distributed power sources, energy storage systems and new energy equipment; the proportion of distributed power source output in the distribution network equipment output in the operating data is higher than the first preset threshold, and the proportion of adjustable load in the load data is lower than the second preset threshold.

[0141] Step 202: Determine the optimization solution task based on the operation data, the objective function and the constraints corresponding to the objective function.

[0142] Among them, the objective function is composed of the main grid electricity purchase cost, distributed power generation cost, energy storage operation cost, demand side response cost, demand side response benefit and network loss cost.

[0143] The constraints include network flow constraints, renewable energy generation constraints, energy storage system constraints, overcurrent and overvoltage constraints, and demand-side response constraints.

[0144] In a feasible implementation manner, the method further includes:

[0145] The nonlinear AC power flow equation constraints corresponding to the distribution network are subjected to second-order cone relaxation to obtain the network power flow constraints.

[0146] The network power flow constraints are composed of node power balance constraints, branch voltage drop constraints, and the second-order cone relaxation relationship between power loss and electrical quantity.

[0147] Step 203, based on the dispatch optimization model, solve the optimization solution task and determine the optimal solution of the objective function; wherein the optimal solution is the output data of the distribution network equipment that makes the objective function obtain the minimum value.

[0148] Among them, the dispatch optimization model is used to generate the dispatch optimization solution for distribution network equipment; the dispatch optimization model takes the total operating cost as the objective function, and takes network flow constraints, renewable energy power generation constraints, energy storage system constraints, overcurrent and overvoltage constraints, and demand-side response constraints as constraints.

[0149] In one example, step 203 includes the following steps:

[0150] The optimization task is split to obtain the subtasks corresponding to the optimization task, so that each agent unit can iteratively solve the corresponding subtask based on the gradient descent method to determine the solution result after each subtask converges.

[0151] The optimization solution task is updated and iterated according to the solution results of each subtask until the optimization solution task converges and the optimal solution of the objective function is obtained; wherein the solution result of each subtask is a local solution of the optimization solution task.

[0152] In a feasible implementation, the optimization solution task is updated and iterated according to the solution results of each subtask until the optimization solution task converges and the optimal solution of the objective function is obtained, including:

[0153] According to the solution results of each subtask, update the optimization solution task.

[0154] The updated optimization solution task is sent to each subtask, and the optimization solution task is repeatedly split to obtain the steps of each subtask corresponding to the optimization solution task, until the optimization solution task converges and the optimal solution of the objective function is obtained.

[0155] Among them, according to the solution results of each subtask, the optimization solution task is updated, including:

[0156] Based on the multi-agent consensus update strategy, the solution results of each subtask are integrated to obtain the global solution of the optimization solution task.

[0157] Based on the global solution, update the optimization solution task.

[0158] Step 204: Generate a dispatch instruction for the distribution network equipment according to the optimal solution; wherein the dispatch instruction is used to dispatch the operating output of the distribution network equipment.

[0159] In one example, step 204 includes the following steps: sending a scheduling instruction to a carrier controller, and executing the scheduling instruction based on the carrier controller to implement scheduling control of the distribution network equipment.

[0160] In one example, Figure 3A schematic diagram of the topology of a distribution network system provided in an embodiment of the present application, in which the distribution network operator supplies power to 330 single-phase residential loads through three radial feeders, namely feeder 1, feeder 2, and feeder 3, wherein the rated voltages of the low-voltage distribution network and the medium-voltage distribution network are 0.4 kV and 10 kV, respectively, and the rated capacity of the transformer is 800 kVA. Household photovoltaic systems are randomly assigned to a portion of the 330 loads, and the capacity of the photovoltaic system is randomly assigned using a uniform distribution or a normal distribution according to the actual penetration rate, such as 20-40% of the total load (3-10 kW per system, uniformly distributed). Similar to photovoltaics, energy storage systems are randomly assigned to a portion of residential loads. Alternatively, several larger energy storage systems can be placed at specific nodes of the network to simulate large-scale energy storage nodes. Assign capacity (e.g., 5-20 kWh) and power level (e.g., 3-10 kW) to each energy storage system. The initial SoC is randomly set at 20-80%, and the dynamic changes of SoC are simulated according to the random load and power generation mode. Randomly distribute EV charging stations in the network with a penetration rate of 15%. Set 6-9 evenly distributed charging piles in the charging station, including Class 1 (3.3 kW) and Class 2 (6.6 kW). Use random EV arrival and departure times based on typical driving patterns. Generate charging demand curves that take into account daily habits and occasional deviations.

[0161] For the above scenario, scheduling optimization is performed based on the scheduling method provided in this application. Figure 4 and Figure 5 The iterative convergence speed of the centralized optimization algorithm and the distributed optimization algorithm are shown respectively. It can be seen that for the above optimization scenarios, the distributed optimization algorithm can achieve a convergence speed similar to that of the traditional centralized algorithm. At the same time, the distributed optimization algorithm can significantly reduce the computational cost. When the algorithm converges, the centralized optimization algorithm takes about 0.529s, while the distributed algorithm only takes 0.158s. Furthermore, for the 300 nodes in the system, Figure 6 The voltage amplitude (converted to per-unit value) is displayed. It can be seen that after the optimization algorithm, there is no voltage over-limit situation in the shown scenario, the voltage amplitude of all user nodes is within the range of 0.9 to 1.1 per-unit value, and the problem of voltage increase at the end of the distribution network is effectively controlled. Figure 7 The voltage phase angles of different nodes in the network are shown in Figure 8 It shows the change of network loss. It can be seen that after applying the scheduling method provided by this application, the network line loss can be reduced by about 20%, ensuring the safe and stable operation of the distribution network. Fig. 9 In summary, it can be seen that the proposed distribution network method reduces the distribution network operation cost and the total network loss at the same time, and realizes the voltage control of the network nodes.

[0162] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0163] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.

[0164] Fig.10 The structural diagram of the distribution network dispatching device provided in the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown, which is described in detail as follows:

[0165] like Fig.10 As shown, the distribution network dispatching device includes:

[0166] The acquisition unit 31 is used to acquire the operation data of the distribution network; wherein the operation data includes the output data, cost data and load data of the distribution network equipment.

[0167] The first processing unit 32 is used to perform data analysis and processing on the operating data based on the scheduling optimization model, and generate scheduling instructions for the distribution network equipment; wherein the scheduling optimization model is used to generate the scheduling optimization solution for the distribution network equipment; the scheduling optimization model takes the total operating cost as the objective function, and takes the network flow constraints, new energy power generation constraints, energy storage system constraints, overcurrent and overvoltage constraints and demand-side response constraints as constraints.

[0168] The control unit 33 is used to perform dispatching control on the distribution network equipment according to the dispatching instructions.

[0169] In a possible implementation, before the first processing unit 32, the device further includes: a second processing unit, configured to perform second-order cone relaxation processing on the nonlinear AC power flow equation constraints corresponding to the distribution network to obtain network power flow constraints.

[0170] The network power flow constraints are composed of node power balance constraints, branch voltage drop constraints, and the second-order cone relaxation relationship between power loss and electrical quantity.

[0171] In a possible implementation, the first processing unit 32 is specifically configured to:

[0172] Based on the operating data, the objective function and the constraints corresponding to the objective function, the optimization solution task is determined.

[0173] The optimization task is solved to determine the optimal solution of the objective function; wherein the optimal solution is the output data of the distribution network equipment that makes the objective function obtain the minimum value.

[0174] According to the optimal solution, a dispatch instruction for the distribution network equipment is generated; wherein the dispatch instruction is used to dispatch the operating output of the distribution network equipment.

[0175] In a possible implementation, the first processing unit 32 is specifically configured to:

[0176] The optimization solution task is split to obtain subtasks corresponding to the optimization solution task, so that each agent unit iteratively solves the corresponding subtask based on the gradient descent method to determine the solution result of each subtask after convergence;

[0177] The optimization solution task is updated and iterated according to the solution results of each subtask until the optimization solution task converges and the optimal solution of the objective function is obtained; wherein the solution result of each subtask is a local solution of the optimization solution task.

[0178] In a possible implementation, the first processing unit 32 is specifically configured to:

[0179] Based on the multi-agent consensus update strategy, the solution results of each subtask are integrated to obtain the global solution of the optimization solution task.

[0180] Based on the global solution, update the optimization solution task.

[0181] The updated optimization solution task is sent to each subtask, and the steps of iterative solution of each subtask based on the gradient descent method are repeated until the optimization solution task converges and the optimal solution of the objective function is obtained.

[0182] In one possible implementation, the distribution network equipment includes distributed power sources, energy storage systems and new energy equipment; the proportion of distributed power source output in the distribution network equipment output in the operating data is higher than a first preset threshold, and the proportion of adjustable load in the load data is lower than a second preset threshold.

[0183] In one possible implementation, the objective function is composed of the main grid electricity purchase cost, distributed power generation cost, energy storage operation cost, demand side response cost, demand side response benefit and network loss cost.

[0184] In a possible implementation, the control unit 33 is specifically configured to:

[0185] Send dispatching instructions to the carrier controller, and execute the dispatching instructions based on the carrier controller to realize dispatching control of distribution network equipment.

[0186] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0187] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0188] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various distribution network dispatching method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0189] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distribution network dispatching method, characterized in that: The method comprises: Acquire operation data of the distribution network; wherein the operation data includes output data, cost data and load data of distribution network equipment; Based on the dispatch optimization model, the operation data is analyzed and processed to generate dispatch instructions for the distribution network equipment; wherein the dispatch optimization model is used to generate the dispatch optimization solution for the distribution network equipment; the dispatch optimization model takes the total operating cost as the objective function, and takes the network flow constraint, the new energy power generation constraint, the energy storage system constraint, the overcurrent and overvoltage constraint and the demand side response constraint as the constraint conditions; The distribution network equipment is dispatched and controlled according to the dispatch instruction.

2. The distribution network dispatching method according to claim 1, characterized in that: Before performing data analysis processing on the operation data based on the scheduling optimization model, the method further includes: Performing second-order cone relaxation processing on the nonlinear AC power flow equation constraints corresponding to the distribution network to obtain the network power flow constraints; The network power flow constraint is composed of a node power balance constraint, a branch voltage drop constraint, and a second-order cone relaxation relation between power loss and electrical quantity.

3. The distribution network dispatching method according to claim 1, characterized in that: The step of performing data analysis and processing on the operation data based on the dispatch optimization model to generate a dispatch instruction for the distribution network equipment includes: Determining an optimization solution task based on the operating data, the objective function, and constraints corresponding to the objective function; Solving the optimization task to determine the optimal solution of the objective function; wherein the optimal solution is the output data of the distribution network equipment that makes the objective function obtain the minimum value; According to the preferred solution, a dispatch instruction for the distribution network equipment is generated; wherein the dispatch instruction is used to dispatch the operating output of the distribution network equipment.

4. The distribution network dispatching method according to claim 3, characterized in that: The step of solving the optimization task to determine the optimal solution of the objective function includes: The optimization solution task is split to obtain subtasks corresponding to the optimization solution task, so that each agent unit iteratively solves the corresponding subtask based on the gradient descent method to determine the solution result of each subtask after convergence; The optimization solution task is updated and iterated according to the solution results of each subtask until the optimization solution task converges and the optimal solution of the objective function is obtained; wherein the solution result of each subtask is a local solution of the optimization solution task.

5. The distribution network dispatching method according to claim 4, characterized in that: The updating and iterating of the optimization solution task according to the solution results of each subtask until the optimization solution task converges to obtain the optimal solution of the objective function includes: Based on the multi-agent consensus update strategy, the solution results of each subtask are integrated to obtain the global solution of the optimization solution task; Based on the global solution, updating the optimization solution task; Repeat the steps of splitting the optimization solution task to obtain subtasks corresponding to the optimization solution task until the optimization solution task converges and the optimal solution of the objective function is obtained.

6. The distribution network dispatching method according to any one of claims 1 to 5, characterized in that: The distribution network equipment includes distributed power sources, energy storage systems and new energy equipment; the proportion of distributed power source output in the operating data in the output of the distribution network equipment is higher than a first preset threshold, and the proportion of adjustable load in the load data is lower than a second preset threshold.

7. The distribution network dispatching method according to any one of claims 1 to 5, characterized in that: The objective function is composed of the main grid electricity purchase cost, distributed power generation cost, energy storage operation cost, demand side response cost, demand side response benefit and network loss cost.

8. The distribution network dispatching method according to any one of claims 1 to 5, characterized in that: The step of performing dispatching control on the distribution network equipment according to the dispatching instruction includes: The dispatching instruction is sent to a carrier controller, and the dispatching instruction is executed based on the carrier controller to realize dispatching control of the distribution network equipment.

9. A distribution network dispatching device, characterized in that: The device comprises: An acquisition unit, used to acquire operation data of the distribution network; wherein the operation data includes output data, cost data and load data of distribution network equipment; A first processing unit is used to perform data analysis and processing on the operation data based on a scheduling optimization model to generate a scheduling instruction for the distribution network equipment; wherein the scheduling optimization model is used to generate a scheduling optimization solution for the distribution network equipment; the scheduling optimization model takes the total operating cost as the objective function, and takes the network flow constraint, the new energy power generation constraint, the energy storage system constraint, the overcurrent and overvoltage constraint, and the demand side response constraint as the constraint conditions; A control unit is used to perform dispatch control on the distribution network equipment according to the dispatch instruction.

10. The distribution network dispatching device according to claim 9, characterized in that: Before the first processing unit, the device further includes: A second processing unit is used to perform second-order cone relaxation processing on the nonlinear AC power flow equation constraint corresponding to the distribution network to obtain the network power flow constraint; The network power flow constraint is composed of a node power balance constraint, a branch voltage drop constraint, and a second-order cone relaxation relation between power loss and electrical quantity.

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