Optimized dispatching method and device for power distribution network and electronic equipment

Abstract

The invention provides a power distribution network optimization scheduling method and device and electronic equipment, and relates to the technical field of power distribution network optimization scheduling, and the method comprises the steps: constructing a day-ahead scheduling model with the minimum operation cost of a power distribution network as a target function, and the day-ahead scheduling model comprises a decision variable; based on the influence mechanism of the N-1 line fault risk on the dynamic network reconstruction of the power distribution network, solving the day-ahead scheduling model to obtain the solving result of the decision variable; and carrying out optimal scheduling on the power distribution network by utilizing a solution result of the decision variable. By adopting the power distribution network optimization scheduling method and device and the electronic equipment, the problem that the economy and the safety cannot be considered at the same time in the existing power distribution network optimization scheduling method is solved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network optimal scheduling. Specifically, it relates to a distribution network optimal scheduling method, device, and electronic device. Background Art

[0002] With the large-scale access of renewable energy such as wind energy and solar energy, the power grid system has become more complex and unstable. When traditional distribution networks encounter line faults (especially N-1 line faults, that is, a line suddenly disconnects), it may lead to problems such as large-scale power outages and system instability. To solve these problems, researchers have proposed methods for active distribution network optimal scheduling to improve the safety and economy of the power grid by real-time adjusting the grid structure.

[0003] However, most of the existing distribution network optimal scheduling methods focus on optimizing economy while ignoring safety issues. Especially when an N-1 line fault occurs, there may be a situation where the power supply is insufficient to meet the load demand, resulting in power outages (load shedding) for some users. Therefore, how to ensure the safety and reliability of the power grid while guaranteeing economy has become an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the purpose of the present application is to provide a distribution network optimal scheduling method, device, and electronic device to solve the problem that the existing distribution network optimal scheduling methods cannot take both economy and safety into account at the same time.

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

[0006] Construct a day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function, where the day-ahead scheduling model includes decision variables;

[0007] Based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, solve the day-ahead scheduling model to obtain the solution results of the decision variables;

[0008] Use the solution results of the decision variables to optimize the scheduling of the distribution network.

[0009] Optionally, constructing a day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function includes: taking the minimum operating cost of the distribution network as the objective function, taking the network topology structure and generation plan as decision variables, and constructing a day-ahead scheduling model that satisfies preset constraint conditions, where the operating cost of the distribution network includes load shedding cost.

[0010] Optionally, based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, solve the day-ahead scheduling model, including: based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, construct a risk verification model with the load shedding amount as an index; use the risk verification model with the load shedding amount as an index to iteratively solve the day-ahead scheduling model.

[0011] Optionally, iteratively solve the day-ahead scheduling model, including: obtaining the solution result of this round based on the solution result and objective function of the previous round; inputting the solution result of this round into the risk verification model to determine the operating cost of the distribution network in this round; determining whether to use the solution result of this round as the final solution result according to the comparison result between the operating cost of the distribution network in this round and the operating cost of the distribution network in the previous round.

[0012] Optionally, obtaining the solution result of this round based on the solution result and objective function of the previous round, including: substituting the solution result of the previous round into the objective function and solving the second-order cone mixed-integer linear programming through a solver to obtain the solution result of this round.

[0013] Optionally, inputting the solution result of this round into the risk verification model to determine the operating cost of the distribution network in this round, including: inputting the solution result of this round into the risk verification model to determine the load shedding cost in this round; inputting the load shedding cost in this round into the day-ahead scheduling model to obtain the operating cost of the distribution network in this round.

[0014] Optionally, determining whether to use the solution result of this round as the final solution result, including: if the operating cost of the distribution network in this round is less than the operating cost of the distribution network in the previous round, determining whether the solution result of this round meets the iteration stop condition; if the iteration stop condition is met, using the solution result of this round as the final solution result.

[0015] Optionally, determining whether the solution result of this round meets the iteration stop condition, including: comparing the difference between the operating cost of the distribution network in this round and the operating cost of the distribution network in the previous round with a set cost threshold to determine whether the iteration stop condition is met according to the comparison result.

[0016] In a second aspect, the embodiments of the present application further provide a distribution network optimal scheduling device, and the device includes:

[0017] A model construction module, configured to construct a day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function, and the day-ahead scheduling model includes decision variables;

[0018] A model solution module, configured to solve the day-ahead scheduling model based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, and obtain the solution result of the decision variables;

[0019] An optimal scheduling module, configured to perform optimal scheduling on the distribution network by using the solution result of the decision variables.

[0020] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the above-mentioned distribution network optimization scheduling method are executed.

[0021] The embodiments of the present application bring the following beneficial effects:

[0022] A distribution network optimization scheduling method, device, and electronic device provided by an embodiment of the present application can solve the problem of the existing distribution network optimization scheduling method that cannot take into account both economy and security by solving the day-ahead scheduling model according to the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, minimizing the operation cost of the distribution network while fully considering the security impact of the N-1 line fault risk on the dynamic reconfiguration of the distribution network, and avoiding the problem of only optimizing economy while ignoring security in the process of distribution network optimization scheduling.

[0023] To make the above objects, features, and advantages of the present application more obvious and understandable, the following preferred embodiments are specifically described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 Shows a flowchart of the distribution network optimization scheduling method provided by an embodiment of the present application;

[0026] Figure 2a Shows a schematic diagram of a network structure change of a radial network when an N-1 line fault occurs in the power grid provided by an embodiment of the present application;

[0027] Figure 2b Shows another schematic diagram of a network structure change of a radial network when an N-1 line fault occurs in the power grid provided by an embodiment of the present application;

[0028] Figure 3a Shows a schematic diagram of a network structure change of a loop network when an N-1 line fault occurs in the power grid provided by an embodiment of the present application;

[0029] Figure 3bIt shows another schematic diagram of the network structure change of the ring network when an N-1 line fault occurs in the power grid provided by the embodiments of the present application;

[0030] Figure 4 It shows a flowchart of the method for solving the day-ahead scheduling model provided by the embodiments of the present application;

[0031] Figure 5 It shows a schematic structural diagram of the distribution network optimal scheduling device provided by the embodiments of the present application;

[0032] Figure 6 It shows a schematic structural diagram of the electronic device provided by the embodiments of the present application. Detailed implementation manners

[0033] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, every other embodiment obtained by those skilled in the art without creative efforts belongs to the scope of protection of the present application.

[0034] It should be noted that before the present application was proposed, with the large-scale access of renewable energy sources such as wind energy and solar energy, the power grid system has become more complex and unstable. When the traditional distribution network encounters a line fault (especially an N-1 line fault, that is, a line suddenly disconnects), it may lead to problems such as large-scale power outages and system instability. To solve these problems, researchers have proposed methods for optimizing the scheduling of active distribution networks to improve the safety and economy of the power grid by adjusting the power grid structure in real time. However, most of the existing distribution network optimal scheduling methods focus on optimizing economy and ignore the safety issues. Especially when an N-1 line fault occurs, there may be a situation where the power supply is insufficient to meet the load demand, resulting in power outages (load shedding) for some users. Therefore, how to ensure the safety and reliability of the power grid while ensuring economy has become an urgent problem to be solved.

[0035] Based on this, the embodiments of the present application provide a distribution network optimal scheduling method to ensure the safety and reliability of the power grid while ensuring economy during the distribution network optimal scheduling process.

[0036] Please refer to Figure 1 , Figure 1It is a flowchart of a distribution network optimal scheduling method provided by an embodiment of the present application. As Figure 1 shown, the distribution network optimal scheduling method provided by the embodiment of the present application includes:

[0037] Step S101, constructing a day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function;

[0038] Step S102, solving the day-ahead scheduling model based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network to obtain the solution results of the decision variables;

[0039] Step S103, using the solution results of the decision variables to optimize the scheduling of the distribution network.

[0040] The distribution network optimal scheduling method provided by the embodiment of the present application can solve the day-ahead scheduling model according to the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network. While minimizing the operating cost of the distribution network, it also fully considers the safety impact of the N-1 line fault risk on the dynamic reconfiguration of the distribution network, avoiding the problem of only optimizing economy while ignoring safety in the process of distribution network optimal scheduling, and solving the problem that the existing distribution network optimal scheduling methods cannot take both economy and safety into account.

[0041] For the convenience of understanding this embodiment, the above exemplary steps provided by the embodiment of the present application will be described separately below.

[0042] In step S101, a day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function is constructed.

[0043] In this step, the distribution network may refer to an urban distribution network, which is an important part of the power system and is mainly responsible for distributing electric energy from the transmission network or power plant to end users after voltage reduction.

[0044] The day-ahead scheduling model may refer to an urban power grid optimal scheduling model based on dynamic network reconfiguration, and the day-ahead scheduling model may also be called a day-ahead optimal scheduling model.

[0045] The day-ahead scheduling model is used to determine the network topology structure and generation plan of the distribution network at each time period in the day-ahead stage considering network dynamic reconfiguration and the changes of wind power, photovoltaics and load at each time period, so as to improve the economy of the urban power grid and reduce the load loss after a line failure in the distribution network.

[0046] In the embodiment of the present application, when the urban power grid structure is a radial network, if a line fault occurs, it will change the topological structure of the power grid, there is a possibility of splitting the network connection, and it is possible to generate an island in the power grid. When an N-1 line fault occurs in the distribution network, first use breadth-first search to find the nodes included in the island, as well as the internal load of the island and the power sources and outputs included. When the total load in an island is greater than the total power generation of the units in the island, it is necessary to cut off the load that exceeds the power output of the power source, and then a load loss amount is generated.

[0047] When the urban power grid structure is a ring network, N-1 faults occurring inside and at the edge of the ring network will not result in islands, and the island load will be greater than the maximum output of the units, directly causing load loss. However, it is still possible that due to the overly long transfer path, the voltage of some nodes is too low, or some lines are overloaded to reach the upper limit of the power flow, thus generating a load loss risk. Only when a line fault occurs at the feeder end, it is possible to generate an island, and the specific analysis method is similar to that of the radial network.

[0048] The following takes Figure 2a 、 Figure 2b and Figure 3a 、 Figure 3b as examples to specifically illustrate the influence mechanism of N-1 line faults on network dynamic reconfiguration.

[0049] Figure 2a shows a schematic diagram of a network structure change of a radial network when an N-1 line fault occurs in the power grid provided by the embodiment of the present application. As shown in Figure 2a , for the radial network, each node in the power grid carries different amounts of load. Among them, the power source P1 has a large output, the power source P2 has a small output, and the power source P3 has a moderate output. When a line fault occurs, the disconnected line divides the power grid into two microgrids, each with a part of the power source and load. If network reconfiguration is not carried out, when a line fault risk occurs, it will cause the load carried by area 1 to be greater than the power output, and the outputs of power sources P2 and P3 are difficult to bear the load demand in the area. At this time, load shedding measures have to be taken. Therefore, it is necessary to reconfigure the network structure into Figure 2a network topology. At this time, the power source P1 with the largest output and the power source P2 with a smaller output can bear most of the load in the power grid, reducing the load loss caused by the mismatch between the source and the load.

[0050] Figure 2b shows another schematic diagram of a network structure change of a radial network when an N-1 line fault occurs in the power grid provided by the embodiment of the present application. As shown in Figure 2b , in the next time period, due to the uncertainty of wind and light and the change of load demand, the outputs of each power source will also change accordingly. In order to reduce the load loss amount, Figure 2a network topology becomes Figure 2b as shown.

[0051] Figure 3a It shows a schematic diagram of the network structure change of the ring network when an N-1 line fault occurs in the power grid provided by the embodiment of the present application. As Figure 3a shown, for the ring network, Figure 3a when a line fault occurs at the network position shown, the load loss is also related to the power output of the power source and the network topology before the fault. If network reconstruction is not carried out, when there is a risk of line fault, the sum of the power outputs of power sources P2 and P3 is much greater than the internal load of the region, while the power output of power source P1 is insufficient to support the current island load, resulting in a risk of load loss. When considering network reconstruction under N-1 line fault, the power outputs of power sources P1 and P3 are sufficient to cope with the large load in the power grid, and the source-load becomes more matched, reducing the load loss.

[0052] Figure 3b It shows another schematic diagram of the network structure change of the ring network when an N-1 line fault occurs in the power grid provided by the embodiment of the present application. As Figure 3b shown, when the line fault occurs at the ring network boundary, that is, at the position shown in Figure 3b , the network does not trip, and the load loss is indirectly caused by the overlong transfer path resulting in too low voltage at some nodes, or some lines being heavily loaded to reach the upper limit of the power flow.

[0053] In summary, the coupling relationship between the N-1 line fault and the network dynamic reconstruction can be expressed by the following formula:

[0054] X = f(P s_load , P G , P load );

[0055] In the above formula, X represents the power grid switch combination in each time period; P s_load represents the load loss of the system under the N-1 contingency situation in each time period; P G represents the power output of the power source in each time period; P load represents the load demand in each time period.

[0056] Therefore, only considering the connection switch combination method with the minimum economy will lead to an increase in the load loss. Based on this, the present application takes the line fault risk as a factor in the dynamic reconstruction of the urban power grid and constructs a day-ahead scheduling model.

[0057] In the embodiment of the present application, the day-ahead scheduling model includes an objective function, decision variables, and preset constraint conditions. Among them, the objective function is constructed with the minimum operation cost of the distribution network as the optimization goal. The operation cost of the distribution network includes but is not limited to: the operation cost of conventional units (such as fuel cost and start-stop cost), the cost of electrochemical energy storage (such as maintenance cost and depreciation cost), the output cost of new energy units, the cost of demand response resources, the network loss cost, the electric vehicle scheduling cost, the battery degradation loss cost, and the load shedding cost. The decision variables include the network topology structure and the generation plan of the distribution network. The constraint conditions include but are not limited to: power balance constraint, power flow constraint, operation constraint of conventional units, output constraint of distributed new energy, transmission power constraint of transmission lines, operation constraint of energy storage power stations, node voltage and current safety constraints, interactive power constraint, radial and connectivity constraints under network reconfiguration, switch constraint, electric vehicle constraint, and load-side demand response resource constraint.

[0058] In an example, the objective function can be expressed as:

[0059]

[0060] In the above formula, f1 represents the operation cost of the distribution network under day-ahead scheduling; f G,t represents the operation cost of conventional units (thermal power units), including fuel cost and start-stop cost; f erss,t represents the cost of electrochemical energy storage; f DG,t represents the output cost of new energy units; f load,t represents the cost of user demand response resources; f loss represents the network loss cost; f EV represents the electric vehicle scheduling cost, including price incentive cost; fs_load represents the load shedding cost.

[0061] Among them,

[0062] In the above formula, N S , p s are respectively the number of typical scenarios after reduction by the synchronous back substitution method and the scenario occurrence probability of each typical scenario; N g is the number of conventional units (such as thermal power units); P Gi,t,s is the power generation of the i-th conventional unit at time t in scenario s; a i , b i , c i are respectively the power generation cost coefficients of the i-th conventional unit; S i is the start-stop cost coefficient of the i-th conventional unit; u Gi,t,s is the start-stop state of the i-th conventional unit at time t in scenario s, 1 is the start state, and 0 is the stop state; N erssis the number of energy storage power stations; P erss,i,t,s is the power output of energy storage power station i at time t in scenario s; C(P erss,i,t,s ) is the cost function of the energy storage power station; W(P erss,i,t,s ) is the maintenance cost function of the energy storage power station; π bt represents the unit time depreciation cost coefficient of electrochemical energy storage; u bti,t,s represents the start-stop state of electrochemical energy storage station i at time t; N DG represents the number of distributed new energy units; P DGi,t,s represents the power output of the i-th new energy unit at time t in scenario s; C(P DGi,t,s ) represents the cost function of the new energy unit at time t in scenario s; u DGi,t,s is the start-stop state of the distributed unit; represents the predicted power output of the new energy unit at time t in scenario s; k IDRA 、k IDRB are the cost coefficients of type A and type B IDR respectively; Δ∣P IDRA,t ∣ is the call volume of type A IDR at time t; Δ∣P IDRB,t,s ∣ is the call volume of type B IDR at time t in scenario s; e t is the time-of-use electricity price; p EVi,t is the charging and discharging power of electric vehicle i in time period t; c is the battery loss coefficient; C change is the battery replacement cost.

[0063] In one example, in order to accurately determine various preset constraint conditions, it is necessary to analyze the characteristics of each adjustable resource on the load side to establish a mathematical model of the adjustable resource on the load side. For example: the characteristics of the electrochemical energy storage power station, electric vehicle, and demand response resource can be analyzed respectively to establish the mathematical models of the above-mentioned adjustable resources on the load side, and then the corresponding preset constraint conditions can be determined. Among them, those skilled in the art can select the characteristic analysis method according to the actual situation to determine the mathematical model of each adjustable resource on the load side, which will not be elaborated here.

[0064] In this way, a day-ahead scheduling model can be constructed with the network topology structure and generation plan as decision variables, the minimum operation cost of the distribution network as the objective function, and satisfying the preset constraint conditions.

[0065] In step S102, based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, the day-ahead scheduling model is solved to obtain the solution results of the decision variables.

[0066] In this step, during the solution process of the day-ahead scheduling model, since the network topology structure and the amount of load shedding have been coupled into the day-ahead scheduling model, an optimal network topology structure and generation plan can be obtained.

[0067] The following is a reference to Figure 4 to introduce the solution process of the day-ahead scheduling model.

[0068] Figure 4 The flowchart of the day-ahead scheduling model solution method provided by the embodiments of the present application is shown. As Figure 4 shown, the day-ahead scheduling model solution method includes:

[0069] Step S1021: Based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, construct a risk verification model with the load shedding amount as an index.

[0070] Here, the risk verification model can be expressed as follows:

[0071]

[0072] In the above formula, f s_load is the load shedding cost of the system under the N-1 contingency fault; k c,load is the load shedding cost coefficient; P loss,n,t,s is the load shedding amount of the island when a certain branch is disconnected in scenario s at time t; S N is the set of opened lines, that is, the set of lines with N-1 faults; T f represents the set of 24 hours of the day-ahead. is the cumulative value of the loads included in the island; is the cumulative value of the generator outputs included in the island.

[0073] Step S1022: Use the risk verification model with the load shedding amount as an index to perform iterative solution on the day-ahead scheduling model.

[0074] Specifically, when performing iterative solution on the day-ahead scheduling model, the method of mutual iteration between the day-ahead scheduling model and the risk verification model can be used for solution. The day-ahead scheduling model is a non-linear integer programming problem. After the operation cost of the conventional units is linearly segmented, it becomes a mixed integer linear programming problem, and the Cplex solver is called through the MATLAB software for solution.

[0075] For example: First, set a maximum value for the risk verification model (i.e., the load shedding cost), input this maximum value into the objective function of the day-ahead scheduling model, and solve the second-order cone mixed integer linear programming through the Cplex solver to obtain the first-round solution result. The first-round solution result includes the day-ahead generation plan, the network topology structure, and the operation cost of the distribution network.

[0076] Then, input the first-round solution result into the risk verification model to obtain the first-round load shedding cost. Input the first-round load shedding cost into the day-ahead scheduling model again to obtain the second-round solution result.

[0077] And so on. For each round of solution process, based on the solution result of the previous round and the objective function, the solution result of this round can be obtained. At this time, the solution result of the previous round can be substituted into the objective function, and the second-order cone mixed-integer linear programming is solved by the Cplex solver to obtain the solution result of this round. Then, the solution result of this round is input into the risk verification model to determine the operation cost of the distribution network in this round. For example: the solution result of this round is input into the risk verification model to obtain the load shedding cost in this round; the load shedding cost in this round is input into the day-ahead scheduling model to obtain the operation cost of the distribution network in this round.

[0078] After determining the operation cost of the distribution network in this round, it can be determined whether to use the solution result of this round as the final solution result according to the comparison result between the operation cost of the distribution network in this round and that in the previous round.

[0079] In one case, if the operation cost of the distribution network in this round is less than that in the previous round, it means that the solution in this round is an effective iterative process, and the solution result of this round is saved. At the same time, it is determined whether the solution result of this round meets the iteration stop condition; if it meets the iteration stop condition, the solution result of this round is used as the final solution result, and if it does not meet the iteration stop condition, the saved solution result of this round is used to continue the solution of subsequent rounds.

[0080] In another case, if the operation cost of the distribution network in this round is greater than or equal to that in the previous round, it means that the solution in this round is not an effective iterative process, and the solution result of this round is not saved.

[0081] In an example, when determining whether the solution result of this round meets the iteration stop condition, the difference between the operation cost of the distribution network in this round and that in the previous round can be compared with the set cost threshold to determine whether the iteration stop condition is met according to the comparison result. For example: if the difference is less than the set cost threshold, it is determined that the iteration stop condition is met; if the difference is greater than or equal to the set cost threshold, it is determined that the iteration stop condition is not met.

[0082] In an example, for photovoltaic and wind power, their output is affected by randomness and uncertainty, making the cost calculation extremely complex. Therefore, for the output of photovoltaic and wind power, when the load shedding cost in this round is input into the day-ahead scheduling model to obtain the operation cost of the distribution network in this round, the scenario analysis method can be used to handle the output uncertainty of these two.

[0083] For example: a preset sampling method is used to generate multiple initial scenarios, and multiple typical scenarios and the scenario occurrence probability of each typical scenario are determined from the multiple initial scenarios through a clustering algorithm. Then, the sum of the products of the operation cost of the distribution network in this round of each typical scenario and the corresponding scenario occurrence probability is determined as the final operation cost of the distribution network in this round.

[0084] Among them, the operation cost of the distribution network is the prediction cost of wind power and photovoltaic power for 24 hours in the day-ahead stage for multiple typical scenarios. The preset sampling method can be the Latin hypercube sampling method, and the clustering algorithm can be the synchronous back substitution method.

[0085] In step S103, the obtained solution result of the decision variables is used to optimize the dispatching of the distribution network.

[0086] In this step, the final solution result includes the power generation plan and network topology corresponding to 24 hours in the day-ahead. The distribution network can be optimized and dispatched according to the power generation plan and network topology corresponding to 24 hours in the day-ahead. When optimizing and dispatching the distribution network, it can not only consider the operation economic cost of the urban power grid, but also take into account the safety impact of the N-1 line fault on the dynamic network reconfiguration of the urban power grid, and reduce the load shedding amount after the line fails.

[0087] Based on the same inventive concept, an embodiment of the present application also provides a distribution network optimization dispatching device corresponding to the distribution network optimization dispatching method. Since the principle of solving problems by the device in the embodiment of the present application is similar to the above-mentioned distribution network optimization dispatching method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0088] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a distribution network optimization dispatching device provided by an embodiment of the present application. As shown in Figure 5 , the distribution network optimization dispatching device 200 includes:

[0089] A model construction module 201, configured to construct a day-ahead dispatching model with the minimum operation cost of the distribution network as the objective function. The day-ahead dispatching model includes decision variables;

[0090] A model solution module 202, configured to solve the day-ahead dispatching model based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, and obtain the solution result of the decision variables;

[0091] An optimization dispatching module 203, configured to use the obtained solution result of the decision variables to optimize the dispatching of the distribution network.

[0092] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 6 , the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0093] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 operates, the processor 310 communicates with the memory 320 via a bus 330. When the machine-readable instructions are executed by the processor 310, the steps of the distribution network optimal scheduling method in the method embodiments as described above can be executed. For the specific implementation manner, reference can be made to the method embodiments and will not be elaborated herein. Figure 1 shown in the method embodiments, and for the specific implementation manner, reference can be made to the method embodiments and will not be elaborated herein.

[0094] The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the distribution network optimal scheduling method in the method embodiments as described above can be executed. For the specific implementation manner, reference can be made to the method embodiments and will not be elaborated herein. Figure 1 shown in the method embodiments, and for the specific implementation manner, reference can be made to the method embodiments and will not be elaborated herein.

[0095] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0096] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0097] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0099] When the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0100] Finally, it should be noted that: the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solutions of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed in this application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing the dispatching of a distribution network, characterized in that, including: Construct a day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function, where the day-ahead scheduling model includes decision variables; Based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, solve the day-ahead scheduling model to obtain the solution results of the decision variables; Use the solution results of the decision variables to optimize the scheduling of the distribution network.

2. The method according to claim 1, wherein The construction of the day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function includes: Taking the minimum operating cost of the distribution network as the objective function, and using the network topology structure and generation plan as decision variables, construct a day-ahead scheduling model that meets the preset constraint conditions, where the operating cost of the distribution network includes load shedding cost.

3. The method according to claim 1, wherein The solution of the day-ahead scheduling model based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network includes: Based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network, construct a risk verification model with the load shedding amount as an index; Use the risk verification model with the load shedding amount as an index to iteratively solve the day-ahead scheduling model.

4. The method according to claim 3, wherein The iterative solution of the day-ahead scheduling model includes: Based on the previous round of solution results and the objective function, obtain the solution results of this round; Input the solution results of this round into the risk verification model to determine the operating cost of the distribution network in this round; According to the comparison result between the operating cost of the distribution network in this round and the operating cost of the distribution network in the previous round, determine whether to use the solution results of this round as the final solution results.

5. The method according to claim 4, wherein The obtaining of the solution results of this round based on the previous round of solution results and the objective function includes: Substitute the previous round of solution results into the objective function, and solve the second-order cone mixed-integer linear programming through a solver to obtain the solution results of this round.

6. The method according to claim 4, characterized in that, The inputting of the solution results of this round into the risk verification model to determine the operating cost of the distribution network in this round includes: Input the solution results of this round into the risk verification model to determine the load shedding cost in this round; Input the load shedding cost in this round into the day-ahead scheduling model to obtain the operating cost of the distribution network in this round.

7. The method according to claim 4, characterized in that, The determination of whether to use the solution results of this round as the final solution results includes: If the operating cost of the distribution network in this round is less than the operating cost of the distribution network in the previous round, determine whether the solution results of this round meet the iteration stop condition; If the iteration stop condition is met, use the solution results of this round as the final solution results.

8. The method according to claim 7, wherein The determination of whether the solution results of this round meet the iteration stop condition includes: Compare the difference between the operating cost of the distribution network in this round and the operating cost of the distribution network in the previous round with a set cost threshold to determine whether the iteration stop condition is met according to the comparison result.

9. An optimized dispatching device for a distribution network, characterized in that, including: A model construction module for constructing a day-ahead scheduling model with the minimum operating cost of the distribution network as the objective function, where the day-ahead scheduling model includes decision variables; A model solution module for solving the day-ahead scheduling model based on the influence mechanism of the N-1 line fault risk on the dynamic network reconfiguration of the distribution network to obtain the solution results of the decision variables; An optimized scheduling module for optimizing the scheduling of the distribution network using the solution results of the decision variables.

10. An electronic device, characterized in that, including: A processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device operates, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the distribution network optimal scheduling method according to any one of claims 1 to 8.

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