Power distribution network unit recovery method and device, storage medium and electronic equipment

Abstract

The invention discloses a power distribution network unit recovery method and device, a storage medium and electronic equipment. The method relates to the field of power distribution networks, and comprises the steps that under the condition of power failure of the power distribution network, selection information of each target user in a plurality of target users for a first station is acquired, the target users are users having electric vehicles, and the first station is a station supporting bidirectional energy exchange between the electric vehicles and the power distribution network; a first mathematical planning model is obtained, the first mathematical planning model is solved based on selection information of each target user for the first site, the starting time point of each unit to be recovered in the power distribution network is obtained, and the first mathematical planning model takes the minimum power distribution network unit recovery time as a target function; and performing unit recovery based on the starting time point of each to-be-recovered unit. According to the invention, the technical problem of low recovery efficiency of the power distribution network unit under the condition of power failure of the power distribution network in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of power distribution networks, and in particular to a method, device, storage medium and electronic equipment for restoring power distribution network units. Background Art

[0002] In recent years, major power outages caused by extreme weather, cyber attacks and other events have occurred frequently. After a major power outage, the power system needs to spend a lot of time restarting the units and grid, which will lead to a long-term outage of important local loads, causing serious economic losses and social impacts.

[0003] In the traditional top-down recovery of the power system, the transmission network restores large non-black start units through black start units to form a stable electrified island, and then gradually restores the power transmission path of the transmission and distribution network, establishes a strong grid structure, and finally restores all loads of the distribution network. The traditional top-down recovery method leads to a long recovery time for the distribution network, which aggravates the adverse effects of load power outages. With the rapid development of electric vehicle technology and the growing number of electric vehicles, it is gradually feasible to use electric vehicles to interact with the power grid. In the early stage of the black start of the distribution network, electric vehicles can supply power to the distribution network through V2G technology (vehicle-to-grid), which has great potential in post-disaster emergency recovery. At present, the relevant technology still restores each unit in the distribution network according to a fixed starting method when the distribution network is out of power, but does not consider the relationship between electric vehicles and the power grid, resulting in the problem of low efficiency of distribution network recovery.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, storage medium and electronic device for restoring a distribution network unit, so as to at least solve the technical problem of low efficiency in restoring a distribution network unit in the case of a power outage in the distribution network in the related art.

[0006] According to one aspect of an embodiment of the present invention, a method for restoring units in a distribution network is provided, comprising: in the event of a power outage in the distribution network, obtaining selection information of each target user among multiple target users for a first site, wherein the target user is a user who owns an electric vehicle, the first site is a site that supports two-way energy exchange between the electric vehicle and the distribution network, and the first site is used to restore the unit based on the power provided by the electric vehicle; obtaining a first mathematical programming model, and solving the first mathematical programming model based on the selection information of each target user for the first site to obtain a start-up time point of each unit to be restored in the distribution network, wherein the first mathematical programming model takes minimizing the recovery time of the distribution network units as an objective function; and restoring the units based on the start-up time points of each unit to be restored.

[0007] Furthermore, the distribution network unit recovery method also includes: obtaining a second mathematical programming model, and solving the second mathematical programming model to obtain the first site to be recommended corresponding to each target user, wherein the second mathematical programming model takes maximizing the attractiveness of the first site to the target user as an objective function; for each target user, generating target recommendation information corresponding to the target user based on the first site to be recommended corresponding to the target user, wherein the target recommendation information is used to recommend the first site to be recommended to the target user; receiving selection information fed back by the target user based on the target recommendation information to obtain selection information of each target user for the first site.

[0008] Furthermore, the distribution network unit recovery method also includes: determining a first constraint condition based on the number of first sites that the electric vehicle can choose to go to; determining a second constraint condition based on the number of electric vehicles supported and accommodated by the first site; determining the attractiveness of the first site based on the distance between the electric vehicle and the first site, the incentive amount of the first site, and the maximum output power of the first site, wherein the incentive amount is used to be distributed to target users who provide electricity to the first site; determining an objective function of a second mathematical programming model to be generated based on the attractiveness of the first site and the recommended relationship between the first site and the target user; and determining the second mathematical programming model based on the first constraint condition, the second constraint condition, and the objective function corresponding to the second mathematical programming model.

[0009] Furthermore, the distribution network unit recovery method also includes: for each target user, sending a geographic location acquisition request to the target user; obtaining the location information of the target user based on the geographic location acquisition request feedback; calculating the distance between the target user's electric vehicle and each first site based on the target user's location information; substituting the distance corresponding to each target user into the second mathematical programming model, solving the second mathematical programming model after the substitution processing, and obtaining the first site to be recommended corresponding to each target user.

[0010] Furthermore, the distribution network unit recovery method also includes: determining the upper and lower limits of active output constraints of the first site based on the selection information of each target user for the first site, the output power of the first site, and the output power of the electric vehicle; determining the time range constraints corresponding to each startup stage of the unit to be restored based on the time required for the startup process of the unit to be restored, the startup time point of the unit to be restored, and the climbing rate of the unit to be restored; determining the objective function of the first mathematical programming model to be generated based on the maximum output power of the unit to be restored, the startup power of the unit to be restored, and the startup time point of the unit to be restored; and determining the first mathematical programming model according to the upper and lower limits of active output constraints, the time range constraints, and the objective function corresponding to the first mathematical programming model.

[0011] Furthermore, the distribution network unit recovery method also includes: determining the energy constraint conditions of the first site based on the selection information of each target user for the first site, the energy of the electric vehicle, and the output power of the electric vehicle; determining the first mathematical programming model based on the energy constraint conditions of the first site, the upper and lower limit constraints of active output, the time range constraints, and the objective function corresponding to the first mathematical programming model.

[0012] Furthermore, the distribution network unit recovery method also includes: determining the power consumption constraint of the electric vehicle moving from the initial position to the first site based on the selection information of each target user for the first site, the distance between the electric vehicle and the first site selected by the target user, and the initial power of the electric vehicle; determining the first mathematical programming model based on the power consumption constraint, the energy constraint of the first site, the upper and lower limit constraints of the active output, the time range constraint and the objective function corresponding to the first mathematical programming model.

[0013] According to another aspect of an embodiment of the present invention, a distribution network unit recovery device is also provided, including: a first acquisition module, used to obtain selection information of each target user among multiple target users for a first site in the event of a power outage in the distribution network, wherein the target user is a user who owns an electric vehicle, and the first site is a site that supports two-way energy exchange between the electric vehicle and the distribution network, and the first site is used to recover the unit based on the power provided by the electric vehicle; a second acquisition module, used to obtain a first mathematical programming model, and solve the first mathematical programming model based on the selection information of each target user for the first site to obtain the start-up time point of each unit to be recovered in the distribution network, wherein the first mathematical programming model takes minimizing the recovery time of the distribution network unit as an objective function; a processing module, used to recover the unit based on the start-up time point of each unit to be recovered.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned distribution network unit recovery method when running.

[0015] According to another aspect of an embodiment of the present invention, an electronic device is also provided, the electronic device comprising one or more processors; a memory for storing one or more programs, which, when the one or more programs are executed by the one or more processors, enables the one or more processors to run the programs, wherein the programs are configured to execute the above-mentioned distribution network unit recovery method when running.

[0016] In an embodiment of the present invention, a method of determining the start time point of the unit to be restored in the distribution network according to the selection information of the electric vehicle user for the V2G station is adopted. When the distribution network is out of power, the selection information of each target user among multiple target users for the first site is obtained, and then the first mathematical programming model is obtained. The first mathematical programming model is solved based on the selection information of each target user for the first site, and the start time point of each unit to be restored in the distribution network is obtained, so that the unit is restored based on the start time point of each unit to be restored. Among them, the target user is a user with an electric vehicle, the first site is a site that supports two-way energy exchange between the electric vehicle and the distribution network, the first site is used to restore the unit based on the power provided by the electric vehicle, and the first mathematical programming model takes minimizing the recovery time of the distribution network unit as the objective function.

[0017] In the above process, by obtaining the selection information of each target user among multiple target users for the first site, the V2G station selected by multiple electric vehicle users to go to is determined, that is, the power supply relationship between the electric vehicle users and the V2G station is determined. By setting the first mathematical programming model to minimize the recovery time of the distribution network units as the objective function, and solving the first mathematical programming model based on the selection information of each target user for the first site, the start time point of each unit to be restored is obtained, and the goal of reducing the recovery time of the unit is achieved. According to the power supply relationship between the electric vehicle users and the V2G station, the start method of multiple units is determined, thereby effectively improving the recovery efficiency of the distribution network units.

[0018] It can be seen that the solution provided in the present application achieves the purpose of determining the start-up time point of the unit to be restored in the distribution network based on the selection information of the V2G station by the electric vehicle user, thereby achieving the technical effect of improving the recovery efficiency of the distribution network unit, and further solving the technical problem of low recovery efficiency of the distribution network unit in the related technology when the distribution network is out of power. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a schematic diagram of an optional method for restoring a distribution network unit according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of an optional distribution network unit recovery device according to an embodiment of the present invention;

[0022] Figure 3is a schematic diagram of an optional electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards in the relevant regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0026] Example 1

[0027] According to an embodiment of the present invention, an embodiment of a method for restoring a distribution network unit is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 is a schematic diagram of an optional method for restoring a distribution network unit according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0029] Step S101, in the event of a power outage in the distribution network, obtain selection information of each target user among multiple target users for the first site, wherein the target user is a user who owns an electric car, the first site is a site that supports two-way energy exchange between the electric car and the distribution network, and the first site is used to restore the unit based on the power provided by the electric car.

[0030] Optionally, electronic devices, application systems, servers and other devices may be used as the execution subject of the present application. In this embodiment, the target processing system is used as the execution subject to execute the above-mentioned distribution network unit recovery method.

[0031] Optionally, in the event of a power outage in the distribution network, the units in the distribution network need to be restored. In this embodiment, in the early stage of the black start of the distribution network, electric vehicles are used to supply power to the distribution network through V2G technology (vehicle-to-grid) to assist in restoring the units in the distribution network.

[0032] Therefore, in this embodiment, when the power distribution network is out of power, selection information of each target user for the first site is obtained among multiple target users. The target user is a user with an electric car, and the first site is a V2G site. The selection information includes information for indicating whether the target user chooses to go to the first site, and the selection information also includes information of the first site selected by the target user.

[0033] Optionally, the distribution network includes multiple V2G stations (i.e., the aforementioned first station). Before obtaining the selection information of each target user, the target processing system can recommend V2G stations to the target user, for example, recommending the first station to the target user based on the target user's geographic location information, or recommending all V2G stations in the distribution station to the target user. The target processing system is a system in the organization to which the distribution network belongs. The organization to which the distribution network belongs can sign an authorization agreement with the target user in advance to obtain the target user's authorization for the target processing system to recommend stations to it, obtain geographic location information, and other behaviors.

[0034] Step S102, obtaining a first mathematical programming model, and solving the first mathematical programming model based on the selection information of each target user for the first site, to obtain the start-up time point of each unit to be restored in the distribution network, wherein the first mathematical programming model takes minimizing the recovery time of the distribution network units as an objective function.

[0035] Optionally, the first mathematical programming model includes an objective function and multiple constraints. The first mathematical programming model may include constraints such as unit ramping, network recovery status, and electric vehicle discharge, and the first mathematical programming model takes minimizing the recovery time of the distribution network units as an objective function, and the start-up time point of each unit to be restored is used as a parameter in the objective function of the first mathematical programming model.

[0036] Optionally, there is a target constraint condition among the multiple constraints in the first mathematical programming model, and the target constraint condition refers to a constraint condition that uses selection information of each target user for the first site as a parameter.

[0037] Optionally, after obtaining the selection information of each target user for the first site, the target processing system can obtain the first mathematical programming model and solve the first mathematical programming model based on the selection information of each target user for the first site, so as to obtain the start-up time point of each unit to be restored in the distribution network.

[0038] In some embodiments, the units to be restored include at least non-black start units and may also include black start units.

[0039] Step S103: Restore the units based on the start-up time points of the units to be restored.

[0040] Optionally, after determining the start time point of each unit to be restored, for each unit to be restored, the target processing system can supply power to the unit to be restored when the current time is at the start time point corresponding to the unit to be restored, so as to realize the recovery of the unit, thereby realizing the recovery of each unit in this way.

[0041] Optionally, the power supply source for the unit to be restored may include the power provided by the electric vehicle collected by the first site, the power provided by the black start unit, and the power provided by other power supply devices.

[0042] Based on the scheme defined in the above steps S101 to S103, it can be known that in an embodiment of the present invention, the start time point of the unit to be restored in the distribution network is determined according to the selection information of the electric vehicle user for the V2G station. In the case of a power outage in the distribution network, the selection information of each target user among multiple target users for the first site is obtained, and then the first mathematical programming model is obtained. The first mathematical programming model is solved based on the selection information of each target user for the first site to obtain the start time point of each unit to be restored in the distribution network, so as to restore the unit based on the start time point of each unit to be restored. Among them, the target user is a user who owns an electric vehicle, the first site is a site that supports two-way energy exchange between the electric vehicle and the distribution network, the first site is used to restore the unit based on the power provided by the electric vehicle, and the first mathematical programming model takes minimizing the recovery time of the distribution network unit as the objective function.

[0043] In the above process, by obtaining the selection information of each target user among multiple target users for the first site, the V2G station selected by multiple electric vehicle users to go to is determined, that is, the power supply relationship between the electric vehicle users and the V2G station is determined. By setting the first mathematical programming model to minimize the recovery time of the distribution network units as the objective function, and solving the first mathematical programming model based on the selection information of each target user for the first site, the start time point of each unit to be restored is obtained, and the goal of reducing the recovery time of the unit is achieved. According to the power supply relationship between the electric vehicle users and the V2G station, the start method of multiple units is determined, thereby effectively improving the recovery efficiency of the distribution network units.

[0044] It can be seen that the solution provided in the present application achieves the purpose of determining the start-up time point of the unit to be restored in the distribution network based on the selection information of the V2G station by the electric vehicle user, thereby achieving the technical effect of improving the recovery efficiency of the distribution network unit, and further solving the technical problem of low recovery efficiency of the distribution network unit in the related technology when the distribution network is out of power.

[0045] In an optional embodiment, in the process of obtaining the selection information of each target user among multiple target users for the first site, the target processing system can obtain the second mathematical programming model, and solve the second mathematical programming model to obtain the first site to be recommended corresponding to each target user, and then for each target user, generate the target recommendation information corresponding to the target user based on the first site to be recommended corresponding to the target user, thereby receiving the selection information fed back by the target user based on the target recommendation information, so as to obtain the selection information of each target user for the first site. The second mathematical programming model takes maximizing the attractiveness of the first site to the target user as the objective function, and the target recommendation information is used to recommend the first site to be recommended to the target user.

[0046] In this embodiment, the V2G station that the target user may select may be predicted first, and then the V2G station may be recommended to the target user, so as to increase the selection rate of the first station by the target user.

[0047] Optionally, the target processing system may obtain a second mathematical programming model, and obtain the first site to be recommended corresponding to each target user by solving the second mathematical programming model. The second mathematical programming model takes maximizing the attractiveness of the first site to the target user as an objective function, and the recommendation relationship between the first site and the target user is used as a parameter in the objective function of the second mathematical programming model, wherein the recommendation relationship between the first site and the target user represents the first site to be recommended corresponding to each target user.

[0048] Optionally, the second mathematical programming model further includes a plurality of constraints, and the constraints in the second mathematical programming model may be associated with the scale and power of the V2G station, the incentive amount, and the distance between the target user's electric vehicle and the V2G. The incentive amount is used to be issued to the target user who provides electricity to the first station.

[0049] Optionally, after obtaining the first site to be recommended corresponding to each target user according to the second mathematical programming model, for each target user, the target processing system may generate target recommendation information corresponding to the target user based on the first site to be recommended corresponding to the target user, and send the target recommendation information to the terminal device held by the target user, and the terminal device held by the target user includes but is not limited to the target user's mobile phone, smart watch, smart terminal on the electric car, etc. The target recommendation information may be "Are you willing to go to V2G station No. 1 to supply power to the distribution network?" The target user may make a selection based on the target recommendation information, and the terminal device held by the target user generates selection information in response to the target user's selection, and then feeds the selection information back to the target processing system.

[0050] Optionally, the target processing system may receive selection information of the target user based on the target recommendation information feedback, thereby obtaining selection information of the target user for the first site.

[0051] It should be noted that, through the above process, it is possible to predict the first site that the target user may choose to go to, and then recommend the predicted first site to the target user, thereby improving the recommendation accuracy and further improving the user selection efficiency and the selection rate of the first site.

[0052] In an optional embodiment, the target processing system can determine the second mathematical programming model in the following manner: determine the first constraint condition according to the number of first stations that the electric vehicle can choose to go to; determine the second constraint condition according to the number of electric vehicles that the first station can accommodate; determine the attractiveness of the first station according to the distance between the electric vehicle and the first station, the incentive amount of the first station, and the maximum output power of the first station, wherein the incentive amount is used to be distributed to the target user who provides electricity to the first station; determine the objective function of the second mathematical programming model to be generated according to the attractiveness of the first station and the recommendation relationship between the first station and the target user; determine the second mathematical programming model according to the first constraint condition, the second constraint condition and the objective function corresponding to the second mathematical programming model.

[0053] Optionally, after a distribution network failure occurs, the dispatcher usually issues an invitation to electric vehicle users based on the fault condition of the distribution network and the information of the units to be started in the system, and sets the fault recovery incentive price for each V2G station in real time. Therefore, how electric vehicle users choose V2G stations is affected by factors such as driving time, V2G station scale and power, and incentive price. Among them, regarding driving time, after an extreme power outage occurs in the distribution network, electric vehicles that are closer can quickly reach the V2G station and speed up the restart of the power-off units. At the same time, choosing a V2G station with a shorter driving time can reduce the power loss on the road accordingly; regarding the scale and power of the V2G station, electric vehicle users need to choose a V2G station with spare charging piles. At the same time, choosing a V2G station with higher power can transmit more power in the same time, so that users can get more benefits; regarding the incentive price, the dispatcher sets a higher incentive price for the initial recovery to encourage users to participate in the unit recovery as soon as possible after the failure. At the same time, the dispatcher sets a higher incentive price for the V2G stations near the units with high importance to guide users to restore important units first.

[0054] Optionally, the attractiveness of each V2G station to electric vehicle users is quantified, the factors that affect the choice of electric vehicle users are comprehensively considered, a second mathematical programming model is constructed, and the second mathematical programming model is solved to obtain a strategy for recommending V2G stations to users. Optionally, the distribution network guides users to discharge at V2G stations by paying incentives to users. The objective function of the second mathematical programming model can be to maximize the sum of the attractiveness of electric vehicle users to choose V2G stations. The objective function is shown in formula (1):

[0055]

[0056] Among them, Ω EV ,Ω ST A is the collection of all electric vehicles and V2G stations. ij is the attractiveness of V2G station j to electric vehicle i, x i ' j It is a 0-1 integer variable, indicating whether the predicted electric vehicle i chooses V2G station j. If the value is 1, the electric vehicle will go to V2G station j, otherwise it will not go to V2G station j.

[0057] Optionally, the first constraint condition is as shown in formula (2):

[0058]

[0059] Among them, formula (2) means that electric vehicle i can only choose to go to one V2G station.

[0060] Optionally, the second constraint is as shown in formula (3):

[0061]

[0062] Among them, N j is the number of charging piles at V2G station j, indicating the maximum number of electric vehicles that V2G station j can accommodate. Formula (3) indicates that V2G station j can accommodate a maximum of N j Electric car.

[0063] Optionally, the calculation formula of attraction is shown in formula (4):

[0064]

[0065] Among them, A ij is the attractiveness of V2G station j to electric vehicle i, a i is the influencing factor coefficient, which is obtained based on the data survey of user preferences and is a constant. is the maximum output power of V2G station j, indicating the scale and power of V2G station j, d ij is the distance between electric vehicle i and V2G station j at the time of fault occurrence, w ij,t is the incentive amount of V2G station j to electric vehicle i at time t, which is pre-set by the dispatcher, Ω T is the set of all recovery periods. Formula (4) includes the three influencing factors of the distance between the electric vehicle and the V2G station, the scale and power of the V2G station, and the incentive amount. The attractiveness is positively correlated with the scale and power of the V2G station and the incentive amount, and negatively correlated with the distance.

[0066] Optionally, the above formulas (1)-(4) together constitute a second mathematical programming model.

[0067] It should be noted that, through the above process, the second mathematical programming model is determined based on the key factors that affect the electric vehicle users' choice of charging and discharging stations, which can effectively characterize the destination selection behavior of electric vehicle users and thus improve the prediction accuracy.

[0068] In an optional embodiment, in the process of solving the second mathematical programming model to obtain the first site to be recommended corresponding to each target user, the target processing system can send a geographic location acquisition request to the target user for each target user, and then obtain the location information of the target user based on the geographic location acquisition request feedback, and then calculate the distance between the target user's electric vehicle and each first site based on the target user's location information, thereby substituting the distance corresponding to each target user into the second mathematical programming model, solving the second mathematical programming model after substitution, and obtaining the first site to be recommended corresponding to each target user.

[0069] Since the distance between the target user's electric vehicle and each first site is used as a parameter in the constraint condition of the second mathematical programming model, after obtaining the second mathematical programming model, the target processing system can send a geographic location acquisition request to each target user. For example, a geographic location acquisition request is sent to a terminal device held by the target user. When the target user receives the geographic location acquisition request, the target user can feedback the target user's current location information to the target processing system. Among them, the location information of the target user is determined as the location information of the target user's electric vehicle.

[0070] Optionally, the target processing system may obtain the location information of the target user based on the geographic location acquisition request feedback, and then calculate the distance between the target user's electric vehicle and each first site based on the target user's location information. For example, the location information of each first site is obtained from the database, and then the distance between the target user's electric vehicle and each first site is calculated based on the target user's location information and the location information of each first site.

[0071] When the distance between each target user and each first site is obtained, the target processing system may substitute the distance corresponding to each target user into the second mathematical programming model, for example, substitute the distance corresponding to each target user into the above formula (4). Then, the second mathematical programming model after substitution is solved to obtain the first site to be recommended corresponding to each target user.

[0072] It should be noted that, through the above process, the parameter values ​​required in the second mathematical programming model are accurately determined, thereby effectively improving the accuracy of the first sites to be recommended corresponding to each target user obtained by solving the problem.

[0073] In an optional embodiment, the target processing system can obtain the first mathematical programming model in the following manner: determine the upper and lower limits of active output constraints of the first site based on the selection information of each target user for the first site, the output power of the first site, and the output power of the electric vehicle; determine the time range constraints corresponding to each startup stage of the unit to be restored based on the time required for the startup process of the unit to be restored, the startup time point of the unit to be restored, and the climbing rate of the unit to be restored; determine the objective function of the first mathematical programming model to be generated based on the maximum output power of the unit to be restored, the startup power of the unit to be restored, and the startup time point of the unit to be restored; determine the first mathematical programming model according to the upper and lower limits of active output constraints, the time range constraints, and the objective function corresponding to the first mathematical programming model.

[0074] During the unit recovery period, the main reason that restricts the unit recovery speed is that there are many non-black start units in the initial recovery period, the starting power demand is large, and the black start resources are scarce, and it is necessary to go through a slow ramp-up process to increase the power generation capacity. At this time, a large number of electric vehicles distributed throughout the city can be used to connect to the power grid through charging piles to discharge, assist the distribution network to achieve unit recovery, so as to alleviate the problem of scarce black start resources in the initial recovery period and speed up the recovery of the distribution network.

[0075] Optionally, in order to speed up the unit recovery process, the objective function of the unit recovery problem is to minimize the unit recovery time, that is, the objective function corresponding to the first mathematical programming model can be shown as formula (5):

[0076]

[0077] Among them, Ω G It represents the set of all nodes connected to the unit in the distribution network. In the power system discipline, the power grid is usually divided into multiple connected nodes. It can be roughly understood that substations and electric poles can be used as nodes. The unit recovery problem requires knowing which power generation resources are available at each node in the power grid. Determining which V2G site the electric vehicle selects, that is, which node in the power grid is selected, is equivalent to determining which power generation resources are contained in each node, so that the unit recovery problem can be further solved; P g max Indicates the maximum output power of the unit g to be restored; P g crk Indicates the starting power of the unit g to be restored; t g st Indicates the start time point of unit g.

[0078] Optionally, the first mathematical programming model may include constraints as shown in formulas (6)-(21):

[0079]

[0080]

[0081] Among them, formula (6) is the unit startup time constraint condition, formula (7)-(18) are the time range constraints corresponding to each startup stage of the unit to be restored; formula (19)-(20) are the recovery conditions of non-black start units; formula (21) is the upper and lower limit constraints of the active output of V2G station j. Ω NBSG ,Ω BSG ,Ω bus ,Ω line ,Ω T ,Ω EV ,Ω STis the set of all non-black start units, black start units, nodes, lines, recovery periods, electric vehicles, and V2G stations. Lines refer to the lines connecting nodes. and They represent the earliest and latest unit start-up time points, corresponding to the minimum critical time for cold start and the maximum critical time for hot start. are integer variables, indicating whether the unit g is in the climbing, maximum output and power absorption stages, respectively. is the auxiliary variable introduced, and formulas (15)-(16) define it, P g t represents the active power generated by unit i at time t, δ(i) represents the set of lines connected to node i, is the output power of V2G station j at time t, P i EV is the output power of electric vehicle i, is the energy of V2G station j at time t, T g c Indicates the time required for the startup process of unit g, R g It indicates the climbing rate of unit g, and Δt is the preset time interval.

[0082] Optionally, the target processing system can construct a first mathematical programming model according to the above formulas (5)-(21).

[0083] It should be noted that, through the above process, the first mathematical programming model is effectively constructed, thereby improving the accuracy of the start-up time point of the unit obtained by solving the first mathematical programming model, thereby effectively improving the recovery efficiency of the distribution network units.

[0084] In an optional embodiment, in the process of determining the first mathematical programming model based on the upper and lower limit constraints of active output, the time range constraints, and the objective function corresponding to the first mathematical programming model, the target processing system can determine the energy constraints of the first site based on the selection information of each target user for the first site, the energy of the electric vehicle, and the output power of the electric vehicle, thereby determining the first mathematical programming model based on the energy constraints of the first site, the upper and lower limit constraints of active output, the time range constraints, and the objective function corresponding to the first mathematical programming model.

[0085] Optionally, the first mathematical programming model may further include constraints as shown in formulas (22)-(23):

[0086]

[0087] Among them, formulas (22)-(23) are the energy constraints of V2G stations. is the energy of electric vehicle i at time t, represents the energy of V2G station j at time t-1, represents the output power of V2G station j at time t-1.

[0088] Optionally, the target processing system can construct a first mathematical programming model according to the above formulas (5)-(23).

[0089] It should be noted that by adding the energy constraint condition of the first site to the first mathematical programming model, the accuracy of the start-up time point of the unit obtained by solving the first mathematical programming model can be further improved, thereby effectively improving the recovery efficiency of the distribution network units.

[0090] In an optional embodiment, in the process of determining the first mathematical programming model based on the energy constraints of the first site, the upper and lower limits of active output constraints, the time range constraints, and the objective function corresponding to the first mathematical programming model, the target processing system can determine the power consumption constraints of the electric vehicle moving from the initial position to the first site based on the selection information of each target user for the first site, the distance between the electric vehicle and the first site selected by the target user, and the initial power of the electric vehicle, thereby determining the first mathematical programming model based on the power consumption constraints, the energy constraints of the first site, the upper and lower limits of active output constraints, the time range constraints, and the objective function corresponding to the first mathematical programming model.

[0091] Optionally, the first mathematical programming model may further include constraints as shown in formula (24):

[0092]

[0093] Wherein, formula (24) is the power consumption constraint condition of the electric vehicle moving from the initial position to the first station. The initial position of the electric vehicle can be understood as the position of the electric vehicle when solving the first mathematical programming model. is the initial charge of electric vehicle i, d ij is the distance between electric vehicle i and V2G station j, and ε is the power consumed by the electric vehicle per unit distance.

[0094] Optionally, the target processing system can construct a first mathematical programming model according to the above formulas (5)-(24).

[0095] It should be noted that by adding the power consumption constraint condition to the first mathematical programming model, the accuracy of the start-up time point of the unit obtained by solving the first mathematical programming model can be further improved, thereby effectively improving the recovery efficiency of the distribution network units.

[0096] It can be seen that the solution provided in the present application achieves the purpose of determining the start-up time point of the unit to be restored in the distribution network based on the selection information of the V2G station by the electric vehicle user, thereby achieving the technical effect of improving the recovery efficiency of the distribution network unit, and further solving the technical problem of low recovery efficiency of the distribution network unit in the related technology when the distribution network is out of power.

[0097] Example 2

[0098] According to an embodiment of the present invention, an embodiment of a device for restoring a generator set in a power distribution network is provided, wherein: Figure 2 is a schematic diagram of an optional distribution network unit recovery device according to an embodiment of the present invention, such as Figure 2 As shown, the device comprises:

[0099] The first acquisition module 201 is used to obtain selection information of each target user among multiple target users for the first site in the case of a power outage in the distribution network, wherein the target user is a user with an electric vehicle, the first site is a site that supports two-way energy exchange between the electric vehicle and the distribution network, and the first site is used to restore the unit based on the power provided by the electric vehicle;

[0100] The second acquisition module 202 is used to acquire the first mathematical programming model, and solve the first mathematical programming model based on the selection information of each target user for the first site to obtain the start-up time point of each unit to be restored in the distribution network, wherein the first mathematical programming model takes minimizing the restoration time of the distribution network units as an objective function;

[0101] The processing module 203 is used to restore the units based on the start-up time point of each unit to be restored.

[0102] It should be noted that the above-mentioned first acquisition module 201, second acquisition module 202 and processing module 203 correspond to steps S101 to S103 in the above-mentioned embodiment, and the examples and application scenarios implemented by the three modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0103] Optionally, the first acquisition module also includes: an acquisition submodule, used to acquire a second mathematical programming model, and solve the second mathematical programming model to obtain the first site to be recommended corresponding to each target user, wherein the second mathematical programming model takes maximizing the attractiveness of the first site to the target user as an objective function; a generation submodule, used to generate, for each target user, target recommendation information corresponding to the target user based on the first site to be recommended corresponding to the target user, wherein the target recommendation information is used to recommend the first site to be recommended to the target user; a receiving submodule, used to receive selection information fed back by the target user based on the target recommendation information, so as to obtain selection information of each target user for the first site.

[0104] Optionally, the distribution network unit recovery device also includes: a first determination module, used to determine the first constraint condition according to the number of first sites that the electric vehicle can choose to go to; a second determination module, used to determine the second constraint condition according to the number of electric vehicles supported and accommodated by the first site; a third determination module, used to determine the attractiveness of the first site according to the distance between the electric vehicle and the first site, the incentive amount of the first site, and the maximum output power of the first site, wherein the incentive amount is used to be distributed to target users who provide electricity to the first site; a fourth determination module, used to determine the objective function of the second mathematical programming model to be generated according to the attractiveness of the first site and the recommended relationship between the first site and the target user; a fifth determination module, used to determine the second mathematical programming model according to the first constraint condition, the second constraint condition and the objective function corresponding to the second mathematical programming model.

[0105] Optionally, the acquisition submodule also includes: a sending unit, used to send a geographic location acquisition request to each target user; an acquisition unit, used to obtain location information of the target user based on the geographic location acquisition request feedback; a calculation unit, used to calculate the distance between the target user's electric vehicle and each first site based on the target user's location information; and a processing unit, used to substitute the distance corresponding to each target user into the second mathematical programming model, and solve the second mathematical programming model after substitution to obtain the first site to be recommended corresponding to each target user.

[0106] Optionally, the distribution network unit recovery device also includes: a sixth determination module, which is used to determine the upper and lower limits of active output constraints of the first site based on the selection information of each target user for the first site, the output power of the first site, and the output power of the electric vehicle; a seventh determination module, which is used to determine the time range constraints corresponding to each startup stage of the unit to be restored based on the time required for the startup process of the unit to be restored, the startup time point of the unit to be restored, and the climbing rate of the unit to be restored; an eighth determination module, which is used to determine the objective function of the first mathematical programming model to be generated based on the maximum output power of the unit to be restored, the startup power of the unit to be restored, and the startup time point of the unit to be restored; a ninth determination module, which is used to determine the first mathematical programming model according to the upper and lower limits of active output constraints, the time range constraints, and the objective function corresponding to the first mathematical programming model.

[0107] Optionally, the distribution network unit recovery device also includes: a tenth determination module, used to determine the energy constraint conditions of the first site based on the selection information of each target user for the first site, the energy of the electric vehicle, and the output power of the electric vehicle; an eleventh determination module, used to determine the first mathematical programming model based on the energy constraint conditions of the first site, the upper and lower limit constraints of active output, the time range constraints and the objective function corresponding to the first mathematical programming model.

[0108] Optionally, the distribution network unit recovery device also includes: a twelfth determination module, which is used to determine the power consumption constraint of the electric vehicle moving from the initial position to the first site based on the selection information of each target user for the first site, the distance between the electric vehicle and the first site selected by the target user, and the initial power of the electric vehicle; a thirteenth determination module, which is used to determine the first mathematical programming model based on the power consumption constraint, the energy constraint of the first site, the upper and lower limits of active output constraints, the time range constraint and the objective function corresponding to the first mathematical programming model.

[0109] Example 3

[0110] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned distribution network unit recovery method when running.

[0111] Example 4

[0112] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, wherein: Figure 3 is a schematic diagram of an optional electronic device according to an embodiment of the present invention, such as Figure 3 As shown, the electronic device includes one or more processors; a memory for storing one or more programs, which, when the one or more programs are executed by the one or more processors, enables the one or more processors to run the programs, wherein the programs are configured to execute the above-mentioned distribution network unit recovery method when running.

[0113] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0114] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0117] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0118] If the integrated 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 technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0119] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for restoring a distribution network unit, characterized in that: include: In the case of a power outage in the distribution network, obtaining selection information of each target user among multiple target users for a first site, wherein the target user is a user who owns an electric vehicle, the first site is a site that supports bidirectional energy exchange between the electric vehicle and the distribution network, and the first site is used to restore the unit based on the power provided by the electric vehicle; Obtaining a first mathematical programming model, and solving the first mathematical programming model based on the selection information of each target user for the first site, to obtain the start-up time point of each unit to be restored in the distribution network, wherein the first mathematical programming model takes minimizing the restoration time of the distribution network units as an objective function; The units are restored based on the start-up time point of each unit to be restored.

2. The method according to claim 1, characterized in that Acquiring selection information of each target user among multiple target users for the first site includes: Acquire a second mathematical programming model, and solve the second mathematical programming model to obtain the first site to be recommended corresponding to each target user, wherein the second mathematical programming model takes maximizing the attractiveness of the first site to the target user as an objective function; For each target user, generating target recommendation information corresponding to the target user based on the first site to be recommended corresponding to the target user, wherein the target recommendation information is used to recommend the first site to be recommended to the target user; The selection information fed back by the target user based on the target recommendation information is received to obtain the selection information of each target user for the first site.

3. The method according to claim 2, characterized in that The second mathematical programming model is determined by: Determining a first constraint condition according to the number of first sites that the electric vehicle can choose to go to; Determining a second constraint condition according to the number of electric vehicles that the first station can accommodate; Determining the attractiveness of the first site according to the distance between the electric vehicle and the first site, the incentive amount of the first site, and the maximum output power of the first site, wherein the incentive amount is used to be distributed to a target user who provides electricity to the first site; Determining an objective function of a second mathematical programming model to be generated according to the attractiveness of the first site and the recommendation relationship between the first site and the target user; The second mathematical programming model is determined according to the first constraint condition, the second constraint condition, and an objective function corresponding to the second mathematical programming model.

4. The method according to claim 3, characterized in that Solving the second mathematical programming model to obtain the first site to be recommended corresponding to each target user includes: For each target user, sending a geographic location acquisition request to the target user; Acquire location information of the target user based on the geographic location to obtain the requested feedback; Calculate the distance between the electric vehicle of the target user and each first site based on the location information of the target user; The distance corresponding to each target user is substituted into the second mathematical programming model, and the second mathematical programming model after the substitution is solved to obtain the first site to be recommended corresponding to each target user.

5. The method according to claim 1, characterized in that The first mathematical programming model is obtained by: Determine active output upper and lower limit constraints of the first site based on selection information of each target user for the first site, output power of the first site, and output power of the electric vehicle; Determine the time range constraint conditions corresponding to each startup phase of the unit to be restored based on the time required for the startup process of the unit to be restored, the startup time point of the unit to be restored, and the ramp rate of the unit to be restored; Determining the objective function of the first mathematical programming model to be generated based on the maximum output power of the unit to be restored, the starting power of the unit to be restored, and the starting time point of the unit to be restored; The first mathematical programming model is determined according to the upper and lower limit constraints of active output, the time range constraints and the objective function corresponding to the first mathematical programming model.

6. The method according to claim 5, characterized in that Determining the first mathematical programming model according to the active output upper and lower limit constraints, the time range constraints, and the objective function corresponding to the first mathematical programming model includes: Determine the energy constraint condition of the first site based on the selection information of each target user for the first site, the energy of the electric vehicle, and the output power of the electric vehicle; The first mathematical programming model is determined based on the energy constraint of the first site, the upper and lower limit constraints of active output, the time range constraint, and the objective function corresponding to the first mathematical programming model.

7. The method according to claim 6, characterized in that Determining the first mathematical programming model based on the energy constraint of the first site, the upper and lower limits of active output constraints, the time range constraint, and the objective function corresponding to the first mathematical programming model includes: Determine a power consumption constraint condition for the electric vehicle to move from an initial position to the first site based on selection information of each target user for the first site, a distance between the electric vehicle and the first site selected by the target user, and an initial power of the electric vehicle; The first mathematical programming model is determined based on the power consumption constraint, the energy constraint of the first site, the upper and lower limit constraints of active output, the time range constraint, and the objective function corresponding to the first mathematical programming model.

8. A distribution network unit recovery device, characterized in that: include: A first acquisition module is used to obtain selection information of each target user among multiple target users for a first site in the case of a power outage in the distribution network, wherein the target user is a user who owns an electric vehicle, the first site is a site that supports two-way energy exchange between the electric vehicle and the distribution network, and the first site is used to restore the unit based on the power provided by the electric vehicle; A second acquisition module is used to acquire a first mathematical programming model, and solve the first mathematical programming model based on the selection information of each target user for the first site to obtain a start-up time point of each unit to be restored in the distribution network, wherein the first mathematical programming model takes minimizing the recovery time of the distribution network units as an objective function; The processing module is used to restore the units based on the start-up time point of each unit to be restored.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the distribution network unit recovery method described in any one of claims 1 to 7 when running.

10. An electronic device, characterized in that: The electronic device includes one or more processors; A memory for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to run the programs, wherein the program is configured to execute the distribution network unit recovery method described in any one of claims 1 to 7 when running.

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