A power distribution network black start method, system, device and medium based on a mobile energy storage type soft switch

Abstract

The present application relates to the technical field of power grid control, and discloses a power distribution network black start method and system based on a mobile energy storage type soft switch, the method comprising: obtaining a power distribution network topology structure and operating parameters, and dividing to obtain an island topology; respectively constructing scheduling constraints of the soft switch, power distribution network operating constraints and root node output constraints; taking minimum load loss and network loss as targets, solving an optimization model under joint constraints of the scheduling constraints, the power distribution network operating constraints and the root node output constraints, determining an access position and power output strategy of the mobile energy storage type soft switch in each period; controlling the access of the target node in the power distribution network, providing active voltage support or power flow transfer for islands in the island topology, and realizing black start recovery of the power distribution network. The present application fully utilizes the advantages of multi-terminal island support and power flow transfer of the EV-SOP, constructs an EV-SOP scheduling optimization model, realizes efficient recovery of the power distribution network black start, and improves power supply reliability and operating economy.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, and in particular to a black start method, system, equipment and medium for distribution networks based on mobile energy storage soft switches. Background Technology

[0002] In recent years, frequent natural disasters have posed challenges to the stable operation of power grids, especially in the face of extreme weather events. As a crucial component of the power system, the distribution network is more vulnerable to natural disasters than the transmission network, and in the event of a distribution network failure caused by an extreme accident, a complete power outage is a possibility. Therefore, researching black start mechanisms in distribution networks is essential to address this extreme scenario.

[0003] Currently, there has been considerable research both domestically and internationally on black start in distribution networks, mainly focusing on utilizing the abundant distributed generation (DG) or power electronic equipment resources of the distribution network to assist in islanding and load restoration after large-scale power outages. However, due to the spatial distribution of resources, distributed resources such as photovoltaic, wind power, and energy storage have drawbacks such as fixed access locations and inflexible scheduling. In particular, some photovoltaic and wind power cannot control the voltage and frequency of islands and require external power sources to provide starting power before they can provide power support to the islands, thus they can only be used as non-black start DG.

[0004] In contrast, mobile energy storage vehicles offer advantages such as flexible dispatching and can also act as black-start distributed generation (DG) units to provide initial voltage and frequency for islanded power supply, thereby activating non-black-start DG units and expanding the load power that the island can restore. However, in terms of emergency vehicle power restoration, existing domestic and international research is largely limited to more traditional energy storage emergency vehicles, with insufficient research on newly emerging energy storage vehicles. A novel mobile energy storage soft switch (EV-SOP) combining intelligent soft switching with an energy storage emergency vehicle has been proposed, giving this equipment the advantages of flexible dispatching of traditional energy storage emergency vehicles and the power flow control characteristics of intelligent soft switching. Its application scenarios have also been studied to some extent. However, none of these studies have considered the possibility of using EV-SOP to provide voltage support for non-black-start power sources to form a power supply island when extreme weather causes a complete power outage in the distribution network. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a black-start method and system for distribution networks based on mobile energy storage soft switches, which solves the problem that under extreme disasters and complete blackout conditions in distribution networks, traditional emergency power supply methods are difficult to flexibly schedule due to the fixed access locations of distributed power sources and their lack of black-start capability, making it impossible to efficiently build power supply islands and optimize the recovery process on a large scale.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a black-start method for a distribution network based on a mobile energy storage soft switch, comprising: Obtain the distribution network topology and operating parameters; Based on the predicted photovoltaic power in the distribution network topology and operating parameters, the island range is divided to obtain an island topology centered on distributed photovoltaic power. Obtain the physical parameters of the mobile energy storage soft switch and establish scheduling constraints for the soft switch; Based on the topology of the distribution network, establish the operation constraints of the distribution network throughout the entire black start process; Based on the black start output ramping characteristics of the root node of the distribution network, the output constraints of the root node are constructed. With the goal of minimizing load loss and network loss, the optimization model is solved under the joint constraints of the scheduling constraints, distribution network operation constraints, and root node output constraints to determine the access location and power output strategy of the mobile energy storage soft switch in each time period. Based on the access location and power output strategy, the mobile energy storage soft switch is controlled to access the target node in the distribution network, providing active voltage support or power flow transfer for the islands in the islanded topology, and realizing black start recovery of the distribution network.

[0008] As a preferred embodiment of the black-start method for distribution networks based on mobile energy storage soft switching described in this invention, the islanding range is divided according to the distribution network topology and the predicted photovoltaic power in the operating parameters, resulting in an islanding topology centered on distributed photovoltaic power, including: All lines in the distribution network are coded, and the on / off status of each line is represented by a binary variable; The output range of distributed photovoltaics is determined based on the predicted photovoltaic power. Taking the node where the distributed photovoltaics is located as the center, the basic power supply range of the island is obtained by dividing it through the binary variables, thus forming an island topology.

[0009] As a preferred embodiment of the black-start method for distribution networks based on mobile energy storage soft switches described in this invention, the method involves: acquiring the physical parameters of the mobile energy storage soft switch and establishing scheduling constraints for the soft switch. These scheduling constraints include accessibility constraints, operating status constraints, port power constraints, and energy storage battery charging / discharging constraints. Define a binary variable for access location and establish the accessible location constraint to limit a single mobile energy storage soft switch to only one node in the distribution network during the same time period. Define a binary variable for the working state and establish the working state constraint to limit each mobile energy storage soft switch to only one of the PQ working state or VF working state at the same time when it is in grid-connected state; wherein, the PQ working state is to provide active power to the grid, and the VF working state is to provide active voltage support to the grid. Define port power variables and loss coefficients, and establish the power balance constraint to ensure that when the mobile energy storage soft switch is connected to the distribution network, the sum of the active and reactive power transmitted at each port and the output power of the energy storage battery inside the mobile energy storage soft switch satisfies the power conservation, and the apparent power of each port does not exceed the rated capacity of the mobile energy storage soft switch. Define energy storage charge / discharge indicator variables and state of charge variables, and establish the energy storage charge / discharge constraints to limit the energy storage battery from being in a charging state and a discharging state at the same time; the charge / discharge power is within a preset upper and lower limit range, and the state of charge is within a preset upper and lower limit range of capacity.

[0010] As a preferred embodiment of the black-start method for distribution networks based on mobile energy storage soft switches described in this invention, the following is included: Based on the distribution network topology, distribution network operation constraints are established for the entire black-start process, including: Based on the active and reactive power output of distributed power sources at each node, the active and reactive power input of mobile energy storage soft switches, the active and reactive loads of nodes, and the line resistance and reactance, the active and reactive power balance relationship of each node is established to construct network power flow constraints. Upper limit constraints are imposed on the apparent power of each branch, upper limit constraints are imposed on the current magnitude of each branch, and upper and lower limit constraints are imposed on the voltage of each node to construct network security operation constraints. By combining the binary variables representing the closed or open state of the line, the relationship between the node voltages at both ends of the branch and the branch current is established to construct Ohm's law constraints; A single commodity flow model is adopted, and radial constraints are constructed based on the power flow of each branch in the virtual network and the power emitted by each distributed power node to ensure the radial topology of the distribution network during operation. The non-convex relationship between the branch head power, node voltage and branch current is treated with second-order cone relaxation to obtain a solvable second-order cone constraint form, so as to construct the second-order cone constraint.

[0011] As a preferred embodiment of the black-start method for distribution networks based on mobile energy storage soft switches described in this invention, the following is included: Based on the black-start output ramp-up characteristics of the distribution network root nodes, a root node output constraint is constructed, comprising: Based on the starting time from the start to the start of power output, the ramping time from the start of ramping to the maximum output, and the ramping rate of the root node in the black start process of the distribution network, the output constraint of the root node is established. The root node output constraint is used to limit the curve characteristics of the output power of the distribution network root node during black start, and the output power curve is used as the source power input boundary condition when solving the optimization model.

[0012] As a preferred embodiment of the black-start method for distribution networks based on mobile energy storage soft switches described in this invention, the optimization model, with the objective of minimizing load loss and network losses, further includes: By introducing virtual network power flow variables, the radial constraints are transformed into linear constraints under virtual network power flow. By combining the linear constraints and the second-order cone constraints to solve the optimization model, the access location and power output strategy of the mobile energy storage soft switch in each time period are determined.

[0013] As a preferred embodiment of the black-start method for distribution networks based on mobile energy storage soft switches described in this invention, the method involves controlling the mobile energy storage soft switch to connect to the target node in the distribution network according to the connection location and power output strategy, providing active voltage support or power flow transfer for islands in an islanded topology, thereby achieving black-start recovery of the distribution network, including: During the black start phase when the power supply to the root node of the distribution network has not been restored, according to the power output strategy, the mobile energy storage soft switch is controlled to connect to the node where the distributed power source is located in the initial islanded topology as a non-black start power source, so that the mobile energy storage soft switch works in an active voltage support state, providing voltage and frequency support for the loads in the islanded topology; After power is restored at the root node of the distribution network, the mobile energy storage soft switch is controlled to switch its working state according to the power output strategy, so that the mobile energy storage soft switch works in the power flow transfer state. Through the multi-port connection of the soft switch, different distribution substations are connected to realize power mutual assistance and power flow optimization between different substations.

[0014] Secondly, the present invention provides a black-start system for distribution networks based on mobile energy storage soft switches, comprising: The data acquisition module is used to acquire the distribution network topology and operating parameters; The islanding module is used to divide the islanding range based on the predicted photovoltaic power in the distribution network topology and operating parameters, and obtain the islanding topology centered on distributed photovoltaics. The first constraint construction module is used to obtain the physical parameters of the mobile energy storage soft switch and establish the scheduling constraints of the soft switch. The second constraint construction module is used to establish distribution network operation constraints for the entire black start process of the distribution network based on the topology of the distribution network. The third constraint construction module is used to construct the root node output constraint based on the black start output ramp-up characteristics of the root node of the distribution network. The solution module is used to solve the optimization model under the joint constraints of the scheduling constraints, distribution network operation constraints, and root node output constraints, with the goal of minimizing load loss and network loss, and to determine the access location and power output strategy of the mobile energy storage soft switch in each time period. The control module is used to control the mobile energy storage soft switch to connect to the target node in the distribution network according to the access location and power output strategy, so as to provide active voltage support or power flow transfer for the islands in the islanded topology and realize the black start recovery of the distribution network.

[0015] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a black-start method for a distribution network based on a mobile energy storage soft switch.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the black-start method for a distribution network based on a mobile energy storage soft switch.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention overcomes the shortcomings of traditional distributed power sources, which have fixed access locations and cannot actively build islands, by introducing a mobile energy storage soft switch with multi-terminal interconnection and power flow regulation capabilities; by acquiring distribution network operation data, the initial island range is delineated with the distributed power source as the center, providing a clear power supply unit basis for subsequent recovery; combined with multi-dimensional scheduling constraints, the mobile energy storage soft switch can ensure flexibility and controllability in spatial layout and operating conditions, thereby providing reliable voltage and frequency for distributed power sources within the island even in a completely dark distribution network state. With high efficiency, the system effectively expands the coverage of isolated areas and restores more lost loads. Simultaneously, based on the black-start ramp-up characteristics of the distribution network root node, an output model is constructed. Multi-stage joint optimization is performed, combining network power flow and safe operation constraints, to ensure coordinated matching between the black-start process and the root node's recovery progress. After the root node restores power, the multi-port interconnection advantage of mobile energy storage soft switches enables power flow transfer and mutual power support between different distribution areas, thereby reducing overall network losses and improving photovoltaic absorption capacity. This enhances the system's reliability in the face of extreme disasters while improving the black-start recovery efficiency of the distribution network. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the overall process of a black start method for a distribution network based on a mobile energy storage soft switch, according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the EV-SOP working state in a black start method for distribution networks based on a mobile energy storage soft switch, as described in one embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the root node output in a black start method for a distribution network based on a mobile energy storage soft switch, as described in one embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of a 109-node system in a black-start method for a distribution network based on a mobile energy storage soft switch, as described in an embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of the islanding configuration in time period 12 of a black start method for a distribution network based on a mobile energy storage soft switch, as described in an embodiment of the present invention.

[0024] Figure 6 This is a schematic diagram of the islanding configuration in time period 13 of a black start method for a distribution network based on a mobile energy storage soft switch, as described in an embodiment of the present invention.

[0025] Figure 7 This is a schematic diagram of the EV-SOP access location in time period 14 and time period 15 of a black start method for distribution networks based on a mobile energy storage soft switch, as described in an embodiment of the present invention.

[0026] Figure 8 This is a schematic diagram of the load restoration amount in a simulation experiment of a black start method for a distribution network based on a mobile energy storage soft switch, as described in an embodiment of the present invention.

[0027] Figure 9 This is a schematic diagram of network losses at different time periods in a simulation experiment of a black start method for distribution networks based on mobile energy storage soft switches, as described in an embodiment of the present invention.

[0028] Figure 10 This is a schematic diagram of photovoltaic output at different time periods in a simulation experiment of a black start method for distribution networks based on a mobile energy storage soft switch, as described in an embodiment of the present invention. Detailed Implementation

[0029] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Example 1, referring to Figures 1-2 As an embodiment of the present invention, a black start method for a distribution network based on a mobile energy storage soft switch is provided, comprising: S100: Obtain the distribution network topology and operating parameters; S200: Based on the predicted photovoltaic power in the distribution network topology and operating parameters, the island range is divided to obtain an island topology centered on distributed photovoltaics; S300: Obtain the physical parameters of the mobile energy storage soft switch and establish scheduling constraints for the soft switch; S400: Based on the topology of the distribution network, establish the operation constraints of the distribution network throughout the entire black start process; S500: Based on the black start output ramp-up characteristics of the root node of the distribution network, construct the output constraint of the root node; S600: With the goal of minimizing load loss and network loss, the optimization model is solved under the joint constraints of the scheduling constraints, distribution network operation constraints, and root node output constraints to determine the access location and power output strategy of the mobile energy storage soft switch in each time period. S700: Based on the access location and power output strategy, it controls the mobile energy storage soft switch to access the target node in the distribution network, providing active voltage support or power flow transfer for islands in the islanded topology, and realizing black start recovery of the distribution network.

[0031] It should be noted that black start means that when the entire power system is shut down due to a fault, it starts up without relying on the external network, but only on the power sources within the system that have self-starting capabilities, thereby driving the remaining units that do not have self-starting capabilities to gradually expand the power supply range of the system and ultimately restore the entire power system.

[0032] In order to make full use of photovoltaic (PV) power, restore as many loads as possible during major power outages in the distribution network, and give full play to the flexible and power flow optimization functions of EV-SOP dispatch, this invention designs a black start optimization dispatch model for the distribution network that comprehensively considers islanding and EV-SOP dispatch through steps S100-S700.

[0033] The characteristic of EV-SOP mobility is reflected in the accessibility constraints, allowing the same mobile energy storage vehicle to access different locations at different times.

[0034] Specifically, in combination with actual working conditions, EV-SOP has four working modes: (1) Multi-point active voltage support mode, which can supply power to the loads of multiple transformer areas at the same time; (2) Multi-point interconnection power flow control mode, which uses its intelligent soft switch to perform multi-point power flow control and achieve power flow optimization; (3) Single-point grid-connected battery charging mode, which connects multiple ports to a bus at the same time, which can charge the energy storage battery and compensate the reactive power to the distribution network; (4) Single-point active voltage support mode, which can double the grid-connected power while also having the ability to compensate reactive power and harmonic control.

[0035] In this invention, there are two main operating modes for EV-SOP, such as... Figure 2 As shown, (a) is the active voltage support mode of EV-SOP, which provides voltage support to the load inside the island; (b) is the multi-point power flow control mode, which gives full play to the power flow transfer function of SOP.

[0036] The actual operating mode switches based on the model optimization results. That is, when the EV-SOP functions as a power source, it can simultaneously and actively support the loads of multiple transformer substations, expanding the power restoration range of traditional single-ended emergency power supply vehicles. Figure 2(a); Alternatively, after its own energy is depleted, it can transfer energy from other transformer areas, playing the role of SOP power flow transfer, such as Figure 2 (b).

[0037] Example 2, based on the above implementation method, referring to Figure 1 and Figure 3 As an embodiment of the present invention, a black start method for a distribution network based on a mobile energy storage soft switch is provided, comprising: S100: Obtain the distribution network topology and operating parameters; Specifically, the operating parameters in S100 include at least line impedance, predicted load power, and predicted photovoltaic power; For example, for a distribution network with N nodes, all lines of the distribution network are coded using natural numbers 1 to N, and information such as the line impedance, predicted load power, and predicted PV power are determined to form the branch impedance matrix, load power matrix, and PV power matrix of the distribution network.

[0038] S200: Based on the predicted photovoltaic power in the distribution network topology and operating parameters, the island range is divided to obtain an island topology centered on distributed photovoltaics; In this embodiment of the application, the specific steps of S200 include A1-A2: A1: Encode all lines in the distribution network and use binary variables to represent the on / off status of each line; A2: Based on the predicted photovoltaic power, determine the output range of distributed photovoltaics. Taking the node where the distributed photovoltaics is located as the center, the basic power supply range of the island is obtained by dividing it through the binary variables, thus forming an island topology.

[0039] Specifically, when constructing islands centered around PV in A1-A2, binary variables are used. This represents the open / closed state of a line with code n, defined as: 1 for closed and 0 for open.

[0040] It should be noted that PV systems mostly adopt grid-connected control and cannot provide voltage support for islanded power grids. Therefore, they cannot independently form power supply islands and require EV-SOPs to provide voltage support. In this invention, the basic range of the island is first divided according to the predicted PV power to form an island topology. Then, the connection location and charging / discharging power of the EV-SOPs are optimized through subsequent steps to support island operation, restore island load, and thus reduce power outage losses.

[0041] S300: Obtain the physical parameters of the mobile energy storage soft switch and establish scheduling constraints for the soft switch; In this embodiment, the soft-switching scheduling constraints of S300 include accessibility location constraints, operating state constraints, power balance constraints, and energy storage charging and discharging constraints; specifically, they are represented as follows: Define a binary variable for access location and establish the accessible location constraint to limit a single mobile energy storage soft switch to only one node in the distribution network during the same time period. Specifically, since an EV-SOP can only connect to one location at a time, there are the following constraints on the connection location: In the formula, It is a 0-1 variable, and its value of 1 represents exist The time period access location is ;No. One EV-SOP, namely The set is ; The scheduling feasible region in this invention refers to the entire distribution network. It covers the entire time period, including the black start period T1 and the normal period T2.

[0042] Define a binary variable for the working state and establish the working state constraint to limit each mobile energy storage soft switch to only one of the PQ working state or VF working state at the same time when it is in grid-connected state; wherein, the PQ working state is to provide active power to the grid, and the VF working state is to provide active voltage support to the grid. Specifically, since an EV-SOP can only operate in one mode at a time when it is connected to the grid, the following constraints apply; In the formula, , , All are 0-1 variables representing the EV-SOP operating status, where, express It is in grid-connected state; express When in PQ operating mode, it provides active power to the power grid, etc. express When in VF operating mode, it provides active voltage support to the power grid.

[0043] Define port power variables and loss coefficients, and establish the power balance constraints to ensure that when the mobile energy storage soft switch is connected to the distribution network, the sum of the active and reactive power transmitted at each port and the output power of the energy storage battery inside the mobile energy storage soft switch satisfies power conservation, and the apparent power of each port does not exceed its rated capacity. Specifically, when EV-SOP is connected to the grid, it needs to meet the following power constraints: In the formula, , They are respectively Time period No. The active and reactive power transmitted through each port; for Time period No. Loss per port; The loss factor for the EV-SOP port; for No. The rated apparent power of each port; for Time period Output power of energy storage batteries; , These are the upper and lower limits of the charging and discharging power of the energy storage battery, respectively. , These are the complete port set and the DC-DC port set of a single EV-SOP, respectively.

[0044] Define energy storage charge / discharge indicator variables and state of charge variables, and establish the energy storage charge / discharge constraints to limit the energy storage battery from being in a charging state and a discharging state at the same time; the charge / discharge power is within a preset upper and lower limit range, and the state of charge is within a preset upper and lower limit range of capacity. Specifically, the energy storage batteries in EV-SOP cannot be charged and discharged simultaneously at the same time, thus there are constraints on the state of charge and discharge. In the formula, , These are variables ranging from 0 to 1, representing the charge and discharge states of the energy storage battery. A value of 1 indicates... Time period The energy storage battery is in a charging or discharging state; Meanwhile, energy storage batteries need to meet charging and discharging power constraints during charging and discharging, which are as follows: In the formula, express Time period The charge capacity, and These are the upper and lower limits of the energy storage battery's charge capacity; To ensure that EV-SOP can be connected for a long time under normal operating conditions of the distribution network, and that there is still residual power to supply subsequent dispatch after the black start of the distribution network, the following constraints apply; In the formula, This represents the initial state of charge of the energy storage battery.

[0045] S400: Based on the topology of the distribution network, establish the operation constraints of the distribution network throughout the entire black start process; It should be noted that in order to ensure its safe and stable operation and meet the limitations of physical laws, certain constraints need to be met.

[0046] In this embodiment, the distribution network operation constraints of S400 include the following specific constraints: network power flow constraints, safe operation constraints, Ohm's law constraints, radial constraints, and second-order cone constraints. Specifically, a dist-flow power flow model is used to represent these constraints. Let... , The specific constraints can be expressed as follows: Based on the active and reactive power output of distributed power sources at each node, the active and reactive power input of mobile energy storage soft switches, the active and reactive loads of nodes, and the line resistance and reactance, the active and reactive power balance relationship of each node is established to construct network power flow constraints. Specifically, network flow constraints are represented as follows: In the formula, Indicates the flow of electricity to the node All upstream nodes that receive injected power; Indicates from node The outflowing power reaches all downstream nodes via the power grid; , They are respectively Time period nodes The active and reactive power output of the DG at the location; , They are respectively Time period nodes Purchase of active and reactive power from the upper-level power grid; , They are respectively Time period nodes The input active and reactive power of the EV-SOP; , They are respectively Time period nodes The active and reactive loads; , The lines are respectively The resistance and reactance values ​​on it.

[0047] Upper limit constraints are imposed on the apparent power of each branch, upper limit constraints are imposed on the current magnitude of each branch, and upper and lower limit constraints are imposed on the voltage of each node to construct network security operation constraints. Specifically, network security operational constraints are expressed as follows: In the formula: , They are respectively Time-of-day branch Active power and reactive power; for Time-of-day branch The upper limit of apparent power; This is the upper limit of the branch current magnitude. for Time period nodes The voltage; and These are its upper and lower limits, respectively.

[0048] By combining the binary variables representing the closed or open state of the line, the relationship between the node voltages at both ends of the branch and the branch current is established to construct Ohm's law constraints; Specifically, the Ohm's law constraint is expressed as: In the formula: It is a variable ranging from 0 to 1, representing whether the line is open or closed; a value of 1 indicates that the line is closed. It is a very large positive number.

[0049] A single commodity flow model is adopted, and radial constraints are constructed based on the power flow of each branch in the virtual network and the power emitted by each distributed power node to ensure the radial topology of the distribution network during operation. Specifically, during the operation of a distribution network, it needs to be ensured that it is radial, thus requiring the satisfaction of radial constraints. These constraints can be represented using a single-commodity flow model as follows: In the formula, for Branch in time-sharing virtual network The trend; It is a set of nodes that contain distributed power sources; for Nodes in a time-sharing virtual network The power generated by the distributed power source.

[0050] The non-convex relationship between the branch head power, node voltage and branch current is relaxed by a second-order cone to obtain a solvable second-order cone constraint form, so as to construct the second-order cone constraint. Specifically, the power, voltage, and current of the first section of a branch circuit should meet the following requirements: Since the above formula is a non-convex constraint, to facilitate the solution, we perform a second-order cone relaxation, resulting in the following formula: S500: Based on the black start output ramp-up characteristics of the root node of the distribution network, construct the output constraint of the root node; Specifically, during a black start in a distribution network, the power at the root node does not immediately rise from 0 to the rated power; instead, it requires a certain amount of time to ramp up (this ramp-up is due to the self-recovery of the external power grid). The simplified output curve of the root node during a black start is shown below. Figure 3 As shown, specifically, the black-start power output process of the root node of the distribution network is divided into three stages, wherein: The first stage is the waiting stage from startup to the start of ramping up and transmitting power outward, during which the active power of the root node is zero; The second stage is the climbing stage from the start of the climb to the point of reaching the rated output, during which the active power of the root node increases linearly with the climbing rate. The third stage is the full-power operation stage after reaching the rated output.

[0051] In the embodiments of this application, based on the above three stages, referring to Figure 3 The output constraints of the root node of S500 include B1-B2: B1: Based on the starting time of the root node of the distribution network from the start to the start of outputting power, the ramping time from the start of ramping to the maximum output, and the ramping rate during the black start process of the distribution network, establish the output constraint of the root node; Specifically, during black start, the output of the root node of the distribution network can be expressed as: In the formula, During black boot, the root node is in Active power at any given moment; This is the time required for the root node to start ramping up and transmitting power outwards from startup. This represents the time required for the root node to go from starting the climb to reaching its maximum output. The climbing rate of the root node; This is the rated output of the root node.

[0052] B2: The root node output constraint is used to limit the curve characteristics of the output power of the distribution network root node during black start, and the output power curve is used as the source power input boundary condition when solving the optimization model.

[0053] S600: With the goal of minimizing load loss and network loss, the optimization model is solved under the joint constraints of the scheduling constraints, distribution network operation constraints, and root node output constraints to determine the access location and power output strategy of the mobile energy storage soft switch in each time period. It should be noted that, in order to make full use of PV power, restore as much load as possible, and give full play to the flexible and power flow optimization functions of EV-SOP scheduling, S600 can obtain a black start optimization model for distribution network regarding EV-SOP scheduling based on the above constraints.

[0054] In this embodiment of the application, when S600 solves the optimization model with the objective of minimizing load loss and network loss, it further includes: By introducing virtual network power flow variables, the radial constraints are transformed into linear constraints under virtual network power flow. By combining the linear constraints and the second-order cone constraints to solve the optimization model, the access location and power output strategy of the mobile energy storage soft switch in each time period are determined.

[0055] It should be noted that in the above steps, the abundant distributed resources on the distribution network side are used to form power supply islands, supplying power to a portion of the load before the root node is fully restored, thereby reducing load loss. Since the distributed generation (DG) used in this invention is a distributed photovoltaic (PV) without black-start capability, voltage support is required through EV-SOPs. Therefore, before the distribution network black-start is complete, it is necessary to consider using EV-SOPs to provide voltage support to the DG islands to reduce power loss. Simultaneously, since the time required for the distribution network black-start from start to finish is short, in order to fully utilize the interconnection and mutual assistance advantages of EV-SOPs and improve the power flow distribution of the distribution network, this invention considers the scheduling of EV-SOPs for a period of time after the black-start is completed to optimize power flow.

[0056] Specifically, based on the above, the objective function of the optimization model in S600 is to minimize the sum of load loss and network line loss during the entire black start process of the distribution network and for a period of time after the black start is completed, as shown in the following formula: In the formula, for Time Branch The square of the current magnitude; Line resistance; Power supply rate to load; , These are the sets of all nodes and branches in the distribution network; , These are the network loss factor and the load power failure factor, respectively. Representative at Time, node The active power of the load.

[0057] S700: Based on the access location and power output strategy, it controls the mobile energy storage soft switch to access the target node in the distribution network, providing active voltage support or power flow transfer for islands in the islanded topology, and realizing black start recovery of the distribution network.

[0058] In this application embodiment, the specific strategy for restoring S700 includes: During the black start phase when the power supply to the root node of the distribution network has not been restored, according to the power output strategy, the mobile energy storage soft switch is controlled to connect to the node where the distributed power source is located in the initial island topology as a non-black start power source, so that it works in an active voltage support state and provides voltage and frequency support for the load in the island topology. After power is restored at the root node of the distribution network, the mobile energy storage soft switch is controlled to switch its working state according to the power output strategy, so that it works in the power flow transfer state. Through its multiple ports, it connects to different distribution substations to realize power mutual assistance and power flow optimization between different substations.

[0059] In summary, this invention addresses the scenario of complete power outage in a distribution network under extreme disasters. Leveraging the interconnectivity, power flow optimization, and flexible scheduling capabilities of mobile energy storage soft switches, it proposes a black-start method for distribution networks based on mobile energy storage soft switches. This method utilizes distributed generation sources and EV-SOPs within the distribution network to construct power supply islands and fully schedules the EV-SOPs, thereby supplying power to some of the lost loads, reducing the cost of load loss, and accelerating distribution network recovery.

[0060] Example 3, based on the above implementation method, with reference to Figures 4-10 Tables 1 and 2, representing an embodiment of the present invention, provide a simulation experiment of a black-start method for distribution networks based on mobile energy storage soft switches to verify the effectiveness of the present invention.

[0061] This implementation method is based on Figure 4 Taking a local 109-node distribution system as an example, black start of the distribution network is performed according to the method described in this invention. The 109-node distribution system used is based on typical distributed generation output forecast curves and load forecast curves. PVs are distributed generation sources, and their access nodes are 13, 42, and 66, respectively. An EV-SOP optimized scheduling model for black start of the distribution network is constructed.

[0062] Black start of the distribution network is performed according to the strategy proposed in this invention, and power supply islanding is constructed using EV-SOP to supply power to some of the lost loads. The islanding construction result is as follows: Figures 5-7 The EV-SOP access locations and islanding construction results shown in the black start diagrams demonstrate that, compared to traditional solutions, the strategy proposed in this invention can construct larger islands, thereby restoring more loads and reducing the cost of load loss.

[0063] The relevant parameters in the examples of this invention are shown in Table 1 below.

[0064] Table 1: Parameters of the Case Study

[0065] Based on the simulation parameters, EV-SOP is used for black start of the distribution network. Assuming the root node black start time is 2 hours and a complete power outage of the distribution network is an extremely low-probability scenario, to fully utilize the power flow optimization function of EV-SOP, this invention also considers the scheduling of EV-SOP within 2 hours after black start. Furthermore, to demonstrate the advantages of the proposed EV-SOP, three schemes are designed for comparison. The three schemes are as follows: Option 1: No emergency vehicles or other emergency equipment of any kind are available.

[0066] Option 2: Connect to a traditional energy storage emergency vehicle, with its energy storage battery capacity set at 280 kW·h.

[0067] Option 3: Connect 3 EV-SOP vehicles.

[0068] Assume that the black start begins at t=12h, that is, the black start of the distribution network is carried out at t=12h and t=13h, and the EV-SOP optimized scheduling is carried out at t=14h and t=15h.

[0069] In terms of restoring power to lost loads, such as Figure 8 As shown, compared to Scheme 1 without an emergency vehicle and Scheme 2 using a traditional emergency vehicle, Scheme 3 proposed in this invention can restore more load during the black start phase. At t=14h and t=15h after the black start is completed, since the root node of the distribution network has been restored to power supply, the entire distribution network has been fully restored. Therefore, the load restoration amount of the three schemes is the same.

[0070] Regarding network loss, such as Figure 9 As shown, due to the advantages of EV-SOP's dedicated power flow and interconnection, the proposed solution three can better reduce network loss, thereby saving economic costs.

[0071] Regarding photovoltaic grid integration rate, such as Figure 10As shown, in Scheme 1, due to the complete power outage of the distribution network, and the inability of PV to provide the necessary voltage and frequency support to the distribution network, it cannot form a power supply island independently, thus the PV absorption rate is 0. Scheme 2's traditional emergency vehicle and Scheme 3's EV-SOP can both provide voltage and frequency support to the island, thereby improving the PV absorption rate and reducing PV curtailment. However, because the EV-SOP has multi-terminal interconnection capabilities, it can restore more loads, thus constructing a larger power supply island. Therefore, Scheme 3 has a better PV absorption rate than Scheme 2.

[0072] In summary, compared with the traditional Scheme 1 and Scheme 2, the black start optimization model for distribution networks proposed in this invention has advantages in reducing network loss costs and load shedding costs, as well as improving photovoltaic absorption, proving the advanced nature of this invention.

[0073] Meanwhile, to reflect the economic benefits of the EV-SOP proposed in this invention, it is assumed that the traditional energy storage emergency vehicle has zero cost, while the cost of each EV-SOP proposed in this invention is 30,000 yuan, with a service life of 20 years, and a major accident of complete power grid failure occurs every 10 years. The total cost of the three schemes over 20 years is shown in Table 2. For comparison, Scheme 1 is the scheme without an emergency vehicle, and Scheme 2 is the traditional emergency vehicle scheme.

[0074] Table 2: Total Economic Costs Over Twenty Years

[0075] Therefore, the EV-SOP and the two-layer optimization model proposed in this invention can effectively reduce the cost of power grid failures caused by extreme disasters and improve the economic efficiency of power grid operation.

[0076] Example 4 illustrates a schematic scheme for a black-start method for a distribution network based on a mobile energy storage soft switch. It should be noted that the technical solution of this black-start system for a distribution network based on a mobile energy storage soft switch is based on the same concept as the aforementioned black-start method for a distribution network based on a mobile energy storage soft switch. Details not described in detail in this example of the black-start system for a distribution network based on a mobile energy storage soft switch can be found in the description of the aforementioned black-start method for a distribution network based on a mobile energy storage soft switch.

[0077] This embodiment provides a black start system for distribution networks based on mobile energy storage soft switches, including: The data acquisition module is used to acquire the distribution network topology and operating parameters; The islanding module is used to divide the islanding range based on the predicted photovoltaic power in the distribution network topology and operating parameters, and obtain the islanding topology centered on distributed photovoltaics. The first constraint construction module is used to obtain the physical parameters of the mobile energy storage soft switch and establish the scheduling constraints of the soft switch. The second constraint construction module is used to establish distribution network operation constraints for the entire black start process of the distribution network based on the topology of the distribution network. The third constraint construction module is used to construct the root node output constraint based on the black start output ramp-up characteristics of the root node of the distribution network. The solution module is used to solve the optimization model under the joint constraints of the scheduling constraints, distribution network operation constraints, and root node output constraints, with the goal of minimizing load loss and network loss, and to determine the access location and power output strategy of the mobile energy storage soft switch in each time period. The control module is used to control the mobile energy storage soft switch to connect to the target node in the distribution network according to the access location and power output strategy, so as to provide active voltage support or power flow transfer for the islands in the islanded topology and realize the black start recovery of the distribution network.

[0078] This embodiment also provides a computer device applicable to a black start scenario for a distribution network based on a mobile energy storage soft switch, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a black start method for a distribution network based on a mobile energy storage soft switch as proposed in the above embodiment.

[0079] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a black-start method for a distribution network based on a mobile energy storage soft switch, as proposed in the above embodiment.

[0080] The storage medium proposed in this embodiment belongs to the same inventive concept as the black start method for a distribution network based on a mobile energy storage soft switch proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0081] From the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A black-start method for distribution networks based on mobile energy storage soft switches, characterized in that, include: Obtain the distribution network topology and operating parameters; Based on the predicted photovoltaic power in the distribution network topology and operating parameters, the island range is divided to obtain an island topology centered on distributed photovoltaic power. Obtain the physical parameters of the mobile energy storage soft switch and establish scheduling constraints for the soft switch; Based on the topology of the distribution network, establish the operation constraints of the distribution network throughout the entire black start process; Based on the black start output ramping characteristics of the root node of the distribution network, the output constraints of the root node are constructed. With the goal of minimizing load loss and network loss, the optimization model is solved under the joint constraints of the scheduling constraints, distribution network operation constraints, and root node output constraints to determine the access location and power output strategy of the mobile energy storage soft switch in each time period. Based on the access location and power output strategy, the mobile energy storage soft switch is controlled to access the target node in the distribution network, providing active voltage support or power flow transfer for the islands in the islanded topology, and realizing black start recovery of the distribution network.

2. The black-start method for distribution networks based on mobile energy storage soft switches as described in claim 1, characterized in that, Based on the predicted photovoltaic power in the distribution network topology and operating parameters, the islanding range is divided, resulting in an islanding topology centered on distributed photovoltaic power, including: All lines in the distribution network are coded, and the on / off status of each line is represented by a binary variable; The output range of distributed photovoltaics is determined based on the predicted photovoltaic power. Taking the node where the distributed photovoltaics is located as the center, the basic power supply range of the island is obtained by dividing it through the binary variables, thus forming an island topology.

3. The black-start method for distribution networks based on mobile energy storage soft switches as described in claim 2, characterized in that, Obtain the physical parameters of the mobile energy storage soft switch and establish scheduling constraints for the soft switch. These scheduling constraints include accessibility location constraints, operating condition constraints, port power constraints, and energy storage battery charge / discharge constraints, wherein: Define a binary variable for access location and establish the accessible location constraint to limit a single mobile energy storage soft switch to only one node in the distribution network during the same time period. Define a binary variable for the working state and establish the working state constraint to limit each mobile energy storage soft switch to only one of the PQ working state or VF working state at the same time when it is in grid-connected state; wherein, the PQ working state is to provide active power to the grid, and the VF working state is to provide active voltage support to the grid. Define port power variables and loss coefficients, and establish the power balance constraint to ensure that when the mobile energy storage soft switch is connected to the distribution network, the sum of the active and reactive power transmitted at each port and the output power of the energy storage battery inside the mobile energy storage soft switch satisfies the power conservation, and the apparent power of each port does not exceed the rated capacity of the mobile energy storage soft switch. Define energy storage charge / discharge indicator variables and state of charge variables, and establish the energy storage charge / discharge constraints to limit the energy storage battery from being in a charging state and a discharging state at the same time; the charge / discharge power is within a preset upper and lower limit range, and the state of charge is within a preset upper and lower limit range of capacity.

4. The black-start method for distribution networks based on mobile energy storage soft switches as described in claim 3, characterized in that, Based on the distribution network topology, establish distribution network operation constraints for the entire black start process, including: Based on the active and reactive power output of distributed power sources at each node, the active and reactive power input of mobile energy storage soft switches, the active and reactive loads of nodes, and the line resistance and reactance, the active and reactive power balance relationship of each node is established to construct network power flow constraints. Upper limit constraints are imposed on the apparent power of each branch, upper limit constraints are imposed on the current magnitude of each branch, and upper and lower limit constraints are imposed on the voltage of each node to construct network security operation constraints. By combining the binary variables representing the closed or open state of the line, the relationship between the node voltages at both ends of the branch and the branch current is established to construct Ohm's law constraints; A single commodity flow model is adopted, and radial constraints are constructed based on the power flow of each branch in the virtual network and the power emitted by each distributed power node to ensure the radial topology of the distribution network during operation. The non-convex relationship between the branch head power, node voltage and branch current is treated with second-order cone relaxation to obtain a solvable second-order cone constraint form, so as to construct the second-order cone constraint.

5. The black-start method for distribution networks based on mobile energy storage soft switches as described in claim 4, characterized in that, Based on the black-start output ramp-up characteristics of the root node of the distribution network, the output constraints of the root node are constructed, including: Based on the starting time from the start to the start of power output, the ramping time from the start of ramping to the maximum output, and the ramping rate of the root node in the black start process of the distribution network, the output constraint of the root node is established. The root node output constraint is used to limit the curve characteristics of the output power of the distribution network root node during black start, and the output power curve is used as the source power input boundary condition when solving the optimization model.

6. The black-start method for distribution networks based on mobile energy storage soft switches as described in claim 5, characterized in that, When solving the optimization model with the goal of minimizing load loss and network loss, the following also applies: By introducing virtual network power flow variables, the radial constraints are transformed into linear constraints under virtual network power flow. By combining the linear constraints and the second-order cone constraints to solve the optimization model, the access location and power output strategy of the mobile energy storage soft switch in each time period are determined.

7. A black-start method for distribution networks based on mobile energy storage soft switches as described in claim 6, characterized in that, Based on the access location and power output strategy, the mobile energy storage soft switch is controlled to connect to the target node in the distribution network, providing active voltage support or power flow transfer for islanded topologies, and realizing black start recovery of the distribution network, including: During the black start phase when the power supply to the root node of the distribution network has not been restored, according to the power output strategy, the mobile energy storage soft switch is controlled to connect to the node where the distributed power source is located in the initial islanded topology as a non-black start power source, so that the mobile energy storage soft switch works in an active voltage support state, providing voltage and frequency support for the loads in the islanded topology; After power is restored at the root node of the distribution network, the mobile energy storage soft switch is controlled to switch its working state according to the power output strategy, so that the mobile energy storage soft switch works in the power flow transfer state. Through the multi-port connection of the soft switch, different distribution substations are connected to realize power mutual assistance and power flow optimization between different substations.

8. A black-start system for distribution networks based on mobile energy storage soft switches, using the method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire the distribution network topology and operating parameters; The islanding module is used to divide the islanding range based on the predicted photovoltaic power in the distribution network topology and operating parameters, and obtain the islanding topology centered on distributed photovoltaics. The first constraint construction module is used to obtain the physical parameters of the mobile energy storage soft switch and establish the scheduling constraints of the soft switch. The second constraint construction module is used to establish distribution network operation constraints for the entire black start process of the distribution network based on the topology of the distribution network. The third constraint construction module is used to construct the root node output constraint based on the black start output ramp-up characteristics of the root node of the distribution network. The solution module is used to solve the optimization model under the joint constraints of the scheduling constraints, distribution network operation constraints, and root node output constraints, with the goal of minimizing load loss and network loss, and to determine the access location and power output strategy of the mobile energy storage soft switch in each time period. The control module is used to control the mobile energy storage soft switch to connect to the target node in the distribution network according to the access location and power output strategy, so as to provide active voltage support or power flow transfer for the islands in the islanded topology and realize the black start recovery of the distribution network.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the black-start method for a distribution network based on a mobile energy storage soft switch as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the steps of the black-start method for a distribution network based on a mobile energy storage soft switch as described in any one of claims 1 to 7.

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