Power system recovery stochastic optimization method and system considering network construction type energy storage auxiliary black start

Abstract

The invention provides an electric power system recovery stochastic optimization method and system considering network construction type energy storage auxiliary black start, and the method comprises the steps: constructing a network construction type energy storage mathematical optimization model for a power grid recovery process based on discrete time steps; embedding the network construction type energy storage mathematical optimization model into a black-start power system recovery stochastic optimization model, wherein the optimization target of the power system recovery stochastic optimization model is that the sum of the full-time recovery load power, the recovered rated power of the thermal power generating unit and the output of the new energy unit in each time step reaches the maximum; and outputting a recovery strategy through the power system recovery stochastic optimization model, and recovering the black-start power system. The method provided by the invention can improve early-stage network formation capability and frequency and voltage stability margin, increase available load and accelerate a recovery process, and is suitable for black start recovery of a high-proportion new energy power system.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a stochastic optimization method and system for power system recovery that considers grid-type energy storage-assisted black start. Background Technology

[0002] With the large-scale grid integration of renewable energy, the operation and control of traditional power systems have encountered unprecedented challenges. Distributed energy sources (such as photovoltaic and wind power) are inherently volatile and intermittent, making grid recovery more complex, especially in black start scenarios. A black start refers to the process of gradually restoring the power grid to operation after a complete blackout without external power supply. Traditional black start solutions primarily rely on centralized power generation resources. However, with the widespread integration of distributed energy sources into the grid, this reliance on centralized resources is no longer sufficient to meet the practical operational needs of modern power grids in terms of both flexibility and stability.

[0003] Grid-based energy storage systems possess the capability to independently provide voltage and frequency support, offering a novel technological direction for grid black start. However, in practical engineering applications, optimizing the coordination mechanism and timing configuration between grid-based energy storage systems and distributed energy resources, while simultaneously achieving the dual goals of shortening recovery time and improving system stability, remains a key unresolved issue.

[0004] Domestic research in this field is continuously deepening, but overall, the core research still focuses on black-start strategies centered on traditional generating units such as hydropower and thermal power. Systematic research on the participation of distributed energy resources and energy storage systems in black-start is relatively scarce. Although some studies have explored the auxiliary roles of photovoltaic and wind power in the black-start process, their inherent intermittent nature limits their feasibility of independently undertaking large-scale grid black-start tasks. On the other hand, grid-based energy storage systems have gradually attracted industry attention due to their significant advantages in frequency support, reactive power regulation, and voltage stability. However, a systematic approach to coupling grid-based energy storage systems with distributed energy resources and incorporating them into the black-start optimization framework is still in the initial exploratory stage, and there is an urgent need to construct a systematic model and strategy capable of coping with uncertainties. Summary of the Invention

[0005] In view of this, the present invention provides a stochastic optimization method and system for power system recovery that considers grid-type energy storage-assisted black start, in order to solve the above problems.

[0006] This invention provides a stochastic optimization method for power system recovery considering grid-based energy storage-assisted black start, comprising: constructing a mathematical optimization model of grid-based energy storage based on discrete time steps of the power grid recovery process; embedding the grid-based energy storage mathematical optimization model into a stochastic optimization model for power system recovery during black start, wherein the optimization objective of the stochastic optimization model is to maximize the total output of restored load power and the rated power of restored thermal power units throughout the entire time period, as well as the total output of new energy units at each time step; and outputting a recovery strategy through the stochastic optimization model for power system recovery during black start to restore the power system.

[0007] In another implementation of the present invention, the constraints of the mathematical optimization model of the grid-type energy storage include charging and discharging constraints of the grid-type energy storage device, energy constraints of the energy storage device, and reactive power droop control constraints of the energy storage device.

[0008] In another implementation of the present invention, the objective function of the power system recovery stochastic optimization model is expressed as:

[0009] in, S A collection of scenes; For the scene s The probability of occurrence; For scenario s, the first t Time step node i The active power of the restored load; for s New energy units in various scenarios r In time step t The active power generated; T The total number of time steps for optimization; N A set of nodes; This refers to a collection of thermal power generating units that do not start from black. For the unit g Rated power; a 0-1 variable representing the unit's rated power. g In the t The power-on status of the time step is 1 if it is powered on, and 0 otherwise.

[0010] In another implementation of the present invention, the constraints of the power system recovery stochastic optimization model include node power balance constraints, line power flow constraints, generator model and corresponding constraints, pumped storage unit constraints, generator self-excitation constraints, new energy unit constraints, grid-type energy storage equipment constraints, single load input constraints, network recovery constraints, and system operation constraints.

[0011] Another aspect of the present invention provides a stochastic optimization method for power system recovery considering grid-based energy storage-assisted black start, comprising: a model building module: constructing a mathematical optimization model of grid-based energy storage based on discrete time steps of the power grid recovery process; embedding the grid-based energy storage mathematical optimization model into a stochastic optimization model for power system recovery during black start, wherein the optimization objective of the stochastic optimization model for power system recovery is to maximize the total output of restored load power and the rated power of restored thermal power units throughout the entire time period, as well as the total output of new energy units at each time step; and a strategy execution module: outputting a recovery strategy through the stochastic optimization model for power system recovery to restore the power system during black start.

[0012] In another implementation of the present invention, the constraints of the mathematical optimization model of the grid-type energy storage include charging and discharging constraints of the grid-type energy storage device, energy constraints of the energy storage device, and reactive power droop control constraints of the energy storage device.

[0013] In another implementation of the present invention, the objective function of the power system recovery stochastic optimization model is expressed as:

[0014] in, S A collection of scenes; For the scene s The probability of occurrence; For scenario s, the first t Time step node i The active power of the restored load; for s New energy units in various scenarios r In time step t The active power generated; T The total number of time steps for optimization; N A set of nodes; This refers to a collection of thermal power generating units that do not start from black. For the unit g Rated power; a 0-1 variable representing the unit's rated power. g In the t The power-on status of the time step is 1 if it is powered on, and 0 otherwise.

[0015] In another implementation of the present invention, the constraints of the power system recovery stochastic optimization model include node power balance constraints, line power flow constraints, generator model and corresponding constraints, pumped storage unit constraints, generator self-excitation constraints, new energy unit constraints, grid-type energy storage equipment constraints, single load input constraints, network recovery constraints, and system operation constraints.

[0016] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of a stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start as described in any of the preceding claims. In another aspect, the present invention provides a computer storage medium, characterized in that the computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a stochastic optimization method for power system recovery considering grid-type energy storage-assisted black start as described in any of the preceding claims.

[0017] This invention presents a stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start. It uses grid-connected energy storage devices as one of the supporting power sources for black start, introduces a droop control model for the grid-connected energy storage system, and simultaneously starts the grid-connected energy storage and the pump at the initial moment of black start, allowing them to operate collaboratively. The method considers the impact of the grid-connected energy storage's power on power balance and its influence on the maximum single-load input of the black start system. It can improve early grid connection capability and frequency and voltage stability margins, increase available load, and accelerate the recovery process, making it suitable for black start recovery in high-proportion renewable energy power systems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of a stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start, according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of a test system network according to an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the recovery process of a non-grid-based energy storage system according to an embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram of the recovery process of a grid-type energy storage system according to an embodiment of the present invention.

[0022] Figure 5 This is a schematic diagram of the load power change curves under different strategies in one embodiment of the present invention.

[0023] Figure 6This is a schematic diagram of the cumulative recovery energy curves under different strategies in one embodiment of the present invention.

[0024] Figure 7 This is a schematic diagram of the restored load and the output of each unit under a non-energy storage strategy according to an embodiment of the present invention.

[0025] Figure 8 This is a schematic diagram of the restored load and the output of each unit under an energy storage strategy according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0027] Figure 1 This is a schematic flowchart of a stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start, provided by an embodiment of the present invention. Figure 1 As shown, this embodiment mainly includes: S101. Based on the discrete time step of the power grid recovery process, construct a mathematical optimization model for grid-type energy storage.

[0028] S102. The grid-type energy storage mathematical optimization model is embedded into the black-start power system recovery stochastic optimization model. The optimization objective of the power system recovery stochastic optimization model is to maximize the total output of the restored load power and the rated power of the restored thermal power units throughout the time period, as well as the total output of the new energy units at each time step.

[0029] S103. The power system that started from black starts is restored by outputting a recovery strategy through the power system recovery stochastic optimization model.

[0030] This invention presents a stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start. It uses grid-connected energy storage devices as one of the supporting power sources for black start, introduces a droop control model for the grid-connected energy storage system, and simultaneously starts the grid-connected energy storage and the pump at the initial moment of black start, allowing them to operate collaboratively. The method considers the impact of the grid-connected energy storage's power on power balance and its influence on the maximum single-load input of the black start system. It can improve early grid connection capability and frequency and voltage stability margins, increase available load, and accelerate the recovery process, making it suitable for black start recovery in high-proportion renewable energy power systems.

[0031] In another implementation of the present invention, the constraints of the mathematical optimization model of the grid-type energy storage include charging and discharging constraints of the grid-type energy storage device, energy constraints of the energy storage device, and reactive power droop control constraints of the energy storage device.

[0032] For example, to adapt to the timing decisions of black start, this invention models the grid recovery process in discrete time steps: within each time step, the grid-type energy storage device provides voltage and frequency references externally using voltage source characteristics, and participates in grid connection, expansion, and paralleling within the power and energy boundaries. To ensure the safety and feasibility of the grid formation stage, it is necessary to simultaneously satisfy the mutual exclusion of energy storage charging and discharging, upper and lower power limits, reactive power regulation capability, and energy evolution constraints, and use droop control to characterize its active support for voltage.

[0033] Grid-connected equipment and pumped storage units are used as black start power sources to assist the black start process and accelerate the recovery of units and loads. As one of the black start units, the grid connection time of grid-connected energy storage is the origin of the optimized time range and should meet the following requirements. With the water pump t =1 time-time coordinated black start. Among them, t 0 represents the black start moment, i.e., the initial moment. Grid-based energy storage actively builds voltage and provides power to unrecovered loads and generating units during the black start process, thus accelerating the black start process.

[0034] The charging and discharging constraints of grid-type energy storage devices are as follows: (1) (2) (3) (4) in, , They are respectively s Energy storage devices in various scenarios t The charging and discharging power of the time step; , A 0-1 variable, representing the energy storage device in t The states of the time step respectively represent t The time step is charging and discharging; , Indicates that energy storage devices are in s In the scene t The active and reactive power output to the outside within the time step; A 0-1 variable, representing the energy storage device in t The state of time step; For energy storage devices es Maximum power limit; For energy storage devices esMaximum reactive power output; Let be the apparent capacity of the energy storage device; Equation (1) ensures that charging and power generation do not occur simultaneously.

[0035] The energy constraints of energy storage devices are as follows: (5) (6) (7) in, t 0 represents the black start time, i.e., the initial time; Initial energy; This refers to the upper limit of energy storage devices; for s In the scene t The energy of time-stepping energy storage devices; , They are respectively t The upper and lower limits of the State of Charge (SOC) of time-phased energy storage devices; , Equation (5) represents the charging and discharging efficiency of the energy storage device; Equation (6) represents the initial energy value of the grid-type energy storage; Equation (7) defines the upper and lower boundaries of the energy, corresponding to the safe range of SOC; Equation (8) is the energy evolution equation, which incorporates the charging and discharging efficiency and the time step length, reflecting the conservation of energy and efficiency loss.

[0036] The reactive power droop control of energy storage devices is as follows: (8) in, , These are reactive power and voltage reference values, respectively. s In the scene t The voltage value at each time step; mq is the slope coefficient for droop control; Equation (8) gives the reactive power-voltage droop control relationship, which is used to provide support under weak grid conditions.

[0037] In another implementation of the present invention, the objective function of the power system recovery stochastic optimization model is expressed as: (9) in, S A collection of scenes; For the scene s The probability of occurrence; For scenario s, the first t Time step node i The active power of the restored load; for s New energy units in various scenarios r In time stept The active power generated; T The total number of time steps for optimization; N A set of nodes; This refers to a collection of thermal power generating units that do not start from black. For the unit g Rated power; a 0-1 variable representing the unit's rated power. g In the t The power-on status of the time step is 1 if it is powered on, and 0 otherwise.

[0038] For example, in investigations of numerous past large-scale power outages, outage duration and load loss were key indicators for assessing the severity of the incident. To ensure system stability during recovery, it is crucial to restore the system's power generation capacity as quickly as possible; therefore, maximizing the total rated power of all restored thermal power units across all time steps is considered one of the optimization objectives. Simultaneously, to reduce losses caused by the outage, maximizing the total restored load power across all time steps during the recovery process is also included as an optimization objective. Furthermore, to fully leverage the role of renewable energy units in the recovery process, a term related to the output of renewable energy units is introduced, with the goal of maximizing the total output of renewable energy units at each time step. Given the differences in renewable energy output under different scenarios and the varying probabilities of each scenario, the optimization objective is set to optimize the expected value of the objective function.

[0039] In another implementation of the present invention, the constraints of the power system recovery stochastic optimization model include node power balance constraints, line power flow constraints, generator model and corresponding constraints, pumped storage unit constraints, generator self-excitation constraints, new energy unit constraints, grid-type energy storage equipment constraints, single load input constraints, network recovery constraints, and system operation constraints.

[0040] For example, grid-connected energy storage devices are embedded into a black-start mathematical optimization model to construct a stochastic optimization model for power system recovery that considers grid-connected energy storage-assisted black start.

[0041] 1) Node power balance The power balance constraint considers the impact of grid-type energy storage power on the power balance, and the maximum single-load constraint considers the impact of grid-type energy storage on the maximum single-load of the black-start system. The power balance of each node considering grid-type energy storage is as follows: (9) (10) (11) (12) (13) (14) (15) in, G(i) Represents nodes i A collection of connected generator units, including black-start generator units and thermal power units that are not black-starting; , for s Scenario 1 t The active and reactive power output of the time-stepping generator unit g; Let g be the starting power of unit g; , The active and reactive power outputs of the energy storage es at time step t in scenario s; for s Scenario 1 t Time step node i The reactive power of the restored load; For nodes i The active power load demand; , They are respectively s In the scene t Time step within the route ij Depend on i Node flow j Active and reactive power of nodes; Br(i) Represents nodes i A set of connected lines; 0-1 variables represent nodes i The recovery status is indicated by a value of 1 if the system has recovered, and 0 otherwise. for s In the scene t The voltage amplitude of node i within the time step; for t Nodes within a time step i Self-susceptivity; for s New energy units in various scenarios r In time step t The reactive power generated; for s In the scene t Time step by line ij The inductive reactive power injected to ground is generated at node i. This represents the upper limit of the node voltage amplitude. Let be the power factor angle of the load at node i; For the line ij The susceptivity to ground; Indicates in s In the scene t Time step by node iThe reactive power injected to ground generated by the parallel susceptance on the bus; considering the grid-type energy storage and taking into account the power consumption of the unit, the active / reactive power balance of each bus at any time step is given by equation (10) and equation (11), respectively. The left side is the net injected power of the bus, and the right side is composed of the output of the grid-connected thermal power unit, the new energy unit, the grid-type energy storage and the power of the adjacent branch; Equation (12) specifies the monotonic recovery and upper limit of the load: in the time step dimension, the recovered load cannot be reversed, and it does not exceed the recoverable upper limit of the bus and is controlled by the energized state of the bus; Equation (13) uses a constant power factor model to associate the reactive and active power of the bus load; Equations (15) and (16) are the constraints of the linearized reactive power to ground of the node and the branch, respectively.

[0042] 2) Power flow constraints of the line The linearization of the AC power flow constraints of the line can be expressed as: (16) (17) In the formula, , The lines are respectively ij The maximum allowable active and reactive power to flow; , The lines are respectively ij Conductivity and susceptance; θsi ( t )for s Scenario 1 t Nodes within a time step i The voltage phase angle; U ij ( t ) is a 0-1 variable, representing the line ij In the t The power-on status of the time step is 1 if it is powered on, and 0 otherwise.

[0043] 3) Generator model The generator model is modeled and linearized based on the startup, ramp-up, and scheduling processes. Its model and corresponding constraints can be expressed as follows: (18) (19) (20) (twenty one) (twenty two) (twenty three) in, , express s Scenario Unitg The difference in output power between the current time step and the previous time step; , For the unit g The upper and lower limits of reactive power output; Equation (19) limits the reactive power of the unit to be within the allowable range in each time step, and is controlled by the unit's operating status. When the unit is in a shutdown state, the output is zero; when the unit starts, its reactive power supply capacity is constrained by the upper and lower limits; Equation (20) gives the change in the unit's active power output. By expressing the difference in active power output between adjacent time steps, the rate of change of the unit's power is represented, and the ramp rate is used to characterize it; Equation (21) ensures that before the unit reaches its minimum technical output after grid connection, the reactive power output is within the allowable range. The constant ramp rate is used for ramping; Equation (22) ensures that the unit output can vary within the ramp rate limit after reaching the minimum technical output; Equation (23) ensures that the unit, after being powered on, at least... After the unit's time step, Only then can the value be positive, and its value cannot be greater than 1. Formula (24) ensures that after the unit climbs to the minimum technical output, the unit output will not be lower than the minimum technical output.

[0044] 4) Constraints of pumped storage units The pumping / power generation constraints of pumped storage units are as follows: (twenty four) (25) (26) in, for s Pumped storage units in various scenarios t Pumping power per hour; This refers to the power of the water pump motor. The variable is 0-1, representing the pumped storage unit in... t The pumping status at each time step; express s Pumped storage units in various scenarios t The active power output to the outside within a time step; for s The active power generated by the pumped storage unit in the scenario; The variable is 0-1, representing the pumped storage unit in... t The power generation state at the time step. Equation (27) ensures that the power generation and pumping states do not occur in the same time step.

[0045] The storage capacity constraints are as follows: (27) (28) in, for t Shibu Reservoir water volume , These are the upper and lower limits of the storage capacity, respectively. The hydroelectric conversion factor under pumping conditions. This is the hydroelectric conversion factor under power generation conditions.

[0046] As a black-start unit, the grid connection time of the pumped storage unit is the origin of the optimized time range, therefore its start-up time... and charging power Both are 0 and Meanwhile, pumped storage units have no minimum technical output requirement, therefore the following constraints apply to pumped storage units: (29) (30) in, express s Pumped storage units in various scenarios t Reactive power generated within a time step , The upper and lower limits of reactive power output of pumped storage units. The ramp rate of pumped storage units.

[0047] 5) Generator self-excitation constraint During system recovery, unloaded lines often generate a large amount of capacitive reactive power. When the generator stator inductance interacts with the line capacitive reactance parameters, parametric resonance may occur, leading to self-excitation in the system. In practical engineering applications, it is generally considered that as long as the product of the unit's rated capacity and the short-circuit ratio exceeds the remaining charging power after line compensation, the system can avoid self-excitation. Therefore, the following constraints apply: (31) In the formula, For generator sets g The short-circuit ratio, This represents the rated capacity of the generator unit. The circuit model of this invention adopts a Π-type equivalent model. for s In the scene t Time step by line ij Earth-to-ground electrical charge at the node i The reactive power injected to ground generated at the terminal.

[0048] 6) Constraints on new energy units During system recovery, the no-load charging of power lines can lead to excess reactive power. Therefore, it is essential to fully utilize the dynamic reactive power regulation capabilities of renewable energy units. Furthermore, renewable energy units typically have low starting power and fast start-up speeds, and do not require ramp-up. Therefore, it can be assumed that once the node containing the renewable energy unit recovers, the unit can begin generating electricity. Based on this, the power constraints for renewable energy units are as follows: (32) (33) In the formula, for s New energy units in various scenarios r exist t The maximum active power that can be generated per hour; This refers to the power factor angle of the new energy unit.

[0049] 7) Constraints of grid-based energy storage devices The mathematical optimization model constraints for grid-type energy storage equipment are Equations (1)-(8).

[0050] 8) Single load limit If the load introduced at one time is too large, it will cause serious system frequency deviation and voltage drop problems. Therefore, the following constraints must be met between the load introduced at one time and the units and equipment already connected to the grid: (34) In the formula, This represents the maximum allowable frequency deviation for normal system operation. , They are the generator sets g Energy storage equipment es The frequency response value; It is a generator set g The rated active power.

[0051] 9) Network recovery constraints To ensure the connectivity of the restored network, the network must meet the following constraints: (35) (36) (37) (38) (39) Equation (36) indicates that only at the node i After recovery, the node iOnly when the generator equipment on the line can be restored; Equation (37) indicates that the node, generator, energy storage and line will not be disconnected again after restoration; Equation (38) indicates that if the line ij It has been restored, and its two ends nodes are now open. i , j It must have already been restored; Equation (39) indicates the relationship with the node. i At least one of the connected lines has been restored, node i Only then can it be restored; Equation (40) represents the circuit. ij Two-end nodes i , j At least one line has been restored in the previous period. ij Only then can it be restored.

[0052] 10) System operation constraints The main system operation constraints are to ensure that node voltages and phase angles do not exceed limits: (40) In the formula, , These are the upper and lower limits of the node voltage amplitude; , These are the upper and lower limits of the node voltage phase angle, respectively.

[0053] Example 1 To verify the effectiveness of the black-start method for power transmission systems proposed in this invention, the following simulation scenario was designed: A New England 10-unit 39-bus system was selected as the test basis, and 14 new energy generating units were added (all units had a power factor uniformly set to 0.9). A grid-type energy storage device was also incorporated. The specific network topology of this simulation system can be found in [reference needed]. Figure 2 Given the randomness of the timing of major power outages and the difficulty in predicting them in advance, this invention fully considers the seasonal variations and intraday fluctuations in renewable energy output—significant differences in renewable energy output under different scenarios—thereby comprehensively covering the uncertainty of renewable energy output.

[0054] To verify that grid-connected energy storage devices can accelerate the black start recovery process, a black start scheme without grid-connected energy storage devices was compared. Figure 3 , 4 The diagram illustrates the recovery processes of recovery schemes using both grid-based and non-grid-based energy storage, with different colors representing components and the recovery sequence. As can be seen from the diagram, due to the coordinated black start of the grid-based energy storage and the pump, the recovery scheme with grid-based energy storage recovers faster in the early and middle stages compared to the scheme without grid-based energy storage. t=At time step 10, all nodes have been restored. Meanwhile, Tables 1 and 2 show the average restored load power and the specific restoration sequence of each generator across all scenarios at each time step for the recovery schemes without and with grid-connected energy storage. As can be seen from the tables, the recovery scheme with grid-connected energy storage restores more units and load faster and in greater quantities in the early and mid-term stages compared to the scheme without grid-connected energy storage.

[0055] Table 1. Restoration sequence and load restoration power excluding energy storage units

[0056] Table 2 Restoration sequence and load restoration power including energy storage units

[0057] Figure 5 The curves showing the change in restored load power during the entire system recovery phase under different strategies are presented. Figure 5 As shown, considering the recovery strategy of grid-connected energy storage, the load recovery was basically completed in time period 9, that is, 135-150 minutes after the black start unit was connected to the grid. This is faster than the recovery without considering grid-connected energy storage, and the overall load recovery time is earlier. Figure 6 The data shows the recovered load energy of the entire system at different times under different strategies. It can be seen that the recovery energy of the recovery strategy with grid-type energy storage is always greater than that without grid-type energy storage, indicating that adding grid-type energy storage can improve the recovery energy of the system during black start. Figure 7 , 8 The output curves of each unit and the power of the restored load are displayed. For example... Figure 7 , 8 As shown, since grid-connected energy storage assists black start as a black start power source, more units are restored in the early and middle stages, enhancing the power generation capacity in the early and middle stages of restoration, enabling the load to be restored more quickly and reducing the economic losses caused by the entire power outage.

[0058] The beneficial effects of this invention are reflected in: (1) This invention proposes a stochastic optimization method for power system recovery that considers grid-type energy storage-assisted black start. This method can effectively improve the load recovery rate and accelerate the recovery of the unit, enabling it to output power as soon as possible and speed up the black start process.

[0059] (2) The present invention can provide support for the voltage and frequency of the power grid by controlling the output power of the grid-type energy storage during the black start process, thereby effectively enhancing the stability of voltage and frequency during the black start process and reducing voltage and frequency fluctuations.

[0060] (3) Based on the traditional black start method, the present invention can provide power support for the uncertain output of new energy in different scenarios by constructing a grid-type energy storage, and has good robustness under the random optimization of the scenario.

[0061] Another aspect of the present invention provides a stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start, comprising: Model building module: Based on the discrete time step of the power grid recovery process, a mathematical optimization model for grid-type energy storage is constructed; the mathematical optimization model for grid-type energy storage is embedded into the black-start stochastic optimization model for power system recovery. The optimization objective of the stochastic optimization model for power system recovery is to maximize the total output of the restored load power and the rated power of the restored thermal power units throughout the entire time period, as well as the total output of the new energy units at each time step.

[0062] Strategy execution module: Outputs a recovery strategy through the power system recovery stochastic optimization model to restore the power system after black start.

[0063] This invention presents a stochastic optimization system for power system recovery that considers grid-connected energy storage-assisted black start. It uses grid-connected energy storage devices as one of the supporting power sources for black start, and introduces a droop control model for the grid-connected energy storage system. At the initial moment of black start, the grid-connected energy storage and the pump are started simultaneously and operate collaboratively. The invention considers the impact of the grid-connected energy storage power on power balance and the impact of the grid-connected energy storage on the maximum single load input of the black start system. It can improve early grid connection capability and frequency and voltage stability margins, increase available load, and accelerate the recovery process, making it suitable for black start recovery in high-proportion renewable energy power systems.

[0064] In another implementation of the present invention, the constraints of the mathematical optimization model of the grid-type energy storage include charging and discharging constraints of the grid-type energy storage device, energy constraints of the energy storage device, and reactive power droop control constraints of the energy storage device.

[0065] In another implementation of the present invention, the objective function of the power system recovery stochastic optimization model is expressed as:

[0066] in, S A collection of scenes; For the scene s The probability of occurrence; For scenario s, the first t Time step node i The active power of the restored load; for s New energy units in various scenarios r In time step t The active power generated; T The total number of time steps for optimization;N A set of nodes; This refers to a collection of thermal power generating units that do not start from black. For the unit g Rated power; a 0-1 variable representing the unit's rated power. g In the t The power-on status of the time step is 1 if it is powered on, and 0 otherwise.

[0067] In another implementation of the present invention, the constraints of the power system recovery stochastic optimization model include node power balance constraints, line power flow constraints, generator model and corresponding constraints, pumped storage unit constraints, generator self-excitation constraints, new energy unit constraints, grid-type energy storage equipment constraints, single load input constraints, network recovery constraints, and system operation constraints.

[0068] In another aspect of the present invention, the electronic device includes: a processor, a memory, and a communication bus and a communication interface.

[0069] in: The processor, memory, and communication interface communicate with each other via a communication bus.

[0070] A communication interface is used to communicate with other electronic devices or servers.

[0071] The processor is used to execute programs, specifically, to perform any of the steps in the above embodiments of the stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start.

[0072] Specifically, the program may include program code, which includes computer operation instructions.

[0073] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0074] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0075] Specifically, the program can be used to cause the processor to execute the steps of any of the stochastic optimization methods for power system recovery considering grid-connected energy storage-assisted black start described in the embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the above-mentioned stochastic optimization methods for power system recovery considering grid-connected energy storage-assisted black start, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments.

[0076] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods of various embodiments of this application.

[0077] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0078] Specific embodiments of the present invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result.

[0079] It should be noted that all directional indications (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components in a certain order (as shown in the figure). If the specific order changes, the directional indication will also change accordingly.

[0080] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.

[0081] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0082] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.

[0083] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start, characterized in that, include: A mathematical optimization model for grid-based energy storage is constructed based on the discrete time step of the grid recovery process. The mathematical optimization model of the grid-type energy storage is embedded into the stochastic optimization model of power system recovery in black start. The optimization objective of the stochastic optimization model of power system recovery is to maximize the total output of the restored load power and the rated power of the restored thermal power units at all times. The power system recovery stochastic optimization model outputs a recovery strategy to restore the black-start power system.

2. The method according to claim 1, characterized in that, The constraints of the mathematical optimization model for grid-type energy storage include charging and discharging constraints of grid-type energy storage devices, energy constraints of energy storage devices, and reactive power droop control constraints of energy storage devices.

3. The method according to claim 1, characterized in that, The objective function of the power system recovery stochastic optimization model is expressed as: in, S A collection of scenes; For the scene s The probability of occurrence; For scenario s, the first t Time step node i The active power of the restored load; for s New energy units in various scenarios r In time step t The active power generated; T The total number of time steps for optimization; N A set of nodes; This refers to a collection of thermal power generating units that do not start from black. For the unit g Rated power; a 0-1 variable representing the unit's rated power. g In the t The power-on status of the time step is 1 if it is powered on, and 0 otherwise.

4. The method according to claim 3, characterized in that, The constraints of the power system recovery stochastic optimization model include node power balance constraints, line power flow constraints, generator model and corresponding constraints, pumped storage unit constraints, generator self-excitation constraints, new energy unit constraints, grid-type energy storage equipment constraints, single load input constraints, network recovery constraints, and system operation constraints.

5. A stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start, characterized in that, include: Model building module: Based on the discrete time step of the power grid recovery process, a mathematical optimization model for grid-type energy storage is constructed; the mathematical optimization model for grid-type energy storage is embedded into the black-start stochastic optimization model for power system recovery. The optimization objective of the stochastic optimization model for power system recovery is to maximize the total output of the restored load power and the rated power of the restored thermal power units throughout the entire time period, as well as the total output of the new energy units at each time step. Strategy execution module: Outputs a recovery strategy through the power system recovery stochastic optimization model to restore the power system after black start.

6. The system according to claim 5, characterized in that, The constraints of the mathematical optimization model for grid-type energy storage include charging and discharging constraints of grid-type energy storage devices, energy constraints of energy storage devices, and reactive power droop control constraints of energy storage devices.

7. The system according to claim 5, characterized in that, The objective function of the power system recovery stochastic optimization model is expressed as: in, S A collection of scenes; For the scene s The probability of occurrence; For scenario s, the first t Time step node i The active power of the restored load; for s New energy units in various scenarios r In time step t The active power generated; T The total number of time steps for optimization; N A set of nodes; This refers to a collection of thermal power generating units that do not start from black. For the unit g Rated power; a 0-1 variable representing the unit's rated power. g In the t The power-on status of the time step is 1 if it is powered on, and 0 otherwise.

8. The system according to claim 7, characterized in that, The constraints of the power system recovery stochastic optimization model include node power balance constraints, line power flow constraints, generator model and corresponding constraints, pumped storage unit constraints, generator self-excitation constraints, new energy unit constraints, grid-type energy storage equipment constraints, single load input constraints, network recovery constraints, and system operation constraints.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the stochastic optimization method for power system recovery considering grid-connected energy storage-assisted black start as described in any one of claims 1 to 4.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps in the stochastic optimization method for power system recovery considering grid-type energy storage-assisted black start as described in any one of claims 1 to 4.

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