Power system dispatching method, device, computer equipment, storage medium and product

Abstract

The present application relates to a method, apparatus, computer equipment, storage medium and product for power system scheduling. The method comprises: solving a power system scheduling optimization model based on an external approximation method, a GUROBI solver and the operating parameters corresponding to the power system in the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system scheduling optimization model is a model constructed based on the dynamic operating model of the at least one battery swap station and the constraints of the power system; the power system is scheduled according to the operating parameters of the power system in the current time period. The use of this method can reduce the complexity of calculations when simulating the scheduling of the power system.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, apparatus, computer equipment, storage medium and product for dispatching an electric power system. Background Art

[0002] With the widespread adoption of electric vehicles, battery swapping has garnered widespread attention in recent years. This model involves replacing batteries at battery swap stations to provide the necessary power for electric vehicles. The batteries are then centrally charged and discharged at these stations, enabling interaction with the power system. The rise of battery swapping presents new challenges to the power system's energy supply, necessitating coordinated power system scheduling to effectively manage power demand at battery swap stations.

[0003] In traditional technology, constructing a microgrid scheduling model containing electric vehicle battery swap stations to achieve scheduling simulation of the power system has the problem of relatively complex calculations. Summary of the Invention

[0004] Based on this, it is necessary to provide a power system scheduling method, device, computer equipment, computer-readable storage medium and product to address the above technical problems, which can reduce the complexity of calculations when simulating the scheduling of power systems.

[0005] In a first aspect, the present application provides a method for dispatching a power system. The method comprises:

[0006] Solving the power system dispatch optimization model based on the external approximation method, the GUROBI solver, and the operating parameters of the power system corresponding to the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on the dynamic operation model of the at least one battery swap station and the constraints of the power system;

[0007] The power system is dispatched according to the operating parameters of the power system in the current time period.

[0008] In one embodiment, the power system scheduling optimization model is solved based on the external approximation method, the GUROBI solver, and the operating parameters of the power system in the previous time period to obtain the operating parameters of the power system containing the battery swap station in the current time period, including:

[0009] According to the external approximation method, the GUROBI solver, the operating parameters corresponding to the power system in the previous time period, and the total number of batteries in the charging and discharging states in each battery swap station corresponding to the power system, the power system scheduling optimization model is solved to obtain the operating parameters of the power system containing the battery swap station in the current time period.

[0010] In one embodiment, the method further comprises:

[0011] Based on the battery state transfer equation corresponding to the at least one battery swap station, a dynamic operation model of the at least one battery swap station is obtained.

[0012] In one embodiment, the method further comprises:

[0013] The battery state transfer equation is established based on the number of batteries in a charging state in the at least one battery swap station, the number of batteries in a discharging state, the number of batteries in the at least one battery swap station, and the number of vehicles providing battery swap services in the at least one battery swap station.

[0014] In one embodiment, the operating parameters include the power generation power of the thermal power unit within a preset period, the power generation power of the wind power unit within a preset period, the power generation power of the photovoltaic unit within a preset period, and the operating status, startup status, and shutdown status of the thermal power unit.

[0015] In one embodiment, the constraints include constraints on the state variables of the thermal power units, constraints on the minimum on / off time of the thermal power units, constraints on the upper and lower limits of the output of the thermal power units, ramp constraints of the thermal power units, constraints on the total installed capacity and real-time resources of wind power and photovoltaic output, constraints on the power balance of the power system, and constraints on the rotating reserve of the power system.

[0016] In a second aspect, the present application further provides a power system dispatching device. The device comprises:

[0017] a calculation module, configured to solve a power system dispatch optimization model based on an external approximation method, a GUROBI solver, and operating parameters corresponding to the power system in a previous time period to obtain operating parameters of the power system in a current time period; the power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on a dynamic operating model of the at least one battery swap station and constraints of the power system;

[0018] A scheduling module is used to schedule the power system according to the operating parameters of the power system in the current time period.

[0019] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0021] In a fifth aspect, the present application further provides a computer program product, comprising a computer program that, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0022] In the above-mentioned power system dispatching method, apparatus, computer equipment, storage medium and product, the operating parameters of the power system in the current time period are obtained by solving the power system dispatching optimization model based on the external approximation method, the GUROBI solver and the corresponding operating parameters of the power system in the previous time period, thereby reducing the complexity of the calculation and improving the efficiency and reliability of the power system dispatching simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is an application environment diagram of a power system dispatching method in one embodiment;

[0024] Figure 2 1 is a flow chart of a power system dispatching method according to an embodiment;

[0025] Figure 3 is a flow chart of a power system dispatching method in an exemplary embodiment;

[0026] Figure 4 1 is a flow chart of a power system dispatching method according to an embodiment;

[0027] Figure 5 This is a diagram of the internal structure of a server in one embodiment;

[0028] Figure 6 FIG. 4 is a diagram showing the internal structure of a terminal in an embodiment. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0030] The power system scheduling method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. The computer device 102 solves the power system scheduling optimization model based on the external approximation method, the GUROBI solver and the operating parameters corresponding to the power system in the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system scheduling optimization model is a model constructed based on the dynamic operation model of at least one battery swap station and the constraints of the power system; the power system is scheduled according to the operating parameters of the power system in the current time period. The computer device 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc.

[0031] In one embodiment, Figure 2 As shown, a power system dispatching method is provided, which is applied to Figure 1 Taking the computer device 102 in the example as an example, the method includes the following steps:

[0032] Step 202: Solve the power system dispatch optimization model based on the external approximation method, the GUROBI solver, and the operating parameters corresponding to the power system in the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on the dynamic operation model of at least one battery swap station and the constraints of the power system.

[0033] Among them, the GUROBI solver is a mathematical programming optimizer that can solve large-scale linear problems, quadratic objective problems, mixed integer linear problems, etc. The operating parameters include but are not limited to the power generation power of the thermal power units in the power system within a preset time period, the power generation power of the wind power units within a preset time period, the power generation power of the photovoltaic units within a preset time period, and the operating status, startup status, and shutdown status of the thermal power units. The power system dispatch optimization model is a model constructed based on the dynamic operation model of at least one battery swap station and the constraints of the power system. Among them, the time span of the previous time period and the time span of the current time period are the same, which can be 48 hours or 72 hours. This embodiment does not limit this.

[0034] Optionally, assuming that the time span of the previous time period and the time span of the current time period are both 48 hours, the operating parameters corresponding to the power system in the previous time period are substituted into the power system dispatch optimization model, and then the model is solved according to the external approximation method and the GUROBI solver to obtain the operating parameters of the power system in the current time period.

[0035] Step 204: dispatching the power system according to the operating parameters of the power system in the current time period.

[0036] Optionally, the power system is configured according to the operating parameters of the power system in the current time period to achieve scheduling of the power system.

[0037] In the above-mentioned power system dispatching method, the power system dispatching optimization model is solved based on the external approximation method, the GUROBI solver, and the operating parameters corresponding to the power system in the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system dispatching optimization model is a model constructed based on the dynamic operating model of at least one battery swap station and the constraints of the power system; the power system is dispatched based on the operating parameters of the power system in the current time period. Among them, the operating parameters of the power system in the current time period are obtained by solving the power system dispatching optimization model based on the external approximation method, the GUROBI solver, and the operating parameters corresponding to the power system in the previous time period, which reduces the complexity of the calculation and improves the efficiency and reliability of the power system dispatching simulation.

[0038] In one embodiment, the power system dispatch optimization model is solved based on the external approximation method, the GUROBI solver, and the operating parameters of the power system corresponding to the previous time period to obtain the operating parameters of the power system including the battery swap station in the current time period, including:

[0039] Based on the external approximation method, the GUROBI solver, the operating parameters of the power system corresponding to the previous time period, and the total number of batteries in the charging and discharging states in each battery swap station corresponding to the power system, the power system scheduling optimization model is solved to obtain the operating parameters of the power system containing the battery swap station in the current time period.

[0040] Optionally, the objective function in the power system dispatch optimization model is as shown in formula (1).

[0041] (1)

[0042] In formula (1), For thermal power units in power systems exist Power generation during the time period; and They are The power generation power of wind turbines and photovoltaic units in the power system during this period; 、 and Both are 0-1 state variables, representing thermal power units exist Operation status, startup status and shutdown status within the time period; and Thermal power units The variable operation energy consumption coefficient and the fixed operation energy consumption coefficient; For thermal power units Unit energy consumption corresponds to fuel price; and Thermal power units Unit startup energy consumption and unit shutdown energy consumption.

[0043] Assuming that there are multiple battery swap stations in the power system, such as battery swap station 1, battery swap station 2...battery swap station n, according to the external approximation method, battery swap station 1, battery swap station 2...battery swap station n can be regarded as a whole during the calculation, that is, only the total number of batteries in charging state and batteries in discharging state in battery swap station 1, battery swap station 2...battery swap station n is considered during the calculation, without considering the number of batteries in charging state and batteries in discharging state in battery swap station 1, the number of batteries in charging state and batteries in discharging state in battery swap station 2...the number of batteries in charging state and batteries in discharging state in battery swap station n.

[0044] Optionally, according to the operating parameters corresponding to the power system in the previous time period, formula (1) is calculated according to the external approximation method and the GUROBI solver to obtain the operating parameters of the power system containing the battery swap station in the current time period. The operating parameters include the power generation power of the thermal power unit, the power generation power of the wind turbine unit, the power generation power of the photovoltaic unit, the operating status, startup status, and shutdown status of the thermal power unit in the power system containing the battery swap station in the current time period.

[0045] In this embodiment, the power system scheduling optimization model is solved based on the external approximation method, the GUROBI solver, the operating parameters of the power system corresponding to the previous time period, and the total number of batteries in the charging and discharging states at each battery swap station corresponding to the power system. The operating parameters of the power system containing the battery swap stations in the current time period are obtained. The calculation based on the external approximation method treats the multiple battery swap stations in the power system as a whole, reducing the complexity of the calculation and improving the efficiency.

[0046] In one embodiment, based on the battery state transfer equation corresponding to at least one battery swap station, a dynamic operation model of at least one battery swap station is obtained.

[0047] Alternatively, assuming that the number of battery swap stations is 1, in order to reduce the computational complexity of the battery state transfer equation corresponding to the battery swap station, the following assumptions are proposed: Assumption 1: The operation of each battery swap station is independent, that is, the battery allocation between battery swap stations is not considered; Assumption 2: For the same battery swap station, the battery equipped with the electric vehicle it serves and the reserve battery in the station have the same battery capacity, and the battery replaced by the vehicle during the battery swap service also has the same remaining power; Assumption 3: The number of battery charging compartments at each battery swap station is the same as the number of batteries in the station; Assumption 4: Each battery in the same battery swap station can only be charged or discharged at a fixed rate, and the charging or discharging process must be maintained continuously within a unit time interval; Assumption 5: The impact of battery degradation on battery capacity is not considered. Based on the above assumptions, the battery state transfer equation corresponding to the battery swap station is constructed, and the additional battery reserve rate is determined according to the battery state transfer equation, as shown in Formula (2).

[0048] (2)

[0049] In formula (2), represents the number of batteries in the Lth group of the kth battery swap station at time (t-1), Indicates the battery reserve rate of fully charged state at battery swap station k, It represents the number of vehicles that receive battery swapping services at battery swap station k during period t.

[0050] In addition, the charging and discharging power of the battery swap station in each time period is determined by the total number of batteries in the charging and discharging states, as shown in formulas (3) and (4).

[0051] (3)

[0052] (4)

[0053] In formula (3), represents the charging power of battery swap station k during period t, Indicates the energy conversion efficiency during battery charging. represents the rated charging power of each battery in the battery swap station k, It represents the number of batteries in the lth battery pack in the battery swap station k that are in the charging state during the t period.

[0054] In formula (4), represents the discharge power of the battery swap station k during period t, Indicates the energy conversion efficiency during battery discharge. represents the rated discharge power of each battery in the battery swap station k, It represents the number of batteries in the lth battery pack in the battery swap station k that are in the discharged state during the t period.

[0055] In the power system, the battery swap station can provide the power system with spinning reserve capacity. In this implementation, only the upward spinning reserve capacity in the spinning reserve capacity is considered. The calculation formula of the upward spinning reserve capacity is shown in formula (5).

[0056] (5)

[0057] In formula (5), represents the maximum upward rotation reserve capacity that the battery swap station k can provide during period t, represents the charging power of battery swap station k during period t, represents the discharge power of the battery swap station k during period t, It represents the theoretical maximum discharge power of battery swap station k during period t, The calculation formula is shown in formula (6).

[0058] (6)

[0059] In formula (6), represents the number of batteries in the Lth battery pack in the battery swap station k at the end of the (t-1) period, represents the number of vehicles that receive battery swap services at battery swap station k during period t, represents the number of batteries in the lth battery pack in the battery swap station k that are in a charging state during the t period, Indicates the energy conversion efficiency during battery discharge. Indicates the rated discharge power of each battery in battery swap station k.

[0060] The battery state transfer equation corresponding to the battery swap station and formulas (2), (3), (4), (5), and (6) corresponding to at least one battery swap station are used as the dynamic operation model of at least one battery swap station.

[0061] In this embodiment, a dynamic operation model of at least one battery swap station is derived based on the battery state transition equation corresponding to at least one battery swap station. Derived based on the battery state transition equation corresponding to the battery swap station, the dynamic operation model of the battery swap station can more accurately characterize the operating state of the battery swap station while considering the interaction between the battery swap station and the power system.

[0062] In one embodiment, the method further comprises:

[0063] A battery state transfer equation is established based on the number of batteries in a charging state in at least one battery swap station, the number of batteries in a discharging state, the number of batteries in at least one battery swap station, and the number of vehicles providing battery swap services in at least one battery swap station.

[0064] Alternatively, assuming that the number of battery swap stations is 1, denoted as battery swap station k, at any time, the batteries in battery swap station k are divided into L groups according to the state of charge from low to high, where the average state of charge of the batteries in group l is denoted as , so there is Assuming that each battery in battery swap station k can only be charged or discharged at a fixed rate, and the charging or discharging process must be maintained continuously within a unit time interval, then the average state of charge intervals between adjacent battery groups can be considered equal and the same as the amount of charge gained (lost) when the battery is charged (discharged) at a fixed rate within a unit time interval. (i.e. the average state of charge of the first group of batteries) is the theoretical minimum charge of the batteries in the station. The remaining charge of the battery replaced by the vehicle during the battery replacement service is also assumed to be ; (i.e. the average state of charge of the Lth group of batteries) is the maximum charge of the batteries in the station. On the premise that the charged state of batteries can be put into battery replacement service, it can be assumed that each battery can maintain the same charge state and remain in the original battery pack within any unit time interval, or increase or decrease the charge state through fixed power charging and discharging and transfer to the adjacent battery pack.

[0065] According to the number of batteries in the charging state, the number of batteries in the discharging state, the number of batteries, and the number of vehicles that provide battery swap services in the battery swap station k, the battery state transfer equation is established, as shown in formulas (7), (8), (9), and (10).

[0066] (8)

[0067] In formula (7), is the first battery pack in the battery swap station k at the end of period t (the average state of charge is ) Number of batteries in is the first battery pack in the battery swap station k at the end of time period t-1 (the average state of charge is ) Number of batteries in is the number of batteries in the first battery pack in the battery swap station k that are in a charging state during time period t, is the number of batteries in the second battery pack in the battery swap station k that are in a discharged state during time period t, The number of vehicles that receive battery swap services at battery swap station k during period t.

[0068] In formula (8), is the lth battery pack in the battery swap station k at the end of period t (the average state of charge is ) Number of batteries in is the lth battery pack in the battery swap station k at the end of time period t-1 (the average state of charge is ) Number of batteries in is the number of batteries in the lth battery pack in the battery swap station k in the t period that are in the charging state, is the number of batteries in the lth battery pack in the battery swap station k in the t period that are in the discharged state, is the number of batteries in the (l-1)th battery pack in the battery swap station k in the t period that are in a charging state, is the number of batteries in the (l+1)th battery group in the battery swap station k that are in a discharged state during time period t.

[0069] In formula (9), is the Lth battery pack in the battery swap station k at the end of the t-1 period (the average state of charge is ) is the number of batteries in is the number of batteries in the Lth battery pack in the battery swap station k that are in a discharged state during time period t, The number of vehicles that receive battery swap services at battery swap station k during period t.

[0070] (10)

[0071] In formula (10), is the lth battery pack in the battery swap station k at the end of period t (the average state of charge is ) Number of batteries in is the total number of batteries in battery swap station k.

[0072] In addition, the number of batteries in the charging state and the number of batteries in the discharging state in each battery group in the battery swap station k must meet the non-negative condition, and the sum of the two must not exceed the total number of batteries in the group, as shown in formulas (11), (12), (13), (14), (15), and (16).

[0073]

[0074]

[0075] In formula (11), is the first battery pack in the battery swap station k at the end of time period t-1 (the average state of charge is ) Number of batteries in is the number of batteries in the first battery pack in the battery swap station k that are in a charging state during time period t.

[0076] In formula (12), is the lth battery pack in the battery swap station k at the end of time period t-1 (the average state of charge is ) Number of batteries in is the number of batteries in the lth battery pack in the battery swap station k in the t period that are in the charging state, is the number of batteries in the lth battery pack in the battery swap station k that are in a discharged state during time period t.

[0077] In formula (13), is the Lth battery pack in the battery swap station k at the end of the t-1 period (the average state of charge is ) is the number of batteries in is the number of batteries in the Lth battery pack in the battery swap station k that are in a discharged state during time period t, The number of vehicles that receive battery swap services at battery swap station k during period t.

[0078] In formula (14), is the lth battery pack in the battery swap station k at the end of period t (the average state of charge is ) Number of batteries in the battery pack.

[0079] In formula (15), is the number of batteries in the lth battery pack in the battery swap station k that are in a charging state during time period t.

[0080] In formula (16), is the number of batteries in the lth battery pack in the battery swap station k that are in a discharged state during time period t.

[0081] In this embodiment, a battery state transition equation is established based on the number of batteries in a charging state, the number of batteries in a discharging state, the number of batteries in at least one battery swap station, and the number of vehicles performing battery swapping services at at least one battery swap station. The battery state transition equation is determined based on the number of batteries in a charging state, the number of batteries in a discharging state, the number of batteries, and the number of vehicles performing battery swapping services at the battery swap station, and can characterize the operating state of the battery swap station from multiple dimensions.

[0082] In one embodiment, the operating parameters include the power generation power of the thermal power unit within a preset period, the power generation power of the wind power unit within a preset period, the power generation power of the photovoltaic unit within a preset period, and the operating status, startup status, and shutdown status of the thermal power unit.

[0083] Optionally, assuming that the power system includes thermal power units, wind power units and photovoltaic units, the operating parameters of the power system include the power generation power of the thermal power units within a preset period of time, the power generation power of the wind power units within a preset period of time, the power generation power of the photovoltaic units within a preset period of time, and the operating status, startup status and shutdown status of the thermal power units.

[0084] In this embodiment, the operating parameters include the power generated by the thermal power generation unit within a preset time period, the power generated by the wind power generation unit within a preset time period, the power generated by the photovoltaic power generation unit within a preset time period, and the operating status, startup status, and shutdown status of the thermal power generation unit. That is, by configuring the power generated by the thermal power generation unit within a preset time period, the power generated by the wind power generation unit within a preset time period, the power generated by the photovoltaic power generation unit within a preset time period, and the operating status, startup status, and shutdown status of the thermal power generation unit, the power system can be dispatched.

[0085] In one embodiment, the constraints include constraints on the state variables of the thermal power units, constraints on the minimum on / off time of the thermal power units, constraints on the upper and lower limits of the output of the thermal power units, ramp constraints on the thermal power units, constraints on the total installed capacity and real-time resources of wind power and photovoltaic output, constraints on the power balance of the power system, and constraints on the rotating reserve of the power system.

[0086] Optionally, the constraints on the state variables of the thermal power unit are shown in formulas (17), (18), and (19).

[0087] (17)

[0088] (18)

[0089] (19)

[0090] In formula (17), and Thermal power units exist Startup status and shutdown status within the time period.

[0091] In formula (18), and Thermal power units exist Startup status and shutdown status within the time period, For thermal power units exist Operation status within the time period, For thermal power units exist Operation status within the time period.

[0092] In formula (19), and Represent thermal power units exist Running status, startup status and shutdown status within the time period.

[0093] The constraints for the minimum on / off time of thermal power units are shown in formulas (20) and (21).

[0094] (20)

[0095] (twenty one)

[0096] In formula (20), Indicates thermal power unit exist Operation status within the time period, Indicates thermal power unit Minimum startup time, Indicates thermal power unit exist The startup status within the time period.

[0097] In formula (21), Indicates thermal power unit exist Operation status within the time period, Indicates thermal power unit The minimum shutdown time, Indicates thermal power unit exist The shutdown status during the time period.

[0098] The constraints on the upper and lower limits of the output of thermal power units are shown in formula (22).

[0099] (twenty two)

[0100] In formula (22), Indicates thermal power unit exist Operation status within the time period, and Thermal power units The lower and upper limits of output, For thermal power units in power systems exist Power generation during the period.

[0101] The ramp constraints of thermal power units are shown in formulas (22) and (23).

[0102] (twenty two)

[0103] (twenty three)

[0104] In formula (22), For thermal power units The maximum power reduction limit, For thermal power units The power reduction limit during the shutdown phase is For thermal power units in power systems exist Power generation during the period, For thermal power units in power systems exist Power generation during the period, and Represent thermal power units exist The running status and shutdown status within the time period.

[0105] In formula (23), For thermal power units in power systems exist Power generation during the period, For thermal power units in power systems exist Power generation during the period, For thermal power units The maximum power rise limit, For thermal power units The power rise limit during the startup phase, For thermal power units exist Start status within the time period, For thermal power units exist Operation status within the time period.

[0106] The wind power and photovoltaic power output is subject to the constraints of total installed capacity and real-time resources as shown in formulas (24) and (25).

[0107] (twenty four)

[0108] (25)

[0109] In formula (24), is the total installed capacity of wind power generation, for Wind power generation capacity factor during the period, for The power generated by the wind turbine during the period.

[0110] In formula (25), is the total installed capacity of photovoltaic power generation, for Photovoltaic power generation capacity factor during the period, for The power generation power of the photovoltaic unit during the period. The constraints of the power balance of the power system are shown in formula (26).

[0111] (26)

[0112] In formula (26), for The regular power load of the power system during the period, For thermal power units in power systems exist Power generation during the period, and They are The power generation power of wind turbines and photovoltaic units in the power system during this period, represents the charging power of battery swap station k during period t, represents the discharge power of battery swap station k during period t.

[0113] The constraints of the power system spinning reserve are shown in formula (27).

[0114] (27)

[0115] In formula (27), and They are the spinning reserve rate considering the electricity load, the spinning reserve rate considering the wind power output, and the spinning reserve rate considering the uncertainty of photovoltaic output. To meet the emergency backup needs of the power system, The thermal power unit is determined by the upper and lower output constraints of the thermal power unit and the climbing constraints of the thermal power unit. exist Maximum power generation during the period, For thermal power units in power systems exist Power generation during the period, and They are The power generation power of wind turbines and photovoltaic units in the power system during this period, represents the maximum upward rotation reserve capacity that the battery swap station k can provide during period t, Indicates thermal power unit exist Operation status within the time period.

[0116] In this embodiment, the constraints include constraints on the state variables of thermal power units, constraints on the minimum on / off time of thermal power units, constraints on the upper and lower output limits of thermal power units, constraints on the ramping of thermal power units, constraints on the total installed capacity and real-time resources of wind and photovoltaic power output, constraints on power system power balance, and constraints on the power system's spinning reserve. By adding these constraints to the power system dispatch optimization model, the resulting operating parameters can be converged.

[0117] In an exemplary embodiment, a power system dispatching method is provided, the process is as follows: Figure 3 As shown, including:

[0118] Step 301: Establish a battery state transfer equation based on the number of batteries in a charging state in at least one battery swap station, the number of batteries in a discharging state, the number of batteries in at least one battery swap station, and the number of vehicles providing battery swap services in at least one battery swap station.

[0119] Step 302: Based on the battery state transfer equation corresponding to at least one battery swap station, a dynamic operation model of at least one battery swap station is obtained.

[0120] Step 303 , the operating parameters include the power generation of the thermal power unit in a preset period, the power generation of the wind power unit in a preset period, the power generation of the photovoltaic unit in a preset period, and the operating status, startup status, and shutdown status of the thermal power unit.

[0121] In step 304, the constraints include the constraints on the state variables of the thermal power units, the constraints on the minimum on / off time of the thermal power units, the constraints on the upper and lower limits of the output of the thermal power units, the ramping constraints of the thermal power units, the constraints on the total installed capacity and real-time resources of the wind and photovoltaic output, the constraints on the power balance of the power system, and the constraints on the spinning reserve of the power system.

[0122] Step 305: Solve the power system scheduling optimization model based on the external approximation method, the GUROBI solver, and the operating parameters corresponding to the power system in the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system scheduling optimization model is a model constructed based on the dynamic operation model of at least one battery swap station and the constraints of the power system.

[0123] Step 306: dispatching the power system according to the operating parameters of the power system in the current time period.

[0124] In the above-mentioned power system dispatching method, the operating parameters of the power system in the current time period are obtained by solving the power system dispatching optimization model based on the external approximation method, the GUROBI solver, and the corresponding operating parameters of the power system in the previous time period, which reduces the complexity of the calculation and improves the efficiency and reliability of the power system dispatching simulation.

[0125] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0126] Based on the same inventive concept, the present application also provides an electric power system dispatching device for implementing the above-mentioned electric power system dispatching method. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more electric power system dispatching device embodiments provided below can be found in the above-mentioned limitations of the electric power system dispatching method, and will not be repeated here.

[0127] In one embodiment, Figure 4 As shown, a power system dispatching device 400 is provided, including: a calculation module 420 and a dispatching module 440, wherein:

[0128] A calculation module 420 is configured to solve a power system dispatch optimization model based on an external approximation method, a GUROBI solver, and the operating parameters of the power system corresponding to the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on the dynamic operating model of the at least one battery swap station and the constraints of the power system;

[0129] The scheduling module 440 is used to schedule the power system according to the operating parameters of the power system in the current time period.

[0130] In one embodiment, the calculation module 420 is also used to solve the power system scheduling optimization model based on the external approximation method, the GUROBI solver and the operating parameters corresponding to the power system in the previous time period, and the total number of batteries in the charging and discharging states in each battery swap station corresponding to the power system, to obtain the operating parameters of the power system containing the battery swap station in the current time period.

[0131] In one embodiment, the power system dispatching device 400 further includes:

[0132] The model building module is used to obtain a dynamic operation model of at least one battery swap station based on the battery state transfer equation corresponding to at least one battery swap station.

[0133] In one embodiment, the power system dispatching device 400 further includes:

[0134] The state equation construction module is used to establish a battery state transfer equation based on the number of batteries in a charging state in at least one battery swap station, the number of batteries in a discharging state, the number of batteries in at least one battery swap station, and the number of vehicles providing battery swap services in at least one battery swap station.

[0135] Each module in the above-mentioned power system dispatching device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor of the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0136] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store XX data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a power system scheduling method is implemented.

[0137] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication. Wireless communication can be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a power system dispatching method. The display screen of the computer device can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer covering the display screen, keys, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0138] Those skilled in the art will understand that Figure 5 and Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0139] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0140] The power system dispatch optimization model is solved using the external approximation method, the GUROBI solver, and the operating parameters of the power system in the previous time period to obtain the operating parameters of the power system in the current time period. The power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on the dynamic operating model of the at least one battery swap station and the constraints of the power system.

[0141] The power system is dispatched according to its operating parameters in the current time period.

[0142] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0143] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0144] The power system dispatch optimization model is solved using the external approximation method, the GUROBI solver, and the operating parameters of the power system in the previous time period to obtain the operating parameters of the power system in the current time period. The power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on the dynamic operating model of the at least one battery swap station and the constraints of the power system.

[0145] The power system is dispatched according to its operating parameters in the current time period.

[0146] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0147] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0148] The power system dispatch optimization model is solved using the external approximation method, the GUROBI solver, and the operating parameters of the power system in the previous time period to obtain the operating parameters of the power system in the current time period. The power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on the dynamic operating model of the at least one battery swap station and the constraints of the power system.

[0149] The power system is dispatched according to its operating parameters in the current time period.

[0150] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0151] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0152] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A power system dispatching method, characterized in that: The method comprises: Solving the power system dispatch optimization model based on the external approximation method, the GUROBI solver, and the operating parameters of the power system corresponding to the previous time period to obtain the operating parameters of the power system in the current time period; the power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on the dynamic operation model of the at least one battery swap station and the constraints of the power system; Dispatching the power system according to operating parameters of the power system in a current time period; The method further comprises: Establishing a battery state transfer equation according to the number of batteries in a charging state in the at least one battery swap station, the number of batteries in a discharging state, the number of batteries in the at least one battery swap station, and the number of vehicles performing battery swapping services in the at least one battery swap station; Obtaining a dynamic operation model of the at least one battery swap station based on a battery state transfer equation corresponding to the at least one battery swap station; Among them, the objective function of the power system dispatch optimization model is: In the objective function, P g，t is the power generation power of the thermal power unit g in the power system during period t; and are the power generation power of wind turbines and photovoltaic units in the power system during period t respectively; u g，t 、y g，t With z g，t are all 0-1 state variables, representing the operating state, startup state and shutdown state of thermal power unit g in period t; b g with c g are the variable operation energy consumption coefficient and fixed operation energy consumption coefficient of thermal power unit g; FP g is the fuel price corresponding to the unit energy consumption of thermal power unit g; Cs g with cd g are the unit startup energy consumption and unit shutdown energy consumption of thermal power unit g respectively; The constraints include the constraints on the state variables of the thermal power units, the constraints on the minimum on / off time of the thermal power units, the constraints on the upper and lower limits of the output of the thermal power units, the ramp constraints of the thermal power units, the constraints on the total installed capacity and real-time resources of wind power and photovoltaic output, the constraints on the power balance of the power system and the constraints on the rotating reserve of the power system.

2. The method according to claim 1, characterized in that The power system dispatch optimization model is solved based on the external approximation method, the GUROBI solver, and the operating parameters of the power system in the previous time period to obtain the operating parameters of the power system containing the battery swap station in the current time period, including: According to the external approximation method, the GUROBI solver, the operating parameters corresponding to the power system in the previous time period, and the total number of batteries in the charging and discharging states in each battery swap station corresponding to the power system, the power system scheduling optimization model is solved to obtain the operating parameters of the power system containing the battery swap station in the current time period.

3. The method according to any one of claims 1-2, characterized in that The operating parameters include the power generation power of the thermal power unit within a preset period, the power generation power of the wind power unit within a preset period, the power generation power of the photovoltaic unit within a preset period, and the operating status, startup status, and shutdown status of the thermal power unit.

4. A power system dispatching device, characterized in that: The device comprises: a calculation module, configured to solve a power system dispatch optimization model based on an external approximation method, a GUROBI solver, and operating parameters corresponding to the power system in a previous time period to obtain operating parameters of the power system in a current time period; the power system corresponds to at least one battery swap station, and the power system dispatch optimization model is a model constructed based on a dynamic operating model of the at least one battery swap station and constraints of the power system; a scheduling module, configured to schedule the power system according to operating parameters of the power system in a current time period; The device further comprises: Establishing a battery state transfer equation according to the number of batteries in a charging state in the at least one battery swap station, the number of batteries in a discharging state, the number of batteries in the at least one battery swap station, and the number of vehicles performing battery swapping services in the at least one battery swap station; Obtaining a dynamic operation model of the at least one battery swap station based on a battery state transfer equation corresponding to the at least one battery swap station; Among them, the objective function of the power system dispatch optimization model is: In the objective function, P g，t is the power generation power of the thermal power unit g in the power system during period t; and are the power generation power of wind turbines and photovoltaic units in the power system during period t respectively; u g，t 、y g，t With z g，t are all 0-1 state variables, representing the operating state, startup state and shutdown state of thermal power unit g in period t; b g with c g are the variable operation energy consumption coefficient and fixed operation energy consumption coefficient of thermal power unit g; FP g is the fuel price corresponding to the unit energy consumption of thermal power unit g; Cs g with cd g are the unit startup energy consumption and unit shutdown energy consumption of thermal power unit g respectively; The constraints include the constraints on the state variables of the thermal power units, the constraints on the minimum on / off time of the thermal power units, the constraints on the upper and lower limits of the output of the thermal power units, the ramp constraints of the thermal power units, the constraints on the total installed capacity and real-time resources of wind power and photovoltaic output, the constraints on the power balance of the power system and the constraints on the rotating reserve of the power system.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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