Multi-resource flexible cooperative control method and system for ultra-large urban power grid load shedding working condition scene

Abstract

The invention relates to a multi-resource flexible cooperative control method and system for an ultra-large urban power grid load shedding working condition scene. The method comprises the following steps: constructing a target function corresponding to the multi-resource flexible cooperative control system based on networking state information of a first power supply, power information of a second power supply, power information of an interruptible load and a preset cost conversion coefficient; the objective function is used for representing the power supply cost of the multi-resource flexible cooperative control system; constructing a power supply constraint function corresponding to the multi-resource flexible cooperative control system based on the power information of the second power supply and the power information of the interruptible load; and performing power supply control on the first power supply, the second power supply and the interruptible load based on the objective function and the power supply constraint function. When facing an ultra-large urban power grid load shedding working condition scene, power supply requirements of an urban power grid in any scene are met by coordinating various power supplies and interruptible loads, so that the overall operation efficiency of the power grid is improved.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a multi-resource flexible collaborative control method and system for load shedding scenarios in super-large urban power grids. Background Art

[0002] Driven by urbanization and economic growth, urban power grids are growing in size and complexity. As densely populated areas with concentrated economic activity, rising electricity demand in urban areas poses significant challenges to grid stability and energy management.

[0003] Traditionally, when a city power grid faces a situation where some power generation sources are disconnected from the grid, it usually adopts the load shedding method to disconnect some loads to ensure normal power supply to other loads.

[0004] However, the traditional use of load shedding conditions to solve the power supply problem when the power source is disconnected from the grid has the problem of poor overall power grid operation efficiency. Summary of the Invention

[0005] Based on this, it is necessary to provide a multi-resource flexible collaborative control method, device, multi-resource flexible collaborative control system, computer equipment, computer-readable storage medium and computer program product for the load shedding operating scenario of super-large urban power grids, which can meet the various power supply needs of urban power grids and improve the overall operating efficiency of the power grid.

[0006] In a first aspect, the present application provides a multi-resource flexible collaborative control method for a super-large urban power grid load shedding operating scenario, which is applied to a multi-resource flexible collaborative control system. The multi-resource flexible collaborative control system includes a first power supply, a second power supply, and an interruptible load. The second power supply includes at least one type of power supply, including:

[0007] Based on the network status information of the first power supply source, the power information of the second power supply source, the power information of the interruptible load, and the preset cost conversion coefficient, an objective function corresponding to the multi-resource flexible collaborative control system is constructed; the objective function is used to represent the power supply cost of the multi-resource flexible collaborative control system;

[0008] Based on the power information of the second power supply source and the power information of the interruptible load, a power supply constraint function corresponding to the multi-resource flexible collaborative control system is constructed;

[0009] Based on the objective function and the power supply constraint function, power supply control is performed on the first power supply source, the second power supply source, and the interruptible load.

[0010] In one embodiment, based on the network status information of the first power source, the power information of the second power source, the power information of the interruptible load, and a preset cost conversion coefficient, an objective function corresponding to the multi-resource flexible collaborative control system is constructed, including:

[0011] constructing a first cost function of the first power source based on the network status information of the first power source and the first cost conversion coefficient;

[0012] constructing a second cost function for the second power supply based on the power information of the second power supply and the second cost conversion coefficient;

[0013] constructing a third cost function of the interruptible load based on the power information of the interruptible load and the third cost conversion coefficient;

[0014] Based on the first cost function, the second cost function and the third cost function, an objective function is constructed; the preset cost conversion coefficients include the first cost conversion coefficient, the second cost conversion coefficient and the third cost conversion coefficient.

[0015] In one embodiment, a first power source includes a first sub-power source in an activated state and a second sub-power source in an inactivated state. A first cost function for the first power source is constructed based on network status information of the first power source and a first cost conversion coefficient, including:

[0016] Constructing a first cost item corresponding to the first sub-power source based on the network status information of the first sub-power source, the output power parameter of the first sub-power source, and the operating cost coefficient;

[0017] constructing a second cost item corresponding to the second sub-power supply source based on the startup state information and the startup cost coefficient of the second sub-power supply source;

[0018] Based on the first cost item and the second cost item, a first cost function is constructed; the first cost conversion coefficient includes an operating cost coefficient and a startup cost coefficient.

[0019] In one embodiment, the second power source includes a wind farm and a photovoltaic power station. Based on the power information of the second power source and the second cost conversion coefficient, a second cost function of the second power source is constructed, including:

[0020] Based on the available power information and output power parameters of the wind farm, as well as the wind curtailment penalty coefficient, a third cost item corresponding to the wind farm is constructed;

[0021] Based on the available power information and output power parameters of the photovoltaic power station, as well as the penalty coefficient for abandoned light, a fourth cost item corresponding to the photovoltaic power station is constructed;

[0022] Based on the third cost item and the fourth cost item, a second cost function is constructed; the second cost conversion coefficient includes a wind abandonment penalty coefficient and a solar abandonment penalty coefficient.

[0023] In one embodiment, based on the power information of the second power source and the power information of the interruptible load, a power supply constraint function corresponding to the multi-resource flexible collaborative control system is constructed, including:

[0024] Constructing a first constraint function based on the output power parameter of the first power supply, the output power parameter of the second power supply, the power information of the target load, and the power information of the interruptible load; the target load is a load that needs to be powered, and the target load includes the interruptible load;

[0025] Constructing a second constraint function corresponding to the second power supply based on the power information and output power parameters of the second power supply;

[0026] Constructing a third constraint function corresponding to the interruptible load based on the power information of the interruptible load;

[0027] A power supply constraint function is constructed based on the first constraint function, the second constraint function, and the third constraint function.

[0028] In one embodiment, the second power supply includes a wind farm, a photovoltaic power station, an energy storage station, and a charging station. Based on the power information and output power parameters of the second power supply, a second constraint function corresponding to the second power supply is constructed, including:

[0029] Constructing a first power generation restriction function corresponding to the wind farm based on the available power information and output power parameters of the wind farm;

[0030] Based on the available power information and output power parameters of the photovoltaic power station, a second power generation restriction function corresponding to the photovoltaic power station is constructed;

[0031] Constructing a first operation constraint function corresponding to the energy storage station based on the maximum power information and output power parameters of the energy storage station and the energy information of the energy storage station;

[0032] Constructing a second operation constraint function corresponding to the charging station based on the maximum power information and output power parameters of the charging station and the energy information of the charging station;

[0033] A second constraint function is constructed based on the first power generation limit function, the second power generation limit function, the first operation constraint function, and the second operation constraint function.

[0034] In one embodiment, power supply control for the first power supply source, the second power supply source, and the interruptible load is performed based on the objective function and the power supply constraint function, including:

[0035] Based on the power supply constraint function, the objective function is minimized to determine the power supply information of the first power supply, the second power supply and the interruptible load;

[0036] Power supply control is performed on the first power supply, the second power supply and the interruptible load according to power supply information of the first power supply, the second power supply and the interruptible load.

[0037] In a second aspect, the present application also provides a multi-resource flexible collaborative control device for a super-large urban power grid load shedding operating scenario, which is applied to a multi-resource flexible collaborative control system. The multi-resource flexible collaborative control system includes a first power supply, a second power supply, and an interruptible load. The second power supply includes at least one type of power supply, including:

[0038] An objective function construction module is configured to construct an objective function corresponding to the multi-resource flexible collaborative control system based on the network status information of the first power supply source, the power information of the second power supply source, the power information of the interruptible load, and a preset cost conversion coefficient; the objective function is used to characterize the power supply cost of the multi-resource flexible collaborative control system;

[0039] A constraint function construction module, configured to construct a power supply constraint function corresponding to the multi-resource flexible collaborative control system based on the power information of the second power supply source and the power information of the interruptible load;

[0040] The power supply control module is used to control the power supply of the first power supply, the second power supply and the interruptible load based on the objective function and the power supply constraint function.

[0041] In the third aspect, the present application also provides a multi-resource flexible collaborative control system, including a first power supply, a second power supply, an interruptible load, a memory and a processor, the second power supply includes at least one type of power supply, the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the multi-resource flexible collaborative control method for the load shedding operating scenario of the super-large urban power grid in the above-mentioned first aspect.

[0042] In a fourth aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the multi-resource flexible collaborative control method for the load shedding operating scenario of the super-large urban power grid in the above-mentioned first aspect.

[0043] In the fifth aspect, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the multi-resource flexible collaborative control method for the load shedding operating scenario of the super-large urban power grid in the above-mentioned first aspect are implemented.

[0044] In a sixth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the multi-resource flexible collaborative control method for the load shedding operating scenario of a super-large urban power grid in the above-mentioned first aspect.

[0045] The above-mentioned multi-resource flexible collaborative control method, device, multi-resource flexible collaborative control system, computer equipment, storage medium and computer program product for the load shedding operating condition scenario of the super-large urban power grid can first construct an objective function corresponding to the multi-resource flexible collaborative control system based on the network status information of the first power supply, the power information of the second power supply, the power information of the interruptible load and the preset cost conversion coefficient. The objective function is used to characterize the power supply cost of the multi-resource flexible collaborative control system; then, based on the power information of the second power supply and the power information of the interruptible load, a power supply constraint function corresponding to the multi-resource flexible collaborative control system is constructed; and then, based on the objective function and the power supply constraint function, the power supply of the first power supply, the second power supply and the interruptible load is controlled. That is to say, the power supply control method proposed in the embodiment of the present application can combine multiple power supply sources and interruptible loads in the urban power grid to establish a multi-resource control model of a super-large urban power grid, and construct the objective function and power supply constraint function of the multi-resource control model, so as to meet the power supply needs of the urban power grid in any scenario by coordinating multiple power supply sources and interruptible loads in the urban power grid, especially when traditional power generation sources are disconnected from the grid or load shedding conditions occur, it can quickly coordinate and control multiple resources to achieve optimal energy distribution, maintain stable operation of the power grid, and thus improve the overall operating efficiency of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 This is a diagram illustrating an application environment of a multi-resource flexible collaborative control method for a super-large urban power grid load shedding scenario in one embodiment;

[0048] Figure 2 1. A flow chart of a multi-resource flexible collaborative control method for a load shedding scenario in a super-large urban power grid according to an embodiment;

[0049] Figure 3 A flowchart of a multi-resource flexible collaborative control method for a super-large urban power grid load shedding scenario in another embodiment;

[0050] Figure 4 A flowchart of a multi-resource flexible collaborative control method for a super-large urban power grid load shedding scenario in another embodiment;

[0051] Figure 5 A complete flow chart of a multi-resource flexible collaborative control method for a super-large urban power grid load shedding scenario in one embodiment;

[0052] Figure 6 A topological diagram of a city power grid control structure in one embodiment;

[0053] Figure 7 A diagram showing the change in discharge power of an energy storage station and an electric vehicle charging station before and after a fault in one embodiment;

[0054] Figure 8 This is a structural block diagram of a multi-resource flexible collaborative control device for a super-large urban power grid load shedding scenario in one embodiment;

[0055] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] 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.

[0057] Driven by urbanization and economic growth, urban power grids are growing in size and complexity. As densely populated areas with concentrated economic activity, rising electricity demand in urban areas poses significant challenges to grid stability and energy management.

[0058] Traditionally, when urban power grids face the loss of some power generation sources, they typically resort to load shedding, disconnecting some loads to ensure the normal supply of power to other loads. However, this traditional approach to solving power supply problems when power generation sources go offline results in poor overall grid operational efficiency. In other words, traditional grid control strategies are no longer able to adapt to the operational demands of urban power grids facing the loss of some traditional power generation sources and load shedding conditions. Therefore, achieving coordinated control of multiple resources in urban power grids to ensure stable grid operation and optimize energy management efficiency has become a key technical issue that needs to be addressed urgently.

[0059] Based on this, the embodiment of the present application proposes a multi-resource flexible collaborative control method for the load shedding operating scenario of a super-large urban power grid, which coordinates and controls a variety of different resources in the urban power grid to meet the different power supply needs of the urban power grid, such as resource collaborative control when the power source is disconnected from the grid, resource collaborative control under load shedding conditions, etc. For example, the various different resources in the urban power grid may include but are not limited to traditional power plants, energy storage stations, electric vehicle charging stations, distributed photovoltaic power stations, wind power stations, and interruptible loads. In response to the different power supply needs of the urban power grid, the optimal energy distribution can be achieved by quickly coordinating and controlling a variety of resources to maintain the stable operation of the power grid, thereby improving the overall operating efficiency of the power grid, wherein the operating efficiency can include economic operating benefits and power grid energy balance, that is, ensuring the balance of power grid energy and the optimization of economic benefits at the same time.

[0060] The multi-resource flexible collaborative control method for the load shedding scenario of a super-large urban power grid provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the multi-resource flexible collaborative control system 10 may include a first power supply 101, a second power supply 102 and an interruptible load 103, wherein the first power supply 101 may include a traditional power plant, in which a plurality of generators are arranged, and after the generator is started, active power can be output to provide energy for the urban power grid. The second power supply 102 may include at least one type of power supply, such as at least one of an energy storage station, an electric vehicle charging station, a new energy field group, etc., wherein the new energy field group may include a distributed photovoltaic power station, a wind power station, etc. The interruptible load may include at least one load in the urban power grid power supply load. When the power supply is overloaded due to the power source being disconnected from the grid or other reasons in the urban power grid, the power supply of the interruptible load may be temporarily disconnected to meet the normal power supply of other loads.

[0061] In other words, when faced with different power supply demands of the urban power grid, the coordinated control of multiple resources can be achieved by coordinating the first power supply source, the second power supply source and the interruptible load, thereby building a super-large urban power grid with diversified resources. For example, under load shedding conditions, multiple resources can be quickly coordinated and controlled to achieve the economically optimal scheduling of multiple resources and ensure the stable operation of the power grid.

[0062] It should be noted that the multi-resource collaborative control method proposed in the embodiment of the present application can not only be applied to collaborative control under conditions of power generation source disconnection and load shedding, but can also be applied to collaborative control under any other power supply scenarios / conditions. On the basis of achieving stable operation of the power grid, it can also improve the overall economic benefits of the power grid, control the overall operating costs of the urban power grid, and achieve optimal energy scheduling.

[0063] In an exemplary embodiment, Figure 2 As shown in the figure, a multi-resource flexible collaborative control method for the load shedding scenario of super-large urban power grid is provided. Figure 1 The multi-resource flexible collaborative control system in FIG. 1 is described as an example, including the following steps 201 to 203. Among them:

[0064] Step 201: Based on the network status information of the first power supply, the power information of the second power supply, the power information of the interruptible load and the preset cost conversion coefficient, an objective function corresponding to the multi-resource flexible collaborative control system is constructed; the objective function is used to characterize the power supply cost of the multi-resource flexible collaborative control system.

[0065] For example, the first power supply source may include multiple generators within a power plant. The power plant may start some or all of the generators to generate electricity based on the power supply demand of the user-end load. Generators that are started and functioning normally may be in a connected state, generators that fail after starting may be in an off-grid state, and generators that have not started may be in a disconnected state. When constructing an objective function, the objective function may be constructed based on the network status information of at least one started generator in the first power supply source, or based on the network status information of at least one started generator in the first power supply source and the startup status information of at least one unstarted generator.

[0066] Exemplarily, when performing multi-resource collaborative control, the multi-resource flexible collaborative control system can obtain the networking status information of each started generator in the first power supply, for example, sending a networking status request to each started generator, and receiving the networking status information returned by each started generator.

[0067] Exemplarily, the power information of the second power supply may include but is not limited to the available power information (or usable power information) and maximum power information of each second power supply. For example, taking the second power supply including a new energy field group as an example, the power information of the second power supply may include the available power information of the new energy field group. As an optional example, a wind farm may provide different available powers according to different wind speeds, and a photovoltaic power station may provide different available powers according to different light intensity; or, in the case where the second power supply includes an energy storage station and a charging station, the power information of the second power supply may include the maximum power information of the energy storage station, the maximum power information of the charging station, etc.

[0068] Exemplarily, when performing multi-resource collaborative control, the multi-resource flexible collaborative control system can obtain power information of each second power supply source, such as sending a power information acquisition request to each second power supply source and receiving power information returned by each second power supply source.

[0069] Exemplarily, the power information of the interruptible load can be determined based on the interruption status information of the interruptible load. When the interruption status information of the interruptible load is interruption, the current power of the interruptible load can be used as the power information of the interruptible load. When the interruption status information of the interruptible load is non-interruption, the power information of the interruptible load can be zero.

[0070] It should be noted that the interruption status information (interruption or non-interruption) of the interruptible load can be determined according to the objective function of the collaborative control, that is, in the process of solving the optimal solution of the objective function, with the goal of achieving the optimal objective function, it is possible to choose to interrupt or not interrupt the interruptible load. In other words, the interruption status information of the interruptible load can be determined after the objective function is solved, that is, it is determined whether the interruptible load needs to be interrupted when performing collaborative control; for example, the power information of each interruptible load can be obtained by default first, that is, the multi-resource flexible collaborative control system can send a power information acquisition request to each interruptible load, and receive the power information returned by each interruptible load.

[0071] When the multi-resource flexible collaborative control system obtains the networking status information of the first power supply, the power information of the second power supply, and the power information of the interruptible load, it can construct an objective function corresponding to the multi-resource flexible collaborative control system based on the networking status information of the first power supply, the power information of the second power supply, the power information of the interruptible load, and the preset cost conversion coefficient, wherein the preset cost conversion coefficient may include multiple conversion systems to be applicable to the first power supply, the second power supply, the interruptible load, etc.

[0072] Exemplarily, the objective function may include a power supply cost item for the first power supply source, a power supply cost item for the second power supply source, and a power supply cost item for the interruptible load, so as to characterize the comprehensive power supply cost of the first power supply source, the second power supply source, and the interruptible load when performing coordinated control.

[0073] Step 202: construct a power supply constraint function corresponding to the multi-resource flexible collaborative control system based on the power information of the second power supply source and the power information of the interruptible load.

[0074] Exemplarily, when the multi-resource flexible collaborative control system obtains the power information of each second power supply source and the power information of the interruptible load, it can also construct a power supply constraint function corresponding to the multi-resource flexible collaborative control system, which is used as a constraint condition in the objective function optimization process. As an optional implementation method, power constraint items related to urban power grid nodes and branches can be constructed based on the power information of the second power supply source and the power information of the interruptible load. Power constraint items related to the second power supply source can also be constructed based on the power information of the second power supply source. Power constraint items related to the interruptible load can also be constructed based on the power information of the interruptible load, etc., thereby constructing a power supply constraint function corresponding to the multi-resource flexible collaborative control system based on at least one power constraint item.

[0075] Step 203 : Based on the objective function and the power supply constraint function, power supply control is performed on the first power supply source, the second power supply source, and the interruptible load.

[0076] For example, based on the power supply constraint function, an objective function can be minimized to determine power supply information for the first power source, the second power source, and the interruptible load. Then, based on the power supply information for the first power source, the second power source, and the interruptible load, power supply control for the first power source, the second power source, and the interruptible load can be performed. The power supply information may include, but is not limited to, the output power (e.g., active output power) of the first power source, the output power of the second power source, and whether the interruptible load needs to be interrupted.

[0077] As an optional implementation method, the multi-resource power supply system can control the first power supply according to the output power of the first power supply to adjust the output power of the first power supply, and control the second power supply according to the output power of the second power supply to adjust the output power of the second power supply. In addition, when the interruptible load needs to be interrupted, the multi-resource flexible collaborative control system can send an interruption instruction to the interruptible load to disconnect the interruptible load, thereby meeting the current power supply demand of the urban power grid through the coordinated control of the first power supply, the second power supply and the interruptible load, achieving energy balance, and maximizing economic benefits.

[0078] In the multi-resource flexible collaborative control method for the load shedding scenario of a super-large urban power grid, the objective function corresponding to the multi-resource flexible collaborative control system can be first constructed based on the network status information of the first power source, the power information of the second power source, the power information of the interruptible load, and the preset cost conversion coefficient. The objective function is used to characterize the power supply cost of the multi-resource flexible collaborative control system; then, based on the power information of the second power source and the power information of the interruptible load, the power supply constraint function corresponding to the multi-resource flexible collaborative control system is constructed; and then, based on the objective function and the power supply constraint function, the power supply of the first power source, the second power source, and the interruptible load is controlled. In other words, the power supply control method proposed in the embodiment of the present application can combine multiple power sources and interruptible loads in the urban power grid to establish a multi-resource control model for a super-large urban power grid, and construct the objective function and power supply constraint function of the multi-resource control model, so as to meet the power supply needs of the urban power grid in any scenario by coordinating multiple power sources and interruptible loads in the urban power grid, especially when the traditional power generation source is disconnected from the grid or a load shedding condition occurs, it can quickly coordinate and control multiple resources to achieve optimal energy distribution, maintain the stable operation of the power grid, and thus improve the overall operation efficiency of the power grid.

[0079] In an exemplary embodiment, Figure 3 As shown, the preset cost conversion coefficients may include a first cost conversion coefficient, a second cost conversion coefficient, and a third cost conversion coefficient. Based on this, the objective function constructed in step 201 may include steps 301 to 304.

[0080] Step 301: construct a first cost function of the first power source based on the network status information of the first power source and a first cost conversion coefficient.

[0081] The network status information of the first power source is status information obtained from the first power source by the multi-resource flexible collaborative control system, and the first cost conversion coefficient can be a fixed coefficient or a coefficient that is dynamically adjusted based on the power grid status. In other words, the network status information of the first power source and the first cost conversion coefficient can be known quantities in the first cost function. When constructing the first cost function, at least one unknown quantity can also be included, namely, the result obtained after subsequent function optimization.

[0082] Exemplarily, the unknown quantity may include an output power parameter of the first power source, i.e., a first cost function for the first power source may be constructed based on the network connection status information and output power parameter of the first power source, as well as a first cost conversion coefficient. Exemplarily, the first power source may include at least one generator in an activated state in a power plant, i.e., the first cost function for the first power source may be constructed based on the network connection status information and output power parameter of each activated generator, as well as the first cost conversion coefficient.

[0083] Exemplarily, the first power supply may also include at least one generator in an unactivated state, that is, the first cost function corresponding to the first power supply is constructed by combining the generator in the activated state and the generator in the unactivated state. As an optional implementation, the first power supply includes a first sub-power supply in the activated state and a second sub-power supply in the unactivated state. Then, based on the network status information of the first power supply and the first cost conversion coefficient, constructing the first cost function of the first power supply may include:

[0084] Step A1: constructing a first cost item corresponding to the first sub-power source based on the network status information of the first sub-power source, the output power parameter of the first sub-power source, and the operation cost coefficient.

[0085] Step A2: constructing a second cost item corresponding to the second sub-power source based on the startup state information and the startup cost coefficient of the second sub-power source.

[0086] Step A3: constructing a first cost function based on the first cost item and the second cost item; wherein the first cost conversion coefficient may include the above-mentioned operating cost coefficient and startup cost coefficient.

[0087] The following provides an exemplary expression of the first cost function in combination with the started generators and the unstarted generators in the traditional power plant:

[0088]

[0089] Among them, f1 represents the first cost function, is the first cost item, is the second cost item, is the active power output of the i-th generator in time period t, Is a binary variable (valued as 0 or 1), indicating the disconnection status of the i-th generator from the grid during time period t. 0 indicates disconnection from the grid, and 1 indicates connection to the grid. is the electricity price coefficient of the i-th generator, is the operating cost coefficient of the i-th generator, is a binary variable (valued as 0 or 1), where 1 indicates that the i-th generator is started within time period t. is the startup cost of the i-th generator, NG is the total number of generators in the distribution network, and T is the total operating time of the generators.

[0090] In the expression of the first cost function above, Can be used as the output power parameter of the first sub-power supply, Can be used as the network status information of the first sub-power source, and Can be used as an operating cost coefficient; Can be used as the startup status information of the second sub-power supply, Can be used as a startup cost factor.

[0091] Step 302: construct a second cost function of the second power supply based on the power information of the second power supply and the second cost conversion coefficient.

[0092] The power information of the second power source is the power information obtained from the second power source by the multi-resource flexible coordinated control system, and the second cost conversion coefficient can be a fixed coefficient or a coefficient that is dynamically adjusted based on the power grid status. In other words, the power information of the second power source and the second cost conversion coefficient can be known quantities in the second cost function. When constructing the second cost function, at least two unknown quantities can also be included, namely, the results obtained after subsequent function optimization.

[0093] Exemplarily, the unknown quantity may include an output power parameter of the second power supply source, i.e., a second cost function for the second power supply source may be constructed based on the power information and output power parameter of the second power supply source, as well as a second cost conversion coefficient. Exemplarily, the second power supply source may include a renewable energy cluster in a city power grid, such as a wind farm and a photovoltaic power station. For example, the second cost function for the second power supply source may be constructed based on the power information and output power parameter of the wind farm, as well as the second cost conversion coefficient, and the power information and output power parameter of the photovoltaic power station, as well as the second cost conversion coefficient.

[0094] As an optional implementation, when the second power supply includes a wind farm and a photovoltaic power station, constructing a second cost function for the second power supply based on the power information of the second power supply and the second cost conversion coefficient may include:

[0095] Step B1: constructing a third cost item corresponding to the wind farm based on the available power information and output power parameters of the wind farm and the wind curtailment penalty coefficient.

[0096] Step B2: constructing a fourth cost item corresponding to the photovoltaic power station based on the available power information and output power parameters of the photovoltaic power station and the curtailment penalty coefficient.

[0097] Step B3: construct a second cost function based on the third cost item and the fourth cost item; wherein the second cost conversion coefficient may include the above-mentioned wind abandonment penalty coefficient and solar abandonment penalty coefficient.

[0098] Taking the second power supply source including a wind farm and a photovoltaic power station as an example, an exemplary expression of the second cost function is provided below:

[0099]

[0100] Among them, f2 represents the second cost function, is the third cost item, is the fourth cost item, is the predicted output power of the i-th wind farm in time period t, is the predicted output power of the ith photovoltaic power station in the time period t, is the actual output power of the i-th wind farm in time period t, is the actual output power of the ith photovoltaic power station in time period t, and are the penalty coefficients for wind power curtailment and photovoltaic curtailment, and are the number of wind farms and the number of photovoltaic power stations respectively.

[0101] In the expression of the second cost function above, Can be used as available power information of wind farms, It can be used as the output power parameter of the wind farm. It can be used as a penalty coefficient for wind curtailment; It can be used as the available power information of the photovoltaic power station. It can be used as the output power parameter of photovoltaic power station. Can be used as a penalty coefficient for abandoning light.

[0102] Step 303: construct a third cost function of the interruptible load based on the power information of the interruptible load and the third cost conversion coefficient.

[0103] An exemplary expression of the third cost function is provided below:

[0104]

[0105] Among them, f3 represents the third cost function, is the interruptible load of the node at time, is the interruptible load penalty coefficient, is the number of interruptible loads.

[0106] In the expression of the third cost function above, Can be used as power information of interruptible loads, Can be used as a third cost conversion factor.

[0107] Step 304: construct an objective function based on the first cost function, the second cost function, and the third cost function.

[0108] Exemplarily, an objective function may be constructed based on the sum of the first cost function, the second cost function, and the third cost function, such as the objective function f=f1+f2+f3.

[0109] For example, when minimizing the objective function f, the third cost function f3 should be as small as possible, that is, no interruptible load is interrupted, and no load interruption cost is generated. However, this also means that all loads need to be powered. When the second power supply reaches the maximum output power, the output power of the first power supply, that is, the power generation demand will increase, which will correspondingly cause an increase in the power generation cost, that is, the first cost function f1 increases; therefore, when conducting coordinated control of energy balance and economic costs, if necessary, the interruptible load can be sacrificed, and the power generation cost of the first power supply can be reduced by moderately increasing the load interruption cost, thereby reducing the overall cost and maximizing economic benefits while balancing energy.

[0110] In this embodiment, a first cost function corresponding to the first power supply, a second cost function corresponding to the second power supply, and a third cost function corresponding to the interruptible load are constructed respectively to obtain the final objective function, that is, the coordinated control of the urban power grid is achieved by coordinating the first power supply, the second power supply, and the interruptible load, so as to ensure the overall energy balance of the urban power grid and the maximization of the overall economic benefits to achieve balanced development.

[0111] In an exemplary embodiment, Figure 4 As shown, the above step 202 of constructing the power supply constraint function may include steps 401 to 404. Among them:

[0112] Step 401: construct a first constraint function based on the output power parameters of the first power supply, the output power parameters of the second power supply, the power information of the target load, and the power information of the interruptible load.

[0113] The target loads are all loads requiring power in the city's power grid, and these target loads include interruptible loads. This means that among all loads requiring power, some are interruptible, meaning they can be disconnected when power demand is unsatisfactory. Another portion is non-interruptible, meaning they must be guaranteed to function normally regardless of the power supply scenario. It should be noted that when the target load is an interruptible load, the power information for the interruptible load is the same as the power information for the target load.

[0114] Exemplarily, the first constraint function may be a power constraint function involving the first power supply source, the second power supply source, the target load and the interruptible load; for example: when the first power supply source includes a generator of a power plant and the second power supply source includes a new energy field group (such as a wind farm and a photovoltaic power station), an energy storage station, an electric vehicle charging station, etc., the outputs of the new energy field group, the energy storage station, and the electric vehicle charging station may be added to the power balance constraint of the grid node to obtain the first constraint function. Exemplarily, the first constraint function may be a grid node power and branch flow constraint function.

[0115] The following provides an optional first constraint function, that is, the expression of the grid node power and branch flow constraint function:

[0116]

[0117]

[0118]

[0119]

[0120] in, is the node susceptance matrix, is the angle of node j at time t, is the load power of node i at time t, is the interruptible load of node i at time t, and Respectively represent the charging and discharging power of the energy storage station (ESS) at time t, and Represent the charging and discharging power of the electric vehicle charging station (EVS) at time t, is the generator connection matrix. If the jth generator is located at node i, then the element at (i, j) can be defined as 1, otherwise it is set to 0. and They are the connection matrices of wind farms and photovoltaic power stations respectively, and They are the energy storage station (ESS) connection matrix and the charging station (EVS) connection matrix, and their definitions are the same as similar, is the total number of nodes, is the total number of branches, is the running time, is the maximum flow rate of the i-th branch, It is a sparse diagonal matrix, which can be defined as the susceptance of branch l at its element (i, j), and is used to construct the sparse connection matrix of the system admittance matrix and It can be defined as follows: For each branch l connecting nodes j and k, The element at (i,j) and middle The elements at are all equal to 1, and All other elements in are 0.

[0121] Step 402: Construct a second constraint function corresponding to the second power supply based on the power information and output power parameters of the second power supply.

[0122] For example, when the second power supply includes multiple types of power supplies, corresponding constraint functions can be constructed for each type of second power supply. As an optional implementation, when the second power supply includes a new energy field group (including a wind farm and a photovoltaic power station), an energy storage station, and an electric vehicle charging station, constraint functions for the wind farm, photovoltaic power station, energy storage station, and electric vehicle charging station can be constructed separately, where:

[0123] Step C1: constructing a first power generation restriction function corresponding to the wind farm based on the available power information and output power parameters of the wind farm.

[0124] The following is an optional expression of the first power generation limit function corresponding to the wind farm:

[0125]

[0126] in, is the wind farm output power (i.e. available power) predicted in time period t, is the actual output power of the wind farm in time period t, For the running time.

[0127] Step C2: constructing a second power generation restriction function corresponding to the photovoltaic power station based on the available power information and output power parameters of the photovoltaic power station.

[0128] The following is an optional expression for the second power generation limit function corresponding to the photovoltaic power station:

[0129]

[0130] in, is the predicted output power of the photovoltaic power station in time period t (i.e., available power), is the actual output power of the photovoltaic power station in the time period t, For the running time.

[0131] Step C3: constructing a first operation constraint function corresponding to the energy storage station based on the maximum power information and output power parameters of the energy storage station and the energy information of the energy storage station.

[0132] The following is an optional expression of the first operating constraint function corresponding to the energy storage station (ESS):

[0133] ①Charge and discharge state constraint function of energy storage station:

[0134]

[0135] ② Maximum power limit constraint function during charging and discharging:

[0136]

[0137]

[0138] ③ State of charge constraint function:

[0139]

[0140]

[0141] in, and are binary variables that define the charge / discharge status of the ESS, e.g. Indicates that the ESS is being charged from the grid. Indicates that the ESS is discharging from the grid. At the same time, the ESS can only be in one state, charging or discharging. and are the charging power and discharging power of ESS at time t, and are the maximum power flowing through the ESS when charging and discharging, and are the state of charge of ESS at time t-1 and the energy stored at time t, respectively. and is the efficiency coefficient of ESS during charging and discharging, which can be set to 0.9. is the maximum capacity of ESS, is the minimum storage energy of ESS, which can be set to , For the running time.

[0142] Step C4: constructing a second operation constraint function corresponding to the charging station based on the maximum power information and output power parameters of the charging station and the energy information of the charging station.

[0143] The following is an optional expression of the second operating constraint function corresponding to the charging station (EVS):

[0144] ①Charging and discharging state restriction constraint function of the charging station:

[0145]

[0146] ② Maximum power limit constraint function during charging and discharging:

[0147]

[0148]

[0149] ③ State of charge constraint function:

[0150]

[0151]

[0152] in, and are binary variables that define the charge / discharge status of the EVS, e.g. Indicates that the EVS is being charged from the grid, Indicates that the EVS is discharging from the grid. At the same time, the EVS can only exist in one state, that is, charging or discharging. and are the charging power and discharging power of EVS at time t, and are the maximum power flowing through the EVS when charging and discharging, and are the state of charge of EVS at time t-1 and the energy stored at time t, and is the efficiency coefficient of EVS during charging and discharging, which can be set to 0.9. is the maximum capacity of EVS, is the minimum storage energy of EVS, set to , For the running time.

[0153] Step C5: constructing a second constraint function based on the first power generation restriction function, the second power generation restriction function, the first operation constraint function, and the second operation constraint function.

[0154] Exemplarily, the second constraint function may include a first power generation constraint function, a second power generation constraint function, a first operation constraint function, and a second operation constraint function.

[0155] Step 403: construct a third constraint function corresponding to the interruptible load based on the power information of the interruptible load.

[0156] Among them, the power information of the interruptible load is related to the power information of the target load. When the target load is an interruptible load and the state of the interruptible load is interrupted, the power information of the interruptible load is the power information of the target load. When the target load is an interruptible load, but the state of the interruptible load is not interrupted, the power information of the interruptible load is 0.

[0157] The following is an optional expression of the third constraint function corresponding to the interrupt load:

[0158]

[0159] in, is a binary variable, indicating whether the load of node i is interrupted at time t. , indicating an interruption, , means no interruption, is the load power of node i at time t, is the power of the load interruption at node i at time t, A collection of interruptible loads.

[0160] Step 404 : constructing a power supply constraint function based on the first constraint function, the second constraint function, and the third constraint function.

[0161] Exemplarily, the power supply constraint function may include a first constraint function, a second constraint function, and a third constraint function.

[0162] In this embodiment, a power supply constraint function of the urban power grid is formed by constructing a first constraint function for characterizing the power of the urban power grid nodes, constructing a second constraint function for the second power supply source, and constructing a third constraint function for the interruptible load. The first power supply source, the second power supply source, and the interruptible load in the urban power grid can be combined for collaborative power supply. The power supply constraint function can ensure the overall and partial power supply constraints in the collaborative power supply process, provide constraint restrictions for the collaborative power supply, thereby providing power guarantee for the collaborative power supply and improving the stability and rationality of the collaborative power supply.

[0163] In an exemplary embodiment, a complete embodiment of a multi-resource flexible collaborative control method for a super-large urban power grid load shedding scenario is provided. Figure 5 As shown in FIG, by establishing a multi-resource control model for a super-large urban power grid, covering traditional power generation sources, loads, energy storage stations, electric vehicle charging stations, and distributed new energy field groups, the super-large urban power grid can be selected as needed. For example, as an optional implementation method, taking the urban power grid as an IEEE39 node system as an example, as shown in FIG. Figure 6 As shown in the figure, it shows a topological diagram of the urban power grid control structure. In this system, not only traditional power generation equipment and loads are included, but also multiple resource station groups such as energy storage stations, electric vehicle charging stations, interruptible loads, and distributed new energy field groups. The system contains 39 nodes and 46 branches, of which 10 nodes are generator nodes and 19 nodes are load nodes. For example, 10 generators can be located at nodes 30 to 39 respectively. In addition, the system is also connected to a variety of controllable resource station groups: the energy storage station is located at node 13, the new energy field group is located at node 14, the electric vehicle charging station is located at node 20, and the interruptible load is located at node 24. The number of two-dimensional populations is 30.

[0164] For this multi-resource control model, an objective function and a power supply constraint function can be constructed to characterize the economic benefits of the multi-resources of the power grid. The power supply constraint function may include the power and branch flow constraint functions of the power grid nodes, the power generation restriction function of the wind farm, the power generation restriction function of the photovoltaic power station, the operation constraint function of the energy storage station (including the charge and discharge state restriction of the energy storage station, the maximum power restriction during charge and discharge, the state of charge restriction, etc.), the operation constraint function of the charging station (the charge and discharge state restriction of the charging station, the maximum power restriction during charge and discharge, the state of charge restriction, etc.), and the operation constraint function of the interruptible load.

[0165] In order to verify the reliability of the multi-resource flexible collaborative control method for the load shedding operating condition of the super-large urban power grid proposed in the embodiment of the present application, simulation verification was carried out in this embodiment. The simulation and optimization were carried out in the R2021b version of the MATLAB software on a PC with a 2.8-GHz CPU and 32-GB RAM environment. In this embodiment, the generator at node 30 is set to be disconnected from the grid in the 12th hour. Under this operating condition, the multi-resource flexible collaborative control method for the load shedding operating condition of the super-large urban power grid proposed in the embodiment of the present application is adopted. The operating states of the energy storage station, electric vehicle charging station, traditional load and interruptible load in the power grid all change accordingly, as shown in Table 1, which shows the changes in the operating states of the energy storage station and electric vehicle charging station before and after the fault.

[0166] Table 1

[0167] Energy storage station state of charge State of charge at electric vehicle charging stations Before the failure 0.8895 1 After the failure 0.3571 0.7297

[0168] As can be seen from Table 1, when traditional loads are disconnected from the grid, the state of charge of the energy storage station and the electric vehicle charging station decreases and the output power increases, thereby providing stable power output for more loads.

[0169] like Figure 7 As shown in Figure 2, it shows the discharge power change curves of the energy storage station and the electric vehicle charging station before and after the fault. Figure 7 As can be seen, after the fault occurs, the power grid system experiences a 50-millisecond delay before issuing control commands to the energy storage station, adjusting its output power from 4.0267kW to 11.0526kW within 200 milliseconds, and adjusting the output power of the electric vehicle charging station from 5.9028kW to 7.0637kW within 150 milliseconds. These simulation results demonstrate that when load shedding occurs, this multi-resource flexible collaborative control approach for ultra-large urban power grids can rapidly coordinate the power output of various resources in the grid, fully utilizing the potential of each controllable subunit to maintain frequency and voltage stability and significantly improve overall system performance.

[0170] The embodiment of the present application proposes a multi-resource flexible collaborative control method for the load shedding operating scenario of a super-large urban power grid. By establishing a multi-resource control model for a super-large urban power grid, it covers power grid resources such as traditional power generation sources, loads, energy storage stations, electric vehicle charging stations, and distributed new energy clusters. It also constructs a multi-resource economic benefit objective function for the power grid, sets power constraints for power grid nodes and branch currents, and operation constraints for new energy clusters, where the new energy clusters include wind farms and photovoltaic power stations, energy storage stations, electric vehicle charging stations, and interruptible load operation constraints. Based on the objective function and constraints, when some traditional power generation sources are disconnected from the grid, mixed integer programming can be applied to achieve system power stability control and economic benefit optimal control of energy management. The method proposed in this embodiment can not only achieve optimal economic benefit scheduling of multiple resources by rapidly coordinating and controlling multiple resources under load shedding conditions, but also maintain stable operation of the power grid, ensuring the continuity of power supply and the reliability of the power system.

[0171] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence 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 executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0172] Based on the same inventive concept, the embodiment of the present application also provides a multi-resource flexible collaborative control device for a super-large city power grid load shedding operating condition scenario for realizing the multi-resource flexible collaborative control method for the super-large city power grid load shedding operating condition scenario involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in the embodiments of one or more multi-resource flexible collaborative control devices for super-large city power grid load shedding operating conditions provided below can be found in the above limitations on the multi-resource flexible collaborative control method for super-large city power grid load shedding operating conditions, and will not be repeated here.

[0173] In an exemplary embodiment, Figure 8 As shown, a multi-resource flexible collaborative control device for a super-large urban power grid load shedding operating scenario is provided, comprising: an objective function construction module 801, a constraint function construction module 802, and a power supply control module 803, wherein:

[0174] The objective function construction module 801 is used to construct the objective function corresponding to the multi-resource flexible collaborative control system based on the network status information of the first power supply source, the power information of the second power supply source, the power information of the interruptible load and the preset cost conversion coefficient; the objective function is used to characterize the power supply cost of the multi-resource flexible collaborative control system.

[0175] The constraint function building module 802 is used to build a power supply constraint function corresponding to the multi-resource flexible collaborative control system based on the power information of the second power supply source and the power information of the interruptible load.

[0176] The power supply control module 803 is configured to control the power supply to the first power supply source, the second power supply source, and the interruptible load based on the objective function and the power supply constraint function.

[0177] In one embodiment, the objective function construction module 801 includes:

[0178] A first constructing unit, configured to construct a first cost function of the first power source based on the network status information of the first power source and a first cost conversion coefficient;

[0179] a second constructing unit, configured to construct a second cost function of the second power supply source based on the power information of the second power supply source and the second cost conversion coefficient;

[0180] a third constructing unit, configured to construct a third cost function of the interruptible load based on the power information of the interruptible load and a third cost conversion coefficient;

[0181] The fourth construction unit is used to construct an objective function based on the first cost function, the second cost function and the third cost function; the preset cost conversion coefficients include the first cost conversion coefficient, the second cost conversion coefficient and the third cost conversion coefficient.

[0182] In one embodiment, the first power supply includes a first sub-power supply in a started state and a second sub-power supply in a non-started state. The first construction unit is specifically used to construct a first cost item corresponding to the first sub-power supply based on the networking status information of the first sub-power supply, the output power parameter of the first sub-power supply, and the operating cost coefficient; construct a second cost item corresponding to the second sub-power supply based on the startup status information and the startup cost coefficient of the second sub-power supply; construct a first cost function based on the first cost item and the second cost item; the first cost conversion coefficient includes an operating cost coefficient and a startup cost coefficient.

[0183] In one embodiment, the second power supply includes a wind farm and a photovoltaic power station, and the second construction unit is specifically used to construct a third cost item corresponding to the wind farm based on the available power information and output power parameters of the wind farm, and the wind abandonment penalty coefficient; construct a fourth cost item corresponding to the photovoltaic power station based on the available power information and output power parameters of the photovoltaic power station, and the light abandonment penalty coefficient; construct a second cost function based on the third cost item and the fourth cost item; the second cost conversion coefficient includes the wind abandonment penalty coefficient and the light abandonment penalty coefficient.

[0184] In one embodiment, the constraint function construction module 802 includes:

[0185] a fifth constructing unit, configured to construct a first constraint function based on the output power parameter of the first power supply, the output power parameter of the second power supply, the power information of the target load, and the power information of the interruptible load; the target load is a load that requires power supply, and the target load includes an interruptible load;

[0186] a sixth constructing unit, configured to construct a second constraint function corresponding to the second power supply based on the power information and output power parameters of the second power supply;

[0187] a seventh constructing unit, configured to construct a third constraint function corresponding to the interruptible load based on the power information of the interruptible load;

[0188] The eighth constructing unit is configured to construct a power supply constraint function based on the first constraint function, the second constraint function, and the third constraint function.

[0189] In one embodiment, the second power supply source includes a wind farm, a photovoltaic power station, an energy storage station and a charging station. The sixth construction unit is specifically used to construct a first power generation restriction function corresponding to the wind farm based on the available power information and output power parameters of the wind farm; construct a second power generation restriction function corresponding to the photovoltaic power station based on the available power information and output power parameters of the photovoltaic power station; construct a first operation constraint function corresponding to the energy storage station based on the maximum power information and output power parameters of the energy storage station and the energy information of the energy storage station; construct a second operation constraint function corresponding to the charging station based on the maximum power information and output power parameters of the charging station and the energy information of the charging station; and construct a second constraint function based on the first power generation restriction function, the second power generation restriction function, the first operation constraint function and the second operation constraint function.

[0190] In one embodiment, the power supply control module 803 includes:

[0191] a determination unit, configured to minimize an objective function based on a power supply constraint function, and determine power supply information of the first power supply source, the second power supply source, and the interruptible load;

[0192] The control unit is used to control the power supply to the first power supply, the second power supply and the interruptible load according to the power supply information of the first power supply, the second power supply and the interruptible load.

[0193] Each module in the multi-resource flexible collaborative control device for ultra-large urban power grid load shedding scenarios can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device's memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0194] In an exemplary embodiment, a multi-resource flexible collaborative control system is provided, referring to Figure 1As shown, the system may include a first power supply, a second power supply, an interruptible load, a memory (not shown in the figure) and a processor (not shown in the figure), wherein the second power supply may include at least one type of power supply, including but not limited to new energy field groups (such as wind farms, photovoltaic power stations, etc.), energy storage stations and charging stations, etc. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the multi-resource flexible collaborative control method for the load shedding operating scenario of the super-large urban power grid in any of the above embodiments.

[0195] In an exemplary embodiment, a computer device is provided. The computer device may be a control device in a multi-resource flexible collaborative control system, such as a control device including a processor and a memory in the multi-resource flexible collaborative control system. The control device may be a terminal device or a server device, wherein the terminal device may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers. Exemplarily, a computer device may also be used to form a collaborative power supply control center, through which relevant staff can monitor and view the power supply situation of the urban power grid in real time.

[0196] For example, taking a computer device as a terminal device, its internal structure diagram can be as follows: Figure 9As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a multi-resource flexible collaborative control method for load shedding scenarios in ultra-large urban power grids. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0197] Those skilled in the art will understand that Figure 9 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.

[0198] In an exemplary 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 steps of the multi-resource flexible collaborative control method for the load shedding operating scenario of a super-large urban power grid in any of the above embodiments are implemented.

[0199] 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 of the multi-resource flexible collaborative control method for the load shedding operating scenario of a super-large urban power grid in any of the above embodiments are implemented.

[0200] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the multi-resource flexible collaborative control method for a super-large urban power grid load shedding scenario in any of the above embodiments.

[0201] It should be noted that the data involved in this application (including but not limited to data used for analysis, storage, display, etc.) are all information and data fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0202] 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.

[0203] 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.

[0204] The above-described embodiments merely represent 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 application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements 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 multi-resource flexible collaborative control method for load shedding scenarios in super-large urban power grids, characterized by: Applied to a multi-resource flexible collaborative control system, the multi-resource flexible collaborative control system includes a first power supply, a second power supply, and an interruptible load, the second power supply includes at least one type of power supply, the method comprising: Based on the network status information of the first power supply source, the power information of the second power supply source, the power information of the interruptible load, and a preset cost conversion coefficient, an objective function corresponding to the multi-resource flexible collaborative control system is constructed; the objective function is used to characterize the power supply cost of the multi-resource flexible collaborative control system; Based on the power information of the second power supply source and the power information of the interruptible load, constructing a power supply constraint function corresponding to the multi-resource flexible collaborative control system; Based on the objective function and the power supply constraint function, power supply control is performed on the first power supply source, the second power supply source, and the interruptible load.

2. The method according to claim 1, characterized in that The constructing of an objective function corresponding to the multi-resource flexible collaborative control system based on the network status information of the first power supply, the power information of the second power supply, the power information of the interruptible load, and a preset cost conversion coefficient includes: constructing a first cost function of the first power source based on the network status information of the first power source and a first cost conversion coefficient; constructing a second cost function of the second power supply based on the power information of the second power supply and the second cost conversion coefficient; constructing a third cost function of the interruptible load based on the power information of the interruptible load and a third cost conversion coefficient; The objective function is constructed based on the first cost function, the second cost function and the third cost function; the preset cost conversion coefficient includes the first cost conversion coefficient, the second cost conversion coefficient and the third cost conversion coefficient.

3. The method according to claim 2, characterized in that The first power supply includes a first sub-power supply in an activated state and a second sub-power supply in an inactivated state. Constructing a first cost function for the first power supply based on the network status information of the first power supply and a first cost conversion coefficient includes: constructing a first cost item corresponding to the first sub-power source based on the network status information of the first sub-power source, the output power parameter of the first sub-power source, and the operating cost coefficient; constructing a second cost item corresponding to the second sub-power supply source based on the startup state information and the startup cost coefficient of the second sub-power supply source; The first cost function is constructed based on the first cost item and the second cost item; the first cost conversion coefficient includes the operating cost coefficient and the startup cost coefficient.

4. The method according to claim 2, characterized in that The second power supply source includes a wind farm and a photovoltaic power station. Constructing a second cost function of the second power supply source based on the power information of the second power supply source and the second cost conversion coefficient includes: constructing a third cost item corresponding to the wind farm based on the available power information and output power parameters of the wind farm and the wind abandonment penalty coefficient; Constructing a fourth cost item corresponding to the photovoltaic power station based on the available power information and output power parameters of the photovoltaic power station and a penalty coefficient for abandonment of light; Based on the third cost item and the fourth cost item, the second cost function is constructed; the second cost conversion coefficient includes the wind abandonment penalty coefficient and the solar abandonment penalty coefficient.

5. The method according to any one of claims 1 to 4, characterized in that The constructing, based on the power information of the second power supply source and the power information of the interruptible load, a power supply constraint function corresponding to the multi-resource flexible collaborative control system includes: constructing a first constraint function based on the output power parameter of the first power supply, the output power parameter of the second power supply, the power information of the target load, and the power information of the interruptible load; the target load is a load that needs power supply, and the target load includes the interruptible load; Constructing a second constraint function corresponding to the second power supply based on the power information and output power parameters of the second power supply; Constructing a third constraint function corresponding to the interruptible load based on the power information of the interruptible load; The power supply constraint function is constructed based on the first constraint function, the second constraint function and the third constraint function.

6. The method according to claim 5, characterized in that The second power supply includes a wind farm, a photovoltaic power station, an energy storage station, and a charging station. The second constraint function corresponding to the second power supply is constructed based on the power information and output power parameters of the second power supply, including: constructing a first power generation restriction function corresponding to the wind farm based on the available power information and output power parameters of the wind farm; constructing a second power generation limitation function corresponding to the photovoltaic power station based on the available power information and output power parameters of the photovoltaic power station; Constructing a first operation constraint function corresponding to the energy storage station based on the maximum power information and output power parameters of the energy storage station and the energy information of the energy storage station; constructing a second operation constraint function corresponding to the charging station based on the maximum power information and output power parameters of the charging station and the energy information of the charging station; The second constraint function is constructed based on the first power generation limit function, the second power generation limit function, the first operation constraint function, and the second operation constraint function.

7. The method according to any one of claims 1 to 4, characterized in that The performing power supply control on the first power supply, the second power supply, and the interruptible load based on the objective function and the power supply constraint function includes: Based on the power supply constraint function, minimizing the objective function to determine power supply information of the first power supply, the second power supply, and the interruptible load; Power supply control is performed on the first power supply, the second power supply, and the interruptible load according to power supply information of the first power supply, the second power supply, and the interruptible load.

8. A multi-resource flexible collaborative control device for load shedding scenarios in super-large urban power grids, characterized by: Applied to a multi-resource flexible collaborative control system, the multi-resource flexible collaborative control system includes a first power supply, a second power supply, and an interruptible load, the second power supply includes at least one type of power supply, and the device includes: an objective function construction module, configured to construct an objective function corresponding to the multi-resource flexible collaborative control system based on the network status information of the first power supply source, the power information of the second power supply source, the power information of the interruptible load, and a preset cost conversion coefficient; the objective function is used to characterize the power supply cost of the multi-resource flexible collaborative control system; A constraint function construction module, configured to construct a power supply constraint function corresponding to the multi-resource flexible collaborative control system based on the power information of the second power supply source and the power information of the interruptible load; A power supply control module is configured to control power supply to the first power supply source, the second power supply source, and the interruptible load based on the objective function and the power supply constraint function.

9. A multi-resource flexible collaborative control system, characterized in that: The method comprises a first power supply, a second power supply, an interruptible load, a memory and a processor, wherein the second power supply comprises at least one type of power supply, the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

10. 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 7 are implemented.

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