Large-scale wind and light storage base group electric power real-time control method and system under severe weather

Abstract

The invention provides a large-scale wind and light storage base group electric power real-time control method under severe weather, and relates to the technical field of electric power system dispatching operation control, and the method comprises the steps: obtaining the electric power information and severe weather evolution path information of a large-scale wind and light storage base group under severe weather, carrying out the safety monitoring of controllable resources and a power grid operation mode, and carrying out the real-time control of the electric power of the large-scale wind and light storage base group; obtaining the sensitivity of active power flow of a power transmission channel in the large-scale wind and light storage base and a branch on-off distribution factor, establishing a severe weather influence risk index, constructing a large-scale wind and light storage base group output optimization model, and obtaining the maximum value of the output sum of the large-scale wind and light storage base group and key limited factors thereof according to the model. Therefore, whether a tie line sending-out requirement is met is judged, and large-scale wind and light storage base group electric power real-time control is realized. The overall risk of insufficient output after the new energy power station receives the severe weather is quantified, and a quantitative basis is provided for a large-scale wind and light storage base group to participate in real-time power regulation and control under the severe weather.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system dispatching operation control, in particular to a large-scale wind-solar-storage base group power real-time control method and system under severe weather. BACKGROUND

[0002] In recent years, with the proposal of the "double carbon" target, the installed capacity of new energy power generation represented by wind power and photovoltaic power continues to grow rapidly. As an important form of large-scale new energy centralized development and external transmission, the wind-solar-storage base plays an increasingly important role in the power system. However, the wind-solar-storage base is mostly located in the "three north" regions with complex climate conditions and harsh natural environment, and is easily affected by extreme weather events, bringing great challenges to the safe and stable operation of the power system. The influence of severe weather on the wind-solar-storage base mainly reflects in the following aspects: intensified power generation output fluctuation, increased equipment operation risk, limited performance of energy storage system, and blocked power transmission channel. However, there is no comprehensive research on the real-time control of large-scale wind-solar-storage base group power under severe weather. When carrying out power balancing and regulation of large-scale wind-solar-storage base group under severe weather, the relevant risk quantification basis and how to introduce risk consideration in the control are still in a state of absence. SUMMARY

[0003] The purpose of the present application is to provide a large-scale wind-solar-storage base group power real-time control method and system under severe weather, which can realize the participation of large-scale wind-solar-storage base group power generation in grid regulation according to the evolution process of severe weather, and provide quantitative basis for the participation of large-scale wind-solar-storage base group in real-time power regulation under severe weather.

[0004] The technical solution of the present application is as follows:

[0005] The present application provides a large-scale wind-solar-storage base group power real-time control method under severe weather, which comprises the following steps:

[0006] Obtain the power information of large-scale wind-solar-storage base group under severe weather and the evolution path information of severe weather;

[0007] According to the evolution path information of severe weather, the safety of controllable resources and grid operation mode is monitored, the sensitivity of active power flow of internal power transmission channel of large-scale wind-solar-storage base group and branch outage distribution factor are obtained;

[0008] According to the power information of large-scale wind-solar-storage base group, the sensitivity of active power flow of internal power transmission channel and branch outage distribution factor, the safety risk is determined, and the severe weather influence risk index is established according to the safety risk;

[0009] Based on the risk indicators of severe weather impact, a power output optimization model for a large-scale wind, solar and energy storage base cluster is constructed. The model is then solved based on the maximum value of the sum of power outputs and key limiting factors to obtain the power output of the stations within the large-scale wind, solar and energy storage base cluster.

[0010] Determine whether the power output of the power stations within the large-scale wind, solar, and energy storage cluster meets the cluster's power transmission request. If yes, issue an instruction; otherwise, release the adjustable capacity of energy storage and high-energy-load self-owned power plants within the region, and reconstruct the large-scale wind, solar, and energy storage cluster power output optimization model until the conditions are met, thus completing power control.

[0011] Furthermore, the power information of the large-scale wind, solar and energy storage base clusters under the aforementioned severe weather conditions includes probabilistic prediction data of new energy power plants, multi-source power generation plan information, output information of the large-scale wind, solar and energy storage base clusters, power grid state estimation data information, and data plan information of key transmission channels; the aforementioned severe weather evolution path information includes the start time of the severe weather affecting the controlled power grid, the range of the controlled power grid, and the intensity of the impact on the large-scale wind and solar power plants.

[0012] Furthermore, the process of obtaining the sensitivity of active power flow and branch interruption distribution factor of the transmission channels within a large-scale wind, solar and energy storage base includes: monitoring the period of severe weather, scanning controllable resources and grid operation modes, and when there are changes in maintenance plans or power generation plans, performing static safety checks on the static safety check faults associated with each transmission channel under the changed grid operation mode to obtain the sensitivity of the active power output of each new energy power station within the large-scale wind, solar and energy storage base to the active power flow of the transmission channels within the base and the branch interruption distribution factor.

[0013] Furthermore, the formula for calculating security risks includes: , ,

[0014] In the formula, R i,o The overall risk of insufficient power output from the combined renewable energy power plants is represented by: t = duration of severe weather evolution, T1 = start time of severe weather evolution, T2 = end time of severe weather evolution, s = predicted power, S = probabilistic predicted power output zone, i = number of renewable energy power plants, and p = ... t,s Let P be the output probability of the i-th renewable energy power station at time t, predicting its power s. t,f_s P is the probabilistic predicted output of the i-th new energy power station in the set of new energy power stations at time t, representing the predicted power s. t,p ψ contributes to the plan at time t i Let be the risk indicator participation factor for the i-th new energy power station in the set N of new energy power stations.

[0015] Furthermore, the objective function of the aforementioned large-scale wind-solar-storage base cluster power output optimization model is: ,

[0016] In the formula, , , Ψ is a unified characterization factor for the participation of new energy power plants, conventional generating units, and energy storage power plants in regulation. n,i Ψ ht,j Ψ bs,k These are the risk indicator participation factors for new energy power plants, conventional generating units, and energy storage power plants, respectively, P′ n,i To constrain the output of new energy sources, P′ ht,j For the output constraint of conventional units, P′ bs,k Constraints on energy storage output.

[0017] Furthermore, the constraints of the aforementioned large-scale wind-solar-storage base cluster output optimization model include:

[0018] Constraints on new energy output: ,

[0019] Output constraints of conventional units: ,

[0020] Energy storage output constraints: ,

[0021] Ramp-up rate constraints for conventional units: ,

[0022] Power balance constraints: ,

[0023] Safety constraints on transmission channels under anticipated fault conditions: ,

[0024] Positive and negative reserve constraints set in the power grid dispatching and operation control procedures: ,

[0025] In the formula, For the power output constraint of the i-th new energy source, The lower limit of the instruction for the i-th new energy power station. Let N be the upper limit of instructions for the i-th renewable energy power station, where i is the number of renewable energy power stations, j is the number of conventional generating units, and N is the set number of renewable energy power stations. This represents the lower limit of the instruction set for conventional generating units at time t0. For the output constraints of conventional units, The upper limit of instructions for conventional units, Let k be the lower limit of the instruction for energy storage station k at time t0. Due to energy storage output constraints, Let be the upper limit of the command for energy storage station k, 'a' be the active power regulation rate of conventional units, and 'Δt' be the set real-time active power control cycle. Let C represent the active power of conventional generating units, and C be the set of conventional generating units. Let m be the planned active power of the external power transmission channel, t be the evolution time of severe weather, T be the period of severe weather arrival, b be the grid loss coefficient, and L be the number of loads in the grid. This represents the predicted active power of the load. For monitoring the active power flow of the channel, S is the maximum limit for monitoring channels. 0.s.i S 1.s.i S 2.s.i S 3.s.i S 4.s.i These represent the online active power sensitivity of the i-th renewable energy power station, the j-th conventional generating unit, the k-th energy storage power station, the m-th external power transmission channel, and the n-th load at time t, respectively. To contribute to the i-th new energy power station To provide power to the j-th conventional unit, To contribute to the kth energy storage station, Let t be the power flow of the m-th external transmission channel. Let s be the active power component of the nth load, s be the predicted power, J be the number of sections for safety and stability monitoring, and P be the active power component. j.max P represents the maximum grid-connected active power of the power plant at the mid-station. k.max P represents the maximum grid-connected active power of the medium-voltage energy storage system. j.min P is the minimum grid-connected active power of the power plant at station H. k.min This represents the minimum grid-connected active power of medium-capacity energy storage. Let be the predicted active power of the i-th new energy power station in the mid-field station, c1 and c2 are the positive and negative reserve coefficients set according to the power grid dispatching and operation control regulations, d1 and d2 are the set proportional coefficients of energy storage and new energy power stations in terms of active power reserve, μ is the set active power reserve constraint relaxation parameter, and ρ is the set parameter.

[0026] This invention also provides a real-time power control system for a large-scale wind, solar, and energy storage cluster under severe weather conditions, comprising:

[0027] The information acquisition module is used to acquire power information and severe weather evolution path information of large-scale wind, solar and energy storage base clusters under severe weather conditions.

[0028] The safety monitoring module is used to monitor the controllable resources and power grid operation mode based on the information of the evolution path of severe weather, and to obtain the sensitivity of the active power flow of the transmission channel inside the large-scale wind, solar and energy storage base and the branch interruption distribution factor.

[0029] The risk indicator establishment module is used to determine safety risks based on the power information of large-scale wind, solar and energy storage base clusters, the sensitivity of active power flow in internal transmission channels and the branch circuit breakage distribution factor, and to establish risk indicators for the impact of severe weather based on safety risks.

[0030] The model building module is used to construct an output optimization model for a large-scale wind, solar and energy storage base cluster based on the risk indicators of severe weather impacts. The model is then solved based on the maximum value of the sum of outputs and key limiting factors to obtain the power output of the stations within the large-scale wind, solar and energy storage base cluster.

[0031] The judgment module is used to determine whether the power output of the power stations in the large-scale wind, solar and energy storage base cluster meets the power transmission request within the cluster. If yes, it issues an instruction; if no, it releases the adjustable capacity of energy storage and high-energy-load self-owned power plants in the area, and reconstructs the power output optimization model of the large-scale wind, solar and energy storage base cluster until the conditions are met and the power control is completed.

[0032] Furthermore, the power information of the large-scale wind, solar and energy storage base clusters under the aforementioned severe weather conditions includes probabilistic prediction data of new energy power plants, multi-source power generation plan information, output information of the large-scale wind, solar and energy storage base clusters, power grid state estimation data information, and data plan information of key transmission channels; the aforementioned severe weather evolution path information includes the start time of the severe weather affecting the controlled power grid, the range of the controlled power grid, and the intensity of the impact on the large-scale wind and solar power plants.

[0033] Furthermore, the process of obtaining the sensitivity of active power flow and branch interruption distribution factor of the transmission channels within a large-scale wind, solar and energy storage base includes: monitoring the period of severe weather, scanning controllable resources and grid operation modes, and when there are changes in maintenance plans or power generation plans, performing static safety checks on the static safety check faults associated with each transmission channel under the changed grid operation mode to obtain the sensitivity of the active power output of each new energy power station within the large-scale wind, solar and energy storage base to the active power flow of the transmission channels within the base and the branch interruption distribution factor.

[0034] Furthermore, the formula for calculating security risks includes: , ,

[0035] In the formula, R i,oThe overall risk of insufficient power output from the combined renewable energy power plants is represented by: t = duration of severe weather evolution, T1 = start time of severe weather evolution, T2 = end time of severe weather evolution, s = predicted power, S = probabilistic predicted power output zone, i = number of renewable energy power plants, and p = ... t,s Let P be the output probability of the i-th renewable energy power station at time t, predicting its power s. t,f_s P is the probabilistic predicted output of the i-th new energy power station in the set of new energy power stations at time t, representing the predicted power s. t,p ψ contributes to the plan at time t i Let be the risk indicator participation factor for the i-th new energy power station in the set N of new energy power stations.

[0036] Furthermore, the objective function of the aforementioned large-scale wind-solar-storage base cluster power output optimization model is: ,

[0037] In the formula, , , These are unified characterization factors for the participation of new energy power plants, conventional generating units, and energy storage power plants in regulation. , , These are the risk indicator participation factors for new energy power plants, conventional generating units, and energy storage power plants, respectively. Constraints on contributing to new energy sources For the output constraints of conventional units, Constraints on energy storage output.

[0038] Furthermore, the constraints of the aforementioned large-scale wind-solar-storage base cluster output optimization model include:

[0039] Constraints on new energy output: ,

[0040] Output constraints of conventional units: ,

[0041] Energy storage output constraints: ,

[0042] Ramp-up rate constraints for conventional units: ,

[0043] Power balance constraints: ,

[0044] Safety constraints on transmission channels under anticipated fault conditions: ,

[0045] Positive and negative reserve constraints set in the power grid dispatching and operation control procedures: ,

[0046] In the formula, For the power output constraint of the i-th new energy source, The lower limit of the instruction for the i-th new energy power station. Let N be the upper limit of instructions for the i-th renewable energy power station, where i is the number of renewable energy power stations, j is the number of conventional generating units, and N is the set number of renewable energy power stations. This represents the lower limit of the instruction set for conventional generating units at time t0. For the output constraints of conventional units, The upper limit of instructions for conventional units, Let k be the lower limit of the instruction for energy storage station k at time t0. Due to energy storage output constraints, Let be the upper limit of the command for energy storage station k, 'a' be the active power regulation rate of conventional units, and 'Δt' be the set real-time active power control cycle. Let C represent the active power of conventional generating units, and C be the set of conventional generating units. Let m be the planned active power of the external power transmission channel, t be the evolution time of severe weather, T be the period of severe weather arrival, b be the grid loss coefficient, and L be the number of loads in the grid. This represents the predicted active power of the load. For monitoring the active power flow of the channel, S is the maximum limit for monitoring channels. 0.s.i S 1.s.i S 2.s.i S 3.s.i S 4.s.i These represent the online active power sensitivity of the i-th renewable energy power station, the j-th conventional generating unit, the k-th energy storage power station, the m-th external power transmission channel, and the n-th load at time t, respectively. To contribute to the i-th new energy power station To provide power to the j-th conventional unit, To contribute to the kth energy storage station, Let t be the power flow of the m-th external transmission channel. Let s be the active power component of the nth load, s be the predicted power, J be the number of sections for safety and stability monitoring, and P be the active power component. j.max P represents the maximum grid-connected active power of the power plant at the mid-station. k.max P represents the maximum grid-connected active power of the medium-voltage energy storage system. j.min P is the minimum grid-connected active power of the power plant at station H. k.min This represents the minimum grid-connected active power of medium-capacity energy storage. Let be the predicted active power of the i-th new energy power station in the mid-field station, c1 and c2 are the positive and negative reserve coefficients set according to the power grid dispatching and operation control regulations, d1 and d2 are the set proportional coefficients of energy storage and new energy power stations in terms of active power reserve, μ is the set active power reserve constraint relaxation parameter, and ρ is the set parameter.

[0047] An electronic device, comprising:

[0048] Memory, used to store one or more programs;

[0049] processor;

[0050] When the processor executes the one or more programs, it implements a real-time power control method for a large-scale wind, solar and energy storage cluster under severe weather conditions, as described in any of the first aspects.

[0051] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a real-time power control method for a large-scale wind, solar and energy storage cluster under severe weather conditions as described in any of the first aspects.

[0052] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0053] This invention provides a method and system for real-time power control of large-scale wind, solar, and energy storage clusters under severe weather conditions. Addressing the lack of existing technologies that apply risk quantification factors for power generation from renewable energy clusters under extreme weather conditions to power system regulation, this invention quantifies the overall risk of insufficient power output from renewable energy power plants in response to severe weather and establishes risk quantification control factors. This provides a quantitative basis for the participation of large-scale wind, solar, and energy storage clusters in real-time power regulation under severe weather conditions, and enables the participation of these clusters in grid regulation based on the evolution of severe weather. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the steps of a real-time power control method for a large-scale wind, solar and energy storage cluster under severe weather conditions, according to the present invention.

[0056] Figure 2 This is a schematic block diagram of an electronic device. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0058] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0059] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0060] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0061] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.

[0062] Example

[0063] Please see Figure 1 , Figure 1 The diagram shows the steps of a real-time power control method for a large-scale wind, solar and energy storage cluster under severe weather conditions, as provided in an embodiment of this application.

[0064] In a first aspect, this application provides a method for real-time power control of a large-scale wind, solar, and energy storage cluster under severe weather conditions, which includes the following steps:

[0065] To obtain power information and severe weather evolution path information for large-scale wind, solar and energy storage base clusters under severe weather conditions;

[0066] Based on the information on the evolution path of severe weather, we can conduct safety monitoring of controllable resources and power grid operation modes, and obtain the sensitivity of active power flow in the transmission channels of large-scale wind, solar and energy storage bases as well as the branch interruption distribution factor.

[0067] Safety risks are determined based on the power information of large-scale wind, solar and energy storage base clusters, the sensitivity of active power flow in internal transmission channels and the branch circuit breakage distribution factor, and risk indicators for the impact of severe weather are established based on the safety risks.

[0068] The model building module is used to construct an output optimization model for a large-scale wind, solar and energy storage base cluster based on the risk indicators of severe weather impacts. The model is then solved based on the maximum value of the sum of outputs and key limiting factors to obtain the power output of the stations within the large-scale wind, solar and energy storage base cluster.

[0069] The judgment module is used to determine whether the power output of the power stations in the large-scale wind, solar and energy storage base cluster meets the power transmission request within the cluster. If yes, it issues an instruction; if no, it releases the adjustable capacity of energy storage and high-energy-load self-owned power plants in the area, and reconstructs the power output optimization model of the large-scale wind, solar and energy storage base cluster until the conditions are met and the power control is completed.

[0070] As a preferred implementation method, the power information of large-scale wind, solar and energy storage base clusters under severe weather conditions includes probabilistic prediction data of new energy power plants, multi-source power generation plan information, output information of large-scale wind, solar and energy storage base clusters, power grid state estimation data information and key transmission channel data plan information; the severe weather evolution path information includes the start time of the severe weather affecting the controlled power grid, the range of the controlled power grid, and the intensity of the impact on large-scale wind and solar power plants.

[0071] As a preferred implementation method, the process of obtaining the sensitivity of active power flow of transmission channels and branch interruption distribution factor within a large-scale wind, solar and energy storage base includes: monitoring the period of severe weather, scanning controllable resources and grid operation modes, and when there are changes in maintenance plans or power generation plans, performing static safety checks on the static safety check faults associated with each transmission channel under the changed grid operation mode to obtain the sensitivity of the active power output of each new energy power station within the large-scale wind, solar and energy storage base to the active power flow of the transmission channels within the base and the branch interruption distribution factor.

[0072] As a preferred implementation method, the formula for calculating security risks includes: , ,

[0073] In the formula, R i,oThe overall risk of insufficient power output from the combined renewable energy power plants is represented by: t = duration of severe weather evolution, T1 = start time of severe weather evolution, T2 = end time of severe weather evolution, s = predicted power, S = probabilistic predicted power output zone, i = number of renewable energy power plants, and p = ... t,s Let P be the output probability of the i-th renewable energy power station at time t, predicting its power s. t,f_s P is the probabilistic predicted output of the i-th new energy power station in the set of new energy power stations at time t, representing the predicted power s. t,p ψ contributes to the plan at time t i Let be the risk indicator participation factor for the i-th new energy power station in the set N of new energy power stations.

[0074] As a preferred implementation method, the objective function of the large-scale wind-solar-storage base cluster output optimization model is: ,

[0075] In the formula, , , These are unified characterization factors for the participation of new energy power plants, conventional generating units, and energy storage power plants in regulation. , , These are the risk indicator participation factors for new energy power plants, conventional generating units, and energy storage power plants, respectively. Constraints on contributing to new energy sources For the output constraints of conventional units, Constraints on energy storage output.

[0076] As a preferred implementation method, the constraints of the large-scale wind-solar-storage base cluster output optimization model include:

[0077] Constraints on new energy output: ,

[0078] Output constraints of conventional units: ,

[0079] Energy storage output constraints: ,

[0080] Ramp-up rate constraints for conventional units: ,

[0081] Power balance constraints: ,

[0082] Safety constraints on transmission channels under anticipated fault conditions: ,

[0083] Positive and negative reserve constraints set in the power grid dispatching and operation control procedures: ,

[0084] In the formula, For the power output constraint of the i-th new energy source, The lower limit of the instruction for the i-th new energy power station. Let N be the upper limit of instructions for the i-th renewable energy power station, where i is the number of renewable energy power stations, j is the number of conventional generating units, and N is the set number of renewable energy power stations. This represents the lower limit of the instruction set for conventional generating units at time t0. For the output constraints of conventional units, The upper limit of instructions for conventional units, Let k be the lower limit of the instruction for energy storage station at time t0. Due to energy storage output constraints, Let be the upper limit of the command for energy storage station k, 'a' be the active power regulation rate of conventional units, and 'Δt' be the set real-time active power control cycle. Let C represent the active power of conventional generating units, and C be the set of conventional generating units. Let m be the planned active power of the external power transmission channel, t be the evolution time of severe weather, T be the period of severe weather arrival, b be the grid loss coefficient, and L be the number of loads in the grid. This represents the predicted active power of the load. For monitoring the active power flow of the channel, S is the maximum limit for monitoring channels. 0.s.i S 1.s.i S 2.s.i S 3.s.i S 4.s.i These represent the online active power sensitivity of the i-th renewable energy power station, the j-th conventional generating unit, the k-th energy storage power station, the m-th external power transmission channel, and the n-th load at time t, respectively. To contribute to the i-th new energy power station To provide power to the j-th conventional unit, To contribute to the kth energy storage station, Let t be the power flow of the m-th external transmission channel. Let s be the active power component of the nth load, s be the predicted power, J be the number of sections for safety and stability monitoring, and P be the active power component. j.max P represents the maximum grid-connected active power of the power plant at the mid-station. k.max P represents the maximum grid-connected active power of the medium-voltage energy storage system. j.min P is the minimum grid-connected active power of the power plant at station H. k.min This represents the minimum grid-connected active power of medium-capacity energy storage. Let be the predicted active power of the i-th new energy power station in the mid-field station, c1 and c2 are the positive and negative reserve coefficients set according to the power grid dispatching and operation control regulations, d1 and d2 are the set proportional coefficients of energy storage and new energy power stations in terms of active power reserve, μ is the set active power reserve constraint relaxation parameter, and ρ is the set parameter.

[0085] Furthermore, the power information of the large-scale wind, solar and energy storage base clusters under the aforementioned severe weather conditions includes probabilistic prediction data of new energy power plants, multi-source power generation plan information, output information of the large-scale wind, solar and energy storage base clusters, power grid state estimation data information, and data plan information of key transmission channels; the aforementioned severe weather evolution path information includes the start time of the severe weather affecting the controlled power grid, the range of the controlled power grid, and the intensity of the impact on the large-scale wind and solar power plants.

[0086] Furthermore, the process of obtaining the sensitivity of active power flow and branch interruption distribution factor of the transmission channels within a large-scale wind, solar and energy storage base includes: monitoring the period of severe weather, scanning controllable resources and grid operation modes, and when there are changes in maintenance plans or power generation plans, performing static safety checks on the static safety check faults associated with each transmission channel under the changed grid operation mode to obtain the sensitivity of the active power output of each new energy power station within the large-scale wind, solar and energy storage base to the active power flow of the transmission channels within the base and the branch interruption distribution factor.

[0087] Furthermore, the formula for calculating security risks includes: , ,

[0088] In the formula, R i,o The overall risk of insufficient power output from the combined renewable energy power plants is represented by: t = duration of severe weather evolution, T1 = start time of severe weather evolution, T2 = end time of severe weather evolution, s = predicted power, S = probabilistic predicted power output zone, i = number of renewable energy power plants, and p = ... t,s Let P be the output probability of the i-th renewable energy power station at time t, predicting its power s. t,f_s P is the probabilistic predicted output of the i-th new energy power station in the set of new energy power stations at time t, representing the predicted power s. t,p ψ contributes to the plan at time t i Let be the risk indicator participation factor for the i-th new energy power station in the set N of new energy power stations.

[0089] Furthermore, the objective function of the above-mentioned large-scale wind-solar-storage base cluster output optimization model is: ,

[0090] In the formula, , , These are unified characterization factors for the participation of new energy power plants, conventional generating units, and energy storage power plants in regulation. , , These are the risk indicator participation factors for new energy power plants, conventional generating units, and energy storage power plants, respectively. Constraints on contributing to new energy sources For the output constraints of conventional units, Constraints on energy storage output.

[0091] Furthermore, the constraints of the aforementioned large-scale wind-solar-storage base cluster output optimization model include:

[0092] Constraints on new energy output: ,

[0093] Output constraints of conventional units: ,

[0094] Energy storage output constraints: ,

[0095] Ramp-up rate constraints for conventional units: ,

[0096] Power balance constraints: ,

[0097] Safety constraints on transmission channels under anticipated fault conditions: ,

[0098] Positive and negative reserve constraints set in the power grid dispatching and operation control procedures: ,

[0099] In the formula, For the power output constraint of the i-th new energy source, The lower limit of the instruction for the i-th new energy power station. Let N be the upper limit of instructions for the i-th renewable energy power station, where i is the number of renewable energy power stations, j is the number of conventional generating units, and N is the set number of renewable energy power stations. This represents the lower limit of the instruction set for conventional generating units at time t0. For the output constraints of conventional units, The upper limit of instructions for conventional units, Let k be the lower limit of the instruction for energy storage station k at time t0. Due to energy storage output constraints, Let be the upper limit of the command for energy storage station k, 'a' be the active power regulation rate of conventional units, and 'Δt' be the set real-time active power control cycle. Let C represent the active power of conventional generating units, and C be the set of conventional generating units. Let m be the planned active power of the external power transmission channel, t be the evolution time of severe weather, T be the period of severe weather arrival, b be the grid loss coefficient, and L be the number of loads in the grid. This represents the predicted active power of the load. For monitoring the active power flow of the channel, S is the maximum limit for monitoring channels. 0.s.i S 1.s.i S 2.s.i S 3.s.i S 4.s.i These represent the online active power sensitivity of the i-th renewable energy power station, the j-th conventional generating unit, the k-th energy storage power station, the m-th external power transmission channel, and the n-th load at time t, respectively. To contribute to the i-th new energy power station To provide power to the j-th conventional unit, To contribute to the kth energy storage station, Let t be the power flow of the m-th external transmission channel. Let s be the active power component of the nth load, s be the predicted power, J be the number of sections for safety and stability monitoring, and P be the active power component. j.max P represents the maximum grid-connected active power of the power plant at the mid-station. k.max P represents the maximum grid-connected active power of the medium-voltage energy storage system. j.min P is the minimum grid-connected active power of the power plant at station H. k.min This represents the minimum grid-connected active power of medium-capacity energy storage. Let be the predicted active power of the i-th new energy power station in the mid-field station, c1 and c2 are the positive and negative reserve coefficients set according to the power grid dispatching and operation control regulations, d1 and d2 are the set proportional coefficients of energy storage and new energy power stations in terms of active power reserve, μ is the set active power reserve constraint relaxation parameter, and ρ is the set parameter.

[0100] Example 2

[0101] Please see Figure 2 , Figure 2 This is a schematic structural block diagram of an electronic device provided in an embodiment of this application.

[0102] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used for signaling or data communication with other node devices.

[0103] The memory 101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0104] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0105] It is understood that the structure shown in the figure is for illustrative purposes only. A real-time power control method and system for a large-scale wind, solar, and energy storage cluster under severe weather conditions may include more or fewer components than those shown in the figure, or have a different configuration. The components shown in the figure can be implemented using hardware, software, or a combination thereof.

[0106] In the embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The embodiments described above are merely illustrative; for example, the flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0107] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0108] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0110] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for real-time power control of a large-scale wind-solar-storage base cluster under severe weather conditions, characterized in that, Includes the following steps: To obtain power information and severe weather evolution path information for large-scale wind, solar and energy storage base clusters under severe weather conditions; Based on the information on the evolution path of severe weather, we can conduct safety monitoring of controllable resources and power grid operation modes, and obtain the sensitivity of active power flow in the transmission channels of large-scale wind, solar and energy storage bases as well as the branch interruption distribution factor. Safety risks are determined based on the power information of large-scale wind, solar and energy storage base clusters, the sensitivity of active power flow in internal transmission channels and the branch circuit breakage distribution factor, and risk indicators for the impact of severe weather are established based on the safety risks. Based on the risk indicators of severe weather impact, a power output optimization model for a large-scale wind, solar and energy storage base cluster is constructed. The model is then solved based on the maximum value of the sum of power outputs and key limiting factors to obtain the power output of the stations within the large-scale wind, solar and energy storage base cluster. Determine whether the power output of the power stations within the large-scale wind, solar, and energy storage cluster meets the cluster's power transmission request. If yes, issue an instruction; otherwise, release the adjustable capacity of energy storage and high-energy-load self-owned power plants within the region, and reconstruct the large-scale wind, solar, and energy storage cluster power output optimization model until the conditions are met, thus completing power control.

2. The method for real-time power control of large-scale wind, solar, and energy storage clusters under severe weather conditions as described in claim 1, characterized in that, The power information of the large-scale wind, solar and energy storage base cluster under severe weather conditions includes probability prediction data of new energy power plants, multi-source power generation plan information, output information of the large-scale wind, solar and energy storage base cluster, power grid state estimation data information and key transmission channel data plan information; the severe weather evolution path information includes the start time of the severe weather affecting the controlled power grid, the range of the controlled power grid, and the intensity of the impact on the large-scale wind and solar power plants.

3. The real-time power control method for large-scale wind, solar, and energy storage clusters under severe weather conditions as described in claim 1, characterized in that... The process of obtaining the sensitivity of active power flow and branch interruption distribution factor of the internal transmission channels of the large-scale wind, solar and energy storage base includes: monitoring the period of severe weather, scanning controllable resources and grid operation modes, and when there are changes in maintenance plans or power generation plans, performing static safety checks on the static safety check faults associated with each transmission channel under the changed grid operation mode to obtain the sensitivity of the active power output of each new energy power station in the large-scale wind, solar and energy storage base to the active power flow of the internal transmission channels and the branch interruption distribution factor.

4. The method for real-time power control of large-scale wind, solar, and energy storage clusters under severe weather conditions as described in claim 1, characterized in that, The formula for determining security risks includes: ， ， In the formula, R i,o The overall risk of insufficient power output from the combined renewable energy power plants is represented by: t = duration of severe weather evolution, T1 = start time of severe weather evolution, T2 = end time of severe weather evolution, s = predicted power, S = probabilistic predicted power output zone, i = number of renewable energy power plants, and p = ... t,s Let P be the output probability of the i-th renewable energy power station at time t, predicting the power s. t,f_s P is the probabilistic predicted output of the i-th new energy power station in the set of new energy power stations at time t, representing the predicted power s. t,p ψ contributes to the plan at time t i Let be the risk indicator participation factor for the i-th new energy power station in the set N of new energy power stations.

5. The method for real-time power control of large-scale wind, solar, and energy storage clusters under severe weather conditions as described in claim 1, characterized in that, The objective function of the large-scale wind-solar-storage base cluster power output optimization model is: ， In the formula, , , These are unified characterization factors for the participation of new energy power plants, conventional generating units, and energy storage power plants in regulation. , , These are the risk indicator participation factors for new energy power plants, conventional generating units, and energy storage power plants, respectively. Constraints on contributing to new energy sources For the output constraints of conventional units, Constraints on energy storage output.

6. The method for real-time power control of large-scale wind, solar, and energy storage clusters under severe weather conditions as described in claim 5, characterized in that, The constraints of the large-scale wind-solar-storage base cluster power output optimization model include: Constraints on new energy output: ， Output constraints of conventional units: ， Energy storage output constraints: ， Ramp-up rate constraints for conventional units: ， Power balance constraints: ， Safety constraints on transmission channels under anticipated fault conditions: ， Positive and negative reserve constraints set in the power grid dispatching and operation control procedures: ， In the formula, For the i-th new energy source, the power output constraint, The lower limit of the instruction for the i-th new energy power station. Let N be the upper limit of instructions for the i-th renewable energy power station, where i is the number of renewable energy power stations, j is the number of conventional generating units, and N is the set number of renewable energy power stations. This represents the lower limit of the instruction set for conventional generating units at time t0. For the output constraints of conventional units, This is the upper limit of instructions for a conventional unit. Let k be the lower limit of the instruction for energy storage station k at time t0. Due to energy storage output constraints, Let be the upper limit of the command for energy storage station k, 'a' be the active power regulation rate of conventional units, and 'Δt' be the set real-time active power control cycle. Let C represent the active power of conventional generating units, and C be the set of conventional generating units. Let m be the planned active power of the external power transmission channel, t be the evolution time of severe weather, T be the period of severe weather arrival, b be the grid loss coefficient, and L be the number of loads in the grid. This represents the predicted active power of the load. For monitoring the active power flow of the channel, S is the maximum limit for monitoring channels. 0.s.i S 1.s.i S 2.s.i S 3.s.i S 4.s.i These represent the online active power sensitivity of the i-th renewable energy power station, the j-th conventional generating unit, the k-th energy storage power station, the m-th external power transmission channel, and the n-th load at time t, respectively. To contribute to the i-th new energy power station To provide power to the j-th conventional unit, To contribute to the kth energy storage station, Let the power flow of the m-th external transmission channel be at time t. Let s be the active power component of the nth load, s be the predicted power, J be the number of sections for safety and stability monitoring, and P be the active power component. j.max P represents the maximum grid-connected active power of the power plant at the mid-station. k.max P represents the maximum grid-connected active power of the medium-voltage energy storage system. j.min P is the minimum grid-connected active power of the power plant at station H. k.min This represents the minimum grid-connected active power of medium-capacity energy storage. Let be the predicted active power of the i-th new energy power station in the mid-field station, c1 and c2 are the positive and negative reserve coefficients set according to the power grid dispatching and operation control regulations, d1 and d2 are the set proportional coefficients of energy storage and new energy power stations in terms of active power reserve, μ is the set active power reserve constraint relaxation parameter, and ρ is the set parameter.

7. A real-time power control system for a large-scale wind-solar-storage power grid cluster under severe weather conditions, characterized in that, include: The information acquisition module is used to acquire power information and severe weather evolution path information of large-scale wind, solar and energy storage base clusters under severe weather conditions. The safety monitoring module is used to monitor the controllable resources and power grid operation mode based on the information of the evolution path of severe weather, and to obtain the sensitivity of the active power flow of the transmission channel inside the large-scale wind, solar and energy storage base and the branch interruption distribution factor. The risk indicator establishment module is used to determine safety risks based on the power information of large-scale wind, solar and energy storage base clusters, the sensitivity of active power flow in internal transmission channels and the branch circuit breakage distribution factor, and to establish risk indicators for the impact of severe weather based on safety risks. The model building module is used to construct an output optimization model for a large-scale wind, solar and energy storage base cluster based on the risk indicators of severe weather impacts. The model is then solved based on the maximum value of the sum of outputs and key limiting factors to obtain the power output of the stations within the large-scale wind, solar and energy storage base cluster. The judgment module is used to determine whether the power output of the power stations in the large-scale wind, solar and energy storage base cluster meets the power transmission request within the cluster. If yes, it issues an instruction; if no, it releases the adjustable capacity of energy storage and high-energy-load self-owned power plants in the area, and reconstructs the power output optimization model of the large-scale wind, solar and energy storage base cluster until the conditions are met and the power control is completed.

Images