Power grid system control method, device and equipment and readable storage medium

Abstract

The invention relates to a power grid system control method, device and equipment and a readable storage medium. The method comprises the steps that a day-ahead optimization scheduling result corresponding to a future prediction day is generated based on distributed resources in a power grid system, the day-ahead optimization scheduling result comprises output corresponding to multiple time points of the distributed resources in the prediction day, and the time intervals between the adjacent time points are consistent; determining a target operation mode of the power grid system based on the optimal scheduling result corresponding to each time point, and partitioning the power grid system based on the target operation mode to determine a key power flow section; obtaining the actual power flow of the key power flow section, and selecting a frequency modulation response terminal from the plurality of edge side intelligent control terminals based on the actual power flow; and when the power grid system reaches the frequency modulation condition, sending a power response demand to the frequency modulation response terminal, so that the frequency modulation response terminal calls the corresponding standby capability based on the power response demand. The method can improve the stability of power grid operation.

Description

Technical Field

[0001] This application relates to the field of network security technology, and in particular to a power grid system control method, device, equipment and readable storage medium. Background Art

[0002] With the deepening of the global energy transformation and the rapid development of digital technology, the proportion of distributed resources in the power grid shows an increasing trend. Due to the geographical dispersion of distributed resources, as well as the randomness and volatility of power generation output, etc., it brings challenges to the safe and stable operation of the power grid.

[0003] In order to effectively address the challenges, it is urgent to manage the distributed resources in the power grid system through scientific and reasonable control strategies to improve the stability of power grid operation. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a power grid system control method, device, equipment and readable storage medium that can improve the stability of power grid operation.

[0005] On the one hand, this application provides a power grid system control method, including: generating a day-ahead optimal scheduling result corresponding to a future predicted day based on the distributed resources in the power grid system, the day-ahead optimal scheduling result including the power outputs corresponding to multiple time points in the predicted day, and the time intervals between adjacent time points being the same; performing power flow calculation based on the day-ahead optimal scheduling result to determine the target operation mode of the power grid system, and partitioning the power grid system based on the target operation mode to determine key power flow sections; obtaining the actual power flow of the key power flow sections, and selecting frequency modulation response terminals from multiple edge-side intelligent control terminals based on the actual power flow; in the case where the power grid system meets the frequency modulation condition, sending a power response demand to the frequency modulation response terminals, so that the frequency modulation response terminals call the corresponding standby capabilities based on the power response demand.

[0006] On the other hand, the present application also provides a power grid system control device, including: a scheduling result generation module, configured to generate a day-ahead optimal scheduling result corresponding to a future predicted day based on distributed resources in the power grid system, where the day-ahead optimal scheduling result includes the power outputs of the distributed resources corresponding to multiple time points in the predicted day respectively, and the time intervals between adjacent time points are the same; a power flow section determination module, configured to perform power flow calculation based on the day-ahead optimal scheduling result, determine the target operation mode of the power grid system, and partition the power grid system based on the target operation mode to determine key power flow sections; a terminal selection module, configured to obtain the actual power flow of the key power flow sections, and select frequency modulation response terminals from multiple edge-side intelligent control terminals based on the actual power flow; a demand trigger module, configured to send a power response demand to the frequency modulation response terminals when the power grid system reaches the frequency modulation condition, so that the frequency modulation response terminals call corresponding standby capabilities based on the power response demand.

[0007] On the other hand, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps in the above power grid system control method when executing the computer program.

[0008] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored, and the computer program implements the steps in the above video file compression method when being executed by a processor.

[0009] Fifthly, the present application also provides a computer program product, including a computer program, and the computer program implements the steps in the above power grid system control method when being executed by a processor.

[0010] The above grid system control method, device, computer equipment, computer-readable storage medium and computer program product generate a day-ahead optimal scheduling result corresponding to a future predicted day based on distributed resources in the grid system. The day-ahead optimal scheduling result includes the power outputs corresponding to multiple time points in the predicted day for the distributed resources, and the time intervals between adjacent time points are the same. Based on the day-ahead optimal scheduling result, a power flow calculation is performed to determine the target operation mode of the grid system, and the grid system is partitioned based on the target operation mode to determine the key power flow sections. The actual power flow of the key power flow sections is obtained, and the frequency modulation response terminals are selected from multiple edge-side intelligent control terminals based on the actual power flow. When the grid system reaches the frequency modulation condition, a power response demand is sent to the frequency modulation response terminals, so that the frequency modulation response terminals call the corresponding standby capabilities based on the power response demand. Since the day-ahead optimal scheduling result includes the optimal scheduling results corresponding to multiple time points in the predicted day for the distributed resources, and a power flow calculation is performed based on the day-ahead optimal scheduling result to determine the target operation mode of the grid system, the target operation mode and the key power flow sections comprehensively consider the distributed resources at multiple time points, giving play to the support role of the edge-side intelligent control terminals in the collaborative regulation of distributed resources at multiple scales and improving the stability of grid operation. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is an application environment diagram of the grid system control method in an embodiment;

[0013] Figure 2 It is a schematic flowchart of the grid system control method in an embodiment;

[0014] Figure 3 It is a schematic diagram of the principle of the grid system control method in another embodiment;

[0015] Figure 4 It is a structural block diagram of the grid system control device in an embodiment;

[0016] Figure 5 It is an internal structure diagram of the computer equipment in an embodiment;

[0017] Figure 6 It is an internal structure diagram of the computer equipment in another embodiment. Detailed Description of the Embodiments

[0018] In order to make the objectives, technical solutions, and advantages of this application more clear and understandable, the following further elaborates on this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain this application and are not used to limit this application.

[0019] The power grid system control method provided by the embodiments of this application can be applied to an application environment as Figure 1 shown. In this application environment, there are computer devices, key section perception terminals, multiple edge-side intelligent control terminals (respectively edge-side intelligent control terminal 1 to edge-side intelligent control terminal J), and multiple distributed resources (respectively distributed resource 1 to distributed resource I). Each edge-side intelligent control terminal can be connected to at least one distributed resource. Among them, the edge-side intelligent control terminal is an intelligent device on the edge side of the power grid (such as the distribution network, user side), and has functions of data acquisition, calculation, and control. The edge-side intelligent control terminal can provide functions such as real-time data acquisition, intelligent decision-making, and remote control, and can comprehensively consider the operation requirements of the power grid and the standby situation of distributed resources, and through algorithms or models, realize the optimal scheduling and coordinated control of distributed resources on multiple time scales, which can not only improve the safety and stability of power grid operation, but also give full play to the potential of distributed resources, improve energy utilization efficiency, and reduce the operation cost of the power grid. The distributed resource can be, but is not limited to, a power generation device, an energy storage device, or a load resource, etc. For example, a charging pile is a kind of distributed resource. The key section perception terminal can monitor the active power of the key power flow section, and when the change amount of the section power flow exceeds the preset dead zone range, it can determine the power deficit according to the change amount of the section power flow and the dead zone range, and allocate the power deficit according to the power transfer distribution factor.

[0020] The computer device can be a terminal or a server. The terminal can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, etc. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The edge - side intelligent control terminal is implemented based on edge - computing technology. Edge computing moves the network functions and resources of cloud computing from the core network to the network edge, making data processing and analysis closer to the data source or actuator. Through diverse wireless access options and intelligent network function control, edge computing can more efficiently process and analyze massive, real - time distributed resource data, providing a more efficient and flexible means for the management and control of distributed resources. Edge - computing technology makes it possible to achieve an autonomous system for regional resources. The autonomous system for regional resources can suppress the random fluctuations brought by distributed resources, reduce the pressure on the main - network scheduling, and improve the flexibility and adaptability of the power grid. This application provides a power - grid system control method that can support the collaborative work of distributed resources on multiple time scales and form an autonomous system for regional resources.

[0021] In some embodiments, as Figure 2 shown, a power - grid system control method is provided. This method can be applied to a terminal or a server. Taking the computer device in Figure 1 as an example for illustration, it includes the following steps 202 to step 208. Among them:

[0022] Step 202: Based on the distributed resources in the power - grid system, generate a day - ahead optimal scheduling result corresponding to the future forecast day. The day - ahead optimal scheduling result includes the power outputs of the distributed resources corresponding to multiple time points in the forecast day, and the time intervals between adjacent time points are the same.

[0023] Among them, the optimal scheduling result corresponding to a time point can include at least one of, but is not limited to, the power output of the distributed resource, the load demand, or the energy - storage state, etc. The number of time points can be set according to actual needs. For example, it can be 96, and the time interval between adjacent time points is 15 minutes.

[0024] In some embodiments, the computer device can predict the curve prediction result corresponding to the power - grid system within the forecast day. Among them, the curve prediction result includes at least one of a predicted wind - power output curve, a predicted photovoltaic - power output curve, a predicted load - demand curve, or a predicted electricity - price curve. The computer device can generate the optimal scheduling results corresponding to multiple time points in the forecast day according to the curve prediction result. The day - ahead optimal scheduling result includes the optimal scheduling results corresponding to multiple time points in the forecast day.

[0025] Step 204: Conduct a power flow calculation based on the optimized scheduling results of the previous day, determine the target operating mode of the power grid system, and partition the power grid system based on the target operating mode to determine the key power flow sections.

[0026] Specifically, the computer device can partition the power grid system under the target typical operating mode to obtain each power grid partition corresponding to the power grid system; and use the tie lines between the power grid partitions as the key power flow sections under the target typical operating mode.

[0027] In some embodiments, for the power grid system under the target operating mode, the computer device can use the self-impedance and mutual-impedance between nodes in the node impedance matrix to determine the equivalent electrical distance between nodes, use the equivalent electrical distance between nodes to replace the original line weight, calculate the modularity, and partition the power grid using a complex network partitioning algorithm. The tie lines between the partitions are the key power flow sections for the target operating mode. Among them, the equivalent electrical distance between nodes is: , is the equivalent electrical distance between node i and node j, is the self-impedance of node i, is the self-impedance of node j, is the mutual-impedance between node i and node j. The complex network partitioning algorithm can be but is not limited to the Louvain algorithm.

[0028] Step 206: Obtain the actual power flow of the key power flow section, and select the frequency modulation response terminals from multiple edge-side intelligent control terminals based on the actual power flow.

[0029] In some embodiments, the computer device can select the frequency modulation response terminals from multiple edge-side intelligent control terminals according to the power transfer distribution factor corresponding to the distributed resources connected to the edge-side intelligent control terminals.

[0030] Step 208: When the power grid system meets the frequency modulation condition, send a power response demand to the frequency modulation response terminals, so that the frequency modulation response terminals can call the corresponding standby capacity based on the power response demand.

[0031] Among them, the frequency modulation demand can be but is not limited to that the change in the power flow of the key section exceeds the preset dead zone.

[0032] In some embodiments, when the power grid is disturbed and the active power of the critical power flow section changes suddenly, if the change amount of the critical section power flow exceeds the preset dead zone, the computer device can notify the critical section sensing terminal to allocate the power deficit according to the power transfer distribution factor of the distributed resources, obtain the power response demand, and send the power response demand to the frequency modulation response terminal. The frequency modulation response terminal exerts the role of the spare capacity of the distributed resources according to the power response demand, so that the frequency of the power grid system reaches stability and frequency modulation is achieved.

[0033] In the above power grid system control method, based on the distributed resources in the power grid system, a day-ahead optimal scheduling result corresponding to a future predicted day is generated. The day-ahead optimal scheduling result includes the power outputs of the distributed resources corresponding to multiple time points in the predicted day, and the time intervals between adjacent time points are the same. Based on the day-ahead optimal scheduling result, power flow calculation is performed to determine the target operation mode of the power grid system, and the power grid system is partitioned based on the target operation mode to determine the critical power flow section. The actual power flow of the critical power flow section is obtained, and the frequency modulation response terminal is selected from multiple edge-side intelligent control terminals based on the actual power flow. When the power grid system meets the frequency modulation condition, a power response demand is sent to the frequency modulation response terminal, so that the frequency modulation response terminal calls the corresponding spare capacity based on the power response demand. Since the day-ahead optimal scheduling result includes the power outputs of the distributed resources corresponding to multiple time points in the predicted day, based on the optimal scheduling results corresponding to each time point, the target operation mode of the power grid system is determined. Therefore, the target operation mode and the critical power flow section comprehensively consider the distributed resources at multiple time points, giving play to the supporting role of the edge-side intelligent control terminal in coordinating and controlling the distributed resources at multiple scales and improving the stability of the power grid operation.

[0034] In response to the challenges brought by the access of a large number of distributed resources to the safe and stable operation of the power grid, this application gives play to the supporting role of the edge-side intelligent control terminal in coordinating and controlling the distributed resources at multiple scales and improves the stability of the power grid operation.

[0035] In some embodiments, generating a day-ahead optimal scheduling result corresponding to a future predicted day based on the distributed resources in the power grid system includes: predicting the curve prediction result corresponding to the power grid system within the predicted day; and generating the day-ahead optimal scheduling result corresponding to the predicted day according to the curve prediction result. The curve prediction result includes at least one of a predicted wind power output curve, a predicted photovoltaic power output curve, a predicted load demand curve, or a predicted electricity price curve.

[0036] In some embodiments, the computer device may normalize the historical curves of wind power, photovoltaic power, load, and electricity price to obtain the normalized curves of wind power, photovoltaic power, load, and electricity price respectively. The per-unit value may be normalization. For example, the computer device may normalize the historical output curves of wind power and photovoltaic power, the historical curve of load, and the historical curve of electricity price according to the historical installed capacity, the maximum load of the current year, and the average value of the electricity price curve respectively. For example, the following formula may be used for normalization:

[0037]

[0038] Wherein, is the historical output value of wind power at time t, is the historical output value of photovoltaic power at time t, is the historical installed capacity of the wind farm, is the historical installed capacity of the photovoltaic power station, is the output value obtained by normalizing the historical output value of wind power at time t, is the output value obtained by normalizing the historical output value of photovoltaic power at time t, is the historical load value at time t, is the maximum load of the current year, is the load value obtained by normalizing the historical load value at time t, is the historical electricity price value at time t, is the average value of the electricity price curve, is the electricity price value obtained by normalizing the historical electricity price value at time t.

[0039] Then, the computer device may cluster multiple typical scenarios of the power grid system according to the normalized curves of wind power, photovoltaic power, load, and electricity price respectively. The term "multiple" means at least two. For example, the K-means algorithm may be used for clustering to obtain 5 typical scenarios. Among them, in the process of using the K-means algorithm to cluster and obtain the typical scenarios, the sum of the squares of the distances from the data points within the cluster to the cluster center is minimized as the clustering objective function. For example, the clustering objective function is: , where J is the clustering objective function, k is the number of clusters, is the i-th cluster, is the data point belonging to the cluster , represents the cluster center of the cluster , represents and is the square of the Euclidean distance.

[0040] In some embodiments, after determining multiple typical scenarios of the power grid system, the computer device may determine a target typical scenario that matches the prediction day from the multiple typical scenarios; based on the wind power and photovoltaic output curves of the target typical scenario and in combination with the installed capacity of the prediction year, predict the corresponding curve prediction results of the power grid system within the prediction day.

[0041] In some embodiments, the computer device may use the Monte Carlo method for prediction. Based on historical data, determine the probability distributions of various influencing factors such as wind speed, light intensity, and national economy, and set the probability density functions of influencing factors such as wind speed v, light intensity I, and national economic indicators. Generate random numbers to simulate future changes, and calculate the corresponding wind and light output, load demand, and electricity price. After repeating the simulation multiple times, perform statistical analysis to obtain the prediction ranges and possible probability distributions of the wind and light output curves, load demand curves, and electricity price curves within the preset future day, and select the curves with the highest probability and multiply them by the installed capacity of the prediction year, etc., to obtain the actual prediction results (i.e., the curve prediction results).

[0042] In this embodiment, since the curve prediction results include at least one of the predicted wind power output curve, predicted photovoltaic output curve, predicted load demand curve, or predicted electricity price curve, based on the curve prediction results, the optimized scheduling results corresponding to multiple time points in the prediction day can be accurately generated.

[0043] In some embodiments, generating the day-ahead optimized scheduling result corresponding to the prediction day according to the curve prediction results includes: minimizing the objective function based on the curve prediction results, and determining the output of distributed resources at multiple time points when the objective function satisfies minimization, to obtain the day-ahead optimized scheduling result corresponding to the prediction day. Among them, minimizing the objective function can be expressed as:

[0044]

[0045] The constraint conditions when minimizing the objective function include:

[0046]

[0047]

[0048]

[0049]

[0050] Among them, N is the number of time points, is the output of distributed resource m at time t (i.e., the t-th time point), and are the output constraint values, is the ramp - up ability characterization value (such as ramp - up rate) of distributed resource m, which is used to characterize the ramp - up ability of distributed resource m at time t. and is the constraint value of the ramp - up ability characterization value. is the number of times the current state of distributed resource m has been converted. is the constraint value of the number of state conversions of distributed resource m. is the total power generation output. is the total load demand. is the cost of generating a unit of electric energy by distributed resource i at time t. is the comprehensive frequency - modulation performance index of the i - th distributed resource participating in frequency modulation. is the capacity bid of the i - th distributed resource participating in frequency modulation. is the mileage bid of the i - th distributed resource participating in frequency modulation. is the frequency - modulation capacity of the i - th distributed resource participating in frequency modulation. is the actual frequency - modulation mileage of the i - th distributed resource participating in frequency modulation. is the number of all distributed resources. is the number of distributed resources participating in frequency modulation.

[0051] That is, solve the minimization function of the objective function according to the following formula:

[0052] , and determine the day - ahead optimal scheduling result. The day - ahead optimal scheduling result includes the optimal value of the optimization objective (objective function), and the output results of various power generation resources such as wind power, photovoltaic power, and energy storage when the optimal value is obtained.

[0053] In this embodiment, by solving the minimization function of the objective function, the optimal scheduling result can be accurately obtained.

[0054] In some embodiments, based on the day - ahead optimal scheduling result, perform power flow calculation to determine the target operation mode of the power grid system, including: performing power flow calculation based on the day - ahead optimal scheduling result to determine multiple reference operation modes; clustering the multiple reference operation modes to obtain at least two typical operation modes; obtaining the net load curve under each typical operation mode among the at least two typical operation modes; and taking the typical operation mode to which the net load curve with the highest peak in each net load curve belongs as the target operation mode of the power grid system.

[0055] Among them, the clustering method can be but is not limited to the k-means clustering algorithm. For the optimal scheduling result corresponding to each time point, a reference operation mode can be determined. For example, if there are 96 time points, 96 reference operation modes are determined. The reference operation mode includes but is not limited to at least one of the voltage amplitude of nodes in the power grid system, line power flow, or total system loss, etc.

[0056] Specifically, the computer device can determine the load demand curve of the power grid system under each typical operation mode, subtract the wind-solar output curve from the load demand curve to obtain the net load curve under this typical operation mode. For example, , where, is is the net load curve, is the load demand curve, is the wind-solar output curve.

[0057] In this embodiment, the typical operation mode to which the net load curve with the highest peak in each net load curve belongs is used as the target operation mode of the power grid system, so as to improve the rationality of the target operation mode.

[0058] In some embodiments, obtaining the actual power flow of the key power flow section and selecting the frequency modulation response terminal from multiple edge-side intelligent control terminals based on the actual power flow includes: determining the power transfer distribution factors corresponding to multiple distributed resources in the power grid system; arranging the spare capacities corresponding to the distributed resources in the multiple distributed resources in descending order of the power transfer distribution factors, and when the accumulated spare capacity is greater than or equal to the actual power flow, taking the edge-side intelligent control terminal connected to the distributed resources participating in the accumulation as the frequency modulation response terminal.

[0059] Among them, the edge-side intelligent control terminal is connected to at least one distributed resource. The distributed resources can be regarded as nodes, and the power transfer distribution factors of the distributed resources are calculated. The calculation of the power transfer distribution factor is as follows:

[0060]

[0061] Among them, is the active power change amount on line caused by the injected active power change, is the change amount of the injected active power of node , is 's power transfer distribution factor.

[0062] Specifically, the computer device can sort multiple distributed resources in descending order of the power transmission distribution factor to obtain a distributed resource sequence. Then, the computer device can accumulate the spare capacities corresponding to the distributed resources in the distributed resource sequence in the order from front to back. When the accumulated spare capacity is greater than or equal to the actual power flow, the edge - side intelligent control terminals connected to the distributed resources participating in the accumulation are used as the frequency - modulation response terminals for the critical power - flow section, thereby obtaining a set of frequency - modulation response terminals. For example, if the distributed resource sequence is [A, B, C, D, E, F], first, the spare capacity corresponding to A is used as the accumulated spare capacity. If the accumulated spare capacity is greater than or equal to the actual power flow, then A is used as the frequency - modulation response terminal. Otherwise, the spare capacities corresponding to A and B are accumulated respectively to obtain the accumulated spare capacity. If the accumulated spare capacity is greater than or equal to the actual power flow, the edge - side intelligent control terminals connected to A and B respectively are used as the frequency - modulation response terminals. Otherwise, continue to accumulate backward.

[0063] In this embodiment, since the larger the power transmission distribution factor corresponding to the distributed resource, the greater the impact of the distributed resource on the power transmission of the power grid system. Therefore, the edge - side intelligent control terminals connected to the distributed resources participating in the accumulation are used as the frequency - modulation response terminals for the critical power - flow section. When the power grid frequency fluctuates, it can give priority to adjusting the output of the distributed resources that have a greater impact on the power transmission of the power grid system, so that the frequency of the power system can quickly return to stability.

[0064] In some embodiments, the method further includes: obtaining the real - time operation data during the prediction day; using the real - time operation data to perform real - time dynamic correction on the day - ahead optimal scheduling result.

[0065] Specifically, based on the generated day - ahead optimal scheduling result, considering the possible differences between the actual operation result within the day and it, in order to more accurately cope with this uncertainty, negative feedback is introduced to perform rolling optimization on the day - ahead optimal scheduling result, and the real - time operation data within the day is used as the key feedback.

[0066] In some embodiments, the day - ahead formulated optimal scheduling plan can be dynamically and timely corrected according to the following formula:

[0067]

[0068] where, is the actual output at time t - 1, is the optimal prediction at time t - 1, is the actual output at time t, is the optimal prediction at time t, is the error at time t - 1, is the error at time t, is the modified control quantity at time t+1.

[0069] In this embodiment, through error correction and rolling optimization, it can better adapt to the changing power market environment and system operating conditions.

[0070] In some embodiments, such as Figure 3 as shown, a power grid system control method is provided, including:

[0071] 1. After normalizing the historical curves of wind power, photovoltaic, load, and electricity price respectively, cluster typical scenarios. Using the Monte Carlo method, introduce random parameters, simulate future changes, and obtain the prediction range and probability distribution after multiple statistics. Select the curve with the maximum probability and multiply it by the installed capacity in the predicted year to obtain the actual prediction result.

[0072] 2. Divide a day into 96 points at a granularity of 15 minutes. According to the actual prediction result, optimize the output of distributed resources, consider constraints, costs, and the benefits of auxiliary frequency modulation, construct an optimization model, and solve to obtain the optimized scheduling results at 96 time points in a day, so as to obtain the day-ahead optimized scheduling result.

[0073] 3. Based on the day-ahead optimized scheduling result, obtain 96 operating modes through power flow calculation, use the clustering algorithm to cluster multiple typical operating modes, and select the typical operating mode with the highest net load peak as the target operating mode.

[0074] 4. According to the target operating mode, identify the key sections. Use the power transfer distribution factor as the evaluation index to accumulate the spare capacity from large to small until it exceeds the actual power flow of the key power flow section, so as to obtain the frequency modulation response terminal set.

[0075] 5. Based on the day-ahead optimized scheduling result, consider the difference from the actual operating result within the day, use the real-time operating data within the day as feedback, and dynamically correct the day-ahead optimized scheduling result to adapt to the changing power market environment and system operating conditions.

[0076] 6. For the key power flow section, analyze that when the power grid receives a disturbance resulting in an abrupt change in the active power of the key power flow section and exceeding the dead zone, trigger the key power flow section sensing terminal to allocate the power deficit according to the power transfer distribution factor, issue a response demand to the edge-side intelligent control terminal, and the edge-side intelligent control terminal exerts its frequency modulation standby ability to respond and fill the active power deficit of the power grid to maintain the frequency stability of the power grid.

[0077] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0078] Based on the same inventive concept, an embodiment of the present application also provides a power grid system control device for implementing the power grid system control method described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the power grid system control device provided below can refer to the limitations on the power grid system control method in the above text, and will not be repeated here.

[0079] In some embodiments, as Figure 4 shown, a power grid system control device is provided, including: a scheduling result generation module 402, a power flow section determination module 404, a terminal selection module 406, and a demand trigger module 408, where:

[0080] The scheduling result generation module 402 is configured to generate a day-ahead optimal scheduling result corresponding to the future predicted day based on distributed resources in the power grid system. The day-ahead optimal scheduling result includes the optimal scheduling results corresponding to multiple time points in the predicted day for the distributed resources, and the time intervals between adjacent time points are the same.

[0081] The power flow section determination module 404 is configured to perform power flow calculation based on the day-ahead optimal scheduling result, determine the target operation mode of the power grid system, and partition the power grid system based on the target operation mode to determine the key power flow sections.

[0082] The terminal selection module 406 is configured to obtain the actual power flow of the key power flow section and select a frequency modulation response terminal from multiple edge-side intelligent control terminals based on the actual power flow.

[0083] The demand trigger module 408 is configured to send a power response demand to the frequency modulation response terminal when the power grid system reaches the frequency modulation condition, so that the frequency modulation response terminal calls the corresponding standby capacity based on the power response demand.

[0084] In some embodiments, the scheduling result generation module 402 is further configured to predict the curve prediction result corresponding to the power grid system within the prediction day, where the curve prediction result includes at least one of a predicted wind power output curve, a predicted photovoltaic power output curve, a predicted load demand curve, or a predicted electricity price curve; and generate a day-ahead optimal scheduling result corresponding to the prediction day according to the curve prediction result.

[0085] In some embodiments, the scheduling result generation module 402 is further configured to: minimize the objective function based on the curve prediction result, and determine the output of the distributed resources at multiple time points when the objective function meets the minimization, so as to obtain the day-ahead optimal scheduling result corresponding to the prediction day; where the objective function is:

[0086]

[0087] N is the number of time points, is the output of the distributed resource m at the t-th moment (i.e., the t-th time point), is the cost of the distributed resource m for generating a unit of electric energy at the t-th moment, is the comprehensive frequency modulation performance index of the i-th distributed resource participating in frequency modulation, is the capacity quotation of the i-th distributed resource participating in frequency modulation, is the mileage quotation of the i-th distributed resource participating in frequency modulation, is the frequency modulation capacity of the i-th distributed resource participating in frequency modulation, is the actual frequency modulation mileage of the i-th distributed resource participating in frequency modulation, is the total number of all distributed resources, is the number of distributed resources participating in frequency modulation.

[0088] In some embodiments, the power flow section determination module 404 is further configured to perform power flow calculation based on the day-ahead optimal scheduling result to determine multiple reference operating modes; cluster the multiple reference operating modes to obtain at least two typical operating modes; obtain the net load curves under each typical operating mode among the at least two typical operating modes; and use the typical operating mode to which the net load curve with the highest peak in each net load curve belongs as the target operating mode of the power grid system.

[0089] In some embodiments, the terminal selection module 406 is further configured to determine the power transfer distribution factors corresponding to multiple distributed resources in the power grid system; accumulate the spare capacities corresponding to the distributed resources in the multiple distributed resources in descending order of the power transfer distribution factors, and use the edge-side intelligent control terminal connected to the distributed resources participating in the accumulation as the frequency modulation response terminal when the accumulated spare capacity is greater than or equal to the actual power flow.

[0090] In some embodiments, the device is further configured to: obtain real-time operation data on the prediction day; and use the real-time operation data to perform real-time dynamic correction on the day-ahead optimized scheduling result.

[0091] Each module in the above grid system control device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0092] In some embodiments, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data involved in the grid system control method. 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 an external terminal through a network connection. When the computer program is executed by the processor, it implements a grid system control method.

[0093] In some embodiments, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, 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 computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The 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 in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a power grid system control method. The display unit of the computer device is used to form a visually visible picture, which 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. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0094] Those skilled in the art can understand that Figure 5 and Figure 6 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0095] In some embodiments, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0096] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above embodiments are implemented.

[0097] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above embodiments are implemented.

[0098] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0099] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0100] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope recorded in this application.

[0101] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several variations and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application should be subject to the appended claims.

Claims

1. A power grid system control method, characterized in that: The method comprises: Based on the distributed resources in the power grid system, a day-ahead optimization dispatch result corresponding to a future forecast day is generated, wherein the day-ahead optimization dispatch result includes the output of the distributed resources corresponding to multiple time points on the forecast day, and the time intervals between adjacent time points are consistent; Performing power flow calculation based on the day-ahead optimization scheduling result to determine a target operation mode of the power grid system, and partitioning the power grid system based on the target operation mode to determine key power flow sections; Acquire the actual power flow of the key power flow section, and select a frequency modulation response terminal from a plurality of edge-side intelligent control terminals based on the actual power flow; When the power grid system meets the frequency regulation condition, a power response requirement is sent to the frequency regulation response terminal, so that the frequency regulation response terminal calls the corresponding backup capacity based on the power response requirement.

2. The method according to claim 1, characterized in that: The method of generating a day-ahead optimization dispatch result corresponding to a future forecast day based on distributed resources in the power grid system includes: Predicting a curve prediction result corresponding to the power grid system within the prediction day, wherein the curve prediction result includes at least one of a predicted wind power output curve, a predicted photovoltaic output curve, a predicted load demand curve, or a predicted electricity price curve; According to the curve prediction result, the day-ahead optimization scheduling result corresponding to the prediction day is generated.

3. The method according to claim 2, characterized in that Generating the day-ahead optimization scheduling result corresponding to the predicted day according to the curve prediction result includes: Minimize the objective function based on the curve prediction result, and determine the output of the distributed resources at multiple time points when the objective function satisfies the minimization, so as to obtain the day-ahead optimization scheduling result corresponding to the prediction day; Among them, the objective function is: N is the number of time points, is the actual output of distributed resource m at time t (i.e., the tth time point), is the cost of distributed resource m to generate unit power at time t, is the comprehensive frequency modulation performance index of the i-th distributed resource participating in frequency modulation, is the capacity quotation of the i-th distributed resource participating in frequency regulation, is the mileage quotation of the i-th distributed resource participating in frequency modulation, is the frequency modulation capacity of the i-th distributed resource participating in frequency modulation, is the actual frequency modulation mileage of the i-th distributed resource participating in frequency modulation, is the number of all distributed resources, The number of distributed resources participating in frequency modulation.

4. The method according to claim 1, characterized in that: The performing of power flow calculation based on the day-ahead optimization dispatch result to determine the target operation mode of the power grid system includes: Performing power flow calculation based on the day-ahead optimization scheduling result to determine multiple reference operation modes; Clustering the multiple reference operating modes to obtain at least two typical operating modes; Obtaining a net load curve under each typical operating mode of the at least two typical operating modes; The typical operation mode to which the net load curve with the highest peak value belongs among the net load curves is used as the target operation mode of the power grid system.

5. The method according to any one of claims 1 to 4, characterized in that: The obtaining of the actual power flow of the key power flow section and selecting a frequency modulation response terminal from a plurality of edge-side intelligent control terminals based on the actual power flow include: Determine power transmission distribution factors corresponding to a plurality of distributed resources in a power grid system; The spare capacities corresponding to the distributed resources in the multiple distributed resources are accumulated in the order of power transmission distribution factors from large to small. When the accumulated spare capacity is greater than or equal to the actual power flow, the edge-side intelligent control terminal connected to the distributed resources participating in the accumulation is used as a frequency modulation response terminal.

6. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Obtaining real-time operation data on the forecast day; The real-time operation data is used to perform real-time dynamic correction on the day-ahead optimization scheduling result.

7. A power grid system control device, characterized in that: The device comprises: A dispatch result generation module, used to generate a day-ahead optimization dispatch result corresponding to a future forecast day based on distributed resources in the power grid system, wherein the day-ahead optimization dispatch result includes the output of the distributed resources corresponding to multiple time points on the forecast day, and the time intervals between adjacent time points are consistent; A power flow section determination module, configured to perform power flow calculation based on the day-ahead optimization scheduling result, determine a target operation mode of the power grid system, and partition the power grid system based on the target operation mode to determine a key power flow section; A terminal selection module, used for obtaining the actual power flow of the key power flow section, and selecting a frequency modulation response terminal from a plurality of edge-side intelligent control terminals based on the actual power flow; The demand triggering module is used to send a power response demand to the frequency modulation response terminal when the power grid system meets the frequency modulation condition, so that the frequency modulation response terminal calls the corresponding backup capacity based on the power response demand.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. 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 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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