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

Abstract

The application relates to a power grid system control method, device and equipment and a readable storage medium. The method comprises the following steps: based on distributed resources in a power grid system, a day-ahead optimization scheduling result corresponding to a predicted day in the future is generated, the day-ahead optimization scheduling result comprises output of the distributed resources at a plurality of time points in the predicted day, and time intervals between adjacent time points are consistent; based on the optimization scheduling result corresponding to each time point, a target operation mode of the power grid system is determined, and the power grid system is partitioned based on the target operation mode to determine a key power flow section; actual power flow of the key power flow section is acquired, a frequency modulation response terminal is selected from a plurality of edge side intelligent control terminals based on the actual power flow; in the case that the power grid system reaches a frequency modulation condition, a power response demand is sent to the frequency modulation response terminal, so that the frequency modulation response terminal calls corresponding standby capacity 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 cybersecurity technology, and in particular to a power grid system control method, apparatus, device, and readable storage medium. Background Technology

[0002] With the deepening of the global energy transition and the rapid development of digital technologies, the proportion of distributed resources in the power grid is showing an increasing trend. Due to their geographically dispersed nature, as well as the randomness and volatility of power generation, distributed resources pose challenges to the safe and stable operation of the power grid.

[0003] In order to effectively address the challenges, it is urgent to manage 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] Therefore, 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 in response to the above-mentioned technical problems.

[0005] On one hand, this application provides a power grid system control method, comprising: generating day-ahead optimized scheduling results for a future forecast date based on distributed resources in the power grid system, wherein the day-ahead optimized scheduling results include the output of the distributed resources at multiple time points on the forecast date, with consistent time intervals between adjacent time points; performing power flow calculation based on the day-ahead optimized scheduling results to determine the target operating mode of the power grid system, and partitioning the power grid system based on the target operating mode to determine key power flow sections; obtaining the actual power flow of the key power flow sections, and selecting a frequency regulation response terminal from multiple edge-side intelligent control terminals based on the actual power flow; and sending a power response demand to the frequency regulation response terminal when the power grid system reaches the frequency regulation conditions, so that the frequency regulation response terminal can call the corresponding reserve capacity based on the power response demand.

[0006] On the other hand, this application also provides a power grid system control device, comprising: a scheduling result generation module, configured to generate a day-ahead optimized scheduling result corresponding to a future forecast date based on distributed resources in the power grid system, wherein the day-ahead optimized scheduling result includes the output of the distributed resources at multiple time points on the forecast date, and the time interval between adjacent time points is consistent; a power flow section determination module, configured to perform power flow calculation based on the day-ahead optimized scheduling result, determine the target operating mode of the power grid system, and partition the power grid system based on the target operating 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 a frequency regulation response terminal from multiple edge-side intelligent control terminals based on the actual power flow; and a demand triggering module, configured to send a power response demand to the frequency regulation response terminal when the power grid system reaches the frequency regulation condition, so that the frequency regulation response terminal can call the corresponding reserve capacity based on the power response demand.

[0007] On the other hand, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described power grid system control method.

[0008] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described video file compression method.

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

[0010] The aforementioned power grid system control method, apparatus, computer equipment, computer-readable storage medium, and computer program product generate day-ahead optimized scheduling results for the predicted future date based on distributed resources in the power grid system. These day-ahead optimized scheduling results include the output of distributed resources at multiple time points within the predicted date, with consistent time intervals between adjacent time points. Power flow calculations are performed based on these day-ahead optimized scheduling results to determine the target operating mode of the power grid system. The power grid system is then partitioned based on the target operating mode to identify key power flow sections. The actual power flow of these key sections is obtained. Based on the actual power flow, frequency regulation response terminals are selected from multiple edge-side intelligent control terminals. When the power grid system meets frequency regulation conditions, power response demands are sent to the frequency regulation response terminals, enabling them to utilize corresponding reserve capacity based on these demands. Because the day-ahead optimized scheduling results include optimized scheduling results for distributed resources at multiple time points within the predicted date, and power flow calculations are performed based on these results to determine the target operating mode of the power grid system, the target operating mode and key power flow sections comprehensively consider distributed resources at multiple time points. This leverages the supporting role of edge-side intelligent control terminals in the coordinated regulation of distributed resources across multiple scales, thereby improving the stability of power grid operation. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is an application environment diagram of a power grid system control method in one embodiment;

[0013] Figure 2 This is a flowchart illustrating a power grid system control method in one embodiment;

[0014] Figure 3 This is a schematic diagram of a power grid system control method in another embodiment;

[0015] Figure 4 This is a structural block diagram of a power grid system control device in one embodiment;

[0016] Figure 5 This is an internal structural diagram of a computer device in one embodiment;

[0017] Figure 6 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The power grid system control method provided in this application embodiment can be applied to, for example, Figure 1 The application environment shown includes computer equipment, key section sensing terminals, multiple edge-side intelligent control terminals (Edge-side Intelligent Control Terminal 1 to Edge-side Intelligent Control Terminal J), and multiple distributed resources (Distributed Resource 1 to Distributed Resource I). Each edge-side intelligent control terminal can connect to at least one distributed resource. The edge-side intelligent control terminal is an intelligent device at the edge of the power grid (such as the distribution network or the user side), possessing data acquisition, calculation, and control functions. It can provide real-time data acquisition, intelligent decision-making, and remote control functions. It can comprehensively consider the power grid's operational needs and the reserve status of distributed resources, and through algorithms or models, achieve optimized scheduling and coordinated control of distributed resources across multiple time scales. This not only improves the safety and stability of power grid operation but also fully utilizes the potential of distributed resources, increases energy utilization, and reduces power grid operating costs. Distributed resources can be, but are not limited to, power generation equipment, energy storage equipment, or load resources; for example, charging piles are a type of distributed resource. The critical section sensing terminal can monitor the active power of critical power flow sections, and when the power flow change exceeds the preset dead zone range, it can determine the power deficit based on the power flow change and the dead zone range, and allocate the power deficit according to the power transmission distribution factor.

[0020] Computer equipment can be terminals or servers. Terminals can be, but are not limited to, various desktop computers, laptops, smartphones, tablets, etc. Servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing cloud computing services. Edge-side intelligent control terminals are 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 realize an autonomous system for regional resources. An autonomous system for regional resources can smooth out random fluctuations caused by distributed resources, reduce the pressure on the main grid 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 at multiple time scales, forming an autonomous system for regional resources.

[0021] In some embodiments, such as Figure 2 As shown, a power grid system control method is provided. This method can be applied to a terminal or a server. Figure 1 The following steps are used as an example of computer equipment, including steps 202 to 208. Wherein:

[0022] Step 202: Based on the distributed resources in the power grid system, generate the day-ahead optimized scheduling results corresponding to the future forecast date. The day-ahead optimized scheduling results include the output of the distributed resources at multiple time points in the forecast date, and the time interval between adjacent time points is consistent.

[0023] The optimized scheduling result corresponding to each time point can include, but is not limited to, at least one of the following: output of distributed resources, load demand, or energy storage status. The number of time points can be set according to actual needs, for example, up to 96, with a time interval of 15 minutes between adjacent time points.

[0024] In some embodiments, the computer device can predict the curve forecast results corresponding to the power grid system within the forecast day. The curve forecast results include at least one of the predicted wind power output curve, predicted photovoltaic power output curve, predicted load demand curve, or predicted electricity price curve. Based on the curve forecast results, the computer device can generate optimized scheduling results corresponding to multiple time points within the forecast day, whereby the day-ahead optimized scheduling results include optimized scheduling results corresponding to multiple time points within the forecast day.

[0025] Step 204: Perform power flow calculation based on the day-ahead optimized scheduling results, 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, computer equipment can partition the power grid system under the target typical operating mode to obtain the corresponding power grid partitions; and use the interconnection lines between the various power grid partitions as the key power flow sections under the target typical operating mode.

[0027] In some embodiments, for a power grid system operating under a target mode, computer equipment can determine the equivalent electrical distance between nodes using the self-impedance and mutual impedance between nodes in the node impedance matrix. This equivalent electrical distance is then used to replace the original line weights to calculate the modularity. A complex network partitioning algorithm is then employed to partition the power grid, and the interconnecting lines between these partitions are designated as the critical power flow sections for the target operating mode. The equivalent electrical distance between nodes is: , Let be the equivalent electrical distance between node i and node j. Let be the self-impedance of node i. Let J be the self-impedance of node j. Let be the mutual impedance between node i and node j. Complex network partitioning algorithms can include, but are not limited to, Louvain's algorithm.

[0028] Step 206: 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.

[0029] In some embodiments, the computer device may select a frequency modulation response terminal from multiple edge-side intelligent control terminals based on the power transmission distribution factor corresponding to the distributed resources connected to the edge-side intelligent control terminal.

[0030] Step 208: When the power grid system meets the frequency regulation conditions, a power response request is sent to the frequency regulation response terminal so that the frequency regulation response terminal can call up the corresponding backup capacity based on the power response request.

[0031] Among them, frequency modulation demand can be, but is not limited to, changes in the power flow at key sections exceeding the preset dead zone.

[0032] In some embodiments, when a disturbance to the power grid causes a sudden change in active power at a critical power flow section, if the change in power flow at the critical section exceeds a preset dead zone, the computer equipment can notify the critical section sensing terminal to allocate the power deficit according to the power transmission distribution factor of the distributed resources, obtain the power response demand, and then send the power response demand to the frequency regulation response terminal. The frequency regulation response terminal, based on the power response demand, utilizes the reserve capacity of the distributed resources to stabilize the frequency of the power grid system, thus achieving frequency regulation.

[0033] In the aforementioned power grid system control method, based on the distributed resources in the power grid system, day-ahead optimized scheduling results corresponding to the predicted future date are generated. These day-ahead optimized scheduling results include the output of distributed resources at multiple time points within the predicted date, with consistent time intervals between adjacent time points. Power flow calculations are performed based on these day-ahead optimized scheduling results to determine the target operating mode of the power grid system. The power grid system is then partitioned based on the target operating mode to identify key power flow sections. The actual power flow of these key power flow sections is obtained. Based on the actual power flow, frequency regulation response terminals are selected from multiple edge-side intelligent control terminals. When the power grid system meets the frequency regulation conditions, power response demands are sent to the frequency regulation response terminals, enabling them to utilize corresponding reserve capacity based on these demands. Since the day-ahead optimized scheduling results include the output of distributed resources at multiple time points within the predicted date, and the target operating mode of the power grid system is determined based on the optimized scheduling results at each time point, the target operating mode and key power flow sections comprehensively consider distributed resources at multiple time points. This leverages the supporting role of edge-side intelligent control terminals in the coordinated regulation of distributed resources at multiple scales, thereby improving the stability of power grid operation.

[0034] To address the challenges posed by the access of a large number of distributed resources to the safe and stable operation of the power grid, this application leverages the role of edge-side intelligent control terminals in supporting the coordinated regulation of distributed resources at multiple scales, thereby improving the stability of power grid operation.

[0035] In some embodiments, based on distributed resources in the power grid system, generating day-ahead optimal scheduling results for the predicted future date includes: predicting the curve prediction results for the power grid system within the predicted date; and generating day-ahead optimal scheduling results for the predicted date based on the curve prediction results. The curve prediction results include 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 can normalize the historical curves of wind power, solar power, load, and electricity price to obtain the normalized curves for each of these components. The normalized value can be a normalization. For example, the computer device can normalize the historical output curves of wind power and solar power, the historical load curves, and the historical electricity price curves according to historical installed capacity, the maximum load of the current year, and the average value of the electricity price curves, respectively, for example, using the following formula:

[0037]

[0038] in, Let be the historical power output of the wind power at time t. Let be the historical power output of the photovoltaic system at time t. This refers to the historical installed capacity of the wind farm. This refers to the historical installed capacity of photovoltaic power plants. This is the output value obtained by normalizing the historical wind power output value at time t. This is the output value obtained by normalizing the historical photovoltaic output value at time t. The historical load value at time t. This was the maximum load of the year. This is the load value after standardizing the historical load value at time t. The historical electrical value at time t. This represents the average value of the electricity price curve. This is the electrical value obtained by standardizing the historical electrical value at time t.

[0039] Then, the computer equipment can cluster multiple typical scenarios of the power grid system based on the per-unit curves of wind power, photovoltaics, load, and electricity price. "Multiple" means at least two; for example, the K-means algorithm can be used to cluster and obtain five typical scenarios. In the process of using the K-means algorithm to obtain typical scenarios, the clustering objective function is to minimize the sum of squared distances from data points within a cluster to the cluster center. For example, the clustering objective function is: Where J is the clustering objective function, and k is the number of clusters. For the i-th cluster, Belongs to cluster Data points, Representative cluster The cluster center, represent and The square of the Euclidean distance.

[0040] In some embodiments, after determining multiple typical scenarios of the power grid system, the computer device can identify a target typical scenario that matches the forecast date from among the multiple typical scenarios; based on the wind power and photovoltaic output curves of the target typical scenario, combined with the installed capacity of the forecast year, the computer device can predict the curve prediction results of the power grid system on the forecast date.

[0041] In some embodiments, the computer equipment can employ the Monte Carlo method for prediction, determining the probability distribution of various influencing factors such as wind speed, solar irradiance, and national economic indicators based on historical data, and defining the probability density functions of these factors, including wind speed v, solar irradiance I, and national economic indicators. Random numbers are generated to simulate future changes, calculating the corresponding wind and solar power output, load demand, and electricity price. After repeated simulations, statistical analysis is performed to obtain the predicted range and possible probability distribution of the wind and solar power output curve, load demand curve, and electricity price curve for the future preset daily period. The curve with the highest probability is selected and multiplied by the installed capacity of the predicted year to obtain the actual prediction result (i.e., the curve prediction result).

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

[0043] In some embodiments, generating the day-ahead optimized scheduling result corresponding to the predicted date based on the curve prediction result includes: minimizing the objective function based on the curve prediction result, and determining the output of distributed resources at multiple time points when the objective function is minimized, thereby obtaining the day-ahead optimized scheduling result corresponding to the predicted date. Minimizing the objective function can be expressed as:

[0044]

[0045] The constraints for minimizing the objective function include:

[0046]

[0047]

[0048]

[0049]

[0050] Where N is the number of time points, Let m be the output of the distributed resource m at time t (i.e., the t-th time point). and This is the output constraint value. This is a characterization value (e.g., climbing rate) of the climbing ability of distributed resource m, used to characterize the climbing ability of distributed resource m at time t. and These are constraint values ​​representing the climbing ability. Let m be the number of times the current state of the distributed resource m has been transformed. Let m be the constraint value for the number of state transitions of the distributed resource m. The total power output. This represents the total load demand. Let i be the cost of generating one unit of electrical energy from distributed resource i at time t. Let be the comprehensive frequency modulation performance index of the i-th distributed resource participating in frequency modulation. The capacity quotation for the i-th distributed resource participating in frequency regulation. The mileage quote for the i-th distributed resource participating in frequency modulation. Let be the frequency modulation capacity of the distributed resource participating in frequency modulation for the i-th unit. Let represent the actual frequency modulation mileage of the distributed resource participating in frequency modulation for the i-th unit. This represents the number of all distributed resources. The number of distributed resources participating in frequency modulation.

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

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

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

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

[0055] The clustering method can be, but is not limited to, the k-means clustering algorithm. The optimized scheduling result for each time point can determine a reference operating mode. For example, if there are 96 time points, then 96 reference operating modes can be determined. The reference operating modes include, but are not limited to, at least one of the following: voltage amplitude of nodes in the power grid system, line power flow, or total system loss.

[0056] Specifically, computer equipment can determine the load demand curve of the power grid system under each typical operating mode, and subtract the wind and solar power output curve from the load demand curve to obtain the net load curve under that typical operating mode. For example, , among which, This is the net load curve. For the load demand curve, The output curve for wind and solar power.

[0057] In this embodiment, the typical operating mode to which the net load curve with the highest peak value among all net load curves belongs is taken as the target operating mode of the power grid system, thereby improving the rationality of the target operating mode.

[0058] In some embodiments, obtaining the actual power flow of a key power flow section and selecting a frequency regulation response terminal from multiple edge-side intelligent control terminals based on the actual power flow includes: determining the power transmission distribution factor corresponding to multiple distributed resources in the power grid system; accumulating the reserve capacity corresponding to the distributed resources in the multiple distributed resources in descending order of the power transmission distribution factor; and, if the accumulated reserve capacity is greater than or equal to the actual power flow, using the edge-side intelligent control terminal connected to the distributed resource participating in the accumulation as the frequency regulation response terminal.

[0059] The edge-side intelligent control terminal connects to at least one distributed resource. The distributed resource can be viewed as a node, and its power transmission distribution factor is calculated. The calculation of the power transmission distribution factor is as follows:

[0060]

[0061] in, To inject active power changes caused by the line The change in active power on the surface, For nodes The change in injected active power, for The power transmission distribution factor.

[0062] Specifically, the computer equipment can sort multiple distributed resources in descending order of power transmission distribution factor to obtain a distributed resource sequence. Then, the computer equipment can accumulate the reserve capacity corresponding to the distributed resources in the distributed resource sequence in a sequential manner. If the accumulated reserve 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 frequency modulation response terminals for critical power flow sections, thus obtaining a set of frequency modulation response terminals. For example, if the distributed resource sequence is [A, B, C, D, E, F], firstly, the reserve capacity corresponding to A is used as the accumulated reserve capacity. If the accumulated reserve capacity is greater than or equal to the actual power flow, then A is used as a frequency modulation response terminal; otherwise, the reserve capacities corresponding to A and B are accumulated to obtain the accumulated reserve capacity. If the accumulated reserve capacity is greater than or equal to the actual power flow, then the edge-side intelligent control terminals connected to A and B are used as frequency modulation response terminals; otherwise, the accumulation continues.

[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, the edge-side intelligent control terminal connected to the distributed resource participating in the accumulation is used as the frequency regulation response terminal of the key power flow section. When the power grid fluctuates, the output of the distributed resource that has a greater impact on the power transmission of the power grid system can be adjusted first, so that the frequency of the power system can be quickly restored to stability.

[0064] In some embodiments, the method further includes: acquiring real-time operational data during the forecast day; and using the real-time operational data to dynamically correct the day-ahead optimized scheduling results in real time.

[0065] Specifically, based on the generated day-ahead optimized scheduling results, and considering the possible differences between the actual intraday operating results and these results, in order to more accurately address this uncertainty, negative feedback is introduced to perform rolling optimization of the day-ahead optimized scheduling results, using real-time intraday operating data as key feedback.

[0066] In some embodiments, the previously established optimized scheduling scheme can be dynamically and promptly modified according to the following formula:

[0067]

[0068] in, This represents the actual output at time t-1. The optimized prediction value at time t-1 Let be the actual output at time t. The optimized prediction value at time t. The error at time t-1, Let be the error at time t. This is the control variable modified at time t+1.

[0069] In this embodiment, error correction and rolling optimization can better adapt to the ever-changing electricity market environment and system operating status.

[0070] In some implementations, for example, Figure 3 As shown, a power grid system control method is provided, including:

[0071] 1. After normalizing the historical curves of wind power, photovoltaics, load, and electricity price, typical scenarios are clustered. The Monte Carlo method is used to introduce random parameters to simulate future changes. After multiple statistical analyses, the prediction range and probability distribution are obtained. The curve with the highest probability is selected and multiplied by the predicted annual installed capacity to obtain the actual prediction result.

[0072] 2. Divide the day into 96 points with a 15-minute granularity. Based on the actual forecast results, optimize the output of distributed resources, considering constraints, costs, and the benefits of auxiliary frequency regulation. Construct an optimization model and solve it to obtain the optimized scheduling results for the 96 time points of the day, thus obtaining the day-ahead optimized scheduling results.

[0073] 3. Based on the day-ahead optimized scheduling results, 96 operating modes were obtained through power flow calculation. Multiple typical operating modes were clustered using a clustering algorithm. The typical operating mode with the highest net load peak was selected as the target operating mode.

[0074] 4. Based on the target operating mode, identify the critical sections, and use the power transmission distribution factor as the evaluation index to accumulate the reserve capacity from large to small until it exceeds the actual power flow of the critical power flow section, thus obtaining the frequency regulation response terminal set.

[0075] 5. Based on the day-ahead optimized scheduling results, and considering the differences with the actual intraday operating results, the day-ahead optimized scheduling results are dynamically corrected using real-time intraday operating data as feedback, in order to adapt to the ever-changing power market environment and system operating status.

[0076] 6. For critical power flow sections, when the power grid receives a disturbance that causes a sudden change in active power and exceeds the dead zone, the critical power flow section sensing terminal is triggered to allocate the power deficit according to the power transmission distribution factor and issue a response demand to the edge-side intelligent control terminal. The edge-side intelligent control terminal responds by leveraging its frequency regulation reserve capability to fill the active power deficit in the power grid and maintain the stability of the power grid frequency.

[0077] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed 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 performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0078] Based on the same inventive concept, this 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 is similar to the solution described in the above method; therefore, the specific limitations in one or more power grid system control device embodiments provided below can be found in the limitations of the power grid system control method described above, and will not be repeated here.

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

[0080] The scheduling result generation module 402 is used to generate the day-ahead optimized scheduling result corresponding to the future forecast date based on the distributed resources in the power grid system. The day-ahead optimized scheduling result includes the optimized scheduling result corresponding to the distributed resources at multiple time points in the forecast date, and the time interval between adjacent time points is consistent.

[0081] The power flow section determination module 404 is used to perform power flow calculations based on the day-ahead optimized scheduling results, 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.

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

[0083] The demand triggering module 408 is used to send a power response demand to the frequency regulation response terminal when the power grid system reaches the frequency regulation conditions, so that the frequency regulation response terminal can call the corresponding backup 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 results of the power grid system on the prediction day, wherein the curve prediction results include at least one of the predicted wind power output curve, the predicted photovoltaic power output curve, the predicted load demand curve, or the predicted electricity price curve; and generate the day-ahead optimized scheduling results corresponding to the prediction day based on the curve prediction results.

[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 satisfies minimization, thereby obtaining the day-ahead optimized scheduling result corresponding to the prediction date; wherein the objective function is:

[0086]

[0087] N is the number of time points. Let m be the output of the distributed resource m at time t (i.e., the t-th time point). Let m be the cost of generating a unit of electrical energy from a distributed resource m at time t. Let be the comprehensive frequency modulation performance index of the i-th distributed resource participating in frequency modulation. The capacity quotation for the i-th distributed resource participating in frequency regulation. The mileage quote for the i-th distributed resource participating in frequency modulation. Let be the frequency modulation capacity of the distributed resource participating in frequency modulation for the i-th unit. Let represent the actual frequency modulation mileage of the distributed resource participating in frequency modulation for the i-th unit. This represents the total number of distributed resources. 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 optimized scheduling results to determine multiple reference operating modes; cluster the multiple reference operating modes to obtain at least two typical operating modes; obtain the net load curve under each typical operating mode in the at least two typical operating modes; and take the typical operating mode to which the net load curve with the highest peak value among the net load curves 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 transmission distribution factor corresponding to each of the multiple distributed resources in the power grid system; and to accumulate the reserve capacity corresponding to the distributed resources in the multiple distributed resources in descending order of the power transmission distribution factor. If the accumulated reserve capacity is greater than or equal to the actual power flow, the edge-side intelligent control terminal connected to the distributed resource participating in the accumulation is used as the frequency regulation response terminal.

[0090] In some embodiments, the apparatus is further configured to: acquire real-time operational data during the forecast day; and use the real-time operational data to dynamically correct the day-ahead optimized scheduling results in real time.

[0091] Each module in the aforementioned power grid system control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0092] In some embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data involved in the power grid system control method. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a power grid system control method.

[0093] In some embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, 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 is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0094] Those skilled in the art will understand that Figure 5 and Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0095] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0096] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above embodiments.

[0097] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above embodiments.

[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 used for analysis, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0099] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media 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), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0100] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0101] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power grid system control method, characterized in that, The method includes: Based on the distributed resources in the power grid system, the day-ahead optimized scheduling result corresponding to the future forecast date is generated. The day-ahead optimized scheduling result includes the output of the distributed resources at multiple time points on the forecast date, and the time interval between adjacent time points is consistent. Based on the day-ahead optimized scheduling results, power flow calculation is performed to determine multiple reference operating modes. The multiple reference operating modes are clustered to obtain at least two typical operating modes. The net load curve under each of the at least two typical operating modes is obtained. The typical operating mode to which the net load curve with the highest peak value belongs is taken as the target operating mode of the power grid system. The power grid system is partitioned based on the target operating mode to determine key power flow sections. Determine the power transmission distribution factor corresponding to multiple distributed resources in the power grid system. According to the power transmission distribution factor from large to small, accumulate the reserve capacity corresponding to the distributed resources. If the accumulated reserve capacity is greater than or equal to the actual power flow of the key power flow section, the edge-side intelligent control terminal connected to the distributed resources participating in the accumulation is used as the frequency regulation response terminal. When the power grid system reaches the frequency regulation condition, a power response request is sent to the frequency regulation response terminal, so that the frequency regulation response terminal can call the corresponding backup capacity based on the power response request, and make use of the backup capacity of the distributed resources connected to the frequency regulation response terminal.

2. The method according to claim 1, characterized in that, The process of generating day-ahead optimized scheduling results for future predicted dates based on distributed resources in the power grid system includes: The prediction results of the curves corresponding to the power grid system within the prediction day are predicted, wherein the curve prediction results include at least one of the predicted wind power output curve, predicted photovoltaic power output curve, predicted load demand curve, or predicted electricity price curve. Based on the curve prediction results, the day-ahead optimized scheduling results corresponding to the predicted day are generated.

3. The method according to claim 2, characterized in that, The step of generating the day-ahead optimized scheduling result corresponding to the predicted date based on the curve prediction result includes: Based on the curve prediction results, the objective function is minimized, and the output of distributed resources at multiple time points is determined when the objective function is minimized, so as to obtain the day-ahead optimized scheduling result corresponding to the prediction date. The objective function is: N is the number of time points. This represents the actual output of distributed resource m at time t. Let m be the cost of generating a unit of electrical energy from a distributed resource m at time t. Let be the comprehensive frequency modulation performance index of the i-th distributed resource participating in frequency modulation. The capacity quotation for the i-th distributed resource participating in frequency regulation. The mileage quote for the i-th distributed resource participating in frequency modulation. Let be the frequency modulation capacity of the distributed resource participating in frequency modulation for the i-th unit. Let represent the actual frequency modulation mileage of the distributed resource participating in frequency modulation for the i-th unit. This represents the total number of distributed resources. The number of distributed resources participating in frequency modulation.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtain the real-time operational data during the predicted day; The real-time operational data is used to dynamically correct the day-ahead optimized scheduling results.

5. A power grid system control device, characterized in that, The device includes: The scheduling result generation module is used to generate the day-ahead optimized scheduling result corresponding to the future forecast date based on the distributed resources in the power grid system. The day-ahead optimized scheduling result includes the output of the distributed resources at multiple time points on the forecast date, and the time interval between adjacent time points is consistent. The power flow section determination module is used to perform power flow calculation based on the day-ahead optimized scheduling results to determine multiple reference operating modes, cluster the multiple reference operating modes to obtain at least two typical operating modes, obtain the net load curve under each of the at least two typical operating modes, take the typical operating mode to which the net load curve with the highest peak value belongs as the target operating mode of the power grid system, and partition the power grid system based on the target operating mode to determine key power flow sections. The terminal selection module is used to determine the power transmission distribution factor corresponding to multiple distributed resources in the power grid system. According to the power transmission distribution factor from large to small, the reserve capacity corresponding to the distributed resources is accumulated. If the accumulated reserve capacity is greater than or equal to the actual power flow of the key power flow section, the edge-side intelligent control terminal connected to the distributed resource participating in the accumulation is used as the frequency regulation response terminal. The demand triggering module is used to send a power response demand to the frequency regulation response terminal when the power grid system reaches the frequency regulation condition, so that the frequency regulation response terminal can call the corresponding backup capacity based on the power response demand and make use of the backup capacity of the distributed resources connected to the frequency regulation response terminal.

6. The apparatus according to claim 5, characterized in that, The scheduling result generation module is also used for: The prediction results of the curves corresponding to the power grid system within the prediction day are predicted, wherein the curve prediction results include at least one of the predicted wind power output curve, predicted photovoltaic power output curve, predicted load demand curve, or predicted electricity price curve. Based on the curve prediction results, the day-ahead optimized scheduling results corresponding to the predicted day are generated.

7. The apparatus according to claim 6, characterized in that, The scheduling result generation module is also used for: Based on the curve prediction results, the objective function is minimized, and the output of distributed resources at multiple time points is determined when the objective function is minimized, so as to obtain the day-ahead optimized scheduling result corresponding to the prediction date. The objective function is: N is the number of time points. This represents the actual output of distributed resource m at time t. Let m be the cost of generating a unit of electrical energy from a distributed resource m at time t. Let be the comprehensive frequency modulation performance index of the i-th distributed resource participating in frequency modulation. The capacity quotation for the i-th distributed resource participating in frequency regulation. The mileage quote for the i-th distributed resource participating in frequency modulation. Let be the frequency modulation capacity of the distributed resource participating in frequency modulation for the i-th unit. Let represent the actual frequency modulation mileage of the distributed resource participating in frequency modulation for the i-th unit. This represents the total number of distributed resources. The number of distributed resources participating in frequency modulation.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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