Power grid load control method, system and device and storage medium

Abstract

The embodiment of the invention provides a power grid load control method, system and device and a storage medium, and belongs to the field of power system protection and control. The method comprises the following steps: entering a load regulation and control process in response to periodically detecting that the operation state of the power distribution network meets a regulation and control starting condition; wherein the load regulation and control process divides a corresponding control period into a plurality of unit regulation and control intervals; the load regulation and control process executes the following steps in each unit regulation and control interval: obtaining a feedback regulation and optimization model of the current unit regulation and control interval, and according to the initial load deviation and the feedback regulation and optimization model in the current unit regulation and control interval, adjusting the load of the current unit regulation and control interval. Determining unit target regulation and control power and determining a target regulation and control object from the plurality of regulation and control objects; and stopping the load regulation and control flow when the regulation and optimization load deviation of the current unit regulation and control interval meets a regulation and control stop condition. The power supply reliability can be considered while the resource utilization rate of the power grid is improved.

Description

Technical Field

[0001] This application relates to the field of power system protection and control, and in particular to a method, system, device and storage medium for controlling power grid loads. Background Technology

[0002] With the transformation and optimization of the global energy structure, the widespread application of clean energy has become a significant trend in the development of the power industry. Against this backdrop, renewable energy resources such as photovoltaic power generation, wind power generation, and battery energy storage technology are gradually becoming important components of the distribution network. However, due to the volatility and uncertainty of these renewable resources, load management of the distribution network faces numerous challenges, significantly increasing its complexity. Photovoltaic power generation is affected by weather, seasons, and other factors, resulting in unstable power generation capacity; the charging and discharging status of energy storage systems is also constrained by battery performance and current electricity demand; and flexible loads require a balance between user experience and grid security. These factors collectively lead to increased grid load fluctuations, affecting grid operational stability and power supply quality. Therefore, how to achieve flexible control of grid load to improve resource utilization while ensuring power supply reliability is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The main objective of this application is to propose a method, system, device, and storage medium for controlling power grid load, which can improve the resource utilization rate of the power grid while ensuring power supply reliability.

[0004] To achieve the above objectives, a first aspect of this application proposes a method for controlling power grid load, the method comprising: In response to the periodic detection that the operating status of the distribution network meets the control initiation conditions, the load control process is initiated; wherein, the load control process divides the corresponding control period into multiple unit control intervals; the load control process executes the following steps in each unit control interval: Obtain the feedback regulation optimization model for the current unit regulation interval, wherein the feedback regulation optimization model characterizes the relationship between the unit target regulation power and the initial load deviation, the cumulative deviation and the deviation change rate corresponding to the initial load deviation within the current unit regulation time; and the weight parameters corresponding to the initial load deviation, the cumulative deviation and the deviation change rate are the initial weight parameters in the first unit regulation interval of the corresponding period, and are updated and determined based on a preset weight parameter update strategy in non-first unit regulation intervals. Based on the initial load deviation and feedback regulation optimization model within the current unit control interval, the unit target control power is determined and the target control object is determined from multiple control objects, including distributed resource objects, flexible load objects, and rigid load objects. If the optimized load deviation of a given unit control interval meets the control stop condition, the load control process is stopped; the optimized load deviation represents the load deviation after adjusting the power of the target control object based on the unit target control power within the corresponding unit control interval.

[0005] To achieve the above objectives, a second aspect of this application provides a control system for power grid loads, characterized in that it includes: Photovoltaic modules; Energy storage components; Flexible load-bearing components; Rigid load components; The actuator is electrically connected to the photovoltaic module, energy storage module, flexible load module, and rigid load module. A control module, which is electrically connected to the actuator; the control module performs the power grid load control method as described in any of the first aspects.

[0006] To achieve the above objectives, a third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power grid load control method as described in any one of the first aspects.

[0007] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power grid load control method described in any of the first aspects.

[0008] The power grid load control method, system, equipment, and storage medium proposed in this application optimize the utilization of renewable energy by encompassing distributed resource objects, flexible load objects, and rigid load objects in the distribution network. Simultaneously, by adjusting the feedback regulation optimization model in multiple unit control intervals and updating the model based on the results of each unit control interval adjustment, more refined control of each control object in the distribution network can be achieved to ensure the reliability of power supply for users. Therefore, compared with related technologies, the embodiments of this application can improve the resource utilization rate of the power grid while ensuring power supply reliability. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the power grid load control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the application process of one embodiment of the power grid load control method provided in this application. Figure 3 This is a system schematic diagram corresponding to the power grid load control system provided in the embodiments of this application; Figure 4 This is a schematic diagram illustrating an embodiment of the power grid load control system provided in this application. Figure 5 This is a schematic diagram of the hardware structure corresponding to the power grid load control method provided in the embodiments of this application. Detailed Implementation

[0010] 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.

[0011] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0013] The following is an explanation of the terms used in the embodiments of this application: Flexible loads refer to loads whose electricity consumption behavior can be flexibly adjusted; the main objects of adjustment include smart air conditioners, electric water heaters (utilizing heat / cold storage capacity), electric vehicle charging stations, etc. on the residential side.

[0014] Rigid loads refer to loads with fixed electrical characteristics that are difficult to adjust. The main objects to be adjusted include precision machine tools and chemical reactors on the industrial side (the production process is continuous, and the product is scrapped when the machine is stopped), or public service side: hospital ventilators, operating room equipment, data center servers (running 24 hours a day), etc.

[0015] Distributed resources: refer to small power sources, energy storage, or controllable loads that are distributed and connected to the power distribution network.

[0016] With the transformation and optimization of the global energy structure, the widespread application of clean energy has become a significant trend in the development of the power industry. Against this backdrop, renewable energy resources such as photovoltaic power generation, wind power generation, and battery energy storage technology are gradually becoming important components of the distribution network. However, due to the volatility and uncertainty of these renewable resources, load management in the distribution network faces numerous challenges, significantly increasing its complexity. Photovoltaic power generation is affected by weather, seasons, and other factors, resulting in unstable power generation capacity; the charging and discharging states of energy storage systems are constrained by battery performance and current electricity demand; and flexible loads require a balance between user experience and grid security. These factors collectively exacerbate grid load fluctuations, affecting grid operational stability and power quality. Currently, many power systems still use traditional load control and dispatch methods, relying solely on rigid load shedding for control, which is insufficient to respond to rapidly changing load conditions in real time. Furthermore, traditional control strategies neglect the comprehensive utilization of distributed adjustable resources when regulating various loads, leading to resource waste and low operational efficiency. Therefore, a more advanced and flexible control method is urgently needed to improve resource utilization efficiency. Based on this, embodiments of this application propose a method, system, device, and storage medium for controlling power grid load, which can improve the resource utilization rate of the power grid.

[0017] Understandably, referring to Figure 1 As shown, a power grid load control method according to an embodiment of this application includes: Step S100: In response to the periodic detection indicating that the operating status of the distribution network meets the control initiation conditions, the load control process is initiated; wherein, the load control process divides the corresponding control cycle into multiple unit control intervals; the load control process executes the following steps in each unit control interval: Step S210: Obtain the feedback regulation optimization model for the current unit control interval. The feedback regulation optimization model represents the relationship between the unit target control power and the initial load deviation, the cumulative deviation and the deviation change rate corresponding to the initial load deviation within the current unit control time. The weight parameters corresponding to the initial load deviation, the cumulative deviation and the deviation change rate are the initial weight parameters in the first unit control interval of the corresponding period, and are updated and determined based on the preset weight parameter update strategy when they are not in the first unit control interval. Step S220: Based on the initial load deviation and feedback regulation optimization model within the current unit control interval, determine the unit target control power and identify the target control object from multiple control objects, including distributed resource objects, flexible load objects, and rigid load objects; Step S230: When the optimized load deviation of the current unit control interval meets the control stop condition, the load control process is stopped; the optimized load deviation represents the load deviation after adjusting the power of the target control object based on the unit target control power within the corresponding unit control interval.

[0018] Therefore, by encompassing distributed resource objects, flexible load objects, and rigid load objects in the control of the distribution network, the utilization of renewable energy can be optimized. Simultaneously, by adjusting the feedback control optimization model across multiple unit control intervals and updating the model based on the results of each unit control interval adjustment, more refined control of each control object in the distribution network can be achieved to ensure the reliability of power supply for users. Therefore, the embodiments of this application can improve the resource utilization rate of the power grid while ensuring power supply reliability.

[0019] In some embodiments, periodically detecting that the operating state of the distribution network meets the control activation conditions indicates that the operating state of the distribution network is periodically detected, so that the operating state and control are integrated into the same device for processing. In other embodiments, periodically detecting that the operating state of the distribution network meets the control activation conditions indicates that the target expected load has been received periodically, so that the operating state detection and control are deployed separately. Those skilled in the art can selectively set these based on the device deployment status of operating state detection and control.

[0020] The control activation condition indicates that the power output of the distribution network is insufficient to support current electricity demand, requiring power adjustment of the distribution network. This application embodiment does not limit the specific control activation condition; those skilled in the art can selectively set it according to actual needs. This application embodiment also does not limit the duration of the control cycle for periodic detection; those skilled in the art can selectively set it according to actual needs. By dividing each control cycle requiring load regulation into multiple unit control intervals, the distribution network can be adjusted multiple times, thereby improving control accuracy.

[0021] The embodiments of this application do not limit the duration of the unit control interval, and those skilled in the art can selectively set it according to actual needs.

[0022] The unit target control power characterizes the power expected to be adjusted within the current unit control interval. The initial load deviation characterizes the difference between the actual total load and the target expected load before power adjustment within the current unit control interval. The cumulative deviation represents the cumulative value of the initial load deviation over time within the unit control interval, and the deviation change represents the rate of change of the initial load deviation over time within the unit control interval. In some embodiments, the initial load deviation... ;in, Indicates the initial load deviation. Indicates the target expected load; This represents the actual total load; where, the actual total load It can be calculated in real time based on real-time data collected from user load, photovoltaic power generation, energy storage system charging and discharging power and power data.

[0023] In some embodiments, feedback regulation optimization model ;in, Adjust power for a unit target; The duration of the unit adjustment time. The weighting parameter for the initial load deviation. The weighting parameter is the cumulative deviation. This is the cumulative deviation; The weighting parameter for the rate of change of deviation. This represents the rate of change of deviation.

[0024] Distributed resource objects are entities that provide distributed resources, such as photovoltaic power generation modules and energy storage modules. Flexible load objects are entities whose electricity consumption behavior can be flexibly adjusted, such as electric vehicle charging stations on the residential side. Rigid load objects are entities whose electricity consumption characteristics are fixed and difficult to adjust, such as operating room equipment. By dividing the distribution network into at least three categories of objects—distributed resource objects, flexible load objects, and rigid load objects—power supply security can be further ensured.

[0025] This application does not limit the specific content of the weight parameter update strategy. Those skilled in the art can selectively set it according to actual needs. For example, they can adjust each weight parameter simultaneously according to the load difference between the optimized load deviations of two adjacent unit control intervals, or select to adjust one of the weight parameters according to the size of the load difference. Alternatively, they can first adjust the weight parameter of the initial load deviation according to the actual deviation change, and then gradually adjust the weight parameters corresponding to the cumulative deviation and the deviation change rate according to the operating status of the distribution network after adjustment.

[0026] For example, assuming that the control initiation condition is met during control period T, and t1~tn are n unit control intervals within control period T, starting from t1, the unit target control power and target control object are determined based on the feedback control optimization model of t1. Within t1, the unit target control power is issued to the target control object. After issuance, the process enters the next unit control interval, i.e., t2. If, in t2, it is determined that the optimized load deviation of t1 meets the control cessation condition, the load control process is stopped. If, in t2, it is determined that the optimized load deviation of t1 does not meet the control cessation condition, the weight parameters of the feedback control optimization model of t2 are updated. Based on the updated feedback control optimization model, the unit target control power and target control object of t2 are calculated, and the unit target control power is issued to the corresponding target control object within t2; then, the process enters the next unit control interval. This process continues until the control cessation condition is met. Assuming that the control stop condition is not met at tn, after the control is completed at tn, the operating status of the distribution network is re-checked in the next control cycle of T to see if the control start condition is met. At this time, if the operating status is detected to be unmet in the next control cycle, the load control process will be re-entered.

[0027] Understandably, the weight parameter update strategy includes: When the current unit control interval is the second unit control interval of the corresponding control cycle or the historical control trend of the same control cycle does not meet the preset convergence condition, the weight parameters of the initial load deviation are adjusted according to the preset initial load deviation step, while maintaining the weight parameters of the cumulative deviation and the deviation change rate. Once the historical control trend is determined to meet the convergence condition, the weight parameters of the cumulative deviation and the rate of change of deviation are adjusted stepwise according to the preset deviation trend, while keeping the weight parameter of the initial load deviation unchanged.

[0028] The convergence condition table includes the convergence rate and overshoot. Meeting the convergence condition indicates that the convergence rate matches the expectation and there is no overshoot.

[0029] The initial load deviation step and the deviation trend step can be the same or different. In some embodiments, the deviation trend step is smaller than the initial load deviation step. The cumulative deviation and the deviation change rate can be set with their respective corresponding deviation trend steps, or they can be set separately. This application does not limit this, and those skilled in the art can selectively set them according to actual needs.

[0030] Historical control trends characterize the changes in the initial load deviation of each unit control interval within the same control cycle. Slow changes indicate slow convergence, rapid changes indicate fast convergence, and fluctuations indicate overshoot.

[0031] For example, let the current unit control interval be t3, where t3 is the third unit control interval of the current control cycle, and the step size is... Taking the weighting parameter of the initial load deviation of the previous unit control interval as A as an example, assuming that the historical control trend does not meet the convergence condition, the weighting parameter of the initial load deviation of the next unit control interval will be adjusted to A+. This process continues until the convergence condition is met, at which point the weight parameters corresponding to the cumulative deviation and the rate of change of deviation are adjusted.

[0032] Understandably, the initial load deviation step is determined through the following steps: Obtain the current control type, which is one of the following: planned control type, accident control type, or emergency control type; The duration of regulation is determined based on the type of regulation. The step adjustment ratio is determined based on the duration of the control measures; the step adjustment ratio ranges from 5% to 20%; the step adjustment ratio for planned control measures is greater than that for accident control measures, and the step adjustment ratio for accident control measures is greater than that for emergency control measures. The initial weighting parameter of the initial load deviation is multiplied by the step adjustment ratio to determine the initial load deviation step.

[0033] By setting different step sizes for different control types, the control rate can be accelerated. This allows for maintaining control precision while minimizing the impact on the user.

[0034] For example, if the emergency control type is 1 minute and the planned control type is 10 minutes, the step value for emergency control needs to be smaller to avoid over-adjustment leading to oscillations; the step value for planned control can be appropriately increased to accelerate parameter convergence. In this case, a larger proportion (e.g., 20%) can be selected from 5% to 25% to calculate the initial load deviation step under the planned control type, while a smaller proportion (e.g., 5%) can be selected to calculate the initial load deviation step under the emergency control type.

[0035] Understandably, the historical control trend is calculated based on the actual initial load deviation change rate of each unit control interval that has completed control within the same cycle; determining whether the historical control trend meets the convergence conditions includes: When the historical control trend is downward and the difference in actual load change between the current unit control interval and the actual starting load deviation of the previous unit control interval is less than the preset change threshold, it is determined that the historical control trend meets the convergence condition. When the historical control trend includes both downward and upward trends, it is determined that the historical control trend meets the convergence condition.

[0036] Historical control trends, including both downward and upward trends, indicate that historical control trends have experienced fluctuations, i.e., over-adjustment.

[0037] The change threshold characterizes the actual load change difference when the convergence rate, based on the initial load deviation as the main control factor, fails to meet the requirements. The actual load change difference can be obtained through data testing.

[0038] Understandably, the target regulatory object is determined from multiple regulatory objects, including: Obtain the priority of each control object, where the priority of distributed resource objects is greater than that of flexible load objects, and the priority of flexible load objects is greater than that of rigid load objects; Based on the priority of each control object, the highest priority control object is selected as the target control object from among all control objects that are currently allowed to adjust power.

[0039] By first adjusting the distributed resource objects, then the flexible load objects, and finally the rigid load objects, we can ensure that electricity supply is guaranteed and resources are used more fully.

[0040] For example, assuming a distributed resource object has a maximum power of 500W and a current power of 300W, the controlled objects currently allowed to adjust power include the distributed resource object, flexible load objects, and rigid load objects. If the distributed resource object has reached its maximum power and there are flexible load objects that can adjust power, then the flexible load objects and rigid load objects are the objects allowed to adjust power. If the flexible load objects cannot be adjusted further, then the rigid load objects are the objects allowed to adjust power. This application does not restrict how the power borne by each flexible load object is allocated when multiple flexible load objects exist, nor does it restrict the order of adjustment for each flexible load object. Those skilled in the art can perform control based on existing power grid control algorithms.

[0041] It is understandable that distributed resource objects include photovoltaic power generation objects and energy storage objects. Power adjustment of the target control objects based on unit target control power includes: Determine the first unit target control power for the photovoltaic power generation object based on the unit target control power. Based on the unit target control power, determine the second unit target control power of the energy storage object; Adjusting the first unit target control power for photovoltaic power generation and adjusting the second unit target control power for energy storage.

[0042] This application does not limit the proportion of the first unit target control power and the second unit target control power to the total unit target control power. The first unit target control power and the second unit target control power can be determined based on the ratio of the adjustable power of the photovoltaic power generation object to the adjustable power of the energy storage object, or they can be allocated in a fixed ratio. For example, if the unit target control power is 80W, the first unit target control power can be set to 40W and the second unit target control power can be set to 30W.

[0043] Understandably, in response to periodic detection that the operating status of the distribution network meets the control activation conditions, including at least one of the following: In response to the periodic detection that the current operating state of the distribution network does not meet the system stability boundary conditions, the system determines the control activation conditions that meet the emergency control type. The system stability boundary conditions include at least one of voltage deviation, frequency deviation, transformer load rate, and line load rate that meets the corresponding boundary stability conditions. In response to the periodic detection of a main grid fault in the distribution network, the control activation conditions for the accident control type are determined to be met. In response to the periodic detection of power outage time that meets the main grid plan, the control activation conditions that meet the planned control type are determined; In response to the periodic detection that the optimized load deviation of the previous control cycle exceeds the preset deviation threshold, it is determined that the current control cycle meets the control start-up conditions.

[0044] For example, a voltage deviation within ±7% is the boundary stability condition corresponding to the voltage deviation, a frequency deviation within ±0.2Hz is the boundary stability condition corresponding to the frequency deviation, a transformer load rate of less than 80% is the boundary stability condition corresponding to the transformer, and a line load rate of less than 80% is the boundary stability condition corresponding to the line load rate.

[0045] For example, refer to Figure 2 The illustration shows an embodiment of the power grid load control method based on emergency control type triggering control according to this application. The specific steps are as follows: S1. If the operating status of the distribution network is detected to meet the control activation conditions corresponding to the emergency control type within the control cycle T, determine the target expected load corresponding to the emergency control type. =500MW and system variables =600MW, where, when the stability assessment indicators such as load, voltage, and frequency in the distribution network operation state show continuous fluctuations, such as voltage deviation repeatedly within ±7%, frequency deviation repeatedly within ±0.2HZ, and transformer and line load rates frequently exceeding or falling below 80%; or when voltage deviation is consistently outside ±7%, frequency deviation is consistently outside ±0.2HZ, and transformer and line load rates are frequently and consistently exceeding 80%, then it is determined that the control activation conditions corresponding to the emergency control type are met. The system variables include real-time load data collected such as actual total load, user load, photovoltaic power generation, energy storage system charging and discharging power, and energy data. The actual total load is calculated based on the real-time load data.

[0046] S2. Within each unit control interval, determine the initial load deviation based on the target expected load and real-time load data. ,in, This means that the current power distribution network needs to reduce its load by 100MW.

[0047] S3. Within each unit control interval, optimize the model through feedback adjustment. Generate unit target control power .

[0048] S4. Within each unit control interval, the target control object is determined according to the preset priority of each control object. Priority is given to adjusting distributed resource objects. If load stabilization cannot be achieved by adjusting distributed resources, flexible and rigid loads need to be adjusted. Priority is given to adjusting flexible loads (flexible loads include air conditioners, electric water heaters, and electric vehicle charging piles (V2G mode)). If the adjustment of flexible loads cannot reach the control target value, rigid loads are cut off, and the operating status of certain load devices is temporarily changed. Non-critical users or adjustable load devices can be scheduled to flexibly respond to load changes. Distributed resource objects include photovoltaic power generation objects (i.e., photovoltaic power generation modules) and energy storage objects (i.e., energy storage modules). When the unit target control power is negative, the output power of photovoltaic power generation objects and energy storage objects is increased. When the unit target control power is positive, the discharge power of energy storage objects is reduced or the charging power of photovoltaic power generation objects is increased. In some embodiments, the specific control involves adjusting the output of photovoltaic power generation objects to increase by 40MW and adjusting the discharge of energy storage objects to increase by 30MW.

[0049] S5. Obtain the optimized load deviation corresponding to the target control power; S6. Before initiating control in the current unit control interval, determine whether the optimized load deviation of the previous unit control interval meets the control stop condition; if a deviation still exists (i.e., the control stop condition is not met), then adjust the parameters. , , Adjustments will be made gradually. Specifically, this involves gradually increasing... (e.g., kp increases by 0.1 or 0.05 each time). In practical applications, it needs to be dynamically set according to system characteristics, and is usually... Use 5%-20% of the initial value (e.g., initial value of kp is 1, step size 0.1) to observe the convergence speed and overshoot, and find a suitable value. Then (i.e., when the convergence speed cannot meet the requirements or overshoot occurs) proceed again. , The adjustment yields a new feedback regulation optimization model, and the process jumps to step S3 to perform the adjustment of the next unit control interval.

[0050] Therefore, the load control method for the distribution network in this application embodiment, by precisely controlling the deviation between the target load and the actual total load, adjusts the generation and consumption of distributed resources in real time, ensuring the efficient operation of the distribution network under different load conditions and avoiding resource waste caused by load fluctuations. Secondly, it optimizes the utilization of renewable energy, effectively integrating photovoltaic power generation and energy storage systems. By dynamically adjusting the output of photovoltaic power generation and energy storage resources, it maximizes the efficiency of renewable energy use, reduces dependence on traditional energy sources, and promotes the widespread application of clean energy. Simultaneously, it enhances the stability of the power grid by adopting a feedback regulation optimization model to achieve dynamic and refined control of various grid resources, ensuring the reliability of power supply for users.

[0051] Understandably, referring to Figure 3 As shown, a power grid load control system according to an embodiment of this application includes: Photovoltaic modules; Energy storage components; Flexible load-bearing components; Rigid load components; The actuator is electrically connected to photovoltaic modules, energy storage modules, flexible load modules, and rigid load modules. The control module is electrically connected to the actuator; the control module executes steps S100 to S230.

[0052] The actuators include power distribution equipment and terminal equipment (such as electricity meters, distribution boxes, etc.). Flexible load components are a collection of flexible load components, and rigid load components are a collection of rigid load components. Photovoltaic modules are components that convert solar energy into electrical energy and transmit it to the actuators. Energy storage components are used to store electrical energy generated by the actuators or to transmit electrical energy to the actuators. The control module is used to control the actuators to perform power control on the photovoltaic modules, energy storage components, flexible load components, and rigid load components based on steps S100~S230.

[0053] For example, refer to Figure 4 As shown, after receiving the target expected load, the control module determines that the control activation conditions are met, initiates the control process, identifies the target control object and the unit target control power, and notifies the actuator of the target control object and the unit target control power. When the actuator determines that the photovoltaic modules, energy storage modules, and flexible load modules can adjust the power, it adjusts the unit target control power to the target control object. After adjustment, a compliance check is performed to obtain the actual total load. The difference between the actual total load and the target expected load is calculated to obtain the initial load deviation for the next unit control interval. The initial load deviation is then input to the control module for power adjustment in the next unit control interval. If the photovoltaic modules, energy storage modules, and flexible load modules cannot adjust the power, rigid load shedding is performed.

[0054] Please see Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 502 can be a NAND flash memory. The relevant program code is stored in the memory 502 and is called by the processor 501 to execute the power grid load control method of the embodiment of this application. The input / output interface 503 is used to implement information input and output; The communication interface 504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 505 transmits information between various components of the device (e.g., processor 501, memory 502, input / output interface 503, and communication interface 504); The processor 501, memory 502, input / output interface 503, and communication interface 504 are connected to each other within the device via bus 505.

[0055] This application also provides a computer-readable storage medium that stores a computer program that, when executed by a processor, implements the above-described power grid load control method.

[0056] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0057] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0058] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0059] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0061] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0062] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0064] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0066] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for controlling power grid load, characterized in that, The method includes: In response to the periodic detection that the operating status of the distribution network meets the control initiation conditions, the load control process is initiated; wherein, the load control process divides the corresponding control period into multiple unit control intervals; the load control process executes the following steps in each unit control interval: Obtain the feedback regulation optimization model for the current unit regulation interval, wherein the feedback regulation optimization model characterizes the relationship between the unit target regulation power and the initial load deviation, the cumulative deviation and the deviation change rate corresponding to the initial load deviation within the current unit regulation time; and the weight parameters corresponding to the initial load deviation, the cumulative deviation and the deviation change rate are the initial weight parameters in the first unit regulation interval of the corresponding period, and are updated and determined based on a preset weight parameter update strategy in non-first unit regulation intervals. Based on the initial load deviation and feedback regulation optimization model within the current unit control interval, the unit target control power is determined and the target control object is determined from multiple control objects, including distributed resource objects, flexible load objects, and rigid load objects. If the optimized load deviation of a given unit control interval meets the control stop condition, the load control process is stopped; the optimized load deviation represents the load deviation after adjusting the power of the target control object based on the unit target control power within the corresponding unit control interval.

2. The power grid load control method according to claim 1, characterized in that, The weight parameter update strategy includes: When the current unit control interval is the second unit control interval of the corresponding control cycle or the historical control trend of the same control cycle does not meet the preset convergence condition, the weight parameter of the initial load deviation is adjusted according to the preset initial load deviation step, while maintaining the weight parameters of the cumulative deviation and the deviation change rate. When it is determined that the historical control trend characterization meets the convergence condition, the weight parameters of the cumulative deviation and the deviation change rate are adjusted according to the preset deviation trend step, while keeping the weight parameter of the initial load deviation unchanged.

3. The power grid load control method according to claim 2, characterized in that, The initial load deviation step is determined through the following steps: Obtain the current control type, which is one of the following: planned control type, accident control type, and emergency control type; The duration of regulation is determined based on the regulation type. The step adjustment ratio is determined based on the control duration; wherein the step adjustment ratio ranges from 5% to 20%; the step adjustment ratio for the planned control type is greater than that for the accident control type, and the step adjustment ratio for the accident control type is greater than that for the emergency control type. The initial weighting parameter of the initial load deviation is multiplied by the step adjustment ratio to determine the initial load deviation step.

4. The power grid load control method according to claim 2, characterized in that, The historical control trend is calculated based on the actual initial load deviation change rate of each unit control interval that has completed control within the same cycle. The determination that the historical regulation trend characterization satisfies the convergence condition includes: When the historical control trend is downward and the actual load change difference between the current unit control interval and the actual starting load deviation of the previous unit control interval is less than the preset change threshold, it is determined that the historical control trend satisfies the convergence condition. When the historical control trend includes both downward and upward trends, it is determined that the historical control trend satisfies the convergence condition.

5. The power grid load control method according to claim 1, characterized in that, The step of determining the target control object from multiple control objects includes: Obtain the priority of each of the control objects, wherein the priority of the distributed resource object is greater than the priority of the flexible load object, and the priority of the flexible load object is greater than the priority of the rigid load object; Based on the priority of each control object, the control object with the highest priority is selected as the target control object from among the control objects that are currently allowed to adjust power.

6. The power grid load control method according to claim 1, characterized in that, The distributed resource objects include photovoltaic power generation objects and energy storage objects. The power adjustment of the target control objects based on the unit target control power includes: Based on the unit target control power, determine the first unit target control power of the photovoltaic power generation object; Based on the aforementioned unit target control power, determine the second unit target control power of the energy storage object; The photovoltaic power generation object is adjusted by a first unit of target control power, and the energy storage object is adjusted by a second unit of target control power.

7. The power grid load control method according to claim 1, characterized in that, In response to periodic detection that the operating status of the distribution network meets the control activation conditions, including at least one of the following: In response to the periodic detection that the current operating state of the distribution network does not meet the system stability boundary conditions, the system determines the control activation conditions that meet the emergency control type; the system stability boundary conditions include at least one of voltage deviation, frequency deviation, transformer load rate and line load rate that meets the corresponding boundary stability conditions. In response to the periodic detection of a main grid fault in the distribution network, the control activation conditions for the accident control type are determined to be met. In response to the periodic detection of power outage time that meets the main grid plan, the control activation conditions that meet the planned control type are determined; In response to the periodic detection that the optimized load deviation of the previous control cycle exceeds the preset deviation threshold, it is determined that the current control cycle meets the control start-up conditions.

8. A control system for power grid load, characterized in that, include: Photovoltaic modules; Energy storage components; Flexible load-bearing components; Rigid load components; The actuator is electrically connected to the photovoltaic module, energy storage module, flexible load module, and rigid load module. A control module, which is electrically connected to the actuator; the control module performs the power grid load control method as described in any one of claims 1 to 7.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the power grid load control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the power grid load control method according to any one of claims 1 to 7.

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