Power distribution network regulation method and device, electronic equipment and computer readable storage medium

Abstract

The application discloses a power distribution network regulation method and device, electronic equipment and computer readable storage medium. Wherein, the method comprises: predicting the operation parameters of the power distribution network in the future time period based on the current operation parameters of the power distribution network, and obtaining the first operation parameters; based on the first operation parameters, determining the resource demand parameters of the first resource in the power distribution network in the future time period for the power supply area, wherein the first resource is used to represent the resource that can be regulated in the power distribution network, and the power supply area is used to represent the area that needs to be provided with the power resource by the power distribution network; based on the resource demand parameters and the resource regulation conditions, the resource regulation strategy of the first resource is constructed, wherein the resource regulation conditions are used to control at least the resource regulation cost of the resource regulation strategy when the resource regulation strategy is executed; and the first resource is regulated based on the resource regulation strategy. The application solves the technical problem that the effect of resource potential release is poor when the flexible load resource is regulated by using the traditional method.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a distribution network control method, device, electronic equipment and computer-readable storage medium. Background Art

[0002] As the penetration of distributed renewable energy sources, such as wind power and photovoltaics, continues to increase in distribution networks, the diverse loads present in these networks, such as air conditioning, electric heating, electric vehicle charging, and energy storage, have significantly increased their complexity and uncertainty. These loads, collectively referred to as flexible load resources, possess a degree of adjustability and energy storage capabilities and demonstrate significant potential for grid regulation.

[0003] However, current distribution network control technology fails to effectively utilize the adjustable potential of flexible load resources such as air conditioning cooling loads, electric heating loads, electric vehicle charging loads and energy storage, and lacks systematicity and intelligence in regulating flexible load resources, resulting in poor results in releasing resource potential.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] Embodiments of the present invention provide a distribution network control method, device, electronic device, and computer-readable storage medium to at least solve the technical problem of poor resource potential release when using traditional methods to control flexible load resources.

[0006] According to one aspect of an embodiment of the present invention, a distribution network control method is provided, comprising: predicting the operating parameters of the distribution network in a future time period based on current operating parameters of the distribution network to obtain first operating parameters; determining, based on the first operating parameters, a resource demand parameter of a power supply area for a first resource in the distribution network in the future time period, wherein the first resource is used to characterize resources that can be controlled in the distribution network, and the power supply area is used to characterize an area that requires the distribution network to provide power resources; constructing a resource control strategy for the first resource based on the resource demand parameter and a resource control condition, wherein the resource control condition is used at least to control the resource control cost of the resource control strategy when it is executed; and controlling the first resource based on the resource control strategy.

[0007] Furthermore, based on the resource demand parameters and resource control conditions, a resource control strategy for the first resource is constructed, including: obtaining the resource control level of the first resource in different power supply areas, and the resource impact parameters corresponding to the first resource, wherein the resource impact parameters are used to characterize the parameters that will affect the usage of the first resource; based on the resource control level, resource impact parameters and resource demand parameters, an initial control strategy is constructed; and the initial control strategy is adjusted based on the resource control conditions to obtain a resource control strategy.

[0008] Furthermore, based on the resource control level, resource impact parameters and resource demand parameters, an initial control strategy is constructed, including: based on the resource demand parameters, determining the initial operating parameters of the distribution network when the first resource is controlled; based on the resource control level and resource impact parameters, determining the initial control order and initial control scale when different first resources are controlled; based on the initial operating parameters, the initial control order and the initial control scale, constructing an initial control strategy.

[0009] Furthermore, the resource control conditions include at least: distribution network operating conditions and control cost conditions. The initial control strategy is adjusted based on the resource control conditions to obtain the resource control strategy, including: adjusting the initial operating parameters based on the distribution network operating conditions to obtain target operating parameters; adjusting the initial control sequence and initial control scale based on the control cost conditions to obtain target control sequence and target control scale; and constructing the resource control strategy based on the target operating parameters, target control sequence and target control scale.

[0010] Furthermore, the initial control order and the initial control scale are adjusted based on the control cost condition to obtain the target control order and the target control scale, including: integrating the resource demand parameters to obtain the demand control scale of the first resource; in response to the maximum control scale of the first resource being able to meet the demand control scale, the first resource is divided based on the resource type of the first resource to obtain at least one resource control scale; based on the unit control cost corresponding to the resource type and the first resource set, a control cost function is constructed; based on the control cost function, the initial control order and the initial control scale are adjusted to obtain the target control order and the target control scale.

[0011] Furthermore, the first operating parameter includes at least: a first power parameter corresponding to the power supply side in the distribution network, and a first load parameter corresponding to the load side. The method also includes: in response to the maximum control scale of the first resource not being able to meet the demand control scale, determining the new energy operating state on the power supply side based on the first power parameter, and determining the equipment operating state on the load side based on the first load parameter; obtaining a load adjustment strategy that matches the new energy operating state and the equipment operating state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operating state, the equipment operating state and the load adjustment strategy, and the load adjustment strategy is used to control the maximum control scale of the first resource to meet the demand control scale; and executing the load adjustment strategy.

[0012] Furthermore, obtaining the resource regulation level of the first resource in different power supply areas includes: obtaining the regional resource scale of the first resource in different power supply areas, and the user regulation perception parameters corresponding to the different power supply areas, wherein the user regulation perception parameters are used to reflect the perception ability of users in the power supply area when regulating the first resource; determining the resource regulation level based on the first resource scale and the user regulation perception parameters.

[0013] Furthermore, based on the current operating parameters of the distribution network, the operating parameters of the distribution network in a future time period are predicted to obtain first operating parameters, including: monitoring the current operating parameters of the distribution network to obtain second operating parameters; based on the second operating parameters, determining resource usage parameters of the distribution network at different operating sides; obtaining grid influence parameters of the distribution network, wherein the power supply influence parameters are used to characterize parameters that will affect the resource allocation of the distribution network; and determining the first operating parameters based on the resource usage parameters and the grid influence parameters.

[0014] Furthermore, based on the first operating parameter, the resource demand parameters of the power supply area for the first resource in the distribution network in the future time period are determined, including: obtaining the regional type of the power supply area and the season type of the future time period; based on the regional type and the season type, determining the operating scenario of the power supply area in the future time period, wherein the power supply scenario includes at least one of the following: a normal operating scenario, a heavy overload scenario, and a new energy consumption scenario; based on the operating scenario and the first operating parameter, determining the resource demand parameters.

[0015] According to another aspect of an embodiment of the present invention, a distribution network control device is also provided, including: a parameter prediction module, used to predict the operating parameters of the distribution network in a future time period based on the current operating parameters of the distribution network to obtain a first operating parameter; a parameter determination module, used to determine the resource demand parameters of the power supply area for the first resource in the distribution network in the future time period based on the first operating parameter, wherein the first resource is used to characterize the resources that can be controlled in the distribution network, and the power supply area is used to characterize the area that needs the distribution network to provide power resources; a strategy construction module, used to construct a resource control strategy for the first resource based on the resource demand parameter and the resource control condition, wherein the resource control condition is at least used to control the resource control cost of the resource control strategy when it is executed; and a resource control module, used to control the first resource based on the resource control strategy.

[0016] According to another aspect of an embodiment of the present invention, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention when running.

[0017] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0018] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0019] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0020] According to another aspect of the embodiments of the present invention, a computer program is provided. When the computer program is executed by a processor, the methods in various embodiments of the present invention are implemented.

[0021] In an embodiment of the present invention, the operating parameters of the distribution network in a future time period are predicted based on the current operating parameters of the distribution network to obtain first operating parameters; based on the first operating parameters, the resource demand parameters of the power supply area for the first resource in the distribution network in the future time period are determined; based on the resource demand parameters and the resource control conditions, a resource control strategy for the first resource is constructed; in a manner of regulating the first resource based on the resource control strategy, the control system predicts the above-mentioned first operating parameters based on the current actual operating parameters of the distribution network, and determines the resource demand parameters based on the above-mentioned first operating parameters, so that the above-mentioned resource demand parameters can have a high degree of matching with the actual control demand, so as to ensure that when the resource control strategy constructed by using the above-mentioned resource demand parameters and resource control conditions is used for resource control, not only the actual control demand can be met, but also the control cost of the flexible load resources can be minimized, thereby achieving low-cost and high-efficiency control of flexible load resources when formulating differentiated scenario control strategies, forming an effective distribution network optimization methodology, and thus solving the technical problem of poor resource potential release when using traditional methods to control flexible load resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 is a flow chart of a distribution network control method according to an embodiment of the present invention;

[0024] Figure 2 is a detailed flow chart of a distribution network control method according to an embodiment of the present invention;

[0025] Figure 3 2 is a schematic diagram of a distribution network control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present invention, an embodiment of a distribution network control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 FIG. 1 is a flow chart of a distribution network control method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0030] Step S102 : predicting the operating parameters of the distribution network in a future time period based on the current operating parameters of the distribution network to obtain first operating parameters.

[0031] The current operating parameters of the distribution network may be parameters that can reflect the operating conditions of the various components of the distribution network. For example, the current operating parameters of the distribution network may include at least one or more of the following: grid-side operating parameters, power supply-side operating parameters, load-side operating parameters, and energy storage-side operating parameters, but are not limited thereto. The future time period may be the time period to be analyzed and predicted by the method, for example, the next few hours, days, or a specific season, but are not limited thereto. The prediction may be performed using an artificial intelligence algorithm. The first operating parameter may be a parameter calculated based on the operating parameters and used to reflect the operating conditions of the various components of the power grid within the future time period.

[0032] In an optional embodiment, considering that the load and output of the power system are affected by multiple factors, these factors may affect the above-mentioned prediction process, thereby causing inaccurate prediction results. Therefore, when regulating the flexible load resources existing in the distribution network, the current operating parameters of the distribution network can be obtained first, and the operating parameters of the distribution network in the future time period can be predicted based on the current operating parameters to obtain the above-mentioned first operating parameters.

[0033] For example, to improve the accuracy of the predicted first operating parameter, the control system can perform predictions based on a prediction model pre-trained by a machine learning algorithm. Before performing data prediction, the control system can first obtain operating parameters for the grid, power supply, load, and energy storage sides of the distribution network. The control system can then use these operating parameters as inputs to the prediction model and, based on this model, output a prediction result for the operating parameter for a future time period, i.e., the first operating parameter.

[0034] For another example, considering that the distribution network resources may show obvious seasonality, the control system can make predictions based on the STL (Seasonal and Trend decomposition using Loess) method, which has the ability to decompose seasonality, thereby improving the accuracy and reliability of the prediction. Before performing data prediction, the control system can first obtain the operating parameters of the grid side, power side, load side and energy storage side in the distribution network. Then, the control system can use the above-mentioned operating parameters as input to the above-mentioned STL method, and output the prediction results of the operating parameters for the future time period based on this method, that is, output the above-mentioned first operating parameters.

[0035] For another example, the control system can also combine the model trained by the machine learning algorithm with the simulation model of the power system to obtain a hybrid prediction model for prediction. Before performing data prediction, the control system can first obtain the operating parameters of the grid side, power side, load side, and energy storage side of the distribution network. Then, the control system can use the above operating parameters as inputs to the above hybrid prediction model. The above hybrid prediction model uses the simulation model of the power system as the prediction basis, and then combines the machine learning model to correct errors, thereby improving the comprehensiveness and accuracy of the prediction. Finally, the control system outputs the prediction results of the operating parameters for the future time period based on the hybrid prediction model.

[0036] Specifically, grid-side operating parameters can include user importance and historical operating data, which can be obtained by analyzing the topology of the regional distribution network. Power supply-side operating parameters can include the output characteristics of renewable energy sources, which can be obtained by analyzing the historical operating data of renewable energy sources in the current region. Load-side operating parameters can include the scale and adjustable potential of flexible load resources, which can be obtained by analyzing user access in the current region. Energy storage-side operating parameters can include the access location, scale, and capacity of energy storage resources, which can be obtained by analyzing energy storage resource access.

[0037] Step S104: Based on the first operating parameter, determine the resource demand parameter of the power supply area for the first resource in the distribution network in the future time period, wherein the first resource is used to characterize the resources that can be regulated in the distribution network, and the power supply area is used to characterize the area that needs the distribution network to provide power resources.

[0038] The above-mentioned power supply area may be an area that requires power resources provided by the distribution network, including at least one or more of the following: urban areas, industrial parks, and rural areas, but not limited to these. The above-mentioned first resource may be a resource that can adjust its power consumption or production according to the needs of the power grid. For example, the above-mentioned first resource may refer to a flexible load resource that can be regulated, and may include at least one or more of the following: air conditioning cooling load, electric heating load, electric vehicle charging load, energy storage resources, but not limited to these. The above-mentioned resource demand parameters may be variables used to regulate the above-mentioned first resources. For example, the above-mentioned resource demand parameters may include at least one or more of the following: regulation time period, regulation scale, but not limited to these.

[0039] In an optional embodiment, the control system has obtained the above-mentioned first operating parameters based on the above-mentioned steps. Based on the first operating parameters, the control system can determine the resource demand parameters of the power supply area for the first resource in the distribution network in the future time period, so as to improve the matching degree between the above-mentioned resource demand parameters and the actual control requirements, thereby improving the execution effect of subsequent resource control strategies.

[0040] For example, based on the first operating parameter, it can be inferred that the load of the urban distribution network will be too high in the future time period. At this time, the resource demand parameter can be: by regulating the first resource, reduce the load of the urban distribution network by 1000kW in the future time period.

[0041] For another example, based on the first operating parameter, it can be inferred that the rural distribution network will be overloaded in the future time period. At this time, the resource demand parameter may be: by regulating the first resource, reduce the load of the rural distribution network by 1200kW in the future time period.

[0042] For another example, based on the above-mentioned first operating parameter, it can be inferred that the park distribution network will have excess new energy output in the future time period. At this time, the resource demand parameter can be: by regulating the above-mentioned first resource, the park's new energy consumption will reach 2000kW in the future time period.

[0043] Step S106: constructing a resource control strategy for the first resource based on the resource demand parameter and the resource control condition, wherein the resource control condition is at least used to control the resource control cost when the resource control strategy is executed.

[0044] The resource control conditions may refer to conditions that must be met by the generated resource control strategy. The resource control strategy may be a control scheme tailored to the needs of distribution network resources in different operating scenarios. For example, in a scenario where the distribution network is overloaded, the resource control strategy may be used to control various distribution network resources to reduce the load. For another example, in a scenario where the distribution network has an excess of renewable energy, the resource control strategy may be used to control various distribution network resources to increase the absorption of renewable energy. The resource control cost may be the overhead incurred when controlling and scheduling distribution network resources.

[0045] In an optional embodiment, the control system can determine the resource demand parameters of the power supply area for the first resource in the distribution network in a future time period based on the operating status of the distribution network. Considering that a variety of resource control schemes may be generated based on the above resource demand parameters, some of these schemes may have excessively high execution costs and do not meet the actual resource control scenarios. Therefore, the control system needs to determine the first resource type that needs to be regulated and its control scale based on the resource demand parameters and resource control conditions, so as to construct a resource control strategy for the above first resource, so that the resource control strategy can reduce the resource control cost as much as possible while meeting the above resource demand parameters, so as to improve the operating efficiency and economic benefits of the distribution network.

[0046] For example, the resource demand parameter may be: by regulating the first resource, reduce the load of the urban distribution network by 1000kW in the future time period. The resource regulation condition may be to ensure the lowest total regulation cost. The unit regulation cost relationship of each resource included in the first resource may be shown as follows:

[0047] Air conditioning cooling load = electric vehicle charging load < electric heating load < energy storage resources.

[0048] Based on the above resource demand parameters and resource control conditions, the following resource control strategy can be constructed: reduce the urban air conditioning cooling load by 500kW in the future time period, and at the same time reduce the urban electric vehicle charging load by 500kW, so as to meet the above resource demand parameters and minimize the total control cost.

[0049] For another example, the resource demand parameter may be: by regulating the first resource, the park's new energy consumption reaches 2000kW in the future time period. The resource regulation condition may be to ensure the lowest total regulation cost. The unit regulation cost of each resource included in the first resource may be expressed as follows:

[0050] Energy storage resources < electric vehicle charging load < electric heating load < air conditioning cooling load.

[0051] Based on the above resource demand parameters and resource control conditions, the following resource control strategy can be constructed: in the future time period, by regulating the park's energy storage resources, 2000kW of new energy in the park can be absorbed to meet the above resource demand parameters and the total control cost does not exceed 1000 yuan.

[0052] In another optional embodiment, considering that multiple resource control schemes may be generated based on the above-mentioned resource demand parameters, the overall response time of some of these schemes may be too long, resulting in too low efficiency of resource control. Therefore, the control system needs to determine the first resource type that needs to be regulated and its control scale based on the resource demand parameters and resource control conditions, so as to construct a resource control strategy for the above-mentioned first resource, so that the resource control strategy can reduce the response time as much as possible while meeting the above-mentioned resource demand parameters, so as to achieve a rapid response of the distribution network to the resource control strategy.

[0053] For example, the resource demand parameter may be: by regulating the first resource, reduce the load of the rural distribution network by 1200kW in the future time period. The resource regulation condition may be to ensure the minimum overall response time of resource regulation. The unit response time relationship of each resource included in the first resource may be shown as follows:

[0054] Electric heating load < air conditioning cooling load < electric vehicle charging load < energy storage resources.

[0055] Based on the above resource demand parameters and resource control conditions, the following resource control strategy can be constructed: reduce the rural electric heating load by 1200kW in the future time period to meet the above resource demand parameters and minimize the overall response time of the control.

[0056] For another example, the resource demand parameter may be: by regulating the first resource, the park's new energy consumption will reach 3100kW in the future time period. The resource regulation condition may be to ensure the minimum overall response time for resource regulation. The unit response time relationship of each resource included in the first resource may be as shown in the following formula:

[0057] Air conditioning cooling load < electric heating load < energy storage resources < electric vehicle charging load.

[0058] Based on the above resource demand parameters and resource control conditions, the following resource control strategy can be constructed: in the future time period, by regulating the air-conditioning cooling load of the park, 3100kW of new energy in the park can be absorbed to meet the above resource demand parameters and the overall response time of the control is minimized.

[0059] Step S108: regulating the first resource based on the resource regulation policy.

[0060] In an optional embodiment, the control system can construct a resource control strategy based on the above-mentioned resource demand parameters and resource control conditions, and then the control system executes the above-mentioned resource control strategy to control the air-conditioning cooling load, electric heating load, electric vehicle charging load or energy storage resources in the power supply area, so that the operating status of the distribution network in the future time period meets the preset goals, thereby enhancing the reliability and stability of the distribution network.

[0061] For example, the resource control strategy could be: reduce the urban area's air conditioning cooling load by 500kW over a future time period, while also reducing the urban area's electric vehicle charging load by 500kW. The control system implements this resource control strategy, rotating the use of distributed air conditioners within the urban area, raising the air conditioning temperature from 24°C to 26.7°C, thereby reducing the urban area's air conditioning cooling load by 500kW. Simultaneously, the control system regulates the electric vehicle charging load. Specifically, through pricing policies, with user approval, the system interrupts electric vehicle charging when the electric vehicle charge exceeds 50%, thereby reducing the urban area's electric vehicle charging load by 500kW.

[0062] For example, the resource control strategy could be to reduce the rural electric heating load by 800 kW over a future period. The control system implements this resource control strategy, rotating the distributed heating systems for rural residents, lowering the temperature from 24.6°C to 17.6°C, thereby reducing the rural electric heating load by 800 kW.

[0063] As another example, the resource control strategy could be: In the future, the park's energy storage resources are controlled to accommodate 2,000 kW of renewable energy, while the electric vehicle charging load is controlled to accommodate 1,500 kW of renewable energy. The control system implements this resource control strategy by regulating the energy storage resources of public buildings within the park. Specifically, by collaboratively optimizing charging, the system increases charging power to accommodate the park's 2,000 kW of renewable energy. Simultaneously, by increasing power and concurrency, the charging module increases charging power for low-power charging vehicles within the park with less than 50% of their charge, thereby accommodating the park's 1,500 kW of renewable energy.

[0064] In an embodiment of the present invention, the operating parameters of the distribution network in a future time period are predicted based on the current operating parameters of the distribution network to obtain first operating parameters; based on the first operating parameters, the resource demand parameters of the power supply area for the first resource in the distribution network in the future time period are determined; based on the resource demand parameters and the resource control conditions, a resource control strategy for the first resource is constructed; in a manner of regulating the first resource based on the resource control strategy, the control system predicts the above-mentioned first operating parameters based on the current actual operating parameters of the distribution network, and determines the resource demand parameters based on the above-mentioned first operating parameters, so that the above-mentioned resource demand parameters can have a high degree of matching with the actual control demand, so as to ensure that when the resource control strategy constructed by using the above-mentioned resource demand parameters and resource control conditions is used for resource control, not only the actual control demand can be met, but also the control cost of the flexible load resources can be minimized, thereby achieving low-cost and high-efficiency control of flexible load resources when formulating differentiated scenario control strategies, forming an effective distribution network optimization methodology, and thus solving the technical problem of poor resource potential release when using traditional methods to control flexible load resources.

[0065] Furthermore, based on the resource demand parameters and resource control conditions, a resource control strategy for the first resource is constructed, including: obtaining the resource control level of the first resource in different power supply areas, and the resource impact parameters corresponding to the first resource, wherein the resource impact parameters are used to characterize the parameters that will affect the usage of the first resource; based on the resource control level, resource impact parameters and resource demand parameters, an initial control strategy is constructed; and the initial control strategy is adjusted based on the resource control conditions to obtain a resource control strategy.

[0066] The resource regulation levels may be classifications of varying degrees of resource regulation. For example, the resource regulation levels may include, but are not limited to, general peak regulation (peak shaving), deep peak regulation (peak shaving), emergency support (peak shaving), and new energy consumption (valley filling). The resource influencing parameters may include, but are not limited to, at least one or more of the following: human body temperature sensitivity, green travel characteristics, and demand response costs.

[0067] In an optional embodiment, in order to improve the matching degree between the resource control strategy and the actual resource control demand, the control system can first obtain the resource control level of the above-mentioned first resource in different power supply areas, and then determine the resource type and control scale that need to be regulated based on the resource control level, resource impact parameters and resource demand parameters of the above-mentioned different resources to construct an initial control strategy, and adjust the resource type and control scale in the initial control strategy based on the resource control conditions to obtain a resource control strategy.

[0068] For ease of understanding, the load type and resource regulation level of the above-mentioned first resource can be as shown in Table 1, but are not limited thereto. According to Table 1, the above-mentioned first resource can be an air conditioning cooling load, an electric heating load, an electric vehicle charging load, and an energy storage resource. Specifically, the air conditioning cooling load can include a decentralized air conditioning load and a central air conditioning load, the electric heating load can include decentralized heating and a central air conditioning (heat pump), the electric vehicle charging load can include a slow charging pile and a fast charging pile, and the energy storage resource can include public building energy storage. The distribution network can divide the regulation levels of the air conditioning cooling load, the electric heating load, and the energy storage into general peak shaving level (peak shaving), deep peak shaving level (peak shaving), emergency guarantee level (peak shaving), and new energy consumption (valley filling), and can divide the regulation levels of the electric vehicle charging load into general peak shaving level (peak shaving), deep peak shaving level (peak shaving), emergency guarantee level (peak shaving), general peak shaving level (valley filling), and deep peak shaving level (valley filling).

[0069] Table 1

[0070]

[0071]

[0072]

[0073]

[0074] For example, the resource demand parameter may be: by regulating the first resource, reduce the load of the urban distribution network by 1000kW in the future time period. The resource impact parameter may be human body temperature sensitivity. The resource control condition may be: while considering the resource impact parameter, reduce the control cost as much as possible. The unit control cost relationship of each resource included in the first resource may be as follows:

[0075] Air conditioning cooling load < electric vehicle charging load < electric heating load < energy storage resources.

[0076] The control system first obtains the resource control level of the above-mentioned first resource in different power supply areas, and then based on the resource control level, resource impact parameter and resource demand parameter of the above-mentioned different resources, the determined initial control strategy can be: adopt the emergency guarantee level (peak shaving) of the fast charging pile for charging electric vehicles to reduce the load of 1000kW in the urban distribution network. At this time, although the above-mentioned initial control strategy meets the above-mentioned resource demand parameters, it does not meet the above-mentioned resource control conditions. Therefore, based on the above-mentioned resource control conditions, the initial control strategy is adjusted to obtain the following resource control strategy: adopt the deep peak-shaving level (peak shaving) of the decentralized air-conditioning of users in the urban area to reduce the load of 800kW in the urban distribution network. At the same time, adopt the general peak-shaving level (peak shaving) of the slow charging pile for charging electric vehicles to reduce the load of 200kW in the urban distribution network. This resource control strategy meets the above-mentioned resource demand parameters and meets the above-mentioned resource control conditions.

[0077] For another example, the resource demand parameter may be: by regulating the first resource, reduce the load of the park distribution network by 1200kW in the future time period. The resource impact parameter may be human body temperature sensitivity, green travel characteristics, and demand response cost. The resource control condition may be: while considering the resource impact parameters, reduce the control cost as much as possible. The size relationship of the unit control cost of each resource included in the first resource may be as follows:

[0078] Electric heating load < electric vehicle charging load < air conditioning cooling load < energy storage resources.

[0079] The relationship between the unit demand response costs of the above-mentioned resources can be shown as follows:

[0080] Energy storage resources < air conditioning cooling load < electric vehicle charging load < electric heating load.

[0081] The control system first obtains the resource control level of the above-mentioned first resource in different power supply areas, and then based on the resource control level, resource impact parameter and resource demand parameter of the above-mentioned different resources, the initial control strategy determined can be: adopt the emergency guarantee level (peak shaving) of the energy storage resource to reduce the load of 1200kW of the urban distribution network. At this time, although the above-mentioned initial control strategy meets the above-mentioned resource demand parameters, it does not meet the above-mentioned resource control conditions. Therefore, based on the above-mentioned resource control conditions, the initial control strategy is adjusted to obtain the following resource control strategy: adopt the general peak shaving level (peak shaving) of the decentralized heating of public buildings in the park and the deep peak shaving level of the central air conditioning (heat pump) of the public buildings to reduce the load of 900kW of the park distribution network. At the same time, adopt the general peak shaving level (peak shaving) of the slow charging pile for electric vehicle charging and the general peak shaving level (peak shaving) of the fast charging pile to reduce the load of 300kW of the park distribution network. This resource control strategy meets the above-mentioned resource demand parameters and meets the above-mentioned resource control conditions.

[0082] For another example, the resource demand parameter may be: by regulating the first resource, the park's new energy consumption reaches 2000kW in the future time period. The resource impact parameter may be green travel characteristics and demand response costs. The resource control condition may be: while considering the resource impact parameters, reduce the control cost as much as possible. The unit control cost relationship of each resource included in the first resource may be as follows:

[0083] Electric vehicle charging load < air conditioning cooling load < electric heating load < energy storage resources.

[0084] The relationship between the unit demand response costs of the above-mentioned resources can be shown as follows:

[0085] Energy storage resources < electric vehicle charging load < air conditioning cooling load < electric heating load.

[0086] The control system first obtains the resource control level of the above-mentioned first resource in different power supply areas, and then based on the resource control level, resource impact parameters and resource demand parameters of the above-mentioned different resources, the determined initial control strategy can be: adopting the new energy consumption (filling the valley) of energy storage resources to consume 1600kW of new energy, and at the same time, adopting the general peak-shaving level (filling the valley) of the fast charging pile for charging electric vehicles to consume 400kW of new energy. At this time, although the above-mentioned initial control strategy meets the above-mentioned resource demand parameters, it does not meet the above-mentioned resource control conditions. Therefore, based on the above-mentioned resource control conditions, the initial control strategy is adjusted to obtain the following resource control strategy: adopting the deep peak-shaving level (filling the valley) of the fast charging pile for charging electric vehicles and the general peak-shaving level (filling the valley) of the slow charging pile for charging electric vehicles to consume 2000kW of new energy. This resource control strategy meets the above-mentioned resource demand parameters and meets the above-mentioned resource control conditions.

[0087] Furthermore, based on the resource control level, resource impact parameters and resource demand parameters, an initial control strategy is constructed, including: based on the resource demand parameters, determining the initial operating parameters of the distribution network when the first resource is controlled; based on the resource control level and resource impact parameters, determining the initial control order and initial control scale when different first resources are controlled; based on the initial operating parameters, the initial control order and the initial control scale, constructing an initial control strategy.

[0088] The initial operating parameters may be operating parameters of the distribution network when regulating the first resource. For example, the initial operating parameters may include, but are not limited to, node voltages and branch currents. The initial regulation order may refer to the order in which operations are performed on different resources in the initial regulation strategy. The initial regulation scale may be the scope of execution of the initial regulation strategy.

[0089] In an optional embodiment, in order to make the initial control strategy meet the actual control needs as much as possible, the control system can first calculate the voltage of each node and the current flowing through each branch of the distribution network when the first resource is controlled based on the current resource demand parameters by using an analysis method. Then, based on the above-mentioned resource control level and resource impact parameters, the control system can use the resources that better meet the resource impact parameters as the resources for priority control, and at the same time, use the resources that deviate more from the resource impact parameters as the resources for subsequent control, thereby determining the initial control order when regulating different first resources. Similarly, the control system can also calculate the range of different resources that need to be regulated based on the above-mentioned resource control level and resource impact parameters, so that the overall control range of all resources meets the above-mentioned resource control needs. Finally, the control system can construct an initial control strategy based on the above-mentioned node voltages and branch currents, as well as the determined control order and control scale.

[0090] For example, the analysis method may be the Newton-Raphson method. The resource control levels may be as shown in Table 1. The resource influencing parameters may be human body temperature sensitivity and demand response cost. The relationship between the response costs of different resources may be as follows:

[0091] Energy storage resources < electric vehicle charging load < air conditioning cooling load < electric heating load.

[0092] To construct an initial control strategy, the control system can first use the Newton-Raphson method to calculate the voltage at each node and the current flowing through each branch. Then, based on the resource control levels and resource impact parameters, the control system can determine the initial control order and control scale for different first resources. The control order and control scale can be: 500kW for general peak-shaving (peak shaving) of energy storage resources, 1000kW for deep peak-shaving (peak shaving) of energy storage resources, 600kW for general peak-shaving (peak shaving) of slow charging piles for electric vehicle charging loads, 1000kW for deep peak-shaving (peak shaving) of slow charging piles for electric vehicle charging loads, 1500kW for emergency support (peak shaving) of slow charging piles for electric vehicle charging loads, 300kW for general peak-shaving (peak shaving) of central air conditioning in public buildings for cooling loads, and 350kW for general peak-shaving (peak shaving) of central air conditioning in public buildings for electric heating loads. Finally, based on the above initial operating parameters, initial control sequence and initial control scale, an initial control strategy is constructed. That is, the control system controls the above different types of resources in sequence according to the calculated node voltage and branch current, according to the above resource control sequence and resource control scale, so as to meet the actual resource control needs.

[0093] For another example, the analysis method may be a DC power flow method. The resource control level may be as shown in Table 1. The resource impact parameter may be a demand response cost. The relationship between the response costs of different resources may be as follows:

[0094] Electric vehicle charging load < air conditioning cooling load < energy storage resources < electric heating load.

[0095] To construct the initial control strategy, the control system can use the DC power flow method to calculate the voltage at each node and the current flowing through each branch. Then, based on the above resource control level and resource impact parameters, the control system can determine the initial control order and control scale when controlling different first resources. The control order and control scale can be: the general peak-shaving level (valley filling) of the slow charging pile for electric vehicle charging load is 500kW, the general peak-shaving level (valley filling) of the fast charging pile for electric vehicle charging load is 600kW, the deep peak-shaving level (valley filling) of the fast charging pile for electric vehicle charging load is 1200kW, the new energy consumption (valley filling) of the air conditioning cooling load is 2200kW, the new energy consumption (valley filling) of the energy storage resource is 3000kW, and the new energy consumption (valley filling) of the electric heating load is 2300kW. Finally, based on the above initial operating parameters, initial control sequence and initial control scale, an initial control strategy is constructed. That is, the control system controls the above different types of resources in sequence according to the calculated node voltage and branch current, according to the above resource control sequence and resource control scale, so as to meet the actual resource control needs.

[0096] Furthermore, the resource control conditions include at least: distribution network operating conditions and control cost conditions. The initial control strategy is adjusted based on the resource control conditions to obtain the resource control strategy, including: adjusting the initial operating parameters based on the distribution network operating conditions to obtain target operating parameters; adjusting the initial control sequence and initial control scale based on the control cost conditions to obtain target control sequence and target control scale; and constructing the resource control strategy based on the target operating parameters, target control sequence and target control scale.

[0097] The above-mentioned distribution network operating conditions may be conditions to ensure stable and safe operation of the distribution network. For example, the above-mentioned distribution network operating conditions may include at least one or more of the following: system steady-state operation constraints, grid operation boundary constraints, distributed power supply output constraints, and flexible load resource regulation constraints, but are not limited to these.

[0098] Specifically, the system steady-state operation constraints may include at least node voltage and branch current not exceeding limits. The node voltage not exceeding limits may be as follows:

[0099]

[0100] U represents the lower limit of the node voltage, Indicates the upper limit of the node voltage. It should be noted that the upper and lower limits of the voltage of all nodes are the same. represents the voltage of node i at time t in the scheduling, For any node i.

[0101] The branch current limit can be shown as follows:

[0102]

[0103] Where, Indicates the upper limit of the current of branch j. Affected by the line specifications, the upper limits of the current of different branches in the system are different. represents the current of branch j at time t in scheduling, For any branch j.

[0104] The grid operation boundary constraints may at least include that the grids at all levels are not overloaded and that renewable energy generation does not cause power flow backflow. The grids at all levels are not overloaded as shown below:

[0105] P i (t)≤Q i ×80%.

[0106] Among them, P i (t) is the load demand of the i-level power grid at time t, Q i For rated capacity, i can be divided into 110, 35 and 10 kV, but not limited to this.

[0107] The renewable energy generation does not cause power flow reverse transmission as shown below:

[0108] P PV (t)+P wind (t)-P total (t)≤0.

[0109] Among them, P PV (t) is the output power of photovoltaic power generation equipment at time t, P wind (t) is the output power of wind power generation equipment at time t, P total (t) is the total load of the 110 kV power grid at time t. It should be noted that here only 110 kV is limited to not being transmitted.

[0110] Distributed power generation output constraints may include at least photovoltaic output constraints and wind power output constraints. Photovoltaic output constraints may be as follows:

[0111] 0≤P PV (t)≤P PV0 .

[0112] Among them, P PV (t) is the output power of photovoltaic power generation equipment at time t; P PV0 (t) is the maximum output power of the photovoltaic power generation equipment at time t.

[0113] The wind power output constraint can be shown as follows:

[0114] 0≤Pwind (t)≤P wind0 .

[0115] Among them, P wind (t) is the output power of the wind power generation equipment at time t; P wind0 is the maximum output power of the wind turbine generator at time t.

[0116] Flexible load resource control constraints can include at least air conditioning load / electric heating load power constraints, electric vehicle charging power constraints, and energy storage operation constraints. The air conditioning load / electric heating load power constraints can be as follows:

[0117]

[0118] Among them, P kt (t) is the operating power of the air conditioning refrigeration unit at time t, is the rated power of the air conditioning refrigeration unit, P dcn (t) is the operating power of the electric heating unit at time t, is the rated power of the electric heating unit. are the operating power and rated power of the air conditioning refrigeration unit and the electric heating unit respectively.

[0119] The EV charging power constraint can be shown as follows:

[0120]

[0121] Among them, P charge (t) is the charging power of the electric vehicle at time t, Rated charging power for electric vehicles.

[0122] The power constraints of energy storage operation constraints can be shown as follows:

[0123] 10% × SOC max ≤SOC(t)≤SOC max .

[0124] Among them, SOC(t) is the current energy storage capacity at time t, SOC max is the energy storage capacity.

[0125] The charging power constraint and discharging power constraint of energy storage operation constraints can be shown as follows:

[0126]

[0127] Among them, P battery,c (t) is the energy storage charging power at time t, P battery,f (t) is the energy storage discharge power at time t, is the maximum charging power of energy storage, is the maximum discharge power of energy storage.

[0128] The aforementioned control cost condition may refer to a condition for controlling the resource control cost during the execution of a resource control strategy. The aforementioned target operating parameters may be operating parameters adjusted based on the aforementioned distribution network operating conditions. The aforementioned target control order may refer to the resource control order adjusted from the initial control order based on the control cost condition. The aforementioned target control scale may refer to the resource control scale adjusted from the initial control order based on the control cost condition.

[0129] In an optional embodiment, in order to regulate flexible load resources at low cost and high efficiency, the control system can adjust the calculated initial operating parameters based on the above-mentioned system steady-state operation constraints, grid operation boundary constraints, distributed power output constraints and flexible load resource control constraints to meet the above-mentioned constraints, thereby obtaining the target operating parameters.

[0130] For example, the control system can adjust the node voltage and branch current calculated above based on the above-mentioned system steady-state operation constraints, so that each node voltage satisfies the above-mentioned node voltage limit, and each branch current satisfies the above-mentioned branch current limit. The control system can adjust the load demand of each level of the power grid and the output power of new energy equipment based on the above-mentioned power grid operation boundary constraints, so that the load demand of each level of the power grid satisfies the above-mentioned power grid operation without overload, and the output power of various new energy equipment satisfies the above-mentioned new energy power generation without causing power flow reverse. The control system can adjust the output power of photovoltaic power generation equipment and wind power generation equipment based on the above-mentioned distributed power supply output constraints, so that the output power of photovoltaic power generation equipment satisfies the photovoltaic output constraint, and the output power of wind power generation equipment satisfies the wind power output constraint. The control system can adjust the operating power of various types of flexible load resources based on the flexible load resource control constraints, so that the operating power of the air-conditioning load meets the above-mentioned air-conditioning load power constraint, the operating power of the electric heating load meets the above-mentioned electric heating load power constraint, the electric vehicle charging power meets the above-mentioned electric vehicle charging power constraint, the energy storage power meets the power constraint of the above-mentioned energy storage operation constraint, the energy storage charging power meets the above-mentioned charging power constraint, and the energy storage discharge power meets the above-mentioned discharge power constraint.

[0131] After obtaining the above-mentioned target operating parameters, the control system can rearrange the above-mentioned initial control order in order from low to high unit control cost, and simultaneously adjust the control scale of various resources to reduce the resource control cost, thereby obtaining the target control order and target control scale.

[0132] For example, the initial regulation order and initial regulation scale can be: the general peak-shaving level (valley-filling) of slow charging piles for electric vehicle charging loads is 500kW, the general peak-shaving level (valley-filling) of fast charging piles for electric vehicle charging loads is 600kW, the deep peak-shaving level (valley-filling) of fast charging piles for electric vehicle charging loads is 1200kW, the new energy consumption (valley-filling) of central air-conditioning in public buildings for air-conditioning cooling loads is 2200kW, the new energy consumption (valley-filling) of energy storage resources is 3000kW, and the new energy consumption (valley-filling) of central air-conditioning in public buildings for electric heating loads is 2300kW. The unit regulation cost relationship of the above-mentioned various flexible load resources can be as follows:

[0133] Energy storage resources < electric vehicle charging load < air conditioning cooling load < electric heating load.

[0134] The control system adjusts the initial control order and initial control scale based on the control cost conditions, so that the target control order and target control scale can be: new energy consumption (valley filling) of energy storage resources is 5000kW, general peak-shaving level (valley filling) of slow charging piles for electric vehicle charging load is 1000kW, general peak-shaving level (valley filling) of fast charging piles for electric vehicle charging load is 1200kW, deep peak-shaving level (valley filling) of fast charging piles for electric vehicle charging load is 1500kW, new energy consumption (valley filling) of air-conditioning cooling load is 700kW, and new energy consumption (valley filling) of electric heating load is 400kW.

[0135] Finally, the control system can build a resource control strategy based on the above target operating parameters, target control sequence and target control scale.

[0136] Furthermore, the initial control order and the initial control scale are adjusted based on the control cost condition to obtain the target control order and the target control scale, including: integrating the resource demand parameters to obtain the demand control scale of the first resource; in response to the maximum control scale of the first resource being able to meet the demand control scale, the first resource is divided based on the resource type of the first resource to obtain at least one resource control scale; based on the unit control cost corresponding to the resource type and the first resource set, a control cost function is constructed; based on the control cost function, the initial control order and the initial control scale are adjusted to obtain the target control order and the target control scale.

[0137] The aforementioned demand control scale may refer to the total amount of resources required to achieve the control target. The aforementioned maximum control scale may refer to the maximum controllable amount of all resources in the current region. The aforementioned control cost function may refer to a calculation method that minimizes the cost of controlling flexible load resources, provided that the control capacity meets the control target.

[0138] In an optional embodiment, considering that there are many types of flexible load resources, it is necessary to determine the corresponding resource control scale for different flexible load resources and determine the execution order of different types of flexible load resources, so as to make the above resource control strategy more refined. Therefore, the control system can first integrate the resource demand parameters and calculate the demand control scale of the flexible load resources. The maximum control scale of the above flexible load resources meets the demand control scale. Then, the control system can divide the above flexible load resources into air conditioning cooling load, electric heating load, electric vehicle charging load, and energy storage resources. Then, each type of flexible load resource satisfies the following formula:

[0139] γ kt0 +γ dcn0 +γ cd0 +γ cn0 ≥ΔP0.

[0140] Among them, γ kt0 represents the maximum adjustable scale of air conditioning cooling load, γ dcn0 represents the maximum adjustable scale of electric heating load, γ cd0 represents the maximum controllable scale of electric vehicle charging load, γ cn0 It represents the maximum controllable scale of energy storage resources, and ΔP0 represents the demand control scale.

[0141] The control system can obtain the control scales of general peak regulation, deep peak regulation, emergency guarantee and new energy consumption levels of the above-mentioned various types of flexible load resources respectively. The above-mentioned control scales can be shown as follows:

[0142]

[0143] Among them, γ kt represents the control scale of air conditioning cooling load, γ dcn represents the control scale of electric heating load, γ cd represents the control scale of electric vehicle charging load, γ cn Represents the control scale of energy storage resources. γ kt1 , γ kt2 , γ kt3 , γ kt4 represents the control scale of air conditioning cooling load at the levels of general peak regulation, deep peak regulation, emergency guarantee and new energy consumption; γ dcn1 , γ dcn2 , γ dcn3 , γ dcn4 represents the control scale of electric heating load at the levels of general peak regulation, deep peak regulation, emergency guarantee and new energy consumption; γ cd1 , γ cd2 , γ cd3 , γ cd4represents the control scale of electric vehicle charging load at the levels of general peak regulation, deep peak regulation, emergency guarantee and new energy consumption; γ cn1 , γ cn2 , γ cn3 , γ cn4 Represents the scale of energy storage regulation at the general peak-shaving, deep peak-shaving, emergency support, and new energy consumption levels. It should be noted that general peak-shaving, deep peak-shaving, and emergency support regulation occur in load peak-shaving scenarios, which is a process of load reduction (energy storage discharge), while new energy consumption regulation occurs in load valley-filling scenarios, which is a process of load increase (energy storage charging).

[0144] Then, the control system can construct a control cost function based on the unit control cost corresponding to the flexible load resource type and the flexible load resource set. The control cost function can be as follows:

[0145] C=min(C kt γ kt +C dcn γ dcn +C cd γ cd +C cn γ cn ).

[0146] Among them, C kt represents the unit control cost of air conditioning cooling load, C dcn represents the unit control cost of electric heating load, C cd represents the unit control cost of electric vehicle charging load, C cn Represents the unit regulation cost of energy storage resources.

[0147] Finally, the control system may adjust the initial control order and initial control scale based on a preset adjustment formula to ensure that the execution costs of the adjusted target control order and target control scale meet the control cost conditions. The adjustment formula may be:

[0148] γ kt0 +γ dcn0 +γ cd0 +γ cn0 ≥ΔP0.

[0149] Furthermore, the first operating parameter includes at least: a first power parameter corresponding to the power supply side in the distribution network, and a first load parameter corresponding to the load side. The method also includes: in response to the maximum control scale of the first resource not being able to meet the demand control scale, determining the new energy operating state on the power supply side based on the first power parameter, and determining the equipment operating state on the load side based on the first load parameter; obtaining a load adjustment strategy that matches the new energy operating state and the equipment operating state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operating state, the equipment operating state and the load adjustment strategy, and the load adjustment strategy is used to control the maximum control scale of the first resource to meet the demand control scale; and executing the load adjustment strategy.

[0150] The first power supply parameter may be, but is not limited to, the output characteristics of a new energy power generation device. The first load parameter may be, but is not limited to, the scale and adjustable potential of a flexible load resource. The preset strategy table may be a table that describes the mapping relationship between the operating status of a new energy device, the device operating status, and the load adjustment strategy, and may be, for example, as shown in Table 2, but is not limited thereto.

[0151] In an optional embodiment, considering the stability of the distribution network, when the maximum control scale of the above-mentioned flexible load resources cannot meet the above-mentioned control requirements, as shown in the above-mentioned adjustment formula, at this time, the control system can analyze the new energy operation status on the power supply side based on the output characteristics of the above-mentioned new energy power generation equipment, and can analyze the equipment operation status on the load side based on the scale and adjustable potential of the above-mentioned flexible load resources. Then, the control system can match the corresponding load adjustment strategy from Table 2 based on the above-mentioned new energy operation status and equipment operation status, so that the maximum control scale of the above-mentioned flexible load resources meets the demand control scale. Finally, the control system executes the above-mentioned load adjustment strategy to ensure the reliability and stability of the power grid.

[0152] Table 2

[0153] New energy operating status Equipment operating status Load adjustment strategy Excess output Lower load curtailment of wind and solar power Insufficient contribution Too high a load Load shedding

[0154] For example, when the maximum control scale of the above-mentioned flexible load resources cannot meet the above-mentioned control needs, the control system can analyze that the new energy operation status on the power supply side is insufficient output based on the output characteristics of the above-mentioned new energy power generation equipment, and can analyze that the equipment operation status on the load side is too high based on the scale and adjustable potential of the above-mentioned flexible load resources. Therefore, on the basis of carrying out load control, the control system also needs to adopt a load shedding plan, that is, the control system can combine the importance of each user in the area and cut off part of the load to achieve the control target, thereby ensuring the safe and stable operation of the power grid.

[0155] For another example, when the maximum control scale of the flexible load resources cannot meet the control needs, the control system can analyze that the new energy operation status on the power supply side is excess output based on the output characteristics of the new energy power generation equipment, and can analyze that the equipment operation status on the load side is low load based on the scale and adjustable potential of the flexible load resources. Therefore, on the basis of load control, the control system also needs to adopt a wind and solar power abandonment plan, that is, the control system can generate a wind and solar power abandonment exclusion list, abandon some wind and photovoltaic energy to achieve the control goals, and thus ensure the safe and stable operation of the power grid.

[0156] Furthermore, obtaining the resource regulation level of the first resource in different power supply areas includes: obtaining the regional resource scale of the first resource in different power supply areas, and the user regulation perception parameters corresponding to the different power supply areas, wherein the user regulation perception parameters are used to reflect the perception ability of users in the power supply area when regulating the first resource; determining the resource regulation level based on the first resource scale and the user regulation perception parameters.

[0157] The regional resource scale may refer to the load range of the first resource in the current region. The user control perception parameter may refer to the user's perception of resource control.

[0158] In an optional embodiment, in order to improve the accuracy of formulating resource control strategies, the control system can first obtain the load range of various types of flexible load resources in different areas, and obtain the perception level of users corresponding to different power supply areas on the above-mentioned flexible load resource control. Then, the control system can determine the resource control level corresponding to different flexible load resources based on the above-mentioned load range and user perception level.

[0159] For example, the control system may first obtain that the resource scale of decentralized air conditioning for urban residents is 50,000 kW, the resource scale of decentralized air conditioning for public buildings is 35,000 kW, and the resource scale of central air conditioning for public buildings is 20,000 kW.

[0160] If the current control demand is to reduce the load, when the control scale of the air conditioning load is small, the control boundary is within the range of 24-26.7°C, and users are unaware of the control strategy's implementation. The resource control level at this control boundary is the general peak-shaving level. When the control scale of the cooling load of the residential distributed air conditioning increases further, the control boundary is within the range of 24-26.7°C, and users still are unaware of the control strategy's implementation. The resource control level at this control boundary is the deep peak-shaving level for the residential distributed air conditioning. When the control scale of the cooling load of the public building's air conditioning increases further, the control boundary is 29°C, and users can just feel the control strategy's implementation. The resource control level at this control boundary is the deep peak-shaving level for the public building's distributed air conditioning and central air conditioning. When the control scale of the residential distributed air conditioning's cooling load increases again, the control boundary is 29°C, and users can just feel the control strategy's implementation. The resource control level at this control boundary is the emergency guarantee level for the residential distributed air conditioning. When the control scale of the cooling load of the decentralized air-conditioning and central air-conditioning in public buildings increases again, the control boundary is that the compressor interruption time does not exceed 50 minutes, and users can clearly feel the execution of the control strategy. The resource control level of the above control boundary is the emergency guarantee level of the decentralized air-conditioning and central air-conditioning in public buildings.

[0161] If the current control demand is to absorb new energy, when the control boundary of the air-conditioning cooling load is at 22-24℃, the user cannot perceive the execution of the resource control strategy, and the resource control level of the above control boundary is the new energy absorption level of the above air-conditioning load.

[0162] For another example, the control system may first obtain that the resource scale of decentralized heating for urban residents is 60,000 kW, the resource scale of decentralized heating for public buildings is 50,000 kW, and the resource scale of central air conditioning for public buildings is 30,000 kW.

[0163] If the current control demand is to reduce the load, when the control scale of the above-mentioned electric heating load is small, the control boundary is within the range of 17.6-24.6℃, and the user cannot perceive the execution of the control strategy. The resource control level of the above-mentioned control boundary is the general peak-shaving level of the above-mentioned electric heating load. When the control scale of the above-mentioned electric heating load is further increased, the control boundary is within the range of 13.6-17.6℃, and the user can just perceive the execution of the control strategy. The resource control level of the above-mentioned control boundary is the deep peak-shaving level of the above-mentioned electric heating load. When the control scale of the above-mentioned electric heating load is increased again, the control boundary is within the range of 10.0-13.6℃, ​​and the user can clearly perceive the execution of the control strategy. The resource control level of the above-mentioned control boundary is the emergency guarantee level of the above-mentioned electric heating load.

[0164] If the current regulation demand is to absorb new energy, when the regulation boundary of the electric heating load is at 24.6-26℃, the user cannot perceive the execution of the resource regulation strategy, and the resource regulation level of the above regulation boundary is the new energy absorption level of the above electric heating load.

[0165] For another example, the control system can first obtain that the resource scale of the electric vehicle charging load of the fast charging pile in the park is 60,000 kW, and the resource scale of the electric vehicle charging load of the slow charging pile is 50,000 kW.

[0166] If the current control demand is to reduce the load, when the control scale of the EV charging load is small, the control boundary is full charge, and users cannot perceive the implementation of the control strategy. The resource control level of this control boundary is the general peak-shaving level. When the control scale of the EV charging load of the slow charging pile increases further, the control boundary is the load interruption when the charge exceeds 50%, and users can just perceive the implementation of the control strategy. The resource control level of this control boundary is the deep peak-shaving level of the slow charging pile. When the control scale of the EV charging load of the fast charging pile increases further, the control boundary is the charging power reduction when the charge exceeds 30%, and users can just perceive the implementation of the control strategy. The resource control level of this control boundary is the deep peak-shaving level of the fast charging pile. When the control scale of the EV charging load of the slow charging pile increases again, the control boundary is the full load interruption, and users can clearly feel the implementation of the control strategy. The resource control level of this control boundary is the emergency guarantee level of the slow charging pile. When the control scale of the electric vehicle charging load of the fast charging pile increases again, the control boundary is that the load is interrupted when the power exceeds 30%. Users can clearly feel the execution of the control strategy. The resource control level of the above control boundary is the emergency guarantee level of the fast charging pile.

[0167] If the current regulation demand is to accommodate new energy, when the EV charging load regulation boundary of a slow-charging pile is to remind charging when the battery level is less than 50%, users can perceive the implementation of the regulation strategy, and the resource regulation level at this regulation boundary is the new energy absorption level of the slow-charging pile. When the EV charging load regulation boundary of a fast-charging pile is to increase the charging power when the battery level is less than 50%, users can perceive the implementation of the regulation strategy, and the resource regulation level at this regulation boundary is the new energy absorption level of the fast-charging pile.

[0168] For another example, the control system may first obtain that the resource scale of rural energy storage resources is 20,000 kW.

[0169] If the current regulation requirement is to reduce the load, and the energy storage resource is discharging steadily, the energy storage resource regulation level is normal peak shaving. If the energy storage resource further increases its discharge power, ultra-short-term load forecasting will be used to predict the overall load factor within the optimization range, and collaborative optimization will be performed based on the peaks and valleys of the load curve. At this point, the energy storage resource regulation level is deep peak shaving. If the energy storage resource further increases its charging power, the energy storage capacity, fault range, and capacity demand will be considered to control the power flow and achieve collaborative optimization. At this point, the energy storage resource regulation level is emergency guarantee.

[0170] If the current regulation demand is to absorb new energy, the energy storage resources will achieve collaborative optimization by controlling the flow based on the new energy absorption demand through short-term load forecasting. At this time, the energy storage resource regulation level is the new energy absorption level.

[0171] Furthermore, based on the current operating parameters of the distribution network, the operating parameters of the distribution network in a future time period are predicted to obtain first operating parameters, including: monitoring the current operating parameters of the distribution network to obtain second operating parameters; based on the second operating parameters, determining resource usage parameters of the distribution network at different operating sides; obtaining grid impact parameters of the distribution network, wherein the grid impact parameters are used to characterize parameters that will affect the resource allocation of the distribution network; and determining the first operating parameters based on the resource usage parameters and the grid impact parameters.

[0172] The second operating parameter may be a factor used to predict the operating state of the distribution network in a future time period. For example, it may be an operating state parameter on the grid side, an operating state parameter on the power supply side, an operating state parameter on the load side, or an operating state parameter on the energy storage side, but is not limited thereto. The resource usage parameter may be a parameter obtained by analyzing the second operating parameter. The grid-influencing parameters may include at least one or more of the following: holidays, weather, time-of-use electricity prices, etc., but are not limited thereto.

[0173] In an optional embodiment, to improve the accuracy of prediction results, the control system can monitor the current operating parameters of the distribution network and obtain operating status parameters for the grid, power supply, load, and energy storage systems. The control system can then analyze each of these operating parameters to obtain resource usage parameters for each of these different operating parameters. For example, for grid-side operating parameters, the control system can analyze the regional distribution network topology to determine the importance of users and historical operating data. For power supply operating parameters, the control system can analyze the historical operating data of renewable energy resources in the region to determine the output characteristics of renewable energy. For load-side operating parameters, the control system can analyze user access in the region to determine the scale and adjustable potential of flexible load resources. For energy storage operating parameters, the control system can analyze energy storage resource access to determine the access location, scale, and capacity of the energy storage resources. The control system can also obtain grid-influencing parameters of the distribution network. For example, the control system can determine that the upcoming period will be a holiday, potentially leading to overload of electric vehicle charging loads. For another example, the control system may detect that the weather in the future will be very hot, so the air conditioning cooling load may be overloaded. Another example is that the control system may detect that the current electricity price is high, so regional electricity consumption may decrease in the future. Finally, based on the above resource usage parameters and grid impact parameters, the control system can predict the operating parameters for the future time period, that is, predict the above-mentioned first operating parameters.

[0174] Furthermore, based on the first operating parameter, the resource demand parameters of the power supply area for the first resource in the distribution network in the future time period are determined, including: obtaining the regional type of the power supply area and the season type of the future time period; based on the regional type and the season type, determining the operating scenario of the power supply area in the future time period, wherein the power supply scenario includes at least one of the following: a normal operating scenario, a heavy overload scenario, and a new energy consumption scenario; based on the operating scenario and the first operating parameter, determining the resource demand parameters.

[0175] In an optional embodiment, to further refine the resource control strategy, the control system may first obtain the current power supply region type and the season type in the future. Then, based on these power supply region and season types, the control system matches the operating scenario that best matches the current power supply region and seasonal characteristics among normal operation scenarios, heavy overload scenarios, and new energy consumption scenarios. This operating scenario is then used as the operating scenario for the current power supply region in the future time period. Finally, based on these operating scenarios and the first operating parameter, the control system determines the resource demand parameter.

[0176] For example, the power supply area type obtained by the control system may be an urban area, and the season type obtained may be summer. Because urban areas generally have higher load density and power supply reliability requirements, and the utilization rate of air conditioning cooling loads is higher in summer, the control system determines that the urban area will be in a heavy overload scenario in the future based on these two factors: urban area and summer. Based on this heavy overload scenario and the first operating parameter, the control system can determine the resource demand parameter as follows: regulate the decentralized air conditioning for urban residents, the decentralized air conditioning for urban public buildings, and the central air conditioning for urban public buildings to reduce the urban area's load by 2000kW.

[0177] For another example, the power supply region type obtained by the control system may be rural, and the season type obtained may be winter. Since the load density in rural areas is generally low, electricity consumption is mainly concentrated in residential electricity consumption, and the power supply reliability requirements are relatively low, while the utilization rate of electric heating loads is high in winter. Therefore, based on the two factors of rural areas and winter, it can be determined that rural areas will be in a heavy overload scenario in the future time period. Based on the above heavy overload scenario and the first operating parameter, the control system can determine the resource demand parameter as follows: regulate the decentralized heating of rural residents to reduce the rural load by 2000kW.

[0178] For another example, the control system might obtain a power supply area type of a park, and a season type of spring. Because the park has a high load density and requires high power supply reliability, and the utilization of air conditioning cooling loads and electric heating loads is low in spring, the control system can determine that the park will be in a renewable energy consumption scenario in the future based on these two factors: the park's location and the spring season. Based on this renewable energy consumption scenario and the first operating parameter, the control system can determine the resource demand parameter: regulating the park's energy storage resources to accommodate a renewable energy load of 1000 kW.

[0179] For ease of understanding, Figure 2 is a detailed flow chart of a distribution network control method according to an embodiment of the present invention, such as Figure 2As shown, the distribution network's operating status is first monitored and analyzed to obtain the regional flexible load resource scale, source-grid-load-storage scale and historical data, grid structure, and user levels. Based on the regional flexible load resource scale, the resource control cascade and boundaries are clarified. Based on the source-grid-load-storage scale and historical data, combined with factors such as holidays, weather, and time-of-use electricity prices, the source-load state is predicted to obtain prediction results. Based on these prediction results, the control scenarios are divided into normal operation scenarios, heavy overload scenarios, and renewable energy consumption scenarios, taking into account seasonal factors and power supply regions. The control requirements for each scenario, including the control time period and control scale, are further clarified. Then, combining the aforementioned resource control cascade, control boundaries, and control requirements, a resource control potential analysis is conducted. This analysis considers factors such as the controllable resource scale, control response time, and economic feasibility. If the analysis results indicate that the regional control potential meets the demand, the optimal control plan is generated using the distribution network control model for flexible load resources, taking into account demand response costs, safe supply, economic efficiency, and green and low-carbon characteristics. If the analysis results indicate that the regional control potential cannot meet the demand, load shedding and wind and solar curtailment plans are necessary. Finally, the control plan is implemented, and the completion of the control objectives and the control results are analyzed. At the same time, the problems existing in the distribution network control process are collected and feedback is provided to facilitate the proposal of grid optimization and equipment configuration.

[0180] According to another aspect of an embodiment of the present invention, a distribution network control device is also provided. It should be noted that the device can be used to execute the above-mentioned distribution network control method. The specific implementation method and application scenario are the same as those in the above-mentioned embodiment and will not be repeated here.

[0181] Figure 3 Schematic diagram of a distribution network control device according to an embodiment of the present invention. Figure 3 As shown, the device includes:

[0182] The parameter prediction module 302 is used to predict the operating parameters of the distribution network in a future time period based on the current operating parameters of the distribution network to obtain the first operating parameters. The parameter determination module 304 is used to determine the resource demand parameters of the power supply area for the first resource in the distribution network in the future time period based on the first operating parameters, wherein the first resource is used to represent the resources that can be regulated in the distribution network, and the power supply area is used to represent the area that requires the distribution network to provide power resources. The strategy construction module 306 is used to construct a resource control strategy for the first resource based on the resource demand parameters and the resource control conditions, wherein the resource control conditions are used to at least control the resource control cost of the resource control strategy during execution. The resource control module 308 is used to control the first resource based on the resource control strategy.

[0183] Furthermore, the strategy construction module is also used to: obtain the resource control level of the first resource in different power supply areas, and the resource impact parameters corresponding to the first resource, wherein the resource impact parameters are used to characterize the parameters that will affect the usage of the first resource; construct an initial control strategy based on the resource control level, the resource impact parameters and the resource demand parameters; adjust the initial control strategy based on the resource control conditions to obtain the resource control strategy.

[0184] Furthermore, the strategy construction module is also used to: determine the initial operating parameters of the distribution network when the first resource is regulated based on the resource demand parameters; determine the initial regulation order and initial regulation scale when regulating different first resources based on the resource regulation level and the resource impact parameters; and construct the initial regulation strategy based on the initial operating parameters, the initial regulation order and the initial regulation scale.

[0185] Furthermore, the strategy construction module is also used to: adjust the initial operating parameters based on the distribution network operating conditions to obtain target operating parameters; adjust the initial control sequence and the initial control scale based on the control cost conditions to obtain target control sequence and target control scale; and construct the resource control strategy based on the target operating parameters, the target control sequence and the target control scale.

[0186] Furthermore, the policy construction module is also used to: integrate the resource demand parameters to obtain the demand control scale of the first resource; in response to the maximum control scale of the first resource being able to meet the demand control scale, divide the first resource based on the resource type of the first resource to obtain at least one resource control scale; construct a control cost function based on the unit control cost corresponding to the resource type and the first resource set; adjust the initial control order and the initial control scale based on the control cost function to obtain the target control order and the target control scale.

[0187] Furthermore, the first operating parameter includes at least: a first power parameter corresponding to the power supply side in the distribution network, and a first load parameter corresponding to the load side. The above-mentioned device also includes: a state determination module, which is used to determine the new energy operating state on the power supply side based on the first power parameter and determine the equipment operating state on the load side based on the first load parameter in response to the maximum control scale of the first resource not being able to meet the demand control scale; a strategy acquisition module, which is used to obtain a load adjustment strategy that matches the new energy operating state and the equipment operating state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operating state, the equipment operating state and the load adjustment strategy, and the load adjustment strategy is used to control the maximum control scale of the first resource to meet the demand control scale; a strategy execution module, which is used to execute the load adjustment strategy.

[0188] Furthermore, the policy construction module is also used to: obtain the regional resource scale of the first resource in different power supply areas, and the user regulation perception parameters corresponding to different power supply areas, wherein the user regulation perception parameters are used to reflect the perception ability of users in the power supply area when regulating the first resource; determine the resource regulation level based on the first resource scale and the user regulation perception parameters.

[0189] Furthermore, the parameter prediction module is also used to: monitor the current operating parameters of the distribution network to obtain a second operating parameter; based on the second operating parameter, determine the resource usage parameters of the distribution network at different operating sides; obtain the grid impact parameters of the distribution network, wherein the grid source impact parameters are used to characterize the parameters that will affect the resource allocation of the distribution network; based on the resource usage parameters and the grid impact parameters, determine the first operating parameters.

[0190] Furthermore, the parameter determination module is also used to: obtain the area type of the power supply area and the season type of the future time period; based on the area type and the season type, determine the operating scenario of the power supply area in the future time period, wherein the power supply scenario includes at least one of the following: normal operation scenario, heavy overload scenario and new energy consumption scenario; based on the operating scenario and the first operating parameter, determine the resource demand parameter.

[0191] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention when running.

[0192] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0193] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0194] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0195] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0196] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0198] The units described as separate components may or may not be physically separate, and 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 units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0199] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0200] If the integrated unit is implemented in the form of 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 the present invention, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0201] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A distribution network control method, characterized in that: include: Predicting the operating parameters of the distribution network in a future time period based on the current operating parameters of the distribution network to obtain first operating parameters; Determining, based on the first operating parameter, a resource demand parameter of a power supply region for a first resource in the distribution network in the future time period, wherein the first resource is used to represent a resource that can be regulated in the distribution network, and the power supply region is used to represent a region that requires power resources provided by the distribution network; constructing a resource control policy for the first resource based on the resource demand parameter and the resource control condition, wherein the resource control condition is at least used to control a resource control cost of the resource control policy when executing the resource control policy; regulating the first resource based on the resource regulation policy; Wherein, based on the resource demand parameter and the resource control condition, constructing a resource control strategy for the first resource, including: obtaining the resource control level of the first resource in different power supply areas, and the resource impact parameter corresponding to the first resource, wherein the resource impact parameter is used to characterize the parameter that affects the usage of the first resource; constructing an initial control strategy based on the resource control level, the resource impact parameter and the resource demand parameter; adjusting the initial control strategy based on the resource control condition to obtain the resource control strategy; Constructing an initial control strategy based on the resource control level, the resource impact parameter, and the resource demand parameter, including: determining initial operating parameters of the distribution network when the first resource is controlled based on the resource demand parameter; determining an initial control sequence and an initial control scale when controlling different first resources based on the resource control level and the resource impact parameter; and constructing the initial control strategy based on the initial operating parameters, the initial control sequence, and the initial control scale; The first operating parameter includes at least: a first power parameter corresponding to the power supply side in the distribution network, and a first load parameter corresponding to the load side. The method also includes: integrating the resource demand parameters to obtain the demand control scale of the first resource; in response to the maximum control scale of the first resource not being able to meet the demand control scale, determining the new energy operating state on the power supply side based on the first power parameter, and determining the equipment operating state on the load side based on the first load parameter; obtaining a load adjustment strategy that matches the new energy operating state and the equipment operating state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operating state, the equipment operating state and the load adjustment strategy, and the load adjustment strategy is used to control the maximum control scale of the first resource to meet the demand control scale; and executing the load adjustment strategy.

2. The method according to claim 1, characterized in that The resource control conditions include at least: distribution network operating conditions and control cost conditions. The initial control strategy is adjusted based on the resource control conditions to obtain the resource control strategy, including: Adjusting the initial operating parameters based on the distribution network operating conditions to obtain target operating parameters; Adjusting the initial control sequence and the initial control scale based on the control cost condition to obtain a target control sequence and a target control scale; The resource regulation strategy is constructed based on the target operating parameters, the target regulation order, and the target regulation scale.

3. The method according to claim 2, characterized in that Adjusting the initial control sequence and the initial control scale based on the control cost condition to obtain a target control sequence and a target control scale includes: In response to the maximum control scale of the first resource being able to meet the required control scale, dividing the first resource based on the resource type of the first resource to obtain at least one resource control scale; Constructing a control cost function based on the unit control cost corresponding to the resource type and the first resource set; The initial control sequence and the initial control scale are adjusted based on the control cost function to obtain the target control sequence and the target control scale.

4. The method according to claim 1, wherein Obtaining a resource regulation level of the first resource in different power supply areas includes: Obtaining regional resource scales of the first resource in different power supply areas, and user regulation perception parameters corresponding to different power supply areas, wherein the user regulation perception parameters are used to reflect the perception ability of users in the power supply area when regulating the first resource; The resource regulation level is determined based on the first resource scale and the user regulation perception parameter.

5. The method according to claim 1, wherein Predicting the operating parameters of the distribution network in a future time period based on the current operating parameters of the distribution network to obtain first operating parameters includes: Monitoring current operating parameters of the distribution network to obtain second operating parameters; determining resource usage parameters of the distribution network at different operating sides based on the second operating parameters; Acquiring a grid impact parameter of the distribution network, wherein the grid impact parameter is used to characterize a parameter that may affect resource allocation of the distribution network; The first operating parameter is determined based on the resource usage parameter and the grid impact parameter.

6. The method according to claim 1, characterized in that Determining, based on the first operating parameter, a resource demand parameter of the power supply area for the first resource in the distribution network in the future time period includes: Obtaining the regional type of the power supply area and the seasonal type of the future time period; Based on the region type and the season type, determining an operation scenario of the power supply region in the future time period, wherein the operation scenario includes at least one of the following: a normal operation scenario, a heavy overload scenario, and a new energy consumption scenario; The resource requirement parameter is determined based on the operation scenario and the first operation parameter.

7. A distribution network control device, characterized in that: include: a parameter prediction module, configured to predict the operating parameters of the distribution network in a future time period based on the current operating parameters of the distribution network to obtain a first operating parameter; a parameter determination module, configured to determine, based on the first operating parameter, a resource demand parameter of a power supply region for a first resource in the distribution network in the future time period, wherein the first resource is used to represent a resource that can be regulated in the distribution network, and the power supply region is used to represent an area that requires power resources provided by the distribution network; a policy construction module, configured to construct a resource control policy for the first resource based on the resource demand parameter and the resource control condition, wherein the resource control condition is at least used to control a resource control cost of the resource control policy when it is executed; A resource control module, configured to control the first resource based on the resource control policy; The strategy construction module is further configured to: obtain the resource control level of the first resource in different power supply areas, and the resource impact parameter corresponding to the first resource, wherein the resource impact parameter is used to characterize the parameter that affects the usage of the first resource; construct an initial control strategy based on the resource control level, the resource impact parameter, and the resource demand parameter; and adjust the initial control strategy based on the resource control condition to obtain the resource control strategy; The strategy construction module is further configured to: determine, based on the resource demand parameters, initial operating parameters of the distribution network when regulating the first resource; determine, based on the resource regulation level and the resource impact parameter, an initial regulation order and an initial regulation scale when regulating different first resources; and construct the initial regulation strategy based on the initial operating parameters, the initial regulation order, and the initial regulation scale; The first operating parameter includes at least: a first power parameter corresponding to the power supply side in the distribution network, and a first load parameter corresponding to the load side. The device is also used to: integrate the resource demand parameters to obtain the demand control scale of the first resource; in response to the maximum control scale of the first resource not being able to meet the demand control scale, determine the new energy operating state on the power supply side based on the first power parameter, and determine the equipment operating state on the load side based on the first load parameter; obtain a load adjustment strategy that matches the new energy operating state and the equipment operating state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operating state, the equipment operating state and the load adjustment strategy, and the load adjustment strategy is used to control the maximum control scale of the first resource to meet the demand control scale; and execute the load adjustment strategy.

8. An electronic device, characterized in that: include: a memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 6 when running.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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