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

Abstract

The invention discloses a power distribution network regulation and control method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: predicting an operation parameter of the power distribution network in a future time period based on a current operation parameter of the power distribution network to obtain a first operation parameter; based on the first operation parameter, determining a resource demand parameter of a power supply area for a first resource in the power distribution network in a future time period, the first resource being used for representing a resource capable of being regulated and controlled in the power distribution network, and the power supply area being used for representing an area requiring the power distribution network to provide power resources; based on the resource demand parameter and a resource regulation condition, constructing a resource regulation strategy of the first resource, the resource regulation condition being at least used for controlling a resource regulation cost when the resource regulation strategy is executed; and regulating and controlling the first resource based on the resource regulation and control strategy. The technical problem that the resource potential release effect is poor when flexible load resources are regulated and controlled through a traditional method is solved.

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 rate of distributed renewable energy represented by wind power and photovoltaic power in the distribution network continues to increase, the diversified loads in the distribution network, such as air conditioning cooling, electric heating, electric vehicle charging and energy storage, have also significantly increased the complexity and uncertainty of the distribution network. These loads have certain adjustability and energy storage capabilities, collectively referred to as flexible load resources, and they show great potential in power grid regulation.

[0003] However, the current distribution network control technology fails to effectively utilize the adjustable potential of flexible load resources such as air-conditioning cooling load, electric heating load, electric vehicle charging load 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 solution has been proposed yet. 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 effect 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 the 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 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; 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 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 the 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, the resource impact parameters and the resource demand parameters, an initial control strategy is constructed; and based on the resource control conditions, the initial control strategy is adjusted to obtain the resource control strategy.

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

[0009] Furthermore, the resource control conditions at least include: 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 conditions to obtain the target control sequence and the target control scale; and constructing the resource control strategy based on the target operating parameters, the target control sequence and the 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, 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; and adjusting 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.

[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 being unable to meet the demand control scale, determining the new energy operation state on the power supply side based on the first power parameter, and determining the equipment operation state on the load side based on the first load parameter; obtaining a load adjustment strategy matching the new energy operation state and the equipment operation state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operation state, the equipment operation 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 power 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; based on the resource usage parameters and the power grid influence parameters, determining the first operating 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 in the future time period; based on the regional type and the season type, determining the operating scenario in which the power supply area will be 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 parameters and the resource control conditions, wherein the resource control conditions are at least used to control the resource control cost of the resource control strategy when it is executed; 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, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is 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 the 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 the 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; based on the resource control strategy, the first resource is controlled, and 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 and the actual control requirements have a high degree of matching, so as to ensure that when the resource control strategy constructed by the above-mentioned resource demand parameters and resource control conditions is used for resource control, not only the actual control requirements can be met, but also the control cost of the flexible load resources can be minimized, so that when the differentiated scenario control strategy is formulated, low-cost and high-efficiency control of flexible load resources is achieved, an effective distribution network optimization methodology is formed, and then the technical problem of poor effect of releasing resource potential when the traditional method is used to control flexible load resources is solved. 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 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 scheme of the present invention, the technical scheme 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 described embodiments 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 creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification 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 data 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 that are 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 is a flow chart of a distribution network control method according to an embodiment of the present invention. Figure 1 As shown, the method comprises 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 accomplished by 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 in the future time period.

[0032] In an optional embodiment, taking into account that the load and output of the power system are affected by many 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, in order to improve the accuracy of the predicted first operating parameter, the control system can make predictions based on a prediction model pre-trained by a machine learning algorithm. Before making data predictions, 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 operating parameters as inputs to the above prediction model, and output the prediction results of the operating parameters for the future time period based on the model, that is, output the above first operating parameters.

[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 supply 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 in the distribution network. Then, the control system can use the above-mentioned operating parameters as inputs to the above-mentioned hybrid prediction model. The above-mentioned hybrid prediction model uses the simulation model of the power system as the basis for prediction, and then combines the machine learning model for error correction, 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, the operating parameters on the grid side may include the importance of users and historical operating data, which can be obtained by analyzing the topological structure of the regional distribution network. The operating parameters on the power supply side may include the output characteristics of new energy sources, which can be obtained by analyzing the historical operating data of new energy sources in the current region. The operating parameters on the load side may include the scale and adjustable potential of flexible load resources, which can be obtained by analyzing the user access situation in the current region. The operating parameters on the energy storage side may include the access location, scale and capacity of the energy storage resources, which can be obtained by analyzing the access situation of the energy storage resources.

[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 where the distribution network needs to provide power resources, 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, which 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 in the future time period is too high. 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 first operating parameter, it can be inferred that the park distribution network will have excess new energy output in the future period. At this time, the resource demand parameter can be: by regulating the first resource, the park's new energy consumption will reach 2000kW in the future period.

[0043] Step S106: 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 when the resource control strategy is executed.

[0044] The resource control conditions may refer to the conditions that the generated resource control strategy needs to meet. The resource control strategy may be a control scheme for distribution network resources in different operating scenarios. For example, for a scenario where the distribution network load is too high, the resource control strategy may be used to control various types of distribution network resources to reduce the load of the distribution network. For another example, for a scenario where there is an excess of new energy in the distribution network, the resource control strategy may be used to control various types of distribution network resources to increase the amount of new energy consumed. 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 the 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, and the unit regulation cost relationship of each resource included in the first resource may be as shown in the following formula:

[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 period, and 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 new energy consumption of the park will reach 2000kW in the future 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 as shown in the following formula:

[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 period, by regulating the park's energy storage resources, 2000kW of new energy in the park can be consumed to meet the above resource demand parameters and the total control cost does not exceed 1,000 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 and its control scale that need to be regulated 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 satisfying the above-mentioned resource demand parameters, so as to achieve 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 that the overall response time of resource regulation is the lowest, and the unit response time relationship of each resource included in the first resource may be as shown in the following formula:

[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 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 new energy consumption of the park will reach 3100kW in the future time period. The resource regulation condition may be to ensure that the overall response time of resource regulation is minimized. The unit response time of each resource included in the first resource may be 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 period, by adjusting 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 strategy.

[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 can be: reduce the cooling load of urban air conditioners by 500kW in the future time period, and reduce the charging load of urban electric vehicles by 500kW. The control system implements the resource control strategy, implements rotation stop control on the decentralized air conditioners of residents in the urban area, and raises the air conditioner temperature from 24℃ to 26.7℃, thereby reducing the cooling load of urban air conditioners by 500kW. At the same time, the control system controls the charging load of electric vehicles, that is, through the price policy, under the condition of user confirmation, when the electric vehicle power exceeds 50%, the charging load of the electric vehicle is interrupted, thereby reducing the charging load of urban electric vehicles by 500kW.

[0062] For another example, the resource control strategy may be: reduce the rural electric heating load by 800kW in the future period. The control system executes the resource control strategy, implements rotation control on the decentralized heating of rural residents, and reduces the decentralized heating temperature from 24.6℃ to 17.6℃, thereby reducing the rural electric heating load by 800kW.

[0063] For another example, the resource control strategy can be: in the future time period, by regulating the energy storage resources of the park, 2000kW of new energy in the park is consumed, and at the same time, the charging load of electric vehicles is regulated to consume 1500kW of new energy in the park. The control system executes the above resource control strategy to regulate the energy storage resources of public buildings in the park, that is, by coordinating and optimizing charging, the charging power is increased, thereby consuming 2000kW of new energy in the park. At the same time, by increasing the power and increasing the simultaneous rate, the charging power is increased through the charging module for low-power charging vehicles with less than 50% power in the park, thereby consuming 1500kW of new energy in the park.

[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 the 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; based on the resource control strategy, the first resource is controlled, and 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 and the actual control requirements have a high degree of matching, so as to ensure that when the resource control strategy constructed by the above-mentioned resource demand parameters and resource control conditions is used for resource control, not only the actual control requirements can be met, but also the control cost of the flexible load resources can be minimized, so that when the differentiated scenario control strategy is formulated, low-cost and high-efficiency control of flexible load resources is achieved, an effective distribution network optimization methodology is formed, and then the technical problem of poor effect of releasing resource potential when the traditional method is used to control flexible load resources is solved.

[0065] Furthermore, based on the resource demand parameters and the 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, the resource impact parameters and the resource demand parameters, an initial control strategy is constructed; and based on the resource control conditions, the initial control strategy is adjusted to obtain the resource control strategy.

[0066] The resource regulation level may be a classification of different degrees of regulation of resources. For example, the resource regulation level may include: general peak regulation level (peak shaving), deep peak regulation level (peak shaving), emergency guarantee level (peak shaving), new energy consumption (valley filling), but not limited thereto. The resource influencing parameters may include at least one or more of the following: human body temperature sensitivity, green travel characteristics, and demand response cost, but not limited thereto.

[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 the 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 levels (peak shaving), deep peak shaving levels (peak shaving), emergency guarantee levels (peak shaving), and new energy consumption (valley filling), and can divide the regulation levels of the electric vehicle charging load into general peak shaving levels (peak shaving), deep peak shaving levels (peak shaving), emergency guarantee levels (peak shaving), general peak shaving levels (valley filling), and deep peak shaving levels (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 the temperature sensitivity of the human body. 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 first resource in different power supply areas, and then based on the resource control level, resource impact parameters and resource demand parameters of the 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 initial control strategy meets the above resource demand parameters, it does not meet the above resource control conditions. Therefore, based on the above 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 conditioners of users in the urban area to reduce the load of 800kW in the urban distribution network. At the same time, the general peak shaving level (peak shaving) of the slow charging pile for charging electric vehicles is adopted to reduce the load of 200kW in the urban distribution network. This resource control strategy meets the above resource demand parameters and meets the above resource control conditions.

[0077] For another example, the resource demand parameter may be: by regulating the first resource, the load of the park distribution network is reduced 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 first resource in different power supply areas, and then based on the resource control level, resource impact parameter and resource demand parameter of the different resources, the determined initial control strategy can be: adopt the emergency guarantee level (peak shaving) of energy storage resources to reduce the load of 1200kW of the urban distribution network. At this time, although the above initial control strategy meets the above resource demand parameters, it does not meet the above resource control conditions. Therefore, based on the above 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 public buildings to reduce the load of 900kW of the park distribution network. At the same time, 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 are adopted to reduce the load of 300kW of the park distribution network. The resource control strategy meets the above resource demand parameters and meets the above resource control conditions.

[0082] For another example, the resource demand parameter may be: by regulating the first resource, the new energy consumption of the park will reach 2000kW in the future time period. The resource impact parameter may be 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 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 regulation level, resource impact parameters and resource demand parameters, an initial regulation strategy is constructed, including: based on the resource demand parameters, determining the initial operating parameters of the distribution network when regulating the first resource; based on the resource regulation level and resource impact parameters, determining the initial regulation order and initial regulation scale when regulating different first resources; based on the initial operating parameters, the initial regulation order and the initial regulation scale, constructing an initial regulation strategy.

[0088] The above-mentioned initial operating parameters may be operating parameters of the distribution network when regulating the first resource. For example, the above-mentioned initial operating parameters may include node voltage, branch current, etc., but are not limited thereto. The above-mentioned initial regulation order may refer to the order in which operations are performed on different resources in the above-mentioned initial regulation strategy. The above-mentioned initial regulation scale may be the execution scope of the above-mentioned 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 regulated based on the current resource demand parameters by using an analysis method. Then, based on the above 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 regulation, and at the same time, use the resources that deviate more from the resource impact parameters as the resources for subsequent regulation, so as to determine 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 resource control level and resource impact parameters, so that the overall control range of all resources meets the above resource control needs. Finally, the control system can construct an initial control strategy based on the above node voltages and branch currents, as well as the determined control order and control scale.

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

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

[0092] In order to construct the initial control strategy, the control system can first use the Newton-Raphson method to calculate the voltage of each node and the current flowing through each branch. Then, the control system can determine the initial control order and control scale when regulating different first resources based on the above resource control level and resource impact parameters. The control order and control scale can be: the general peak-shaving level (peak shaving) of energy storage resources is 500kW, the deep peak-shaving level (peak shaving) of energy storage resources is 1000kW, the general peak-shaving level (peak shaving) of slow charging piles for electric vehicle charging load is 600kW, the deep peak-shaving level (peak shaving) of slow charging piles for electric vehicle charging load is 1000kW, the emergency guarantee level (peak shaving) of slow charging piles for electric vehicle charging load is 1500kW, the general peak-shaving level (peak shaving) of central air conditioning in public buildings for air conditioning cooling load is 300kW, and the general peak-shaving level (peak shaving) of central air conditioning in public buildings for electric heating load is 350kW. Finally, based on the above initial operating parameters, initial control order and initial control scale, an initial control strategy is constructed. That is, the control system controls the above different types of resources in turn based on the calculated node voltage and branch current, according to the above resource control order 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 regulation level may be as shown in Table 1. The resource impact parameter may be a demand response cost. The magnitude relationship of the response costs of the different resources may be as follows:

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

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

[0096] Furthermore, the resource control conditions at least include: 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 conditions to obtain the target control sequence and the target control scale; and constructing the resource control strategy based on the target operating parameters, the target control sequence and the 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, power grid operation boundary constraints, distributed power source output constraints, and flexible load resource regulation constraints, but are not limited to these.

[0098] Specifically, the system steady-state operation constraints may at least include node voltage not exceeding the limit and branch current not exceeding the limit. The node voltage not exceeding the limit 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 scheduling, For any node i.

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

[0102]

[0103] In the formula, It represents the upper limit of the current of branch j. Affected by the line specifications, the upper limits of the currents of different branches in the system vary. represents the current of branch j at time t in scheduling, Represents 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 new energy generation does not cause power flow reverse. 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 the flow to reverse 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 110 kV power grid at time t. It should be noted that here only 110 kV is limited to no transmission.

[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 the 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 may 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 may 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 For 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 above-mentioned control cost condition may refer to the condition for controlling the resource control cost when the resource control strategy is executed. The above-mentioned target operating parameters may be the operating parameters adjusted based on the above-mentioned distribution network operating conditions. The above-mentioned target control order may refer to the resource control order after the initial control order is adjusted based on the control cost condition. The above-mentioned target control scale may refer to the resource control scale after the initial control order is adjusted based on the control cost condition.

[0129] In an optional embodiment, in order to regulate flexible load resources with 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, power 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 above-calculated node voltages and branch currents based on the above-mentioned system steady-state operation constraints, so that each node voltage satisfies the above-mentioned node voltage within the limit, and each branch current satisfies the above-mentioned branch current within the limit. The control system can adjust the load demand of each level of 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 power grid meets the above-mentioned power grid operation without overloading, and the output power of various new energy equipment meets the above-mentioned new energy power generation without causing power flow reverse transmission. 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 source output constraints, so that the output power of photovoltaic power generation equipment meets the photovoltaic output constraint, and the output power of wind power generation equipment meets 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 load is 500kW, the general peak-shaving level (valley filling) of fast charging piles for electric vehicle charging load is 600kW, the deep peak-shaving level (valley filling) of fast charging piles for electric vehicle charging load is 1200kW, the new energy consumption (valley filling) of central air-conditioning in public buildings for air-conditioning cooling load 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 load is 2300kW. The unit regulation cost relationship of the above-mentioned various types of flexible load resources can be shown 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, 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; and adjusting 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.

[0137] The above-mentioned demand control scale may refer to the total amount of resources that need to be controlled to achieve the control target. The above-mentioned maximum control scale may be the maximum amount of all resources that can be controlled in the current area. The above-mentioned control cost function may refer to a calculation method for achieving the lowest cost of flexible load resource control under the premise 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 adjustable 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 level 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. 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 It indicates the regulation scale of energy storage at the levels of general peak regulation, deep peak regulation, emergency guarantee and new energy consumption. It should be noted that general peak regulation, deep peak regulation and emergency guarantee regulation occur in the load peak reduction scenario, which is the process of load reduction (energy storage discharge), while new energy consumption regulation occurs in the load valley filling scenario, which is the 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 above 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 can adjust the initial control order and initial control scale based on a preset adjustment formula to ensure that the execution cost of the adjusted target control order and target control scale meets the control cost condition, wherein the adjustment formula can 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 being unable to meet the demand control scale, determining the new energy operation state on the power supply side based on the first power parameter, and determining the equipment operation state on the load side based on the first load parameter; obtaining a load adjustment strategy matching the new energy operation state and the equipment operation state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operation state, the equipment operation 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 the output characteristic of the new energy power generation equipment, but is not limited thereto. The first load parameter may be the flexible load resource scale and adjustable potential, but is not limited thereto. The preset strategy table may be a table that illustrates the mapping relationship between the operating status of the new energy equipment, the equipment operating status, and the load adjustment strategy, 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 required 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 operation status Equipment operating status Load adjustment strategy Excessive output Lower load Abandoning wind and solar Insufficient contribution Too high 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 the new energy operation status on the power supply side as insufficient output 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 as overload 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 goals, thereby ensuring the safe and stable operation of the power grid.

[0155] For another 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 excess 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 low load 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 wind and solar power abandonment plan, that is, the control system can generate a wind and solar power abandonment removal list, abandon some wind power and photovoltaic energy to achieve the control goals, thereby ensuring 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 can 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 above air-conditioning load is small, the control boundary is within the range of 24-26.7℃, and the user cannot perceive the execution of the control strategy. The resource control level of the above control boundary is the general peak-shaving level. When the control scale of the cooling load of the residents' decentralized air-conditioning is further increased, the control boundary is 24-26.7℃, and the user still cannot perceive the execution of the control strategy. The resource control level of the above control boundary is the deep peak-shaving level of the residents' decentralized air-conditioning. When the control scale of the cooling load of the air-conditioning of the above public buildings is further increased, the control boundary is 29℃, and the user can just feel the execution of the control strategy. The resource control level of the above control boundary is the deep peak-shaving level of the decentralized air-conditioning and central air-conditioning of public buildings. When the control scale of the cooling load of the decentralized air-conditioning of the residents increases again, the control boundary is 29℃, and the user can just 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 of the residents. 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 regulation demand is to absorb new energy, when the regulation boundary of the air-conditioning cooling load is at 22-24℃, 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 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 regulation demand is to reduce the load, when the regulation scale of the above-mentioned electric heating load is small, the regulation boundary is within the range of 17.6-24.6℃, and the user cannot perceive the execution of the regulation strategy. The resource regulation level of the above-mentioned regulation boundary is the general peak-shaving level of the above-mentioned electric heating load. When the regulation scale of the above-mentioned electric heating load is further increased, the regulation boundary is within the range of 13.6-17.6℃, and the user can just perceive the execution of the regulation strategy. The resource regulation level of the above-mentioned regulation boundary is the deep peak-shaving level of the above-mentioned electric heating load. When the regulation scale of the above-mentioned electric heating load is increased again, the regulation boundary is within the range of 10.0-13.6℃, ​​and the user can clearly perceive the execution of the regulation strategy. The resource regulation level of the above-mentioned regulation 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 regulation demand is to reduce the load, when the regulation scale of the electric vehicle charging load is small, the regulation boundary is full power, and the user cannot perceive the execution of the regulation strategy. The resource regulation level of the above regulation boundary is the general peak-shaving level. When the regulation scale of the electric vehicle charging load of the slow charging pile is further increased, the regulation boundary is the load interruption when the power exceeds 50%, and the user can just perceive the execution of the regulation strategy. The resource regulation level of the above regulation boundary is the deep peak-shaving level of the slow charging pile. When the regulation scale of the electric vehicle charging load of the fast charging pile is further increased, the regulation boundary is the charging power reduction when the power exceeds 30%, and the user can just perceive the execution of the regulation strategy. The resource regulation level of the above regulation boundary is the deep peak-shaving level of the fast charging pile. When the regulation scale of the electric vehicle charging load of the slow charging pile increases again, the regulation boundary is the full interruption load, and the user can clearly feel the execution of the regulation strategy. The resource regulation level of the above regulation boundary is the emergency guarantee level of the slow charging pile. When the regulation scale of the electric vehicle charging load of the fast charging pile increases again, the regulation boundary is that the load is interrupted when the power exceeds 30%. Users can clearly feel the execution of the regulation strategy. The resource regulation level of the above regulation boundary is the emergency guarantee level of the fast charging pile.

[0167] If the current regulation demand is to absorb new energy, when the regulation boundary of the electric vehicle charging load of the slow charging pile is to remind charging when the power is less than 50%, the user can feel the execution of the regulation strategy, and the resource regulation level of the above regulation boundary is the new energy absorption level of the slow charging pile. When the regulation boundary of the electric vehicle charging load of the fast charging pile is to increase the charging power when the power is less than 50%, the user can feel the execution of the regulation strategy, and the resource regulation level of the above regulation boundary is the new energy absorption level of the fast charging pile.

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

[0169] If the current regulation demand is to reduce the load, when the energy storage resources are discharged steadily, the energy storage resource regulation level at this time is the general peak-shaving level. When the energy storage resources further increase the discharge power, the overall load rate within the optimization range will be predicted through ultra-short-term load forecasting, and the peak and valley conditions of the load curve will be coordinated and optimized. At this time, the energy storage resource regulation level is the deep peak-shaving level. When the energy storage resources increase the charging power again, the energy storage capacity, fault range and capacity demand control flow will be considered to achieve coordinated optimization. At this time, the energy storage resource regulation level is the emergency guarantee level.

[0170] If the current regulation demand is to absorb new energy, the energy storage resources will achieve collaborative optimization by controlling the power flow through short-term load forecasting and according to the new energy absorption demand. 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 power grid influence parameters of the distribution network, wherein the power grid influence parameters are used to characterize parameters that will affect the resource allocation of the distribution network; based on the resource usage parameters and the power grid influence parameters, determining the first operating parameters.

[0172] The second operating parameter may be a factor used to predict the operating state of the distribution network in the 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, an operating state parameter on the energy storage side, but 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 not limited thereto.

[0173] In an optional embodiment, in order to improve the accuracy of the prediction results, the control system can monitor based on the current operating parameters of the distribution network, and obtain the operating status parameters of the grid side, the operating status parameters of the power supply side, the operating status parameters of the load side, and the operating status parameters of the energy storage side. Then, the control system can analyze the above operating parameters and obtain the resource usage parameters of the above different operating parameters. For example, for the operating status parameters of the grid side, the control system can obtain the importance and historical operating data of the user by analyzing the topological structure of the regional distribution network. For the operating status parameters of the power supply side, the control system can obtain the output characteristics of the new energy by analyzing the historical operating data of the new energy in the current area. For the operating status parameters of the load side, the control system can obtain the scale and adjustable potential of the flexible load resources by analyzing the user access situation in the current area. For the operating status parameters of the energy storage side, the control system can obtain the access location, scale and capacity of the energy storage resources by analyzing the access situation of the energy storage resources. The control system can also obtain the grid impact parameters of the distribution network. For example, the control system can obtain that the future time period is on holidays, so the charging load of electric vehicles may be overloaded. For another example, the control system can obtain that the weather in the future time period is very hot, so the air conditioning cooling load may be overloaded. For another example, the control system can obtain that the current electricity price is high, so the regional electricity consumption in the future time period may be reduced. Finally, based on the above resource usage parameters and grid influencing parameters, the control system can predict the prediction results of the operating parameters in the future time period, that is, predict the above 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 in the future time period; based on the regional type and the season type, determining the operating scenario in which the power supply area will be 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, in order to make the resource regulation strategy more refined, the regulation system can first obtain the current power supply area type and the season type in the future time. Then, based on the above power supply area type and season type, in the normal operation scenario, the heavy overload scenario and the new energy consumption scenario, match the operation scenario that best meets the current power supply area characteristics and seasonal characteristics, and use the operation scenario as the operation scenario of the current power supply area in the future time period. Finally, the regulation system determines the resource demand parameter based on the above operation scenario and the above first operation 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. Since the load density and power supply reliability requirements in urban areas are generally high, and the utilization rate of air conditioning cooling loads is high in summer, it is determined that the urban area will be in a heavy overload scenario in the future time period based on the two factors of urban area and summer. Based on the above-mentioned heavy overload scenario and the first operating parameter, the control system can determine the resource demand parameters as follows: regulate the decentralized air conditioning of urban residents, the decentralized air conditioning of urban public buildings, and the central air conditioning of urban public buildings to reduce the load of 2000kW in the urban area.

[0177] For another example, the power supply area 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, so based on the two factors of rural and winter, it can be determined that rural areas will be in a heavy overload scenario in the future period. Based on the above-mentioned 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 power supply area type obtained by the control system may be a park, and the season type obtained may be spring. Since the load density of the park is high and the power supply reliability is very high, and the utilization rate of air conditioning cooling load and electric heating load is low in spring, based on the two factors of the park and spring, it can be determined that the park will be in a new energy consumption scenario in the future time period. Based on the above new energy consumption scenario and the first operating parameter, the control system can determine the resource demand parameter as follows: regulate the park energy storage resources to consume 1000kW of new energy load.

[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 in the figure, firstly, the distribution network operation status is monitored and analyzed to obtain the scale of regional flexible load resources, source-grid-load-storage scale and historical data, grid structure and user level. Based on the scale of regional flexible load resources, the resource regulation level and boundary are clarified. Based on the scale of source-grid-load-storage and historical data, combined with influencing factors such as holidays, weather, and time-of-use electricity prices, the source-load state is predicted to obtain the prediction results. Based on the prediction results, combined with seasonal factors and power supply areas, the regulation scenarios are divided into normal operation scenarios, heavy overload scenarios, and new energy consumption scenarios, and the regulation requirements under different scenarios are further clarified, including the regulation time section and regulation scale. Then, combined with the above-mentioned resource regulation levels, regulation boundaries and regulation requirements, the resource regulation potential is analyzed. The analysis process needs to consider factors such as the scale of adjustable resources, regulation response time, and economic feasibility. If the analysis results show that the regional regulation potential meets the demand, then combined with the demand response cost, safe supply, economic efficiency, green and low-carbon characteristics, the distribution network regulation model of flexible load resources is used to generate the optimal regulation plan. If the analysis results show that the regional regulation potential cannot meet the demand, it is necessary to carry out load shedding and wind and solar abandonment plans. 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 is a schematic diagram of a distribution network control device according to an embodiment of the present invention. Figure 3 As shown, the device comprises:

[0182] The parameter prediction module 302 is used to predict the operating parameters of the distribution network in the 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 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. The strategy construction module 306 is used to construct a resource regulation strategy for the first resource based on the resource demand parameters and the resource regulation conditions, wherein the resource regulation conditions are at least used to control the resource regulation cost of the resource regulation strategy when it is executed. The resource regulation module 308 is used to regulate the first resource based on the resource regulation 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 order and the initial control scale based on the control cost conditions to obtain target control order and target control scale; and construct the resource control strategy based on the target operating parameters, the target control order and the target control scale.

[0186] Furthermore, the strategy 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 at least includes: 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 operation state of the power supply side based on the first power parameter in response to the maximum control scale of the first resource being unable to meet the demand control scale, and determine the equipment operation state of the load side based on the first load parameter; a strategy acquisition module, which is used to obtain a load adjustment strategy matching the new energy operation state and the equipment operation state from a preset strategy table, wherein the preset strategy table is used to store the mapping relationship between the new energy operation state, the equipment operation 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 influence parameters of the distribution network, wherein the grid source influence 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 influence 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: a normal operation scenario, a heavy overload scenario, and a 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 in 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 emphasis. 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 schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, 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 separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on 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, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of 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, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a 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 and other 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 principle of the present invention. These improvements and modifications should also be regarded as 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; Based on the first operating parameter, determining 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 a resource that can be regulated in the distribution network, and the power supply area is used to characterize an area that requires the distribution network to provide power resources; Based on the resource demand parameter and the resource control condition, construct a resource control strategy for the first resource, wherein the resource control condition is at least used to control the resource control cost of the resource control strategy when executing; The first resource is regulated based on the resource regulation policy.

2. The method according to claim 1, characterized in that Based on the resource demand parameter and the resource control condition, construct a resource control strategy for the first resource, including: Obtaining a resource control level of the first resource in different power supply areas and a resource impact parameter corresponding to the first resource, wherein the resource impact parameter is used to characterize a 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; The initial control strategy is adjusted based on the resource control condition to obtain the resource control strategy.

3. The method according to claim 2, characterized in that Based on the resource control level, the resource impact parameter and the resource demand parameter, an initial control strategy is constructed, including: Based on the resource demand parameter, determining initial operating parameters of the distribution network when the first resource is regulated; Determining an initial regulation order and an initial regulation scale when regulating different first resources based on the resource regulation level and the resource impact parameter; The initial control strategy is constructed based on the initial operating parameters, the initial control sequence and the initial control scale.

4. The method according to claim 3, characterized in that The resource control conditions at least include: distribution network operation 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 regulation order and the initial regulation scale based on the regulation cost condition to obtain a target regulation order and a target regulation scale; The resource regulation strategy is constructed based on the target operating parameters, the target regulation order and the target regulation scale.

5. The method according to claim 4, characterized in that The initial regulation order and the initial regulation scale are adjusted based on the regulation cost condition to obtain a target regulation order and a target regulation scale, including: Integrating the resource demand parameters to obtain a demand control scale of the first resource; In response to the maximum regulation scale of the first resource being able to meet the demand regulation scale, dividing the first resource based on the resource type of the first resource to obtain at least one resource regulation 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.

6. The method according to claim 5, characterized in that The first operating parameter at least includes: a first power supply parameter corresponding to the power supply side in the distribution network, and a first load parameter corresponding to the load side. The method further includes: In response to the maximum regulation scale of the first resource being unable to meet the demand regulation scale, determining the new energy operation state of the power supply side based on the first power supply parameter, and determining the equipment operation state of the load side based on the first load parameter; Obtaining a load adjustment strategy that matches the new energy operation state and the equipment operation state from a preset strategy table, wherein the preset strategy table is used to store a mapping relationship between the new energy operation state, the equipment operation state, and the load adjustment strategy, and the load adjustment strategy is used to control the maximum regulation scale of the first resource to meet the demand regulation scale; The load adjustment strategy is executed.

7. The method according to claim 2, characterized in that: Obtaining resource regulation levels of the first resource in different power supply areas includes: Acquire 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.

8. The method according to claim 1, characterized in that 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 the first operating parameters includes: Monitoring the current operating parameters of the distribution network to obtain a second operating parameter; Determining resource usage parameters of the distribution network at different operation sides based on the second operation parameters; Acquire a power grid influencing parameter of the distribution network, wherein the power grid influencing 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.

9. 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: Acquire 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, determining an operation 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 operation scenario, a heavy overload scenario, and a new energy consumption scenario; Based on the operation scenario and the first operation parameter, the resource requirement parameter is determined.

10. A distribution network control device, characterized in that: include: 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, configured to determine, based on the first operating parameter, 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 a resource that can be regulated in the distribution network, and the power supply area is used to characterize an area that requires the distribution network to provide power resources; A policy building module, configured to build 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 the resource control cost of the resource control policy when it is executed; A resource control module is used to control the first resource based on the resource control strategy.

11. 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 9 when running.

12. 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 9.

13. 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 9.

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