A power control method, device and system based on distributed energy

Abstract

The application provides a kind of power control method, equipment and system based on distributed energy, it is related to distributed energy control technical field, comprising: the region division is carried out according to preset size to the region to be handled, obtains several sub-regions to be handled;According to the preset regional division algorithm, the sub-regional reorganization is carried out to several sub-regions to be handled, obtains several collaborative control regions;The networking is carried out to all power generation equipment in each collaborative control region, to carry out power control to corresponding collaborative control region.Effect improves the operating efficiency and stability of distributed energy system, fundamentally solves the problem of uneven distribution of energy production and consumption.The application improves the operating efficiency and stability of distributed energy system, fundamentally solves the problem of uneven distribution of energy production and consumption.

Description

Technical Field

[0001] This application relates to the field of distributed energy control technology, and in particular to a power control method, device and system based on distributed energy. Background Technology

[0002] With the increasing global demand for clean energy, distributed energy systems have become a research hotspot and development direction in the energy field due to their advantages such as local power supply, high energy efficiency, and environmental friendliness. Distributed energy systems typically consist of multiple decentralized power generation devices (such as solar photovoltaic panels, wind turbines, and small hydropower stations) and numerous energy-consuming devices of various types (such as industrial electrical equipment and residential appliances), which are connected and interact through a complex network.

[0003] Currently, existing distributed energy power control technologies mainly rely on traditional centralized control strategies or simple local control methods. Centralized control strategies typically involve setting up a central control unit in the system to collect operational data from all power-generating and energy-consuming devices, and then distributing power according to a pre-set algorithm. However, this approach has many drawbacks: firstly, as the scale of distributed energy systems continues to expand, the number of devices and the amount of data grow exponentially, drastically increasing the computational pressure and communication burden on the central control unit, leading to delays in data processing and command issuance, making it difficult to quickly respond to real-time changes in the status of devices in the system; secondly, centralized control is highly dependent on the communication network, and once the communication link fails, the power distribution of the entire system will fall into chaos.

[0004] A simple local control method involves each region or device controlling itself independently based on its own energy supply and demand. While this approach reduces reliance on a central control unit and communication network to some extent, the lack of global coordination and optimization means that each region or device often makes decisions solely based on its own interests, failing to consider the overall operational status of the distributed energy system. For example, at certain times, some regions' energy-producing devices generate a large amount of electricity due to favorable conditions such as sunlight and wind, but due to the lack of an effective coordination mechanism, this surplus electricity cannot be promptly delivered to other regions with insufficient capacity, leading to energy waste. Conversely, in other regions, the electricity demand of energy-consuming devices cannot be met, affecting normal user operations. This uneven distribution of capacity, with some regions experiencing surplus and others insufficient, severely reduces the overall operational efficiency and stability of the distributed energy system, hindering its large-scale promotion and application. Therefore, an effective technical means is urgently needed to solve the problem of rational allocation between energy-producing and energy-consuming devices in distributed energy systems, achieving balanced power supply and efficient utilization. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a power control method, device, and system based on distributed energy resources, which at least partially solves the problems existing in the prior art.

[0006] In a first aspect of this application, a power control method based on distributed energy resources is provided, the method comprising: S100, the area to be processed is divided into several sub-areas according to a preset size, wherein any two sub-areas to be processed have the same size.

[0007] S200, according to a preset region division algorithm, several sub-regions to be processed are reorganized to obtain several collaborative control regions; wherein, each collaborative control region has a corresponding preset reorganization center; the preset region division algorithm reorganizes the sub-regions according to the matching degree of power production capacity characteristics, the matching degree of power consumption characteristics, the spatial distance, and the magnitude of the power production capacity-power consumption complementarity parameter between each sub-region to be processed and each preset reorganization center; the power production capacity-power consumption complementarity parameter represents the matching degree between the total power production capacity characteristics and the total power consumption characteristics between the sub-region to be processed and the preset reorganization center.

[0008] The S300 network connects all power generation equipment within each collaborative control area to control the power supply within that area.

[0009] In a second aspect of this application, a power control system based on distributed energy resources is provided, the system comprising: The first partitioning module is used to divide the area to be processed into several sub-regions according to a preset size; wherein any two sub-regions to be processed have the same size.

[0010] The second partitioning module is used to reorganize several sub-regions to be processed according to a preset region partitioning algorithm to obtain several collaborative control regions. Each collaborative control region has a corresponding preset reorganization center. The preset region partitioning algorithm reorganizes the sub-regions according to the matching degree of power production capacity characteristics, the matching degree of power consumption characteristics, the spatial distance, and the magnitude of the power production capacity-power consumption complementarity parameter between each sub-region to be processed and each preset reorganization center. The power production capacity-power consumption complementarity parameter represents the matching degree between the total power production capacity characteristics and the total power consumption characteristics between the sub-region to be processed and the preset reorganization center.

[0011] The network control module is used to network all power generation equipment in each collaborative control area in order to control the power of the corresponding collaborative control area.

[0012] In a third aspect of this application, an electronic device is provided for the aforementioned power control method based on distributed energy resources.

[0013] This application has at least the following beneficial effects: The power control method based on distributed energy provided in this application first divides the area to be processed into several sub-regions of the same size according to a preset size. Then, using a preset region division algorithm, the sub-regions are reorganized based on the matching degree of power production capacity characteristics, the matching degree of power consumption characteristics, the spatial distance, and the magnitude of the power production-consumption complementarity parameters between each sub-region and each preset reorganization center. This not only accurately grasps the essence of energy supply and demand in each sub-region but also solves the problem of power transmission caused by neglecting geographical factors. Sub-regions with similar or complementary energy supply and demand relationships are grouped together, greatly enhancing the synergistic complementarity of power production and consumption within the collaborative control area. Finally, the power production equipment within the collaborative control area is networked to achieve collaborative work under unified control. Power generation and power output are flexibly adjusted according to the overall energy consumption demand of the area, achieving precise power supply, avoiding overcapacity or undercapacity, effectively improving the operating efficiency and stability of the distributed energy system, and fundamentally solving the problem of uneven distribution of power production and consumption. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating the power control method based on distributed energy resources provided in this application embodiment; Figure 2 This is a structural block diagram of a power control system based on distributed energy resources provided in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any corresponding variations, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] It should be noted that the following description covers various aspects of embodiments within the scope of the appended claims. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0019] Please refer to Figure 1 As shown, an embodiment of this application provides a power control method based on distributed energy resources, the method comprising: S100, the area to be processed is divided into several sub-areas according to a preset size, wherein any two sub-areas to be processed have the same size.

[0020] Specifically, the area to be processed can be a city, containing several clean energy-based power generation devices, such as photovoltaic power generation devices and wind power generation devices. These clean energy-based power generation devices are distributed in different locations within the area to be processed. The area to be processed is divided into several sub-areas according to a preset size. For example, each sub-area is a 1km × 1km square sub-area. Several sub-areas are adjacent to each other, realizing the division of a large area to be processed. As the sub-areas are divided, the clean energy-based power generation devices are placed in different sub-areas.

[0021] S200, according to a preset region division algorithm, several sub-regions to be processed are reorganized to obtain several collaborative control regions; wherein, each collaborative control region has a corresponding preset reorganization center; the preset region division algorithm reorganizes the sub-regions according to the matching degree of power production capacity characteristics, the matching degree of power consumption characteristics, the spatial distance, and the magnitude of the power production capacity-power consumption complementarity parameter between each sub-region to be processed and each preset reorganization center; the power production capacity-power consumption complementarity parameter represents the matching degree between the total power production capacity characteristics and the total power consumption characteristics between the sub-region to be processed and the preset reorganization center.

[0022] Specifically, the preset region segmentation algorithm is an improved superpixel algorithm. A superpixel is a pixel block formed by merging adjacent pixels in an image that have similar color, texture, brightness, and other characteristics. Essentially, it is a kind of "oversegmentation" of the image (the segmentation granularity is finer than the actual target). In this embodiment, the improved superpixel algorithm treats each sub-region to be processed as a pixel. Based on the matching degree of power production capacity characteristics, matching degree of power consumption characteristics, spatial distance, and the magnitude of the power production capacity-consumption complementarity parameter between each sub-region to be processed and each preset recombination center, the sub-regions to be processed that have similar or complementary energy supply and demand relationships are clustered into one category.

[0023] It should be noted that in this embodiment, sub-region reorganization is performed not only based on the matching degree of power production capacity characteristics, power consumption characteristics, and spatial distance between each sub-region to be processed and each preset reorganization center, but also based on the power production capacity-power consumption complementarity parameter. This parameter represents the matching degree between the total power production capacity characteristics and total power consumption characteristics between the sub-region to be processed and the preset reorganization center. In other words, during the sub-region reorganization process, not only the similarity of power characteristics and production capacity characteristics between each sub-region to be processed and each preset reorganization center, but also the spatial distance are considered. Furthermore, the complementary nature of the capacity and energy consumption characteristics between each sub-region to be processed and each preset reorganization center is considered. Specifically, when a sub-region to be processed and a preset reorganization center are reorganized into a single region for joint control, if the similarity of their electrical characteristics is high and the spatial distance is small, it facilitates coordinated control of multiple devices. If the matching degree between the total electrical capacity and total electrical consumption characteristics of the resulting region is high, it indicates that after the sub-region to be processed and the preset reorganization center are reorganized into a single region, in addition to facilitating coordinated control, a relative supply-demand balance in the overall capacity and energy consumption of the newly formed region can also be achieved. Therefore, if, based on high similarity of electrical characteristics and small spatial distance, the matching degree between the total electrical capacity and total electrical consumption characteristics of the resulting region is high (i.e., the capacity-energy consumption complementarity parameter is small), then the final matching degree will be high. In another scenario, if the electrical energy characteristics are not similar and the spatial distance is large (i.e., the electrical energy characteristics of the sub-region to be processed and the preset reorganization center are dissimilar and far apart, making coordinated control difficult), and if the matching degree between the total electrical energy production capacity and total electrical energy consumption characteristics of the resulting region is large, it indicates that after the sub-region to be processed and the preset reorganization center are reorganized into one region, the overall production capacity and energy consumption of the newly formed region are in relative supply-demand balance. Alternatively, they can be clustered into one class. Specifically, clustering is controlled through a reasonable threshold. The sub-region reorganization method provided in this embodiment can achieve the most uniform energy distribution possible.

[0024] This embodiment determines the collaborative control region to which each sub-region to be processed belongs by jointly considering power generation characteristics, power consumption characteristics, spatial distance, and power generation-energy consumption complementarity parameters. It takes into account not only the distance between the sub-region to be processed and the preset reorganization center and energy similarity, but also energy complementarity. This allows for a balance between power generation and energy consumption as much as possible while minimizing the difficulty of controlling the equipment system within the final collaborative control region.

[0025] It is understood that the aforementioned preset reorganization center can be any one of the regions to be processed. The preset reorganization center is any key sub-region to be processed; the average energy generation of the key sub-region within the target time window is greater than a preset average energy generation threshold; and the average energy consumption within the target time window is greater than a preset average energy consumption threshold. These regions, as preset reorganization centers, are prominent in both energy production and consumption, and are core areas of the energy system. Using them as preset reorganization centers, collaborative control areas are constructed around them. Because the dynamic changes in energy in these regions have a significant impact on the entire system, reorganizing sub-regions around them allows for more efficient management and allocation of energy, ensuring the stability and reliability of energy supply. Compared to randomly selecting preset reorganization centers, this method better reflects the actual supply and demand relationship and geographical distribution of the energy system, making the division of collaborative control areas more in line with energy management needs, thereby improving the operational efficiency of the entire distributed energy system.

[0026] The S300 network connects all power generation equipment within each collaborative control area to control the power supply within that area.

[0027] Specifically, a ring topology can be used to network all power generation equipment within each collaborative control area to control the power supply within that area. The specific networking and power control methods can be any technology known to those skilled in the art, and will not be elaborated upon here.

[0028] The power control method based on distributed energy provided in this application first divides the area to be processed into several sub-regions of the same size according to a preset size. Through a preset region division algorithm, the method performs region clustering by comprehensively considering the power capacity, energy consumption characteristics, spatial distance, and power capacity-energy consumption complementarity parameters of each sub-region. This not only accurately grasps the essence of energy supply and demand in each sub-region, but also solves the problem of power transmission caused by neglecting geographical factors. Sub-regions with similar or complementary energy supply and demand relationships are grouped together, which greatly enhances the synergistic complementarity of power capacity and energy consumption within the collaborative control area. Finally, the method networks the power capacity equipment within the collaborative control area to achieve collaborative work under unified control. The method flexibly adjusts the power generation and power output according to the overall energy consumption demand of the area to achieve precise power supply, avoid overcapacity or undercapacity, effectively improve the operating efficiency and stability of the distributed energy system, and fundamentally solve the problem of uneven distribution of power capacity and energy consumption.

[0029] In one exemplary embodiment of this application, the preset region partitioning algorithm performs sub-region reorganization according to the following steps; S210, Obtain the electrical energy characteristic Z of the a-th sub-region to be processed within the target time window. a =(ZC a ZH a); a = 1, 2, ..., b; where b is the number of sub-regions to be processed; ZC a ZC represents the electrical energy production capacity characteristics of the a-th sub-region to be processed within the target time window. a =(ZC a，1 ZC a，2 , ..., ZC a，i , ..., ZC a，n YS a，1 YS a，2 , ..., YS a，j , ..., YS a，m ); i = 1, 2, ..., n; n is the number of data collection points for the energy production capacity characteristics of the a-th sub-region to be processed within the target time window; ZC a，i YS represents the sum of electrical energy generation from all power-generating devices within the a-th sub-region to be processed, collected at the i-th acquisition time point within the target time window; the time interval between any two adjacent acquisition time points is the same; j = 1, 2, ..., m; m is the number of preset power-generating device types; YS a，j ZH represents the total electrical energy generation of all electrical energy devices corresponding to the j-th type of electrical energy production equipment in the a-th sub-region to be processed within the target time window; a ZH represents the energy consumption characteristics of the a-th sub-region to be processed within the target time window; a =(ZH a，1 ZH a，2 , ..., ZH a，i , ..., ZH a，n ); ZH a，i Let be the sum of power consumption collected at the i-th acquisition time point within the target time window for the a-th sub-region to be processed.

[0030] Specifically, this includes the electricity output at each data collection point within the target time window, where electricity output can be represented by power. The electricity characteristics of the sub-region to be processed also include the number of devices included in each preset power capacity device type; the data on the number of device types can reveal the energy production composition of the sub-region (such as the proportion of photovoltaic and wind power devices), and combined with the output at each time point, the power generation characteristics of different device types can be analyzed (such as the temporal fluctuations of photovoltaic devices affected by sunlight). For example, if a sub-region has a high proportion of photovoltaic devices, its output peaks significantly during the daytime, potentially complementing sub-regions with a high proportion of wind power devices.

[0031] By analyzing the energy consumption at each data collection point within the target time window, the energy consumption characteristics of the sub-region to be processed are obtained. This clearly presents the changes in electricity consumption of the sub-region at different times. For example, if the energy consumption of a sub-region remains high during the daytime working hours and decreases significantly at night, it indicates that the region may be dominated by industrial or commercial electricity consumption; if the energy consumption remains at a certain level during the nighttime rest period for residents, it may indicate that there are a large number of public facilities operating at night or that residents have a strong demand for electricity at night.

[0032] S220, Obtain the electrical energy characteristic X corresponding to the c-th preset reorganization center within the target time window. c =(XC c XH c ); c = 1, 2, ..., d; where d is the number of preset recombination centers; where XC c XC represents the power generation characteristics of the c-th pre-defined reorganization center within the target time window. C =(XC c，1 XC c，2 , ..., XC c，i , ..., XC c，n ES c，1 ES c，2 , ..., ES c，j , ..., ES c，m ); XC c，i The sum of the electrical energy generation of all power-producing devices included in the c-th preset reorganization center, collected at the i-th acquisition time point within the target time window; ES c，j XH represents the total electrical energy generation of all electrical equipment corresponding to the j-th type of electrical energy production equipment in the c-th preset reorganization center within the target time window; c XH represents the energy consumption characteristics of the c-th preset reorganization center within the target time window. c =(XH c，1 XH c，2 , ..., XH c，i , ..., XH c，n ); XH c，i The sum of power consumption collected by the c-th preset reorganization center at the i-th collection time point within the target time window.

[0033] Specifically, since the preset reorganization center is any sub-region to be processed, here, the power characteristics corresponding to the preset reorganization center within the target time window are obtained, which is the same as the sub-region to be processed mentioned above, and will not be repeated here.

[0034] S230, if the matching degree D between the a-th sub-region to be processed and the c-th preset recombination center is... a，cIf the matching degree between the a-th sub-region to be processed and each preset recombination center except the c-th preset recombination center is greater than the matching degree between the a-th sub-region to be processed and each preset recombination center, then the a-th sub-region to be processed is assigned to the collaborative control region where the c-th preset recombination center is located, so as to obtain d initial collaborative control regions; where D a，c The following conditions must be met: ; δ a，c The capacity-energy consumption complementarity parameters between the a-th sub-region to be processed and the c-th preset reorganization center; α, β, and γ are the capacity weight, energy consumption weight, and distance weight, respectively; DC a，c The capacity matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; DH a，c Dk represents the energy consumption matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; a，c α is the spatial distance between the a-th sub-region to be processed and the c-th preset recombination center; α + β + γ = 1; 0 < δ a，c ≤1.

[0035] Specifically, if the matching degree D between the a-th sub-region to be processed and the c-th preset recombination center... a，c If the matching degree between the a-th sub-region to be processed and every preset reorganization center except the c-th preset reorganization center is greater than the matching degree between the a-th sub-region to be processed and the c-th preset reorganization center, then the a-th sub-region to be processed and the c-th preset reorganization center are considered to be the most matched. α, β, and γ are used to reflect the relative importance of the three factors of production capacity, energy consumption, and spatial distance in the matching degree calculation. For example, if the value of α is larger, it means that the production capacity matching degree has a greater impact on determining the matching degree. The specific values ​​of α, β, and γ can be determined according to the actual situation, and the specific determination method can be any weight determination method known to those skilled in the art.

[0036] Where, δ a，c Determine according to the following steps: S231, Obtain the comprehensive production capacity curve ZXC corresponding to the a-th sub-region to be processed and the c-th preset reorganization center within the target time window. a，c and comprehensive energy consumption curve ZXH a，c Among them, ZXC a，c =(ZXC a，c，1 ZXC a，c，2 ..., ZXC a，c，i ..., ZXC a，c，n ); ZXC a，c，i ZXC represents the combined electrical energy generation of the a-th sub-region to be processed and the c-th preset recombination center at the i-th acquisition time point within the target time window; a，c，i =ZC a，i +XC c，i ZXHa，c =(ZXH a，c，1 ZXH a，c，2 ..., ZXH a，c，i ..., ZXH a，c，n ); ZXH a，c，i ZXH represents the combined power consumption of the a-th sub-region to be processed and the c-th preset recombination center at the i-th acquisition time point within the target time window; a，c，i =ZH a，i +XH c，i .

[0037] S232, according to ZXC a，c and ZXH a，c δ a，c ; where δ a，c The following conditions must be met: ; in, δ represents the maximum difference between the combined energy generation and combined energy consumption of the a-th sub-region to be processed and the c-th preset reorganization center within the target time window. a，c ∈[0,1].

[0038] Specifically, the combined production capacity curve ZXC corresponding to the a-th sub-region to be processed and the c-th preset reorganization center within the target time window. a，c and comprehensive energy consumption curve ZXH a，c Since it is a sequence of discrete points of equal length, and each point corresponds one-to-one, it is considered a one-dimensional data sequence. The matching degree between the two curves is obtained based on the Euclidean distance. Here, the comprehensive production capacity curve ZXC is obtained based on the Euclidean distance. a，c and comprehensive energy consumption curve ZXH a，c The degree of matching between them. And based on Normalize the Euclidean distance.

[0039] Wherein, the original Euclidean distance is ,here, The range of values ​​is [0, +∞). Let be the maximum difference between the combined power generation and combined power consumption of the a-th sub-region to be processed and the c-th preset reorganization center within the target time window, where =max(|max(ZXC a，c )-min(ZXH a，c )|,|max(ZXH a，c )-min(ZXC a，c `max()` is a function that determines the preset maximum value; `min()` is a function that determines the preset minimum value; that is... This represents the maximum possible difference between power generation and power consumption at a single point in time; that is, the difference is largest when power generation reaches its maximum value and power consumption reaches its minimum value, or vice versa. The theoretical maximum Euclidean distance for the entire sequence is... That is, the sum of Euclidean distances when the maximum difference is taken at each time point. Therefore, with Normalize the Euclidean distance so that The value range of is [0, 1]. Since Euclidean distance is inversely proportional to the matching degree, the final matching degree calculation formula is: That is, the smaller the Euclidean distance, the greater the matching degree; conversely, the larger the Euclidean distance, the smaller the matching degree.

[0040] δ a，c The smaller the value, the better the overall capacity curve ZXC. a，c and comprehensive energy consumption curve ZXH a，c The higher the matching degree between them, the more complementary their energy production-energy consumption characteristics are if the a-th sub-region to be processed and the c-th preset recombination center are clustered. Conversely, δ a，c The larger the value, the better the overall capacity curve ZXC. a，c and comprehensive energy consumption curve ZXH a，c The lower the matching degree between them, the less complementary the energy production-energy consumption characteristics are to the a-th sub-region to be processed and the c-th preset recombination center are if they are clustered.

[0041] This embodiment comprehensively considers factors such as capacity-energy consumption complementarity, capacity matching, energy consumption matching, and spatial distance to measure the degree of correlation between the sub-region to be processed and the preset reorganization center, thereby determining the clustering affiliation of the sub-region. Considering the capacity-energy consumption complementarity parameter, sub-regions with complementary characteristics in terms of time and type of capacity and energy consumption can be matched with the preset reorganization center. For example, photovoltaic sub-regions with high daytime capacity and low nighttime capacity, and industrial sub-regions with high nighttime energy consumption and low daytime energy consumption, can achieve efficient energy flow at different times by clustering based on complementarity, reducing overall energy waste and ensuring timely supply of electricity from areas with excess capacity to areas with insufficient capacity, thus alleviating the problem of uneven energy distribution. Energy characteristics differ in different application scenarios, and the weight of each factor can be flexibly adjusted by comprehensive multi-factor clustering. In industrial-dominated areas, the weight of capacity matching can be appropriately increased; in densely populated residential areas, the weight of energy consumption matching is emphasized. This better adapts to the needs of different scenarios and ensures the efficient operation of the energy system.

[0042] Taking multiple factors into account allows the system to gain a more comprehensive understanding of the energy situation in each sub-region, thereby enabling the rational allocation of resources such as energy storage devices and transmission lines. For example, in sub-regions where energy production and consumption are well-matched but geographically distant, large-capacity transmission lines can be rationally planned; in sub-regions that are highly complementary and geographically close, energy storage devices can be centrally deployed to optimize resource allocation and enhance the overall stability and reliability of the system.

[0043] S240, obtain the supply and demand balance parameters of each of the d initial cooperative control regions to obtain the supply and demand balance parameter list GX = (GX1, GX2, ..., GX...). c ..., GX d ); among which, GX c GX represents the supply and demand balance parameter corresponding to the c-th initial coordinated control region. c The following conditions must be met: ;

[0044] ZC c，e，i For the c-th initial collaborative control region, the sum of the electrical energy generation of all power generation equipment in the e-th sub-region to be processed is collected at the i-th acquisition time point within the target time window; e = 1, 2, ..., f(c); f(c) is the number of sub-regions to be processed in the c-th initial collaborative control region; ZH c，e，i The sum of power consumption collected at the i-th acquisition time point within the target time window for the e-th sub-region to be processed, which is contained in the c-th initial collaborative control region; GX represents the maximum difference between the sum of energy generated and the sum of energy consumed in the c-th initial cooperative control region within the target time window. c ∈[0,1]; Specifically, after the initial clustering, since the improved superpixel clustering method only focuses on the matching degree between each sub-region to be processed and the preset recombination center, and does not focus on the matching degree between any two sub-regions to be processed within the same collaborative control region, it is necessary to evaluate the energy production and energy consumption of the initial collaborative control region obtained by the initial clustering. That is, to obtain the supply and demand balance parameters of each initial collaborative control region. Here, the supply and demand balance parameters are the similarity between the total energy consumption and total energy output of all sub-regions to be processed (including the sub-region where the preset recombination center is located) in the entire collaborative control region within the target time window. If the similarity between the two curves is high, it indicates that the supply and demand of the collaborative control region is relatively balanced. Conversely, if the similarity between the two curves is low, it indicates that the supply and demand of the collaborative control region is unbalanced for a certain period of time or as a whole.

[0045] S250, if g / d≥YB, then the region division ends, and all d initial collaborative control regions are determined as collaborative control regions; g is the number of initial collaborative control regions whose supply and demand balance parameter is greater than the preset supply and demand balance threshold; YB is the preset supply and demand balance ratio threshold.

[0046] Specifically, if g / d≥YB, it indicates that most of the initial collaborative control areas are relatively balanced in terms of supply and demand. In this case, the area division is considered to be complete. For a small number of initial collaborative control areas where supply and demand are not very balanced, the method of using mains power to supply electricity or storing excess electricity in a designated space, or other processing methods known to those skilled in the art, can be used for power control.

[0047] In one exemplary implementation of this application, after step S250, the method further includes: S260, at preset time intervals, obtain the current supply and demand balance parameters of each of the d collaborative control areas within the first time window, to obtain the current supply and demand balance parameter list DGX = (DGX1, DGX2, ..., DGX...). c ..., DGX d ); among them, DGX c The current supply and demand balance parameters for the c-th coordinated control area; the end time of the first time window is the current time, and the duration of the first time window is the same as the duration of the target time window.

[0048] S270, if h / d≥EB, then update each preset reorganization center and jump to step S210; where h is the number of collaborative control regions for d changes; EB is a preset change ratio threshold; the collaborative control region for changes is the collaborative control region where the current supply and demand balance parameter is less than the key supply and demand balance parameter, and the difference between the current supply and demand balance parameter and the key supply and demand balance parameter is greater than a preset difference threshold; the key supply and demand balance parameter is the supply and demand balance parameter corresponding to the collaborative control region when the region division is completed.

[0049] Specifically, since the capacity or energy consumption characteristics of each collaborative control area may change over time, for example, the capacity equipment may increase, which may cause the originally relatively balanced supply and demand collaborative control area to become unbalanced over time. Therefore, in this embodiment, the supply and demand balance parameters of each collaborative control area in the most recent period are re-acquired at regular intervals. If the proportion of collaborative control areas with supply and demand imbalance is large, the global collaborative control area is re-divided.

[0050] When supply and demand are imbalanced in a collaboratively controlled area, wasteful production capacity or insufficient supply may occur. Timely re-division can optimize resource allocation and allow production capacity and energy consumption to be rationally matched again. For example, if a region that was originally in a balanced supply and demand situation experiences reduced production due to aging equipment, re-division can introduce external production capacity to supplement it, avoiding energy waste or shortages, improving overall energy efficiency, and reducing system operating costs.

[0051] In one exemplary implementation of this application, after step S250, the method further includes: S280, for each preset time interval, based on the power characteristics and power characteristic prediction model of each of the d collaborative control regions within the first time window, the predicted power characteristics of each collaborative control region within the second time window are obtained; wherein, the end time of the first time window is the current time; the start time of the second time window is the current time; the time length of the first time window, the time length of the second time window, and the time length of the target time window are all the same; the power characteristic prediction model is trained based on the power characteristics within the target time window.

[0052] S290, if k / d≥SB, then update each preset reorganization center and jump to step S210; where k is the number of predicted change collaborative control regions; SB is the preset predicted change ratio threshold; the predicted change collaborative control region is the collaborative control region in which the matching degree between the actual power characteristics and the predicted power characteristics of each collaborative control region in the second time window is less than the preset predicted matching degree threshold.

[0053] Specifically, an energy feature prediction model is trained based on the energy features within the target time window. Based on the energy features and the energy feature prediction model of each of the d collaborative control regions within the first time window, the predicted energy features of each collaborative control region within the second time window are obtained. If the predicted features differ from the actual features, it indicates that the actual features have changed significantly compared to the features during clustering. If there are many regions with such changes, the global collaborative control region is re-divided.

[0054] By comparing predicted and actual values ​​using an energy characteristic prediction model, deviations from expectations in the energy characteristics of a collaboratively controlled area can be detected in advance. For example, if the predicted photovoltaic power generation in a certain area differs significantly from the actual value, it may indicate the addition of new photovoltaic equipment or weather anomalies affecting production capacity. When many areas exhibit such changes, it suggests a significant alteration in the overall energy system's supply and demand structure. In such cases, triggering a re-division can prevent reactive responses to imbalances. Furthermore, this embodiment does not require continuous real-time calculation of supply and demand parameters for all areas; instead, it uses a prediction model to filter characteristic changes. Only when the difference between prediction and actual values ​​exceeds a threshold is the characteristic change of that area given priority attention. Detailed analysis of only a few areas reduces the resource consumption of real-time calculation of all data, making it particularly suitable for large-scale distributed energy systems, reducing computational costs while ensuring efficiency.

[0055] Please refer to Figure 2 As shown, an embodiment of this application provides a power control system 100 based on distributed energy resources, the system comprising: The first division module 110 is used to divide the area to be processed into several sub-areas according to a preset size; wherein any two sub-areas to be processed have the same size.

[0056] The second partitioning module 120 is used to reorganize several sub-regions to be processed according to a preset region partitioning algorithm to obtain several collaborative control regions; wherein, each collaborative control region has a corresponding preset reorganization center; the preset region partitioning algorithm reorganizes the sub-regions according to the matching degree of power production capacity characteristics, the matching degree of power consumption characteristics, the spatial distance, and the magnitude of the power production capacity-power consumption complementarity parameter between each sub-region to be processed and each preset reorganization center; the power production capacity-power consumption complementarity parameter represents the matching degree between the total power production capacity characteristics and the total power consumption characteristics between the sub-region to be processed and the preset reorganization center.

[0057] The network control module 130 is used to network all power generation equipment in each collaborative control area in order to control the power of the corresponding collaborative control area.

[0058] In an exemplary embodiment of this application, an electronic device capable of implementing the above-described method is also provided.

[0059] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0060] An electronic device according to this embodiment of the present application. The electronic device is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0061] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0062] The memory stores program code that can be executed by a processor, causing the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this application.

[0063] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0064] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0065] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0066] The electronic device can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0067] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this application.

[0068] In exemplary embodiments of this application, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this application may also be implemented as a program product including program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this application described in the "Exemplary Methods" section above.

[0069] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0070] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0071] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0072] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0073] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0074] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0075] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A power control method based on distributed energy resources, characterized in that, The method includes: S100: Divide the area to be processed into several sub-regions according to a preset size; wherein any two sub-regions to be processed have the same size. S200, according to a preset region division algorithm, several sub-regions to be processed are reorganized to obtain several collaborative control regions; wherein, each collaborative control region has a corresponding preset reorganization center; the preset region division algorithm reorganizes the sub-regions according to the matching degree of power generation characteristics, the matching degree of power consumption characteristics, the spatial distance, and the magnitude of the power generation-power consumption complementarity parameter between each sub-region to be processed and each preset reorganization center; the power generation-power consumption complementarity parameter represents the matching degree between the total power generation characteristics and the total power consumption characteristics between the sub-region to be processed and the preset reorganization center; the preset reorganization center is the sub-region to be processed in which the average power generation within the target time window is greater than the preset average power generation threshold, and the average power consumption within the target time window is greater than the preset average power consumption threshold; Based on a preset region partitioning algorithm, several sub-regions to be processed are reorganized, including: S230, if the matching degree D between the a-th sub-region to be processed and the c-th preset recombination center is... a，c If the matching degree between the a-th sub-region to be processed and each preset recombination center except the c-th preset recombination center is greater than the matching degree between the a-th sub-region to be processed and each preset recombination center, then the a-th sub-region to be processed is assigned to the collaborative control region where the c-th preset recombination center is located, so as to obtain d initial collaborative control regions; where D a，c Meets the following conditions: ； δ a，c The capacity-energy consumption complementarity parameters between the a-th sub-region to be processed and the c-th preset reorganization center; α, β, and γ are the capacity weight, energy consumption weight, and distance weight, respectively; DC a，c The capacity matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; DH a，c Dk represents the energy consumption matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; a，c α is the spatial distance between the a-th sub-region to be processed and the c-th preset recombination center; α + β + γ = 1; 0 < δ a，c ≤1; The S300 network connects all power generation equipment within each collaborative control area to control the power supply within that area.

2. The power control method based on distributed energy resources according to claim 1, characterized in that, The preset region partitioning algorithm reorganizes sub-regions according to the following steps: S210, Obtain the electrical energy characteristic Z of the a-th sub-region to be processed within the target time window. a =(ZC a ZH a ); a = 1, 2, ..., b; where b is the number of sub-regions to be processed; ZC a ZC represents the electrical energy production capacity characteristics of the a-th sub-region to be processed within the target time window. a =(ZC a，1 ZC a，2 , ..., ZC a，i , ..., ZC a，n YS a，1 YS a，2 , ..., YS a，j , ..., YS a，m ); i = 1, 2, ..., n; n is the number of data collection points for the energy production capacity characteristics of the a-th sub-region to be processed within the target time window; ZC a，i YS represents the sum of electrical energy generation from all power-generating devices within the a-th sub-region to be processed, collected at the i-th acquisition time point within the target time window; the time interval between any two adjacent acquisition time points is the same; j = 1, 2, ..., m; m is the number of preset power-generating device types; YS a，j ZH represents the total electrical energy generation of all electrical energy devices corresponding to the j-th type of electrical energy production equipment in the a-th sub-region to be processed within the target time window; a ZH represents the energy consumption characteristics of the a-th sub-region to be processed within the target time window; a =(ZH a，1 ZH a，2 , ..., ZH a，i , ..., ZH a，n ); ZH a，i The sum of power consumption collected at the i-th acquisition time point within the target time window for the a-th sub-region to be processed; S220, Obtain the electrical energy characteristic X corresponding to the c-th preset reorganization center within the target time window. c =(XC c XH c ); c = 1, 2, ..., d; where d is the number of preset recombination centers; where XC c XC represents the power generation characteristics of the c-th pre-defined reorganization center within the target time window. C =(XC c，1 XC c，2 , ..., XC c，i , ..., XC c，n ES c，1 ES c，2 , ..., ES c，j , ..., ES c，m ); XC c，i The sum of the electrical energy generation of all power-producing devices included in the c-th preset reorganization center, collected at the i-th acquisition time point within the target time window; ES c，j XH represents the total electrical energy generation of all electrical equipment corresponding to the j-th type of electrical energy production equipment in the c-th preset reorganization center within the target time window; c XH represents the energy consumption characteristics of the c-th preset reorganization center within the target time window. c =(XH c，1 XH c，2 , ..., XH c，i , ..., XH c，n ); XH c，i The sum of power consumption collected by the c-th preset reorganization center at the i-th collection time point within the target time window; S230, if the matching degree D between the a-th sub-region to be processed and the c-th preset recombination center is... a，c If the matching degree between the a-th sub-region to be processed and each preset recombination center except the c-th preset recombination center is greater than the matching degree between the a-th sub-region to be processed and each preset recombination center, then the a-th sub-region to be processed is assigned to the collaborative control region where the c-th preset recombination center is located, so as to obtain d initial collaborative control regions; where D a，c Meets the following conditions: ； δ a，c The capacity-energy consumption complementarity parameters between the a-th sub-region to be processed and the c-th preset reorganization center; α, β, and γ are the capacity weight, energy consumption weight, and distance weight, respectively; DC a，c The capacity matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; DH a，c Dk represents the energy consumption matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; a，c α is the spatial distance between the a-th sub-region to be processed and the c-th preset recombination center; α + β + γ = 1; 0 < δ a，c ≤1; S240, obtain the supply and demand balance parameters of each of the d initial cooperative control regions to obtain the supply and demand balance parameter list GX = (GX1, GX2, ..., GX...). c , ..., GX d ); among which, GX c GX represents the supply and demand balance parameter corresponding to the c-th initial coordinated control region. c Meets the following conditions: ； ZC c，e，i For the c-th initial collaborative control region, the sum of the electrical energy generation of all power generation equipment in the e-th sub-region to be processed is collected at the i-th acquisition time point within the target time window; e = 1, 2, ..., f(c); f(c) is the number of sub-regions to be processed in the c-th initial collaborative control region; ZH c，e，i The sum of power consumption collected at the i-th acquisition time point within the target time window for the e-th sub-region to be processed, which is included in the c-th initial collaborative control region; GX represents the maximum difference between the sum of energy generated and the sum of energy consumed in the c-th initial cooperative control region within the target time window. c ∈[0,1]; S250, if g / d≥YB, then the region division ends, and all d initial collaborative control regions are determined as collaborative control regions; g is the number of initial collaborative control regions whose supply and demand balance parameter is greater than the preset supply and demand balance threshold; YB is the preset supply and demand balance ratio threshold.

3. The power control method based on distributed energy resources according to claim 2, characterized in that, δ a，c Determine according to the following steps: S231, Obtain the comprehensive production capacity curve ZXC corresponding to the a-th sub-region to be processed and the c-th preset reorganization center within the target time window. a，c and comprehensive energy consumption curve ZXH a，c Among them, ZXC a，c =(ZXC a，c，1 ZXC a，c，2 ..., ZXC a，c，i ..., ZXC a，c，n ); ZXC a，c，i ZXC represents the combined electrical energy generation of the a-th sub-region to be processed and the c-th preset recombination center at the i-th acquisition time point within the target time window; a，c，i =ZC a，i +XC c，i ZXH a，c =(ZXH a，c，1 ZXH a，c，2 ..., ZXH a，c，i ..., ZXH a，c，n ); ZXH a，c，i ZXH represents the combined power consumption of the a-th sub-region to be processed and the c-th preset recombination center at the i-th acquisition time point within the target time window; a，c，i =ZH a，i +XH c，i ; S232, according to ZXC a，c and ZXH a，c δ a，c ; where δ a，c Meets the following conditions: ； in, δ represents the maximum difference between the combined power generation and combined power consumption of the a-th sub-region to be processed and the c-th preset reorganization center within the target time window; a，c ∈[0,1].

4. The power control method based on distributed energy resources according to any one of claims 1-3, characterized in that, The preset reorganization center is any key sub-region to be processed; the average power generation of the key sub-region to be processed within the target time window is greater than the preset average power generation threshold. Furthermore, the average energy consumption within the target time window is greater than the preset average energy consumption threshold.

5. The power control method based on distributed energy resources according to claim 2, characterized in that, After step S250, the method further includes: S260, at preset time intervals, obtain the current supply and demand balance parameters of each of the d collaborative control areas within the first time window, to obtain the current supply and demand balance parameter list DGX = (DGX1, DGX2, ..., DGX...). c ..., DGX d ); among them, DGX c The current supply and demand balance parameters for the c-th coordinated control area; the end time of the first time window is the current time, and the duration of the first time window is the same as the duration of the target time window; S270, if h / d≥EB, then update each preset reorganization center and jump to step S210; where h is the number of collaborative control regions for d changes; EB is a preset change ratio threshold; the collaborative control region for changes is the collaborative control region where the current supply and demand balance parameter is less than the key supply and demand balance parameter, and the difference between the current supply and demand balance parameter and the key supply and demand balance parameter is greater than a preset difference threshold; the key supply and demand balance parameter is the supply and demand balance parameter corresponding to the collaborative control region when the region division is completed.

6. The power control method based on distributed energy resources according to claim 2, characterized in that, After step S250, the method further includes: S280, at each preset time interval, based on the power characteristics and power characteristic prediction model of each of the d collaborative control regions within the first time window, the predicted power characteristics of each collaborative control region within the second time window are obtained; wherein, the end time of the first time window is the current time; the start time of the second time window is the current time; the time lengths of the first time window, the second time window, and the target time window are all the same; the power characteristic prediction model is trained based on the power characteristics within the target time window; S290, if k / d≥SB, then update each preset reorganization center and jump to step S210; where k is the number of predicted change collaborative control regions; SB is the preset predicted change ratio threshold; the predicted change collaborative control region is the collaborative control region in which the matching degree between the actual power characteristics and the predicted power characteristics of each collaborative control region in the second time window is less than the preset predicted matching degree threshold.

7. A power control system based on distributed energy resources, characterized in that, The system includes: The first partitioning module is used to divide the area to be processed into several sub-regions according to a preset size; wherein any two sub-regions to be processed have the same size. The second partitioning module is used to reorganize several sub-regions to be processed according to a preset region partitioning algorithm to obtain several collaborative control regions. Each collaborative control region has a corresponding preset reorganization center. The preset region partitioning algorithm reorganizes the sub-regions based on the matching degree of power generation characteristics, the matching degree of power consumption characteristics, the spatial distance, and the magnitude of the power generation-power consumption complementarity parameter between each sub-region to be processed and each preset reorganization center. The power generation-power consumption complementarity parameter represents the matching degree between the total power generation characteristics and the total power consumption characteristics between the sub-region to be processed and the preset reorganization center. The preset reorganization center is a sub-region to be processed whose average power generation within the target time window is greater than a preset average power generation threshold and whose average power consumption within the target time window is greater than a preset average power consumption threshold. Based on a preset region partitioning algorithm, several sub-regions to be processed are reorganized, including: If the matching degree D between the a-th sub-region to be processed and the c-th preset reorganization center a，c If the matching degree between the a-th sub-region to be processed and each preset recombination center except the c-th preset recombination center is greater than the matching degree between the a-th sub-region to be processed and each preset recombination center, then the a-th sub-region to be processed is assigned to the collaborative control region where the c-th preset recombination center is located, so as to obtain d initial collaborative control regions; where D a，c Meets the following conditions: ； δ a，c The capacity-energy consumption complementarity parameters between the a-th sub-region to be processed and the c-th preset reorganization center; α, β, and γ are the capacity weight, energy consumption weight, and distance weight, respectively; DC a，c The capacity matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; DH a，c Dk represents the energy consumption matching degree between the a-th sub-region to be processed and the c-th preset reorganization center; a，c α is the spatial distance between the a-th sub-region to be processed and the c-th preset recombination center; α + β + γ = 1; 0 < δ a，c ≤1; The network control module is used to network all power generation equipment in each collaborative control area in order to control the power of the corresponding collaborative control area.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory storing a computer program, wherein when the processor executes the computer program, it implements the power control method based on distributed energy source as described in any one of claims 1-6.

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