Power utilization side power management method and system of centralized photovoltaic power station

Abstract

The invention provides a power utilization side power management method and system for a centralized photovoltaic power station, and belongs to the technical field of electric power systems.The method comprises the steps that predicted power generation power of the centralized photovoltaic power station in the next operation period is obtained, obtaining the energy storage state of an energy storage unit in the centralized photovoltaic power station in the next operation period and the predicted demand power of a plurality of power utilization sides in the next operation period, and determining the supply and demand matching degree of the centralized photovoltaic power station and the power utilization sides according to the predicted power generation power, the energy storage state of the energy storage unit and the predicted demand power, and obtaining a trusted level of each power utilization side, determining a first power utilization side and a second power utilization side from the plurality of power utilization sides according to the supply and demand matching degree and the trusted levels, executing a first power management strategy on the first power utilization side, and executing a second power management strategy on the second power utilization side. The invention aims to improve the problem that the stability of system operation and the accuracy of power management are poor when power management is carried out on a power utilization side in an existing scheme.

Description

Technical Field

[0001] This application relates to the technical field of power systems, and in particular to a power management method and system for the power consumption side of a centralized photovoltaic power station. Background Art

[0002] A centralized photovoltaic power station refers to a large-scale photovoltaic power generation system that centrally installs a large number of photovoltaic modules in a specific area, and after converging and boosting, connects to a medium-voltage or high-voltage power grid for the main purpose of power generation and grid connection. In order to ensure the safe and stable operation of the centralized photovoltaic power station and meet the requirements of grid dispatching, it is necessary to perform power management on the power consumption side of the centralized photovoltaic power station.

[0003] Currently, when performing power management on the power consumption side of a centralized photovoltaic power station, the stability of system operation and the accuracy of power management are poor. Summary of the Invention

[0004] The embodiments of this application provide a power management method and system for the power consumption side of a centralized photovoltaic power station. This application adopts the following technical solutions: In a first aspect, a power management method for the power consumption side of a centralized photovoltaic power station is provided. The method includes: Obtaining the predicted power generation of the centralized photovoltaic power station in the next operation cycle; Obtaining the energy storage state of the energy storage unit in the next operation cycle of the centralized photovoltaic power station and the predicted demand power of multiple power consumption sides in the next operation cycle, and determining the supply-demand matching degree between the centralized photovoltaic power station and the power consumption sides according to the predicted power generation, the energy storage state of the energy storage unit, and the predicted demand power; Obtaining the trust level of each power consumption side, and determining a first power consumption side and a second power consumption side from multiple power consumption sides according to the supply-demand matching degree and the trust level. The number of the first power consumption sides is positively correlated with the supply-demand matching degree, and the number of the second power consumption sides is negatively correlated with the supply-demand matching degree; Executing a first power management strategy for the first power consumption side and a second power management strategy for the second power consumption side. The first power management strategy is used to provide a stable power supply for the first power consumption side, and the second power management strategy is used to provide a flexible power supply for the second power consumption side.

[0005] In the embodiment of the first aspect of this application, obtaining the predicted power generation of the centralized photovoltaic power station in the next operation cycle includes: Obtaining the historical photovoltaic conversion efficiency of the centralized photovoltaic power station and the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; Constructing the input features of the first dimension according to the historical photovoltaic conversion efficiency of the centralized photovoltaic power station; Constructing the input features of the second dimension according to the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; Input the input features of the first dimension and the input features of the second dimension into the pre-trained power prediction model for a centralized photovoltaic power station, and output the predicted power generation of the centralized photovoltaic power station for the next cycle.

[0006] In an embodiment of the first aspect of the present application, obtaining the energy storage state of the energy storage unit in the centralized photovoltaic power station for the next operating cycle includes: Obtain the energy storage state of the energy storage unit in the centralized photovoltaic power station for the current operating cycle; Obtain the dynamic correction coefficient of the energy storage state, and determine the energy storage state of the energy storage unit in the centralized photovoltaic power station for the next operating cycle according to the energy storage state of the current operating cycle and the dynamic correction coefficient.

[0007] In an embodiment of the first aspect of the present application, determining the supply-demand matching degree between the centralized photovoltaic power station and the power consumption side according to the predicted power generation, the energy storage state of the energy storage unit, and the predicted demand power includes: Determine the first matching index according to the deviation between the predicted power generation and the predicted demand power; Determine the second matching index according to the energy storage state of the energy storage unit; Perform a fusion calculation on the first matching index and the second matching index to determine the supply-demand matching degree between the centralized photovoltaic power station and the power consumption side.

[0008] In an embodiment of the first aspect of the present application, the trusted level of each power consumption side is determined through the following steps: Determine the difference degree between the historical predicted demand power and the historical actual demand power of each power consumption side; Determine the power influence ability of each power consumption side on other adjacent power consumption sides; Determine the trusted level of each power consumption side according to the difference degree and the power influence ability.

[0009] In an embodiment of the first aspect of the present application, determining the power influence ability of each power consumption side on other adjacent power consumption sides includes: Determine the target power consumption side as the first node, and determine the adjacent power consumption sides of the target power consumption side as the second nodes; Determine the first connection relationship between the first node and the second nodes according to the power supply influence propagation relationship between the target power consumption side and the adjacent power consumption sides; Determine the second connection relationship between the first node and the second nodes according to the power demand influence propagation relationship between the target power consumption side and the adjacent power consumption sides; Construct a topology graph of the power influence ability of each target power consumption side according to the first connection relationship and the second connection relationship; Determine the power influence ability of each power consumption side on other adjacent power consumption sides according to the intersection relationship between the topology graphs of the power influence ability.

[0010] In an embodiment of the first aspect of the present application, determining a first power consumption side and a second power consumption side from multiple power consumption sides according to the supply-demand matching degree and the trusted level includes: Determining the quantities of the first power consumption side and the second power consumption side according to the supply-demand matching degree; Determining the first power consumption side with a matching quantity from multiple power consumption sides according to the level of the trusted level, and determining the remaining power consumption sides as the second power consumption side.

[0011] In a second aspect, based on the same inventive concept, a power consumption side power management system for a centralized photovoltaic power station is provided. The system includes: A first acquisition module, configured to acquire the predicted power generation of the centralized photovoltaic power station in the next operation cycle; A second acquisition module, configured to acquire the energy storage state of the energy storage unit in the centralized photovoltaic power station in the next operation cycle and the predicted demand power of multiple power consumption sides in the next operation cycle, and determine the supply-demand matching degree between the centralized photovoltaic power station and the power consumption sides according to the predicted power generation, the energy storage state of the energy storage unit, and the predicted demand power; A third acquisition module, configured to acquire the trusted level of each power consumption side, and determine a first power consumption side and a second power consumption side from multiple power consumption sides according to the supply-demand matching degree and the trusted level. The quantity of the first power consumption side is positively correlated with the supply-demand matching degree, and the quantity of the second power consumption side is negatively correlated with the supply-demand matching degree; An execution module, configured to execute a first power management strategy for the first power consumption side and a second power management strategy for the second power consumption side. The first power management strategy is used to provide a stable power supply for the first power consumption side, and the second power management strategy is used to provide a flexible power supply for the second power consumption side.

[0012] In an embodiment of the second aspect of the present application, the first acquisition module includes: A first acquisition sub-module, configured to acquire the historical photovoltaic conversion efficiency of the centralized photovoltaic power station and the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; A first feature construction sub-module, configured to construct an input feature of the first dimension according to the historical photovoltaic conversion efficiency of the centralized photovoltaic power station; A second feature construction sub-module, configured to construct an input feature of the second dimension according to the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; A power determination sub-module, configured to input the input feature of the first dimension and the input feature of the second dimension into a pre-trained power generation prediction model, and output the predicted power generation of the centralized photovoltaic power station in the next cycle.

[0013] In an embodiment of the second aspect of the present application, the second acquisition module includes: A second acquisition sub-module, configured to acquire the energy storage state of the energy storage unit in the current operation cycle of the centralized photovoltaic power station; A correction sub-module, configured to acquire a dynamic correction coefficient of the energy storage state, and determine the energy storage state of the energy storage unit in the next operation cycle of the centralized photovoltaic power station according to the energy storage state in the current operation cycle and the dynamic correction coefficient.

[0014] In summary, the above-mentioned power management method and system for the power consumption side of the centralized photovoltaic power station have the following technical effects: By introducing a multi-dimensional supply-demand matching degree and trusted level mechanism, the present application realizes precise dynamic adjustment in the power management of the centralized photovoltaic power station to solve the problems of poor system stability and accuracy in the power management process of the existing solutions. In the existing solutions, the uncertainty of photovoltaic power generation, the dynamic regulation ability of the energy storage unit, and the differential impact on the power consumption side are not fully considered, resulting in the power management being unable to be flexibly adjusted according to the actual operation conditions. The present application acquires the predicted power generation power of the photovoltaic power station, the energy storage state of the energy storage unit, and the power demand of the power consumption side, evaluates the supply-demand matching degree from multiple dimensions, and ensures that the system can accurately identify the relationship between power generation and demand, and solves the problem of information error accumulation caused by supply-demand imbalance. In addition, through the evaluation of the trusted level based on the power consumption side, the stability, reliability, and schedulability of the power consumption side are fully considered, and combined with the supply-demand matching degree, it is ensured that the system preferentially guarantees the power supply demand of the high-trust power consumption side, reduces the interference of the low-trust power consumption side on the system stability, and enables the system to quickly respond and achieve dynamic scheduling when the power supply fluctuates or the demand changes. In addition, by separately managing the first power consumption side and the second power consumption side and adopting different power management strategies respectively, the first power management strategy guarantees the stability of the high-priority power consumption side, and the second power management strategy enhances the flexible regulation ability of the lower-priority power consumption side, thereby improving the adaptability and robustness of the system under various load conditions, optimizing the overall operation efficiency of the photovoltaic power station, enabling the system to improve the power utilization rate and reduce the abandonment of electricity while ensuring power supply. Overall, the precise scheduling, efficient load regulation strategy, and multi-dimensional demand response mechanism for power management in the present application provide guarantee for the stability of the photovoltaic power station, and effectively improve the accuracy and stability of power management. Description of the Drawings

[0015] Figure 1 It is a schematic diagram of the steps of a power management method for the power consumption side of a centralized photovoltaic power station provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the functional modules of a power management system for the power consumption side of a centralized photovoltaic power station provided by an embodiment of the present application. Detailed Embodiments

[0016] The terms used in the following embodiments are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "", "the above", "the", and "this" are also intended to include expressions such as "one or more", unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of the present application, "at least one" and "one or more" mean one or more than two (including two). The character " / " generally indicates that the related objects before and after are in an "or" relationship.

[0017] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0018] Hereinafter, terms such as "first" and "second" are only for convenience of description and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more. For example, a plurality of processing units means two or more processing units.

[0019] In addition, in the embodiments of the present application, "up", "down", "left", and "right" are not defined only in terms of the orientation of the components in the relative drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly with the change of the orientation of the components in the drawings. In the drawings, for the sake of clarity, the thickness of the layers and regions is exaggerated, and the dimensional proportional relationship between the various parts in the drawings does not reflect the actual dimensional proportional relationship.

[0020] In the embodiments of the present application, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" may be a fixed connection, a detachable connection, or integrated; it may be directly connected, or indirectly connected through an intermediate medium. In addition, the term "electrical connection" may be a direct electrical connection, or an indirect electrical connection through an intermediate medium.

[0021] In the embodiments of the present application, the term "module" may be a functional structure divided according to logic, and this "module" may be implemented by pure hardware, or by a combination of software and hardware. In the embodiments of the present application, "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B may represent: A exists alone, B exists alone, and A and B exist simultaneously.

[0022] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0023] Next, the technical solutions in the present application will be described with reference to the accompanying drawings.

[0024] Currently, when performing power management on the power consumption side of a centralized photovoltaic power station, management is first carried out based on the matching state between the power generation power of the power consumption side of the centralized photovoltaic power station and the demand power of the power consumption side. And all power consumption sides are treated uniformly or power management is carried out based on their importance. However, the above solutions ignore the attributes of the power consumption side itself. Specifically, these solutions ignore the core attribute that reflects its predictability and regulation reliability, which is the degree of difference between the historical predicted demand power and the actual demand power of the power consumption side, and also do not consider the influencing index of the degree of interference or coupling of its power state on adjacent power consumption sides during actual operation. As a result, when the system power supply and demand are tense or photovoltaic power generation fluctuates, the scheduling strategy is difficult to accurately match according to the true behavior characteristics of the power consumption side, which in turn leads to unreasonable resource allocation. Therefore, when the existing solutions perform power management on the power consumption side of a centralized photovoltaic power station, the stability of system operation and the accuracy of power management are relatively poor.

[0025] Based on this, the inventors proposed the inventive concept of the present application: determining the corresponding trusted level through the power demand attributes of the power consumption side itself and its influencing ability on other power consumption sides. The higher the trusted level, in the case where the importance of each power consumption side is the same, when the system power resources are limited or the supply and demand fluctuate, the power supply of the power consumption side with a higher trusted level is preferentially guaranteed, the stability of system scheduling and the reliability of execution are improved, and at the same time, the planning deviation and system disturbance caused by low-trust power consumption sides are reduced, thereby improving the accuracy of power management and the stability of system operation.

[0026] Referring to Figure 1 , an embodiment of the present invention provides a method for power management of the power consumption side of a centralized photovoltaic power station, which is applied to a server and may specifically include the following steps: S101: Obtain the predicted power generation of the centralized photovoltaic power station in the next operation cycle.

[0027] In this embodiment, first, it is necessary to obtain the predicted power generation of the centralized photovoltaic power station in the next operation cycle. The next operation cycle is the operation cycle after the current operation cycle and is a predicted value of future power generation. The specific acquisition steps may include: S1011: Obtain the historical photovoltaic conversion efficiency of a centralized photovoltaic power station and the predicted weather data for the next cycle in the area where the centralized photovoltaic power station is located; S1012: Construct the input features of the first dimension based on the historical photovoltaic conversion efficiency of the centralized photovoltaic power station; S1013: Construct the input features of the second dimension based on the predicted weather data for the next cycle in the area where the centralized photovoltaic power station is located; S1014: Input the input features of the first dimension and the input features of the second dimension into a pre-trained power generation prediction model, and output the predicted power generation of the centralized photovoltaic power station for the next cycle.

[0028] In the implementation manners of S1011 to S1014, the photovoltaic conversion efficiency is a key indicator for measuring the ability of a photovoltaic module to convert light energy into electrical energy, and is affected by various factors such as component aging, pollution, temperature, and installation angle. Therefore, using the historical photovoltaic conversion efficiency can reflect the evolution trend of the system's own power generation performance; at the same time, weather is the main external factor affecting photovoltaic power generation. Factors such as solar radiation intensity, cloud cover, temperature, humidity, and wind speed will directly affect the lighting conditions and component performance. Obtaining the predicted weather data for the next cycle is the basis for perceiving the future external environment for power generation.

[0029] Based on the historical photovoltaic conversion efficiency, statistical or time series features such as average efficiency, maximum efficiency, efficiency response characteristics under different weather conditions, and seasonal change trends can be extracted as the first dimension of the input model. These features reflect the intrinsic power generation ability of the photovoltaic system and its performance over time.

[0030] Convert the predicted weather data into a feature vector recognizable by the model, which commonly includes the light intensity, temperature, humidity, wind speed, cloud cover, and weather type (sunny, cloudy, rainy, etc.) at future times. These constitute the input features of the second dimension and represent the impact of the future environment on the photovoltaic system.

[0031] Input the input features of the above two dimensions into the pre-trained power generation prediction model together. During the prediction stage, the predicted power generation value for the next operation cycle can be output, serving as an important reference basis for subsequent energy storage management, power allocation on the power consumption side, and global scheduling optimization.

[0032] The training process of the above power generation prediction model comprehensively considers the historical photovoltaic conversion efficiency and historical meteorological data of the centralized photovoltaic power station, and constructs the mapping relationship between the input features and the power generation output through the method of supervised learning. Among them, the photovoltaic conversion efficiency, as an important indicator reflecting the actual power generation performance of the photovoltaic module, reflects the ability to convert into electric energy under unit light intensity. It is affected by factors such as component aging, pollution, and temperature. Therefore, it participates in the modeling as a key input feature in the model training. Specifically, first, collect the sequence of photovoltaic conversion efficiency of the photovoltaic module in the historical period and meteorological data such as solar radiation intensity, temperature, humidity, wind speed, and weather type in the corresponding period. After cleaning, removing outliers, and normalizing these data, they are used as the training samples of the model. Use these samples to train a model that can fit the non-linear relationship between the input features and the actual power generation. During the training process, continuously adjust the model parameters to minimize the error between the predicted value and the true power generation value. Finally, form a power generation prediction model that can accurately reflect the comprehensive influence of light conditions, weather factors, and component conversion efficiency, and is used to output the power generation prediction value of the next period in actual applications by combining the predicted weather data and the latest component efficiency data.

[0033] S102: Obtain the energy storage state of the energy storage unit in the next operation period of the centralized photovoltaic power station and the predicted demand power of multiple power consumption sides in the next operation period, and determine the supply-demand matching degree between the centralized photovoltaic power station and the power consumption side according to the predicted power generation, the energy storage state of the energy storage unit, and the predicted demand power.

[0034] In this embodiment, due to the volatility and uncontrollability of photovoltaic power generation, relying solely on the predicted power generation cannot truly reflect the dispatchable capacity of the power station. By introducing the energy storage state of the energy storage unit, the accuracy of the regulation capacity evaluation in the centralized photovoltaic power station's supply-demand matching judgment can be improved, thereby realizing a more secure, stable, flexible, and economical power management strategy on the power consumption side. The energy storage unit refers to an energy storage device deployed in a centralized photovoltaic power station for energy regulation when the photovoltaic power generation and power consumption demand do not match. It can store electrical energy during excess power generation and release electrical energy during insufficient power generation, playing a role in balancing grid fluctuations, alleviating load shocks, and enhancing the stability of power supply. The energy storage state of the energy storage unit refers to a set of parameters reflecting the charge-discharge capacity of the energy storage unit during the current and predicted time periods, which can specifically include information such as the current remaining available power, charge-discharge rate, charge / discharge efficiency, historical charge-discharge cycle data, and the predicted released / stored power in the next cycle. These state parameters can reflect whether the energy storage unit has the regulation ability and the degree of its participation in supply-demand balance. When determining the supply-demand matching degree between the centralized photovoltaic power station and the power consumption side, by combining the predicted power generation, predicted power consumption, and energy storage state, it is possible to dynamically evaluate whether the power generation meets the power consumption demand within the target cycle and whether the energy storage unit can effectively perform power regulation, thereby obtaining a more accurate and responsive supply-demand matching evaluation result. The specific steps for obtaining the energy storage state of the energy storage unit in the centralized photovoltaic power station in the next operation cycle can include: S1021: Obtain the energy storage state of the energy storage unit in the centralized photovoltaic power station in the current operation cycle; S1022: Obtain the dynamic correction coefficient of the energy storage state, and determine the energy storage state of the energy storage unit in the centralized photovoltaic power station in the next operation cycle according to the energy storage state in the current operation cycle and the dynamic correction coefficient.

[0035] In the implementation manners of S1021 to S1022, the energy storage state of the energy storage unit in the current operation cycle is mainly used to reflect the real-time operation ability and energy surplus of the current energy storage unit, and usually includes parameters such as the current state of charge, remaining capacity, current charge-discharge power, historical charge-discharge trend, and health state. By collecting the data provided by the battery management system through the sensing data of the energy storage system, the current energy reserve level and charge-discharge state of the energy storage unit can be comprehensively grasped, providing a basis for predicting the energy storage capacity in the next cycle. The dynamic correction coefficient is used to standardize the change factors that affect the future charge-discharge ability of the energy storage unit, such as environmental temperature change, equipment aging, charge-discharge efficiency fluctuation, and grid dispatching strategy change. This coefficient can be obtained by establishing a mathematical model or fitting using historical data, or can be dynamically updated based on a prediction model trained with multi-cycle operation data. Then, according to the energy storage state of the current operation cycle and the dynamic correction coefficient, combined with the charge-discharge characteristics, load demand prediction, and dispatching strategy, the available charge capacity and the releasable electric energy of the energy storage unit in the next operation cycle are dynamically estimated, so as to determine the energy storage state in the next operation cycle.

[0036] The specific steps for determining the supply-demand matching degree between the centralized photovoltaic power station and the power consumption side according to the predicted power generation power, the energy storage state of the energy storage unit, and the predicted demand power may include: S1023: Determine the first matching index according to the deviation between the predicted power generation power and the predicted demand power; S1024: Determine the second matching index according to the energy storage state of the energy storage unit; S1025: Perform a fusion calculation on the first matching index and the second matching index to determine the supply-demand matching degree between the centralized photovoltaic power station and the power consumption side.

[0037] In the implementation manners of S1023 to S1025, the first matching index reflects the static deviation between the power generation side and the demand side, and the second matching index reflects the ability of power dynamic regulation. The deviation between the predicted power generation power and the predicted demand power refers to the numerical difference between the electric power that the centralized photovoltaic power station is expected to generate (i.e., the predicted power generation power) and the total electric power that multiple power consumption sides are expected to consume in the same cycle (i.e., the predicted demand power) within a certain operation cycle. This deviation can be used to measure the supply-demand balance degree between power generation and power consumption. The smaller the deviation, the more matching the power generation ability and the power consumption demand, and the higher the corresponding value of the first matching index; on the contrary, the larger the deviation, the lower the value of the first matching index. The first matching index mainly describes the initial matching relationship between the power generation side and the power consumption side from a static perspective, without considering factors such as energy storage regulation.

[0038] When the predicted power generation cannot fully meet the demand or there is an excess, dynamic power regulation is performed through the charging and discharging behavior of the energy storage unit to alleviate the problem of supply-demand mismatch. The energy storage state includes information such as the current state of charge (SoC), remaining capacity, maximum charging and discharging power, etc. Combining the scheduling flexibility within the prediction period, the effective regulation capacity of the energy storage system in the next period can be calculated. The stronger the energy storage regulation ability, the more it means that the system can still maintain balance in the face of uncertain loads or light fluctuations, and the higher the second matching index.

[0039] By introducing the second matching index, the system no longer simply relies on the power generation prediction results to make decisions, but comprehensively considers the regulation potential of the energy storage system, making the evaluation results more flexible and robust, and capable of coping with practical problems such as the uncertainty of photovoltaic output and the deviation of load prediction. In summary, the first matching index mainly reflects the static relationship between power generation capacity and load demand, while the second matching index reflects the dynamic ability of the system to achieve regulation balance through energy storage. The combination of the two can comprehensively reflect the supply-demand coordination ability between the centralized photovoltaic power station and each power consumption side in the next operation period, thus providing a more accurate basis for subsequent power scheduling strategies.

[0040] S103: Obtain the trusted level of each power consumption side, and determine the first power consumption side and the second power consumption side from multiple power consumption sides according to the supply-demand matching degree and the trusted level.

[0041] In this embodiment, the number of the first power consumption sides is positively correlated with the supply-demand matching degree, and the number of the second power consumption sides is negatively correlated with the supply-demand matching degree. That is, the higher the supply-demand matching degree, the better it indicates that the power generation capacity and energy storage regulation ability of the centralized photovoltaic power station can meet the power demands of each power consumption side in the next operation period, and the overall system is in a supply-demand balance state. Therefore, more power consumption sides can be included in the first power consumption sides to provide them with stable and reliable power supply, and the corresponding number of the second power consumption sides is less. On the contrary, the lower the supply-demand matching degree, the more difficult it is for the power generation capacity and energy storage regulation ability of the centralized photovoltaic power station to meet the power demands of all power consumption sides, and there is a large power supply gap or fluctuation risk. At this time, it is necessary to adjust and cut some power consumption sides through flexible power allocation strategies. Therefore, the number of the first power consumption sides is less, and the number of the second power consumption sides is more to ensure the stable operation of the system under limited resources.

[0042] And the trusted level of each power consumption side can be obtained through the following steps: S1031: Determine the difference degree between the historical predicted demand power and the historical actual demand power of each power consumption side; S1032: Determine the power influence ability of each power consumption side on other adjacent power consumption sides; S1033: Determine the trusted level of each power consumption side according to the difference degree and the power influence ability.

[0043] In the implementation manners of S1031 to S1033, by comparing the predicted demand power of the power consumption side with the actually collected power usage data in multiple past operation cycles, indexes such as the mean absolute deviation, standard deviation, or mean square error between the two are calculated to quantify the regularity and controllability of the demand fluctuation of the power consumption side. The smaller the difference degree, the stronger the predictability of the power demand of the power consumption side and the higher the stability of its scheduling execution. Since there may be a coupling effect of power fluctuation or voltage disturbance in the power grid for some power consumption sides, which constitutes a certain interference to the operating states of other power consumption sides, it is necessary to evaluate the influence degree of the operation change of a power consumption side on the surrounding power consumption sides. The lower the influence ability, the weaker the coupling and the stronger the independence of the power consumption side in the system, which is beneficial to the local isolation control and dynamic regulation of the overall system. The trusted level can be constructed by a weight model based on the above two indexes, or comprehensively evaluated by a scoring and ranking method.

[0044] Specifically, for a power consumption side with a lower difference degree and a greater influence ability, its trusted level is higher, indicating that the deviation between its historical predicted demand power and actual demand power is smaller, that is, its power demand is relatively stable and predictable. At the same time, because it has a strong power influence ability on adjacent power consumption sides, if it fails to obtain stable power support, it may trigger a chain effect, resulting in fluctuations in the operation of other power consumption sides in the system. Therefore, it is more suitable to be included in the first power consumption side to preferentially ensure its stable power supply. For a power consumption side with a greater difference degree and a smaller influence ability, the predictability of its power demand is poor, its operating state has stronger volatility, and its influence on other power consumption sides is weak. Even if its operation is unstable, the interference to the overall system is small. Therefore, its trusted level is relatively low, and it is more suitable to be classified into the second power consumption side and scheduled through flexible management strategies such as peak shaving and valley filling, off-peak operation, or dynamic power adjustment to avoid resource waste and ensure the overall stability of the system and the scientific nature of scheduling.

[0045] It should be noted that the above scheme is based on the premise of the importance degree of each power consumption side. If there are differences in the importance degree of each power consumption side, differential correction can be made based on the high or low importance degree.

[0046] And the specific steps for determining the power influence ability of each power consumption side on other adjacent power consumption sides may include: S10321: Determine the target power consumption side as the first node and the adjacent power consumption sides of the target power consumption side as the second nodes; S10322: Determine the first connection relationship between the first node and the second nodes according to the power supply influence propagation relationship between the target power consumption side and the adjacent power consumption sides; S10323: Determine the second connection relationship between the first node and the second nodes according to the power demand influence propagation relationship between the target power consumption side and the adjacent power consumption sides; S10324: Construct a topology diagram of the power influence ability for each target power consumption side according to the first connection relationship and the second connection relationship; S10325: Determine the power influence ability of each power consumption side on other adjacent power consumption sides according to the intersection relationship between the topology diagrams of the power influence ability.

[0047] In the implementation manners of S10321 to S10325, the currently analyzed power consumption side is taken as the first node, and all other power consumption sides in the system that are electrically connected to it, or geographically adjacent, or have interactions in power dispatching are regarded as its second nodes (i.e., adjacent nodes). The power supply influence propagation relationship reflects the possible influence on the voltage, current, or power quality of adjacent power consumption sides when a power mutation (such as a sharp increase in load or power outage) occurs in the target power consumption side. For example, the influence intensity can be quantified by simulating a short-term load fluctuation scenario and monitoring the responses of other nodes in the power grid. If the influence is significant, record this first connection relationship in the topology diagram and assign a corresponding influence weight. The power demand influence propagation relationship evaluates the induced effect of the target power consumption side on other nodes in the demand-side regulation. For example, when a sudden increase in demand occurs in a certain power consumption side, in order to maintain local supply-demand balance, it may trigger energy storage dispatching or current reallocation of adjacent nodes, thereby affecting their operating states. This relationship can also be determined through the co-variation pattern of load fluctuations in historical data. Incorporate the above two connection relationships into the graph structure uniformly. Each node represents a power consumption side, and the directed edges between nodes represent the direction and degree of influence. The topology diagram of the influence ability can be modeled and analyzed using an adjacency matrix, a graph theory model, or a network propagation algorithm to form the influence radius and influence intensity of each target node. By comparing the overlapping degree and the number of cross paths between the topology diagrams constructed for different power consumption sides, further evaluate the influence core degree of a power consumption side in the entire network. The nodes with more intersections and denser paths have stronger influence ability. The influence ability can be quantified using network centrality indicators or a propagation ability scoring model.

[0048] As an example, assume that there are power consumption sides A, B, C, and D in a centralized photovoltaic power station system. A is electrically connected to B and C, and B is connected to D. Analyze A. A is the first node, and B and C are the second nodes. When simulating a sudden increase in the load of A, the voltage of B drops by 2%, and there is no obvious change in C, indicating that there is a strong power supply propagation influence from A to B, and the influence from A to C is weak. At the same time, through the historical load demand data, it is found that when the load of A increases, the power response of B often fluctuates in a short time, indicating that there is also a demand propagation influence from A to B. The constructed topology diagram shows that the connection weight of A→B is high, and the weight of A→C is low. After cross-analysis with the topology diagrams of other nodes, it is found that the influence paths of A on B and indirectly on D are relatively obvious. Therefore, the power consumption side A has a strong power influence ability on adjacent power consumption sides.

[0049] Specifically, for the power consumption side with a lower degree of difference and a greater influence ability, the higher its trusted level indicates that it is more suitable to be included in the first power consumption side to obtain stable power support; while for the power consumption side with a greater degree of difference and a smaller influence ability, its trusted level is relatively lower, and it is more suitable to be classified into the second power consumption side to implement flexible management strategies, thereby enhancing the stability of the overall system operation and the scientific nature of scheduling.

[0050] The specific steps for determining the first power consumption side and the second power consumption side from multiple power consumption sides according to the supply-demand matching degree and the trusted level further include: S1034: Determine the quantities of the first power consumption side and the second power consumption side according to the supply-demand matching degree; S1035: Determine the first power consumption side with a matching quantity from multiple power consumption sides according to the high or low trusted level, and determine the remaining power consumption sides as the second power consumption side.

[0051] In the implementation manners of S1034 to S1035, the supply-demand matching degree reflects whether the power supply capacity of the centralized photovoltaic power station can meet the demands of all power consumption sides. When the supply-demand matching degree is high, it indicates that the predicted power generation power and the energy storage state can meet or nearly meet the power demands of all power consumption sides. At this time, more power consumption sides can be classified as the first power consumption side to provide stable power supply for them and ensure that their operations are not affected by power fluctuations; while when the supply-demand matching degree is low, it indicates that the power station cannot fully meet the demands of all power consumption sides. To ensure the system stability and the operation of key loads, it is necessary to reduce the quantity of the first power consumption side and appropriately increase the quantity of the second power consumption side, so as to implement strategies such as curtailment, peak shifting or dynamic control for these power consumption sides with flexible power adjustment.

[0052] The higher the trusted level indicates that the predicted load of this power consumption side is more stable, the error is smaller, and at the same time, the influence ability on other power consumption sides is stronger. Therefore, it is more suitable to be classified into the first power consumption side to obtain stable power supply. Based on this, when the system determines the first and second power consumption sides, it preferentially selects several with the highest trusted level from all power consumption sides and classifies them as the first power consumption side.

[0053] As an example, if the supply-demand matching degree is 0.8, it can support 8 first power consumption sides, then select the power consumption sides ranked top 8 in terms of trusted level as the first power consumption side. The remaining power consumption sides are automatically classified as the second power consumption side.

[0054] Through the above steps, not only the dynamic matching between the quantity of power consumption sides and the power station capacity is achieved, but also the differential management is carried out based on the attributes of the power consumption sides themselves.

[0055] S104: Execute the first power management strategy for the first power consumption side and execute the second power management strategy for the second power consumption side.

[0056] In this embodiment, after completing the evaluation of the trust levels of multiple power consumption sides and dividing the first power consumption side and the second power consumption side according to the supply-demand matching degree, different power management strategies can be adopted for dispatching control according to the importance of the power consumption side and its power stability requirements. Specifically, the first power management strategy is for the first power consumption side with a relatively high trust level, high operation stability requirements, strong prediction accuracy, and significant influence on other nodes in the system. The goal of the strategy is to ensure its continuous, stable, and priority power supply as much as possible. This usually includes preferentially allocating power generation, reasonably using energy storage resources, and ensuring that it can still operate normally under load fluctuations or power supply shortages to reduce the possible risk of system-level chain fluctuations. The second power management strategy is for the second power consumption side with a relatively low trust level, large deviation in power demand prediction, and small impact on the overall operation of the system. The focus of this strategy is to improve flexibility and regulation elasticity, usually including implementing dynamic load curtailment, demand response, time-of-use power limitation, or dispatching delay for this type of power consumption side to achieve optimal allocation of resources on the basis of ensuring the overall supply-demand balance of the system. Through such differentiated management means, the stability, power utilization efficiency, and refinement level of dispatching control of the entire centralized photovoltaic power station system can be effectively improved under different supply-demand states.

[0057] The power consumption side power management method of the centralized photovoltaic power station provided by this application evaluates the supply-demand matching degree from multiple dimensions by obtaining the predicted power generation of the photovoltaic power station, the energy storage state of the energy storage unit, and the power demand of the power consumption side, ensuring that the system can accurately identify the relationship between power generation and demand and solving the problem of information error accumulation caused by supply-demand imbalance. In addition, through the evaluation of the trust level of the power consumption side, the stability, reliability, and dispatchability of the power consumption side are fully considered and combined with the supply-demand matching degree to ensure that the system preferentially guarantees the power supply demand of the high-trust power consumption side and reduces the interference of the low-trust power consumption side on the system stability, enabling the system to respond quickly and achieve dynamic dispatch when the power supply fluctuates or the demand changes. Moreover, by separately managing the first power consumption side and the second power consumption side and adopting different power management strategies, the first power management strategy ensures the stability of the high-priority power consumption side, and the second power management strategy enhances the flexible regulation ability of the lower-priority power consumption side, thereby improving the adaptability and robustness of the system under various load conditions, optimizing the overall operation efficiency of the photovoltaic power station, enabling the system to improve the power utilization rate and reduce power abandonment while ensuring power supply. Overall, the precise dispatching, efficient load regulation strategy, and multi-dimensional demand response mechanism of power management in this application guarantee the stability of the photovoltaic power station and effectively improve the precision and stability of power management.

[0058] In a second aspect, based on the same inventive concept, referring to Figure 2, which shows a power management system 200 on the power consumption side of a centralized photovoltaic power station provided by an embodiment of the present application. The system includes: A first acquisition module 201, configured to acquire the predicted power generation of the centralized photovoltaic power station in the next operation cycle; A second acquisition module 202, configured to acquire the energy storage state of the energy storage unit in the centralized photovoltaic power station in the next operation cycle and the predicted demand power of multiple power consumption sides in the next operation cycle, and determine the supply-demand matching degree between the centralized photovoltaic power station and the power consumption side according to the predicted power generation, the energy storage state of the energy storage unit, and the predicted demand power; A third acquisition module 203, configured to acquire the trusted level of each power consumption side, and determine a first power consumption side and a second power consumption side from multiple power consumption sides according to the supply-demand matching degree and the trusted level. The number of the first power consumption sides is positively correlated with the supply-demand matching degree, and the number of the second power consumption sides is negatively correlated with the supply-demand matching degree; An execution module 204, configured to execute a first power management strategy for the first power consumption side and a second power management strategy for the second power consumption side. The first power management strategy is used to provide stable power supply for the first power consumption side, and the second power management strategy is used to provide flexible power supply for the second power consumption side.

[0059] In an embodiment of the second aspect of the present application, the first acquisition module includes: A first acquisition sub-module, configured to acquire the historical photovoltaic conversion efficiency of the centralized photovoltaic power station and the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; A first feature construction sub-module, configured to construct input features in a first dimension according to the historical photovoltaic conversion efficiency of the centralized photovoltaic power station; A second feature construction sub-module, configured to construct input features in a second dimension according to the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; A power determination sub-module, configured to input the input features in the first dimension and the input features in the second dimension into a pre-trained power generation prediction model, and output the predicted power generation of the centralized photovoltaic power station in the next cycle.

[0060] In an embodiment of the second aspect of the present application, the second acquisition module includes: A second acquisition sub-module, configured to acquire the energy storage state of the energy storage unit in the centralized photovoltaic power station in the current operation cycle; A correction sub-module, configured to acquire a dynamic correction coefficient of the energy storage state, and determine the energy storage state of the energy storage unit in the centralized photovoltaic power station in the next operation cycle according to the energy storage state in the current operation cycle and the dynamic correction coefficient.

[0061] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer program or instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0062] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0063] In the present application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0064] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0065] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0066] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0067] In several embodiments provided in this application, it should be understood that the disclosed systems, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or units can be electrical, mechanical, or other forms.

[0068] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0069] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0070] If a function 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 this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0071] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. A power management method for the power consumption side of a centralized photovoltaic power station, characterized in that The method includes: Obtaining the predicted power generation of the centralized photovoltaic power station for the next operation cycle; Obtaining the energy storage state of the energy storage unit in the centralized photovoltaic power station for the next operation cycle and the predicted demand power of multiple power consumption sides for the next operation cycle, and determining the supply-demand matching degree between the centralized photovoltaic power station and the power consumption sides according to the predicted power generation, the energy storage state of the energy storage unit, and the predicted demand power; Obtaining the trust level of each power consumption side, and determining a first power consumption side and a second power consumption side from the multiple power consumption sides according to the supply-demand matching degree and the trust level. The number of the first power consumption sides is positively correlated with the supply-demand matching degree, and the number of the second power consumption sides is negatively correlated with the supply-demand matching degree; Executing a first power management strategy for the first power consumption side and a second power management strategy for the second power consumption side. The first power management strategy is used to provide stable power supply for the first power consumption side, and the second power management strategy is used to provide flexible power supply for the second power consumption side.

2. The power management method for the power consumption side of the centralized photovoltaic power station according to claim 1, characterized in that The obtaining the predicted power generation of the centralized photovoltaic power station for the next operation cycle includes: Obtaining the historical photovoltaic conversion efficiency of the centralized photovoltaic power station and the predicted weather data of the area where the centralized photovoltaic power station is located for the next cycle; Constructing input features in the first dimension according to the historical photovoltaic conversion efficiency of the centralized photovoltaic power station; Constructing input features in the second dimension according to the predicted weather data of the area where the centralized photovoltaic power station is located for the next cycle; Inputting the input features in the first dimension and the input features in the second dimension into a pre-trained power generation prediction model, and outputting the predicted power generation of the centralized photovoltaic power station for the next cycle.

3. The power management method for the power consumption side of a centralized photovoltaic power station according to claim 1, wherein The obtaining the energy storage state of the energy storage unit in the centralized photovoltaic power station for the next operation cycle includes: Obtaining the energy storage state of the energy storage unit in the centralized photovoltaic power station for the current operation cycle; Obtaining the dynamic correction coefficient of the energy storage state, and determining the energy storage state of the energy storage unit in the centralized photovoltaic power station for the next operation cycle according to the energy storage state of the current operation cycle and the dynamic correction coefficient.

4. The power management method on the power consumption side of the centralized photovoltaic power station according to claim 1, characterized in that The determining the supply-demand matching degree between the centralized photovoltaic power station and the power consumption sides according to the predicted power generation, the energy storage state of the energy storage unit, and the predicted demand power includes: Determining a first matching index according to the deviation between the predicted power generation and the predicted demand power; Determining a second matching index according to the energy storage state of the energy storage unit; Performing fusion calculation on the first matching index and the second matching index to determine the supply-demand matching degree between the centralized photovoltaic power station and the power consumption sides.

5. The power management method on the power consumption side of the centralized photovoltaic power station according to claim 1, wherein The trust level of each power consumption side is determined through the following steps: Determining the difference degree between the historical predicted demand power and the historical actual demand power of each power consumption side; Determining the power influence ability of each power consumption side on other adjacent power consumption sides; Determining the trust level of each power consumption side according to the difference degree and the power influence ability.

6. The power management method on the power consumption side of the centralized photovoltaic power station according to claim 5, characterized in that, The determining the power influence ability of each power consumption side on other adjacent power consumption sides includes: Determine the target power consumption side as the first node, and determine the adjacent power consumption side of the target power consumption side as the second node; Determine the first connection relationship between the first node and the second node according to the power supply influence propagation relationship between the target power consumption side and the adjacent power consumption side; Determine the second connection relationship between the first node and the second node according to the power demand influence propagation relationship between the target power consumption side and the adjacent power consumption side; Construct a power influence capacity topology graph for each target power consumption side according to the first connection relationship and the second connection relationship; Determine the power influence capacity of each power consumption side on other adjacent power consumption sides according to the intersection relationship between the power influence capacity topology graphs; 7. The power management method for the power consumption side of a centralized photovoltaic power station according to claim 1, characterized in that, The determining the first power consumption side and the second power consumption side from the multiple power consumption sides according to the supply-demand matching degree and the trust level includes: Determine the quantities of the first power consumption side and the second power consumption side according to the supply-demand matching degree; Determine the matching quantity of the first power consumption sides from the multiple power consumption sides according to the level of the trust level, and determine the remaining power consumption sides as the second power consumption sides; 8. A power management system on the power consumption side of a centralized photovoltaic power station, characterized in that, A system for implementing the method according to any one of claims 1-7, the system includes: A first acquisition module, configured to acquire the predicted power generation power of the centralized photovoltaic power station in the next operation cycle; A second acquisition module, configured to acquire the energy storage state of the energy storage unit in the centralized photovoltaic power station in the next operation cycle and the predicted demand power of multiple power consumption sides in the next operation cycle, and determine the supply-demand matching degree between the centralized photovoltaic power station and the power consumption sides according to the predicted power generation power, the energy storage state of the energy storage unit, and the predicted demand power; A third acquisition module, configured to acquire the trust level of each power consumption side, and determine a first power consumption side and a second power consumption side from the multiple power consumption sides according to the supply-demand matching degree and the trust level, wherein the quantity of the first power consumption side is positively correlated with the supply-demand matching degree, and the quantity of the second power consumption side is negatively correlated with the supply-demand matching degree; An execution module, configured to execute a first power management strategy for the first power consumption side and execute a second power management strategy for the second power consumption side, wherein the first power management strategy is used to provide stable power supply for the first power consumption side, and the second power management strategy is used to provide flexible power supply for the second power consumption side; 9. The power management system on the power consumption side of the centralized photovoltaic power station according to claim 8, wherein The first acquisition module includes: A first acquisition sub-module, configured to acquire the historical photovoltaic conversion efficiency of the centralized photovoltaic power station and the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; A first feature construction sub-module, configured to construct input features in a first dimension according to the historical photovoltaic conversion efficiency of the centralized photovoltaic power station; A second feature construction sub-module, configured to construct input features in a second dimension according to the predicted weather data of the area where the centralized photovoltaic power station is located in the next cycle; A power determination sub-module, configured to input the input features in the first dimension and the input features in the second dimension into a pre-trained power generation power prediction model, and output the predicted power generation power of the centralized photovoltaic power station in the next cycle; 10. The power management system on the electricity consumption side of the centralized photovoltaic power station according to claim 8, characterized in that, The second acquisition module includes: The second acquisition sub-module is used to acquire the energy storage state of the energy storage unit in the current operation cycle of the centralized photovoltaic power station; The correction sub-module is used to acquire the dynamic correction coefficient of the energy storage state, and determine the energy storage state of the energy storage unit in the next operation cycle of the centralized photovoltaic power station according to the energy storage state of the current operation cycle and the dynamic correction coefficient.

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