Intelligent photovoltaic power distribution method, system and device and storage medium

Abstract

The invention provides an intelligent photovoltaic power distribution method, system and device and a storage medium, and relates to the technical field of electric energy distribution, and the method comprises the steps: predicting the target power generation amount of a photovoltaic power generation device in a preset time period through combining the environment information of the photovoltaic power generation device and the initial power generation amount at the current moment; obtaining a historical power distribution strategy of a plurality of electric devices corresponding to the photovoltaic power generation equipment, and generating an initial power distribution strategy of each electric device within a preset time length in combination with the target power generation amount and the historical power distribution strategy; and adjusting the initial power distribution strategy by combining the load state and the equipment characteristic of each electric equipment at the current moment, generating a target power distribution strategy, and performing power distribution on the electric equipment according to the target power distribution strategy within a preset duration. The method has the technical effects that the flexibility and pertinence of power distribution are improved, and the dynamic power utilization requirement of the electric equipment is met.

Description

Technical Field

[0001] The present application relates to the technical field of electric energy distribution, and in particular to an intelligent photovoltaic power distribution method, system, device and storage medium. Background Art

[0002] With the continuous development of photovoltaic power generation technology, its application in industrial production and daily life is becoming more and more extensive. However, due to the strong instability and intermittent characteristics of photovoltaic power generation, how to reasonably allocate power resources according to the real-time power generation status and environmental conditions of power generation equipment to meet the power demand of different power-consuming equipment has become a technical problem that needs to be solved urgently.

[0003] In the prior art, a fixed power distribution scheme is usually adopted to distribute power according to the rated power of the power-consuming equipment or the historical power consumption data. Although this method solves the problem of power resource allocation to a certain extent, due to the lack of perception and dynamic adjustment mechanism of the real-time operating status of photovoltaic power generation equipment and power-consuming equipment, the power distribution lacks flexibility and pertinence, and cannot meet the dynamic power consumption demand of power-consuming equipment. Summary of the invention

[0004] The present application provides an intelligent photovoltaic power distribution method, system, device and storage medium for improving the flexibility and pertinence of power distribution and meeting the dynamic power demand of power-consuming equipment.

[0005] In a first aspect, the present application provides an intelligent photovoltaic power distribution method, the method comprising: obtaining the initial power generation of the photovoltaic power generation equipment at the current moment, and environmental information of the environment in which the photovoltaic power generation equipment is located; combining the environmental information and the initial power generation, predicting the target power generation of the photovoltaic power generation equipment within a preset time period; obtaining the historical power distribution strategies of multiple power-consuming devices corresponding to the photovoltaic power generation equipment, combining the target power generation and the historical power distribution strategies, generating an initial power distribution strategy for each of the power-consuming devices within the preset time period; obtaining the load status of each of the power-consuming devices at the current moment, and the device characteristics of each of the power-consuming devices; combining the load status and the device characteristics of each of the power-consuming devices, adjusting the initial power distribution strategy, generating a target power distribution strategy, and distributing power to each of the power-consuming devices according to the target power distribution strategy within the preset time period.

[0006] By adopting the above technical solution, by obtaining the initial power generation and environmental information of the photovoltaic power generation equipment, the target power generation within the preset time period can be accurately predicted, and the initial power distribution strategy can be generated in combination with the historical power distribution strategy of the power-consuming equipment. Then, based on the real-time load status and equipment characteristics of the power-consuming equipment, the initial power distribution strategy is dynamically adjusted to form a more reasonable target power distribution strategy, which can not only improve the utilization efficiency of photovoltaic power generation resources, but also ensure that the power demand of important power-consuming equipment is given priority, effectively improve the flexibility and pertinence of power distribution, and meet the dynamic power demand of power-consuming equipment.

[0007] Optionally, the combination of the environmental information and the initial power generation to predict the target power generation of the photovoltaic power generation equipment within a preset time period includes: predicting the power generation change trend of the photovoltaic power generation equipment within the preset time period based on the initial power generation; determining the first power generation of the photovoltaic power generation equipment within the preset time period based on the power generation change trend; and adjusting the first power generation based on the environmental information to obtain the target power generation of the photovoltaic power generation equipment within the preset time period.

[0008] By adopting the above technical scheme, the power generation change trend is predicted based on the initial power generation and the first power generation is determined, and then the first power generation is adjusted in combination with environmental information to obtain the target power generation. This technical scheme can superimpose the influence of environmental factors on the historical change law of power generation, thereby improving the power generation prediction accuracy of photovoltaic power generation equipment within a preset time period, and providing more reliable data support for subsequent power distribution strategies.

[0009] Optionally, the environmental information includes light intensity and temperature, and adjusting the first power generation according to the environmental information to obtain the target power generation of the photovoltaic power generation equipment within a preset time period includes: obtaining a first changing trend of the light intensity and a second changing trend of the temperature within a preset time period; adjusting the first power generation according to the first changing trend to obtain a second power generation; wherein the second power generation is positively correlated with the first changing trend; adjusting the second power generation according to the second changing trend to obtain the target power generation of the photovoltaic power generation equipment within a preset time period; wherein, when the temperature value represented by the second changing trend is higher than a preset temperature threshold, the target power generation is negatively correlated with the second changing trend; when the temperature value represented by the second changing trend is not higher than the preset temperature threshold, the target power generation is positively correlated with the second changing trend.

[0010] By adopting the above technical scheme, by respectively obtaining the first change trend of light intensity and the second change trend of temperature, and obtaining the second power generation according to the positive correlation between light intensity and power generation, and then considering the dual influence mechanism of temperature on photovoltaic power generation, that is, the positive correlation when the temperature is lower than the preset temperature threshold and the negative correlation when the temperature is higher than the preset temperature threshold, the second power generation is further adjusted to obtain the target power generation, thereby realizing the accurate modeling of the impact of the two key environmental factors of light and temperature on photovoltaic power generation, and improving the prediction accuracy of the target power generation.

[0011] Optionally, the target power generation and the historical power allocation strategy are combined to generate an initial power allocation strategy for each of the power users within the preset time period, including: obtaining the historical power consumption proportion of each of the power users in the historical power allocation strategy; allocating the target power generation according to the historical power consumption proportion of each of the power users, to generate an initial power allocation strategy for each of the power users within the preset time period.

[0012] By adopting the above technical solution, the historical power consumption proportion of each power-consuming equipment in the historical power distribution strategy is obtained, and the target power generation is distributed based on the proportion to generate an initial power distribution strategy. The historical power consumption pattern of the power-consuming equipment can be used as a benchmark to ensure the continuity and stability of power distribution, avoid drastic fluctuations in power distribution, and provide a reasonable initial solution for subsequent refined adjustments.

[0013] Optionally, the initial power allocation strategy is adjusted in combination with the load state and the device characteristics of each of the electrical devices to generate a target power allocation strategy, including: obtaining the load level corresponding to the load state of each of the electrical devices, and the device priority corresponding to the device characteristics; wherein the load level includes a high load state, a medium load state and a low load state; the device priority is used to characterize the importance of each of the electrical devices; when there is a first electrical device whose load level is the high load state and whose device priority is higher than a first preset priority, increasing the power allocation amount of the first electrical device in the target power allocation strategy; when there is a second electrical device whose load level is the low load state and / or whose device priority is lower than a second preset priority, reducing the power allocation amount of the second electrical device in the target power allocation strategy; when there is a third electrical device whose load level is the medium load state and whose device priority is between the first preset priority and the second preset priority, keeping the power allocation amount of the third electrical device in the target power allocation strategy unchanged.

[0014] By adopting the above technical scheme, by comprehensively considering the load level and equipment priority of the electrical equipment, the power allocation amount for the first electrical equipment that is in a high load state and has a high priority is increased, and the power allocation amount for the second electrical equipment that is in a low load state or has a low priority is reduced. At the same time, the power allocation amount for the third electrical equipment that is in a medium load state and has a moderate priority remains unchanged, thereby achieving differentiated power distribution based on load status and importance, which not only guarantees the power demand of important equipment under high load, but also avoids the waste of power resources for low load or low priority equipment, thereby improving the efficiency of power resource utilization.

[0015] Optionally, after distributing power to each of the electrical devices according to the target power distribution strategy within the preset time period, it also includes: monitoring the real-time load status of each of the electrical devices, and when it is detected that the real-time load status of any of the electrical devices has changed, obtaining the changed load status and corresponding device characteristics; adjusting the target power distribution strategy in real time according to the changed load status and the corresponding device characteristics to obtain an updated target power distribution strategy; and re-distributing power to each of the electrical devices according to the updated target power distribution strategy.

[0016] By adopting the above technical solution, the load status of electrical equipment is monitored in real time, and new load status and equipment characteristics are obtained when load changes are detected. The target power distribution strategy is adjusted and updated in real time accordingly, and then power is redistributed, thereby achieving dynamic response and real-time optimization of the power distribution strategy, enabling the distribution system to adapt to load changes of electrical equipment in a timely manner, and ensuring that power resources are always reasonably allocated according to the latest power demand.

[0017] Optionally, after allocating power to each of the power-consuming devices according to the target power allocation strategy within the preset time period, it also includes: obtaining the actual power consumption of each of the power-consuming devices within the preset time period; and updating the historical power allocation strategy of each of the power-consuming devices based on the actual power consumption of each of the power-consuming devices.

[0018] By adopting the above technical solution, a continuous optimization feedback mechanism is established by obtaining the actual power consumption of power-consuming equipment within a preset period of time and updating the historical power distribution strategy accordingly, so that the system can continuously adjust and improve the power distribution strategy based on the latest power consumption data, thereby improving the accuracy and adaptability of subsequent power distribution plans, and realizing self-optimization and iterative evolution of the power distribution strategy.

[0019] In a second aspect, the present application provides an intelligent photovoltaic power distribution system, the system comprising: a first acquisition module, a prediction module, a generation module, a second acquisition module, and an output module; wherein the first acquisition module is used to acquire the initial power generation of the photovoltaic power generation device at the current moment, and the environmental information of the environment in which the photovoltaic power generation device is located; the prediction module is used to combine the environmental information and the initial power generation to predict the target power generation of the photovoltaic power generation device within a preset time period; the generation module is used to acquire the historical power allocation strategies of multiple power-consuming devices corresponding to the photovoltaic power generation device, and combine the target power generation and the historical power allocation strategies to generate the initial power allocation strategy of each of the power-consuming devices within the preset time period; the second acquisition module is used to acquire the load state of each of the power-consuming devices at the current moment, and the device characteristics of each of the power-consuming devices; the output module is used to adjust the initial power allocation strategy in combination with the load state and the device characteristics of each of the power-consuming devices, generate a target power allocation strategy, and allocate power to each of the power-consuming devices according to the target power allocation strategy within the preset time period.

[0020] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned intelligent photovoltaic distribution methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned intelligent photovoltaic power distribution methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: By obtaining the initial power generation and environmental information of photovoltaic power generation equipment, the target power generation within a preset time period can be accurately predicted, and the initial power distribution strategy can be generated in combination with the historical power distribution strategy of the power-consuming equipment. The initial power distribution strategy is then dynamically adjusted based on the real-time load status and equipment characteristics of the power-consuming equipment, thereby forming a more reasonable target power distribution strategy, which can not only improve the utilization efficiency of photovoltaic power generation resources, but also ensure that the power demand of important power-consuming equipment is given priority, effectively improve the flexibility and pertinence of power distribution, and meet the dynamic power demand of power-consuming equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of an intelligent photovoltaic power distribution method provided in an embodiment of the present application; Figure 2It is a structural schematic diagram of an intelligent photovoltaic power distribution system provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] Figure 1 Schematic diagram of a process flow of an intelligent photovoltaic power distribution method provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining the initial power generation of the photovoltaic power generation equipment at the current moment and the environmental information of the environment in which the photovoltaic power generation equipment is located.

[0028] In this embodiment, in order to realize the intelligent power distribution of photovoltaic power generation equipment, it is first necessary to obtain the initial power generation and environmental information of the photovoltaic power generation equipment at the current moment. The photovoltaic power generation equipment may include a solar panel array, an inverter, and a power distribution control unit, and the environmental information includes but is not limited to light intensity and temperature.

[0029] Specifically, the power sensor installed on the photovoltaic power generation equipment can collect the power generation data at the current moment in real time, and the collected power generation data can be used as the initial power generation; at the same time, the light intensity and temperature data at the current moment can be collected by the light sensor and temperature sensor installed around the photovoltaic power generation equipment. The light sensor can use devices such as photoresistors or photodiodes, and the temperature sensor can use devices such as thermocouples or thermistors to ensure the accuracy and real-time nature of the collected data.

[0030] S102, combining environmental information and initial power generation, predicting a target power generation of the photovoltaic power generation equipment within a preset time period.

[0031] In this embodiment, after obtaining the initial power generation and environmental information of the photovoltaic power generation equipment, it is necessary to predict the power generation of the photovoltaic power generation equipment within a preset time period in order to reasonably allocate power resources. The preset time period can be flexibly set according to the actual application scenario, for example, it can be set to 1 hour, 2 hours or 4 hours.

[0032] In the specific implementation, firstly, based on the initial power generation, a time series prediction algorithm (such as moving average method, exponential smoothing method, etc.) is used to predict the power generation change trend of photovoltaic power generation equipment within a preset time period to obtain the first power generation. This prediction process mainly considers the time continuity characteristics of power generation, and estimates the basic value of power generation in the future by analyzing the historical change law of power generation.

[0033] Subsequently, the first power generation needs to be corrected in combination with the environmental information. Specifically, the first change trend of the light intensity and the second change trend of the temperature within a preset time period are obtained. These trend information can be obtained through meteorological forecast data or a prediction model based on historical environmental data. First, the first power generation is adjusted according to the first change trend to obtain the second power generation. Since the light intensity is positively correlated with the power generation efficiency, the second power generation will increase with the increase of the light intensity. Then, the second power generation is adjusted according to the second change trend to obtain the final target power generation.

[0034] It should be noted that the effect of temperature on power generation efficiency has a critical characteristic: when the temperature is lower than the preset temperature threshold (for example, 45°C), the temperature increase is conducive to improving the power generation efficiency, and the target power generation is positively correlated with the second change trend; but when the temperature exceeds the preset temperature threshold, the excessively high temperature will reduce the photoelectric conversion efficiency of the solar panel, and the target power generation is negatively correlated with the second change trend.

[0035] Based on the above embodiment, as an optional implementation, in S102, combining environmental information and initial power generation, predicting the target power generation of the photovoltaic power generation equipment within a preset time period specifically includes S21-S23: S21, predicting the power generation change trend of the photovoltaic power generation equipment within a preset time period based on the initial power generation.

[0036] In this embodiment, in order to accurately predict the target power generation of the photovoltaic power generation equipment within the preset time, it is first necessary to predict the power generation change trend based on the initial power generation at the current moment. This prediction is of great significance for subsequent power distribution, because photovoltaic power generation has obvious time series characteristics, and the power generation in adjacent time periods usually has a certain correlation.

[0037] In specific implementation, first obtain the historical power generation data of the photovoltaic power generation equipment for a period of time before the current moment (for example, the previous 4 hours), and combine these data with the initial power generation at the current moment. Then, the power generation trend is predicted by calculating the change rate of the historical data. For example, the power generation difference at each adjacent time point can be calculated to obtain the growth or decline rate of power generation. For the power generation change trend within a preset time period, it can be predicted based on the change rate at the last few time points, where the change rate closer to the current moment has a higher reference value.

[0038] In the prediction process, the daily cycle characteristics of photovoltaic power generation need to be considered. For example, when predicting the power generation trend after 10 am on a sunny day, you can focus on the power generation change pattern of the same period on sunny days recently. Specifically, you can select data from the same period with similar weather conditions in the last 3-5 days, observe the changing characteristics of its power generation, such as the rising rate, peak time, etc., so as to predict the power generation change trend within the current preset time.

[0039] S22, determining a first power generation of the photovoltaic power generation equipment within a preset time period according to a power generation change trend.

[0040] In the specific implementation, it is first necessary to calculate the power generation based on the initial power generation and the obtained power generation change trend. Taking the preset duration of 2 hours as an example, the preset duration can be divided into several time periods (such as 15 minutes as a time period), and the power generation of each time period is predicted. In the prediction process, for each time period, the initial power generation is combined with the change rate corresponding to the time period to obtain the predicted power generation of the time period. For example, if the initial power generation at the current moment is 10kW, and the change rate of a certain time period increases by 5% every 15 minutes, then the predicted power generation of the time period is 10.5kW. By calculating and accumulating section by section, the first power generation within the preset time length can be obtained.

[0041] During the calculation process, the continuity characteristics of photovoltaic power generation need to be considered. Since photovoltaic power generation usually does not change suddenly, the power generation in adjacent time periods should have the characteristics of smooth transition. Therefore, when determining the predicted power generation of each time period, the correlation with adjacent time periods should be considered to avoid unreasonable jumps. For example, if it is found that the predicted value of a certain time period is too different from the previous and next time periods (such as the change in power generation in adjacent time periods exceeds 20%), the predicted value needs to be smoothed to make it more consistent with the gradual characteristics of power generation.

[0042] S23, adjusting the first power generation according to the environmental information to obtain a target power generation of the photovoltaic power generation equipment within a preset time period.

[0043] Based on the above embodiment, as an optional implementation, in S23, the environmental information includes light intensity and temperature, and the first power generation is adjusted according to the environmental information to obtain the target power generation of the photovoltaic power generation equipment within the preset time, which specifically includes S231-S233: S231, obtaining a first change trend of light intensity and a second change trend of temperature within a preset time period.

[0044] S232, adjusting the first power generation according to the first change trend to obtain a second power generation; wherein the second power generation is positively correlated with the first change trend.

[0045] S233, according to the second change trend, the second power generation is adjusted to obtain the target power generation of the photovoltaic power generation equipment within the preset time period; wherein, when the temperature value represented by the second change trend is higher than the preset temperature threshold, the target power generation is negatively correlated with the second change trend; when the temperature value represented by the second change trend is not higher than the preset temperature threshold, the target power generation is positively correlated with the second change trend.

[0046] In this embodiment, in order to make the power generation prediction more accurate, the impact of environmental factors on photovoltaic power generation needs to be considered. Since photovoltaic power generation efficiency is mainly affected by light intensity and temperature, the first power generation needs to be further adjusted based on these two environmental factors to obtain a more realistic target power generation.

[0047] In the specific implementation, firstly, the environmental data within the preset time period is collected through the light sensor and the temperature sensor. By analyzing the changes in light intensity at adjacent time points, the first change trend of light intensity is obtained; similarly, by analyzing the changes in temperature data, the second change trend of temperature is obtained. For example, if the light intensity gradually increases from 800W / m² to 1000W / m² during a period of time, it can be determined that the first change trend of this period is an upward trend with an increase of 25%; if the temperature gradually increases from 25℃ to 40℃, it can be determined that the second change trend of this period is an upward trend with an increase of 60%.

[0048] Then, the first power generation is adjusted according to the first change trend. Since light intensity is positively correlated with photoelectric conversion efficiency, the second power generation should increase with the increase of light intensity. When making specific adjustments, the proportional coefficient of light intensity change to power generation change can be set. For example, when the light intensity increases by 25%, the first power generation can be increased by 20% to obtain the second power generation, reflecting the positive effect of light intensity on power generation efficiency.

[0049] Next, consider the effect of temperature on power generation efficiency. The relationship between temperature and power generation efficiency has critical characteristics, and needs to be processed in stages according to the preset temperature threshold (such as 35°C). When the temperature is lower than the preset temperature threshold, the temperature increase is conducive to improving the photoelectric conversion efficiency. At this time, the target power generation should increase with the increase in temperature; when the temperature exceeds the preset temperature threshold, the excessively high temperature will reduce the photoelectric conversion efficiency. At this time, the target power generation should decrease with the increase in temperature. For example, when the temperature is 30°C (lower than the threshold) and is on an upward trend, the second power generation can be increased by 5% to obtain the target power generation; when the temperature is 40°C (higher than the threshold) and is on an upward trend, the second power generation needs to be reduced by 10% to obtain the target power generation.

[0050] S103, obtaining historical power allocation strategies of multiple power-consuming devices corresponding to the photovoltaic power generation device, and generating an initial power allocation strategy for each power-consuming device within a preset time period in combination with the target power generation amount and the historical power allocation strategies.

[0051] In this embodiment, after the target power generation of the photovoltaic power generation equipment within the preset time is predicted, the power generation needs to be initially allocated. In order to ensure the rationality of the allocation, this step makes full use of the guidance of historical data and formulates an initial allocation plan by analyzing historical power allocation strategies.

[0052] In the specific implementation, it is first necessary to obtain the historical power distribution strategies of multiple power-consuming devices corresponding to the photovoltaic power generation equipment. These historical data can be stored in the database of the power distribution system, which contains the power consumption records of each power-consuming device in different time periods. Based on these historical data, the historical power consumption proportion of each power-consuming device is calculated. For example, if the average power consumption of a certain power-consuming device in the past period of time accounts for 20% of the total power consumption, then its historical power consumption proportion is 0.2. In order to improve the reference value of historical data, you can select historical time periods similar to the current period (such as the same period of workdays, records under similar weather conditions, etc.) for statistical analysis, so as to obtain a more representative historical power consumption proportion.

[0053] After obtaining the historical electricity consumption ratio of each power-consuming device, use it as a weight coefficient and multiply it with the predicted target power generation to obtain the initial power allocation strategy for each power-consuming device within the preset time. For example, if the target power generation is 100kWh and the historical electricity consumption ratio of a certain power-consuming device is 0.2, then the initial power allocation of the device is 20kWh. It should be noted that in order to avoid the greater impact of accidental factors in historical data, the weighted average of multiple historical periods can be used as the final historical power consumption ratio, among which more recent historical data can be given a higher weight.

[0054] Based on the above embodiment, as an optional implementation, in S103, combining the target power generation and the historical power allocation strategy, generating the initial power allocation strategy for each power-consuming device within a preset time period specifically includes S31-S32: S31, obtaining the historical power consumption proportion of each power-consuming device in the historical power distribution strategy.

[0055] S32, allocating the target power generation according to the historical power consumption proportion of each power-consuming device, and generating an initial power allocation strategy for each power-consuming device within a preset time period.

[0056] In the specific implementation, it is first necessary to obtain the historical power distribution strategy of each power-consuming device from the database of the power distribution system. In order to improve the reference value of historical data, historical records similar to the current time period conditions can be selected for analysis, such as selecting power consumption data for the same period in the past month (for example, from 10 am to 12 pm on weekdays). After obtaining the historical data, calculate the proportion of each power-consuming device in the total power consumption. For example, the power consumption of a refrigeration device in the historical records is 200kWh, and the total power consumption of all equipment in the same period is 1000kWh. The historical power consumption proportion of this device is 0.2. In order to avoid the possible contingency of data in a single time period, the average proportion of multiple similar time periods can be calculated to make the historical power consumption proportion more representative.

[0057] After obtaining the historical power consumption ratio of each power-consuming device, use it as the allocation weight and multiply it by the predicted target power generation to obtain the initial power allocation of each device. For example, if the predicted target power generation is 500kWh and the historical power consumption ratio of a certain power-consuming device is 0.2, the initial power allocation of the device is 100kWh. It should be noted that when allocating, it should be ensured that the sum of the allocations of all devices is equal to the target power generation to ensure the balance of power supply and demand.

[0058] S104, obtaining the load status of each electrical device at the current moment and the device characteristics of each electrical device.

[0059] In this embodiment, after generating the initial power distribution strategy, it is necessary to obtain the real-time operation information and characteristic information of each power-consuming device in order to optimize and adjust the initial distribution strategy. This is because relying solely on historical data for power distribution may not be able to respond to the real-time demand changes of power-consuming devices in a timely manner, and it is also difficult to reflect the differences in the importance of different devices.

[0060] In the specific implementation, firstly, the load status data at the current moment is collected in real time through the load monitoring device deployed on each electrical device. The load status can be characterized by measuring the real-time power, current and other electrical parameters of the equipment. According to the collected load data, the load status of the electrical equipment is divided into high load status, medium load status and low load status. For example, when the real-time power of the equipment exceeds 80% of its rated power, it is judged as a high load status; when the real-time power is between 30% and 80% of the rated power, it is judged as a medium load status; when the real-time power is lower than 30% of the rated power, it is judged as a low load status. This division of load status can intuitively reflect the current power demand intensity of each electrical equipment.

[0061] At the same time, it is necessary to obtain the equipment feature information of each power-consuming device. The equipment feature is mainly reflected in the equipment priority, which is used to characterize the importance of each power-consuming device. The determination of equipment priority can take into account multiple factors: for example, the criticality of the equipment in the production process, the impact of equipment downtime on the system, and the economic benefit contribution of the equipment. Scoring criteria can be set based on these factors, and the priority score of each device can be obtained through weighted calculation. The higher the priority score, the more important the equipment is, and its power supply needs should be prioritized in subsequent power distribution adjustments.

[0062] S105, adjusting the initial power allocation strategy in combination with the load status and device characteristics of each power-consuming device, generating a target power allocation strategy, and allocating power to each power-consuming device according to the target power allocation strategy within a preset time period.

[0063] In this embodiment, in order to make power distribution more accurately meet the actual needs of each power-consuming device, it is necessary to optimize and adjust the initial power distribution strategy based on the load status and device characteristic information obtained above, so as to generate a more targeted target power distribution strategy. This adjustment mechanism can further combine the real-time operating status and importance of the equipment on the basis of considering the historical power consumption rules to achieve a more reasonable power distribution plan.

[0064] In the specific implementation, it is necessary to first clarify the load level and equipment priority of each electrical device. The load level is divided based on real-time monitoring data, including high load state, medium load state and low load state; the equipment priority reflects the importance of the equipment, and the first preset priority (for example, 0.8) and the second preset priority (for example, 0.4) can be set as the priority judgment criteria. On this basis, the initial power allocation strategy is adjusted differently: when it is found that a first electrical device is in a high load state (such as the real-time power exceeds 80% of the rated power) and its equipment priority is higher than the first preset priority, the power allocation of the device should be increased, and the increase can be set to 20% of the initial allocation; when it is found that a second electrical device is in a low load state (such as the real-time power is lower than 30% of the rated power) or its equipment priority is lower than the second preset priority, the power allocation of the device is reduced accordingly, and the reduction can be set to 15% of the initial allocation; for the third electrical device in a medium load state and whose priority is between the first preset priority and the second preset priority, its initial power allocation is maintained unchanged.

[0065] Based on the above embodiment, as an optional implementation, in S105, the initial power distribution strategy is adjusted in combination with the load status and device characteristics of each power-consuming device, and the target power distribution strategy is generated, which specifically includes S51-S54: S51, obtaining the load level corresponding to the load state of each electrical device and the device priority corresponding to the device characteristics; wherein the load level includes a high load state, a medium load state and a low load state; and the device priority is used to characterize the importance of each electrical device.

[0066] S52: When the load level of the first electrical device is in a high load state and the device priority is higher than the first preset priority, increase the power allocation amount of the first electrical device in the target power allocation strategy.

[0067] S53: When there is a second electric device whose load level is in a low load state and / or whose device priority is lower than a second preset priority, reduce the power allocation amount of the second electric device in the target power allocation strategy.

[0068] S54: When there is a third electrical device whose load level is in a medium load state and whose device priority is between the first preset priority and the second preset priority, the power allocation amount of the third electrical device in the target power allocation strategy is kept unchanged.

[0069] In this embodiment, in order to make power distribution more accurately meet the actual needs of each power-consuming device, it is necessary to optimize and adjust the initial power distribution strategy based on the real-time load status and importance of the device. This dynamic adjustment mechanism based on multi-dimensional features can ensure that the power supply needs of important equipment are prioritized and improve the utilization efficiency of power resources.

[0070] In the specific implementation, it is first necessary to obtain and determine the load level and equipment priority of each electrical equipment. The load level is determined by real-time monitoring of the power consumption of the equipment: when the real-time power exceeds 80% of the rated power, it is judged as a high load state; when the real-time power is between 30% and 80% of the rated power, it is judged as a medium load state; when the real-time power is less than 30% of the rated power, it is judged as a low load state. The equipment priority is determined according to the importance of the equipment in production operations. The first preset priority (such as 0.8) and the second preset priority (such as 0.4) can be set as the priority judgment criteria. For example, the priority of key production equipment may be 0.9 (higher than the first preset priority), and the priority of auxiliary equipment may be 0.3 (lower than the second preset priority).

[0071] After determining these characteristics, the initial power allocation strategy is adjusted differently. When it is found that a first power-consuming device is in a high-load state (such as the real-time power reaches 85% of the rated power) and its priority is higher than 0.8, it is necessary to increase the power allocation of the device, and the increase can be set to 20% of the initial allocation. For example, if the initial allocation of a key production equipment is 100kWh, the adjusted allocation is 120kWh. When it is found that a second power-consuming device is in a low-load state (such as the real-time power is only 20% of the rated power) or its priority is lower than 0.4, the power allocation of the device is reduced accordingly, and the reduction can be set to 15% of the initial allocation. For the third power-consuming device that is in a medium-load state and has a priority between 0.4 and 0.8, its initial power allocation is maintained unchanged to maintain the stability of the system.

[0072] When making adjustments, it is necessary to ensure that the total amount of electricity allocated after adjustment does not exceed the target power generation. When the allocation of a certain device needs to be increased, the electricity should be reduced from low-load or low-priority devices first to supplement the power demand of high-load and high-priority devices. For example, 5kWh is reduced from each of three low-priority devices, and a total of 15kWh of electricity is obtained to increase the allocation of a high-load and high-priority device.

[0073] After allocating power to each power-consuming device according to the target power allocation strategy within the preset time, the method further includes: Monitor the real-time load status of each electrical device. When it is detected that the real-time load status of any electrical device has changed, obtain the changed load status and the corresponding device characteristics; adjust the target power allocation strategy in real time according to the changed load status and the corresponding device characteristics to obtain an updated target power allocation strategy; and reallocate power to each electrical device according to the updated target power allocation strategy.

[0074] In specific implementation, the power distribution system continuously collects operating data through the load monitoring devices installed on each power-consuming device. When the real-time load status of the power-consuming device is detected to have changed (for example, the load rate increases from 30% to 85%), the system will immediately capture the change and obtain the latest status. At this time, the system will combine the changed load status with the characteristic information of the device (such as device priority) for analysis. For example, if a sudden increase in the load of a key production device with a priority of 0.9 is detected, the system will immediately start the adjustment process.

[0075] Based on the latest data obtained, the system updates the target power allocation strategy with reference to the aforementioned adjustment mechanism. When there is a significant increase in load, such as when the load level of a key device changes from a low-load state to a high-load state, its power allocation needs to be increased accordingly. For example, if the original allocation of the device is 100kWh, it can be increased to 120kWh based on the load change. In order to maintain the overall supply and demand balance of the system, the system will reduce the allocation from other low-load or low-priority devices. For example, 7kWh can be reduced from each of the three low-priority devices to obtain an adjustment amount of 21kWh to meet the new demand of high-load devices.

[0076] After the adjustment is completed, the system will immediately execute the updated target power distribution strategy and redistribute power to each power-consuming device. This adjustment and distribution process is ongoing. As long as a significant change in the load status of the device is detected, the system will start a new round of adjustment process. For example, if the load of a previously adjusted device returns to normal, the system will adjust it again accordingly to ensure that the power distribution is always in the optimal state.

[0077] After allocating power to each power-consuming device according to the target power allocation strategy within the preset time, the method further includes: The actual power consumption of each power-consuming device within a preset time period is obtained; and the historical power allocation strategy of each power-consuming device is updated according to the actual power consumption of each power-consuming device.

[0078] In specific implementation, after the preset duration ends, the system obtains the actual power consumption data of each power-consuming device during the period through the power metering device. For example, if the target allocation of a device within the preset duration (such as 2 hours) is 120kWh, and the actual power consumption is 110kWh, the actual power consumption data will be recorded. The system will collect the actual power consumption data of all power-consuming devices during this period to form a complete power consumption record.

[0079] After obtaining the actual power consumption data, the system will update the historical power distribution strategy based on this data. During the update process, the system will integrate the actual power consumption data with the original historical data. For example, a sliding time window can be used to retain the power consumption data for the last month, and the latest actual power consumption data can be added each time the data is updated, while the earliest historical data can be removed. When integrating data, different weights can be assigned based on the timeliness of the data. Newer data has a higher reference value and can be assigned a greater weight.

[0080] The historical power allocation strategy updated in this way will serve as an important basis for the subsequent formulation of the initial power allocation strategy. For example, if the actual power consumption of a device is lower than the allocated amount for multiple consecutive periods, then after the historical strategy is updated, the system will reduce the initial allocation ratio of the device accordingly in the subsequent allocation, so that the allocation is more in line with actual needs.

[0081] Based on the above method, the present application also discloses an intelligent photovoltaic power distribution system, such as Figure 2 As shown, Figure 2 is a structural diagram of an intelligent photovoltaic power distribution system provided in an embodiment of the present application, the system comprises: a first acquisition module, a prediction module, a generation module, a second acquisition module, and an output module; wherein, The first acquisition module is used to obtain the initial power generation of the photovoltaic power generation equipment at the current moment, and the environmental information of the environment in which the photovoltaic power generation equipment is located; the prediction module is used to combine the environmental information and the initial power generation to predict the target power generation of the photovoltaic power generation equipment within a preset time period; the generation module is used to obtain the historical power allocation strategy of multiple power-consuming devices corresponding to the photovoltaic power generation equipment, and generate the initial power allocation strategy of each power-consuming device within a preset time period in combination with the target power generation and the historical power allocation strategy; the second acquisition module is used to obtain the load status of each power-consuming device at the current moment, and the device characteristics of each power-consuming device; the output module is used to adjust the initial power allocation strategy in combination with the load status and device characteristics of each power-consuming device, generate a target power allocation strategy, and allocate power to each power-consuming device according to the target power allocation strategy within the preset time period.

[0082] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0083] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0084] The communication bus 1002 is used to realize the connection and communication between these components.

[0085] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0086] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0087] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0088] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of an intelligent photovoltaic power distribution method.

[0089] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program storing an intelligent photovoltaic power distribution method in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0090] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0091] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0092] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0097] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent photovoltaic power distribution method, characterized in that: The method comprises: Obtaining the initial power generation of the photovoltaic power generation equipment at the current moment, and environmental information of the environment in which the photovoltaic power generation equipment is located; Combining the environmental information and the initial power generation, predicting the target power generation of the photovoltaic power generation equipment within a preset time period; Obtaining historical power allocation strategies of multiple power-consuming devices corresponding to the photovoltaic power generation device, and generating an initial power allocation strategy for each of the power-consuming devices within the preset time period in combination with the target power generation amount and the historical power allocation strategy; Obtaining the load status of each of the electrical devices at the current moment and the device characteristics of each of the electrical devices; In combination with the load status and device characteristics of each of the electrical devices, the initial power allocation strategy is adjusted to generate a target power allocation strategy, and power is allocated to each of the electrical devices according to the target power allocation strategy within the preset time period.

2. The intelligent photovoltaic power distribution method according to claim 1, characterized in that: The step of combining the environmental information and the initial power generation to predict the target power generation of the photovoltaic power generation equipment within a preset time period includes: According to the initial power generation, predicting the power generation change trend of the photovoltaic power generation equipment within a preset time period; Determining a first power generation amount of the photovoltaic power generation equipment within a preset time period according to the power generation amount change trend; The first power generation is adjusted according to the environmental information to obtain a target power generation of the photovoltaic power generation equipment within a preset time period.

3. The intelligent photovoltaic power distribution method according to claim 2, characterized in that: The environmental information includes light intensity and temperature, and the first power generation is adjusted according to the environmental information to obtain a target power generation of the photovoltaic power generation device within a preset time period, including: Obtaining a first change trend of the light intensity and a second change trend of the temperature within a preset time period; According to the first change trend, the first power generation is adjusted to obtain a second power generation; wherein the second power generation is positively correlated with the first change trend; According to the second change trend, the second power generation is adjusted to obtain the target power generation of the photovoltaic power generation equipment within a preset time period; wherein, when the temperature value represented by the second change trend is higher than the preset temperature threshold, the target power generation is negatively correlated with the second change trend; when the temperature value represented by the second change trend is not higher than the preset temperature threshold, the target power generation is positively correlated with the second change trend.

4. The intelligent photovoltaic power distribution method according to claim 1, characterized in that: The step of combining the target power generation and the historical power allocation strategy to generate an initial power allocation strategy for each of the power-consuming devices within the preset time period includes: Obtaining the historical power consumption ratio of each of the power-consuming devices in the historical power distribution strategy; The target power generation is allocated according to the historical power consumption proportion of each of the power-consuming devices, and an initial power allocation strategy for each of the power-consuming devices within the preset time period is generated.

5. The intelligent photovoltaic power distribution method according to claim 1, characterized in that: The adjusting the initial power allocation strategy in combination with the load state and the device characteristics of each of the power-consuming devices to generate a target power allocation strategy includes: Obtaining the load level corresponding to the load state of each of the electrical devices and the device priority corresponding to the device characteristics; wherein the load level includes a high load state, a medium load state and a low load state; and the device priority is used to characterize the importance of each of the electrical devices; When there is a first power-consuming device whose load level is in the high load state and whose device priority is higher than a first preset priority, increasing the power allocation amount of the first power-consuming device in the target power allocation strategy; When there is a second electric device whose load level is in the low load state and / or whose device priority is lower than a second preset priority, reducing the power allocation amount of the second electric device in the target power allocation strategy; When there is a third electrical device whose load level is in the medium load state and whose device priority is between the first preset priority and the second preset priority, the power allocation amount of the third electrical device in the target power allocation strategy is kept unchanged.

6. The intelligent photovoltaic power distribution method according to claim 1, characterized in that: After allocating power to each of the power-consuming devices according to the target power allocation strategy within the preset time period, the method further includes: Monitor the real-time load status of each of the electrical devices, and when it is detected that the real-time load status of any of the electrical devices changes, obtain the changed load status and corresponding device characteristics; According to the changed load state and the corresponding equipment characteristics, the target power allocation strategy is adjusted in real time to obtain an updated target power allocation strategy; According to the updated target power distribution strategy, power is redistributed to each of the power-consuming devices.

7. The intelligent photovoltaic power distribution method according to claim 1, characterized in that: After allocating power to each of the power-consuming devices according to the target power allocation strategy within the preset time period, the method further includes: Obtaining actual power consumption of each of the power-consuming devices within the preset time period; According to the actual power consumption of each of the power-consuming devices, the historical power allocation strategy of each of the power-consuming devices is updated.

8. An intelligent photovoltaic power distribution system, characterized in that: The system comprises: a first acquisition module, a prediction module, a generation module, a second acquisition module, and an output module; wherein, The first acquisition module is used to acquire the initial power generation of the photovoltaic power generation equipment at the current moment, and the environmental information of the environment in which the photovoltaic power generation equipment is located; The prediction module is used to predict the target power generation of the photovoltaic power generation equipment within a preset time period by combining the environmental information and the initial power generation; The generating module is used to obtain the historical power allocation strategies of the plurality of power-consuming devices corresponding to the photovoltaic power generation device, and generate the initial power allocation strategy of each of the power-consuming devices within the preset time period in combination with the target power generation amount and the historical power allocation strategy; The second acquisition module is used to acquire the load status of each of the electrical devices at the current moment and the device characteristics of each of the electrical devices; The output module is used to adjust the initial power allocation strategy in combination with the load status and device characteristics of each of the electrical devices, generate a target power allocation strategy, and allocate power to each of the electrical devices according to the target power allocation strategy within the preset time period.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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