Power supply control method, storage medium and electronic equipment

Abstract

The invention discloses a power supply control method, a storage medium and electronic equipment. The method comprises the steps that power utilization characteristics of a first device end from interaction equipment are received, and the power utilization characteristics are obtained by processing power utilization data of a power utilization account in the current sampling time period through the first device end; power generation data of a distributed power generation system in the micro-grid system in the current sampling time period and charging and discharging data of an energy storage system in the current sampling time period are obtained; determining power supply parameters based on the power utilization characteristics, the power generation data and the charging and discharging data; the power supply parameter is sent to the first equipment end, the first equipment end is used for sending the power supply parameter and the power supply parameter to the second equipment end, the second equipment end is used for displaying the power supply parameter and the power supply parameter, and the interaction equipment comprises the first equipment end and the second equipment end. According to the invention, the technical problem of poor flexibility of a power supply control method in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of smart grids, and in particular to a power supply control method, a storage medium and an electronic device. Background Art

[0002] In the field of power grid control, for example, the high penetration rate of renewable energy grid connection introduces randomness and uncertainty to the microgrid system, which is a significant feature of the new microgrid system with new energy as the main body, and also brings huge challenges to the actual operation and comprehensive energy management of the microgrid. Therefore, it is a prerequisite to ensure the safe and stable operation of the power grid to quickly and accurately obtain demand-side electricity consumption information, identify and intelligently perceive it, and then realize load feature classification and precise regulation. The power supply control method in the relevant technology is currently difficult to meet the high-precision requirements of the model in the new microgrid system, especially it cannot meet the large-scale data calculation and demand response two-way interaction, resulting in a large amount of redundant power data without usability value, unable to realize real-time demand response management, and low power supply control flexibility.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a power supply control method, a storage medium and an electronic device, so as to at least solve the technical problem of poor flexibility existing in the power supply control method in the related art.

[0005] According to one aspect of an embodiment of the present invention, there is provided a power supply control method, comprising: receiving a power usage characteristic from a first device end of an interactive device, wherein the power usage characteristic is obtained by the first device end by processing the power usage data of the power usage account in a current sampling period; obtaining power generation data of a distributed power generation system in a microgrid system in the current sampling period, and charging and discharging data of an energy storage system in the current sampling period; determining power supply parameters based on the power usage characteristic, the power generation data and the charging and discharging data; and sending the power supply parameters to the first device end, wherein the first device end is used to send the power supply parameters and the power supply parameters to a second device end, for the second device end to display the power supply parameters and the power supply parameters, and the interactive device includes the first device end and the second device end.

[0006] According to another aspect of an embodiment of the present invention, another power supply control method is provided, including: a first device end in an interactive device collects electricity consumption data of an electricity consumption account in a current sampling period; the first device processes the electricity consumption data to obtain electricity consumption characteristics of the electricity consumption account; a main control device obtains power generation data of a distributed power generation system in a microgrid system in the current sampling period, and charging and discharging data of an energy storage system in the current sampling period; the main control device determines power supply parameters based on the power consumption characteristics, the power generation data and the charging and discharging data, and sends the power supply parameters to the first device; the first device sends the power supply parameters and the power supply parameters to a second device end; the second device end displays the power supply parameters and the power supply parameters, wherein the interactive device includes the first device end and the second device end.

[0007] According to another aspect of an embodiment of the present invention, a power supply control system is also provided, including: a main control device, an interactive device, wherein the interactive device includes a first device end and a second device end, wherein the first device end is used to collect the electricity consumption data of the electricity consumption account in the current sampling period, process the electricity consumption data, and obtain the electricity consumption characteristics of the electricity consumption account; the main control device is used to obtain the power generation data of the distributed power generation system in the microgrid system in the current sampling period, and the charging and discharging data of the energy storage system in the current sampling period; based on the power consumption characteristics, the power generation data and the charging and discharging data, determine the power supply parameters, and send the power supply parameters to the first device; the first device is used to send the power supply parameters and the power supply parameters to the second device end; the second device end is used to display the power supply parameters and the power supply parameters.

[0008] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing any one of the power supply control methods.

[0009] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the power supply control methods described.

[0010] In an embodiment of the present invention, by receiving a power consumption feature from a first device end of an interactive device, wherein the power consumption feature is obtained by the first device end by processing the power consumption data of the power account in the current sampling period; obtaining the power generation data of the distributed power generation system in the microgrid system in the current sampling period, and the charging and discharging data of the energy storage system in the current sampling period; determining the power supply parameters based on the power consumption feature, the power generation data and the charging and discharging data; sending the power supply parameters to the first device end, wherein the first device end is used to send the power supply parameters and the power supply parameters to the second device end, for the second device end to display the power supply parameters and the power supply parameters, the interactive device includes the first device end and the second device end, so as to achieve the purpose of using the interactive device as a medium for data interaction between the microgrid system and the power user end, and performing flexible control of power demand response, thereby achieving the technical effect of improving the flexibility of power supply control, and further solving the technical problem of poor flexibility of the power supply control method in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0012] Figure 1 is a flow chart of a power supply control method according to an embodiment of the present invention;

[0013] Figure 2 is a flow chart of another power supply control method according to an embodiment of the present invention;

[0014] Figure 3 is a structural schematic diagram of a power supply control system according to an embodiment of the present invention;

[0015] Figure 4 is a schematic diagram of a demand response interaction process within an optional interaction device according to an embodiment of the present invention;

[0016] Figure 5 is a schematic structural diagram of an optional first device end (ie, device end A) according to an embodiment of the present invention;

[0017] Figure 6 It is a modular hardware structure diagram of an optional first device end (ie, device end A) according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention are explained below:

[0021] DL / T 645 is an electric power industry standard used to regulate the communication between multi-function electric energy meters and data acquisition terminals to ensure the accuracy and compatibility of data transmission.

[0022] According to an embodiment of the present invention, a method embodiment of power supply control is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] Figure 1 is a flow chart of a power supply control method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0024] Step S102, receiving a power usage feature from a first device end of an interactive device, wherein the power usage feature is obtained by the first device end by processing power usage data of a power usage account in a current sampling period;

[0025] Optionally, the execution subject of steps S102 to S108 can be a main control device in the microgrid system, and the main control device can serve as a microgrid manager in the microgrid system. The power consumption characteristics can reflect the power consumption pattern and demand of the user during the current sampling period. The power consumption characteristics may include but are not limited to indicator characteristics and load identification results. By analyzing these data, the microgrid manager can understand the user's power demand in real time, including peak and trough periods of demand, and stability of demand. Based on the received power consumption characteristics, the microgrid manager can customize personalized power supply parameters for different users, such as dynamic electricity prices, power supply or power supply time. The above method can more accurately match user needs and avoid waste of power resources, while also providing users with a more economical power consumption plan.

[0026] In an optional embodiment, the electricity usage characteristics include: load identification results, power characteristic indicators, and other characteristic indicators, wherein the power characteristic indicators include at least one of the following: the minimum load rate, peak-to-valley difference, peak-to-valley difference rate, load rate, peak-time power consumption rate, valley-time power consumption rate, flat-time power consumption percentage, and load peak time of the electricity usage account in the current sampling period; other characteristic indicators include at least one of the following: temperature, weather, and preset special time periods; the flat-time power consumption percentage is used to indicate the percentage of the electricity usage of the electricity usage account in the flat period to the total electricity usage in the current sampling period, and the flat period is used to indicate the period in the current sampling period when the electricity usage is within a predetermined range.

[0027] Optionally, the special period may be, but is not limited to, holidays, major events, etc.

[0028] Optionally, microgrid managers can obtain a more comprehensive understanding of loads by including power characteristic indicators such as load identification results, minimum load rate, peak-to-valley difference, and other characteristic indicators such as temperature, weather, and special time periods. These indicators not only cover the user's power usage pattern, but also take into account the impact of the external environment and specific time periods, which helps to more accurately predict load demand. Power characteristic indicators such as peak power consumption rate, valley power consumption rate, and flat power consumption percentage, as well as other characteristic indicators such as temperature and weather, enable microgrid managers to formulate refined demand response strategies based on different demand scenarios. For example, when high temperature weather is predicted, the electricity price strategy can be adjusted in advance to guide the air conditioning load to be more reasonably distributed and avoid excessive concentration of power demand during peak hours. By analyzing these characteristic indicators, microgrid managers can better optimize the allocation of power resources. For example, by identifying the peak load moment, the energy storage system can be dispatched to discharge or increase power generation in advance to ensure sufficient power supply during peak demand periods; while in flat or valley sections, energy storage charging or users can be encouraged to use electricity to make full use of excess power resources. The combination of power characteristic indicators and other characteristic indicators provides a data basis for the implementation of the dynamic electricity price mechanism. The microgrid manager can adjust the electricity price according to the preset special time period, such as during holidays or major events, taking into account the particularity of electricity demand in these time periods, and formulate corresponding electricity price strategies to adapt to market changes. Taking into account the indicator of the percentage of electricity consumption in the flat period, the microgrid manager can more accurately predict the electricity demand in the flat period. This helps to reduce prediction errors and improve the efficiency of power supply and demand management. The user obtains the power supply parameters adjusted based on these characteristic indicators through the second device, such as dynamic electricity prices, and can respond according to their own needs and external environmental conditions, such as adjusting the power consumption time or power consumption, and enhancing the user's participation in power consumption management. Through refined management, the microgrid manager can better dispatch renewable energy generation and energy storage systems, promote the efficient consumption of renewable energy, help reduce carbon emissions, and promote the use of green energy. In summary, based on the power consumption characteristics in this embodiment, detailed demand-side data can be provided for the power supply control method, which helps to implement more accurate demand response strategies, optimize power resource allocation, improve prediction accuracy, enhance user participation, and promote the efficient use of renewable energy, which plays an important role in the stable operation and sustainable development of the microgrid.

[0029] Optionally, one day can be used as a sampling period, and the corresponding power characteristic indicators can include but are not limited to: daily minimum load rate, daily peak-to-valley difference, daily peak-to-valley difference rate, daily load rate, peak power consumption rate, valley power consumption rate, flat power consumption percentage, daily load peak time Tmax. The calculation method of each power characteristic indicator is as follows:

[0030] Daily minimum load rate = daily minimum load / daily maximum load;

[0031] Daily peak-to-valley difference: Daily peak-to-valley difference = daily maximum load - daily minimum load;

[0032] Daily peak-to-valley difference rate: Daily peak-to-valley difference rate = (daily maximum load - daily minimum load) / daily maximum load;

[0033] Daily load rate: Daily load rate = user average load / maximum load;

[0034] Peak power consumption rate: Peak power consumption rate = peak power consumption / total power consumption;

[0035] Off-peak power consumption rate: Off-peak power consumption rate = off-peak power consumption / total power consumption;

[0036] The percentage of electricity consumption in the flat section: The percentage of electricity consumption in the flat section = the electricity consumption in the flat section / the total electricity consumption;

[0037] Daily load peak time Tmax: Daily load peak time = Pd.T max, where Pd.max represents the maximum operating load in a day.

[0038] Step S104, obtaining power generation data of the distributed power generation system in the microgrid system in the current sampling period, and charging and discharging data of the energy storage system in the current sampling period;

[0039] Optionally, the distributed power generation system includes distributed power generation equipment such as wind power and solar energy. By real-time monitoring of the power generation data of the distributed power generation system and the charge and discharge data of the energy storage system, the microgrid manager can grasp the power supply capacity of the microgrid in the current period. Combined with the power consumption characteristics, the matching degree of power supply and demand can be evaluated in real time, providing a basis for dynamically adjusting the power supply parameters and achieving an instant balance of supply and demand. Based on the power generation data and the charge and discharge data, the resources in the microgrid can be intelligently dispatched. For example, when the power generation is sufficient, the purchase of electricity from the external power grid can be reduced to reduce the cost of purchasing electricity; when the power generation is insufficient, the discharge of the energy storage system or the dispatch of other distributed energy sources can be used to ensure the continuity and stability of the power supply. By obtaining power generation and energy storage data, the microgrid manager can adjust the electricity price based on the supply and demand situation. When the power generation is high and the energy storage is sufficient, a lower electricity price can be implemented to encourage users to increase electricity consumption; conversely, when the power generation is low and the energy storage is insufficient, the electricity price can be increased to guide users to reduce electricity consumption and avoid tight power supply and demand. Real-time monitoring of power generation data and charge and discharge data helps prevent and reduce the uncertainty of power supply and improve the reliability of the microgrid system. For example, when insufficient power generation is predicted, adjusting the energy storage strategy in advance or finding alternative power generation sources can avoid system failures caused by insufficient power supply. Microgrid managers can optimize the scheduling of renewable energy based on power generation data, reduce wind and solar abandonment, and improve the absorption capacity of renewable energy. At the same time, by interacting with users and guiding users to increase electricity consumption during the peak period of renewable energy generation, the use of green energy can be further promoted. Through the power supply parameters received by the second device, users can understand the real-time supply and demand status of the microgrid, including power generation and energy storage status. This enables users to respond based on this information, such as adjusting power consumption time or usage mode, so as to participate more actively in power demand response and improve user participation. With accurate power generation and energy storage data, microgrid managers can make more economical power generation and supply decisions, avoid excessive power purchase or sales, reduce unnecessary cost expenditures, and improve the efficiency of microgrid economic operation. Real-time acquisition of power generation and energy storage data, combined with comprehensive analysis of power consumption characteristics, helps to realize the intelligent management of power systems. The real-time and accuracy of data are the key to the operation of smart grids. The above methods can improve the intelligence level of microgrids through data-driven. In summary, through the real-time acquisition of power generation data and charging and discharging data in the microgrid system, not only can the microgrid managers accurately grasp the supply and demand situation, reasonably dispatch resources, and formulate dynamic electricity price strategies, but also enhance user participation and improve the economy and reliability of the system, which is of great significance to the stable operation and intelligent management of the microgrid.

[0040] Step S106, determining power supply parameters based on power consumption characteristics, power generation data, and charge and discharge data;

[0041] Optionally, the power supply parameter may be, but is not limited to, electricity price, price adjustment index (or coefficient), etc. The power supply parameter is determined based on the power consumption characteristics on the user side, the real-time power generation data on the power generation side, and the charge and discharge status of the energy storage system. Such a comprehensive analysis can evaluate the power supply and demand balance of the microgrid in real time, and achieve a dynamic balance of power supply and demand by adjusting power supply parameters such as electricity price, power supply, energy storage strategy, etc., to ensure the stable operation of the power system. By analyzing power generation data and charge and discharge data, distributed power generation systems and energy storage systems can be intelligently dispatched to maximize the use of renewable energy and energy storage resources, reduce dependence on external power grids, optimize the configuration of power resources, and improve the self-sufficiency and economy of microgrids. The power supply parameters include information such as dynamic electricity prices, and the adjustment of these parameters can effectively guide users to adjust their electricity consumption behavior according to changes in electricity prices. For example, increase power consumption when electricity prices are low, or reduce non-essential power consumption when electricity prices are high, thereby achieving demand-side management and alleviating the pressure on the power system during peak hours. By comprehensively analyzing power supply and demand data, microgrid managers can formulate more economical operation strategies, such as lowering electricity prices when power generation is sufficient to attract users to increase power consumption and reduce power abandonment; raising electricity prices when power generation is insufficient to suppress demand and avoid additional power purchases, thereby reducing operating costs and improving the economic benefits of microgrids. Based on accurate power consumption, power generation and energy storage data, dynamic adjustment of power supply parameters can improve the operating efficiency of the power system, avoid waste of power resources and imbalance between supply and demand, and ensure that the power system maintains efficient operation while meeting user needs.

[0042] In an optional embodiment, power supply parameters are determined based on power consumption characteristics, power generation data, and charge and discharge data, including: obtaining a load forecast result for a prediction period based on power consumption characteristics, wherein the prediction period is a period after a current sampling period; obtaining a power generation forecast result for the prediction period based on power generation data; obtaining a storage state forecast result for the prediction period based on charge and discharge data; and obtaining power supply parameters based on the load forecast result, the power generation forecast result, and the energy storage state forecast result.

[0043] Optionally, by predicting the load demand in the future period based on the current power consumption characteristics, predicting the power generation capacity in the future period based on the current power generation data, and predicting the energy storage status in the future period based on the current charging and discharging data, the microgrid manager can plan the power supply strategy in advance to ensure that the power resources are effectively allocated in the forecast period. This method helps to avoid the impact of sudden changes in power supply and demand on the operation of the power grid and improve the foresight and flexibility of decision-making. Predictive analysis can help microgrid managers predict the supply and demand dynamics more accurately and achieve a dynamic balance of supply and demand by adjusting power supply parameters such as dynamic electricity prices, power generation scheduling, and energy storage scheduling. When the load peak is predicted, the energy storage system can be scheduled to charge and discharge in advance, or the distributed power generation system can be mobilized to increase the power generation; when the trough is predicted, the power generation strategy can be adjusted to reduce power waste, and at the same time, energy storage or users can be encouraged to use excess power through lower electricity prices. The load, power generation and energy storage status in the forecast period can help system managers take measures to prevent potential imbalances in power supply and demand, stabilize the operation of the power grid through timely regulation, and avoid power grid failures or instability caused by fluctuations in power supply and demand. Based on the predicted load demand, power generation capacity and energy storage status, microgrid managers can optimize resource allocation, such as reasonably allocating renewable energy power generation and adjusting the charging and discharging strategy of the energy storage system to ensure the most effective and economical use of power resources, while also promoting the efficient consumption of renewable energy. By predicting load demand and adjusting power supply parameters accordingly, users can be provided with a more stable and economical power supply. When load peaks are predicted, measures can be taken to avoid power supply shortages, and when low loads are predicted, more favorable power conditions can be provided to users by adjusting electricity prices, thereby improving user satisfaction with power services.

[0044] In an optional embodiment, when the electricity consumption characteristics include power characteristic indicators and other characteristic indicators, the load forecast result for the forecast period is obtained based on the electricity consumption characteristics, including: determining the correlation coefficient between the other characteristic indicators and the power characteristic indicators; and obtaining the load forecast result based on the power characteristic indicators, other characteristic indicators and the correlation coefficient.

[0045] Optionally, by determining the correlation coefficients between non - electrical characteristic indicators (such as holidays, temperature, weather, major events, etc.) and electricity consumption data, a more comprehensive and accurate load forecasting model can be constructed. These non - electrical characteristics often have a significant impact on users' electricity consumption demands. Considering them can make the forecasting model closer to the actual electricity consumption situation and improve the forecasting accuracy. The calculation of the correlation coefficient helps to identify which non - electrical characteristics have an important impact on the electricity load and which have a smaller impact. Micro - grid managers can conduct intelligent demand analysis based on this, and more accurately predict the electricity consumption demands in different scenarios, such as the increase in air - conditioner electricity load in hot weather, and the peak period of household electricity consumption during holidays. Based on the load forecasting results of electrical characteristic indicators and other characteristic indicators, micro - grid managers can implement a more refined power supply parameter adjustment strategy. For example, when it is predicted that there will be a peak in electricity consumption in a certain area due to weather changes, the power supply parameters in this area can be adjusted in advance, such as raising the electricity price to suppress demand or increasing power supply, so as to avoid power shortages. By introducing the correlation coefficient, the forecasting model can better consider the impact of external environmental factors on electricity consumption demands and reduce the errors in the forecasting process. This is crucial for the stable operation of the micro - grid and can avoid the power supply - demand imbalance caused by inaccurate forecasting. More accurate load forecasting helps to formulate more reasonable power supply parameters, such as dynamic electricity prices. After receiving these parameters through the second device terminal, users can make more reasonable electricity - using decisions according to their own demands and external conditions, such as weather, etc., enhancing the users' participation and initiative in electricity consumption management.

[0046] The correlation coefficients between other characteristic indicators and electricity consumption data can be calculated, but are not limited to, the following methods:

[0047]

[0048] Where: R is the correlation coefficient; cov(x, y) is the covariance of x and y; D(x) and D(y) are the mean square deviations between the two characteristics respectively. The value of the correlation coefficient r is between - 1 and 1. If r > 0, it indicates a positive correlation between the two elements; if r < 0, it indicates a negative correlation between the two elements. If the absolute value of r is closer to 1, there is a stronger linear relationship between elements x and y; if the absolute value of r is closer to 0, it means that the linear relationship between x and y is weaker; generally, when r is between 0 and 1, it indicates that x and y have a correlation relationship, but it is a non - linear relationship; when r ≥ 0.7, it is regarded as highly correlated; when 0.4 < r < 0.7, it is regarded as significantly correlated; when r < 0.4, it is regarded as weakly correlated.

[0049] In an optional embodiment, based on the load forecast results, the power generation forecast results and the energy storage status forecast results, the power supply parameters are obtained, including: based on the load forecast results, the power generation forecast results and the energy storage status forecast results, determining an adjustment method for adjusting a preset basic electricity price, wherein the adjustment method includes: an adjustment direction, an adjustment range; adjusting the basic electricity price based on the adjustment method to obtain a target electricity price; and obtaining the power supply parameters based on the target electricity price.

[0050] Optionally, the basic electricity price can be a pre-set benchmark price range. By combining the load forecast, power generation forecast and energy storage status forecast results with the preset basic electricity price, the adjustment direction and amplitude of the electricity price are determined. This means that the electricity price can be adjusted according to the dynamic changes in the supply and demand forecasts in the future period to form a dynamic electricity price mechanism, which is more in line with the actual needs of the power market and can promote the rational allocation of power resources. If it is predicted that the power demand in the future period will exceed the supply (the load forecast result is higher than the power generation and energy storage forecast results), the electricity price can be set to increase, thereby encouraging users to save electricity or shift the power demand to non-peak hours. Conversely, if there is an oversupply, the electricity price can be set to decrease, encouraging users to increase electricity consumption and improve the absorption capacity of renewable energy. Adjusting the power supply parameters based on the target electricity price can promote the optimal allocation of resources within the microgrid. For example, by predicting the power generation and energy storage status, the microgrid manager can decide whether to mobilize more distributed power generation resources or adjust the charging and discharging strategy of the energy storage system to ensure the balance of power supply and demand. The dynamic electricity price mechanism can automatically adjust the electricity price according to the supply and demand relationship, avoiding the problem of power surplus or shortage that may occur under the traditional fixed electricity price. During the peak demand period, the operating pressure of the power system can be effectively reduced, while during the low demand period, the excess power resources can be fully utilized. The dynamic electricity price mechanism provides users with more flexible price signals, enabling users to make more reasonable electricity consumption decisions based on their own needs and economic capabilities. In some cases, users can even save electricity bills by adjusting the electricity consumption time, which improves the user's satisfaction with the power service. In addition, through the implementation of the dynamic electricity price mechanism, the additional costs caused by the mismatch between supply and demand can also be reduced, such as the emergency call for backup power generation resources and the high cost of electricity purchase. Through advance planning and precise control, microgrid managers can reduce management costs and improve overall economic benefits. In summary, the above mechanism of adjusting electricity prices based on prediction results can optimize the power supply and demand relationship by dynamically adjusting electricity prices, which can not only improve the operating efficiency and economy of the power system, but also enhance the user's participation and satisfaction in electricity consumption, which is of great significance to the stable operation and sustainable development of microgrids.

[0051] Step S108, sending the power supply parameters to the first device end, wherein the first device end is used to send the power supply parameters and the power supply parameters to the second device end, and the second device end is used to display the power supply parameters and the power supply parameters, and the interactive device includes the first device end and the second device end.

[0052] Optionally, the power supply parameters (such as dynamic electricity prices, power supply adjustment, etc.) are displayed to the user through the second device end of the interactive device, so that the user can understand the operation status and cost information of the power system in real time. The transparency of this information can enhance the user's participation in power consumption management and encourage users to adjust their power consumption behavior according to changes in electricity prices, such as using more electricity when electricity prices are low, or reducing electricity consumption during peak hours, thereby improving the efficiency of demand-side response. The real-time display of power supply parameters can promote the interaction between supply and demand. Users can adjust their power consumption behavior according to the displayed parameters, and microgrid managers can achieve real-time supply and demand matching based on the user's power consumption response to ensure the stable operation of the power system. The instant transmission and display of power supply parameters can ensure that users can receive the latest power cost information in a timely manner, which helps users make immediate power consumption decisions, respond to the dispatch of microgrids, and improve the immediacy and effectiveness of power demand response. Users can adjust their power consumption behavior according to the displayed power supply parameters (such as dynamic electricity prices), which helps to optimize power demand and reduce unnecessary power consumption during high power price periods, thereby reducing electricity bills and improving personal economic benefits. At the same time, this adjustment of user behavior also helps to improve the overall energy efficiency of the microgrid and reduce electricity waste.

[0053] Through the above steps S102 to S108, the purpose of flexibly controlling the power demand response by using the interactive device as a medium for data interaction between the microgrid system and the power user end can be achieved, thereby achieving the technical effect of improving the flexibility of power supply control, and further solving the technical problem of poor flexibility of the power supply control method in the related technology.

[0054] According to an embodiment of the present invention, another method embodiment of power supply control is also provided. Figure 2 is a flow chart of another power supply control method according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0055] Step S202: The first device in the interactive device collects electricity usage data of the electricity usage account in the current sampling period; the first device processes the electricity usage data to obtain electricity usage characteristics of the electricity usage account;

[0056] Step S204, the main control device obtains the power generation data of the distributed power generation system in the microgrid system in the current sampling period, and the charging and discharging data of the energy storage system in the current sampling period;

[0057] Step S206, the main control device determines power supply parameters based on the power consumption characteristics, power generation data, and charge and discharge data, and sends the power supply parameters to the first device;

[0058] Step S208, the first device sends the power supply parameter and the power supply parameter to the second device end;

[0059] Step S210, the second device end displays the power supply parameter and the power supply parameter, wherein the interactive device includes the first device end and the second device end.

[0060] Optionally, the first device collects power consumption data during the current sampling period and processes it to obtain power consumption characteristics, which enables the microgrid manager to monitor the user's power demand in real time, analyze the user's power consumption pattern and behavior, and provide real-time data support for subsequent supply and demand balance regulation. The main control device collects the power generation data of the distributed power generation system in the microgrid and the charging and discharging data of the energy storage system, and combines them with the user's power consumption characteristics to realize the integration and analysis of power supply and demand data. Based on the power consumption characteristics, power generation data and charging and discharging data, the main control device can dynamically adjust the power supply parameters, such as electricity price, power supply, etc., to achieve supply and demand balance. This method can respond to changes in power supply and demand in a timely manner, ensure the effective use of power resources and the stable operation of the microgrid. The first device sends the power supply parameters to the second device, and the user can view these parameters in real time through the second device, such as dynamic electricity price information. This two-way interactive mechanism not only enables users to adjust their power consumption behavior according to electricity prices, but also enhances users' participation and management capabilities in power consumption. Through the use of interactive devices, users can directly receive the power supply parameters determined by the main control device. This improvement in information transparency allows users to better understand the operation of the microgrid and encourages users to respond according to their personal needs and cost-effectiveness, such as using more electricity when electricity prices are low or reducing unnecessary electricity consumption when electricity prices are high. Dynamically adjusted power supply parameters help optimize the allocation of power resources, reduce power waste, and improve the economic operation efficiency of the power system. Users' response to electricity prices, such as increasing electricity consumption during low electricity price periods, helps reduce electricity costs and improve personal economic benefits. In the above methods, through real-time data collection, integrated analysis of supply and demand data, dynamic supply and demand balance adjustment, and two-way information interaction with users, the efficiency, stability, and economy of power system operation can be improved. At the same time, it can also promote users' intelligent management and participation in power consumption and promote the construction of smart grids.

[0061] In an optional embodiment, when the electricity usage characteristics include load identification results, the first device processes the electricity usage data to obtain the electricity usage characteristics of the electricity account, including: the first device extracts target characteristics from the electricity usage data, wherein the target characteristics are characteristics that reflect the characteristics of the electricity equipment; the first device uses a load identification algorithm to analyze the target characteristics to obtain the load characteristics of the electricity account in the current sampling period, wherein the load characteristics are used to indicate the predicted operating status and electricity usage mode of the electricity equipment; the first device obtains a load identification result based on the load characteristics and an electrical appliance feature library, wherein the load identification result includes the equipment type that matches the load characteristics, and the operating status corresponding to the equipment type; the electrical appliance feature library includes key electricity usage characteristics corresponding to multiple equipment types.

[0062] Optionally, by extracting target features from power consumption data and analyzing them, the first device can identify the specific device type and its operating status used by the user during the current sampling period, which can achieve refined management of user loads and more accurately predict and control power demand. The load identification algorithm is used to analyze power consumption data, which can intelligently identify the user's power consumption pattern, such as daily power consumption habits, device usage frequency, etc. This helps microgrid managers better understand user needs and formulate power supply strategies that are more in line with users' actual power consumption habits. Based on the matching of load characteristics with the electrical appliance feature library, the first device can more accurately predict the user's power demand during the forecast period. The key power consumption characteristics of various types of equipment stored in the electrical appliance feature library can provide rich data support for load identification. Through accurate load identification, microgrid managers can allocate power resources more intelligently. For example, when a large number of air-conditioning loads are identified, the charging and discharging strategies of the energy storage system can be adjusted in advance to ensure power supply during peak periods; and during low-load periods, energy storage charging or users can be encouraged to use electricity to make full use of excess resources. The first device end can send the load identification result to the second device end. By viewing this information, the user can understand the operating status and mode of his own electrical equipment, so as to participate in power management more actively, such as adjusting the power consumption plan according to the equipment usage and saving electricity bills. The identification of load characteristics can also help detect equipment failures or abnormal usage, such as high energy consumption caused by abnormal equipment operation status, which helps users and managers take timely measures to reduce energy consumption and improve equipment operation efficiency. By implementing the load identification algorithm and matching with the electrical appliance feature library, the first device end can realize automatic data processing and analysis, which can enhance the intelligence level of the system, reduce the need for manual intervention, and improve the efficiency of power supply and demand management. In the above method, through the intelligent processing of power consumption data and accurate identification of load characteristics by the first device end, the refined management of user loads can be realized, the accuracy of power supply and demand forecasting and resource optimization configuration can be improved, and the user's participation in power consumption management can also be promoted to realize the intelligent operation and stable management of microgrids.

[0063] Optionally, the original real-time values ​​of current and voltage in the power consumption data can be converted into a feature set required for load identification to obtain target features.

[0064] According to an embodiment of the present invention, a system embodiment for implementing the above power supply control method is also provided. Figure 3 is a schematic diagram of a power supply control system according to an embodiment of the present invention. Figure 3 As shown, the power supply control system includes: a main control device 10, an interactive device 20, wherein the interactive device 20 includes a first device end 22 and a second device end 22, wherein:

[0065] The first device end 22 is used to collect the electricity consumption data of the electricity consumption account in the current sampling period, process the electricity consumption data, and obtain the electricity consumption characteristics of the electricity consumption account;

[0066] The main control device 10 is used to obtain the power generation data of the distributed power generation system in the microgrid system in the current sampling period, and the charge and discharge data of the energy storage system in the current sampling period; based on the power consumption characteristics, the power generation data and the charge and discharge data, determine the power supply parameters, and send the power supply parameters to the first device 22;

[0067] The first device 22 is used to send the power supply parameters and the power supply parameters to the second device end 24;

[0068] The second device terminal 24 is used to display the power supply parameters and the power supply parameters.

[0069] Optionally, the first device end can collect and process power consumption data in real time, and obtain power consumption characteristics, including load identification results, power characteristic indicators and other characteristic indicators. This real-time data processing capability can provide a basis for the rapid response of the system, can monitor the user's power consumption behavior and pattern changes in real time, and provide data support for the demand response mechanism of the smart grid. The main control device is responsible for collecting the power generation data and charging and discharging data in the microgrid, and combining them with the power consumption characteristics uploaded by the first device end for comprehensive analysis. This integrated analysis capability helps the system to fully understand the power supply and demand situation, provide a decision-making basis for dynamically adjusting the power supply parameters, and ensure the stable operation of the power system under various working conditions. The main control device determines the power supply parameters, such as dynamic electricity prices, based on power consumption characteristics, power generation data, and charging and discharging data. This parameter adjustment mechanism can flexibly adjust according to real-time changes in supply and demand, effectively balance power resources, avoid supply and demand gaps or surpluses, and improve the economy and efficiency of the power system. The setting of the first device end and the second device end can realize two-way interaction of information. The first device sends the power supply parameters to the second device, and the user can view them in real time and adjust the power consumption behavior according to these parameters, such as increasing power consumption during low-price periods or reducing unnecessary power consumption during peak demand periods. This mechanism can enhance the user's ability to manage power consumption, improve user participation, promote interaction between supply and demand, and optimize the use of power resources. By comprehensively analyzing power supply and demand data, the system can make intelligent decisions on power supply parameters, such as electricity prices and power supply, so as to optimize resource allocation, ensure efficient use of power resources and stable operation of microgrids. This intelligent decision-making ability helps to reduce power waste and improve the economic and environmental benefits of the power system. The design of the interactive device (the first device and the second device) takes into account the changes in the number of users and demand, and can adapt to the increase or decrease in the number of users in the microgrid, seamlessly add or replace users in the system, enhance the adaptability and scalability of the system, and provide a flexible solution for the growth of future power demand. The second device provides users with intuitive price signals by displaying power supply parameters, such as dynamic electricity price information, so that users can respond according to their needs and cost-effectiveness, enhance the transparency and controllability of power consumption, and improve the user's power experience. In summary, the power supply control system in this embodiment can improve the operating efficiency, economy and stability of the microgrid system through real-time data collection, integrated analysis of supply and demand data, dynamic power supply parameter adjustment and two-way information interaction with users. It can also promote the active participation of users in power consumption management, which plays an important role in promoting the development of smart grids and the management of power supply and demand balance.

[0070] It should be noted that in this application Figure 3 The specific structure of the power supply control system shown in the figure is only for reference. In specific applications, the power supply control system in this application can be compared to Figure 3The electrical control systems shown have more or less structures.

[0071] It should be noted that any optional or preferred power supply control method in the above method embodiments can be executed or implemented in the power control system provided in this embodiment.

[0072] In addition, it is still necessary to explain that the optional or preferred implementation modes of this embodiment can refer to the relevant descriptions in the method embodiments, which will not be repeated here.

[0073] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation mode: Figure 4 It is a schematic diagram of the demand response interaction process within an optional interactive device according to an embodiment of the present invention. Each microgrid includes a master control device as a microgrid manager, which is responsible for managing and regulating all distributed power generation systems and energy storage systems within its jurisdiction, and formulating electricity sales prices by calculating the costs of all parties and its own benefits, so as to provide new energy supply services to users in the microgrid. Specifically, the microgrid manager first provides a benchmark price range as the basic electricity price, and then dynamically adjusts the current energy price (i.e., electricity price) according to the real-time output of the new energy power generation unit (i.e., distributed power generation system) and the charge and discharge of the integrated energy storage system.

[0074] The new type of power system demand response two-way interactive device (i.e., interactive device) designed in this embodiment consists of two parts: one part is connected to the circuit entrance and is fixedly installed at the first device end (i.e., device end A) at the power entry point; the other part is the second device end (i.e., user interaction terminal B) used for user interaction, communication and display.

[0075] The demand response bidirectional interactive device can realize the following functions: (1) 220V AC voltage and current sampling; (2) load characteristic monitoring and extraction algorithm; (3) uploading and downloading of large amounts of real-time data, compatible with multiple communication methods, and realizing bidirectional interaction with users and microgrid side; (4) good operation of the whole machine (including power supply, status display, fault prevention, etc.).

[0076] Figure 5It is a structural diagram of an optional first device end (i.e., device end A) according to an embodiment of the present invention. Its main components include: a voltage measurement port, a current measurement port, a power conversion and power supply module, a processor, a memory, a functional module, a communication and interaction module, a wireless transmission module, and a wireless transmission antenna. Among them, the processor is responsible for processing tasks such as data acquisition, load event detection, and feature extraction; the memory stores real-time data, an electrical appliance feature database, and a terminal identification result library; the functional module includes tasks such as load identification, power consumption status monitoring, energy consumption metering, and fee control strategies. The communication and interaction module is used to communicate with the grid-side administrator to obtain uplink and downlink information; the wireless transmission module is used to connect to the wireless transmission antenna and to communicate with the user interaction terminal B.

[0077] For device end A, real-time voltage and current transient data collection can be realized, and the device itself can be powered through the power conversion and power supply module. The collected voltage and current data are converted and input into the processor for processing to realize functions such as load event detection and feature extraction. Furthermore, combined with the real-time database, electrical appliance feature library and terminal identification result library in the memory, the functional module can realize tasks including load identification, power consumption status monitoring, energy consumption metering and fee control strategy. The power consumption information and load identification results on the user side will be transmitted to the grid administrator and the user interaction terminal B by the communication module at the same time. The user can understand the information collected by the device end A and the dynamic electricity price information given by the grid side in real time.

[0078] The detailed power information on the user side will be obtained by the feature extraction module in the device side A. The microgrid manager can monitor the user-side energy consumption data, load usage details, obtain the user-side load composition information through the interactive device, and purposefully adjust and publish dynamic electricity prices. For the new energy power generation side, the microgrid manager can also obtain detailed data on the microgrid grid connection, as well as power generation and consumption. Power data feature extraction and analysis is one of the main functions of device A. In this embodiment, the demand response behavior feature indicators contained in the feature extraction module include but are not limited to: daily minimum load rate, daily peak-to-valley difference, daily peak-to-valley difference rate, daily load rate, peak power consumption rate, valley power consumption rate, flat section power consumption percentage, daily load peak time.

[0079] For the non-power characteristics that the processor module needs to obtain, including holidays, temperature, weather, and major events, this embodiment performs a correlation analysis based on the quantized external data feature quantity and the user curve to obtain the correlation coefficient R to reflect the correlation between the external data and power consumption behavior. The calculation formula of the correlation coefficient is the same as mentioned above and will not be repeated here.

[0080] The demand-side interactive device device end A designed in this embodiment is installed at the household port, collects the total voltage and current data of the power inlet, processes it through the load identification algorithm, and transmits the results to the power grid manager server and the user interactive terminal B via wired or wireless communication methods. Different from the traditional smart meter, the device end A in this embodiment not only has the functions of power collection and data communication, but also undertakes complex data processing, algorithm execution, two-way information interaction and other tasks.

[0081] Figure 6 is a modular hardware structure diagram of an optional first device end (ie, device end A) according to an embodiment of the present invention, such as Figure 6 The sampling module shown contains voltage and current input and conditioning circuits, which are input into STMicroelectronics (STM) 32 processor after the metering chip for subsequent load event detection and feature extraction. At the same time, in order to power the device itself, a power conversion module is also designed to convert the mains electricity into low-voltage direct current. The multi-dimensional load data and feature library collected by the device are stored in the memory, and communicate with the power grid manager or user interaction terminal in a wired or wireless manner through the communication and interaction module.

[0082] The demand-side interaction device of this embodiment can meet the functions of power signal collection, power conversion, data processing, communication, etc.

[0083] The data acquisition and analysis module can convert the original real-time values ​​of current and voltage into the feature set required for load identification. The device is equipped with a 6.4kHz high-frequency sampling chip to sample the voltage and current in real time, and each complete waveform will be restored using 128 data points.

[0084] Furthermore, the data acquisition and analysis module of this embodiment converts the obtained hexadecimal data into decimal floating point data under the calculation of the processor, and further converts them into real-time data required for electric energy metering and feature set data required for the algorithm. The real-time data of electric energy will be directly uploaded to the server program through the DL / T645 protocol.

[0085] The power supply voltage conversion module converts the 220V 50Hz AC voltage to DC voltage at the device end A, and is designed with a protection circuit. For important chips (mainly acquisition chips and processor chips, etc.), additional lithium batteries are used to avoid data loss and device loss caused by power outages.

[0086] The communication and interaction module includes wired modes such as four-wire balanced transmission standard interface RS-485, serial two-way communication standard interface RS-232, and Ethernet, and wireless modes such as General Packet Radio Service (GPRS), third-generation mobile communication technology (3G), fourth-generation mobile communication technology (4G), narrowband Internet of Things (NB-Io), and wireless fidelity technology (Wi-Fi). In different scenarios of user categories, the device has multiple different communication chips to cope with different environments, and can quickly switch to the backup communication channel for breakpoint resumption when the main communication channel fails to avoid losses caused by data loss.

[0087] The event detection module can start the load event detection step when the difference exceeds a predetermined threshold; if the load is started or shut down, the event will be identified, the corresponding data will be recorded, labeled, and uploaded to the server program through the DL / T645 protocol.

[0088] The second device end (i.e., user end B) in the new power system demand response two-way interactive device is an interactive terminal that can wirelessly communicate with device end A. The hardware structure of user end B includes the device body and shell, display screen, power button, mode operation button (+), mode operation button (-), wireless transmission antenna, sound playback device and working indicator light. There are adhesive or fixing screws on the back of the device, and the user can freely choose to hold or fix the device on the wall to interact with device end A.

[0089] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any of the power supply control methods.

[0090] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group, and the non-volatile storage medium includes a stored program.

[0091] Optionally, when the program is running, the device where the non-volatile storage medium is located is controlled to perform the following functions: receiving power usage characteristics from the first device end of the interactive device, wherein the power usage characteristics are obtained by the first device end by processing the power usage data of the power account in the current sampling period; obtaining the power generation data of the distributed power generation system in the microgrid system in the current sampling period, and the charging and discharging data of the energy storage system in the current sampling period; determining the power supply parameters based on the power usage characteristics, the power generation data and the charging and discharging data; sending the power supply parameters to the first device end, wherein the first device end is used to send the power supply parameters and the power supply parameters to the second device end, and the second device end is used to display the power supply parameters and the power supply parameters, and the interactive device includes the first device end and the second device end.

[0092] According to an embodiment of the present application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any one of the power supply control methods when it is run.

[0093] According to an embodiment of the present application, an embodiment of a computer program product is also provided. When executed on a data processing device, it is suitable for executing a program that initializes any one of the power supply control method steps described above.

[0094] Optionally, the above-mentioned computer program product, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: receiving power usage characteristics from a first device end of an interactive device, wherein the power usage characteristics are obtained by the first device end by processing the power usage data of the power usage account in the current sampling period; obtaining power generation data of a distributed power generation system in a microgrid system in the current sampling period, and charging and discharging data of an energy storage system in the current sampling period; determining power supply parameters based on the power usage characteristics, power generation data, and charging and discharging data; and sending the power supply parameters to the first device end, wherein the first device end is used to send the power supply parameters and the power supply parameters to the second device end, for the second device end to display the power supply parameters and the power supply parameters, and the interactive device includes the first device end and the second device end.

[0095] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: receiving power usage characteristics from a first device end of an interactive device, wherein the power usage characteristics are obtained by the first device end by processing the power usage data of the power usage account in the current sampling period; obtaining power generation data of a distributed power generation system in a microgrid system in the current sampling period, and charging and discharging data of an energy storage system in the current sampling period; determining power supply parameters based on the power usage characteristics, power generation data, and charging and discharging data; and sending the power supply parameters to the first device end, wherein the first device end is used to send the power supply parameters and the power supply parameters to the second device end, so that the second device end can display the power supply parameters and the power supply parameters. The interactive device includes the first device end and the second device end.

[0096] The above sequence of the embodiments of the present invention is for description only and does not represent the superiority or inferiority of the embodiments.

[0097] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above modules can be a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

[0099] The modules described above as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0100] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0101] If the above-mentioned integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0102] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A power supply control method, characterized in that: include: Receiving a power usage feature from a first device end of an interactive device, wherein the power usage feature is obtained by the first device end by processing power usage data of a power account in a current sampling period; Acquire power generation data of a distributed power generation system in a microgrid system during the current sampling period, and charge and discharge data of an energy storage system during the current sampling period; Determining power supply parameters based on the power consumption characteristics, the power generation data, and the charge and discharge data; The power supply parameter is sent to the first device end, wherein the first device end is used to send the power supply parameter and the power supply parameter to the second device end, and the second device end is used to display the power supply parameter and the power supply parameter, and the interactive device includes the first device end and the second device end.

2. The method according to claim 1, characterized in that The determining of power supply parameters based on the power consumption characteristics, the power generation data and the charge and discharge data includes: Based on the power consumption characteristics, a load forecast result for a forecast period is obtained, wherein the forecast period is a period after the current sampling period; Based on the power generation data, obtaining a power generation prediction result for the prediction period; Based on the charging and discharging data, obtaining a storage state prediction result for the prediction period; The power supply parameter is obtained based on the load forecast result, the power generation forecast result and the energy storage state forecast result.

3. The method according to claim 2, characterized in that In the case where the power consumption characteristics include power characteristic indicators and other characteristic indicators, the load forecasting result for the forecast period is obtained based on the power consumption characteristics, including: Determining a correlation coefficient between the other characteristic indicators and the power characteristic indicator; The load forecasting result is obtained based on the power characteristic index, the other characteristic indexes and the correlation coefficient.

4. The method according to claim 2, characterized in that: The obtaining of the power supply parameter based on the load prediction result, the power generation prediction result and the energy storage state prediction result comprises: Based on the load forecast result, the power generation forecast result and the energy storage state forecast result, determining an adjustment method for adjusting the preset basic electricity price, wherein the adjustment method includes: adjustment direction and adjustment range; Adjusting the basic electricity price based on the adjustment method to obtain a target electricity price; The power supply parameter is obtained based on the target electricity price.

5. The method according to any one of claims 1 to 4, characterized in that: The electricity usage characteristics include: Load identification results, power characteristic indicators, other characteristic indicators, including: The power characteristic index includes at least one of the following: the minimum load rate, peak-to-valley difference, peak-to-valley difference rate, load rate, peak-time power consumption rate, valley-time power consumption rate, flat-time power consumption percentage, and load peak time of the power consumption account in the current sampling period; The other characteristic indicators include at least one of the following: temperature, weather, and a preset special period; The flat-segment electricity consumption percentage is used to indicate the percentage of the electricity consumption of the electricity consumption account in the flat segment to the total electricity consumption in the current sampling period, and the flat segment is used to indicate the period in the current sampling period when the electricity consumption is within a predetermined range.

6. A power supply control method, characterized in that: include: The first device end in the interactive device collects the electricity consumption data of the electricity consumption account in the current sampling period; The first device processes the electricity usage data to obtain electricity usage characteristics of the electricity usage account; The main control device obtains the power generation data of the distributed power generation system in the microgrid system during the current sampling period, and the charging and discharging data of the energy storage system during the current sampling period; The main control device determines power supply parameters based on the power consumption characteristics, the power generation data, and the charge and discharge data, and sends the power supply parameters to the first device; The first device sends the power supply parameter and the power supply parameter to the second device end; The second device end displays the power supply parameter and the power supply parameter, wherein the interactive device includes the first device end and the second device end.

7. The method according to claim 6, characterized in that In the case where the power usage characteristics include a load identification result, the first device processes the power usage data to obtain the power usage characteristics of the power usage account, including: The first device extracts a target feature from the power consumption data, wherein the target feature is a feature reflecting a characteristic of the power consumption device; The first device analyzes the target feature using a load identification algorithm to obtain a load feature of the electricity account in the current sampling period, wherein the load feature is used to indicate a predicted operating state and electricity consumption mode of the electric device; The first device obtains a load identification result based on the load characteristics and the electrical appliance characteristic library, wherein the load identification result includes the equipment type matching the load characteristics and the operating status corresponding to the equipment type; the electrical appliance characteristic library includes key power consumption characteristics corresponding to multiple equipment types.

8. A power supply control system, characterized in that: include: A main control device and an interactive device, wherein the interactive device includes a first device end and a second device end, wherein: The first device is used to collect electricity usage data of the electricity usage account in the current sampling period, process the electricity usage data, and obtain electricity usage characteristics of the electricity usage account; The main control device is used to obtain the power generation data of the distributed power generation system in the microgrid system in the current sampling period, and the charge and discharge data of the energy storage system in the current sampling period; based on the power consumption characteristics, the power generation data and the charge and discharge data, determine the power supply parameters, and send the power supply parameters to the first device; The first device is used to send the power supply parameter and the power supply parameter to the second device end; The second device end is used to display the power supply parameters and the power supply parameters.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the power supply control method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the power supply control method described in any one of claims 1 to 7.

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