Energy aggregation scheduling method, system and device and storage medium

Abstract

The invention provides an energy aggregation scheduling method, system and device and a storage medium, and relates to the technical field of electric power engineering, and the method comprises the steps: according to the first power consumption time period of a user in a first preset time period and the first power consumption of each first power consumption time period, calculating the first power consumption of each first power consumption time period; predicting second electricity consumption time periods of the user in a second preset time period and second electricity consumption of each second electricity consumption time period, and generating total electricity consumption of the user in the second preset time period according to the second electricity consumption; calculating the power generation quantity of the distributed energy equipment in each second power utilization period according to the environment information; and when the sum of the residual electricity quantity and the electricity generation quantity is not less than the total electricity consumption, obtaining an electric energy transaction price of each time period of the electricity market, and on the basis of the electric energy transaction price, combining the residual electricity quantity, the electricity generation quantity and the second electricity consumption of each second electricity consumption time period to generate an electric energy selling scheme. The technical effect of the invention is that the energy scheduling scheme realizes the maximization of economic benefits.

Description

Technical Field

[0001] The present application relates to the field of electric power engineering technology, and in particular to an energy aggregation scheduling method, system, device and storage medium. Background Art

[0002] With the rapid development of distributed energy systems, the management and scheduling of distributed energy devices on the user side are becoming increasingly important. Especially when users act as both consumers and producers of electricity, accurately predicting electricity demand, rationally arranging electricity production and trading, and maximizing economic benefits have become urgent technical challenges.

[0003] Currently, common energy scheduling methods rely primarily on simple demand forecasts based on historical electricity usage data, and then formulate corresponding scheduling strategies based on the forecast results. While this approach can achieve optimal energy scheduling to a certain extent, it lacks the ability to dynamically respond to market factors and suffers from low forecast accuracy, making it difficult for energy scheduling solutions to maximize economic benefits. Summary of the Invention

[0004] The present application provides an energy aggregation scheduling method, system, device and storage medium for maximizing the economic benefits of energy scheduling schemes.

[0005] In the first aspect, the present application provides an energy aggregation scheduling method, which includes: obtaining a first electricity consumption period of a user within a first preset time period, and the first electricity consumption of each of the first electricity consumption periods; predicting a second electricity consumption period of the user within a second preset time period, and the second electricity consumption of each of the second electricity consumption periods based on the first electricity consumption period and the first electricity consumption, and generating the total electricity consumption of the user within the second preset time period based on the second electricity consumption, wherein the first preset time period is before the second preset time period; obtaining environmental information of the location of the user's distributed energy equipment within the second preset time period, and the remaining power of the distributed energy equipment; calculating the power generation of the distributed energy equipment in each of the second electricity consumption periods based on the environmental information; when the sum of the remaining power and the power generation is not less than the total power consumption, obtaining the electricity trading price of each time period in the electricity market, and on the basis of the electricity trading price, combining the remaining power and the power generation, and the second power consumption of each of the second electricity consumption periods, generating an electricity sales plan for the distributed energy equipment within the second preset time period.

[0006] By employing this technical solution, a multi-dimensional analysis of historical electricity consumption data and environmental information is conducted to accurately predict user electricity usage behavior. Furthermore, by matching predicted electricity demand with the generation capacity of distributed energy devices, the system accurately determines whether conditions for electricity trading are met. When trading conditions are met, the solution incorporates price fluctuations in the electricity market to develop a targeted electricity sales plan, ensuring reliable electricity supply for users while maximizing the economic value of surplus electricity. This prediction-based intelligent scheduling mechanism maximizes the economic benefits of energy scheduling solutions.

[0007] Optionally, the method also includes: when the sum of the remaining power and the power generation is less than the total power consumption, arithmetically adding the remaining power to the power generation to generate the total power generation, and calculating the power difference between the total power generation and the total power consumption; obtaining the available capacity of the central energy storage station in the area where the user is located, and the power consumption information of other users in the area where the user is located; combining the available capacity and the power consumption information, determining the target power that the central energy storage station can sell to the user; on the basis of the power transaction price, combining each of the power differences and the target power, generating an power purchase plan for the distributed energy equipment within the second preset time period.

[0008] By adopting the above technical solution, the difference between total power generation and total power consumption can be calculated to accurately identify users' power shortages. Based on this, by obtaining the available capacity of the central energy storage station and regional power consumption information, the system can rationally assess the scheduling space for energy storage resources. By combining the power shortage with the target power capacity provided by the central energy storage station and considering the electricity trading price, an optimal power purchase plan can be formulated. This multi-source complementary power supply strategy not only ensures reliable power supply for users, but also optimizes power costs by prioritizing the use of power from the central energy storage station, thereby improving the overall operational efficiency of the regional energy system.

[0009] Optionally, the electricity purchasing plan includes: purchasing a first amount of electricity from the central energy storage station and a first purchasing time, and purchasing a second amount of electricity from the power grid and a second purchasing time.

[0010] By adopting the above technical solution, refined management of electricity purchases is achieved by subdividing the electricity purchase plan into two sources: the central energy storage station and the power grid, and clearly defining the purchase quantity and time of each. By rationally arranging the first quantity, the first purchase time, the second quantity, and the second purchase time, the system can flexibly adjust the electricity purchase strategy based on the differences in electricity prices during different time periods, prioritizing electricity purchases during periods with lower prices. This tiered, multi-source electricity purchase plan not only reduces users' overall electricity costs, but also improves power supply reliability and promotes the coordinated operation of regional energy systems.

[0011] Optionally, the method of predicting the second power usage period of the user within the second preset time period and the second power consumption of each second power usage period based on the first power usage period and the first power consumption includes: predicting the third power usage period of the user within the second preset time period and the third power consumption of each third power usage period based on the first power usage period; obtaining environmental information of the location of the user's distributed energy equipment within the second preset time period; adjusting the third power usage period and the third power consumption based on the environmental information, and correspondingly generating the second power usage period of the user within the second preset time period and the second power consumption of each second power usage period.

[0012] By adopting this technical solution, a two-step forecasting mechanism is implemented. Initially, a preliminary third power consumption period and third power consumption are generated based on historical power consumption data. The forecast is then dynamically adjusted based on environmental information, ultimately generating a more accurate second power consumption period and second power consumption. This environmentally sensitive forecasting approach effectively improves the accuracy of power consumption predictions, enabling the system to better adapt to fluctuations in power demand caused by environmental changes and providing a more reliable basis for subsequent energy scheduling decisions.

[0013] Optionally, on the basis of the electricity trading price, combined with the remaining electricity and the electricity production, and the second electricity consumption in each second electricity consumption period, an electricity sales plan for the distributed energy equipment within the second preset time period is generated, including: calculating the remaining saleable electricity of the user in each second electricity consumption period, the remaining saleable electricity is the sum of the remaining electricity and the electricity production minus the second electricity consumption; identifying the high-price period in the second preset time period when the electricity trading price is higher than a preset threshold; determining the electricity sales amount and sales time of the distributed energy equipment in the high-price period, and generating an electricity sales plan for the distributed energy equipment within the second preset time period.

[0014] By employing this technical solution, the system accurately calculates the remaining saleable electricity in each time period and identifies periods with high electricity prices exceeding a preset threshold, enabling precise timing for electricity trading. By scheduling electricity sales during high-price periods, the system can leverage market price differences and trade surplus electricity during the most economically profitable periods. This price-driven sales approach not only ensures that users' electricity needs are met, but also maximizes the economic value of surplus electricity, improving the return on investment for distributed energy equipment.

[0015] Optionally, determining the amount of electricity sold and the time of sale of the distributed energy equipment during the high-price period includes: sorting the high-price period from high to low according to the electricity trading price; allocating the remaining saleable electricity in order from high to low according to the electricity trading price, allocating the maximum allocable electricity in the period with the highest electricity trading price, and when the allocable electricity in the period with the highest electricity trading price reaches the upper limit or the remaining saleable electricity has been fully allocated, allocating the remaining unallocated electricity to the period with the second highest electricity trading price; and determining the amount of electricity sold and the time of sale of the distributed energy equipment during the high-price period based on the allocation result.

[0016] By employing this technical solution, we prioritize high-price periods by price ranking and employ a progressive electricity allocation strategy, prioritizing the remaining saleable electricity to the highest-price periods. Once the allocable limit is reached or the electricity allocation is exhausted, the remaining electricity is allocated to the next-highest-price periods. This refined allocation method, based on price priority, ensures maximum trading revenue for each unit of electricity. By setting an allocation cap, we also mitigate the risk of over-concentration in trading, optimize electricity trading revenue, and improve the economic benefits of distributed energy devices.

[0017] Optionally, after generating the electricity sales plan for the distributed energy equipment within the second preset time period, it also includes: sending the electricity sales plan to the user's energy management system; receiving the user's confirmation information on the electricity sales plan; submitting the electricity sales plan to the power trading platform based on the confirmation information; monitoring the actual power consumption and actual power generation of the distributed energy equipment within the second preset time period; when the difference between the actual power consumption or the actual power generation and the second power consumption or the power generation exceeds a preset deviation threshold, regenerating the electricity sales plan.

[0018] By adopting the above technical solution and establishing a comprehensive plan execution and monitoring mechanism, the entire process of electricity trading is managed. By interacting with the user's energy management system, plans are submitted to the trading platform only after user confirmation, safeguarding user rights. Furthermore, the system monitors actual electricity consumption and production in real time, triggering a plan regeneration mechanism when deviations from predicted values ​​exceed preset thresholds. This dynamic adjustment mechanism improves the adaptability and reliability of the plan, ensuring the stability and cost-effectiveness of electricity trading during actual execution.

[0019] In a second aspect, the present application provides an energy aggregation scheduling system, the system comprising: a first acquisition module, a prediction module, a second acquisition module, a calculation module and a generation module; wherein, The first acquisition module is configured to acquire a first electricity usage period of a user within a first preset duration, and a first electricity consumption in each of the first electricity usage periods; the prediction module is configured to predict a second electricity usage period of the user within a second preset duration, and a second electricity consumption in each of the second electricity usage periods, based on the first electricity usage period and the first electricity consumption, and generate the total electricity consumption of the user within the second preset duration based on the second electricity consumption, where the first preset duration is before the second preset duration; the second acquisition module is configured to acquire environmental information about the location of the user's distributed energy device within the second preset duration, and the remaining power of the distributed energy device; the calculation module is configured to calculate the power generation of the distributed energy device in each of the second electricity usage periods based on the environmental information; and the generation module is configured to acquire, when the sum of the remaining power and the power generation is not less than the total power consumption, the electricity trading price for each time period in the electricity market, and generate, based on the electricity trading price, an electricity sales plan for the distributed energy device within the second preset duration in combination with the remaining power, the power generation, and the second power consumption in each of the second electricity usage periods.

[0020] In the third aspect, the present application provides an electronic device that 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 energy aggregation scheduling 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 energy aggregation scheduling methods.

[0022] In summary, this application includes at least one of the following beneficial technical effects: By collecting historical electricity consumption data and conducting multi-dimensional analysis of environmental information, the system accurately predicts user electricity usage behavior. Furthermore, by matching predicted electricity demand with the generation capacity of distributed energy devices, the system accurately determines whether conditions for electricity trading are met. When trading conditions are met, the solution incorporates price fluctuations in the electricity market to develop a targeted electricity sales plan, ensuring reliable electricity supply for users while maximizing the economic value of surplus electricity. This prediction-based intelligent scheduling mechanism maximizes the economic benefits of energy scheduling solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an energy aggregation scheduling method provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of an energy aggregation scheduling system provided in an embodiment of the present application; Figure 3 This is a structural 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 people 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 this application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0027] Figure 1 This is a flow chart of an energy aggregation scheduling method provided by an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining a first power usage period of a user within a first preset time period, and a first power usage in each first power usage period.

[0028] Specifically, the first preset duration can be a fixed duration such as one day, one week or one month. In this embodiment, one week is taken as an example; the first power usage period is the time period when the user actually uses electricity within the first preset duration, which can be accurate to the hour. For example, 8:00-12:00 and 14:00-18:00 every day in a week are the user's power usage periods; the first power consumption is the actual power consumption of the user in each first power consumption period.

[0029] To accurately predict a user's future electricity usage, it's first necessary to obtain the user's historical electricity usage data. Specifically, electricity usage data can be collected through the user's smart meter, which can record the user's electricity usage at different time periods and upload this data to the energy management system in real time. The energy management system can store and process this data to obtain the user's first electricity usage period within a first preset time period, as well as the first electricity usage for each first electricity usage period. For example, the energy management system can record the user's electricity usage data for each period over the past week, including the specific time period in which electricity usage occurred and the corresponding electricity consumption.

[0030] In practical applications, energy management systems can also pre-process collected electricity consumption data, including but not limited to: data cleaning to remove outliers and noise; data standardization to unify electricity consumption data from different time periods under the same metric; and data classification to categorize and store data based on the characteristics of electricity consumption periods (such as weekdays and holidays). This can improve the accuracy of subsequent electricity consumption forecasts.

[0031] By acquiring historical user electricity usage data, the energy management system can understand the patterns and characteristics of users' electricity use, providing a reliable data foundation for subsequent electricity demand forecasts. This data not only reflects users' basic electricity needs but also includes characteristics of their electricity usage behavior at different times and in different scenarios. This is crucial for accurately predicting users' future electricity needs. Furthermore, this data is an important basis for developing reasonable energy scheduling plans, helping the system better balance user electricity demand with the supply capacity of distributed energy resources.

[0032] S102, based on the first power usage period and the first power consumption, predict the user's second power usage period within the second preset time period, and the second power consumption of each second power usage period, and generate the user's total power consumption within the second preset time period based on the second power consumption, where the first preset time period is before the second preset time period.

[0033] Specifically, the second preset duration is the time period for which electricity consumption forecasting is required, which can be a fixed duration such as one day, one week or one month. In this embodiment, one day is taken as an example for illustration; the second electricity consumption period is the time period in which the user is predicted to consume electricity in the future; the second electricity consumption is the predicted electricity consumption of the user in each second electricity consumption period; the total electricity consumption is the sum of all electricity that the user is expected to consume within the second preset duration.

[0034] To properly schedule the operation of distributed energy devices, the system needs to predict users' future electricity usage. Specifically, the energy management system can perform statistical analysis based on historical electricity usage data, analyzing users' electricity usage patterns within a first preset time period to predict future electricity usage behavior. The prediction process primarily involves analyzing the temporal distribution characteristics of electricity usage periods in historical data, such as the patterns of peak and low electricity usage periods; and analyzing the changing characteristics of electricity consumption, such as the amount of electricity used during different time periods and its changing trends.

[0035] During the forecasting process, the system analyzes factors such as differences in electricity usage between weekdays and weekends, the impact of seasonal changes on electricity demand, and the characteristics of electricity consumption during special holidays. It then classifies and compiles historical data based on these factors to produce forecasts that are more realistic. Based on these statistical analysis results, the system determines the user's likely electricity usage time periods (i.e., second electricity usage periods) within the second preset duration and the estimated electricity consumption for each period (i.e., second electricity consumption). After the forecast is complete, the system arithmetically accumulates the second electricity consumption for each second electricity usage period to determine the user's total electricity consumption for the second preset duration.

[0036] Based on the above embodiment, as an optional implementation, in S102, based on the first power usage period and the first power consumption, predicting the second power usage period of the user within the second preset duration, and the second power consumption of each second power usage period specifically includes S21-S23: S21 , predicting a third power usage period of the user within a second preset time period and a third power consumption in each third power usage period based on the first power usage period.

[0037] In this embodiment, the third power consumption period is a power consumption period that is preliminarily predicted based only on historical power consumption data; the third power consumption is the predicted power consumption corresponding to the third power consumption period; the results of these preliminary predictions will be adjusted according to environmental information, and ultimately form a more accurate second power consumption period and second power consumption.

[0038] To improve the accuracy of electricity consumption forecasts, the system uses a step-by-step forecasting and dynamic adjustment approach. First, the system performs a preliminary forecast based on the user's electricity usage data within a first preset time period. Specifically, by analyzing the temporal distribution characteristics of the first electricity usage period, such as the occurrence patterns of peak and trough periods, and the duration of electricity consumption, the system predicts the user's likely electricity usage period within a second preset time period, i.e., the third electricity usage period. Simultaneously, based on the first electricity consumption of each first electricity usage period and combining it with the changing patterns of electricity consumption, the system predicts the third electricity consumption corresponding to each third electricity usage period.

[0039] S22, obtaining environmental information of the location of the user's distributed energy device within a second preset time period.

[0040] However, predictions based solely on historical electricity usage data may not fully reflect actual electricity usage, as user electricity consumption is often significantly affected by environmental factors. Therefore, the system needs to obtain environmental information about the location of the distributed energy device over a second preset time period, including meteorological data such as temperature, humidity, and light intensity. This environmental information helps the system better understand and predict changes in user electricity demand.

[0041] S23 , adjusting the third power usage period and the third power consumption according to the environmental information, and correspondingly generating the second power usage period of the user within the second preset time period and the second power consumption of each second power usage period.

[0042] After acquiring environmental information, the system will adjust the initial forecast results. This adjustment process primarily considers the impact of environmental factors on electricity consumption. For example, during predicted high temperatures, users may increase air conditioning use, so the system will adjust power consumption accordingly during these periods. During periods of abundant sunlight, users may reduce lighting use, so the system will adjust power consumption accordingly. Through this environmental information-based adjustment, the system generates the final second power consumption period and the corresponding second power consumption.

[0043] The system uses an environmental factor impact model to dynamically adjust the preliminary prediction results. The model includes the temperature impact coefficient (α), the light impact coefficient (β) and the weather condition coefficient (γ), and the comprehensive environmental impact coefficient (K) is obtained through weighted calculation. The calculation formula for the adjusted second power consumption is: P2=P3×K, where P2 is the adjusted second power consumption and P3 is the preliminary predicted third power consumption. The comprehensive environmental impact coefficient K=α×Wt+β×Wl+γ×Ww, where Wt, Wl, and Ww are the weight coefficients of temperature, light, and weather conditions, respectively. The temperature impact coefficient α shows a nonlinear relationship with temperature changes. When the temperature exceeds the comfort range (18℃-26℃), the α value increases; the light impact coefficient β is negatively correlated with the natural light intensity; the weather condition coefficient γ takes different discrete values ​​according to the weather type (sunny, cloudy, rainy).

[0044] Specifically, the calculation of the temperature influence coefficient α is as follows: when the temperature T is in different ranges: T < 18°C: α = 1 + 0.05 × (18 - T); 18°C ​​≤ T ≤ 26°C: α = 1; when T > 26°C: α = 1 + 0.08 × (T - 26); the calculation of the light influence coefficient β is as follows: β = 1 - (L - Lbase) × 0.01, where: L is the current light intensity (lux); Lbase is the reference light intensity (usually 500 lux); the value range of β is limited to [0.7, 1.2]; the value of the weather condition coefficient γ is as follows: sunny: γ = 1.0; cloudy: γ = 1.1; overcast: γ = 1.2; rainy: γ = 1.3; The system also sets an upper limit (Pmax) and lower limit (Pmin) for power consumption adjustment to ensure that the adjusted power consumption forecast remains within a reasonable range. Based on the model's dynamic adjustments, the system generates a second power consumption period and a second power consumption forecast that better reflects actual environmental conditions. The power consumption constraints are: Pmin ≤ P2 ≤ Pmax; where Pmin = 0.6 × P3 and Pmax = 1.5 × P3.

[0045] S103, obtaining environmental information of the location of the user's distributed energy device within a second preset time period, and the remaining power of the distributed energy device.

[0046] Distributed energy equipment may include renewable energy power generation equipment such as photovoltaic power generation equipment and energy storage equipment; environmental information includes meteorological information (such as light intensity, temperature, wind speed, etc.) and geographic information (such as equipment installation location, inclination angle, etc.); remaining power refers to the amount of power currently stored in the energy storage device.

[0047] To accurately assess the power generation capacity of distributed energy devices, the system needs to obtain information about the environment in which the devices are located and their current status. Specifically, the system collects real-time environmental data from sensors installed on the distributed energy devices, including light intensity data collected by light intensity sensors and temperature data collected by temperature sensors. Furthermore, the system can also obtain weather forecast information for the next two preset time periods through a data interface with meteorological authorities, including projected light intensity, temperature, and other data. This environmental information directly impacts the power generation efficiency and output of distributed energy devices.

[0048] To obtain the remaining charge of the energy storage device, the system reads the current charge status through the energy storage device's battery management system (BMS). The BMS monitors parameters such as the battery's charge and discharge status and remaining capacity in real time and transmits this data to the energy management system. The system needs to accurately understand the remaining charge of the energy storage device, as it is a key indicator for evaluating the system's power supply capacity.

[0049] By acquiring this information, the system can gain a comprehensive understanding of the operating environment and current status of distributed energy devices. Environmental information helps the system assess future power generation potential, while remaining power information reflects the system's current available energy reserves. This information is crucial for developing appropriate energy scheduling plans, helping the system maximize the use of renewable energy while ensuring power supply reliability.

[0050] S104: Calculate the power generation of the distributed energy equipment in each second power consumption period according to the environmental information.

[0051] Electricity production refers to the electricity that distributed energy equipment is expected to generate within a specific time period; environmental information includes meteorological information (such as light intensity, temperature, wind speed, etc.) and geographic information (such as equipment installation location, inclination, etc.); the second electricity consumption period is the time period when users are predicted to consume electricity in the future.

[0052] To rationally manage electricity production and usage, the system needs to calculate the power generation of distributed energy devices during each second power-usage period based on acquired environmental information. Specifically, the system first selects a power generation calculation method appropriate for the type of distributed energy device. For example, photovoltaic power generation is primarily influenced by factors such as light intensity, temperature, and device efficiency. The system calculates the theoretical power generation of the device during different time periods based on the rated power and photoelectric conversion efficiency of the PV modules, combined with environmental parameters such as light intensity and temperature.

[0053] During the calculation process, the system considers multiple factors, such as how light intensity changes over time, the impact of temperature on PV module efficiency, and the degree of equipment aging. Regarding light intensity, the system determines the effective duration and intensity of sunlight for each period based on forecasted weather conditions and historical data. Regarding temperature effects, the system calculates a correction factor for the effect of temperature on photovoltaic conversion efficiency based on the predicted ambient temperature. For equipment efficiency, the system considers the actual conversion efficiency and losses of the equipment. By comprehensively considering these factors, the system can produce a more accurate power generation forecast.

[0054] When calculating power generation for each time period, the system uses a time-based accumulation method. First, the second preset duration is divided into several calculation periods, each of which can be accurate to the hour or less. Then, the power generation is calculated for each period separately. Finally, based on the time range of the second power usage period, the power generation of the corresponding period is combined to obtain the power generation of each second power usage period.

[0055] By accurately calculating power generation, the system can better assess the power supply capabilities of distributed energy devices, providing an important basis for subsequent power scheduling and energy trading. This method of calculating power generation based on environmental information is highly accurate and reliable, helping the system better match power demand with power supply capabilities and improving energy efficiency.

[0056] S105, when the sum of the remaining electricity and the generated electricity is not less than the total electricity consumption, the electricity trading price of each time period in the electricity market is obtained. On the basis of the electricity trading price, combined with the remaining electricity and the generated electricity, and the second electricity consumption in each second electricity consumption period, an electricity sales plan for the distributed energy equipment within the second preset time period is generated.

[0057] The electricity trading price refers to the buying and selling price of electricity in different time periods in the power market; the electricity sales plan includes the specific electricity sales amount and the corresponding electricity sales time in different time periods; the sum of the surplus electricity and the generated electricity is not less than the total electricity consumption, indicating that the system has an electricity surplus and has the ability to participate in market transactions.

[0058] To maximize user economic benefits, the system needs to develop a reasonable electricity sales plan when it discovers that the sum of surplus electricity and generated electricity exceeds the user's total electricity consumption. Specifically, the system first obtains electricity trading prices for each time period within a second preset duration through a data interface with the power trading platform. This price information typically includes time-of-use electricity prices and real-time electricity prices, reflecting the market value of electricity at different times. Obtaining this price information provides a crucial basis for developing an optimal electricity sales plan.

[0059] After obtaining the electricity trading price, the system calculates the amount of electricity available for sale in each time period. This calculation method involves first determining the electricity demand for each time period (i.e., the second electricity demand). Then, the system deducts the corresponding electricity demand from the sum of the surplus electricity and the generated electricity in that time period to obtain the amount of electricity available for sale. This calculation method ensures that the sale of surplus electricity is rationally arranged while meeting the user's own electricity needs.

[0060] When developing electricity sales plans, the system adopts a price-priority strategy, prioritizing sales during periods with higher electricity prices. In practice, the system first sorts each time period by electricity trading price from highest to lowest, then allocates available electricity, starting with the period with the highest electricity price. During this allocation process, the system considers electricity market trading rules and restrictions, such as the maximum trading volume limit for a single period, to ensure that the generated sales plan meets market requirements.

[0061] This market-based electricity sales solution offers significant economic advantages. By selling more electricity during periods of high electricity prices, higher economic returns can be achieved. At the same time, this solution considers users' actual electricity needs, ensuring that the sale of excess electricity does not impact their regular electricity needs. This approach, balancing electricity demand and market returns, ensures both power supply reliability and maximizes economic benefits.

[0062] In practice, the system-generated electricity sales plan will include specific implementation details, such as the amount of electricity sold during each period and the time of sale. This information will be used to guide subsequent actual transactions. By implementing the optimized electricity sales plan, users can fully utilize the generation capacity of distributed energy devices, convert surplus electricity into economic benefits, and improve the overall system's operational efficiency.

[0063] Based on the above embodiment, as an optional implementation, in S105, based on the electric energy transaction price, combined with the remaining power and the generated power, and the second power consumption in each second power consumption period, generating an electric energy sales plan for the distributed energy device within the second preset time period specifically includes S51-S53: S51, calculating the remaining saleable electricity of the user in each second electricity consumption period, where the remaining saleable electricity is the sum of the remaining electricity and the generated electricity minus the second electricity consumption.

[0064] In this embodiment, the remaining saleable electricity refers to the electricity available for market transactions after meeting the user's own electricity needs; the high-price period refers to the time period when the electricity trading price exceeds the preset threshold; the preset threshold is the electricity price judgment standard set by the system, which is used to identify the time period suitable for electricity trading; the electricity sales plan includes the specific amount of electricity sold and the sales time schedule.

[0065] To maximize users' economic benefits, the system needs to develop a scientific and rational electricity sales plan. First, the system needs to accurately calculate the amount of electricity available for trading in each time period. Specifically, the system arithmetically adds the user's remaining electricity in each second power consumption period to the generated electricity, then subtracts the corresponding second power consumption period to calculate the remaining saleable electricity. This calculation method ensures that the amount of tradable electricity is reasonably determined while meeting the user's own electricity needs, avoiding the impact of excessive electricity sales on normal user electricity use.

[0066] S52, identifying a high-price period during a second preset time period when the electric energy transaction price is higher than a preset threshold.

[0067] After determining the remaining saleable electricity in each time period, the system needs to identify the most suitable time periods for electricity trading. The system compares electricity trading prices in each time period with preset price thresholds to identify periods with high prices. These thresholds are typically set based on historical trading data and market analysis, ensuring both sufficient economic returns and sufficient trading opportunities. This price threshold-based screening method helps the system identify the most economically profitable trading opportunities.

[0068] S53, determining the amount and time of electricity sold by the distributed energy equipment during the high-price period, and generating an electricity selling plan for the distributed energy equipment during a second preset time period.

[0069] After identifying high-price periods, the system determines a specific electricity sales plan. It prioritizes selling more electricity during periods with the highest electricity prices, while also taking into account electricity market trading rules and restrictions. When determining the specific sales time and amount, the system also considers practical factors such as transmission losses and market acceptance to ensure the generated sales plan is feasible.

[0070] This time-based, price-driven electricity sales solution offers significant economic advantages. By accurately calculating the amount of electricity available for sale, the system can avoid supply-demand conflicts caused by improper energy scheduling. By identifying periods of high electricity prices, the system can seize optimal trading opportunities. By rationally allocating the amount of electricity sold, the system can maximize profits. Furthermore, this solution contributes to the stable operation of the electricity market and avoids excessive concentration in electricity trading.

[0071] During actual execution, the system dynamically monitors and adjusts the sales plan. If actual conditions deviate from expectations, such as sudden changes in electricity demand or market price fluctuations, the system can promptly adjust the amount and timing of sales to ensure the feasibility and economic viability of the plan. This dynamic adjustment mechanism enhances the flexibility and adaptability of electricity trading.

[0072] By executing optimized electricity sales plans, users can fully utilize the generation capacity of distributed energy devices and convert surplus electricity into economic benefits. This market-based trading method also promotes the efficient use of renewable energy and promotes the clean transition of the energy system. Furthermore, by recording and analyzing the effectiveness of plan execution, the system can continuously accumulate experience, optimize trading strategies, and further improve the efficiency of future transactions.

[0073] Based on the above embodiment, as an optional implementation, in S53, determining the amount and time of electric energy sold by the distributed energy device during the high-price period specifically includes S531-S533: S531, sorting the high-price periods from high to low according to the electricity transaction prices.

[0074] In this embodiment, the high-price period refers to the time period when the electricity trading price exceeds the preset threshold; the maximum allocable electricity refers to the maximum electricity allowed to be traded in a single period, which is usually restricted by the electricity market trading rules and technical conditions; the remaining unallocated electricity refers to the electricity that has not been arranged for trading after the electricity allocation for a certain period is completed.

[0075] To maximize electricity trading profits, the system adopts a price-priority electricity allocation strategy. First, the system compares and ranks electricity trading prices during all high-price periods, generating a price sequence from high to low. This ranking method intuitively demonstrates the price advantages of different time periods, providing clear priority guidance for subsequent electricity allocation. During the sorting process, the system records the specific price and time information for each period to ensure accurate alignment during subsequent allocation.

[0076] S532, allocate the remaining saleable electricity in order from high to low electricity trading prices, and allocate the maximum allocable electricity in the period with the highest electricity trading price. When the allocable electricity in the period with the highest electricity trading price reaches the upper limit or the remaining saleable electricity has been fully allocated, allocate the remaining unallocated electricity to the period with the second highest electricity trading price.

[0077] After completing the price ranking, the system begins to allocate electricity. The system first allocates the remaining saleable electricity to the time period with the highest electricity price. During this allocation process, the system needs to consider two constraints: the maximum allocable electricity limit for that time period, which is generally determined by the trading rules of the electricity market and network transmission capacity; and the total amount of remaining saleable electricity. When the allocated electricity for a certain time period reaches the maximum allocable electricity, or the remaining saleable electricity has been fully allocated, the system will transfer the remaining unallocated electricity (if any) to the time period with the next highest electricity price for allocation. This progressive allocation method ensures maximum returns on electricity trading.

[0078] S533: Determine the amount and time of electricity sold by the distributed energy equipment during the high-price period based on the allocation result.

[0079] Based on the power allocation results, the system ultimately determines the specific amount of power to be sold and the specific time of sale during each high-price period. This determination process not only considers the economic feasibility of the allocation results but also the feasibility of actual implementation. For example, the system needs to assess technical factors such as the continuity requirements of power transmission and the stability requirements of equipment operation to ensure that the final sales plan not only maximizes profits but also ensures practical implementation.

[0080] This price-priority-based electricity allocation method offers several advantages. First, it leverages market price differences to prioritize the allocation of limited saleable electricity to the most profitable periods, maximizing revenue. Second, by setting a maximum allocable electricity limit, it avoids the risk of excessive trading concentration in a single period. Finally, its progressive allocation approach ensures the rational use of remaining saleable electricity, improving resource efficiency.

[0081] Based on the above embodiment, as an optional implementation manner, the method further includes S201-S204: S201: When the sum of the remaining power and the generated power is less than the total power consumption, the remaining power is arithmetically added to the generated power to generate the total generated power, and the power difference between the total generated power and the total power consumption is calculated.

[0082] In this embodiment, the total power generation refers to the sum of the remaining power of the distributed energy equipment and the expected power generation; the power difference refers to the difference between the user's total power consumption and the total power generation, indicating the additional power that the user needs to purchase; the central energy storage station is a centralized energy storage facility in the region; the available capacity refers to the power currently available for dispatch at the central energy storage station; the target power refers to the power that the central energy storage station can sell to a specific user; the power purchase plan includes the source, quantity, and time schedule of the purchased power.

[0083] When the system discovers that a user's total power generation cannot meet their projected electricity demand, it must develop a reasonable power purchase plan to ensure reliable power supply. First, the system arithmetically adds the remaining power of the distributed energy devices to the projected power generation to obtain the total power generation. This total power generation is then compared with the projected total power consumption to calculate the difference in additional power required for purchase. This calculation provides a specific power demand target for the subsequent power purchase plan.

[0084] S202: Obtain the available capacity of the central energy storage station in the area where the user is located, and the electricity consumption information of other users in the area where the user is located.

[0085] To optimize electricity purchasing plans, the system prioritizes purchasing electricity from central energy storage stations, as they typically offer more competitive pricing than purchasing directly from the grid. The system first obtains information about the central energy storage station's available capacity. It also obtains information about electricity consumption by other users in the area, including their electricity demand and time of day. This information helps the system assess the central energy storage station's power supply capacity and scheduling capabilities.

[0086] S203 , combining available capacity and electricity usage information, determining the target amount of electricity that the central energy storage station can sell to users.

[0087] After acquiring relevant information, the system needs to determine the target amount of electricity that the central energy storage station can provide to users. This determination takes into account several factors: first, the available capacity of the central energy storage station, which determines the maximum amount of electricity that can be supplied; second, the electricity demand of other users in the area, which must ensure fair and reasonable energy distribution; and finally, the central energy storage station's operating strategy and scheduling rules. By comprehensively evaluating these factors, the system can determine a reasonable target amount of electricity.

[0088] S204: Based on the electricity transaction price, combined with the power differences and the target power, generate an electricity purchase plan for the distributed energy equipment within a second preset time period.

[0089] After determining the target amount of electricity that the central energy storage station can provide, the system needs to develop a specific electricity purchasing plan. This plan takes into account electricity market prices at different time periods and, based on the time distribution of user electricity demand, rationally arranges the timing and amount of electricity purchased from the central energy storage station and the grid. Specifically, the system prioritizes electricity purchases during periods with lower electricity prices and uses electricity from the central energy storage station first, with the remaining balance purchased from the grid.

[0090] This tiered, multi-source electricity purchasing solution offers significant economic and reliability advantages. By prioritizing the use of electricity from the central energy storage station, users' electricity costs can be reduced; by rationally scheduling electricity purchase times, large purchases during peak hours can be avoided; and by leveraging multiple sources, power supply reliability can be improved. Furthermore, this solution will improve the overall operational efficiency of the regional energy system and promote the absorption and utilization of renewable energy.

[0091] During actual implementation, the system dynamically adjusts the power purchase plan based on real-time conditions to ensure its feasibility and cost-effectiveness. By implementing the optimized power purchase plan, users are guaranteed reliable power supply while minimizing electricity costs, while also promoting the coordinated operation and sustainable development of the regional energy system.

[0092] Based on the above embodiment, as an optional implementation, in S204, the electricity purchase plan includes: purchasing a first amount of electricity and a first purchase time from the central energy storage station, and purchasing a second amount of electricity and a second purchase time from the power grid.

[0093] In this embodiment, the first amount of electricity refers to the amount of electricity purchased from the central energy storage station, and the first purchase time refers to the specific time period during which electricity from the central energy storage station is purchased. The second amount of electricity refers to the amount of electricity purchased from the power grid, and the second purchase time refers to the specific time period during which electricity from the power grid is purchased. This design, which distinguishes between power purchase sources and times, aims to achieve optimal allocation of power from multiple sources.

[0094] To minimize electricity costs and maximize power supply reliability, the system needs to develop a detailed electricity purchasing plan. After determining the energy difference and the target amount of energy that the central energy storage station can provide, the system first arranges for the purchase of electricity from the central energy storage station. Specifically, the system determines the specific amount of the first electricity quantity and the first purchase time based on the central energy storage station's electricity price and available time period. Since central energy storage stations generally provide lower prices, the system prioritizes electricity from central energy storage stations and purchases from them whenever possible during periods when electricity prices are lower.

[0095] For any electricity that cannot be obtained from the central energy storage station, the system must purchase it from the grid, determining a secondary quantity and a secondary purchase time. During this process, the system analyzes electricity price fluctuations across different time periods in the power market, prioritizing periods with lower prices as the secondary purchase time. At the same time, it ensures that the secondary quantity purchased can cover the remaining electricity demand gap. This time-of-day, source-based electricity purchasing solution takes into account both economic efficiency and power supply reliability.

[0096] By segmenting power purchases into two sources, the central energy storage station and the grid, the system can more flexibly arrange power purchase strategies. For example, during periods of high grid electricity prices, the system can increase power purchases from the central energy storage station. When the central energy storage station is running low on power, it can promptly replenish it from the grid. This flexible scheduling strategy effectively reduces users' overall electricity costs while also improving power supply reliability.

[0097] During actual implementation, the system monitors the progress of the power purchase plan in real time and dynamically adjusts it based on actual conditions. If actual power demand during a particular period deviates from expectations, or if there are significant changes in electricity prices, the system can adjust the first power consumption, first purchase time, second power consumption, and second purchase time accordingly, ensuring that the power purchase plan remains optimal.

[0098] After generating the electric energy selling plan of the distributed energy device within the second preset time period, the method further includes: The electricity sales plan is sent to the user's energy management system; the user's confirmation information on the electricity sales plan is received; based on the confirmation information, the electricity sales plan is submitted to the power trading platform; the actual power consumption and actual power generation of the distributed energy equipment within a second preset time period are monitored; when the difference between the actual power consumption or actual power generation and the second power consumption or power generation exceeds a preset deviation threshold, the electricity sales plan is regenerated.

[0099] To ensure the feasibility and effectiveness of the electricity sales plan, the system needs to establish a comprehensive plan execution and monitoring mechanism. First, the system sends the generated electricity sales plan to the user's energy management system for review and decision-making. The sales plan includes detailed transaction information, such as the amount of electricity sold by time period, the time of sale, and the expected profit. This information transmission mechanism ensures that users fully understand the plan content and can make reasonable decisions.

[0100] After receiving an electricity sales proposal, users need to evaluate and confirm it. Users can decide whether to accept the proposal based on their electricity usage plans and actual needs. The system receives confirmation from the user and only proceeds to the next step after receiving the user's confirmation. This interactive mechanism fully respects the user's decision-making power and ensures that the proposal is implemented in accordance with their wishes.

[0101] After receiving user confirmation, the system submits the electricity sales plan to the power trading platform. During the submission process, the system ensures that the plan complies with the power trading platform's rules and requirements, including transaction power limits, quotation rules, and submission time. This standardized transaction process helps ensure smooth transactions.

[0102] During plan execution, the system monitors the operating status of distributed energy devices in real time, including actual power consumption and production. This data is collected and transmitted through the energy management system for real-time tracking. This continuous monitoring mechanism can promptly identify operational deviations and provide a basis for plan adjustments.

[0103] If the system detects that the difference between actual electricity consumption or production and the predicted value (secondary electricity consumption or production) exceeds a preset deviation threshold, it triggers a plan regeneration mechanism. Specifically, the system reassesses the saleable electricity based on the latest operating data and generates a new electricity sales plan based on the original optimization strategy. This dynamic adjustment mechanism ensures that the plan always adapts to actual operating conditions.

[0104] Based on the above method, this application also discloses an energy aggregation scheduling system, such as Figure 2 As shown, Figure 2 This is a structural diagram of an energy aggregation scheduling system provided by an embodiment of the present application. The system includes: a first acquisition module, a prediction module, a second acquisition module, a calculation module and a generation module; wherein, The first acquisition module is used to obtain the user's first electricity consumption period within a first preset time period, and the first electricity consumption of each first electricity consumption period; the prediction module is used to predict the user's second electricity consumption period within a second preset time period, and the second electricity consumption of each second electricity consumption period based on the first electricity consumption period and the first electricity consumption, and generate the user's total electricity consumption within the second preset time period based on the second electricity consumption, the first preset time period being before the second preset time period; the second acquisition module is used to obtain the environmental information of the location of the user's distributed energy equipment within the second preset time period, and the remaining power of the distributed energy equipment; the calculation module is used to calculate the power generation of the distributed energy equipment in each second electricity consumption period based on the environmental information; the generation module is used to obtain the electricity trading price of each time period in the power market when the sum of the remaining power and the power generation is not less than the total power consumption, and on the basis of the electricity trading price, combine the remaining power and the power generation, and the second power consumption of each second electricity consumption period to generate an electricity sales plan for the distributed energy equipment within the second preset time period.

[0105] It should be noted that the above embodiments provide systems that implement their functions using only the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0106] See 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 .

[0107] The communication bus 1002 is used to implement the connection and communication between these components.

[0108] 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.

[0109] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0110] Processor 1001 may include one or more processing cores. Using various interfaces and circuits, processor 1001 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in memory 1005, as well as accesses data stored in memory 1005, to perform various server functions and process data. Optionally, processor 1001 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). Processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may not be integrated into processor 1001 but implemented as a separate chip.

[0111] Among them, the memory 1005 may include a random access memory (RAM) or 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 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 energy aggregation scheduling method.

[0112] 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 energy aggregation scheduling 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.

[0113] 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.

[0114] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this 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 this application.

[0115] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, 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 the devices or units can be electrical or other forms.

[0117] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0118] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0119] If the integrated unit is implemented as 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 this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing 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 this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0120] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. 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 herein, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An energy aggregation scheduling method, characterized in that: The method comprises: Obtaining a first power usage period of a user within a first preset time period, and a first power usage of each of the first power usage periods; predicting, based on the first power usage period and the first power consumption, a second power usage period of the user within a second preset duration, and a second power consumption of each of the second power usage periods, and generating, based on the second power consumption, a total power consumption of the user within the second preset duration, where the first preset duration is before the second preset duration; Obtaining environmental information of the location of the user's distributed energy device within the second preset time period and the remaining power of the distributed energy device; Calculating the amount of electricity generated by the distributed energy device during each of the second electricity consumption periods according to the environmental information; When the sum of the remaining electricity and the electricity production is not less than the total electricity consumption, the electricity trading price of each time period in the electricity market is obtained. On the basis of the electricity trading price, combined with the remaining electricity and the electricity production, and the second electricity consumption in each second electricity consumption period, an electricity sales plan for the distributed energy equipment within the second preset time period is generated.

2. The energy aggregation scheduling method according to claim 1, characterized in that: The method further comprises: When the sum of the remaining power and the power generation is less than the total power consumption, the remaining power is arithmetically added to the power generation to generate the total power generation, and the power difference between the total power generation and the total power consumption is calculated; Obtaining the available capacity of the central energy storage station in the area where the user is located, and the electricity consumption information of other users in the area where the user is located; Determining a target amount of electricity that the central energy storage station can sell to the user based on the available capacity and the electricity usage information; On the basis of the electric energy transaction price, combined with each of the electric energy differences and the target electric energy, an electric energy purchase plan for the distributed energy device within the second preset time period is generated.

3. The energy aggregation scheduling method according to claim 2, characterized in that: The electric energy purchase plan includes: purchasing a first amount of electricity from the central energy storage station and a first purchase time, and purchasing a second amount of electricity from the power grid and a second purchase time.

4. The energy aggregation scheduling method according to claim 1, characterized in that: The predicting, based on the first power usage time period and the first power consumption, of a second power usage time period of the user within a second preset duration, and a second power consumption of each of the second power usage time periods, includes: predicting, based on the first power usage time period, a third power usage time period of the user within the second preset time period, and a third power consumption in each of the third power usage time periods; Obtaining environmental information of the location of the user's distributed energy device within the second preset time period; The third power usage period and the third power consumption are adjusted according to the environmental information, and a second power usage period of the user within a second preset time period and a second power consumption of each second power usage period are correspondingly generated.

5. The energy aggregation scheduling method according to claim 1, characterized in that: The generating of an electric energy selling plan for the distributed energy device within the second preset time period based on the electric energy transaction price, in combination with the remaining electric energy and the electric energy generated, and the second electric energy consumption in each second electric energy consumption period, includes: Calculating the remaining saleable electricity of the user in each of the second electricity consumption periods, where the remaining saleable electricity is the sum of the remaining electricity and the generated electricity minus the second electricity consumption; Identifying a high-price period during the second preset time period in which the electric energy transaction price is higher than a preset threshold; The amount and time of electricity sold by the distributed energy device during the high-price period are determined, and an electricity selling plan for the distributed energy device during the second preset time period is generated.

6. The energy aggregation scheduling method according to claim 5, characterized in that: The determining of the amount and time of electric energy sold by the distributed energy device during the high-price period includes: Sort the high-price periods by electricity transaction prices from high to low; Allocate the remaining saleable electricity in descending order of electricity trading prices, allocate the maximum allocable electricity in the period with the highest electricity trading price, and when the allocable electricity in the period with the highest electricity trading price reaches the upper limit or the remaining saleable electricity has been fully allocated, allocate the remaining unallocated electricity to the period with the second highest electricity trading price; According to the allocation result, the amount and time of selling electric energy of the distributed energy equipment during the high-price period are determined.

7. The energy aggregation scheduling method according to claim 1, characterized in that: After generating the electric energy selling plan of the distributed energy device within the second preset time period, the method further includes: Sending the electricity selling plan to the user's energy management system; receiving confirmation information of the user on the electric energy selling plan; Submitting the electricity sales plan to the power trading platform according to the confirmation information; Monitoring the actual power consumption and actual power generation of the distributed energy device within the second preset time period; When the difference between the actual power consumption or the actual power generation and the second power consumption or the power generation exceeds a preset deviation threshold, the power selling plan is regenerated.

8. An energy aggregation scheduling system, characterized in that: The system includes: a first acquisition module, a prediction module, a second acquisition module, a calculation module and a generation module; wherein, The first acquisition module is used to acquire a first power usage period of the user within a first preset time period, and a first power consumption of each of the first power usage periods; The prediction module is configured to predict, based on the first power usage period and the first power consumption, a second power usage period of the user within a second preset duration, and a second power consumption of each of the second power usage periods, and generate, based on the second power consumption, a total power consumption of the user within the second preset duration, where the first preset duration is before the second preset duration; The second acquisition module is used to obtain environmental information of the location of the user's distributed energy device within the second preset time period and the remaining power of the distributed energy device; The calculation module is configured to calculate the amount of electricity generated by the distributed energy device in each of the second electricity consumption periods according to the environmental information; The generation module is used to obtain the electricity trading price of each time period in the electricity market when the sum of the remaining electricity and the electricity production is not less than the total electricity consumption, and on the basis of the electricity trading price, combine the remaining electricity and the electricity production, and the second electricity consumption in each second electricity consumption period to generate an electricity sales plan for the distributed energy equipment within the second preset time period.

9. An electronic device, characterized in that: The electronic device comprises a processor, a memory, a user interface and a network interface, wherein 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 according to any one of claims 1 to 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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