Power utilization strategy adjustment method, device and equipment and storage medium

Abstract

The invention relates to the technical field of power supply, in particular to a power utilization strategy adjustment method and device, equipment and a storage medium. When the analysis result is that the power grid is in an unstable state, analyzing the standardized power grid data according to a preset time sequence clustering algorithm to obtain a current power utilization mode; if the current power utilization mode is the photovoltaic side dominant power supply mode, obtaining the current residual capacity of the battery, the commercial power voltage output phase and the photovoltaic voltage output phase; analyzing the current residual capacity of the battery, the commercial power voltage output phase and the photovoltaic voltage output phase according to a preset power utilization mode and preset simulation parameters to obtain a power utilization adjustment strategy; when the stability of the power grid is judged based on the household power angle parameter and the power grid is unstable, the power utilization mode is identified firstly, a differential adjustment strategy is generated under photovoltaic leading, the problems of phase synchronization and the like are solved, the power supply stability and the photovoltaic utilization rate are improved, various scenes are adapted, and the requirements of power grid safety and efficient energy utilization are met.

Description

A method, apparatus, device, and storage medium for adjusting power consumption strategies. Technical Field

[0001] This invention relates to the field of power supply technology, and specifically to a method, apparatus, device, and storage medium for adjusting power consumption strategies. Background Technology

[0002] Existing photovoltaic (PV) priority power supply control technology for integrated PV-energy storage systems (applicable to residential, commercial, industrial, and integrated energy service scenarios, with commercial and integrated energy services as the core application scenario) has significant drawbacks: Under grid instability conditions, due to the lack of dynamic assessment of the power angle of grid nodes, continuous grid connection of inverters can exacerbate grid oscillations through reverse power flow; and at the moment of grid instability, some systems are prone to battery over-discharge or grid power cut-off, triggering secondary impacts and power outages. Furthermore, existing control strategies are difficult to adapt to diverse operating scenarios involving PV dominance, grid supplementation, and energy storage adjustment, failing to generate targeted and differentiated solutions, resulting in low energy utilization, insufficient user comfort, and weak grid support. Especially in commercial and integrated energy service scenarios, where the load scale is larger and energy interaction is more complex, these problems are more likely to cause serious impacts. In summary, existing technologies cannot meet the dual requirements of grid security and stability and efficient energy utilization under high distributed energy penetration, necessitating a refined control method to address these issues. Summary of the Invention

[0003] In order to overcome the shortcomings of the prior art, the present invention aims to provide a method, apparatus, device and storage medium for adjusting power consumption strategy.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The present invention provides a method for adjusting electricity consumption strategy, including: acquiring photovoltaic-side data, battery-side data, and grid-side data; preprocessing the photovoltaic-side data, battery-side data, and grid-side data to obtain standardized grid data; analyzing the standardized grid data according to a preset grid influence threshold and a preset power angle range to obtain analysis results; when the analysis result indicates that the grid is in an unstable state, analyzing the standardized grid data according to a preset time-series clustering algorithm to obtain the current electricity consumption mode; analyzing the current electricity consumption mode; if the current electricity consumption mode is a photovoltaic-dominated power supply mode, acquiring the current remaining battery capacity, grid voltage output phase, and photovoltaic voltage output phase; analyzing the current remaining battery capacity, grid voltage output phase, and photovoltaic voltage output phase according to the preset electricity consumption mode and preset simulation parameters to obtain an electricity consumption adjustment strategy.

[0005] Further, the step of analyzing the current remaining battery capacity, mains voltage output phase, and photovoltaic voltage output phase according to a preset power consumption mode and preset simulation parameters to obtain a power consumption adjustment strategy includes: performing simulation tests on simulation parameters and standardized power grid data according to the power consumption mode to obtain simulation test results and test effect scores; analyzing the mains voltage output phase and photovoltaic voltage output phase according to the simulation test results and test effect scores to obtain a first adjustment power; determining whether the test effect score is less than or equal to a preset effect score threshold; if the test effect score is less than or equal to the effect score threshold, obtaining the current remaining battery capacity; analyzing the current remaining battery capacity according to the test effect score to obtain a second adjustment power; generating power adjustment parameters according to the first adjustment power and the second adjustment power; obtaining the current power consumption strategy, and adjusting the current power consumption strategy according to the power adjustment parameters to obtain the power consumption adjustment strategy.

[0006] Furthermore, the step of analyzing standardized power grid data according to a preset power grid impact threshold and a preset power angle range to obtain analysis results includes: analyzing standardized power grid data according to the power grid impact threshold to obtain the power angle of key power grid nodes; determining whether the power angle of key power grid nodes is less than a preset node power angle threshold; and when the power angle of key power grid nodes is less than the node power angle threshold, analyzing the power angle of key power grid nodes according to the power angle range to obtain analysis results.

[0007] Furthermore, the step of analyzing standardized power grid data according to a preset time-series clustering algorithm to obtain the current electricity consumption pattern includes: performing feature analysis on the standardized power grid data to obtain time-domain features, correlation features, and frequency-domain features; constructing a feature vector matrix based on the time-domain features, correlation features, and frequency-domain features; and analyzing the feature vector matrix according to the time-series clustering algorithm to obtain the current electricity consumption pattern.

[0008] Furthermore, the step of conducting simulation tests on simulation parameters and standardized power grid data based on power consumption patterns to obtain simulation test results and test effect scores includes: analyzing standardized power grid data based on preset available photovoltaic power and preset real-time load demand to obtain actual photovoltaic power supply; calculating the ratio between actual photovoltaic power supply and real-time load demand to obtain load matching degree; and conducting simulation tests on simulation parameters based on power consumption patterns and load matching degree to obtain simulation test results and test effect scores.

[0009] Furthermore, the step of analyzing the mains voltage output phase and photovoltaic voltage output phase based on simulation test results and test effect scores to obtain the first adjusted power includes: calculating the difference between the mains voltage output phase and the photovoltaic voltage output phase to obtain the phase difference; analyzing the simulation test results to obtain the photovoltaic output index; analyzing the phase difference based on the test effect score, real-time load demand, and photovoltaic output index to obtain the first adjustment parameter; obtaining the mains discharge power, and adjusting the mains discharge power according to the first adjustment parameter to obtain the first adjusted power.

[0010] Furthermore, the step of analyzing the current remaining capacity of the battery based on the test performance score to obtain the second adjusted power includes: calculating the ratio of the current remaining capacity of the battery to a preset rated capacity to obtain a capacity ratio; analyzing the capacity ratio based on the test performance score, real-time load demand, and photovoltaic output index to obtain a second adjustment parameter; obtaining the battery discharge power, and adjusting the battery discharge power according to the second adjustment parameter to obtain the second adjusted power.

[0011] Furthermore, an electricity consumption strategy adjustment device includes: a first data acquisition module for acquiring photovoltaic-side data, battery-side data, and grid-side data; a data processing module for preprocessing the photovoltaic-side data, battery-side data, and grid-side data to obtain standardized grid data; a first analysis module for analyzing the standardized grid data according to a preset grid influence threshold and a preset power angle range to obtain analysis results; a second analysis module for analyzing the standardized grid data according to a preset time-series clustering algorithm when the analysis result indicates that the grid is in an unstable state to obtain the current electricity consumption mode; a third analysis module for analyzing the current electricity consumption mode; the second data acquisition module for acquiring the current remaining battery capacity, grid voltage output phase, and photovoltaic voltage output phase if the current electricity consumption mode is a photovoltaic-dominated power supply mode; and an electricity consumption adjustment strategy generation module for analyzing the current remaining battery capacity, grid voltage output phase, and photovoltaic voltage output phase according to a preset electricity consumption mode and preset simulation parameters to obtain an electricity consumption adjustment strategy.

[0012] Furthermore, an electricity consumption policy adjustment device includes: a memory and at least one processor, the memory storing instructions; at least one processor invokes the instructions in the memory to cause the electricity consumption policy adjustment device to perform the steps of an electricity consumption policy adjustment method as described in any one of the above descriptions.

[0013] Furthermore, a computer-readable storage medium stores instructions that, when executed by a processor, implement the steps of a power consumption strategy adjustment method as described in any one of the preceding descriptions.

[0014] In the technical solution of this invention, multi-source data from photovoltaics, batteries, and mains power are first integrated and preprocessed to lay a reliable foundation for subsequent analysis. Then, based on the power angle range and impact threshold of household characteristics, the stability of the power grid is quantitatively determined, solving the deficiency of lack of power angle assessment and avoiding the aggravation of oscillations by blind grid connection. When the power grid is unstable, the time-series clustering algorithm accurately identifies the power consumption pattern, filters instantaneous fluctuations, and overcomes the limitation of insufficient adaptability to multiple scenarios. In the photovoltaic-dominated mode, dual-phase and battery capacity data are accurately collected to generate differentiated adjustment strategies, which not only solves the phase synchronization problem and avoids the secondary impact caused by battery over-discharge and hard switching of mains power, but also improves power supply stability and photovoltaic utilization, extends equipment life, adapts to various scenarios, and meets the dual requirements of power grid security and efficient energy utilization. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments in conjunction with the following drawings, in which: FIG1 is a first flowchart of a power consumption strategy adjustment method provided by an embodiment of the present invention; FIG2 is a second flowchart of a power consumption strategy adjustment method provided by an embodiment of the present invention; FIG3 is a third flowchart of a power consumption strategy adjustment method provided by an embodiment of the present invention; FIG4 is a fourth flowchart of a power consumption strategy adjustment method provided by an embodiment of the present invention; FIG5 is a fifth flowchart of a power consumption strategy adjustment method provided by an embodiment of the present invention; FIG6 is a sixth flowchart of a power consumption strategy adjustment method provided by an embodiment of the present invention; FIG7 is a seventh flowchart of a power consumption strategy adjustment method provided by an embodiment of the present invention; FIG8 is a structural schematic diagram of a power consumption strategy adjustment device provided by an embodiment of the present invention; FIG9 is a structural schematic diagram of a power consumption strategy adjustment equipment provided by an embodiment of the present invention. Detailed Implementation

[0016] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to Figure 1. One embodiment of a power consumption strategy adjustment method in the present invention includes: 101. Acquiring photovoltaic-side data, battery-side data, and grid-side data; 102. Preprocessing the photovoltaic-side data, battery-side data, and grid-side data to obtain standardized grid data; In this embodiment, the preprocessing operation includes unifying the data format of the photovoltaic-side data, battery-side data, and grid-side data, removing noise interference, and achieving time sequence alignment, laying a reliable foundation for subsequent power angle assessment, power consumption pattern identification, and accurate strategy generation; 103. Analyzing the standardized grid data according to a preset grid influence threshold and a preset power angle range. To obtain the analysis results; In this embodiment, based on the power angle range set according to the characteristics of the home photovoltaic energy storage system, targeted analysis is carried out on the standardized power grid data, accurately focusing on high-impact core data, realizing the quantitative determination of power grid stability, providing a reliable basis for subsequent electricity consumption pattern identification and differentiated adjustment strategy generation, and helping to achieve the dual goals of power grid safety and energy efficiency utilization; 104. When the analysis result indicates that the power grid is in an unstable state, the standardized power grid data is analyzed according to the preset time-series clustering algorithm to obtain the current electricity consumption pattern; In this embodiment, when the power grid is unstable, the standardized power grid data is analyzed through the time-series clustering algorithm to accurately identify the current electricity consumption pattern. The algorithm focuses on the core data under the unstable state and retains the time-series data. Continuity and filtering of instantaneous fluctuations ensure accurate pattern recognition, providing a basis for subsequent differentiated adjustment strategies. This avoids problems such as blind grid connection exacerbating oscillations and over-discharging batteries, while strengthening grid security support and helping to improve energy utilization efficiency, meeting the dual needs under the high penetration rate of distributed energy; 105. Analyze the current power consumption mode; 106. If the current power consumption mode is a photovoltaic-dominated power supply mode, obtain the current remaining battery capacity, mains voltage output phase, and photovoltaic voltage output phase; In this embodiment, the mains voltage output phase refers to the phase state of AC voltage change over time when the public grid (mains power) supplies power to the household. It is a core electrical parameter characterizing the time position and change rhythm of the mains AC signal. Through the mains side... Phase measurement unit (PMU) and power quality monitor collect data at a frequency synchronized with the grid frequency (50Hz) to ensure real-time reflection of the grid phase dynamics. The photovoltaic voltage output phase refers to the phase state of the AC voltage output after the DC power generated by the photovoltaic array is converted to AC power by the inverter. It is a key parameter characterizing the AC power supply rhythm on the photovoltaic side. Data is collected by the photovoltaic inverter's built-in phase monitoring module and the AC-side smart meter, maintaining the same sampling frequency (50Hz) as the grid phase to ensure the accuracy of the two phase difference calculations. 107. Based on the preset power consumption mode and preset simulation parameters, the current remaining battery capacity, grid voltage output phase, and photovoltaic voltage output phase are analyzed to obtain a power consumption adjustment strategy.In this embodiment, by combining power consumption patterns and simulation parameters, the remaining battery capacity, mains power, and photovoltaic voltage output phase are analyzed in a targeted manner to accurately generate power consumption adjustment strategies. This not only ensures that the battery avoids over-discharge and extends its service life, but also effectively solves phase synchronization problems, reduces voltage superposition anomalies and equipment impacts, and improves the power supply stability and energy utilization efficiency of the home photovoltaic energy storage system, adapting to diverse power consumption scenarios. In this embodiment, multi-source data from photovoltaics, batteries, and mains power are first integrated and preprocessed to lay a reliable foundation for subsequent analysis. Then, based on the power angle range and impact threshold of home characteristics, grid stability is quantitatively determined, addressing the deficiency of lack of power angle assessment and avoiding blind grid connection that exacerbates oscillations. When the grid is unstable, the time-series clustering algorithm accurately identifies the power consumption pattern, filters instantaneous fluctuations, and overcomes the limitation of insufficient adaptability to diverse scenarios. In the photovoltaic-dominated mode, dual-phase and battery capacity data are accurately collected to generate differentiated adjustment strategies, which not only solves phase synchronization problems and avoids secondary impacts caused by battery over-discharge and hard switching of mains power, but also improves power supply stability and photovoltaic utilization, extends equipment life, adapts to diverse scenarios, and meets the dual requirements of grid security and efficient energy utilization.

[0018] Please refer to Figure 2, which illustrates a second embodiment of a power consumption strategy adjustment method according to the present invention. Step 107 specifically includes: 201. Performing simulation tests on simulation parameters and standardized power grid data according to the power consumption mode to obtain simulation test results and test effect scores. In this embodiment, simulation parameters are adapted according to the power consumption mode, and targeted simulation tests are conducted on standardized power grid data to output quantitative simulation test results and test effect scores. This not only accurately reflects the system operating status and predicts potential problems, but also provides a reliable basis for subsequent power adjustment and strategy optimization, improving the adaptability of the power consumption strategy and the stability of the power grid; 202. Analyzing the mains voltage output phase and photovoltaic voltage output phase based on the simulation test results and test effect scores to obtain the first adjustment power. In this embodiment, by combining simulation test results and test performance scores, the phase of the mains and photovoltaic voltage outputs is analyzed in a targeted manner, and the first adjustment power is accurately generated. This effectively solves the phase synchronization problem, reduces voltage superposition anomalies and equipment impacts, improves grid stability, and provides a reliable basis for subsequent power consumption strategy optimization, avoiding blind adjustments and adapting to the actual operating performance of the system. 203. Determine whether the test performance score is less than or equal to the preset performance score threshold. 204. If the test performance score is less than or equal to the performance score threshold, obtain the current remaining battery capacity. In this embodiment, the performance score threshold (e.g., 60 points, out of 100 points) is set based on system stability and energy utilization targets. The lower the score, the worse the system performance after mains power adjustment. Battery intervention is required for enhancement; a binary judgment rule: if the test result score S≤60, it is marked as insufficient mains power adjustment, triggering the battery-side adjustment process; if S>60, it means that the mains power adjustment has met the requirements, and no battery participation is needed, reducing invalid calculations; 205. Analyze the current remaining capacity of the battery based on the test result score to obtain the second adjustment power; in this embodiment, by combining the test result score and the current remaining capacity of the battery for analysis, the second adjustment power is accurately generated to adapt to the system operation requirements, avoid excessive battery discharge loss, and effectively improve power supply stability and energy utilization efficiency; 206. Generate power adjustment parameters based on the first adjustment power and the second adjustment power; 207. Obtain the current power consumption strategy and adjust the current power consumption strategy based on the power adjustment parameters. The power strategy is adjusted to obtain a power consumption adjustment strategy. In this embodiment, power adjustment parameters are generated by integrating the first adjustment power and the second adjustment power. These parameters are then used to optimize the current power consumption strategy, achieving linkage between mains power and battery adjustment. This allows the strategy to accurately adapt to system performance and load demand, effectively improving power supply stability, reducing voltage fluctuations and equipment impact, while optimizing energy allocation, increasing photovoltaic utilization, reducing mains power consumption and battery loss. It is adaptable to various power consumption scenarios and has strong applicability. In this embodiment, targeted simulations are first performed according to the power consumption mode to provide a reliable basis for adjustment. Then, the first adjustment power is generated through phase analysis to solve the phase synchronization problem and reduce equipment impact. The effect score threshold is used to determine whether to enable battery adjustment to avoid invalid calculations.By combining test scores with remaining battery capacity to generate a second adjustment power, battery safety is ensured. Ultimately, this dual-adjustment power optimization strategy improves power supply stability, increases photovoltaic utilization, reduces mains power consumption and battery wear, and is adaptable to diverse scenarios with strong applicability.

[0019] Please refer to Figure 3, which illustrates the third embodiment of a power consumption strategy adjustment method in this invention. Step 103 specifically includes: 301. Analyzing standardized grid data based on the grid impact threshold to obtain the power angle of key grid nodes. In this embodiment, the grid impact threshold is used to define the range of core parameters strongly correlated with the power angle, such as voltage fluctuation threshold, frequency fluctuation threshold, and power deficit threshold. Only high-impact data that meets this threshold is analyzed. The analysis logic of the power angle of key grid nodes is to reflect the active power balance state of the grid. The power angle of key grid nodes is obtained by analyzing parameters such as frequency dynamic changes in standardized data, and the core indicators reflecting grid stability are accurately calculated. 302. Determining whether the power angle of key grid nodes is less than a preset node power angle threshold. In this embodiment, the node power angle threshold is set based on the characteristics of residential photovoltaic-storage systems, and the preset instability critical threshold is 20° (the smaller the power angle, the more prone the grid is to out-of-synchronization oscillation). 303. When the power angle of key grid nodes is less than the node power angle threshold, the power angle of key grid nodes is analyzed based on the power angle range to obtain the analysis results. In this embodiment, the preset power angle range is... To classify the power angle into three levels: instability level (δ < 15°, the system has shown an oscillation trend), critical level (15° ≤ δ < 20°, insufficient stability reserve), and safe level (δ ≥ 20°, sufficient stability reserve), δ is a range defining coefficient. When the power angle range of δ < 20° is met, a precise analysis result is output according to the above range. For example, if δ = 12°, the power grid is in an unstable state and requires emergency adjustment; if δ = 18°, the power grid is in a critical unstable state and requires early warning and appropriate adjustment. In this embodiment, high-impact data is filtered through the power grid impact threshold to ensure that the power grid... The power angle calculation of key nodes in the grid is more accurate, precisely reflecting the active power balance and stability of the grid. The power angle range is set based on the characteristics of home photovoltaic and energy storage systems, which fits the actual needs of home scenarios. The three-level power angle range classification (instability level, critical level, and safety level) enables precise state definition and outputs targeted analysis results. This solution can detect grid instability trends in advance and intervene before oscillations occur, improving the power supply reliability of home power supply systems. At the same time, the calculation logic is simple, adaptable to the computing power and data scale of home systems, requires no complex equipment, has low implementation cost, and is highly practical and operable.

[0020] Please refer to Figure 4, which shows the fourth embodiment of a power consumption strategy adjustment method in this invention. Step 104 specifically includes: 401. Performing feature analysis on standardized power grid data to obtain time-domain features, correlation features, and frequency-domain features. In this embodiment, the time-domain features focus on the changing patterns of parameters over time, reflecting the instantaneous state and trend of the system. Examples of time-domain features include average photovoltaic output, load mutation coefficient, energy storage SOC (remaining capacity) change rate (the increase or decrease of SOC per unit time), power grid frequency fluctuation range (the deviation from the rated 50Hz), inverter efficiency, and line loss parameters. The extraction logic is to use a sliding time window (window size 30s, step size 10). s) Calculate the above indicators to capture dynamic changes over a short period of time; the correlation features are used to explore the temporal correlation patterns between different parameters, reflecting the relationship between various links in the household power supply system; the core indicators are the correlation coefficient between photovoltaic output and load demand (measuring the linear relationship between supply and demand matching), the lag correlation between energy storage charging and discharging power and photovoltaic output (the response delay of energy storage after changes in photovoltaic output), and the synchronization degree between the grid phase and photovoltaic phase (the temporal mean of the phase difference). The extraction logic is to use methods such as Pearson correlation coefficient and mutual information entropy to quantify the correlation strength between parameters (e.g., correlation coefficient > 0.8 indicates strong correlation, < 0.3 indicates weak correlation); the frequency domain features are obtained through Fourier transform... The time-domain data is converted to the frequency domain to reflect the periodic fluctuation characteristics of the system. Key indicators include the main fluctuation frequency of photovoltaic output, the harmonic content of load demand, and the frequency components of the grid voltage. 402. A feature vector matrix is ​​constructed based on the time-domain features, correlation features, and frequency-domain features. In this embodiment, let the number of time sampling points be n (e.g., 360 points sampled every 10 seconds for 1 hour of data), and the total number of features be m (e.g., 5 time-domain features, 3 correlation features, and 2 frequency-domain features, totaling 10). Then, the feature vector matrix is ​​an m×n two-dimensional matrix (rows represent feature types, and columns represent time points). 403. The feature vector matrix is ​​analyzed using a time-series clustering algorithm to obtain... Current power consumption pattern; In this embodiment, a time-series clustering algorithm (such as improved DBSCAN, time-series K-means) is used to analyze the feature vector matrix to identify the current power consumption pattern. Assuming that the feature vector matrix is ​​analyzed using the improved DBSCAN algorithm, a time neighborhood (such as three consecutive windows) and a feature distance threshold (Euclidean distance < 0.1) are first set. When the feature vectors of multiple consecutive windows meet the similarity condition, they are determined to be the same power consumption pattern. Finally, the power consumption pattern label is output, such as photovoltaic-dominated mode (average photovoltaic output ratio ≥ 60% in time-domain features, correlation coefficient between photovoltaic and load ≥ 0.7 in correlation features), and mixed power supply mode (photovoltaic output ratio ≥ 60%). (The correlation coefficient between energy storage charging and discharging and photovoltaic output lag is ≥0.5). In this embodiment, the power consumption pattern is accurately identified through multi-dimensional feature analysis and time-series clustering. By extracting time-domain, correlation, and frequency-domain features, the dynamic changes, parameter relationships, and periodic patterns of the system are fully captured. The feature vector matrix structures multi-dimensional information, providing reliable input for clustering. The time-series clustering algorithm takes into account the continuity of time, accurately filters instantaneous fluctuations, and improves the accuracy of pattern recognition. Ultimately, it can clearly distinguish between photovoltaic-dominated and hybrid power supply modes, providing a precise basis for subsequent power consumption strategy adjustments. It adapts to the dynamic changes of load and photovoltaic, ensuring power supply stability and improving energy utilization efficiency, and has strong applicability.

[0021] Please refer to Figure 5, which illustrates the fifth embodiment of a power consumption strategy adjustment method according to the present invention. Step 201 specifically includes: 501. Analyzing standardized grid data based on preset available photovoltaic power and preset real-time load demand to obtain the actual photovoltaic power supply. In this embodiment, the formula for calculating the actual photovoltaic power supply is: In the formula, For inverter efficiency, Line loss parameters can be obtained from standardized power grid data, including inverter efficiency and line loss parameters. R represents real-time load demand, and H represents actual photovoltaic power supply. 502. Calculate the ratio of actual photovoltaic power supply to real-time load demand to obtain the load matching degree. In this embodiment, when the load matching degree... Photovoltaics can meet most loads, defined as high matching degree. Simulation tests focus on priority photovoltaic consumption. The photovoltaic (PV) portion meets the load requirement and is defined as having a medium matching degree. The simulation test focuses on the power complementarity between PV, grid power, and energy storage. 503. Simulation tests are conducted on the simulation parameters according to the power consumption mode and load matching degree to obtain simulation test results and test effect scores. In this embodiment, the power consumption mode is classified into PV-dominated mode and hybrid power supply mode. Different modes correspond to different simulation parameters (e.g., in PV-dominated mode, the simulation parameters focus on phase synchronization threshold and energy storage charging and discharging efficiency; in hybrid mode, the simulation parameters focus on power smooth switching threshold). The simulation parameters include the allowable value of phase synchronization error (≤5°), energy storage charging and discharging response time (≤100ms), grid stability recovery time (≤200ms), etc. The simulation test results include the PV utilization curve, phase difference change trajectory, load guarantee rate, etc. The test effect score (S, out of 100 points) directly reflects the adaptability of the strategy. By simulating the power supply performance under different scenarios, quantitative results and comprehensive scores are output to provide... The simulation logic provides verification for actual power consumption strategy adjustments. It uses a photovoltaic-dominated, hybrid power supply mode, configuring differentiated simulation parameters (such as phase synchronization error and charge / discharge response time) to simulate power supply performance. It outputs results such as photovoltaic utilization rate and phase difference trajectory, along with test scores, to verify strategy adaptability and support actual power consumption strategy adjustments. In this embodiment, the actual photovoltaic power supply is calculated by incorporating inverter efficiency and line losses, improving the accuracy of photovoltaic output assessment. It is graded according to load matching degree, with high matching degree emphasizing priority photovoltaic absorption and medium matching degree strengthening multi-source power complementarity, thus improving the targeting of power consumption strategies. Differentiated simulation parameters are configured based on photovoltaic-dominated and hybrid power supply modes to adapt to different power supply scenarios. The simulation outputs quantitative results such as photovoltaic utilization rate and phase difference trajectory, along with test performance scores, to verify strategy adaptability in advance, reducing actual operational risks. This ensures grid stability and power supply continuity while optimizing energy utilization efficiency, reducing grid power consumption and equipment losses, demonstrating strong applicability.

[0022] Please refer to Figure 6, the sixth embodiment of a power consumption strategy adjustment method in this invention. Step 202 specifically includes: 601, calculating the phase difference between the mains voltage output phase and the photovoltaic voltage output phase to obtain the phase difference; in this embodiment, the purpose of calculating the phase difference is to capture the rhythm deviation between the mains voltage and the photovoltaic voltage output, providing a quantifiable core basis for subsequent synchronization adjustment, and avoiding voltage superposition anomalies, equipment impacts, or grid protection actions caused by phase inconsistency; the formula for calculating the phase difference is... In the formula, The mains voltage output phase, The output phase of the photovoltaic voltage. 602. Analyze the simulation test results to obtain the photovoltaic output index; 603. Analyze the phase difference based on the test effect score, real-time load demand, and photovoltaic output index to obtain the first adjustment parameter; In this embodiment, the test effect score (S) is used to reflect the comprehensive performance of the current grid stability and energy utilization rate (full score 100 points). The lower the S, the more urgent the adjustment demand (e.g., when S≤80 points, the adjustment intensity needs to be increased); the real-time load demand (R) is used to reflect the user's immediate electricity demand. The larger the R, the more priority needs to be given to ensuring power supply. The second adjustment parameter tends to adjust the battery discharge power; photovoltaic output index : Reflects the stability of photovoltaic power supply, The larger the value (the more volatile the photovoltaic output), the more conservative the adjustment parameters need to be to avoid losses caused by frequent battery start-ups and shutdowns; a weighted linear model is used to calculate the first adjustment parameter. The typical formula is: In the formula, (System performance weight) (Battery state weight) (Photovoltaic stability weight) The value range is locked between 0.2 and 0.8 to ensure that the adjustment range is both effective and controllable; 604. Obtain the mains power discharge power and adjust the mains power discharge power according to the first adjustment parameter to obtain the first adjusted power; In this embodiment, the voltage and current are collected by a smart meter at the mains power entrance, and after preprocessing, they are calculated according to the active power formula. Taking the positive value yields the mains power discharge power (i.e., ,in, This is the mains phase voltage. For mains power line current, The first adjustment parameter generated is the mains power factor. This will be directly used in the mains discharge power adjustment step (i.e. ,in, For the first adjustment power, The mains discharge power is dynamically adjusted to achieve phase synchronization between the mains and photovoltaic voltages. In this embodiment, the phase difference between the mains and photovoltaic power is precisely quantified to provide a reliable basis for synchronization adjustment, avoiding equipment shocks and grid instability caused by phase conflicts, and improving the operational safety of the home appliance system. The first adjustment parameter generated by combining the test effect score, load demand and photovoltaic stability weighted model takes into account system performance, user needs and power characteristics, making the adjustment more accurate and adaptable to various scenarios. Based on the dynamic adjustment of the mains discharge power, phase synchronization is achieved, ensuring the continuity of power supply to the home appliance system, reducing photovoltaic curtailment, reducing mains power consumption, taking into account energy efficiency and cost optimization, adapting to different home photovoltaic and energy storage systems, and having strong applicability.

[0023] Please refer to Figure 7, the seventh embodiment of a power consumption strategy adjustment method in this invention. Step 205 specifically includes: 701, calculating the ratio of the current remaining battery capacity to the preset rated capacity to obtain a capacity ratio; in this embodiment, the purpose of calculating the capacity ratio is to convert the current remaining battery capacity (SOC_real) into a standardized indicator. The calculation logic is as follows: ,in, The rated capacity of the battery. Given the current remaining battery capacity, 702, the capacity ratio is analyzed based on the test performance score, real-time load demand, and photovoltaic output index to obtain the second adjustment parameter; in this embodiment, a weighted nonlinear model is used to calculate the second adjustment parameter. The typical formula is: In the formula, (System performance weight) (Battery state weight) (Photovoltaic stability weight) The value range is locked between 0.2 and 0.8 to ensure the effectiveness of the adjustment while avoiding excessive battery discharge; 703. Obtain the battery discharge power and adjust the battery discharge power according to the second adjustment parameter to obtain the second adjusted power; In this embodiment, the current actual discharge power of the battery is first obtained. (Extracted from standardized data on the battery side, with an accuracy of 0.1kW); Calculate the second adjusted power. If the calculated result exceeds the rated discharge power range of the battery (e.g., the rated discharge power of a household energy storage battery is 5kW), the rated value is taken as the upper limit. If the adjusted battery SOC prediction value is <20% (safe lower limit), k2 is automatically lowered to the range of 0.2~0.3 to ensure that the battery is not deeply discharged and to adapt to the rapid changes in photovoltaic output and load demand. In this embodiment, the remaining battery capacity is standardized by calculating the capacity ratio, laying the foundation for unified adjustment logic. Based on the weighted nonlinear modeling of test effect score, real-time load demand, and photovoltaic output fluctuation rate, the second adjustment parameter can dynamically adapt to system performance, user needs, and photovoltaic characteristics. The range constraint of k2 value and the protection of the lower limit of SOC prediction effectively avoid battery over-discharge and overload operation, reduce the risk of failure, ensure power supply continuity, improve load matching and energy utilization under photovoltaic-dominated mode, extend battery life, reduce user electricity and equipment replacement costs, and take into account grid stability, energy efficiency, and equipment safety.

[0024] The above describes a method for adjusting electricity consumption strategy in an embodiment of the present invention. The following describes an apparatus for adjusting electricity consumption strategy in an embodiment of the present invention. Referring to Figure 8, one embodiment of the apparatus for adjusting electricity consumption strategy in an embodiment of the present invention includes: a first data acquisition module 1, used to acquire photovoltaic-side data, battery-side data, and grid-side data; a data processing module 2, used to preprocess the photovoltaic-side data, battery-side data, and grid-side data to obtain standardized grid data; a first analysis module 3, used to analyze the standardized grid data according to a preset grid influence threshold and a preset power angle range to obtain analysis results; a second analysis module 4, used to analyze the standardized grid data according to a preset time-series clustering algorithm when the analysis result indicates that the grid is in an unstable state, to obtain the current electricity consumption mode; a third analysis module 5, used to analyze the current electricity consumption mode; and a second data acquisition module 6, used to acquire the current remaining battery capacity if the current electricity consumption mode is a photovoltaic-dominated power supply mode. The system includes a mains voltage output phase and a photovoltaic voltage output phase. A power consumption adjustment strategy generation module 7 analyzes the current remaining battery capacity, mains voltage output phase, and photovoltaic voltage output phase based on preset power consumption modes and preset simulation parameters to obtain power consumption adjustment strategies. In this embodiment, multi-source data from photovoltaics, batteries, and mains are first integrated and preprocessed to lay a reliable foundation for subsequent analysis. Then, based on the power angle range and influence threshold of household characteristics, the system quantitatively determines grid stability, addressing the lack of power angle assessment and avoiding blind grid connection that exacerbates oscillations. When the grid becomes unstable, a time-series clustering algorithm accurately identifies the power consumption mode, filters instantaneous fluctuations, and overcomes the limitations of insufficient adaptation to diverse scenarios. Under photovoltaic-dominated mode, precise dual-phase and battery capacity data are collected to generate differentiated adjustment strategies. This not only solves the phase synchronization problem and avoids secondary impacts caused by battery over-discharge and hard mains power cut-off, but also improves power supply stability and photovoltaic utilization, extends equipment life, adapts to diverse scenarios, and meets the dual requirements of grid security and efficient energy utilization.

[0025] Figure 9 is a schematic diagram of a power consumption policy adjustment device 900 provided in an embodiment of the present invention. This power consumption policy adjustment device 900 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 910 (e.g., one or more processors) and a memory 920, and one or more storage media 930 (e.g., one or more mass storage devices) storing application programs 933 or data 932. The memory 920 and storage media 930 can be temporary or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the figure), each module may include a series of instruction operations on the power consumption policy adjustment device 900. Furthermore, the processor 910 may be configured to communicate with the storage media 930 and execute a series of instruction operations in the storage media 930 on the power consumption policy adjustment device 900 to implement the steps of the power consumption policy adjustment method provided in the above-described method embodiments.

[0026] A power policy adjustment device 900 may further include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating devices 931, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that the structure of a power policy adjustment device 900 shown in FIG9 does not constitute a limitation on the power policy adjustment device 900, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0027] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a power consumption strategy adjustment method.

[0028] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0029] 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 storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a power consumption policy adjustment device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0030] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for adjusting electricity consumption strategies, characterized in that, include: Acquire data from the photovoltaic side, the battery side, and the grid power side; Preprocessing of photovoltaic-side data, battery-side data, and grid-side data yields standardized grid data; analysis of the standardized grid data is performed based on preset grid impact thresholds and preset power angle ranges to obtain analysis results. If the analysis result indicates that the power grid is in an unstable state, the standardized power grid data is analyzed according to the preset time-series clustering algorithm to obtain the current power consumption pattern; the current power consumption pattern is analyzed; if the current power consumption pattern is a photovoltaic-dominated power supply mode, the current remaining battery capacity, mains voltage output phase and photovoltaic voltage output phase are obtained; Based on the preset power consumption mode and preset simulation parameters, the current remaining battery capacity, mains voltage output phase and photovoltaic voltage output phase are analyzed to obtain power consumption adjustment strategies.

2. The electricity consumption strategy adjustment method as described in claim 1, characterized in that, The process of analyzing the current remaining battery capacity, mains voltage output phase, and photovoltaic voltage output phase based on a preset power consumption mode and preset simulation parameters to obtain a power consumption adjustment strategy includes: performing simulation tests on simulation parameters and standardized power grid data according to the power consumption mode to obtain simulation test results and test effect scores; analyzing the mains voltage output phase and photovoltaic voltage output phase based on the simulation test results and test effect scores to obtain a first adjustment power; determining whether the test effect score is less than or equal to a preset effect score threshold; if the test effect score is less than or equal to the effect score threshold, obtaining the current remaining battery capacity; analyzing the current remaining battery capacity based on the test effect score to obtain a second adjustment power; generating power adjustment parameters based on the first and second adjustment powers; obtaining the current power consumption strategy and adjusting the current power consumption strategy according to the power adjustment parameters to obtain the power consumption adjustment strategy.

3. The method for adjusting electricity consumption strategy as described in claim 1, characterized in that, The step of analyzing standardized power grid data according to a preset power grid impact threshold and a preset power angle range to obtain analysis results includes: analyzing standardized power grid data according to the power grid impact threshold to obtain the power angle of key power grid nodes; determining whether the power angle of key power grid nodes is less than a preset node power angle threshold; and when the power angle of key power grid nodes is less than the node power angle threshold, analyzing the power angle of key power grid nodes according to the power angle range to obtain analysis results.

4. The method for adjusting electricity consumption strategy as described in claim 1, characterized in that, The step of analyzing standardized power grid data according to a preset time-series clustering algorithm to obtain the current electricity consumption pattern includes: performing feature analysis on the standardized power grid data to obtain time-domain features, correlation features, and frequency-domain features; constructing a feature vector matrix based on the time-domain features, correlation features, and frequency-domain features; and analyzing the feature vector matrix according to the time-series clustering algorithm to obtain the current electricity consumption pattern.

5. The method for adjusting electricity consumption strategy as described in claim 2, characterized in that, The process of conducting simulation tests on simulation parameters and standardized power grid data based on power consumption patterns to obtain simulation test results and test effect scores includes: analyzing standardized power grid data based on preset available photovoltaic power and preset real-time load demand to obtain actual photovoltaic power supply; calculating the ratio between actual photovoltaic power supply and real-time load demand to obtain load matching degree; and conducting simulation tests on simulation parameters based on power consumption patterns and load matching degree to obtain simulation test results and test effect scores.

6. The electricity consumption strategy adjustment method as described in claim 5, characterized in that, The step of analyzing the mains voltage output phase and photovoltaic voltage output phase based on simulation test results and test effect scores to obtain the first adjusted power includes: calculating the difference between the mains voltage output phase and the photovoltaic voltage output phase to obtain the phase difference; analyzing the simulation test results to obtain the photovoltaic output index; analyzing the phase difference based on the test effect score, real-time load demand, and photovoltaic output index to obtain the first adjustment parameter; obtaining the mains discharge power and adjusting the mains discharge power according to the first adjustment parameter to obtain the first adjusted power.

7. The electricity consumption strategy adjustment method as described in claim 6, characterized in that, The step of analyzing the current remaining capacity of the battery based on the test performance score to obtain the second adjusted power includes: calculating the ratio of the current remaining capacity of the battery to a preset rated capacity to obtain a capacity ratio; analyzing the capacity ratio based on the test performance score, real-time load demand, and photovoltaic output index to obtain a second adjustment parameter; obtaining the battery discharge power, and adjusting the battery discharge power based on the second adjustment parameter to obtain the second adjusted power.

8. A power consumption strategy adjustment device, characterized in that, include: The first data acquisition module is used to acquire photovoltaic-side data, battery-side data, and grid-side data. The data processing module is used to preprocess photovoltaic-side data, battery-side data and grid-side data to obtain standardized grid data; The first analysis module is used to analyze standardized power grid data according to preset power grid impact thresholds and preset power angle ranges to obtain analysis results; The second analysis module is used to analyze standardized power grid data according to a preset time-series clustering algorithm to obtain the current power consumption pattern when the analysis result indicates that the power grid is in an unstable state. The third analysis module is used to analyze the current power consumption mode; the second data acquisition module is used to acquire the current remaining battery capacity, mains voltage output phase and photovoltaic voltage output phase if the current power consumption mode is photovoltaic-dominated power supply mode; the power consumption adjustment strategy generation module is used to analyze the current remaining battery capacity, mains voltage output phase and photovoltaic voltage output phase according to the preset power consumption mode and preset simulation parameters to obtain the power consumption adjustment strategy.

9. A power consumption strategy adjustment device, characterized in that, include: The device includes a memory and at least one processor, the memory storing instructions; at least one processor invokes the instructions in the memory to cause the power consumption policy adjustment device to perform the steps of the power consumption policy adjustment method as claimed in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the steps of the power consumption strategy adjustment method as described in any one of claims 1-7.

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