Energy storage charging and discharging timing optimization scheduling method and system based on maximum benefit

Abstract

The application provides a storage charging and discharging time sequence optimization scheduling method and system based on maximum income, relates to the technical field of power system optimization scheduling, and comprises the following steps: acquiring price data and system parameters, identifying a local extreme point of the price and constructing an envelope line, stripping an oscillation component to obtain a price trend component, establishing a corresponding relationship between a state of charge target value and a time period identifier, making the state of charge trajectory gradually converge to the target value, and dynamically correcting the target value and adjusting a price peak identification method according to real-time feedback data. The application can effectively cope with price fluctuations, improve the economic benefits of the storage system, and realize adaptive optimization control of the charging and discharging process.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling technology, and in particular to a method and system for optimizing the timing of energy storage charging and discharging based on maximizing benefits. Background Technology

[0002] With the continuous expansion of renewable energy grid integration, the uncertainty and volatility of the power system are increasing. Energy storage systems, as an important regulation tool, play a crucial role in power system stability maintenance, demand-side response, and market price arbitrage. In a power market environment, energy storage systems can maximize economic benefits by rationally scheduling charge and discharge times, while also providing ancillary services such as peak shaving and frequency regulation for the grid. Currently, optimized scheduling technology for energy storage charge and discharge has become a research hotspot in the fields of smart grids and the energy internet.

[0003] Traditional optimization dispatching methods rely excessively on the accuracy of electricity price forecasts. However, actual electricity market prices are influenced by various factors, exhibiting strong randomness and volatility, often resulting in significant forecast deviations. This makes it difficult for charge / discharge strategies based on forecasted prices to achieve the expected economic benefits. Most existing methods do not consider real-time operational status feedback from energy storage systems, employing open-loop control models. This fails to address timely adjustments for state-of-charge deviations during actual operation and lacks necessary closed-loop control mechanisms, reducing the practicality and robustness of dispatching strategies. Conventional methods, when identifying peak and valley characteristics in electricity prices, typically use simple threshold judgments or fixed time divisions, which cannot effectively cope with complex and ever-changing price fluctuation patterns. Particularly for electricity price curves with multiple peaks and valleys, it is difficult to accurately identify the optimal charging and discharging timing, thus affecting the arbitrage effectiveness of energy storage systems. Summary of the Invention

[0004] The present invention provides a method and system for optimizing the timing of energy storage charging and discharging based on maximizing benefits, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides a method for optimizing the timing of energy storage charging and discharging based on maximizing returns, comprising:

[0006] Acquire time-series data on electricity prices, state-of-charge data of energy storage systems, and physical constraint parameters;

[0007] Local extreme points of the electricity price time series data are identified, and an upper and lower price envelope are constructed based on the local extreme points and their mean is calculated. Oscillating components are iteratively removed to obtain the trend components of electricity price changes. Price peak periods are identified from the trend components, and a correspondence between the target value of state of charge and the period identifier is established in combination with the physical constraint parameters.

[0008] Based on the correspondence and the power boundary constraints of the physical constraint parameters, the state of charge trajectory of the state of charge data gradually converges to the target value of the state of charge along the time axis, thereby obtaining the charging and discharging power scheduling instructions at each moment.

[0009] Based on the charging and discharging power scheduling command, the energy storage system is controlled to perform charging and discharging actions, and the actual state of charge feedback data and actual market price feedback data of the energy storage system are collected in real time during the execution process.

[0010] Based on the actual state of charge feedback data and the actual market price feedback data, the current state of charge deviation and price prediction deviation are calculated. The state of charge target value is dynamically corrected using the state of charge deviation. At the same time, the price prediction deviation is used to adaptively adjust the method for identifying the price peak period, thereby completing the optimized scheduling of charging and discharging timing.

[0011] Local extreme points in the electricity price time series data are identified. Based on these local extreme points, an upper and lower price envelope are constructed and their mean values ​​are calculated. Oscillating components are iteratively removed to obtain the trend components of electricity price changes, including:

[0012] The electricity price time series data is traversed in time series, and the moment when the price change direction changes in adjacent time moments is taken as the local extreme point. The local extreme point is then divided into a set of local maximum points and a set of local minimum points.

[0013] Interpolation is performed on the time axis to connect the sets of local maxima and local minima respectively, constructing an upper price envelope and a lower price envelope; the average values ​​of the upper and lower price envelopes at each time point are calculated to obtain the intermediate trend component of the current iteration period; the difference between the electricity price time series data and the intermediate trend component is calculated to obtain the residual component of the current iteration period.

[0014] The oscillation amplitude of the residual component is calculated. When the oscillation amplitude is higher than the preset oscillation threshold, the residual component is used as the input data for the next iteration cycle to calculate the intermediate trend component for the next iteration cycle. The iteration continues until the oscillation amplitude is lower than the preset oscillation threshold, and the intermediate trend component of the current iteration cycle is determined as the trend component of the electricity price change.

[0015] Identifying price peak periods from the trend components and establishing a correspondence between the target value of state of charge and the period identifier, in conjunction with the physical constraint parameters, includes:

[0016] Identify continuous time intervals in the trend components where the price value exceeds the average price level, and mark them as candidate high-price periods; verify the duration of the candidate high-price periods, and determine the periods whose duration meets the minimum charge and discharge duration requirement of the energy storage system in the physical constraint parameters as price peak periods;

[0017] The maximum state of charge (SOC) value of the energy storage system in the physical constraint parameters is set as the target SOC value corresponding to the start time of the price peak period, and a discharge period identifier is assigned to the price peak period; for the transition period between the price peak periods, linear interpolation is performed based on the target SOC values ​​corresponding to adjacent price peak periods to obtain the target SOC values ​​corresponding to each time of the transition period, and a transition period identifier is assigned to the transition period.

[0018] The target state of charge and time period identifiers corresponding to each moment of the price peak period and the transition period are summarized and organized into associated data records. The associated data records are stored in chronological order to form a correspondence between the target state of charge and the time period identifier.

[0019] Based on the correspondence and the power boundary constraints of the physical constraint parameters, the state of charge trajectory of the state of charge data gradually converges to the target value of the state of charge along the time axis, resulting in charging and discharging power scheduling instructions at each time point, including:

[0020] Obtain the actual state of charge value, extract the target state of charge value and time period identifier corresponding to the current time from the correspondence, and calculate the state deviation between the target state of charge value and the actual state of charge value;

[0021] Based on the state deviation and the time interval from the current moment to the next moment, calculate the theoretical charging and discharging power required to converge the actual state of charge value to the target state of charge value.

[0022] The maximum charging power limit and maximum discharging power limit of the energy storage system are obtained from the physical constraint parameters; the theoretical charging and discharging power is subjected to power boundary constraint processing; when the absolute value of the theoretical charging and discharging power exceeds the maximum charging power limit or the maximum discharging power limit, the theoretical charging and discharging power is truncated to the corresponding power boundary value to obtain the constrained charging and discharging power at the current moment; the constrained charging and discharging power is used as the charging and discharging power scheduling command at the current moment.

[0023] Based on the constrained charging and discharging power and the time interval from the current moment to the next moment, the predicted state of charge value for the next moment is calculated, and the predicted state of charge value is updated in the state of charge data as the actual state of charge value for the next moment; this process is repeated iteratively for subsequent moments until the charging and discharging power scheduling instructions for all moments within the scheduling period are generated.

[0024] The dynamic correction of the target value of the state of charge using the state of charge deviation includes:

[0025] The state of charge (SOC) deviations from the start of the scheduling cycle to the current time are arranged in chronological order to form a SOC deviation sequence. The rate of change between SOC deviations at adjacent times in the SOC deviation sequence is calculated. The evolution trend of the SOC deviations is determined based on the sign and magnitude of the rate of change. The correction response intensity level and response coefficient are determined based on the evolution trend.

[0026] The absolute value of the price prediction deviation is calculated as the ratio of the historical average of the price prediction deviation within the scheduling period to obtain the price volatility relative strength index. The price volatility relative strength index is then subjected to a nonlinear mapping transformation to map the price volatility relative strength index to a preset sensitivity coefficient range to obtain the price volatility sensitivity coefficient.

[0027] Obtain the response coefficient corresponding to the corrected response intensity level, perform weighted fusion calculation on the response coefficient and the price fluctuation sensitivity coefficient to obtain a comprehensive correction intensity factor, use the comprehensive correction intensity factor to correct the current state of charge deviation to obtain the correction range of the target value of the state of charge; add the correction range of the target value of the state of charge to the target value of the state of charge corresponding to the current time to obtain the corrected target value of the state of charge.

[0028] Simultaneously, the method for identifying the price peak period is adaptively adjusted using the price prediction deviation, including:

[0029] The identification result of the price peak period is used as the random variable to be inferred, and the price prediction deviation is used as observation evidence. A prior probability distribution of the price peak period identification result is established, and the likelihood value is calculated. The prior probability distribution and the likelihood value are fused using Bayesian method to obtain the posterior probability distribution. The relative entropy between the prior probability distribution and the posterior probability distribution is calculated.

[0030] The maximum posterior probability value in the posterior probability distribution is calculated as the confidence value of the price peak period identification result; when the confidence value is lower than the preset confidence threshold and the relative entropy exceeds the preset relative entropy threshold, the weight of the prior probability distribution in the Bayesian fusion calculation is reduced and the Bayesian fusion calculation is re-executed, and a multi-candidate set of price peak periods is constructed based on the updated posterior probability distribution.

[0031] Calculate the time period overlap between the identification results of each candidate price peak period in the multi-candidate set, and mark the time period overlap that is lower than the overlap threshold as the low confidence price peak period; adjust the state of charge target value setting for the low confidence price peak period, and update the correspondence between the state of charge target value and the time period identifier based on the adjusted state of charge target value setting.

[0032] A second aspect of the present invention provides a time-series optimization scheduling system for energy storage charging and discharging based on maximizing returns, comprising:

[0033] The first unit is used to acquire time-series data on electricity prices, state-of-charge data of energy storage systems, and physical constraint parameters.

[0034] The second unit is used to identify local extreme points in the electricity price time series data, construct upper and lower price envelopes based on the local extreme points and calculate the mean, iteratively remove oscillation components, and obtain the trend components of electricity price changes; identify price peak periods from the trend components, and establish a correspondence between the target value of state of charge and the time period identifier in combination with the physical constraint parameters;

[0035] The third unit is used to make the state of charge trajectory of the state of charge data gradually converge to the target value of the state of charge along the time axis based on the correspondence and the power boundary constraints of the physical constraint parameters, so as to obtain the charging and discharging power scheduling instructions at each time.

[0036] The fourth unit is used to control the energy storage system to perform charging and discharging actions based on the charging and discharging power scheduling command, and to collect the actual state of charge feedback data and actual market price feedback data of the energy storage system in real time during the execution process.

[0037] The fifth unit is used to calculate the current state of charge deviation and price prediction deviation based on the actual state of charge feedback data and the actual market price feedback data, and to dynamically correct the target value of the state of charge using the state of charge deviation. At the same time, it uses the price prediction deviation to adaptively adjust the identification method of the price peak period, thereby completing the optimized scheduling of charging and discharging timing.

[0038] A third aspect of the present invention,

[0039] An electronic device is provided, comprising:

[0040] processor;

[0041] Memory used to store processor-executable instructions;

[0042] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0043] Fourth aspect of the embodiments of the present invention,

[0044] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0045] The beneficial effects of this application are as follows:

[0046] This invention identifies local extreme points in electricity price time-series data, constructs upper and lower price envelopes for mean calculation, and iteratively removes oscillating components. This effectively obtains the trend components of electricity price changes, avoiding the oversensitivity to random price fluctuations in traditional methods and improving the accuracy of identifying peak price periods. Based on the price trend components, this invention identifies peak price periods and establishes a correspondence between the target state of charge (SOC) value and the time period identifier, making charging and discharging decisions more rational. This effectively avoids the frequent charging and discharging switching problems caused by price fluctuations in traditional methods, extending the lifespan of the energy storage system. Furthermore, this invention employs an optimization method where the SOC trajectory gradually converges to the target value along the time axis. This satisfies the physical constraints of the energy storage system and achieves a smooth transition in charging and discharging power, improving the stability of system operation. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the energy storage charging and discharging timing optimization scheduling method based on maximizing benefits, as described in an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of the price trend component extraction process. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0051] Figure 1 This is a flowchart illustrating the energy storage charging and discharging timing optimization scheduling method based on maximizing returns, as described in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0052] Acquire time-series data on electricity prices, state-of-charge data of energy storage systems, and physical constraint parameters;

[0053] Local extreme points of the electricity price time series data are identified, and an upper and lower price envelope are constructed based on the local extreme points and their mean is calculated. Oscillating components are iteratively removed to obtain the trend components of electricity price changes. Price peak periods are identified from the trend components, and a correspondence between the target value of state of charge and the period identifier is established in combination with the physical constraint parameters.

[0054] Based on the correspondence and the power boundary constraints of the physical constraint parameters, the state of charge trajectory of the state of charge data gradually converges to the target value of the state of charge along the time axis, thereby obtaining the charging and discharging power scheduling instructions at each moment.

[0055] Based on the charging and discharging power scheduling command, the energy storage system is controlled to perform charging and discharging actions, and the actual state of charge feedback data and actual market price feedback data of the energy storage system are collected in real time during the execution process.

[0056] Based on the actual state of charge feedback data and the actual market price feedback data, the current state of charge deviation and price prediction deviation are calculated. The state of charge target value is dynamically corrected using the state of charge deviation. At the same time, the price prediction deviation is used to adaptively adjust the method for identifying the price peak period, thereby completing the optimized scheduling of charging and discharging timing.

[0057] In one optional implementation, local extreme points of the electricity price time-series data are identified, and an upper and lower price envelope are constructed based on these local extreme points, with their mean calculated. Oscillating components are iteratively removed to obtain the trend components of electricity price changes, including:

[0058] The electricity price time series data is traversed in time series, and the moment when the price change direction changes in adjacent time moments is taken as the local extreme point. The local extreme point is then divided into a set of local maximum points and a set of local minimum points.

[0059] Interpolation is performed on the time axis to connect the sets of local maxima and local minima respectively, constructing an upper price envelope and a lower price envelope; the average values ​​of the upper and lower price envelopes at each time point are calculated to obtain the intermediate trend component of the current iteration period; the difference between the electricity price time series data and the intermediate trend component is calculated to obtain the residual component of the current iteration period.

[0060] The oscillation amplitude of the residual component is calculated. When the oscillation amplitude is higher than the preset oscillation threshold, the residual component is used as the input data for the next iteration cycle to calculate the intermediate trend component for the next iteration cycle. The iteration continues until the oscillation amplitude is lower than the preset oscillation threshold, and the intermediate trend component of the current iteration cycle is determined as the trend component of the electricity price change.

[0061] like Figure 2 As shown, the method includes:

[0062] Obtain time-series electricity price data, which comes from historical records of the electricity trading market and includes electricity price values ​​at different points in time. For example, hourly electricity price data for a specific region over a month can be collected to form a time series of 720 data points.

[0063] The acquired electricity price time-series data is traversed to identify local extrema. Starting from the second point in the time series, the current price is compared with the previous price to determine the direction of price change. This process continues to the third point and subsequent points. When a price change direction differs from the previous segment, the previous point is marked as a local extrema. For example, if the price trend is upward from the first to the fourth point, but downward from the fourth to the fifth point, then the fourth point is a local maximum; conversely, if a price change reverses from downward to upward, the turning point is a local minimum.

[0064] During the identification process, all local extreme points are divided into two categories: the set of local maxima and the set of local minima. For consecutive time points with equal prices, the following processing method can be adopted: if consecutive equal points are located between an upward and a downward trend, the last equal point is marked as a local maximum; if consecutive equal points are located between a downward and an upward trend, the last equal point is marked as a local minimum.

[0065] After identifying local extrema, interpolation is performed on the sets of local maxima and local minima to construct upper and lower price envelopes. Cubic spline interpolation can be used to ensure the smoothness of the connected curves. For example, assuming the identified local maxima are located at times t1, t3, and t7, corresponding to price values ​​p1, p3, and p7, the upper envelope values ​​at times t2, t4, t5, and t6 are calculated through interpolation. Similarly, the lower price envelope is constructed.

[0066] After constructing the envelope, calculate the average values ​​of the upper and lower price envelopes at each time point to obtain the intermediate trend component of the current iteration period. The specific calculation method is as follows: for time t, the intermediate trend component m(t) = (upper envelope value u(t) + lower envelope value l(t)) / 2.

[0067] The residual component of the current iteration period is obtained by performing a difference operation between the original electricity price time series data and the calculated intermediate trend component. For time t, the residual component r(t) = original price p(t) - intermediate trend component m(t).

[0068] The oscillation amplitude of the residual components is calculated to determine whether further iteration is needed. The oscillation amplitude can be measured by the standard deviation of the residual components, i.e., by calculating the standard deviation of the residual sequence. Alternatively, the range (maximum value minus minimum value) or the average absolute value of the residual components can be used as a metric.

[0069] When the oscillation amplitude of the residual component exceeds the preset oscillation threshold, it indicates that the residual still contains significant fluctuations, requiring further iterative stripping. In this case, the current residual component is used as the input data for the next iteration cycle, and the steps of identifying local extrema, constructing the envelope, calculating intermediate trend components, and calculating residual components are repeated. The preset oscillation threshold can be set according to the analysis requirements and data characteristics; for example, it can be set to 5% of the standard deviation of the original data.

[0070] The iteration process continues until the oscillation amplitude of the residual component falls below a preset oscillation threshold. At this point, the residual is considered to no longer contain significant fluctuations, and the intermediate trend component of the current iteration cycle is determined as the trend component of electricity price changes. The final trend component represents the main direction of electricity price changes, eliminating the impact of short-term fluctuations.

[0071] The electricity price change trend components obtained using the above methods can be used for subsequent electricity market analysis, such as price trend forecasting and anomaly price detection. These trend components reflect fundamental changes in the market, eliminate the interference of short-term fluctuations, and provide a more reliable reference for decision-making.

[0072] In one optional implementation, identifying price peak periods from the trend components and establishing a correspondence between the target value of the state of charge and the period identifier, in conjunction with the physical constraint parameters, includes:

[0073] Identify continuous time intervals in the trend components where the price value exceeds the average price level, and mark them as candidate high-price periods; verify the duration of the candidate high-price periods, and determine the periods whose duration meets the minimum charge and discharge duration requirement of the energy storage system in the physical constraint parameters as price peak periods;

[0074] The maximum state of charge (SOC) value of the energy storage system in the physical constraint parameters is set as the target SOC value corresponding to the start time of the price peak period, and a discharge period identifier is assigned to the price peak period; for the transition period between the price peak periods, linear interpolation is performed based on the target SOC values ​​corresponding to adjacent price peak periods to obtain the target SOC values ​​corresponding to each time of the transition period, and a transition period identifier is assigned to the transition period.

[0075] The target state of charge and time period identifiers corresponding to each moment of the price peak period and the transition period are summarized and organized into associated data records. The associated data records are stored in chronological order to form a correspondence between the target state of charge and the time period identifier.

[0076] The process involves analyzing the trend components of electricity price changes to identify peak price periods. The average price level of the acquired time-series price data is calculated; this average price level can be the arithmetic mean of electricity prices over a period of time, such as a day, a week, or a month. The time-series price data is then iterated through to identify consecutive time points where the price value exceeds the average price level. These consecutive time points are then marked as candidate high-price periods. For example, in 24-hour electricity price data for a given day, if the electricity price from 10:00 AM to 2:00 PM is continuously higher than the average price for that day, this period is marked as a candidate high-price period.

[0077] The duration of candidate high-price periods is verified to obtain the minimum charge / discharge duration requirement for the energy storage system from the physical constraints. This parameter is usually determined by the physical characteristics of the energy storage system; for example, some battery energy storage systems require at least half an hour of continuous discharge to ensure high efficiency. The duration of each candidate high-price period is checked sequentially to see if it meets this minimum requirement. If the duration of a candidate high-price period is less than the minimum charge / discharge duration, it is discarded; otherwise, the period is confirmed as a price peak period. This avoids frequent switching of the energy storage system's operating state, improving system stability and lifespan.

[0078] After determining the peak price period, a correspondence is established between the target state of charge (SOC) value and the period identifier. The maximum SOC value of the energy storage system is obtained from the physical constraint parameters; this value represents the maximum energy the energy storage system can store. This maximum SOC value is set as the target SOC value corresponding to the start time of the peak price period. Simultaneously, a discharge period identifier is assigned to this peak price period, indicating that the energy storage system should perform discharge operations during this period to take advantage of the high electricity price and generate revenue. For example, if the maximum SOC value of the energy storage system is 100%, and the peak price period is from 10:00 to 14:00, then the target SOC value for 10:00 is set to 100%, and a discharge period identifier is assigned for 10:00 to 14:00.

[0079] For periods between adjacent price peaks, a transition period is defined. During this transition period, linear interpolation is performed based on the target state of charge (SOC) values ​​corresponding to the adjacent price peak periods to calculate the target SOC values ​​for each moment. The linear interpolation method can be expressed as: the target SOC value at the current moment equals the target SOC value at the end of the previous price peak period plus the time ratio multiplied by the difference in target SOC values ​​at the beginning of the two adjacent price peak periods. Simultaneously, a transition period identifier is assigned to each transition period, indicating that the energy storage system should adjust its charging and discharging behavior according to actual conditions during this period.

[0080] For example, suppose the periods from 10:00 to 14:00 and from 18:00 to 22:00 are two adjacent price peak periods, then the period from 14:00 to 18:00 is a transition period. If the target value of the state of charge (SOC) at 14:00 is 60% and the target value of the SOC at 18:00 is 100%, then the target value of the SOC at 16:00 (located at the midpoint of the transition period) is calculated to be 80% through linear interpolation.

[0081] The target state of charge (SOC) values ​​and time period identifiers for each moment during the price peak and transition periods are aggregated and organized into associated data records. The structure of these associated data records may include: timestamp, target SOC value, and time period identifier (discharge time period identifier or transition time period identifier). These associated data records are stored in chronological order, forming a complete correspondence between the target SOC values ​​and time period identifiers. These associated data records will serve as the basis for subsequent energy storage system scheduling and control, guiding the energy storage system to execute appropriate charging and discharging strategies at different times.

[0082] In this way, the energy storage system can optimize its operation strategy according to the fluctuation of electricity prices, maximize economic benefits while ensuring the physical constraints of the system, and provide effective peak-shaving services for the power grid, thereby achieving efficient utilization of energy storage resources.

[0083] In one optional implementation, based on the correspondence and the power boundary constraints of the physical constraint parameters, the state of charge trajectory of the state of charge data is gradually converged along the time axis to the target value of the state of charge, resulting in charging and discharging power scheduling instructions at each time point, including:

[0084] Obtain the actual state of charge value, extract the target state of charge value and time period identifier corresponding to the current time from the correspondence, and calculate the state deviation between the target state of charge value and the actual state of charge value;

[0085] Based on the state deviation and the time interval from the current moment to the next moment, calculate the theoretical charging and discharging power required to converge the actual state of charge value to the target state of charge value.

[0086] The maximum charging power limit and maximum discharging power limit of the energy storage system are obtained from the physical constraint parameters; the theoretical charging and discharging power is subjected to power boundary constraint processing; when the absolute value of the theoretical charging and discharging power exceeds the maximum charging power limit or the maximum discharging power limit, the theoretical charging and discharging power is truncated to the corresponding power boundary value to obtain the constrained charging and discharging power at the current moment; the constrained charging and discharging power is used as the charging and discharging power scheduling command at the current moment.

[0087] Based on the constrained charging and discharging power and the time interval from the current moment to the next moment, the predicted state of charge value for the next moment is calculated, and the predicted state of charge value is updated in the state of charge data as the actual state of charge value for the next moment; this process is repeated iteratively for subsequent moments until the charging and discharging power scheduling instructions for all moments within the scheduling period are generated.

[0088] The process involves obtaining the actual state of charge (SOC) value, extracting the target SOC value and time period identifier for the current moment from the correspondence, and calculating the SOC deviation between the target SOC value and the actual SOC value. Specifically, the actual SOC value of the current energy storage unit can be obtained through the battery management system, for example, a SOC measured at a certain moment of 45%. Simultaneously, based on the pre-established correspondence between time and target SOC value, the target SOC value for the current moment is extracted (assumed to be 50%), and the current time period identifier is determined, such as peak period, valley period, or normal period. The SOC deviation is calculated as the target value minus the actual value; in this example, the deviation equals 5%.

[0089] Based on the state deviation and the time interval from the current moment to the next moment, the theoretical charging and discharging power required to converge the actual state of charge (SOC) value to the target SOC value is calculated. In the specific implementation, the time interval from the current moment to the next moment is assumed to be 15 minutes, and the rated capacity of the energy storage system is 100 kWh. A positive deviation indicates that charging is required; a negative deviation indicates that discharging is required. In the above example, the theoretical charging and discharging power is 20 kW of charging power.

[0090] The maximum charging power limit and maximum discharging power limit of the energy storage system are obtained from physical constraint parameters. Power boundary constraints are applied to the theoretical charging and discharging power; for example, the maximum charging power limit is 30 kW, and the maximum discharging power limit is -30 kW. When the absolute value of the theoretical charging and discharging power exceeds the maximum charging power limit or the maximum discharging power limit, the theoretical charging and discharging power is truncated to the corresponding power boundary value, resulting in the constrained charging and discharging power at the current moment. Specifically, the judgment logic is as follows: when the theoretical power is greater than the maximum charging power limit, the constrained power equals the maximum charging power limit; when the theoretical power is less than the maximum discharging power limit, the constrained power equals the maximum discharging power limit; otherwise, the constrained power equals the theoretical power. In this example, the theoretical charging power of 20 kW does not exceed the maximum charging power limit of 30 kW, therefore the constrained charging and discharging power remains 20 kW. The constrained charging and discharging power is then used as the charging and discharging power scheduling command for the current moment and sent to the power control unit of the energy storage system for execution.

[0091] After completing the power constraint processing for the current moment, based on the constrained charging and discharging power and the time interval from the current moment to the next moment, the predicted state of charge (SOC) value for the next moment is calculated, and this predicted SOC value is updated in the SOC data as the actual SOC value for the next moment. In the example above, the predicted SOC value for the next moment is 48.5% (45% plus 3.5%). This predicted value will be used as the actual SOC value for the next moment for power scheduling calculations.

[0092] The above steps are repeated iteratively for subsequent time periods until charging and discharging power scheduling instructions for all time periods within the scheduling cycle are generated. The scheduling cycle can be one day, one week, or other time periods, depending on the specific application scenario requirements. Through this scheduling method based on state-of-charge closed-loop control, the energy storage system can achieve smooth convergence of the state of charge according to the preset target trajectory, while satisfying physical power constraints.

[0093] In practical applications, such as photovoltaic energy storage systems, a target state of charge trajectory that meets economic requirements can be generated based on photovoltaic power generation forecasts and electricity load forecasts. This involves charging during periods of surplus photovoltaic power generation and discharging during peak electricity demand periods. The charging and discharging power scheduling achieved through this method enables the actual operating state of the energy storage system to gradually converge to the expected target, while ensuring that it does not violate the physical constraints of the equipment, effectively improving the safety and economy of system operation.

[0094] Furthermore, when an energy storage system consists of multiple energy storage units, different target values ​​for the state of charge (SOC) can be allocated based on the characteristics and state of each unit, achieving system-level coordinated optimization. For example, larger charging and discharging power can be allocated to energy storage units with longer lifespans, while the charging and discharging burden can be reduced for energy storage units nearing retirement, thereby extending the overall lifespan of the energy storage system.

[0095] In one optional implementation, dynamically correcting the target value of the state of charge using the state of charge deviation includes:

[0096] The state of charge (SOC) deviations from the start of the scheduling cycle to the current time are arranged in chronological order to form a SOC deviation sequence. The rate of change between SOC deviations at adjacent times in the SOC deviation sequence is calculated. The evolution trend of the SOC deviations is determined based on the sign and magnitude of the rate of change. The correction response intensity level and response coefficient are determined based on the evolution trend.

[0097] The absolute value of the price prediction deviation is calculated as the ratio of the historical average of the price prediction deviation within the scheduling period to obtain the price volatility relative strength index. The price volatility relative strength index is then subjected to a nonlinear mapping transformation to map the price volatility relative strength index to a preset sensitivity coefficient range to obtain the price volatility sensitivity coefficient.

[0098] Obtain the response coefficient corresponding to the corrected response intensity level, perform weighted fusion calculation on the response coefficient and the price fluctuation sensitivity coefficient to obtain a comprehensive correction intensity factor, use the comprehensive correction intensity factor to correct the current state of charge deviation to obtain the correction range of the target value of the state of charge; add the correction range of the target value of the state of charge to the target value of the state of charge corresponding to the current time to obtain the corrected target value of the state of charge.

[0099] The state of charge deviations from the start of the scheduling cycle to the current time are arranged in chronological order. Assuming the current time is t and the start time of the scheduling cycle is t0, the sequence of state of charge deviations can be represented as [ΔSOC(t0), ΔSOC(t0+1), ..., ΔSOC(t)], where ΔSOC represents the difference between the actual state of charge and the target state of charge.

[0100] Next, the rate of change between adjacent time points in the state of charge deviation sequence is calculated. For time i, the rate of change can be calculated as (ΔSOC(i) - ΔSOC(i-1)) / Δt, where Δt is the time interval between adjacent time points. For example, if sampling is performed every 5 minutes, then Δt is 5 minutes.

[0101] Based on the sign and magnitude of the rate of change, the evolution trend of the state of charge deviation can be determined. The evolution trend can be divided into the following cases: 1. Rapid increase: the rate of change is positive and greater than the preset threshold α1; 2. Slow increase: the rate of change is positive and less than or equal to the preset threshold α1; 3. Remaining stable: the rate of change is close to zero and the absolute value is less than the preset threshold α2; 4. Slow decrease: the rate of change is negative and the absolute value is less than or equal to the preset threshold α3; 5. Rapid decrease: the rate of change is negative and the absolute value is greater than the preset threshold α3.

[0102] Based on the identified evolutionary trend characteristics, the modified response intensity level and response coefficient are determined. For example, five intensity levels can be set, each corresponding to one of the five trend characteristics mentioned above. For a rapid upward trend, a larger negative response coefficient should be used; for a rapid downward trend, a larger positive response coefficient should be used; for a steady state, the response coefficient can be close to zero. Specifically, a response coefficient mapping table can be preset to map each trend characteristic to the corresponding response coefficient k1.

[0103] Simultaneously, the ratio of the absolute value of the price prediction deviation to the historical average of the price prediction deviation within the scheduling period is calculated. Assuming the current actual price is P(t) and the predicted price is P'(t), then the price prediction deviation is ΔP(t) = P(t) - P'(t). The historical average of the price prediction deviation within the scheduling period can be expressed as AvgΔP. The relative strength index of price volatility can be calculated as |ΔP(t)| / AvgΔP.

[0104] A non-linear mapping transformation is applied to the relative strength index of price volatility to map it to a preset range of sensitivity coefficient values. This mapping can be performed using the sigmoid function: k2 = 1 / (1+e -λ×(|ΔP(t)| / AvgΔP-β) ), where λ and β are preset parameters used to adjust the shape of the mapping curve. Through this mapping, the range of the price fluctuation sensitivity coefficient k2 is restricted to the interval [0, 1].

[0105] Obtain the response coefficient k1 corresponding to the corrected response intensity level. Then, perform a weighted fusion calculation between the response coefficient k1 and the price fluctuation sensitivity coefficient k2 to obtain the comprehensive corrected intensity factor k. The weighted fusion can be achieved by k = w1×k1 + w2×k2, where w1 and w2 are preset weights, and w1 + w2 = 1.

[0106] The current state of charge deviation is corrected using a comprehensive correction intensity factor k, yielding the correction magnitude for the target state of charge. The correction magnitude can be expressed as ΔSOC_adj(t) = k × ΔSOC(t).

[0107] The correction magnitude of the target state of charge is added to the target state of charge at the current time to obtain the corrected target state of charge. Assuming the original target value is SOC_target(t), the corrected target value is SOC_target'(t) = SOC_target(t) + ΔSOC_adj(t).

[0108] Using the above methods, the energy storage system can dynamically adjust the target value of the state of charge (SOC) during operation based on the real-time trend of SOC deviation and price prediction deviation, thereby improving the system's adaptability to market price fluctuations and ensuring that the system operates according to the expected trajectory.

[0109] In one optional implementation, simultaneously using the price prediction deviation to adaptively adjust the method for identifying the price peak period includes:

[0110] The identification result of the price peak period is used as the random variable to be inferred, and the price prediction deviation is used as observation evidence. A prior probability distribution of the price peak period identification result is established, and the likelihood value is calculated. The prior probability distribution and the likelihood value are fused using Bayesian method to obtain the posterior probability distribution. The relative entropy between the prior probability distribution and the posterior probability distribution is calculated.

[0111] The maximum posterior probability value in the posterior probability distribution is calculated as the confidence value of the price peak period identification result; when the confidence value is lower than the preset confidence threshold and the relative entropy exceeds the preset relative entropy threshold, the weight of the prior probability distribution in the Bayesian fusion calculation is reduced and the Bayesian fusion calculation is re-executed, and a multi-candidate set of price peak periods is constructed based on the updated posterior probability distribution.

[0112] Calculate the time period overlap between the identification results of each candidate price peak period in the multi-candidate set, and mark the time period overlap that is lower than the overlap threshold as the low confidence price peak period; adjust the state of charge target value setting for the low confidence price peak period, and update the correspondence between the state of charge target value and the time period identifier based on the adjusted state of charge target value setting.

[0113] The identification results of price peak periods are used as random variables to be inferred, and the price prediction deviation is used as observational evidence to establish the prior probability distribution of the price peak period identification results. Price peak periods can typically be represented as several time intervals within a day, for example, T = {[t1_start, t1_end], [t2_start, t2_end], ...}. For each time t, a binary random variable X_t can be established to represent whether that time is a peak period. Its prior probability distribution can be calculated based on historical identification results and denoted as P(X_t).

[0114] Simultaneously, the likelihood value is calculated, which is the conditional probability P(e_t|X_t) of observing a specific price prediction deviation e_t. The price prediction deviation can be obtained by the difference between the actual price and the predicted price. When historical data indicates that the prediction deviation is large for a specific period, the reliability of identifying that period as a peak period decreases.

[0115] The prior probability distribution and the likelihood value are fused using Bayesian methods to obtain the posterior probability distribution. According to Bayes' theorem, the posterior probability P(X_t|e_t) can be calculated as follows: first, multiply the prior probability P(X_t) by the likelihood P(e_t|X_t), and then divide by the marginal probability P(e_t), thus obtaining the probability that time t is the peak period given the observed price prediction deviation e_t.

[0116] After Bayesian fusion, the relative entropy between the prior and posterior probability distributions is calculated to measure the degree of change in the distribution brought about by new information. Relative entropy, also known as KL divergence, measures the difference between two probability distributions. A larger relative entropy indicates a more significant correction of prior judgments by observed evidence.

[0117] Simultaneously, the maximum posterior probability value in the posterior probability distribution is calculated as the confidence value of the price peak period identification result. This confidence value reflects the reliability of the current identification result. When the confidence value is lower than the preset confidence threshold (e.g., 0.75) and the relative entropy exceeds the preset relative entropy threshold (e.g., 0.3), it indicates that the current identification result is not reliable enough and the prior judgment needs to be significantly adjusted.

[0118] In this scenario, it is necessary to reduce the weight of the prior probability distribution in the Bayesian fusion calculation and re-execute the Bayesian fusion calculation. Specifically, a weighting coefficient α (0 < α < 1) can be introduced to adjust the influence of the prior probability P(X_t) with weight α. By reducing the influence of the prior probability, the posterior probability is made more susceptible to the influence of current observational evidence, thereby improving the adaptability of the identification results to current price fluctuations.

[0119] Based on the updated posterior probability distribution, a multi-candidate set of price peak periods is constructed. All time intervals with a posterior probability exceeding a specific threshold (e.g., 0.5) can be selected as candidate peak periods, forming a set T_candidates={[t1_start, t1_end], [t2_start, t2_end], ...}. This multi-candidate design can mitigate the risks associated with a single identification result.

[0120] Calculate the overlap between the peak price periods identified in the multi-candidate set. For any two candidate periods T_i and T_j, the overlap can be calculated as the ratio of the overlap length to the total length. If there is a large discrepancy (i.e., low overlap) in the division of a certain time t among different candidate periods, it indicates that there is uncertainty in the peak price determination at that time.

[0121] Periods with overlap below a threshold (e.g., 0.6) are identified as low-confidence price peak periods. These periods are typically located near price inflection points or in areas with large forecast deviations, requiring special handling to mitigate energy dispatch risks.

[0122] Adjust the target value of the state of charge (SOC) for low-confidence price peak periods. Traditional methods typically use different SOC targets for peak and off-peak periods; for example, a lower SOC is desired during peak periods to maximize discharge profits, while a higher SOC is maintained during off-peak periods for future use. For low-confidence periods, a moderate SOC target value between these two values ​​can be used, or the target value can be set proportionally based on the confidence level, thereby reducing losses from misidentification.

[0123] Based on the adjusted target value for state of charge (SOC), the correspondence between the SOC target value and the time period identifier is updated. For example, the time period can be divided into four categories: "certain peak", "low confidence peak", "low confidence non-peak", and "certain non-peak", and different target values ​​for SOC can be set for each category, such as 20%, 40%, 60%, and 80%. This fine-grained division of time periods and setting of target values ​​can better adapt to the uncertainty caused by price forecast deviations.

[0124] Through the above adaptive adjustment process, the energy storage system can dynamically adjust its price peak period identification strategy based on price forecast deviations, improving operational robustness and economic efficiency in volatile price environments. This method is particularly suitable for scenarios with drastic electricity price fluctuations or unstable price forecasting model performance, effectively reducing the scheduling risk of the energy storage system.

[0125] This invention provides an energy storage charging and discharging timing optimization scheduling system based on maximizing returns. The system includes:

[0126] The first unit is used to acquire time-series data on electricity prices, state-of-charge data of energy storage systems, and physical constraint parameters.

[0127] The second unit is used to identify local extreme points in the electricity price time series data, construct upper and lower price envelopes based on the local extreme points and calculate the mean, iteratively remove oscillation components, and obtain the trend components of electricity price changes; identify price peak periods from the trend components, and establish a correspondence between the target value of state of charge and the time period identifier in combination with the physical constraint parameters;

[0128] The third unit is used to make the state of charge trajectory of the state of charge data gradually converge to the target value of the state of charge along the time axis based on the correspondence and the power boundary constraints of the physical constraint parameters, so as to obtain the charging and discharging power scheduling instructions at each time.

[0129] The fourth unit is used to control the energy storage system to perform charging and discharging actions based on the charging and discharging power scheduling command, and to collect the actual state of charge feedback data and actual market price feedback data of the energy storage system in real time during the execution process.

[0130] The fifth unit is used to calculate the current state of charge deviation and price prediction deviation based on the actual state of charge feedback data and the actual market price feedback data, and to dynamically correct the target value of the state of charge using the state of charge deviation. At the same time, it uses the price prediction deviation to adaptively adjust the identification method of the price peak period, thereby completing the optimized scheduling of charging and discharging timing.

[0131] A third aspect of the present invention provides an electronic device, comprising:

[0132] processor;

[0133] Memory used to store processor-executable instructions;

[0134] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0135] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0136] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A time-series optimization scheduling method for energy storage charging and discharging based on maximizing returns, characterized in that, include: Acquire time-series data on electricity prices, state-of-charge data of energy storage systems, and physical constraint parameters; Identify local extreme points in the electricity price time series data, construct upper and lower price envelopes based on these local extreme points and calculate their mean, iteratively remove oscillation components, and obtain the trend components of electricity price changes; Identify price peak periods from the trend components and, in conjunction with the physical constraint parameters, establish a correspondence between the target value of the state of charge and the period identifier. Based on the correspondence and the power boundary constraints of the physical constraint parameters, the state of charge trajectory of the state of charge data gradually converges to the target value of the state of charge along the time axis, thereby obtaining the charging and discharging power scheduling instructions at each moment. Based on the charging and discharging power scheduling command, the energy storage system is controlled to perform charging and discharging actions, and the actual state of charge feedback data and actual market price feedback data of the energy storage system are collected in real time during the execution process. Based on the actual state of charge feedback data and the actual market price feedback data, calculate the current state of charge deviation and price prediction deviation, and use the state of charge deviation to dynamically correct the target value of the state of charge, including: The state of charge (SOC) deviations from the start of the scheduling cycle to the current time are arranged in chronological order to form a SOC deviation sequence. The rate of change between SOC deviations at adjacent times in the SOC deviation sequence is calculated. The evolution trend of the SOC deviations is determined based on the sign and magnitude of the rate of change. The correction response intensity level and response coefficient are determined based on the evolution trend. The absolute value of the price prediction deviation is calculated as the ratio of the historical average of the price prediction deviation within the scheduling period to obtain the price volatility relative strength index. The price volatility relative strength index is then subjected to a nonlinear mapping transformation to map the price volatility relative strength index to a preset sensitivity coefficient range to obtain the price volatility sensitivity coefficient. Obtain the response coefficient corresponding to the corrected response intensity level, perform weighted fusion calculation on the response coefficient and the price fluctuation sensitivity coefficient to obtain a comprehensive correction intensity factor, use the comprehensive correction intensity factor to correct the current state of charge deviation to obtain the correction range of the target value of the state of charge; add the correction range of the target value of the state of charge to the target value of the state of charge corresponding to the current time to obtain the corrected target value of the state of charge. Simultaneously, the method for identifying the price peak period is adaptively adjusted using the price prediction deviation to complete the optimized scheduling of charging and discharging timing.

2. The method according to claim 1, characterized in that, Local extreme points in the electricity price time series data are identified. Based on these local extreme points, an upper and lower price envelope are constructed and their mean values ​​are calculated. Oscillating components are iteratively removed to obtain the trend components of electricity price changes, including: The electricity price time series data is traversed in time series, and the moment when the price change direction changes in adjacent time moments is taken as the local extreme point. The local extreme point is then divided into a set of local maximum points and a set of local minimum points. Interpolation is performed on the time axis to connect the sets of local maxima and local minima respectively, constructing an upper price envelope and a lower price envelope; the average values ​​of the upper and lower price envelopes at each time point are calculated to obtain the intermediate trend component of the current iteration period; the difference between the electricity price time series data and the intermediate trend component is calculated to obtain the residual component of the current iteration period. The oscillation amplitude of the residual component is calculated. When the oscillation amplitude is higher than the preset oscillation threshold, the residual component is used as the input data for the next iteration cycle to calculate the intermediate trend component for the next iteration cycle. The iteration continues until the oscillation amplitude is lower than the preset oscillation threshold, and the intermediate trend component of the current iteration cycle is determined as the trend component of the electricity price change.

3. The method according to claim 1, characterized in that, Identifying price peak periods from the trend components and establishing a correspondence between the target value of state of charge and the period identifier, in conjunction with the physical constraint parameters, includes: Identify continuous time intervals in the trend components where the price value exceeds the average price level, and mark them as candidate high-price periods; verify the duration of the candidate high-price periods, and determine the periods whose duration meets the minimum charge and discharge duration requirement of the energy storage system in the physical constraint parameters as price peak periods; The maximum state of charge (SOC) value of the energy storage system in the physical constraint parameters is set as the target SOC value corresponding to the start time of the price peak period, and a discharge period identifier is assigned to the price peak period; for the transition period between the price peak periods, linear interpolation is performed based on the target SOC values ​​corresponding to adjacent price peak periods to obtain the target SOC values ​​corresponding to each time of the transition period, and a transition period identifier is assigned to the transition period. The target state of charge and time period identifiers corresponding to each moment of the price peak period and the transition period are summarized and organized into associated data records. The associated data records are stored in chronological order to form a correspondence between the target state of charge and the time period identifier.

4. The method according to claim 1, characterized in that, Based on the correspondence and the power boundary constraints of the physical constraint parameters, the state of charge trajectory of the state of charge data gradually converges to the target value of the state of charge along the time axis, resulting in charging and discharging power scheduling instructions at each time point, including: Obtain the actual state of charge value, extract the target state of charge value and time period identifier corresponding to the current time from the correspondence, and calculate the state deviation between the target state of charge value and the actual state of charge value; Based on the state deviation and the time interval from the current moment to the next moment, calculate the theoretical charging and discharging power required to converge the actual state of charge value to the target state of charge value. The maximum charging power limit and maximum discharging power limit of the energy storage system are obtained from the physical constraint parameters; the theoretical charging and discharging power is subjected to power boundary constraint processing; when the absolute value of the theoretical charging and discharging power exceeds the maximum charging power limit or the maximum discharging power limit, the theoretical charging and discharging power is truncated to the corresponding power boundary value to obtain the constrained charging and discharging power at the current moment; the constrained charging and discharging power is used as the charging and discharging power scheduling command at the current moment. Based on the constrained charging and discharging power and the time interval from the current moment to the next moment, the predicted state of charge value for the next moment is calculated, and the predicted state of charge value is updated in the state of charge data as the actual state of charge value for the next moment; this process is repeated iteratively for subsequent moments until the charging and discharging power scheduling instructions for all moments within the scheduling period are generated.

5. The method according to claim 1, characterized in that, Simultaneously, the method for identifying the price peak period is adaptively adjusted using the price prediction deviation, including: The identification result of the price peak period is used as the random variable to be inferred, and the price prediction deviation is used as observation evidence. A prior probability distribution of the price peak period identification result is established, and the likelihood value is calculated. The prior probability distribution and the likelihood value are fused using Bayesian method to obtain the posterior probability distribution. The relative entropy between the prior probability distribution and the posterior probability distribution is calculated. The maximum posterior probability value in the posterior probability distribution is calculated as the confidence value of the price peak period identification result; when the confidence value is lower than the preset confidence threshold and the relative entropy exceeds the preset relative entropy threshold, the weight of the prior probability distribution in the Bayesian fusion calculation is reduced and the Bayesian fusion calculation is re-executed, and a multi-candidate set of price peak periods is constructed based on the updated posterior probability distribution. Calculate the time period overlap between the identification results of each candidate price peak period in the multi-candidate set, and mark the time period overlap that is lower than the overlap threshold as the low confidence price peak period; adjust the state of charge target value setting for the low confidence price peak period, and update the correspondence between the state of charge target value and the time period identifier based on the adjusted state of charge target value setting.

6. A time-series optimization scheduling system for energy storage charging and discharging based on maximizing returns, used to implement the method as described in any one of claims 1-5, characterized in that, include: The first unit is used to acquire time-series data on electricity prices, state-of-charge data of energy storage systems, and physical constraint parameters. The second unit is used to identify local extreme points in the electricity price time series data, construct an upper price envelope and a lower price envelope based on the local extreme points and perform mean calculation, iteratively remove oscillation components, and obtain the trend component of electricity price changes. Identify price peak periods from the trend components and, in conjunction with the physical constraint parameters, establish a correspondence between the target value of the state of charge and the period identifier. The third unit is used to make the state of charge trajectory of the state of charge data gradually converge to the target value of the state of charge along the time axis based on the correspondence and the power boundary constraints of the physical constraint parameters, so as to obtain the charging and discharging power scheduling instructions at each time. The fourth unit is used to control the energy storage system to perform charging and discharging actions based on the charging and discharging power scheduling command, and to collect the actual state of charge feedback data and actual market price feedback data of the energy storage system in real time during the execution process. The fifth unit is used to calculate the current state of charge deviation and price prediction deviation based on the actual state of charge feedback data and the actual market price feedback data, and to dynamically correct the target value of the state of charge using the state of charge deviation. At the same time, it uses the price prediction deviation to adaptively adjust the identification method of the price peak period, thereby completing the optimized scheduling of charging and discharging timing.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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