Energy storage system configuration method and device, electronic equipment and storage medium

Abstract

The application discloses a configuration method and device of an energy storage system, electronic equipment and a storage medium, and belongs to the technical field of energy management. The method comprises the following steps: acquiring a load time sequence of a target area in a preset period; performing fluctuation analysis on the load time sequence to extract fluctuation characteristic parameters; performing multi-time scale decomposition on the load time sequence according to the fluctuation characteristic parameters to obtain multi-time scale components; determining rated power demand and rated capacity demand of the energy storage system based on the multi-time scale components; and configuring the energy storage system according to the rated power demand and the rated capacity demand. Through fluctuation characteristic extraction and multi-time scale decomposition on load data, the optimal power capacity and energy capacity of the energy storage system are determined, so that a more optimal fluctuation suppression effect and higher configuration rationality are realized, and the accuracy and economy of energy storage configuration are improved.

Description

Technical Field

[0001] This invention belongs to the field of energy management technology, and specifically relates to a configuration method for an energy storage system, a configuration device for an energy storage system, an electronic device, and a computer-readable storage medium. Background Technology

[0002] With the large-scale integration of new energy power generation, distributed power sources, electric vehicle charging facilities, and various flexible loads, the load fluctuation characteristics of the power system are becoming increasingly complex, manifesting as increased peak-to-valley differences, frequent short-term fluctuations, and significant localized impact loads. These changes place higher demands on the safe operation of the distribution network, the improvement of power supply quality, and the optimization of energy efficiency.

[0003] Energy storage systems have become a key supporting equipment in the construction of new power systems due to their advantages such as peak shaving and valley filling, smoothing fluctuations, improving power quality, enhancing the absorption capacity of new energy sources, and increasing power supply reliability. However, the initial investment cost of energy storage systems is relatively high, and their configuration effect is closely related to capacity selection and power matching. Therefore, how to achieve a scientific and reasonable energy storage configuration has become a key issue in current research and engineering applications.

[0004] Existing energy storage configuration methods typically rely on typical daily load curves, maximum load values, average load levels, or empirical formulas to statically design energy storage capacity. These methods do not adequately extract the fluctuation characteristics in the load curve, often focusing only on the difference between peak and trough values, failing to effectively reflect the randomness of the load, resulting in discrepancies between the energy storage configuration results and actual demand. Summary of the Invention

[0005] The purpose of this invention is to provide a configuration method for an energy storage system, a configuration device for an energy storage system, an electronic device, and a corresponding computer-readable storage medium, which can solve the problem of power and capacity mismatch caused by static configuration based solely on peak-valley differences or typical daily curves in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention is implemented as follows: In a first aspect, embodiments of the present invention provide a method for configuring an energy storage system, the method comprising: Obtain the load time series of the target area within a preset period; Fluctuation analysis was performed on the load time series to extract fluctuation characteristic parameters; Based on the fluctuation characteristic parameters, the load time series is decomposed into multiple time scales to obtain multiple time scale components; Based on the multi-timescale components, the rated power requirement and rated capacity requirement of the energy storage system are determined; Configure the energy storage system according to the rated power requirement and the rated capacity requirement.

[0007] Optionally, the multi-timescale components include short-timescale components and long-timescale components; The step of performing multi-time-scale decomposition on the load time series based on the fluctuation characteristic parameters to obtain multi-time-scale components includes: Based on the fluctuation characteristic parameters, determine the parameters for multi-timescale decomposition; Based on the determined parameters of the multi-timescale decomposition, the load time series is decomposed into short-timescale components and long-timescale components; the short-timescale components represent rapidly fluctuating demand, and the long-timescale components represent continuous energy transfer demand. The fluctuation characteristic parameters include at least one of the following: load change at adjacent time points, fluctuation amplitude, fluctuation frequency, fluctuation duration, load ramp rate, peak-to-valley difference, standard deviation, fluctuation energy demand, and fluctuation probability distribution characteristics.

[0008] Optionally, determining the rated power demand and rated capacity demand of the energy storage system based on the multi-timescale components includes: Based on the short-timescale components, the rated power requirement of the energy storage system is determined; Based on the aforementioned long-time scale components, the rated capacity requirement of the energy storage system is determined.

[0009] Optionally, determining the rated power requirement of the energy storage system based on the short-timescale component includes: Determine the desired power output curve; The energy storage compensation power sequence is determined based on the difference between the expected power output curve and the original load time series; the energy storage compensation power sequence includes multiple energy storage compensation powers. Determine the confidence level of the plurality of energy storage compensation powers; The rated power requirement of the energy storage system is determined based on the multiple energy storage compensation powers and the corresponding confidence levels.

[0010] Optionally, determining the rated capacity requirement of the energy storage system based on the long-time scale component includes: Based on the difference between the expected output power curve and the original load curve, the charging and discharging requirements of the energy storage system at each moment are determined. The charging and discharging energy within a preset period is cumulatively statistically analyzed to determine the maximum energy variation range of the energy storage system. Based on preset constraints, the maximum energy variation range of the energy storage system is adjusted to obtain the rated energy capacity of the energy storage system.

[0011] Optionally, configuring the energy storage system according to the rated power requirement and the rated capacity requirement includes: An energy storage configuration optimization model is constructed using the rated power demand and the rated energy capacity demand as variables; Based on the preset optimization objectives, the energy storage configuration optimization model is solved under preset constraints to obtain the corrected rated power demand and rated energy capacity demand; the preset optimization objectives include at least one of load fluctuation smoothing effect, peak shaving and valley filling effect, investment cost, operating cost or energy storage life loss. The energy storage system is configured according to the revised rated power requirement and the rated energy capacity requirement.

[0012] Optionally, the preset constraint conditions include at least one of the following: Constraints include upper limit of energy storage power, upper limit of energy storage capacity, upper and lower limits of state of charge, charging and discharging efficiency, charging and discharging rate, response time, cycle life, and operational safety constraints for the target scenario.

[0013] Secondly, embodiments of the present invention provide an apparatus for configuring an energy storage system, the apparatus comprising: The acquisition module is used to acquire the load time series of the target area within a preset period; The analysis module is used to perform fluctuation analysis on the load time series and extract fluctuation characteristic parameters; The decomposition module is used to perform multi-time-scale decomposition on the load time series based on the fluctuation characteristic parameters to obtain multi-time-scale components. The determination module is used to determine the rated power requirement and rated capacity requirement of the energy storage system based on the multi-timescale components. A configuration module is used to configure the energy storage system according to the rated power requirement and the rated capacity requirement.

[0014] Optionally, the multi-timescale components include short-timescale components and long-timescale components; The decomposition module includes: The first determining submodule is used to determine the parameters for multi-timescale decomposition based on the fluctuation characteristic parameters; The decomposition submodule is used to perform multi-timescale decomposition on the load time series according to the determined parameters of the multi-timescale decomposition, and decompose it into short-timescale components and long-timescale components; the short-timescale components represent rapid fluctuation demand, and the long-timescale components represent continuous energy transfer demand. The fluctuation characteristic parameters include at least one of the following: load change at adjacent time points, fluctuation amplitude, fluctuation frequency, fluctuation duration, load ramp rate, peak-to-valley difference, standard deviation, fluctuation energy demand, and fluctuation probability distribution characteristics.

[0015] Optionally, the determining module includes: The second determining submodule is used to determine the rated power requirement of the energy storage system based on the short time scale component. The third determining submodule is used to determine the rated capacity requirement of the energy storage system based on the long-time scale component.

[0016] Optionally, the second determining submodule includes: The first determining unit is used to determine the desired power output curve; The second determining unit is used to determine the energy storage compensation power sequence based on the difference between the expected power output curve and the original load time series; the energy storage compensation power sequence includes multiple energy storage compensation powers. The third determining unit is used to determine the confidence level of the plurality of energy storage compensation powers; The fourth determining unit is used to determine the rated power requirement of the energy storage system based on the plurality of energy storage compensation powers and the corresponding confidence levels.

[0017] Optionally, the third determining submodule includes: The fifth determining unit is used to determine the charging and discharging requirements of the energy storage system at each moment based on the difference between the expected output power curve and the original load curve. The sixth determining unit is used to accumulate and statistically analyze the charging and discharging energy within a preset period to determine the maximum energy variation range of the energy storage system. The seventh determining unit is used to adjust the maximum energy variation range of the energy storage system according to preset constraints to obtain the rated energy capacity of the energy storage system.

[0018] Optionally, the configuration module includes: A submodule is constructed to build an energy storage configuration optimization model using the rated power requirement and the rated energy capacity requirement as variables. The solution submodule is used to solve the energy storage configuration optimization model under preset constraints according to preset optimization objectives to obtain the corrected rated power demand and rated energy capacity demand; the preset optimization objectives include at least one of load fluctuation smoothing effect, peak shaving and valley filling effect, investment cost, operating cost or energy storage life loss. A configuration submodule is used to configure the energy storage system according to the revised rated power requirement and rated energy capacity requirement.

[0019] Optionally, the preset constraint conditions include at least one of the following: Constraints include upper limit of energy storage power, upper limit of energy storage capacity, upper and lower limits of state of charge, charging and discharging efficiency, charging and discharging rate, response time, cycle life, and operational safety constraints for the target scenario.

[0020] Thirdly, embodiments of the present invention provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0021] Fourthly, embodiments of the present invention provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0022] The embodiments of the present invention have the following advantages: This invention provides a method for configuring an energy storage system, comprising: acquiring a load time series of a target area within a preset period; performing fluctuation analysis on the load time series to extract fluctuation characteristic parameters; decomposing the load time series into multiple time scales based on the fluctuation characteristic parameters to obtain multiple time scale components; determining the rated power demand and rated capacity demand of the energy storage system based on the multiple time scale components; and configuring the energy storage system according to the rated power demand and rated capacity demand. This invention, by extracting fluctuation characteristics from load data and decomposing it into multiple time scales, determines the optimal power capacity and energy capacity of the energy storage system, thereby achieving better fluctuation suppression and higher configuration rationality. It solves the problem of power and capacity mismatch caused by static configuration based solely on peak-valley differences or typical daily curves in existing technologies, improving the accuracy and economy of energy storage configuration. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the steps of a configuration method for an energy storage system provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a configuration device for an energy storage system provided in an embodiment of the present invention. Detailed Implementation

[0024] 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, not all, of the embodiments of the present invention. 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.

[0025] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0026] The following description, in conjunction with the accompanying drawings, details a configuration method for an energy storage system, a configuration device for an energy storage system, an electronic device, and a computer-readable storage medium provided by the embodiments of the present invention through specific examples and application scenarios.

[0027] Reference Figure 1 The diagram illustrates a step flowchart of a configuration method for an energy storage system provided by an embodiment of the present invention. The method may specifically include the following steps: Step 101: Obtain the load time series of the target area within a preset period; This embodiment uses an industrial park as an application scenario. The target area is the power load area where energy storage configuration is to be carried out, which can be a power distribution network, industrial users, park energy systems, microgrids, or integrated energy systems with new energy access. This embodiment focuses on an industrial park that includes multiple production lines, air compressor systems, lighting systems, and centralized charging stations for electric vehicles, among other power loads.

[0028] The preset period is determined based on load variation patterns and energy storage configuration requirements, and should generally cover the complete production cycle or electricity consumption cycle to fully reflect the load variation characteristics at different times. In this embodiment, the preset period is 30 consecutive days, which can cover the complete production shift cycle of the park, including weekdays, rest days, and load changes under different production task schedules.

[0029] The sampling interval for load data is determined based on the time-scale characteristics of load fluctuations and the energy storage response capability. An excessively large sampling interval will result in the loss of short-term, rapid fluctuation information, while an excessively small sampling interval will increase data acquisition costs and subsequent processing complexity. In this embodiment, considering the load characteristics of the park, such as the start-up and shutdown of large motors (seconds to minutes) and the intermittent operation of the air compressor system (minutes), the sampling interval is set to 5 minutes, forming a load time series P(t), t=1,2,…,T, where T is the total number of sampling points within the preset period.

[0030] The data to be collected includes, but is not limited to: total active load data of the park; operating status data of major production equipment; time-of-use electricity pricing information; and distributed photovoltaic power output data of the park. Data can be collected through the park's existing energy management system or smart meters.

[0031] It should be noted that the load data obtained in this step is the raw collected data, which may have data quality issues. It needs to be cleaned and standardized in subsequent preprocessing steps to obtain a standardized load sequence that can be used for fluctuation feature extraction and multi-timescale decomposition.

[0032] In one embodiment, preprocessing is required because the original collected data contains some missing points and abnormal spikes. The preprocessing process is as follows: missing data is filled in using the mean interpolation method of adjacent time periods; abnormal points that deviate significantly from the normal range are identified, and if the difference between a sampling point and the points before and after it exceeds a set threshold, the average value of adjacent time periods is used for replacement; the entire load sequence is aligned on the time axis to ensure consistent sampling periods; and the load sequence is smoothed and filtered as needed to reduce the impact of sampling noise on subsequent fluctuation analysis. After processing, the cleaned standard load sequence P(t) is obtained.

[0033] Step 102: Perform fluctuation analysis on the load time series and extract fluctuation characteristic parameters; Obtain the preprocessed standardized load time series, perform fluctuation analysis on the load time series, and identify and quantify the fluctuation pattern of the load value over time in the load time series from the time dimension.

[0034] Fluctuation characteristic parameters refer to quantitative indicators that can characterize the fluctuation pattern from different dimensions. These dimensions include at least: the intensity of the fluctuation, the frequency of the fluctuation, the duration of the fluctuation, and the rate of change of the fluctuation. These dimensions describe the characteristics of load fluctuation from different perspectives, and together constitute a multi-dimensional fluctuation characteristic description system to replace the single-dimensional evaluation method in the existing technology that only relies on the peak-to-valley difference or standard deviation.

[0035] By extracting the aforementioned multi-dimensional fluctuation characteristic parameters, a comprehensive understanding of the load fluctuations in the target area can be obtained, providing crucial information for subsequent steps. On the one hand, based on the intensity and frequency of fluctuations reflected by the fluctuation characteristic parameters, it can be determined whether multi-timescale decomposition of the load time series is necessary. That is, when the fluctuation characteristic parameters reach or exceed preset conditions, it indicates that there are both significant short-term impact fluctuations and long-term trend fluctuations in the load, making multi-timescale decomposition necessary. On the other hand, based on the statistical characteristics such as the duration and rate of change of fluctuations reflected by the fluctuation characteristic parameters, a data-driven basis can be provided for selecting specific decomposition parameters for multi-timescale decomposition (such as decomposition window length, number of decomposition layers, etc.), enabling the decomposition process to adaptively match the load's own fluctuation patterns rather than relying on manual experience.

[0036] Step 103: Based on the fluctuation characteristic parameters, decompose the load time series into multiple time scales to obtain multiple time scale components; Multi-timescale decomposition refers to the process of separating the fluctuations at different time scales that are superimposed on the original load time series based on the characteristics of load fluctuations. Its purpose is to decompose complex load fluctuations into components with different rates of change, each corresponding to a different type of regulation demand that the energy storage system needs to undertake. Specifically, the actual load curve is usually formed by the combined effects of multiple factors, including high-frequency fluctuations that change rapidly and have short durations, as well as low-frequency fluctuations that change slowly and have long durations.

[0037] There are various ways to implement the multi-timescale decomposition, such as moving average decomposition, empirical mode decomposition, wavelet decomposition, and Fourier frequency domain decomposition. Regardless of the decomposition method used, their common feature is that the scale division criteria for decomposition are determined based on the load fluctuation pattern reflected by the fluctuation characteristic parameters, so that the two components after decomposition are distinguishable in the time scale or frequency domain, thereby providing basic data with clear physical meaning for subsequently determining the rated power demand and rated capacity demand respectively.

[0038] Through the above decomposition steps, the technical objective of mapping the power configuration and energy configuration of energy storage to different types of load fluctuations has been achieved, laying a physical foundation for subsequent quantitative configuration.

[0039] In one embodiment, the multi-timescale components include short-timescale components and long-timescale components; step 103 may include the following sub-steps: The actual load curve is composed of fluctuations at multiple time scales. Short-term fluctuations are typically caused by factors such as equipment start-up and shutdown, and intermittent load switching; these fluctuations are rapid and short-lived, demanding rapid charging and discharging power response from energy storage systems. Long-term fluctuations are typically caused by factors such as shift changes and day-night electricity consumption differences; these fluctuations are slow and long-lasting, demanding continuous energy support from energy storage systems. Failure to differentiate between these two types of fluctuations and a haphazard configuration can easily lead to misalignment between power and capacity allocation. Therefore, this step decomposes the load time series into multiple time scales based on the fluctuation characteristic parameters extracted in the previous steps, obtaining short-term and long-term components.

[0040] Specifically, the short-timescale component reflects rapid load changes over a short period (e.g., minutes to tens of minutes), mainly caused by factors such as equipment start-up and shutdown, and intermittent load switching. This component fluctuates around zero or a reference value, characterizing the rapid charging and discharging power regulation requirements of the energy storage system. The long-timescale component reflects the load change trend over a longer period (e.g., tens of minutes to hours), mainly caused by factors such as shift changes and differences in electricity consumption between day and night. This component reflects the overall energy transfer trend of the load, characterizing the continuous energy support requirements of the energy storage system.

[0041] Sub-step S11: Determine the parameters for multi-timescale decomposition based on the fluctuation characteristic parameters; Based on the fluctuation characteristic parameters extracted in the preceding steps, a reasonable parameter value is selected or calculated for multi-timescale decomposition, ensuring that the decomposition scale matches the fluctuation characteristics of the load itself. The selection of these fluctuation characteristic parameters includes, but is not limited to: fluctuation duration, fluctuation frequency, fluctuation amplitude, and ramp rate. Among these, fluctuation duration is the key basis for determining the decomposition parameters.

[0042] The purpose of decomposition is to separate fluctuations at different time scales, so that short-term rapid fluctuations are fully preserved in the short-term time scale component, while long-term slow changes are attributed to the long-term time scale component. Therefore, the selection of decomposition parameters should be adapted to the fluctuation characteristics of the load itself.

[0043] Specifically, this step determines the decomposition parameters based on the statistical distribution characteristics of the fluctuation duration.

[0044] Taking moving average decomposition as an example: First, statistical analysis is performed on the duration of fluctuations to obtain their probability distribution. Statistical analysis shows that most short-term fluctuations in the industrial park load in this embodiment (such as power spikes caused by motor start-stop and instantaneous power changes caused by intermittent air compressor loading) have durations concentrated in the range of 5 to 15 minutes, with a few fluctuations lasting longer than 30 minutes. Therefore, the window length for the moving average is determined to be 30 minutes, making this window length greater than the typical duration of most short-term fluctuations. Thus, when a moving average is performed on the original load sequence using this window, fluctuations with durations shorter than the window length will be effectively filtered out and assigned to the short-timescale component; trends with durations longer than the window length will be retained in the long-timescale component.

[0045] If wavelet decomposition is used, the number of decomposition levels can be determined based on the fluctuation frequency range; if empirical mode decomposition is used, eigenfunctions with physical meaning can be selected based on the fluctuation amplitude distribution characteristics. Parameter determination under different decomposition methods follows the same principle: based on the statistical results of the fluctuation characteristic parameters, the decomposition scale is matched to the fluctuation characteristics of the load itself.

[0046] Sub-step S12: Based on the determined parameters of the multi-timescale decomposition, the load time series is decomposed into short-timescale components and long-timescale components; the short-timescale components represent rapidly fluctuating demand, and the long-timescale components represent continuous energy transfer demand; the fluctuation characteristic parameters include at least one of the following: load change at adjacent times, fluctuation amplitude, fluctuation frequency, fluctuation duration, load ramp rate, peak-to-valley difference, standard deviation, fluctuating energy demand, and fluctuation probability distribution characteristics.

[0047] In this embodiment, the load time series is decomposed into short-time-scale components and long-time-scale components according to the determined parameters of the multi-time-scale decomposition.

[0048] It should be noted that the fluctuation characteristic parameters used in this step include, but are not limited to, at least one of the following: load change at adjacent time points, fluctuation amplitude, fluctuation frequency, fluctuation duration, load ramp-up rate, peak-to-valley difference, standard deviation, fluctuation energy demand, and fluctuation probability distribution characteristics. These parameters collectively characterize the load fluctuation patterns in the target area from different dimensions, providing a quantitative input basis and parameter setting foundation for the multi-timescale decomposition in this step.

[0049] In this embodiment of the invention, a moving average decomposition method is used to perform a moving average on the original load sequence P(t) over a relatively long time window W (e.g., W=12, corresponding to 60 minutes, i.e., 12 five-minute sampling intervals) to obtain the long-time scale component P. s (t). Then, subtracting the long-time-scale component from the original load sequence yields the short-time-scale component P. f (t), the specific formula is as follows:

[0050] Among them, the long-time scale component P s (t) reflects load changes over a long timescale and is used to determine energy storage capacity demand; the short-timescale component P f (t) reflects the fluctuation characteristics on a short time scale and is used to determine the energy storage power demand. As for the specific implementation method of multi-timescale decomposition and the specific determination process of decomposition parameters, they can be adaptively selected according to the actual load characteristics of the target scenario, and this invention does not impose specific limitations on them.

[0051] Step 104: Based on the multi-timescale components, determine the rated power requirement and rated capacity requirement of the energy storage system; In one embodiment, step 104 may include the following sub-steps: Sub-step S21: Determine the rated power requirement of the energy storage system based on the short time scale component; In one embodiment, sub-step S21 may include the following sub-steps: Sub-step S211: Determine the desired output power curve; To mitigate short-term fluctuations, it is necessary to first define the desired power profile delivered to the grid after energy storage regulation. In this embodiment, a smooth desired power output curve P is obtained by applying a longer-window moving average to the original load sequence. ref (t). This curve reflects the slow changing trend that the load should exhibit after removing short-term rapid fluctuations. In other embodiments, the expected output power curve may also be a constant power value, a time-of-use constant value, or a planned curve issued by the dispatching agency; this invention is not limited to these.

[0052] Sub-step S212: Determine the energy storage compensation power sequence based on the difference between the expected output power curve and the original load time series; the energy storage compensation power sequence includes multiple energy storage compensation powers. For each sampling time t, the difference between the expected transmitted power and the original load power is calculated using the following formula:

[0053] Among them, P es (t) represents the energy storage compensation power sequence. P es (t)>0 indicates that the energy storage needs to be discharged to compensate for the load gap, P es (t)<0 indicates that the energy storage needs to be charged to absorb the load surplus. Since P es (t) has already had short-timescale fluctuations removed by moving average, therefore the difference P es (t) primarily reflects the demand for energy storage power regulation from short-timescale fluctuation components. Thus, the rapid fluctuation components are transformed into a specific compensation power sequence that energy storage needs to undertake.

[0054] Sub-step S213: Determine the confidence level of the plurality of energy storage compensation powers; Get P for the entire time period es After the (t) sequence, take the absolute value of the compensation power at each time step |P es (t)|, forming an energy storage power demand dataset. This embodiment does not use the absolute maximum value in this dataset as the rated power because absolute maximum values ​​are often caused by isolated, extreme operating conditions. If configured in this way, the energy storage system would operate at a low load rate for most of the time, resulting in wasted investment. Therefore, this embodiment introduces a statistical confidence interval method for screening. The confidence level refers to the percentage of load fluctuation events that the configured energy storage power can handle, based on the fluctuation patterns statistically derived from historical data.

[0055] Sub-step S214: Determine the rated power requirement of the energy storage system based on the multiple energy storage compensation powers and the corresponding confidence levels.

[0056] | P es The (t)| sequence is sorted from largest to smallest. The quantile value of this sequence at a preset information level α is taken as the rated power of the energy storage system, that is:

[0057] The confidence level α can be set according to the actual needs of the scenario. In this embodiment, α is set to 95%, indicating that the configured rated power can cover 95% of fluctuation events. For scenarios with higher power quality requirements, α can be set to 98% or higher; for scenarios that are more cost-sensitive, α can be appropriately reduced.

[0058] Taking the 30-day load data of the industrial park in this embodiment as an example, a total of 8640 sampling points were obtained for compensation power values. After sorting, the power value corresponding to the 95th quantile was 350kW, while the absolute maximum value in all data was 620kW. If configured with 620kW, the equipment cost would be about 40% higher, but this extreme peak only occurs once in the entire cycle and lasts for less than 5 minutes. Using a 350kW configuration can effectively cope with the vast majority of fluctuation events, significantly improving the economic efficiency of the configuration.

[0059] This embodiment establishes a quantitative mapping relationship between the power configuration of energy storage and the rapid fluctuation characteristics of the load, so that the configuration result can effectively smooth out fluctuations and avoid over-configuration caused by occasional extreme peaks.

[0060] Sub-step S22: Determine the rated capacity requirement of the energy storage system based on the long-time scale component.

[0061] In one embodiment, sub-step S22 may include the following sub-steps: Sub-step S221: Determine the charging and discharging requirements of the energy storage system at each moment based on the difference between the expected output power curve and the original load curve. Based on the difference between the expected power output curve and the original load curve, the charging and discharging power requirements of the energy storage system at each time point are determined. Specifically, when the load is lower than the expected power, the energy storage system needs to release energy to make up for the difference; when the load is higher than the expected power, the energy storage system needs to absorb energy to reduce the peak value.

[0062] Sub-step S222: Accumulate and statistically analyze the charging and discharging energy within the preset period to determine the maximum energy variation range of the energy storage system; Based on the determined charging and discharging power at each moment, the energy changes over the entire analysis period are cumulatively statistically analyzed to obtain the maximum energy change range that the energy storage system must withstand during continuous adjustment. This energy change range characterizes the minimum energy throughput capacity that the energy storage system should possess to meet long-term energy transfer requirements.

[0063] Sub-step S223: Adjust the maximum energy variation range of the energy storage system according to preset constraints to obtain the rated energy capacity of the energy storage system.

[0064] The maximum energy variation range corresponds to the energy throughput demand under ideal conditions. In practical engineering applications, it is necessary to adjust and recalculate based on the actual constraints of the energy storage system to obtain the rated energy capacity of the energy storage system. The preset constraints include at least: the allowable state of charge operating range of the energy storage system, the charge and discharge efficiency, and the reserved safety margin.

[0065] Specifically, based on the permissible state of charge (SOC) operating range, the maximum energy variation range is converted into an equivalent capacity requirement within the available SOC range; based on the charge and discharge efficiency, the converted capacity requirement is corrected to compensate for energy losses during charging and discharging; based on the safety margin, the corrected capacity requirement is amplified to ensure that regulation requirements can still be met under extreme operating conditions. The final rated energy capacity of the energy storage system is then obtained.

[0066] To further improve the adaptability of the configuration results, the above capacity calculations can be performed based on multiple typical operating conditions, and the capacity value that can meet the preset coverage requirements can be selected as the final configuration result.

[0067] Step 105: Configure the energy storage system according to the rated power requirement and the rated capacity requirement.

[0068] To further improve the overall performance of the configuration results, an energy storage configuration optimization model was constructed to verify and correct the above preliminary results.

[0069] In one embodiment, step 105 may include the following sub-steps: Sub-step S31: Construct an energy storage configuration optimization model using the rated power demand and the rated energy capacity demand as variables; Using the initially determined rated power demand and rated energy capacity demand as initial variables, an optimization model is established within their neighborhood. The optimization model uses the rated power and rated energy capacity of the energy storage system as decision variables. This embodiment employs a multi-objective combination approach to construct the objective function of the optimization model, comprehensively considering at least one of the following optimization objectives: load fluctuation mitigation effect objective, represented by minimizing the fluctuation degree of the grid-connected power after energy storage regulation; peak shaving and valley filling effect objective, represented by maximizing the load peak-valley difference reduction rate or the smoothness of the equivalent load; investment cost and operating cost objective, represented by minimizing the combined investment cost and operation and maintenance cost of the energy storage system; and energy storage lifespan degradation objective, represented by minimizing the impact of energy storage charge-discharge cycles on lifespan degradation. The above objectives can be combined into a composite objective function through a weighted approach, with the weight coefficients adjusted according to the emphasis of the target scenario.

[0070] Sub-step S32: Based on the preset optimization objective, solve the energy storage configuration optimization model under preset constraints to obtain the corrected rated power demand and rated energy capacity demand; the preset optimization objective includes at least one of load fluctuation smoothing effect, peak shaving and valley filling effect, investment cost, operating cost or energy storage life loss; During the optimization process, the following constraints are applied to ensure the technical feasibility and operational safety of the configuration scheme: energy storage power constraint, stipulating that the energy storage charging and discharging power shall not exceed the rated power; energy storage capacity constraint, stipulating that the stored energy must be within the allowable state of charge range; energy storage state of charge recursion constraint, considering the impact of charging and discharging efficiency on energy changes; charge / discharge rate constraint, linked to the load ramp-up rate extracted from the aforementioned fluctuation characteristic analysis, ensuring that the energy storage has sufficient instantaneous response capability; response time constraint, ensuring that the energy storage can track rapid load fluctuations; energy storage cycle life constraint, ensuring that the configuration scheme can operate sustainably within its design service life; and target scenario operational safety constraints, including but not limited to transformer capacity constraints and grid connection point operational constraints. Under the above constraints, an optimization algorithm is used to solve the optimization model.

[0071] Sub-step S33: Configure the energy storage system according to the revised rated power requirement and rated energy capacity requirement.

[0072] After solving the problem, the corrected rated power and rated energy capacity requirements are obtained. The optimized results are compared with the preliminary configuration results: if the deviation is within the preset engineering tolerance range, the optimized result is adopted as the final configuration scheme; if the deviation exceeds the tolerance range, the process returns to the aforementioned fluctuation feature extraction or fluctuation decomposition steps, and after adjustment, the above process is repeated until a stable and convergent configuration scheme is obtained. Based on the finally determined rated power and rated energy capacity requirements, and in conjunction with the energy storage equipment selection requirements, the configuration of the energy storage system is completed.

[0073] In one embodiment, the preset constraint conditions include at least one of the following: Constraints include upper limit of energy storage power, upper limit of energy storage capacity, upper and lower limits of state of charge, charging and discharging efficiency, charging and discharging rate, response time, cycle life, and operational safety constraints for the target scenario.

[0074] The aforementioned constraints are used to ensure the rationality of the energy storage configuration scheme in terms of technical feasibility and operational safety. By applying these constraints to the optimization model, the rated power, rated capacity, and operating parameters of the energy storage can be verified and corrected based on the preliminary configuration scheme, thereby obtaining the final configuration scheme that meets the requirements for load fluctuation smoothing while taking into account equipment safety.

[0075] It should be noted that the configuration method for an energy storage system provided in this embodiment of the invention can be executed by an energy storage system configuration device, or a control module within the energy storage system configuration device for executing the method of loading the configuration of the energy storage system. This embodiment of the invention uses the execution of the method of loading the configuration of the energy storage system by the energy storage system configuration device as an example to illustrate the configuration method for an energy storage system provided in this embodiment of the invention.

[0076] This invention provides a method for configuring an energy storage system, including: acquiring a load time series of a target area within a preset period; performing fluctuation analysis on the load time series to extract fluctuation characteristic parameters; decomposing the load time series into multiple time scales based on the fluctuation characteristic parameters to obtain multiple time scale components; determining the rated power demand and rated capacity demand of the energy storage system based on the multiple time scale components; and configuring the energy storage system according to the rated power demand and rated capacity demand. This invention, by extracting fluctuation characteristics from load data and decomposing it into multiple time scales, determines the optimal power capacity and energy capacity of the energy storage system, achieving better fluctuation suppression and higher configuration rationality. It solves the problem of power and capacity mismatch caused by static configuration based solely on peak-valley differences or typical daily curves in existing technologies, thus improving the accuracy and economy of energy storage configuration.

[0077] Reference Figure 2 The diagram illustrates a structural block diagram of a configuration device for an energy storage system provided in an embodiment of the present invention, which may specifically include the following modules: The acquisition module 201 is used to acquire the load time series of the target area within a preset period; Analysis module 202 is used to perform fluctuation analysis on the load time series and extract fluctuation characteristic parameters; The decomposition module 203 is used to perform multi-time-scale decomposition on the load time series according to the fluctuation characteristic parameters to obtain multi-time-scale components. The determination module 204 is used to determine the rated power requirement and rated capacity requirement of the energy storage system based on the multi-time scale components. Configuration module 205 is used to configure the energy storage system according to the rated power requirement and the rated capacity requirement.

[0078] In one embodiment, the multi-timescale components include short-timescale components and long-timescale components; The decomposition module includes: The first determining submodule is used to determine the parameters for multi-timescale decomposition based on the fluctuation characteristic parameters; The decomposition submodule is used to perform multi-timescale decomposition on the load time series according to the determined parameters of the multi-timescale decomposition, and decompose it into short-timescale components and long-timescale components; the short-timescale components represent rapid fluctuation demand, and the long-timescale components represent continuous energy transfer demand. The fluctuation characteristic parameters include at least one of the following: load change at adjacent time points, fluctuation amplitude, fluctuation frequency, fluctuation duration, load ramp rate, peak-to-valley difference, standard deviation, fluctuation energy demand, and fluctuation probability distribution characteristics.

[0079] In one embodiment, the determining module includes: The second determining submodule is used to determine the rated power requirement of the energy storage system based on the short time scale component. The third determining submodule is used to determine the rated capacity requirement of the energy storage system based on the long-time scale component.

[0080] In one embodiment, the second determining submodule includes: The first determining unit is used to determine the desired power output curve; The second determining unit is used to determine the energy storage compensation power sequence based on the difference between the expected power output curve and the original load time series; the energy storage compensation power sequence includes multiple energy storage compensation powers. The third determining unit is used to determine the confidence level of the plurality of energy storage compensation powers; The fourth determining unit is used to determine the rated power requirement of the energy storage system based on the plurality of energy storage compensation powers and the corresponding confidence levels.

[0081] In one embodiment, the third determining submodule includes: The fifth determining unit is used to determine the charging and discharging requirements of the energy storage system at each moment based on the difference between the expected output power curve and the original load curve. The sixth determining unit is used to accumulate and statistically analyze the charging and discharging energy within a preset period to determine the maximum energy variation range of the energy storage system. The seventh determining unit is used to adjust the maximum energy variation range of the energy storage system according to preset constraints to obtain the rated energy capacity of the energy storage system.

[0082] In one embodiment, the configuration module includes: A submodule is constructed to build an energy storage configuration optimization model using the rated power requirement and the rated energy capacity requirement as variables. The solution submodule is used to solve the energy storage configuration optimization model under preset constraints according to preset optimization objectives to obtain the corrected rated power demand and rated energy capacity demand; the preset optimization objectives include at least one of load fluctuation smoothing effect, peak shaving and valley filling effect, investment cost, operating cost or energy storage life loss. A configuration submodule is used to configure the energy storage system according to the revised rated power requirement and rated energy capacity requirement.

[0083] In one embodiment, the preset constraint conditions include at least one of the following: Constraints include upper limit of energy storage power, upper limit of energy storage capacity, upper and lower limits of state of charge, charging and discharging efficiency, charging and discharging rate, response time, cycle life, and operational safety constraints for the target scenario.

[0084] The configuration device for the energy storage system in this embodiment of the invention can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This embodiment of the invention does not impose specific limitations.

[0085] The configuration device for the energy storage system in this embodiment of the invention can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this embodiment of the invention does not impose specific limitations.

[0086] The configuration device for the energy storage system provided in this embodiment of the invention can achieve Figure 1 The various processes implemented by the configuration device of the energy storage system in the method embodiment will not be described again here to avoid repetition.

[0087] This invention provides a structural block diagram of an energy storage system configuration device, comprising: an acquisition module for acquiring the load time series of a target area within a preset period; an analysis module for performing fluctuation analysis on the load time series and extracting fluctuation characteristic parameters; a decomposition module for performing multi-time-scale decomposition on the load time series based on the fluctuation characteristic parameters to obtain multi-time-scale components; a determination module for determining the rated power demand and rated capacity demand of the energy storage system based on the multi-time-scale components; and a configuration module for configuring the energy storage system according to the rated power demand and the rated capacity demand. By extracting fluctuation characteristics and decomposing the load data at multiple time scales, the optimal power capacity and energy capacity of the energy storage system are determined, thereby achieving better fluctuation suppression and higher configuration rationality. This solves the problem of power and capacity mismatch caused by static configuration based solely on peak-valley differences or typical daily curves in the prior art, and improves the accuracy and economy of energy storage configuration.

[0088] This invention also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described energy storage system configuration method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0089] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0090] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0092] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A configuration method for an energy storage system, characterized in that, include: Obtain the load time series of the target area within a preset period; Fluctuation analysis was performed on the load time series to extract fluctuation characteristic parameters; Based on the fluctuation characteristic parameters, the load time series is decomposed into multiple time scales to obtain multiple time scale components; Based on the multi-timescale components, the rated power requirement and rated capacity requirement of the energy storage system are determined; Configure the energy storage system according to the rated power requirement and the rated capacity requirement.

2. The configuration method of the energy storage system according to claim 1, characterized in that, The multi-timescale components include short-timescale components and long-timescale components; The step of performing multi-time-scale decomposition on the load time series based on the fluctuation characteristic parameters to obtain multi-time-scale components includes: Based on the fluctuation characteristic parameters, determine the parameters for multi-timescale decomposition; Based on the determined parameters of the multi-timescale decomposition, the load time series is decomposed into short-timescale components and long-timescale components; the short-timescale components represent rapidly fluctuating demand, and the long-timescale components represent continuous energy transfer demand. The fluctuation characteristic parameters include at least one of the following: load change at adjacent time points, fluctuation amplitude, fluctuation frequency, fluctuation duration, load ramp rate, peak-to-valley difference, standard deviation, fluctuation energy demand, and fluctuation probability distribution characteristics.

3. The configuration method of the energy storage system according to claim 2, characterized in that, The determination of the rated power demand and rated capacity demand of the energy storage system based on the multi-timescale components includes: Based on the short-timescale components, the rated power requirement of the energy storage system is determined; Based on the aforementioned long-time scale components, the rated capacity requirement of the energy storage system is determined.

4. The configuration method of the energy storage system according to claim 3, characterized in that, Determining the rated power requirement of the energy storage system based on the short-timescale component includes: Determine the desired power output curve; The energy storage compensation power sequence is determined based on the difference between the expected power output curve and the original load time series; the energy storage compensation power sequence includes multiple energy storage compensation powers. Determine the confidence level of the plurality of energy storage compensation powers; The rated power requirement of the energy storage system is determined based on the multiple energy storage compensation powers and the corresponding confidence levels.

5. The configuration method of the energy storage system according to claim 4, characterized in that, Determining the rated capacity requirement of the energy storage system based on the long-time scale component includes: Based on the difference between the expected output power curve and the original load curve, the charging and discharging requirements of the energy storage system at each moment are determined. The charging and discharging energy within a preset period is cumulatively statistically analyzed to determine the maximum energy variation range of the energy storage system. Based on preset constraints, the maximum energy variation range of the energy storage system is adjusted to obtain the rated energy capacity of the energy storage system.

6. The configuration method of the energy storage system according to claim 1, characterized in that, The configuration of the energy storage system based on the rated power requirement and the rated capacity requirement includes: An energy storage configuration optimization model is constructed using the rated power demand and the rated energy capacity demand as variables; Based on the preset optimization objectives, the energy storage configuration optimization model is solved under preset constraints to obtain the corrected rated power demand and rated energy capacity demand; the preset optimization objectives include at least one of load fluctuation smoothing effect, peak shaving and valley filling effect, investment cost, operating cost or energy storage life loss. The energy storage system is configured according to the revised rated power requirement and the rated energy capacity requirement.

7. The configuration method of the energy storage system according to claim 6, characterized in that, The preset constraints include at least one of the following: Constraints include upper limit of energy storage power, upper limit of energy storage capacity, upper and lower limits of state of charge, charging and discharging efficiency, charging and discharging rate, response time, cycle life, and operational safety constraints for the target scenario.

8. A configuration device for an energy storage system, characterized in that, include: The acquisition module is used to acquire the load time series of the target area within a preset period; The analysis module is used to perform fluctuation analysis on the load time series and extract fluctuation characteristic parameters; The decomposition module is used to perform multi-time-scale decomposition on the load time series based on the fluctuation characteristic parameters to obtain multi-time-scale components. The determination module is used to determine the rated power requirement and rated capacity requirement of the energy storage system based on the multi-timescale components. A configuration module is used to configure the energy storage system according to the rated power requirement and the rated capacity requirement.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the configuration method for the energy storage system as described in claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the configuration method for the energy storage system as described in claims 1-7.

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