Energy storage power and capacity evaluation method, device and equipment of photovoltaic system and storage medium

Abstract

This disclosure provides a method, apparatus, equipment, and storage medium for assessing the energy storage power and capacity of a photovoltaic system. The method acquires and preprocesses power generation and load power data, and decomposes the preprocessed data into a power imbalance sequence containing a target deterministic component sequence and a target stochastic component sequence. Based on the power imbalance sequence, the energy storage power demand is determined; based on the target deterministic component sequence, the corresponding basic energy storage capacity is determined; and based on the target stochastic component sequence combined with preset guarantee level parameters, the corresponding stochastic energy storage capacity with random fluctuations is determined. The total energy storage capacity is determined by combining the two types of energy storage capacities. Energy storage configuration is then based on this, giving the energy storage capacity configuration a clear physical orientation. By explicitly incorporating the preset guarantee level parameters into the calculation of the stochastic energy storage capacity, a quantitative correlation between the reliability of the photovoltaic system and the energy storage capacity is established, allowing the assessment results to flexibly match the reliability requirements of different engineering scenarios.

Description

Technical Field

[0001] This disclosure relates to the field of energy storage capacity configuration technology, and in particular to a method, apparatus, equipment and storage medium for evaluating the energy storage power and capacity of a photovoltaic system. Background Technology

[0002] For renewable energy systems, such as photovoltaic systems, the physical interpretability and engineering applicability of their energy storage configuration are crucial, and the assessment of energy storage demand directly affects the corresponding energy storage configuration. In related technologies, when assessing energy storage demand, all fluctuations are often simply treated as random uncertainties, leading to unreasonable energy storage capacity configurations and low reliability. Summary of the Invention

[0003] This disclosure provides a method, apparatus, equipment, and storage medium for evaluating the energy storage power and capacity of a photovoltaic system, in order to at least solve the above-mentioned technical problems existing in the prior art.

[0004] A first aspect of this disclosure provides a method for evaluating the energy storage power and capacity of a photovoltaic system, the method comprising:

[0005] Acquire the power generation data and load power data of the photovoltaic system, and obtain the target power generation data and target load power data after corresponding preprocessing; Based on the decomposition of the target power generation data and the target load power data, a corresponding power imbalance sequence is obtained, which includes a target deterministic component sequence and a target random component sequence. The corresponding energy storage power demand is determined based on the power imbalance sequence. The corresponding basic energy storage capacity is determined based on the target deterministic component sequence; The corresponding random energy storage capacity is determined based on the target random component sequence and the preset protection level parameters. The total energy storage capacity is determined based on the basic energy storage capacity and the random energy storage capacity.

[0006] In one possible implementation, the power generation data includes a power generation time series, and the load power data includes a load power time series. Accordingly, acquiring the power generation data and load power data of the photovoltaic system, and obtaining the target power generation data and target load power data after preprocessing, includes: Obtain the time series of power generation and load power; After normalizing the power generation time series, the target power generation time series is obtained; After normalizing the load power time series, the target load power time series is obtained.

[0007] In one possible implementation, the step of decomposing the target power generation data and the target load power data to obtain the corresponding power imbalance sequence includes: The target power generation data is decomposed to determine the corresponding deterministic power generation component sequence and the random power generation component sequence; The target load power data is decomposed to determine the corresponding deterministic power consumption component sequence and random power consumption component sequence; The corresponding target deterministic component sequence is determined based on the power generation deterministic component sequence and the power consumption deterministic component sequence; The corresponding target random component sequence is determined based on the power generation random component sequence and the power consumption random component sequence; The corresponding power imbalance sequence is determined based on the target deterministic component sequence and the target random component sequence; The formula for the power imbalance sequence is as follows:

[0008] In the formula, This represents the target deterministic component at time t in the target deterministic component sequence. This represents the target random component at time t in the target random component sequence.

[0009] In one possible implementation, determining the corresponding target deterministic component sequence based on the power generation deterministic component sequence and the power consumption deterministic component sequence includes: For each moment in the deterministic component of power generation in the deterministic component sequence, determine the deterministic component of power consumption at the corresponding moment in the deterministic component sequence of power consumption, and construct a deterministic component pair; By combining the differences between the deterministic power generation component and the deterministic power consumption component in each deterministic component pair, the target deterministic component sequence is obtained; The formula for the target deterministic component sequence is as follows:

[0010] In the formula, This represents the deterministic component of power generation at time t. The deterministic component of electricity consumption at time t is represented.

[0011] In one possible implementation, determining the corresponding target random component sequence based on the power generation random component sequence and the power consumption random component sequence includes: For each moment in the power generation random component sequence, determine the corresponding moment in the power consumption random component sequence and construct a random component pair; By combining the differences between the power generation random component and the power consumption random component in each random component pair, the target random component sequence is obtained. The formula for the target random component sequence is as follows:

[0012] In the formula, This represents the random component of power generation at time t. This represents the random component of electricity consumption at time t.

[0013] In one possible implementation, determining the corresponding basic energy storage capacity based on the target deterministic component sequence includes: By integrating the deterministic component sequence of the target over time, the corresponding basic energy storage capacity is obtained, as shown in the following formula: .

[0014] In the formula, It is the basic energy storage capacity.

[0015] In one possible implementation, determining the corresponding stochastic energy storage capacity based on the target stochastic component sequence combined with a preset protection level includes: Parametric modeling is performed on the original time series curve corresponding to the target random component sequence, and model parameters characterizing the statistical features corresponding to the target random component sequence are extracted; Based on the model parameters and combined with the multivariate normal distribution, random simulation is performed to iteratively generate multiple new time-domain power curves that conform to the statistical characteristics corresponding to the target random component sequence. Perform time integration on each of the new time-domain power curves to obtain the corresponding energy demand integral results; Based on all the energy demand integral results, the energy demand distribution of the target random component sequence is determined; The corresponding stochastic energy storage capacity is determined based on the energy demand distribution and the preset guarantee level parameters characterizing power supply reliability.

[0016] A second aspect of this disclosure provides a device for evaluating the energy storage power and capacity of a photovoltaic system, the device comprising: The data acquisition and preprocessing module is used to acquire the power generation data and load power data of the photovoltaic system, and obtain the target power generation data and target load power data after corresponding preprocessing. A power imbalance sequence construction module is used to decompose the target power generation data and the target load power data to obtain the corresponding power imbalance sequence, wherein the power imbalance sequence includes a target deterministic component sequence and a target random component sequence. An energy storage power demand determination module is used to determine the corresponding energy storage power demand based on the power imbalance sequence. The first energy storage capacity determination module is used to determine the corresponding basic energy storage capacity based on the target deterministic component sequence. The second energy storage capacity determination module is used to determine the corresponding random energy storage capacity based on the target random component sequence and the preset protection level. The total capacity determination module is used to determine the total energy storage capacity based on the basic energy storage capacity and the random energy storage capacity.

[0017] A third aspect of this disclosure provides an electronic device comprising: At least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the energy storage power and capacity assessment method for the photovoltaic system described herein.

[0018] A fourth aspect of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the energy storage power and capacity assessment method for a photovoltaic system described in this disclosure.

[0019] This disclosure discloses a method for evaluating the energy storage power and capacity of a photovoltaic (PV) system. The method involves standardizing and preprocessing the acquired PV system power generation and load power data, and then decomposing the preprocessed data to construct a power imbalance sequence corresponding to power generation and load, including a target deterministic component sequence and a target random component sequence. Subsequently, the energy storage power demand of the PV system is calculated separately. Then, the corresponding basic energy storage capacity is determined based on the target deterministic component sequence, and the corresponding random energy storage capacity with random fluctuations is determined based on the target random component sequence combined with preset guarantee level parameters. Finally, by combining the two types of energy storage capacity, the total energy storage capacity that balances periodic adjustment and reliability assurance is determined. Based on this, energy storage configuration is carried out, giving the energy storage capacity configuration a clear physical orientation. Furthermore, the separate evaluation of power demand and capacity demand provides a more accurate basis for selecting energy storage power conversion equipment. By explicitly incorporating the preset guarantee level parameters into the calculation of random energy storage capacity, a quantitative correlation between PV system reliability and energy storage capacity is established, allowing the evaluation results to flexibly match the reliability requirements of different engineering scenarios. The final total energy storage capacity simultaneously covers the basic needs of periodic power imbalance and the reliability needs of power fluctuations under different guarantee levels, effectively improving the engineering applicability and decision-making value of the energy storage configuration results.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0021] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0022] Figure 1 This illustration shows the implementation flow of a photovoltaic system energy storage power and capacity evaluation method according to an embodiment of the present disclosure. Figure 1 ; Figure 2 This illustration shows a schematic diagram of the original power data and corresponding deterministic component sequence in a photovoltaic system energy storage power and capacity assessment method according to an embodiment of the present disclosure; Figure 3 A schematic diagram of a module for evaluating the energy storage power and capacity of a photovoltaic system according to an embodiment of this disclosure is shown. Figure 4 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0023] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0024] In current energy storage demand assessment technologies, all power fluctuations are typically treated as random uncertainties and processed uniformly, making it difficult to determine the true causes of changes in energy storage capacity demand. This leads to discrepancies between the configured energy storage capacity and actual energy storage needs. Furthermore, energy storage configurations usually need to meet reliability requirements such as ensuring power supply levels. These technologies typically evaluate energy storage configurations through simulation calculations after determining the storage capacity. In addition, the mixed assessment of power and energy in these technologies makes it difficult to support the selection of energy storage devices. Moreover, these technologies lack an assessment of the impact of seasonal differences on energy storage configurations.

[0025] Based on this, the present disclosure provides a method for evaluating the energy storage power and capacity of a photovoltaic system to solve the above-mentioned technical problems, such as... Figure 1As shown, the method includes: S101. Obtain the power generation data and load power data of the photovoltaic system, and obtain the target power generation data and target load power data after corresponding preprocessing.

[0026] In this step, after obtaining the original power generation and load power data of the photovoltaic system, the raw data is preprocessed to eliminate the influence of dimensions. The power generation data in this step is renewable energy power generation data, preferably photovoltaic power generation data, and the photovoltaic power generation data and the photovoltaic system load power data have the same time resolution. After the above data preprocessing, the target power generation data and target load power data are obtained.

[0027] Related technologies directly use raw data for energy storage calculations, ignoring the impact of different seasons and installed capacity on the statistical characteristics of energy storage power. Compared to these technologies, this step preprocesses the acquired raw data, using the target power generation and load power data as the basis for subsequent energy storage calculations. This decouples the subsequent processing from the installed capacity scale, effectively improving the overall versatility of the method. For example, power generation and power consumption data may be affected by the installed capacity scale, leading to a mismatch in data magnitude. This step normalizes both power generation and power consumption data, then scales the raw power data to a relative power generation ratio within a 0-1 range, eliminating the magnitude difference caused by the installed capacity scale and retaining only the relative shape and statistical regularity of power fluctuations.

[0028] S102. Based on the decomposition of the target power generation data and the target load power data, the corresponding power imbalance sequence is obtained. The power imbalance sequence includes the target deterministic component sequence and the target random component sequence.

[0029] In this step, the preprocessed target power generation data and target load power data serve as the basis for constructing the power imbalance sequence. The resulting power imbalance sequence is a decomposable object jointly produced by different driving mechanisms. Photovoltaic power generation data and load power data are simultaneously affected by time periods and multiple random factors; directly mixing them into a single signal would affect the reliability of energy storage estimation. This step decomposes the target power generation data and target load power data into a structured power imbalance sequence, clarifying that it is an analytical object that can be further decomposed according to physical causes, rather than a simple integral and aggregated signal. This lays the data foundation for subsequent hierarchical decomposition of periodic and random fluctuations and accurate positioning of the sources of energy storage demand, avoiding the energy storage capacity configuration deviation caused by mixing and analyzing power fluctuations from different driving mechanisms.

[0030] It should be noted that for the power imbalance sequence, deterministic and stochastic decompositions are performed on both photovoltaic (PV) and load components to obtain corresponding target deterministic and target stochastic component sequences. The target deterministic component sequence represents a predictable and repeatable structural configuration, and the energy storage demand determined based on this corresponds to the baseline demand, i.e., a fixed energy storage demand. The target stochastic component sequence represents short-term cloud-induced fluctuations or load stochasticity, and the corresponding energy storage demand increases with increasing reliability preference. Here, reliability preference refers to the reliability of the PV system, typically characterized by probability values; the higher the probability value, the higher the reliability preference.

[0031] This step divides the power imbalance sequence into a target deterministic component sequence and a target random component sequence, and decomposes the total demand into structural limit demand and risk-driven incremental demand, thereby increasing the interpretability of energy storage demand.

[0032] S103. Determine the corresponding energy storage power demand value based on the power imbalance sequence.

[0033] In this step, the energy storage power demand is determined based on the extreme values ​​of the power imbalance sequence, including the maximum charging power demand and the maximum discharging power demand.

[0034] The formula corresponding to the maximum charging power requirement is as follows:

[0035] The formula corresponding to the maximum discharge power requirement is as follows:

[0036] It should be noted that the rated power of the photovoltaic system is the larger of the maximum charging power requirement and the maximum discharging power requirement mentioned above. This step clearly distinguishes between power requirement and capacity requirement. Since the power requirement determines the scale of the inverter and power conversion system, this step provides a direct basis for selecting the energy storage inverter and power conversion system by determining the energy storage power requirement. Furthermore, it determines the maximum charging and discharging power that the photovoltaic system needs to withstand.

[0037] Before determining the final energy storage capacity of the photovoltaic system, this step also identifies the power demand, specifically assessing both power and capacity requirements separately. Driven by stochastic components, power and capacity demands grow at different rates with the level of assurance, with power demand growing faster and capacity demand growing more gradually. This is because, in practical applications, power is primarily driven by instantaneous extreme events, while capacity is also affected by the cumulative effect over time. Related technologies typically combine power and capacity demands for assessment to improve efficiency; however, this approach can easily amplify extreme instantaneous peak values ​​indirectly in capacity estimation through integration, leading to inaccurate capacity results and affecting the final energy storage configuration. This step assesses power demand separately, meaning power and capacity demands are evaluated independently, making the determination of indicators such as the inverter's rated power or the energy storage medium's capacity in the photovoltaic system more reliable.

[0038] S104. Determine the corresponding basic energy storage capacity based on the target deterministic component sequence.

[0039] In this step, the corresponding energy accumulation process is determined by integrating the deterministic component sequence of the target over time. Based on the range of energy changes, the baseline energy storage capacity is then determined to address periodic power imbalances. In other words, this step avoids amplifying the basic energy storage capacity demand by integrating only the deterministic components, ensuring a one-to-one correspondence between the determined baseline energy storage capacity and the periodic adjustment needs. This capacity is determined only by predictable structural fluctuations such as diurnal cycles and seasonal changes, and is independent of the system's reliability level. It serves as the baseline capacity for the photovoltaic system configuration, providing a stable and interpretable quantitative basis for selecting the baseline capacity of the energy storage medium.

[0040] S105. Determine the corresponding random energy storage capacity based on the target random component sequence and the preset protection level parameters.

[0041] In this step, the energy demand corresponding to the target random component sequence is statistically analyzed to construct its corresponding energy demand distribution. Based on this, the random energy storage capacity that meets the preset guarantee level parameters is determined. For example, the corresponding annual cycle base capacity and random capacity under different guarantee levels are determined based on multi-year photovoltaic power and load data.

[0042] It is important to note that this step directly incorporates the guarantee requirement during the energy storage demand determination process. This uses the reliability target, i.e., the guarantee level parameter, as the basis for capacity determination, rather than relying solely on post-event evaluation. That is, after determining the energy storage capacity, simulations are used to calculate the probability of power failure, such as LPSP (Limited Power Supply Probability). Related technologies typically determine the energy storage capacity first based on empirical criteria or energy balance results, and then use indicators such as LPSP for simulation verification. The determined energy storage capacity is prone to discrepancies with the target guarantee level. Consequently, it is easy to encounter problems such as insufficient capacity requiring repeated adjustments, or excessive capacity leading to redundant configuration. This step, however, directly incorporates the requirements corresponding to the target guarantee level during the capacity determination stage. This ensures that the energy storage capacity is directly driven by the target guarantee level, thereby establishing a mapping relationship between the guarantee level and energy storage demand. This ensures that the determined energy storage capacity is consistent with the target guarantee level, reducing the likelihood of insufficient or redundant energy storage capacity.

[0043] It should also be noted that for high-proportion renewable energy systems, the essence of energy storage configuration is to meet given energy supply reliability requirements under conditions of volatility and uncertainty. In the application process, this step can, on the one hand, avoid the trial-and-error process consisting of assumed capacity, simulation verification and iterative correction, thus improving computational efficiency; on the other hand, the energy storage demand corresponding to different guarantee levels can be directly quantified, which can effectively assess the reliability of capacity and ensure the energy supply reliability of renewable energy systems.

[0044] S106. Determine the total energy storage capacity based on the basic energy storage capacity and the random energy storage capacity.

[0045] In this step, the formula corresponding to the total energy storage capacity is as follows:

[0046] In the formula, Indicates the total energy storage capacity. Indicates the basic energy storage capacity. This indicates the stochastic energy storage capacity corresponding to different levels of protection.

[0047] Therefore, the obtained total energy storage capacity can simultaneously meet the requirements of periodic adjustment and random fluctuation protection. The determined total energy storage capacity can clarify the energy storage needs of the photovoltaic system in the time dimension. The energy demand determines the capacity of the battery or energy storage medium, which can support the selection of energy storage equipment.

[0048] This disclosure discloses a method for evaluating the energy storage power and capacity of a photovoltaic (PV) system. The method involves standardizing and preprocessing the acquired PV system power generation and load power data, and then decomposing the preprocessed data to construct a power imbalance sequence corresponding to power generation and load, including a target deterministic component sequence and a target random component sequence. Subsequently, the energy storage power demand of the PV system is calculated separately. Then, the corresponding basic energy storage capacity is determined based on the target deterministic component sequence, and the corresponding random energy storage capacity for random fluctuations is determined based on the target random component sequence combined with preset guarantee level parameters. Finally, by combining the two types of energy storage capacities, the total energy storage capacity that balances periodic regulation and reliability assurance is determined. Based on this, energy storage configuration is performed, giving the energy storage capacity configuration a clear physical orientation. It can clearly determine whether the capacity source is periodic regulation demand or random fluctuation demand, and the separate evaluation of power demand and capacity demand provides a more accurate basis for the selection of energy storage power conversion equipment. By explicitly introducing the preset guarantee level parameters into the calculation of random energy storage capacity, a quantitative correlation between PV system reliability and energy storage capacity is established, allowing the evaluation results to flexibly match the reliability requirements of different engineering scenarios. The final total energy storage capacity simultaneously covers the basic needs of periodic power imbalance and the reliability needs of power fluctuations under different guarantee levels, thereby improving the engineering applicability and decision-making value of the energy storage configuration results.

[0049] In one possible implementation, the power generation data includes a power generation time series, and the load power data includes a load power time series. Accordingly, the power generation data and load power data of the photovoltaic system are acquired, and target power generation data and target load power data are obtained after corresponding preprocessing, including: Obtain the time series of power generation and load power; After normalizing the power generation time series, the target power generation time series is obtained; After normalizing the load power time series, the target load power time series is obtained.

[0050] In this embodiment, the power generation data includes a time series of renewable energy power generation. Preferably, the photovoltaic power generation capacity is included, and the load power data includes the system load power time series. And the time series of renewable energy power generation and system load power time series They have the same time resolution, preferably 1 hour.

[0051] It should be noted that, in order to eliminate the influence of dimensions and improve the comparability of data from different time periods, this embodiment uses a time series of power generation data. and load power time series Both were normalized to obtain the normalized power generation time series as the target power generation time series, and the normalized load power time series as the target load power time series, which served as the data basis for subsequently determining the power imbalance sequence.

[0052] In other words, this embodiment performs unified scaling on the original photovoltaic power generation data and load power data, eliminating the influence of factors such as installed capacity scale and dimensions on the statistical characteristics of power generation, thus decoupling the subsequent decomposition and statistical classification steps from the installed capacity scale. In related technologies, integration or statistical processing is usually performed based on the original power series, and the results obtained are highly dependent on the installed capacity scale, the selected time window, and the sample scale. The lack of unified data time resolution leads to time series mismatch, and the failure to perform scaling unification processing on the original data causes the subsequent analysis results to depend on the installed capacity scale.

[0053] This embodiment reduces the risk of parameter drift during scenario migration by standardizing the original data scale, thereby improving the overall portability and reusability of the method. Furthermore, standardizing the time resolution of power generation and load time series ensures the accuracy of subsequent power imbalance sequence construction and eliminates calculation errors caused by time series misalignment.

[0054] In one possible implementation, based on the decomposition of target power generation data and target load power data, a corresponding power imbalance sequence is obtained, including: The target power generation data is decomposed to determine the corresponding deterministic power generation component sequence and the random power generation component sequence; The target load power data is decomposed to determine the corresponding deterministic and random power consumption component sequences. The corresponding target deterministic component sequence is determined based on the deterministic component sequence of power generation and the deterministic component sequence of power consumption; The corresponding target random component sequence is determined based on the power generation random component sequence and the power consumption random component sequence; The corresponding power imbalance sequence is determined based on the target deterministic component sequence and the target random component sequence; The formula for the power imbalance sequence is as follows:

[0055] In the formula, This represents the target deterministic component at time t in the target deterministic component sequence. This represents the target random component at time t in the target random component sequence.

[0056] In this embodiment, after preprocessing the original power generation data and load power data, as follows: Figure 2In the diagram, A represents a certain original power generation data, and C represents a certain original load power data. The preprocessed target power generation data and the preprocessed target load power data are decomposed to obtain the corresponding power imbalance sequence, which serves as the basic input for determining the subsequent energy storage power and capacity.

[0057] Specifically, by decomposing the preprocessed target power generation data, deterministic and stochastic power generation component sequences are obtained on the power generation side. Similarly, by decomposing the preprocessed target load power data, deterministic and stochastic power consumption component sequences are obtained on the power consumption side. Further, by subtracting the deterministic power generation component sequences and the deterministic power consumption component sequences on the power generation side, an imbalance sequence representing the deterministic components, i.e., the target deterministic component sequence, is obtained, which reflects periodic power imbalances. Similarly, by subtracting the stochastic component sequences on the power generation side and the stochastic power consumption component sequences on the power consumption side, an imbalance sequence representing the uncertain components, i.e., the target stochastic component sequence, is obtained, which reflects stochastic power fluctuations, thus yielding the power imbalance sequence. This embodiment provides a standardized core analysis object for subsequent component decomposition and energy storage power demand calculation by determining the power imbalance sequence.

[0058] In this embodiment, the target power generation data and target load power data are each decomposed into two independent components based on the target decomposition method: a deterministic component sequence and a random component sequence. The target decomposition method includes techniques such as periodic averaging, filtering, or statistical modeling. By displaying the decomposition according to physical source based on the above methods, the mixing of energy storage demands of different natures is avoided, thereby improving the interpretability of energy storage configuration results from the source.

[0059] In other words, when decomposing the preprocessed target power generation and target load data, the fluctuation components from different sources in the original energy storage demand are decoupled, and seasonal trends, intraday patterns (deterministic changes), and random disturbances are identified separately. This avoids the superposition of these effects in the total demand, making them difficult to distinguish. After decomposition, the impact of seasonal differences on energy storage configuration can be further pinpointed to a specific time scale. This is because the target deterministic component sequence represents the superposition of stable seasonal demand and daily demand. If the base energy storage capacity demand of the deterministic component determined in this way changes significantly, the seasonal trend can be determined as either increasing or decreasing. However, if the determined base energy storage capacity demand does not change significantly, but the total energy storage capacity demand increases, it can be determined that it is caused by the influence of random disturbances, i.e., daily-scale random disturbances. For example, a surge in energy storage demand may occur due to several consecutive days of rain or cloudy weather in certain seasons.

[0060] In one possible implementation, determining the corresponding target deterministic component sequence based on the power generation deterministic component sequence and the power consumption deterministic component sequence includes: For each moment in the deterministic power generation component sequence, determine the deterministic power consumption component at the corresponding moment in the deterministic power consumption component sequence, and construct a deterministic component pair. By combining the differences between the deterministic power generation component and the deterministic power consumption component in each deterministic component pair, the target deterministic component sequence is obtained; The formula for the target deterministic component sequence is as follows:

[0061] In the formula, This represents the deterministic component of power generation at time t. The deterministic component of electricity consumption at time t is represented.

[0062] In this embodiment, when determining the target deterministic component sequence, the target deterministic component sequence is constructed by calculating the difference between the generation deterministic component sequence on the generation side and the consumption deterministic component sequence on the consumption side at each time step. That is, the generation deterministic component at each time step in the generation deterministic component sequence and the consumption deterministic component in the consumption deterministic component sequence form a deterministic component pair. By combining the differences between the generation deterministic component and the consumption deterministic component in all deterministic component pairs, the target deterministic component sequence can be obtained. Figure 2 In this context, B represents a deterministic component of power generation in the deterministic power generation component sequence, and D represents a deterministic component of power consumption in the deterministic power consumption component sequence. The deterministic power difference signal characterizing time t is defined as the imbalance between the deterministic component of photovoltaic power generation and the deterministic component of load. It should be noted that the target deterministic component sequence reflects the periodic power imbalance caused by diurnal cycles, seasonal variations, etc. It is the superposition of stable seasonal demand and daily demand, and is independent of the system guarantee level. It is the baseline demand for energy storage capacity, and the basic energy storage capacity can be determined based on it.

[0063] In one possible implementation, determining the corresponding target random component sequence based on the power generation random component sequence and the power consumption random component sequence includes: For each time step in the random component sequence of power generation, determine the random component sequence of electricity consumption at the corresponding time step, and construct a random component pair. By combining the differences between the power generation random component and the power consumption random component in each random component pair, the target random component sequence is obtained. The formula for the target random component sequence is as follows:

[0064] In the formula, This represents the random component of power generation at time t. This represents the random component of electricity consumption at time t.

[0065] In this embodiment, the method used to determine the target random component sequence is the same as that used to determine the target deterministic component sequence: the difference between the generation random component and the consumption random component at each time step is calculated to obtain the corresponding target random component sequence. The random power difference signal characterizing time t is defined as the mismatch between the random component of photovoltaic power generation and the random component of load. The target random component sequence can reflect the power fluctuations caused by meteorological disturbances and random load changes. The corresponding energy storage demand increases with the improvement of the guarantee level parameter, which is the risk-driven increment of energy storage capacity. Based on this, the random energy storage capacity of the photovoltaic system can be determined.

[0066] Therefore, this embodiment treats the power imbalance sequence as a decomposable object jointly generated by different driving mechanisms. Related technologies treat the power imbalance sequence as a single aggregated signal, which can easily lead to over-configuration or configuration from unknown sources. This embodiment obtains target deterministic component sequences and target random component sequences that are independent of each other, which can clearly determine whether the energy storage capacity mainly comes from periodic structures or random fluctuations, i.e., assess their contribution to the energy storage capacity separately, providing an interpretable basis for subsequent equipment selection, such as the selection of long-term or short-term energy storage devices.

[0067] In one possible implementation, determining the corresponding basic energy storage capacity based on the target deterministic component sequence includes: By integrating the deterministic component sequence of the target over time, the corresponding basic energy storage capacity is obtained, as shown in the following formula: .

[0068] In the formula, It is the basic energy storage capacity.

[0069] In this embodiment, time integration is performed on the target deterministic component sequence, and the deterministic component is integrated separately, rather than the entire power imbalance sequence. Since the deterministic component reflects the periodic power imbalance caused by diurnal cycles, seasonal changes, etc., time integration can determine the corresponding energy accumulation process, thereby determining the basic energy storage capacity based on the energy change range.

[0070] In other words, this embodiment determines the basic energy storage capacity by focusing on the deterministic components obtained from the decomposition. It uses time integration to obtain the energy accumulation process over time. Therefore, the basic energy storage capacity is not a single integral value, but rather a range of energy variations with maximum and minimum energy values ​​determined during the energy accumulation process. This capacity is specifically designed to address periodic power imbalances caused by diurnal cycles and seasonal variations, and represents the basic capacity requirement of the photovoltaic system. This embodiment only performs time integration on the deterministic components, avoiding interference from random fluctuations in the basic energy storage capacity calculation. This ensures that the basic energy storage capacity matches the periodic power imbalance requirements, eliminates unnecessary capacity redundancy, and results in a basic energy storage capacity that more closely matches actual energy demand.

[0071] In one possible implementation, determining the corresponding stochastic energy storage capacity based on the target stochastic component sequence combined with a preset protection level includes: Parametric modeling is performed on the original time series curve corresponding to the target random component sequence, and model parameters that characterize the statistical features corresponding to the target random component sequence are extracted. Based on the model parameters and combined with the multivariate normal distribution, random simulation is performed to iteratively generate multiple new time-domain power curves that conform to the statistical characteristics of the target random component sequence. For each new time-domain power curve, perform time integration to obtain the corresponding energy demand integral result; Based on the integral results of all energy requirements, determine the energy requirement distribution of the target random component sequence; The corresponding stochastic energy storage capacity is determined based on the distribution of energy demand and the preset guarantee level parameters that characterize power supply reliability.

[0072] In this embodiment, the random component reflects the power fluctuations caused by meteorological disturbances and random load changes. By statistically analyzing the energy demand corresponding to the random component, the corresponding energy demand distribution can be determined. Based on the guarantee level parameters, the energy storage capacity that meets the reliability of the guarantee level parameters can be determined from the determined energy demand distribution.

[0073] It should be noted that by parameterizing the original data curves corresponding to the target random component sequence, i.e., the original time series curves, the corresponding model parameters are extracted. It should also be noted that extracting the statistical characteristics of the random components through parametric modeling can eliminate the dependence on single historical data, making the construction of energy demand distribution more statistically meaningful. Based on the model parameters, new time-domain curves are generated by combining them with a multivariate normal distribution. The multiple new time-domain power curves generated iteratively, which conform to the statistical characteristics corresponding to the random components, can cover energy demand distributions in various scenarios, further matching the reliability requirements in engineering and improving the robustness of the evaluation results. By integrating each new time-domain power curve over time to determine the cumulative probability distribution of energy demand, the energy demand pattern of the random components can be accurately reflected, providing a reliable basis for subsequent combination with guarantee level parameters to determine random energy storage capacity and improving the accuracy of random energy storage capacity calculation.

[0074] Specifically, this embodiment constructs an empirical cumulative distribution function (CDF) for random power differences, thereby directly mapping different reliability preferences to corresponding power or capacity demands. The power rating under different reliability levels can be directly determined from the empirical CDF of random power differences. That is, the statistical results of random fluctuations are transformed into energy storage demand values ​​under a given assurance level through the CDF empirical cumulative distribution function. Specifically, this includes: First, the sequence of target random components obtained after decomposition is statistically analyzed to form the corresponding probability distribution. Let the random variable be... Then its CDF is defined as:

[0075] In the formula, the cumulative distribution function This indicates that energy storage demand does not exceed The probability of.

[0076] Then, based on the preset protection level (e.g., 90%, 95%, 99%), find the corresponding quantiles on the CDF. , so that:

[0077] The determined That is to say: in Under the guarantee requirements, photovoltaic systems should at least have the required power or capacity to cover [the following conditions]. The proportion fluctuates randomly.

[0078] Therefore, in this embodiment, the CDF (Contingency Design Factor) does not simply describe the probability distribution, but serves as the basis for connecting random fluctuations to the design values ​​corresponding to the assurance level. Using the CDF, random components can be directly mapped to energy storage configuration results under different assurance requirements, thus avoiding the process of repeatedly calculating capacity and then verifying reliability in related technologies.

[0079] It should be noted that related technologies typically lack explicit input for reliability dimensions. This embodiment, however, uses a preset assurance level parameter as the explicit independent variable. Based on the determined capacity demand distribution and assurance level parameter, the corresponding stochastic energy storage capacity is determined, establishing an explicit mapping between energy storage capacity and system reliability. This improves the interpretability and verifiability of the final determined energy storage capacity result. It can be determined how much the energy storage capacity needs to increase when the assurance level is raised from one value to another.

[0080] It should also be noted that the random component represents short-term cloud-induced fluctuations or load randomness, and the corresponding energy storage demand increases with reliability preference, that is, with the increase of the guarantee level parameter. For example, when the guarantee level parameter is in the range of 50%-80%, each 1% increase in the guarantee level results in an average increase of approximately 0.7% in power demand and approximately 0.3% in capacity; however, when the guarantee level parameter is ≥99%, the marginal increase in average power demand is approximately 28.3%, and the capacity is approximately 13.2%. This means that under high guarantee level parameters, such as 99% or 99.9%, a sharp increase in demand will make energy demand unsustainable. Based on the determination of the above marginal indicators, the guarantee level parameter in this embodiment is preferably set to be less than or equal to 95% to ensure a balance between reliability and cost pressure.

[0081] As the level of assurance increases, the stochastic component shifts from secondary to primary, with the corresponding high reliability demand mainly driven by stochastic fluctuations. Related technologies typically treat deterministic and stochastic components together, making it difficult to provide a reasonable explanation for capacity surges under high reliability demands and to determine the source of the capacity increase. This embodiment further improves the interpretability of capacity increases by decomposing the deterministic and stochastic components, and can also specify long-term or short-term energy storage configuration strategies accordingly. Furthermore, the stochastic component is affected by seasonal differences, and seasonal factors amplify the power demand of the stochastic component at high assurance levels, with summer demand approximately twice that of winter demand. This embodiment determines the energy demand distribution based on the energy demand analysis of the stochastic component and maps it to the assurance level, making seasonal patterns quantifiable and resulting in a more flexible energy storage dispatch scheme.

[0082] To implement the above method, an example of this application also provides a photovoltaic system energy storage power and capacity assessment device 200, such as... Figure 3 As shown, the device includes: The data acquisition and preprocessing module 201 is used to acquire the power generation data and load power data of the photovoltaic system, and obtain the target power generation data and target load power data after corresponding preprocessing. The power imbalance sequence construction module 202 is used to decompose the target power generation data and the target load power data to obtain the corresponding power imbalance sequence. The power imbalance sequence includes the target deterministic component sequence and the target random component sequence. The energy storage power demand determination module 203 is used to determine the corresponding energy storage power demand based on the power imbalance sequence. The first energy storage capacity determination module 204 is used to determine the corresponding basic energy storage capacity based on the target deterministic component sequence. The second energy storage capacity determination module 205 is used to determine the corresponding random energy storage capacity based on the target random component sequence and the preset guarantee level. The total capacity determination module 206 is used to determine the total energy storage capacity based on the basic energy storage capacity and the random energy storage capacity.

[0083] In one embodiment, the power generation data includes a power generation time series, and the load power data includes a load power time series. Accordingly, the data acquisition and preprocessing module 201 is used to acquire the power generation time series and the load power time series. After normalizing the power generation time series, the target power generation time series is obtained; After normalizing the load power time series, the target load power time series is obtained.

[0084] In one embodiment, the power imbalance sequence construction module 202 is used to decompose the target power generation data and determine the corresponding power generation deterministic component sequence and power generation random component sequence; The target load power data is decomposed to determine the corresponding deterministic and random power consumption component sequences. The corresponding target deterministic component sequence is determined based on the deterministic component sequence of power generation and the deterministic component sequence of power consumption; The corresponding target random component sequence is determined based on the power generation random component sequence and the power consumption random component sequence; The corresponding power imbalance sequence is determined based on the target deterministic component sequence and the target random component sequence; The formula for the power imbalance sequence is as follows:

[0085] In the formula, This represents the target deterministic component at time t in the target deterministic component sequence. This represents the target random component at time t in the target random component sequence.

[0086] In one embodiment, the power imbalance sequence construction module 202 is further configured to determine the power consumption deterministic component at the corresponding time in the power consumption deterministic component sequence for each moment of the power generation deterministic component sequence, and construct a deterministic component pair. By combining the differences between the deterministic power generation component and the deterministic power consumption component in each deterministic component pair, the target deterministic component sequence is obtained; The formula for the target deterministic component sequence is as follows:

[0087] In the formula, This represents the deterministic component of power generation at time t. The deterministic component of electricity consumption at time t is represented.

[0088] In one embodiment, the power imbalance sequence construction module 202 is further configured to determine the electricity consumption random component at the corresponding time in the electricity consumption random component sequence for each time step of the power generation random component sequence, and construct a random component pair. By combining the differences between the power generation random component and the power consumption random component in each random component pair, the target random component sequence is obtained. The formula for the target random component sequence is as follows:

[0089] In the formula, This represents the random component of power generation at time t. This represents the random component of electricity consumption at time t.

[0090] In one embodiment, the first energy storage capacity determination module 204 is used to perform time integration on the target deterministic component sequence to obtain the corresponding basic energy storage capacity, as shown in the following formula: .

[0091] In the formula, It is the basic energy storage capacity.

[0092] In one embodiment, the second energy storage capacity determination module 205 is used to perform parametric modeling on the original time series curve corresponding to the target random component sequence and extract model parameters that characterize the statistical features corresponding to the target random component sequence. Based on the model parameters and combined with the multivariate normal distribution, random simulation is performed to iteratively generate multiple new time-domain power curves that conform to the statistical characteristics of the target random component sequence. For each new time-domain power curve, perform time integration to obtain the corresponding energy demand integral result; Based on the integral results of all energy requirements, determine the energy requirement distribution of the target random component sequence; The corresponding stochastic energy storage capacity is determined based on the distribution of energy demand and the preset guarantee level parameters that characterize power supply reliability.

[0093] By way of example, this application also provides an electronic device, including: processor; Memory used to store processor-executable instructions; The processor is used to read executable instructions from memory and execute the instructions to implement the above-mentioned method for evaluating the energy storage power and capacity of photovoltaic systems.

[0094] For example, this application also provides a computer-readable storage medium storing a computer program for executing the above-described method for evaluating the energy storage power and capacity of a photovoltaic system.

[0095] Figure 4 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0096] like Figure 4 As shown, device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 302 or a computer program loaded from storage unit 308 into random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0097] Multiple components in device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0098] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as a method for evaluating the energy storage power and capacity of a photovoltaic system. For example, in some embodiments, a method for evaluating the energy storage power and capacity of a photovoltaic system can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the method for evaluating the energy storage power and capacity of a photovoltaic system described above can be performed. Alternatively, in other embodiments, the computing unit 301 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for evaluating the energy storage power and capacity of a photovoltaic system.

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0103] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0104] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0105] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0107] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for evaluating the energy storage power and capacity of a photovoltaic system, characterized in that, The method includes: Acquire the power generation data and load power data of the photovoltaic system, and obtain the target power generation data and target load power data after corresponding preprocessing; Based on the decomposition of the target power generation data and the target load power data, a corresponding power imbalance sequence is obtained, which includes a target deterministic component sequence and a target random component sequence. The corresponding energy storage power demand is determined based on the power imbalance sequence. The corresponding basic energy storage capacity is determined based on the target deterministic component sequence; The corresponding random energy storage capacity is determined based on the target random component sequence and the preset protection level parameters. The total energy storage capacity is determined based on the basic energy storage capacity and the random energy storage capacity.

2. The method for evaluating the energy storage power and capacity of a photovoltaic system according to claim 1, characterized in that, The power generation data includes a power generation time series, and the load power data includes a load power time series. Correspondingly, the acquisition of the photovoltaic system's power generation data and load power data, after preprocessing, yields target power generation data and target load power data, including: Obtain the time series of power generation and load power; After normalizing the power generation time series, the target power generation time series is obtained; After normalizing the load power time series, the target load power time series is obtained.

3. The method for evaluating the energy storage power and capacity of a photovoltaic system according to claim 1, characterized in that, The step of decomposing the target power generation data and the target load power data to obtain the corresponding power imbalance sequence includes: The target power generation data is decomposed to determine the corresponding deterministic power generation component sequence and the random power generation component sequence; The target load power data is decomposed to determine the corresponding deterministic power consumption component sequence and random power consumption component sequence; The corresponding target deterministic component sequence is determined based on the power generation deterministic component sequence and the power consumption deterministic component sequence; The corresponding target random component sequence is determined based on the power generation random component sequence and the power consumption random component sequence; The corresponding power imbalance sequence is determined based on the target deterministic component sequence and the target random component sequence; The formula for the power imbalance sequence is as follows: In the formula, This represents the target deterministic component at time t in the target deterministic component sequence. This represents the target random component at time t in the target random component sequence.

4. The method for evaluating the energy storage power and capacity of a photovoltaic system according to claim 3, characterized in that, The step of determining the corresponding target deterministic component sequence based on the power generation deterministic component sequence and the power consumption deterministic component sequence includes: For each moment in the deterministic component of power generation in the deterministic component sequence, determine the deterministic component of power consumption at the corresponding moment in the deterministic component sequence of power consumption, and construct a deterministic component pair; By combining the differences between the deterministic power generation component and the deterministic power consumption component in each deterministic component pair, the target deterministic component sequence is obtained; The formula for the target deterministic component sequence is as follows: In the formula, This represents the deterministic component of power generation at time t. The deterministic component of electricity consumption at time t is represented.

5. The method for evaluating the energy storage power and capacity of a photovoltaic system according to claim 3, characterized in that, The step of determining the corresponding target random component sequence based on the power generation random component sequence and the power consumption random component sequence includes: For each moment in the power generation random component sequence, determine the corresponding moment in the power consumption random component sequence and construct a random component pair; By combining the differences between the power generation random component and the power consumption random component in each random component pair, the target random component sequence is obtained. The formula for the target random component sequence is as follows: In the formula, This represents the random component of power generation at time t. This represents the random component of electricity consumption at time t.

6. The method for evaluating the energy storage power and capacity of a photovoltaic system according to claim 1, characterized in that, The step of determining the corresponding basic energy storage capacity based on the target deterministic component sequence includes: By integrating the deterministic component sequence of the target over time, the corresponding basic energy storage capacity is obtained, as shown in the following formula: In the formula, It is the basic energy storage capacity.

7. The method for evaluating the energy storage power and capacity of a photovoltaic system according to claim 1, characterized in that, The step of determining the corresponding random energy storage capacity based on the target random component sequence and a preset guarantee level includes: Parametric modeling is performed on the original time series curve corresponding to the target random component sequence, and model parameters characterizing the statistical features corresponding to the target random component sequence are extracted; Based on the model parameters and combined with the multivariate normal distribution, random simulation is performed to iteratively generate multiple new time-domain power curves that conform to the statistical characteristics corresponding to the target random component sequence. Perform time integration on each of the new time-domain power curves to obtain the corresponding energy demand integral results; Based on all the energy demand integral results, the energy demand distribution of the target random component sequence is determined; The corresponding stochastic energy storage capacity is determined based on the energy demand distribution and the preset guarantee level parameters characterizing power supply reliability.

8. A device for evaluating the energy storage power and capacity of a photovoltaic system, characterized in that, The device includes: The data acquisition and preprocessing module is used to acquire the power generation data and load power data of the photovoltaic system, and obtain the target power generation data and target load power data after corresponding preprocessing. A power imbalance sequence construction module is used to decompose the target power generation data and the target load power data to obtain the corresponding power imbalance sequence, wherein the power imbalance sequence includes a target deterministic component sequence and a target random component sequence. An energy storage power demand determination module is used to determine the corresponding energy storage power demand based on the power imbalance sequence. The first energy storage capacity determination module is used to determine the corresponding basic energy storage capacity based on the target deterministic component sequence. The second energy storage capacity determination module is used to determine the corresponding random energy storage capacity based on the target random component sequence and the preset protection level parameters. The total capacity determination module is used to determine the total energy storage capacity based on the basic energy storage capacity and the random energy storage capacity.

9. An electronic device, characterized in that, include: At least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform claim 1. The method for evaluating the energy storage power and capacity of a photovoltaic system as described in any one of the 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the method for evaluating the energy storage power and capacity of a photovoltaic system according to any one of claims 1-7.

Images