Hybrid energy storage capacity allocation method and device based on multi-time scale fluctuation component energy ratio

Abstract

The invention discloses a hybrid energy storage capacity allocation method and device based on a multi-time scale fluctuation component energy ratio, and the method comprises the steps: S1, obtaining wind and light resource time sequence data and load curve data, setting a wind and light matching value through a time sequence production simulation model, and outputting a system net load curve; s2, decomposing the system net load curve to obtain fluctuation components, and calculating the energy ratio of each fluctuation component; s3, based on the fluctuation components and the energy ratio of each fluctuation component, evaluating the constructed wind-light capacity matching scheme, and determining a wind-light preliminary matching interval; s4, power type energy storage and energy type energy storage are configured based on the goal of full life cycle cost minimization; and S5, iteratively adjusting the wind-solar matching value in the initial wind-solar matching interval on the basis of the goal of minimizing the cost of the whole life cycle and under the condition that a preset constraint condition is met, repeating the steps S1 to S4, and taking the hybrid energy storage configuration scheme with the lowest total cost as an optimal configuration scheme.

Description

Technical Field

[0001] This invention relates to the field of new energy power system planning and operation technology, and more specifically, to a method and device for allocating hybrid energy storage capacity based on the energy proportion of fluctuation components at multiple time scales. Background Technology

[0002] As the proportion of fluctuating power sources such as wind and solar power in the power system continues to increase, the complex fluctuation characteristics of the system's net load are becoming increasingly prominent. Traditional wind-solar-storage configuration methods typically treat wind and solar output as a fixed whole for optimization, considering only a single type of energy storage configuration based on power deficits or fluctuations on a single time scale. This approach has significant limitations: First, it ignores the potential to significantly reduce energy storage demand by optimizing the wind-solar installed capacity ratio during the planning process, failing to leverage the natural complementarity of wind and solar output to reduce the system's demand for flexibility resources from the source. Second, it fails to accurately match different types of energy storage according to the time scale differences in fluctuation characteristics, resulting in poor economic efficiency of energy storage configuration and low system operating efficiency. Furthermore, the focus of wind-solar-storage coordinated configuration is on matching the external DC power transmission curve, lacking a quantitative model that directly maps the energy proportion of fluctuation components to the mixed energy storage capacity configuration, and failing to construct an energy distribution-oriented energy storage coordinated planning framework.

[0003] Therefore, a technology is needed to achieve hybrid energy storage capacity allocation based on the energy proportion of fluctuation components across multiple time scales. Summary of the Invention

[0004] The present invention provides a method and apparatus for allocating hybrid energy storage capacity based on the energy ratio of multiple time-scale fluctuation components, in order to solve the problem of how to allocate hybrid energy storage capacity based on the energy ratio of multiple time-scale fluctuation components.

[0005] To address the aforementioned problems, this invention provides a hybrid energy storage capacity allocation method based on the energy proportion of multi-timescale fluctuation components, the method comprising:

[0006] S1: Acquire time-series data of wind and solar resources and load curve data, set the wind and solar power ratio value through the time-series production simulation model, and output the system net load curve;

[0007] S2, decompose the net load curve of the system, obtain the fluctuation components, and calculate the energy proportion of each fluctuation component;

[0008] S3, Evaluate the constructed wind and solar capacity allocation scheme based on the fluctuation components and the energy ratio of each fluctuation component, and determine the preliminary wind and solar capacity allocation range;

[0009] S4 is configured with power-type energy storage and energy-type energy storage respectively, based on the goal of minimizing the total life cycle cost;

[0010] S5. Based on minimizing the total life cycle cost, and under the condition of meeting the preset constraints, iteratively adjust the wind and solar ratio value in the initial wind and solar ratio range, and repeat the above steps S1-S4, and take the hybrid energy storage configuration scheme with the lowest total cost as the optimal configuration scheme.

[0011] Preferably, the step of decomposing the system net load curve to obtain fluctuation components and calculating the energy proportion of each fluctuation component includes:

[0012] The fluctuation components include: intraday high-frequency fluctuation components, interday fluctuation components, and multi-day fluctuation components.

[0013] The period of the intraday high-frequency fluctuation component is ≤4 hours, reflecting minute- to hourly fluctuations caused by sudden changes in wind speed and cloud movement. The characteristic parameters of the intraday high-frequency fluctuation component include fluctuation amplitude, extreme value of the rate of change, and average daily fluctuation frequency. The energy proportion of the intraday high-frequency fluctuation component is:

[0014]

[0015] The period of the diurnal fluctuation component is 4-24 hours, reflecting the intraday wind-solar complementarity characteristics. The characteristic parameters of the diurnal component include the daily average peak-to-valley difference and load factor. The proportion of the diurnal fluctuation component is:

[0016]

[0017] The period of the multi-day fluctuation component is >24 hours, reflecting the sustained multi-day energy imbalance caused by changes in the weather system. The characteristic parameters of the multi-day fluctuation component include the duration of continuous power shortage and the cumulative energy gap. The proportion of the multi-day fluctuation component is:

[0018]

[0019] Among them, E H E M and E L These are the high-frequency fluctuation component, the mid-frequency fluctuation component, and the low-frequency fluctuation component, E. total It represents the total fluctuation.

[0020] Preferably, the evaluation of the wind-solar capacity allocation scheme based on the fluctuation components and the energy proportion of each fluctuation component to determine the initial wind-solar allocation range includes:

[0021] Multiple wind-solar capacity allocation schemes are set, and a set of wind-solar capacity allocation schemes is established as {λ}. i};

[0022] Where λ iThis represents the proportion of wind power installed capacity in the i-th wind-solar capacity allocation scheme. For each wind-solar capacity allocation scheme, the complementarity of wind and solar resources is quantitatively evaluated through assessment indicators, including:

[0023] The standard deviation σ and maximum peak-to-valley difference ΔP of net load fluctuation are calculated using fluctuation mitigation effectiveness indicators. max and the peak-to-valley difference limit ΔP limit ;

[0024] The energy percentage limit E of each fluctuation component is set by the component energy distribution index. Hlim E Mlim E Llim Among them, E Hlim E Mlim E Llim These are the energy percentage limits for intraday high-frequency fluctuation components, interday fluctuation components, and multi-day fluctuation components, respectively.

[0025] η is calculated using system performance indicators. NE η min R NE R min ; where η NE For the utilization rate of new energy, η min To set the minimum utilization requirement, R NE For the proportion of electricity generated by new energy sources, R min The minimum required percentage of renewable energy consumption is set.

[0026] Each wind-solar ratio is comprehensively and quantitatively evaluated using the aforementioned assessment indicators. A multi-objective decision-making method is then employed to select candidate ratio ranges that can maximize the smoothing of fluctuations from the source and achieve the smoothest net load curve.

[0027] .

[0028] Preferably, the configuration of power-type energy storage and energy-type energy storage based on minimizing the total lifecycle cost includes:

[0029] Establish a system with the objective function of minimizing the total lifecycle cost, and configure power-type energy storage and energy-type energy storage respectively. The configuration of power-type energy storage includes:

[0030] To meet the balancing needs of intraday high-frequency fluctuations, the power capacity of power-type energy storage is mainly determined by the short-term variability of the net load, while the energy capacity is determined by the duration of continuous charging and discharging. The power-type energy storage must satisfy the following:

[0031]

[0032] Where k1 and k2 are the adaptation coefficients for power scenario and energy scenario, respectively, Pfmax E represents the maximum intraday system fluctuation power. fTotal P represents the total daily fluctuation energy. power For the power capacity required for power-type energy storage, E power The energy capacity required for power-type energy storage;

[0033] The configuration of energy storage includes: aiming to cope with diurnal and multi-day fluctuations and solve energy deficits. The energy capacity of the energy storage is determined by the cumulative energy gap under extreme weather conditions such as continuous rain and no wind. The energy storage needs to meet the following requirements:

[0034]

[0035] Among them, T dis The typical discharge duration is given by k3, which is the adaptation coefficient, and E is the standard discharge duration. deficit E represents the total energy deficit. energy For the energy capacity required for energy storage, P energy The power capacity required for energy storage.

[0036] Preferably, the process of minimizing the total lifecycle cost, and under preset constraints, iteratively adjusting the wind-solar ratio within the initial wind-solar ratio range, and repeating steps S1-S4 to select the hybrid energy storage configuration with the lowest total cost as the optimal configuration, includes:

[0037] Determine the objective function based on minimizing the total lifecycle cost:

[0038] minC total =C inv +C OM -R benefit

[0039] Among them, C inv For investment costs, C OM For operation and maintenance costs; R benefit For energy storage revenue;

[0040] The constraints include:

[0041] System reliability constraints include: power failure rate ≤ 5%;

[0042] The constraints for renewable energy consumption rate include: a comprehensive curtailment rate of ≤10% and a renewable energy power generation ratio of ≥60%;

[0043] Energy storage technology requires constraints, including setting charge / discharge efficiency, cycle life, and SOC operating range.

[0044] Based on another aspect of the present invention, the present invention provides a hybrid energy storage capacity allocation device based on the energy proportion of multi-timescale fluctuation components, the device being used to perform:

[0045] S1: Acquire time-series data of wind and solar resources and load curve data, set the wind and solar power ratio value through the time-series production simulation model, and output the system net load curve;

[0046] S2, decompose the net load curve of the system, obtain the fluctuation components, and calculate the energy proportion of each fluctuation component;

[0047] S3, Evaluate the constructed wind and solar capacity allocation scheme based on the fluctuation components and the energy ratio of each fluctuation component, and determine the preliminary wind and solar capacity allocation range;

[0048] S4 is configured with power-type energy storage and energy-type energy storage respectively, based on the goal of minimizing the total life cycle cost;

[0049] S5. Based on minimizing the total life cycle cost, and under the condition of meeting the preset constraints, iteratively adjust the wind and solar ratio value in the initial wind and solar ratio range, and repeat the above steps S1-S4, and take the hybrid energy storage configuration scheme with the lowest total cost as the optimal configuration scheme.

[0050] Preferably, the step of decomposing the system net load curve to obtain fluctuation components and calculating the energy proportion of each fluctuation component includes:

[0051] The fluctuation components include: intraday high-frequency fluctuation components, interday fluctuation components, and multi-day fluctuation components.

[0052] The period of the intraday high-frequency fluctuation component is ≤4 hours, reflecting minute- to hourly fluctuations caused by sudden changes in wind speed and cloud movement. The characteristic parameters of the intraday high-frequency fluctuation component include fluctuation amplitude, extreme value of the rate of change, and average daily fluctuation frequency. The energy proportion of the intraday high-frequency fluctuation component is:

[0053]

[0054] The period of the diurnal fluctuation component is 4-24 hours, reflecting the intraday wind-solar complementarity characteristics. The characteristic parameters of the diurnal component include the daily average peak-to-valley difference and load factor. The proportion of the diurnal fluctuation component is:

[0055]

[0056] The period of the multi-day fluctuation component is >24 hours, reflecting the sustained multi-day energy imbalance caused by changes in the weather system. The characteristic parameters of the multi-day fluctuation component include the duration of continuous power shortage and the cumulative energy gap. The proportion of the multi-day fluctuation component is:

[0057]

[0058] Among them, E H E M and E L These are the high-frequency fluctuation component, the mid-frequency fluctuation component, and the low-frequency fluctuation component, E. total It represents the total fluctuation.

[0059] Preferably, the evaluation of the wind-solar capacity allocation scheme based on the fluctuation components and the energy proportion of each fluctuation component to determine the initial wind-solar allocation range includes:

[0060] Multiple wind-solar capacity allocation schemes are set, and a set of wind-solar capacity allocation schemes is established as {λ}. i};

[0061] Where λ i This represents the proportion of wind power installed capacity in the i-th wind-solar capacity allocation scheme. For each wind-solar capacity allocation scheme, the complementarity of wind and solar resources is quantitatively evaluated through assessment indicators, including:

[0062] The standard deviation σ and maximum peak-to-valley difference ΔP of net load fluctuation are calculated using fluctuation mitigation effectiveness indicators. max and the peak-to-valley difference limit ΔP limit ;

[0063] The energy percentage limit E of each fluctuation component is set by the component energy distribution index. Hlim E Mlim E Llim Among them, E Hlim E Mlim E Llim These are the energy percentage limits for intraday high-frequency fluctuation components, interday fluctuation components, and multi-day fluctuation components, respectively.

[0064] η is calculated using system performance indicators. NE η min R NE R min ; where η NE For the utilization rate of new energy, η min To set the minimum utilization requirement, R NE For the proportion of electricity generated by new energy sources, R min The minimum required percentage of renewable energy consumption is set.

[0065] Each wind-solar ratio is comprehensively and quantitatively evaluated using the aforementioned assessment indicators. A multi-objective decision-making method is then employed to select candidate ratio ranges that can maximize the smoothing of fluctuations from the source and achieve the smoothest net load curve.

[0066] .

[0067] Preferably, the configuration of power-type energy storage and energy-type energy storage based on minimizing the total lifecycle cost includes:

[0068] Establish a system with the objective function of minimizing the total lifecycle cost, and configure power-type energy storage and energy-type energy storage respectively. The configuration of power-type energy storage includes:

[0069] To meet the balancing needs of intraday high-frequency fluctuations, the power capacity of power-type energy storage is mainly determined by the short-term variability of the net load, while the energy capacity is determined by the duration of continuous charging and discharging. The power-type energy storage must satisfy the following:

[0070]

[0071] Where k1 and k2 are the adaptation coefficients for power scenario and energy scenario, respectively, P fm ax represents the maximum intraday system fluctuation power, E fTotal P represents the total daily fluctuation energy. power For the power capacity required for power-type energy storage, E power The energy capacity required for power-type energy storage;

[0072] The configuration of energy storage includes: aiming to cope with diurnal and multi-day fluctuations and solve energy deficits. The energy capacity of the energy storage is determined by the cumulative energy gap under extreme weather conditions such as continuous rain and no wind. The energy storage needs to meet the following requirements:

[0073]

[0074] Among them, T dis The typical discharge duration is given by k3, which is the adaptation coefficient, and E is the standard discharge duration. deficit E represents the total energy deficit. energy For the energy capacity required for energy storage, P energy The power capacity required for energy storage.

[0075] Preferably, the process of minimizing the total lifecycle cost, and under preset constraints, iteratively adjusting the wind-solar ratio within the initial wind-solar ratio range, and repeating steps S1-S4 to select the hybrid energy storage configuration with the lowest total cost as the optimal configuration, includes:

[0076] Determine the objective function based on minimizing the total lifecycle cost:

[0077] minC total =C inv +C OM -R benefit

[0078] Among them, Cinv For investment costs, C OM For operation and maintenance costs; R benefit For energy storage revenue;

[0079] The constraints include:

[0080] System reliability constraints include: power failure rate ≤ 5%;

[0081] The constraints for renewable energy consumption rate include: a comprehensive curtailment rate of ≤10% and a renewable energy power generation ratio of ≥60%;

[0082] Energy storage technology requires constraints, including setting charge / discharge efficiency, cycle life, and SOC operating range.

[0083] This invention provides a method and apparatus for allocating hybrid energy storage capacity based on the energy proportion of fluctuation components at multiple time scales. The method includes: S1, acquiring time-series data of wind and solar resources and load curve data, setting the wind-solar ratio value through a time-series production simulation model, and outputting the system net load curve; S2, decomposing the system net load curve to obtain fluctuation components and calculating the energy proportion of each fluctuation component; S3, evaluating the constructed wind-solar capacity allocation scheme based on the fluctuation components and the energy proportion of each fluctuation component, and determining the initial wind-solar ratio range; S4, configuring power-type energy storage and energy-type energy storage respectively based on minimizing the total life-cycle cost; S5, based on minimizing the total life-cycle cost and under preset constraints, iteratively adjusting the wind-solar ratio value in the initial wind-solar ratio range, and repeating the above steps S1-S4, and selecting the hybrid energy storage configuration scheme with the lowest total cost as the optimal configuration scheme. This invention proposes a hybrid energy storage capacity allocation method and device based on the energy proportion of multi-timescale fluctuation components. By establishing a "fluctuation energy distribution-energy storage capacity mapping" model, it achieves a scientific allocation of the capacity ratio between power-type and energy-type energy storage, thereby improving the overall economy and operational effectiveness of the energy storage system. It realizes integrated collaborative design from power structure to flexibility resources, achieving a significant improvement in economy while ensuring system safety and reliability. Attached Figure Description

[0084] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0085] Figure 1 This is a flowchart of a hybrid energy storage capacity allocation method based on the energy proportion of multiple time-scale fluctuation components according to a preferred embodiment of the present invention.

[0086] Figure 2 This is a flowchart of a hybrid energy storage capacity allocation method based on the energy proportion of multiple time-scale fluctuation components according to a preferred embodiment of the present invention.

[0087] Figure 3 This is a schematic diagram of the normalized wind power output curve according to a preferred embodiment of the present invention;

[0088] Figure 4 This is a schematic diagram of net load fluctuation decomposition according to a preferred embodiment of the present invention;

[0089] Figure 5 This is a schematic diagram illustrating the monthly variation trend of each frequency division according to a preferred embodiment of the present invention;

[0090] Figure 6 This is a schematic diagram of the monthly proportion of each frequency division according to a preferred embodiment of the present invention. Detailed Implementation

[0091] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0092] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0093] Figure 1 This is a flowchart of a hybrid energy storage capacity allocation method based on the energy proportion of multiple time-scale fluctuation components according to a preferred embodiment of the present invention.

[0094] To address the shortcomings of traditional wind-solar-storage configuration methods, which fail to fully utilize the complementary characteristics of wind and solar power and suffer from a mismatch between energy storage configuration and fluctuation characteristics, this invention proposes a hybrid energy storage capacity allocation method based on the energy proportion of fluctuation components across multiple time scales. By establishing a "fluctuation energy distribution-energy storage capacity mapping" model, it achieves a scientific allocation of power-type and energy-type energy storage capacity ratios, thereby improving the overall economy and operational effectiveness of the energy storage system. This enables integrated and collaborative design from power structure to flexibility resources, achieving a significant improvement in economic efficiency while ensuring system safety and reliability.

[0095] like Figure 1 As shown, this invention provides a method for allocating hybrid energy storage capacity based on the energy proportion of multi-timescale fluctuation components. The method includes:

[0096] S1: Acquire time-series data of wind and solar resources and load curve data, set the wind and solar power ratio value through the time-series production simulation model, and output the system net load curve;

[0097] In step S1 of this invention, typical data acquisition and 8760 production simulation calculations are performed:

[0098] Based on the time-series data of wind and solar resources in a typical year in the region, wind and solar power output curves were plotted, load curves were obtained, a time-series production simulation model considering grid constraints was established, wind and solar power ratios were set, and 8760 hours of production simulation calculations were performed to obtain the system net load curve.

[0099] S2, decompose the system net load curve, obtain the fluctuation components, and calculate the energy proportion of each fluctuation component;

[0100] Preferably, the system net load curve is decomposed to obtain fluctuation components, and the energy proportion of each fluctuation component is calculated, including:

[0101] The volatility components include: intraday high-frequency volatility components, intraday volatility components, and multi-day volatility components.

[0102] The period of the intraday high-frequency fluctuation component is ≤4 hours, reflecting minute- to hourly fluctuations caused by sudden changes in wind speed and cloud movement. The characteristic parameters of the intraday high-frequency fluctuation component include fluctuation amplitude, extreme value of the rate of change, and average daily fluctuation frequency. The energy proportion of the intraday high-frequency fluctuation component is:

[0103]

[0104] The period of the daytime fluctuation component is 4-24 hours, reflecting the intraday wind-solar complementarity characteristics. The characteristic parameters of the daytime component include the daily average peak-to-valley difference and load factor. The proportion of the daytime fluctuation component is:

[0105]

[0106] The period of the multi-day fluctuation component is >24 hours, reflecting the sustained energy imbalance caused by changes in the weather system. The characteristic parameters of the multi-day fluctuation component include the duration of power shortage and the cumulative energy deficit. The proportion of the multi-day fluctuation component is:

[0107]

[0108] Among them, E H E M and E L These are the high-frequency fluctuation component, the mid-frequency fluctuation component, and the low-frequency fluctuation component, E. total It represents the total fluctuation.

[0109] In step S2 of this invention, time-series production simulation and fluctuation characteristic decomposition are performed:

[0110] This invention utilizes the Empirical Mode Decomposition (EMD) algorithm to decompose the net load fluctuation of the system into three key components and calculates the energy proportion of each component:

[0111] Intraday high-frequency fluctuation component (period ≤ 4 hours): mainly reflects minute- to hourly fluctuations caused by sudden changes in wind speed and cloud movement. Its characteristic parameters include fluctuation amplitude, extreme values ​​of the rate of change, and average daily fluctuation frequency. The percentage is:

[0112]

[0113] Daytime fluctuation component (medium-frequency fluctuation component, period 4-24 hours): reflects the intraday wind-solar complementarity characteristics, and its characteristic parameters include the daily average peak-to-valley difference and load factor. The proportion is:

[0114]

[0115] Multi-day fluctuation component (low-frequency fluctuation component, period > 24 hours): reflects the sustained multi-day energy imbalance caused by changes in weather systems. Its characteristic parameters include the duration of power shortage and the cumulative energy deficit. The percentage is:

[0116]

[0117] Among them, E H E M and E L These are the high-frequency fluctuation component, the mid-frequency fluctuation component, and the low-frequency fluctuation component, E. total It represents the total fluctuation.

[0118] S3, evaluate the constructed wind and solar capacity allocation scheme based on the fluctuation components and the energy proportion of each fluctuation component, and determine the preliminary wind and solar capacity allocation range;

[0119] Preferably, the constructed wind-solar capacity allocation scheme is evaluated based on the fluctuation components and the energy proportion of each fluctuation component to determine the initial wind-solar allocation range, including:

[0120] Multiple wind-solar capacity allocation schemes are set, and a set of wind-solar capacity allocation schemes is established as {λ}. i};

[0121] Where λ i This represents the proportion of wind power installed capacity in the i-th wind-solar capacity allocation scheme. For each wind-solar capacity allocation scheme, the complementarity of wind and solar resources is quantitatively evaluated through assessment indicators, including:

[0122] The standard deviation σ and maximum peak-to-valley difference ΔP of net load fluctuation are calculated using fluctuation mitigation effectiveness indicators. max and the peak-to-valley difference limit ΔP limit ;

[0123] The energy percentage limit E of each fluctuation component is set by the component energy distribution index. Hlim E Mlim E Llim Among them, E Hlim E Mlim E Llim These are the energy percentage limits for intraday high-frequency fluctuation components, interday fluctuation components, and multi-day fluctuation components, respectively.

[0124] η is calculated using system performance indicators. NE η min R NE R min ; where η NE For the utilization rate of new energy, η min To set the minimum utilization requirement, R NE For the proportion of electricity generated by new energy sources, R min The minimum required percentage of renewable energy consumption is set.

[0125] Each wind-solar ratio is comprehensively and quantitatively evaluated using assessment indicators. A multi-objective decision-making method is then employed to select candidate ratio ranges that can maximize the smoothing of fluctuations and achieve the smoothest net load curve from the source.

[0126] .

[0127] In step S3 of this invention, the complementary nature of the wind-solar ratio is considered in the initial screening.

[0128] A series of wind-solar capacity allocation schemes are defined, and the set of wind-solar capacity allocation schemes is {λ}. i}. Where λ i This represents the proportion of wind power installed capacity in the i-th scheme. For each scheme, the complementarity of wind and solar resources is quantitatively evaluated using the following indicators:

[0129] (1) Fluctuation mitigation effect indicators: Calculate the standard deviation σ of net load fluctuation and the maximum peak-to-valley difference ΔP. max ;

[0130] (2) Component energy distribution index: Set the energy proportion limit E for each fluctuation component. Hlim E Mlim E Llim ;

[0131] (3) System performance indicators: Calculate the utilization rate of new energy sources η NE The proportion of new energy power generation R NE ;

[0132] The above indicators are used to conduct a comprehensive quantitative evaluation of each wind-solar ratio. A multi-objective decision-making method is used to screen out the candidate ratio range that can maximize the smoothing of fluctuations from the source and make the net load curve the smoothest.

[0133]

[0134] S4 is configured with power-type energy storage and energy-type energy storage respectively, based on the goal of minimizing the total life cycle cost;

[0135] Preferably, based on the goal of minimizing the total life cycle cost, power-type energy storage and energy-type energy storage are configured respectively, including:

[0136] Establish a system with the objective function of minimizing the total lifecycle cost, and configure power-type energy storage and energy-type energy storage respectively. The configuration of power-type energy storage includes:

[0137] To meet the balancing needs of high-frequency intraday fluctuations, the power capacity of power-type energy storage is mainly determined by the short-term variability of the net load, while the energy capacity is determined by the duration of continuous charging and discharging. Power-type energy storage needs to meet the following requirements:

[0138]

[0139] Where k1 and k2 are the adaptation coefficients for power scenario and energy scenario, respectively, P fmax E represents the maximum fluctuating power of the system during the day, indicating the maximum change in net load over a short period of time. It directly determines the instantaneous power support capacity that power-type energy storage needs to provide. fTotal P is the total daily fluctuation energy (the sum of the absolute values ​​of the total energy fluctuating around the system's net load around its average value), used to quantify the overall degree of "instability" of the system. power The power capacity required for power-type energy storage, i.e., the maximum charge and discharge power of the energy storage system that needs to be configured, E power The energy capacity required for power-type energy storage, i.e., the maximum energy that the energy storage system needs to be configured to store;

[0140] Configuring energy storage includes: addressing diurnal and multi-day fluctuations and resolving energy deficits. The energy capacity of energy storage is determined by the cumulative energy shortfall under extreme weather conditions such as continuous cloudy / rainy weather and no wind. Energy storage needs to meet the following requirements:

[0141]

[0142] Among them, T dis The typical discharge duration is given by k3, which is the adaptation coefficient, and E is the standard discharge duration. deficit E represents the total energy deficit. energy For the energy capacity required for energy storage, P energy The power capacity required for energy storage.

[0143] In step S4 of this invention, a hybrid energy storage capacity configuration model is implemented:

[0144] First, a hybrid energy storage optimization model considering techno-economic factors is established, with the objective function being the minimization of total lifecycle cost. The energy storage configuration mainly consists of two types:

[0145] Power-type energy storage configuration: Aimed at meeting the balancing needs of high-frequency intraday fluctuations. Its power capacity is primarily determined by the short-term variability of the net load, while its energy capacity is determined by the duration of continuous charging and discharging. This type of energy storage can utilize lithium batteries, flywheels, etc., and must meet the following requirements:

[0146]

[0147] Where k1 and k2 are the fitness coefficients, P fmax This represents the maximum frequency fluctuation power.

[0148] Energy storage configuration: Aimed at addressing diurnal and multi-day fluctuations and resolving energy deficits. Its energy capacity is determined by the cumulative energy shortfall under extreme weather conditions such as continuous cloudy / rainy weather and calm conditions. This type of energy storage can utilize compressed air, flow batteries, etc., and must meet the following requirements:

[0149]

[0150] Among them, T dis The typical discharge duration is given by k3, which is the adaptation coefficient, and E is the standard discharge duration. deficit This represents the total energy deficit.

[0151] S5. Based on minimizing the total life cycle cost, and under the condition of meeting the preset constraints, iteratively adjust the wind and solar power ratio in the initial wind and solar power ratio range, and repeat the above steps S1-S4, and take the hybrid energy storage configuration scheme with the lowest total cost as the optimal configuration scheme.

[0152] Preferably, based on minimizing the total lifecycle cost, and under preset constraints, the wind-solar ratio within the initial wind-solar ratio range is iteratively adjusted, and steps S1-S4 are repeated. The hybrid energy storage configuration scheme with the lowest total cost is selected as the optimal configuration scheme, including:

[0153] Determine the objective function based on minimizing the total lifecycle cost:

[0154] minC total =C inv +C OM -R benefit

[0155] Among them, C inv For investment costs, C OM For operation and maintenance costs; Rbenefit For energy storage revenue;

[0156] The constraints include:

[0157] System reliability constraints include: power failure rate ≤ 5%;

[0158] The constraints for renewable energy consumption rate include: a comprehensive curtailment rate of ≤10% and a renewable energy power generation ratio of ≥60%;

[0159] Energy storage technology requires constraints, including setting charge / discharge efficiency, cycle life, and SOC operating range.

[0160] In step S5, the present invention performs multi-objective collaborative optimization and verification of hybrid energy storage.

[0161] This invention aims to minimize total lifecycle cost and maximize reliability.

[0162] (1) The objective function is:

[0163] minC total =C inv +C OM -R benefit

[0164] Among them, C inv Investment cost, related to power capacity and energy capacity; C OM For operation and maintenance costs; R benefit The revenue from energy storage includes peak shaving and valley filling, frequency regulation, and other benefits.

[0165] (2) The constraints are:

[0166] System reliability, for example: power outage rate ≤ 5%.

[0167] New energy consumption rate, for example: comprehensive curtailment rate ≤10%, and new energy power account for ≥60% of energy storage technology requirements: charge and discharge efficiency, cycle life, and SOC operating range.

[0168] In step S5, the present invention performs closed-loop iterative optimization:

[0169] This invention returns to step one, modifies the wind-solar power ratio, and performs optimization calculations using a cyclic hybrid energy storage model. Ultimately, the scheme with the lowest total cost or highest overall benefit from the "wind-solar power ratio + hybrid energy storage configuration" is selected as the final recommended scheme, thereby achieving global optimization from power source to energy storage.

[0170] This invention proposes a wind-solar-storage coordinated planning method based on multi-timescale fluctuation characteristic decomposition. Through time-series data acquisition of wind and solar resources, fluctuation characteristic decomposition, complementary quantitative assessment, and hybrid energy storage optimization, it reveals the coupling relationship between wind and solar power output characteristics and energy storage configuration requirements at different time scales, and their impact on system economy and reliability. By deeply analyzing the multi-timescale fluctuation characteristics of wind and solar power output, the wind-solar capacity ratio can be better optimized, reducing the system's demand for flexibility resources from the source. By establishing a closed-loop optimization mechanism for source-storage coordination, we can accurately match power-type and energy-type energy storage capacities, improve energy storage utilization efficiency, and reduce the total life-cycle cost. This has significant practical implications for achieving the safe, stable operation and sustainable development of high-proportion renewable energy power systems.

[0171] This invention addresses the challenges of complex fluctuation characteristics and mismatch between energy storage configuration and power structure in the coordinated planning of wind-solar-storage power systems with a high proportion of renewable energy. Through multi-timescale fluctuation characteristic decomposition and complementarity quantification analysis, it transforms the traditional single-energy storage planning problem into a global optimization problem of source-storage coordination. The proposed hybrid energy storage capacity allocation method mainly includes five steps, as shown in the flowchart below. Figure 2 As shown.

[0172] To verify the effectiveness of this scheme, this embodiment takes a new energy base in a desert area in Northwest China as an example. This area plans to construct a large-scale new energy base, transmitting electricity via ultra-high-voltage direct current (UHVDC) lines. The project area's annual equivalent utilization hours are: wind power 2200 hours, photovoltaic 1500 hours. The DC transmission capacity is 8000MW, requiring a new energy installed capacity of 8400MW. For example... Figure 3 As shown.

[0173] First, the empirical mode decomposition algorithm is used to decompose the net load fluctuation into:

[0174] Intraday high-frequency fluctuations: average fluctuation range ±850MW, maximum rate of change 180MW / hour;

[0175] Daytime fluctuations: average daily peak-to-valley difference of 1200MW, load factor of 0.65;

[0176] Multi-day fluctuations: The longest continuous power outage lasted 3 days, with a cumulative energy deficit of 210 million kilowatt-hours. For example... Figure 4 As shown.

[0177] Figure 5 This is a schematic diagram illustrating the monthly variation trend of each frequency. Figure 6 This is a diagram illustrating the monthly percentage of each frequency division.

[0178] A lifecycle cost minimization model was established, and hybrid energy storage was configured. The optimization results are as follows:

[0179] Power-type energy storage: configured with 650MW / 4600MWh;

[0180] Energy storage: 200MW / 2000MWh.

[0181] Finally, iterative verification was conducted, and the optimal mixed-use capacity under this wind-solar ratio was determined to be 850MW.

[0182] This invention provides a hybrid energy storage capacity allocation device based on the energy proportion of multi-timescale fluctuation components. The device is used to perform:

[0183] S1: Acquire time-series data of wind and solar resources and load curve data, set the wind and solar power ratio value through the time-series production simulation model, and output the system net load curve;

[0184] S2, decompose the system net load curve, obtain the fluctuation components, and calculate the energy proportion of each fluctuation component;

[0185] Preferably, the system net load curve is decomposed to obtain fluctuation components, and the energy proportion of each fluctuation component is calculated, including:

[0186] The volatility components include: intraday high-frequency volatility components, intraday volatility components, and multi-day volatility components.

[0187] The period of the intraday high-frequency fluctuation component is ≤4 hours, reflecting minute- to hourly fluctuations caused by sudden changes in wind speed and cloud movement. The characteristic parameters of the intraday high-frequency fluctuation component include fluctuation amplitude, extreme value of the rate of change, and average daily fluctuation frequency. The energy proportion of the intraday high-frequency fluctuation component is:

[0188]

[0189] The period of the daytime fluctuation component is 4-24 hours, reflecting the intraday wind-solar complementarity characteristics. The characteristic parameters of the daytime component include the daily average peak-to-valley difference and load factor. The proportion of the daytime fluctuation component is:

[0190]

[0191] The period of the multi-day fluctuation component is >24 hours, reflecting the sustained energy imbalance caused by changes in the weather system. The characteristic parameters of the multi-day fluctuation component include the duration of power shortage and the cumulative energy deficit. The proportion of the multi-day fluctuation component is:

[0192]

[0193] Among them, E H E M and E L These are the high-frequency fluctuation component, the mid-frequency fluctuation component, and the low-frequency fluctuation component, E. total It represents the total fluctuation.

[0194] S3, evaluate the constructed wind and solar capacity allocation scheme based on the fluctuation components and the energy proportion of each fluctuation component, and determine the preliminary wind and solar capacity allocation range;

[0195] Preferably, the constructed wind-solar capacity allocation scheme is evaluated based on the fluctuation components and the energy proportion of each fluctuation component to determine the initial wind-solar allocation range, including:

[0196] Multiple wind-solar capacity allocation schemes are set, and a set of wind-solar capacity allocation schemes is established as {λ}. i};

[0197] Where λ i This represents the proportion of wind power installed capacity in the i-th wind-solar capacity allocation scheme. For each wind-solar capacity allocation scheme, the complementarity of wind and solar resources is quantitatively evaluated through assessment indicators, including:

[0198] The standard deviation σ and maximum peak-to-valley difference ΔP of net load fluctuation are calculated using fluctuation mitigation effectiveness indicators. max and the peak-to-valley difference limit ΔP limit ;

[0199] The energy percentage limit E of each fluctuation component is set by the component energy distribution index. Hlim E Mlim E Llim Among them, E Hlim E Mlim E Llim These are the energy percentage limits for intraday high-frequency fluctuation components, interday fluctuation components, and multi-day fluctuation components, respectively.

[0200] η is calculated using system performance indicators. NE η min R NE R min ; where η NE For the utilization rate of new energy, η min To set the minimum utilization requirement, R NE For the proportion of electricity generated by new energy sources, R min The minimum required percentage of renewable energy consumption is set.

[0201] Each wind-solar ratio is comprehensively and quantitatively evaluated using assessment indicators. A multi-objective decision-making method is then employed to select candidate ratio ranges that can maximize the smoothing of fluctuations and achieve the smoothest net load curve from the source.

[0202] .

[0203] S4 is configured with power-type energy storage and energy-type energy storage respectively, based on the goal of minimizing the total life cycle cost;

[0204] Preferably, based on the goal of minimizing the total life cycle cost, power-type energy storage and energy-type energy storage are configured respectively, including:

[0205] Establish a system with the objective function of minimizing the total lifecycle cost, and configure power-type energy storage and energy-type energy storage respectively. The configuration of power-type energy storage includes:

[0206] To meet the balancing needs of high-frequency intraday fluctuations, the power capacity of power-type energy storage is mainly determined by the short-term variability of the net load, while the energy capacity is determined by the duration of continuous charging and discharging. Power-type energy storage needs to meet the following requirements:

[0207]

[0208] Where k1 and k2 are the adaptation coefficients for power scenario and energy scenario, respectively, P fmax E represents the maximum fluctuating power of the system during the day, indicating the maximum change in net load over a short period of time. It directly determines the instantaneous power support capacity that power-type energy storage needs to provide. fTotal P is the total daily fluctuation energy (the sum of the absolute values ​​of the total energy fluctuating around the system's net load around its average value), used to quantify the overall degree of "instability" of the system. power The power capacity required for power-type energy storage, i.e., the maximum charge and discharge power of the energy storage system that needs to be configured, E power The energy capacity required for power-type energy storage, i.e., the maximum energy that the energy storage system needs to be configured to store;

[0209] Configuring energy storage includes: addressing diurnal and multi-day fluctuations and resolving energy deficits. The energy capacity of energy storage is determined by the cumulative energy shortfall under extreme weather conditions such as continuous cloudy / rainy weather and no wind. Energy storage needs to meet the following requirements:

[0210]

[0211] Among them, T dis The typical discharge duration is given by k3, where k3 is the adaptation coefficient and E is the standard discharge duration. deficit E represents the total energy deficit. energy For the energy capacity required for energy storage, P energy The power capacity required for energy storage.

[0212] S5. Based on minimizing the total life cycle cost, and under the condition of meeting the preset constraints, iteratively adjust the wind and solar power ratio in the initial wind and solar power ratio range, and repeat the above steps S1-S4, and take the hybrid energy storage configuration scheme with the lowest total cost as the optimal configuration scheme.

[0213] Preferably, based on minimizing the total lifecycle cost, and under preset constraints, the wind-solar ratio within the initial wind-solar ratio range is iteratively adjusted, and steps S1-S4 are repeated. The hybrid energy storage configuration scheme with the lowest total cost is selected as the optimal configuration scheme, including:

[0214] Determine the objective function based on minimizing the total lifecycle cost:

[0215] minC total =C inv +C OM -R benefit

[0216] Among them, C inv For investment costs, C OM For operation and maintenance costs; R benefit For energy storage revenue;

[0217] The constraints include:

[0218] System reliability constraints include: power failure rate ≤ 5%;

[0219] The constraints for renewable energy consumption rate include: a comprehensive curtailment rate of ≤10% and a renewable energy power generation ratio of ≥60%;

[0220] Energy storage technology requires constraints, including setting charge / discharge efficiency, cycle life, and SOC operating range.

[0221] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0222] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0223] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0224] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0225] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0226] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0227] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0228] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

Claims

1. A hybrid energy storage capacity allocation method based on multi-time scale fluctuation component energy proportion, the method comprising: S1, obtaining wind and light resource time series data and load curve data, setting wind and light ratio through a time series production simulation model, and outputting system net load curve; S2, decomposing the system net load curve, obtaining fluctuation components, and calculating the energy proportion of each fluctuation component; S3, based on the fluctuation components and the energy proportion of each fluctuation component, evaluating the constructed wind and light capacity ratio scheme to determine the wind and light preliminary ratio interval; S4, based on the minimum whole life cycle cost as the target, respectively configuring power type energy storage and energy type energy storage; S5, based on the minimum whole life cycle cost as the target, and under the condition of meeting the preset constraint condition, iteratively adjusting the wind and light ratio in the wind and light preliminary ratio interval, and repeating the above steps S1-S4, taking the hybrid energy storage configuration scheme with the lowest total cost as the optimal configuration scheme.

2. The method of claim 1, wherein the system net load curve is decomposed to obtain fluctuation components and calculate the energy proportion of each fluctuation component, comprising: The fluctuation components include: intra-day high-frequency fluctuation components, inter-day fluctuation components, and multi-day fluctuation components; The intra-day high-frequency fluctuation component has a period of ≤4 hours, reflects minute-level to hour-level fluctuations caused by wind speed mutations and cloud movement, the characteristic parameters of the intra-day high-frequency fluctuation component include fluctuation amplitude, change rate extreme value, and daily average fluctuation frequency, and the energy proportion of the intra-day high-frequency fluctuation component is: The inter-day fluctuation component has a period of 4-24 hours, reflects the intra-day wind and light complementary characteristics, the characteristic parameters of the inter-day component include daily average peak-valley difference and load rate, and the proportion of the inter-day fluctuation component is: The multi-day fluctuation component has a period of >24 hours, reflects the sustained multi-day energy imbalance caused by weather system changes, the characteristic parameters of the multi-day fluctuation component include sustained power shortage duration and cumulative energy gap, and the proportion of the multi-day fluctuation component is: where E H , E M and E L are the high, medium and low frequency fluctuation components, respectively, and E total is the total fluctuation.

3. The method of claim 2, wherein the constructed wind and light capacity ratio scheme is evaluated based on the fluctuation components and the energy proportion of each fluctuation component to determine the wind and light preliminary ratio interval, comprising: A plurality of wind-solar capacity matching schemes are set, and a wind-solar capacity matching scheme set is established as {λ i}. where λ i represents the installed capacity ratio of wind power of the ith wind-solar capacity matching scheme. Each wind-solar capacity matching scheme quantitatively evaluates the complementarity of wind-solar resources through evaluation indexes, including: The standard deviation σ of the net load fluctuation and the maximum peak-valley difference ΔP are calculated by the fluctuation damping effect index max and the peak-valley difference limit ΔP limit ; The energy percentage limit E of each fluctuation component is set by the component energy distribution index. Hlim E Mlim E Llim Among them, E Hlim E Mlim E Llim These are the energy percentage limits for intraday high-frequency fluctuation components, interday fluctuation components, and multi-day fluctuation components, respectively. η is calculated by system performance indicators NE , η min , R NE , R min ; wherein, η NE is the new energy utilization rate, η min is the minimum utilization rate requirement, R NE is the new energy power ratio, and R min is the minimum new energy power ratio requirement Each wind and light ratio is comprehensively quantitatively evaluated through the evaluation index, and a multi-objective decision method is used to select a candidate ratio interval that can maximize the fluctuation suppression from the source and make the net load curve most smooth:

4. The method of claim 3, wherein the power type energy storage and the energy type energy storage are respectively configured based on the minimum whole life cycle cost as the target, comprising: A target function with the minimum whole life cycle cost is established to respectively configure the power type energy storage and the energy type energy storage, wherein the power type energy storage comprises: The power capacity of the power type energy storage is mainly determined by the short-time variability of the net load, and the energy capacity of the power type energy storage is determined by the duration of the time period during which continuous charging and discharging is required, and the power type energy storage needs to meet: wherein k1, k2 are the power scenario and energy scenario adaptation coefficients, respectively, P fmax is the daily system maximum fluctuation power, E fTotal is the total fluctuation energy per day, P power is the power capacity required for the power-type storage, E power is the energy capacity required for the power-type storage; The energy type energy storage includes: in order to solve the energy shortage, the energy capacity of the energy type energy storage is determined by the cumulative energy gap under extreme weather such as continuous rain and no wind, and the energy type energy storage needs to meet: where T dis is the typical discharge duration, k3 is an adaptation factor, E deficit is the total energy deficit, E energy is the energy capacity required for the energy storage, P energy is the power capacity required for the energy storage.

5. The method of claim 4, wherein the wind-solar preliminary matching interval is iteratively adjusted based on the target of minimizing the total life cycle cost, and the preset constraint condition is met, and the steps S1-S4 are repeated, and the mixed energy storage configuration scheme with the lowest total cost is taken as the optimal configuration scheme, comprising: determining the target function based on the minimization of the total life cycle cost: minC total = C inv + C OM - R benefit where C inv is the investment cost, C OM is the operation and maintenance cost; R benefit is the energy storage benefit; the constraint condition includes: the system reliability constraint condition includes: power shortage rate ≤ 5%; the new energy consumption rate constraint condition includes: comprehensive curtailment rate ≤ 10%, and new energy power ratio ≥ 60%; the energy storage technology requirement constraint condition includes setting the charging and discharging efficiency, cycle life, and SOC operating range.

6. A mixed energy storage capacity allocation device based on energy proportion of multi-time scale fluctuation components, the device is used for executing: S1, obtaining wind-solar resource time series data and load curve data, setting wind-solar matching value through a time series production simulation model, and outputting system net load curve; S2, decomposing the system net load curve, obtaining fluctuation components, and calculating energy proportion of each fluctuation component; S3, evaluating the constructed wind-solar capacity matching scheme based on the fluctuation components and the energy proportion of each fluctuation component, and determining a wind-solar preliminary matching interval; S4, respectively configuring power type energy storage and energy type energy storage based on the target of minimizing the total life cycle cost; S5, iteratively adjusting the wind-solar preliminary matching interval based on the target of minimizing the total life cycle cost, and meeting the preset constraint condition, and repeating the steps S1-S4, and taking the mixed energy storage configuration scheme with the lowest total cost as the optimal configuration scheme.

7. The device of claim 6, wherein the system net load curve is decomposed to obtain fluctuation components and calculate energy proportion of each fluctuation component, comprising: the fluctuation components include: intra-day high-frequency fluctuation components, daily fluctuation components, and multi-day fluctuation components; the intra-day high-frequency fluctuation component has a period of ≤ 4 hours, reflects minute-level to hour-level fluctuation caused by wind speed mutation and cloud movement, the characteristic parameters of the intra-day high-frequency fluctuation component include fluctuation amplitude, change rate extreme value, and daily average fluctuation frequency, and the energy proportion of the intra-day high-frequency fluctuation component is: the daily fluctuation component has a period of 4-24 hours, reflects intra-day wind-solar complementary characteristics, the characteristic parameters of the daily fluctuation component include daily average peak-valley difference and load rate, and the proportion of the daily fluctuation component is: the multi-day fluctuation component has a period of > 24 hours, reflects sustained multi-day energy imbalance caused by weather system change, the characteristic parameters of the multi-day fluctuation component include sustained power shortage time length and cumulative energy gap, and the proportion of the multi-day fluctuation component is: where E H , E M and E L are the high, medium and low frequency fluctuation components, respectively, and E total is the total fluctuation.

8. The apparatus of claim 7, wherein the wind-solar capacity ratio scheme is constructed based on the fluctuation components and the energy proportion of each fluctuation component, and a wind-solar preliminary ratio interval is determined, including: A plurality of wind-solar capacity matching schemes are set, and a wind-solar capacity matching scheme set is established as {λ i}. where λ i represents the installed capacity ratio of wind power of the ith wind-solar capacity matching scheme. Each wind-solar capacity matching scheme quantitatively evaluates the complementarity of wind-solar resources through evaluation indexes, including: The standard deviation σ of the net load fluctuation and the maximum peak-valley difference ΔP are calculated by the fluctuation damping effect index max and the peak-valley difference limit ΔP limit ; By setting the energy proportion limit E of each fluctuation component through the component energy distribution index Hlim 、 Mlim 、 Llim ; wherein E Hlim 、 Mlim 、 Llim ; respectively, the energy proportion limit of the intraday high-frequency fluctuation component, the interday fluctuation component, and the multi-day fluctuation component η is calculated using system performance indicators. NE η min R NE R min ; where η NE For the utilization rate of new energy, η min To set the minimum utilization requirement, R NE For the proportion of electricity generated by new energy sources, R min The minimum required percentage of renewable energy consumption is set. each wind-solar ratio is quantitatively evaluated by the evaluation index, and a multi-objective decision method is used to screen out a candidate ratio interval that can maximize the smoothing of fluctuations from the source and make the net load curve smoothest: 。 9. The apparatus of claim 8, wherein the power-type storage and the energy-type storage are configured based on the minimization of the life cycle cost, including: a target function of the minimization of the life cycle cost is established, and the power-type storage and the energy-type storage are configured, wherein the configuration of the power-type storage includes: to meet the balance demand of high-frequency fluctuations within a day, the power capacity of the power-type storage is mainly determined by the short-time variability of the net load, and the energy capacity of the power-type storage is determined by the duration of continuous charging and discharging, and the power-type storage needs to meet: wherein k1, k2 are the power scenario and energy scenario adaptation coefficients, respectively, P fmax is the maximum fluctuating power of the system during the day, E fTotal is the total fluctuating energy per day, P power is the power capacity required for the power storage, E power is the energy capacity required for the power storage; the configuration of the energy-type storage includes: to cope with day-to-day fluctuations and solve energy shortages, the energy capacity of the energy-type storage is determined by the cumulative energy gap under extreme weather conditions such as continuous rain and no wind, and the energy-type storage needs to meet: where T is the typical discharge duration, k3 is an adaptation factor, E dis is the total energy deficit, E deficit is the total energy deficit, E energy is the energy capacity required for the energy storage, P energy is the power capacity required for the energy storage.

10. The apparatus of claim 9, wherein the wind-solar ratio value in the wind-solar preliminary ratio interval is iteratively adjusted based on the minimization of the life cycle cost and under the satisfaction of preset constraint conditions, and the steps S1-S4 are repeated, and the mixed storage configuration scheme with the lowest total cost is taken as the optimal configuration scheme, including: a target function based on the minimization of the life cycle cost is determined: minC total = C inv + C OM - R benefit Where C inv is the investment cost, C OM is the operation and maintenance cost; R benefit is the energy storage benefit; the constraint conditions include: a system reliability constraint condition, including: power shortage rate ≤ 5%; a new energy consumption rate constraint condition, including: comprehensive curtailment rate ≤ 10%, and new energy power proportion ≥ 60%; a storage technology requirement constraint condition, including setting the charging and discharging efficiency, cycle life, and SOC operating range.

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