A wind-solar-storage new energy station energy storage system optimization configuration method, system, terminal and medium

Abstract

The present application relates to the field of new energy power generation and energy storage technology, and specifically provides a wind-solar-storage new energy station energy storage system optimization configuration method, system, terminal and medium, the method comprises: obtaining grid-connected power fluctuation constraint parameters, including the maximum power variation allowed in the unit discretization control period; based on the maximum power variation, the discretization control period and the theoretical maximum output of the energy storage system, the upper limit value of the first-order filter smoothing time constant is deduced reversely; based on the same upper limit value of the smoothing time constant, the rated power configuration lower limit of the energy storage system is determined according to the proportional relationship between the upper limit value and the maximum power variation and the discretization control period respectively, and the rated capacity configuration lower limit of the energy storage system is determined according to the proportional relationship between the upper limit value of the smoothing time constant and the total rated output power of the wind farm and the photovoltaic power station; finally, the rated power and the rated capacity of the energy storage system are configured based on the lower limit value. The present application reduces the calculation cost of energy storage configuration and improves the power quality under the condition of new energy grid connection.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation and energy storage technology, specifically to an optimized configuration method, system, terminal and medium for energy storage systems in wind, solar and energy storage power plants. Background Technology

[0002] In actual operation, the output of wind power and photovoltaic power generation is significantly affected by natural environmental factors, exhibiting strong volatility and intermittency. This leads to frequent fluctuations in the grid-connected power of wind, solar and energy storage power plants, affecting the voltage and frequency stability of the power grid.

[0003] To address these issues, two main technical solutions are currently employed: one is to configure energy storage systems of a certain capacity within wind and solar power plants, using the charging and discharging of the storage units to smooth fluctuations in wind and solar power output; the other is to control the grid connection of renewable energy sources within the region through power system dispatching. Among these, configuring energy storage systems is widely used due to its flexible adjustment and rapid response.

[0004] However, current capacity configuration methods for energy storage systems have the following drawbacks: First, they rely heavily on historical power data from wind and solar power plants for spectrum analysis or statistical fitting, requiring a large amount of measured data, which makes them unsuitable for newly built sites or scenarios with missing data. Second, existing methods typically require complex iterative optimization processes, resulting in low computational efficiency. Third, power configuration and capacity configuration are often performed independently, lacking a unified parameter correlation, making it difficult to ensure the consistency of configuration results. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method, system, terminal, and medium for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants. By establishing an analytical mapping relationship between grid connection fluctuation constraint parameters and the upper limit of the first-order filter smoothing time constant, the lower limit of the rated power and the lower limit of the rated capacity of the energy storage system are collaboratively determined based on the same upper limit of the smoothing time constant. This achieves coordinated configuration of the energy storage system without the need for historical data or iterative optimization, reducing the computational cost of energy storage configuration. It is suitable for the rapid planning and design of wind, solar, and energy storage power plants and improves the power quality under the grid connection status of new energy sources.

[0006] In a first aspect, the technical solution of the present invention provides a method for optimizing the configuration of energy storage systems in wind, solar and energy storage power stations, comprising the following steps: Obtain grid-connected power fluctuation constraint parameters for wind farms and photovoltaic power plants connected to the grid, wherein the constraint parameters include the maximum allowable power change within a unit discretized control cycle; Based on the maximum power change, the discretized control cycle, and the theoretical maximum output of the energy storage system, the upper limit of the first-order filter smoothing time constant is derived in reverse to ensure that the grid-connected power fluctuation of the wind, solar, and energy storage power station meets the constraint of the maximum power change. The lower limit of the rated power configuration of the energy storage system is determined based on the ratio of the upper limit of the first-order filter smoothing time constant to the maximum power change and the discretized control cycle. The lower limit of the rated capacity configuration of the energy storage system is determined based on the ratio of the upper limit of the first-order filter smoothing time constant to the total rated output power of the wind farm and the photovoltaic power station. The rated power and rated capacity of the energy storage system are configured based on the lower limit of the rated power configuration and the lower limit of the rated capacity configuration.

[0007] Secondly, the technical solution of the present invention provides an optimized configuration system for energy storage systems in wind, solar, and energy storage power stations, comprising: The constraint parameter acquisition module is used to acquire the grid-connected power fluctuation constraint parameters of wind farms and photovoltaic power plants connected to the grid. The constraint parameters include the maximum allowable power change within a unit discretized control period. The upper limit determination module for time constant is used to deduce the upper limit value of the first-order filter smoothing time constant based on the maximum power change, the discretized control cycle, and the theoretical maximum output of the energy storage system, so that the grid-connected power fluctuation of wind, solar and energy storage new energy power plants meets the constraint of the maximum power change. The rated power capacity lower limit determination module is used to determine the rated power configuration lower limit of the energy storage system based on the ratio of the upper limit of the first-order filter smoothing time constant to the maximum power change and the discretized control cycle, and to determine the rated capacity configuration lower limit of the energy storage system based on the ratio of the upper limit of the first-order filter smoothing time constant to the total rated output power of the wind farm and photovoltaic power station. The energy storage system configuration module is used to configure the rated power and rated capacity of the energy storage system based on the lower limit of the rated power configuration and the lower limit of the rated capacity configuration.

[0008] Thirdly, the technical solution of the present invention provides a terminal, comprising: The memory is used to store the optimization configuration program for the energy storage system of wind, solar and energy storage new energy power stations; The processor is used to implement the steps of the wind, solar, and energy storage power station energy storage system optimization configuration method as described above when executing the wind, solar, and energy storage power station energy storage system optimization configuration program.

[0009] Fourthly, the present invention provides a computer-readable storage medium storing an optimization configuration program for a wind-solar-storage energy storage system at a new energy power station. When the optimization configuration program is executed by a processor, it implements the steps of the optimization configuration method for a wind-solar-storage energy storage system at a new energy power station as described above.

[0010] As can be seen from the above technical solutions, this application has the following advantages: By obtaining the grid-connected power fluctuation constraint parameters and deriving the upper limit of the first-order filter smoothing time constant in reverse, a direct mapping relationship between the grid-connected standard and the energy storage configuration parameters is established. This eliminates the need to rely on historical power data to determine the lower limits of the energy storage system's power and capacity, significantly reducing the dependence of energy storage configuration on data integrity and improving the method's universality and engineering applicability. Based on the same upper limit of the smoothing time constant, the lower limit of the power configuration is determined according to its proportional relationship with the maximum power change and the discretized control cycle, and the lower limit of the capacity configuration is determined according to its proportional relationship with the total rated output power of the wind and solar power station, thus realizing the correlation between power configuration and capacity configuration. The coordinated and unified approach avoids the parameter mismatch problem caused by the independent configuration of wind, solar, and energy storage units in traditional methods. By rationally configuring the power and capacity of energy storage units in the wind-solar-energy storage power station system, it smooths out short-term fluctuations in the power generation of wind and solar power stations to the greatest extent, greatly reducing the grid-connected power fluctuations of wind-solar-energy storage power stations and improving the power quality under the grid-connected state of new energy. Furthermore, it ensures that the cost of the energy storage system is within a reasonable range, which has certain reference value for the configuration planning of energy storage units in future wind-solar-energy storage combined power stations. The analytical calculation method is used to determine the energy storage configuration parameters, which does not require spectrum analysis or iterative optimization. The calculation process is simple and efficient, which greatly reduces the calculation cost of energy storage configuration and is suitable for the rapid planning and design of wind-solar-energy storage power stations. Attached Figure Description

[0011] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of the structure of a wind, solar, and energy storage power station system.

[0013] Figure 2 This is a schematic diagram illustrating the principle of the wind, solar, and energy storage filtering and smoothing control strategy.

[0014] Figure 3 This is a schematic flowchart of an optimized configuration method for energy storage systems in wind, solar, and energy storage power plants, provided as an embodiment of the present invention.

[0015] Figure 4 A comparison chart of the grid-connected power of wind, solar and energy storage power stations before and after configuring the energy storage system using the method of this invention.

[0016] Figure 5 To smooth the power fluctuation diagram of the wind-solar-storage power station before configuring the energy storage system using the method of the present invention.

[0017] Figure 6The diagram shows the power fluctuation of a wind-solar-storage power station after smoothing the energy storage system configured using the method of this invention.

[0018] Figure 7 This is a schematic block diagram of an optimized configuration system structure for a wind, solar, and energy storage power station, provided as an embodiment of the present invention.

[0019] Figure 8 This is a schematic diagram of the structure of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this application and in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0022] The output and operation of energy sources such as wind and solar power are also constrained by the natural environment. The instability of wind or sunlight leads to significant fluctuations in wind or solar power output, which in turn affects the voltage and frequency stability of the power grid. To address these issues, relevant grid connection standards have set requirements for the power fluctuations of new energy power plants. For example, they specify the maximum power variation limits for wind farms with different installed capacities over 1-minute and 10-minute time scales, as shown in Table 1.

[0023] Table 1: Maximum Power Fluctuation Limits for Wind Farms

[0024] Conventional wind and solar power plants directly feed the generated power from wind turbines and photovoltaic modules into the grid. However, due to the volatility of wind and solar power output, the grid-connected power often fails to meet the aforementioned standards. Configuring energy storage systems within wind and solar power plants, through the regulation of charge and discharge of the storage units, can effectively smooth out short-term fluctuations in wind and solar power output and improve the power quality of the grid-connected power.

[0025] This invention achieves smooth control of wind and solar power output based on the principle of first-order filtering and smoothing control. For example... Figure 2As shown, this principle involves applying a first-order low-pass filter to the total output of the wind-solar system, using the filtered power as the target grid-connected power for the wind-solar-storage power station. When the actual wind and solar output exceeds the target power, the excess power is absorbed by the energy storage system; when the actual output is lower than the target power, the insufficient power is released by the energy storage system. Through this charging and discharging regulation, the grid-connected power of the wind-solar-storage power station is changed, ensuring that the grid-connected power of the new energy power station remains stable and controllable.

[0026] Based on the first-order filter smoothing control principle, the total power generation of wind and solar power... With the filtered grid-connected target power The transfer function relationship between them is:

[0027] in, This is the smoothing time constant of the energy storage system, and its magnitude directly affects the smoothing effect and the configuration requirements of the energy storage system.

[0028] The power absorbed or released by the energy storage system can be obtained from the law of power conservation. .

[0029] From the above two equations, we can obtain that

[0030] The above relationship is discretized, and the discretization control period is assumed to be... Then in the first Each control cycle time ,have:

[0031] Solving for:

[0032] and The first The and the first The target grid-connected power value for each discrete control cycle. For the first The charging and discharging power of the energy storage system in each discrete control cycle.

[0033] make The target power for grid connection can then be expressed as:

[0034] In the known , as well as Then, we can find as well as .at the same time, and Inversely proportional, that is When it decreases, Increase, and The difference is reduced by smoothing out the power fluctuation rate input to the power grid, resulting in a smoother output power curve.

[0035] Based on the aforementioned smooth control principle and in conjunction with the relevant standards' requirements for the maximum power change rate when wind and solar systems are connected to the grid, this invention proposes an optimized configuration method for energy storage systems in wind-solar-storage new energy power plants. For example... Figure 1 As shown, a wind-solar-storage new energy power station system mainly consists of three parts: wind turbine generators, photovoltaic modules, and an energy storage system. This invention obtains grid-connected power fluctuation constraint parameters, derives the upper limit of the first-order filter smoothing time constant, and then simultaneously determines the lower limit of the rated power configuration and the lower limit of the rated capacity configuration of the energy storage system based on the same upper limit of the smoothing time constant, thereby achieving coordinated and optimized configuration of the energy storage system.

[0036] Figure 3 This is a schematic flowchart illustrating a method for optimizing the configuration of an energy storage system at a wind-solar-storage renewable energy power station, provided as an embodiment of the present invention. Figure 3 The executing entity can be a wind-solar-storage energy storage system optimization and configuration system. The wind-solar-storage energy storage system optimization and configuration method provided in this embodiment of the invention is executed by computer equipment; correspondingly, the wind-solar-storage energy storage system optimization and configuration system runs on the computer equipment. Depending on different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0037] like Figure 3 As shown, the method includes the following steps.

[0038] S1, obtain the grid-connected power fluctuation constraint parameters for wind farms and photovoltaic power plants connected to the grid. The constraint parameters include the maximum allowable power change within a unit discretized control cycle.

[0039] S1.1, Obtain the data for wind farms and photovoltaic power plants per unit time. Maximum permissible power variation .

[0040] According to relevant grid connection standards, when wind, solar, and energy storage power plants are connected to the grid, their grid-connected power fluctuations must meet the maximum power change rate limit requirements. This standard specifies the maximum power change limits for wind farms with different installed capacities over 1-minute and 10-minute timescales, as detailed in Table 1.

[0041] In practical applications, the maximum 10-minute change and the maximum 1-minute change can be determined by referring to a table based on the total installed capacity of the wind farm and photovoltaic power station in the wind-solar-storage new energy power station, and the more stringent value between the two can be taken as the unit time. Maximum permissible power variation .in, You can select 1 minute or 10 minutes, depending on the grid connection standard requirements and actual control needs.

[0042] S1.2, Based on the discretization control requirements of the energy storage system, set the discretization control cycle. .

[0043] In this embodiment, the energy storage system adopts a discrete control method, with a control cycle of The settings need to comprehensively consider factors such as the control response time, data sampling period, and scheduling command period of the energy storage converter (PCS). In practical engineering, Typically, time intervals of 1 minute, 30 seconds, or shorter are used to ensure that the energy storage system can respond promptly to real-time changes in wind and solar power output. In this embodiment, a discrete control cycle is preferably set. =1min.

[0044] S1.3, [This refers to a unit of time] Maximum permissible power variation Converted to unit discretized control cycle Maximum permissible power variation , Since the grid connection standard specifies the power variation limit over a relatively long time scale (such as 1 minute or 10 minutes), while this embodiment uses a discrete control cycle. For real-time control, the limits specified in the standard need to be converted to a time scale that matches the control cycle. The conversion relationship is as follows:

[0045] in, The unit time The number of discretized control cycles within the timeframe. This conversion will... The total allowable power variation over a given time period is evenly distributed across each control cycle, ensuring that in any continuous period... Within each control cycle, the cumulative power change shall not exceed the limit specified in the standard.

[0046] For example, suppose ,but ,therefore That is, the maximum allowable power change within a unit control cycle is equal to the maximum change in 1 minute specified in the standard. If selected... ,but ,at this time That is, the maximum change in 10 minutes as specified in the standard must be evenly distributed across 10 control cycles.

[0047] This embodiment transforms the macroscopic time-scale constraints specified in the grid connection standard into microscopic constraint parameters that match the control cycle. This provides a unified constraint benchmark for the reverse derivation of the upper limit of the smoothing time constant. This conversion process ensures that the configuration results of the energy storage system can strictly meet the grid connection standard requirements, while maintaining time-domain matching consistency with the discretized control model.

[0048] S2, based on the maximum power change, discretized control cycle and the theoretical maximum output of the energy storage system, derives the upper limit of the first-order filter smoothing time constant in reverse, so that the grid-connected power fluctuation of wind, solar and energy storage power plants meets the constraint of the maximum power change.

[0049] First, based on the first-order filter smoothing control principle, the relationship between the grid-connected power fluctuation of wind, solar, and energy storage power plants and the filter time constant is established, expressed as:

[0050] in, and The first The and the first The target grid-connected power value for each discrete control cycle. For the first The charging and discharging power of the energy storage system in a discrete control cycle. This is the time constant for first-order filtering and smoothing.

[0051] Secondly, based on the maximum power change Constraints:

[0052] The derivation leads to:

[0053] Finally, based on the characteristics of the wind and solar resources of the new energy power station to be configured, the theoretical maximum output of the energy storage system is determined. Then the upper limit of the first-order filter smoothing time constant. .

[0054] Specifically, the formula After iteration, we can obtain:

[0055] In the formula, This represents the initial value of the power injected into the power grid.

[0056] According to the above formula, it can be found that... The output power, after being smoothed by the battery energy storage system, is fed into the grid at a value close to that of... A straight line. Therefore, we can conclude that... The magnitude of this constant affects the smoothing quality and corresponding capacity of the energy storage system. Therefore, selecting a suitable energy storage constant is crucial. It is a crucial node for implementing a smooth control strategy.

[0057] From the above formula, we can derive:

[0058] According to relevant regulations, the requirements for the maximum power change rate when connecting it to the power grid are as follows:

[0059] From this, we can deduce that... .

[0060] Energy storage output The theoretical maximum value is Substituting into the above equation, we can obtain the upper limit of the first-order filter smoothing time constant. : .

[0061] In some alternative implementations, fluctuation analysis based on historical data is used to determine... First, obtain the historical operating data of the wind farm over the past year, including the total power output of the wind farm. Total output data of photovoltaic power plants The combined total output of wind and solar power If no historical data is available for the newly established power station, data from similar stations in a nearby climate zone can be used as a substitute. Secondly, for each sampling point t, calculate its power change relative to the previous sampling point. , The sampling interval is defined; then, the power change at all sampling points is calculated. Sort by size from largest to smallest, and take the quantile at a certain confidence level as the benchmark value for the theoretical maximum output, for example, the 99th quantile. Considering the safety margin in extreme cases, multiply by a safety factor. (Take 1.1~1.2): In other words, 99% of the time, the instantaneous change in wind and solar power output does not exceed [a certain value]. That is, the energy storage system needs to have at least Only with sufficient instantaneous power can most fluctuations be smoothed out.

[0062] In some optional implementations, for newly built power stations without historical data, the climate zone to which the station belongs is determined based on its geographical location. Then, 3-5 typical extreme fluctuation scenarios with the same climate type as the station to be configured are selected from a pre-built library of typical operating scenarios. These scenarios meet the following criteria: the power fluctuation rate per unit time exceeds the grid connection standard limit, and they cover different seasons and different weather types. For each selected scenario j, its maximum power change over the entire time series is calculated. The maximum value of the maximum power change for each scenario is taken, taking into account a certain safety margin. ,get .

[0063] This embodiment establishes an analytical relationship between grid connection fluctuation constraints and smoothing time constants through the above derivation. This differs from existing technologies that... As the parameters that need to be optimized or set empirically differ, this invention will... Represented as , and The function achieves the effect of determining the upper limit of the smoothing time constant from the grid connection standard parameters. The significance lies in the fact that, under the premise of meeting grid connection fluctuation constraints, it is the maximum allowable filtering time constant in theory. Its value directly determines the smoothing capability of the energy storage system and the subsequent configuration requirements.

[0064] Simultaneously characterized The intrinsic relationship between the parameters, i.e., the discretized control period Proportional to the change in maximum power Inversely proportional to the theoretical maximum output of the energy storage system These intrinsic relationships are directly proportional, providing a parameter basis for determining the lower limit of power and capacity configuration for subsequent energy storage systems.

[0065] S3. The lower limit of the rated power configuration of the energy storage system is determined based on the ratio of the upper limit of the first-order filter smoothing time constant to the maximum power change and the discretized control cycle. The lower limit of the rated capacity configuration of the energy storage system is determined based on the ratio of the upper limit of the first-order filter smoothing time constant to the total rated output power of the wind farm and the photovoltaic power station.

[0066] The lower limit of rated power configuration refers to the minimum rated power value required by the energy storage system to meet grid connection fluctuation constraints. This is derived from the formula in step S2. and energy storage output The maximum value is It can be seen that the energy storage system in the first... The charging and discharging power required for each discrete control cycle Smoothing time constant The following relationship exists between them:

[0067] In other words, given a smoothing time constant In this case, the maximum charging and discharging power required by the energy storage system is equal to... Proportional to the discretization control period Inversely proportional. In other words, to achieve a stronger smoothing effect (i.e., a larger smoothing effect)... If so, a larger energy storage capacity is required.

[0068] The upper limit of the smoothing time constant determined in step S2. Substituting into the above formula, we can obtain the theoretical maximum charging and discharging power required by the energy storage system to meet grid connection fluctuation constraints:

[0069] in, This is the theoretical maximum power demand value, i.e., under ideal conditions (ignoring SOC constraints, efficiency losses, system response delays, etc.), to meet grid connection fluctuation constraints. Theoretically, an energy storage system needs to be able to provide the maximum instantaneous charging and discharging power.

[0070] However, in practical engineering applications, due to factors such as state of charge (SOC) constraints, charging and discharging efficiency losses, system response delays, measurement and control errors, and extreme fluctuations in wind and solar power output, the actual output power required by the energy storage system is often greater than the theoretical demand. To address these uncertainties and ensure that the energy storage system meets grid connection fluctuation constraints under various operating conditions, this invention introduces a power reserve margin coefficient. The theoretical maximum power demand value is corrected.

[0071] S3.01, Preset power reserve margin .

[0072] S3.02, then the lower limit of rated power configuration. Determined by the following formula: .

[0073] This embodiment achieves a direct mapping from grid-connected standard parameters to the lower limit of energy storage power configuration through the formula in step S3.02. It does not rely on historical power data, spectrum analysis, or iterative optimization; the calculation process is simple and efficient. and 、 Proportional to, with The inverse relationship can reveal the influence of various parameters on power configuration, by introducing a power reserve margin factor. This allows the configuration results to adapt to various uncertainties in actual engineering projects.

[0074] In some alternative implementations, a power reserve margin is preset. Specifically, it includes the following steps.

[0075] Step 11: Obtain a typical operating scenario library covering wind-solar-storage combined power generation systems under different climate types. The typical operating scenario library contains typical wind and solar power output scenario data under different climate zones, seasons, and weather types.

[0076] The scenario database contains typical wind and solar power output scenario data under different climate zones (high-altitude, cold, high-latitude, and warm-temperate regions, etc.), different seasons (spring, summer, autumn, and winter), and different weather types (sunny, cloudy, overcast, rainy, and snowy weather, etc.). The data sources for the scenario database can be photovoltaic energy storage empirical experimental datasets, publicly available meteorological and new energy power output datasets, or constructed from historical data accumulated over a long period of operation.

[0077] Step 12: Select historical wind and solar power output data for several typical extreme fluctuation scenarios from the typical operating scenario library. The typical extreme fluctuation scenario refers to a power fluctuation rate exceeding the maximum power change per unit time. The scene.

[0078] Step 13, for each typical extreme fluctuation scenario Based on the wind power output characteristics of this scenario, the theoretical maximum output of the energy storage system required to meet grid connection fluctuation constraints is calculated. And the actual maximum output of the energy storage system after considering the state of charge constraints and charge / discharge efficiency of the energy storage system. .

[0079] Theoretical maximum output This refers to: under ideal conditions, in order to meet grid connection fluctuation constraints The energy storage system theoretically needs to be able to provide a maximum instantaneous charge and discharge power. This can be determined using the same method as described above. The method is calculated based on the wind and solar power output characteristics of the scene.

[0080] Actual maximum output This refers to the maximum output power actually required by the energy storage system under a given scenario, obtained through simulation after considering practical engineering factors such as the state of charge (SOC) constraints, charging and discharging efficiency, and system response delay.

[0081] The simulation process includes: constructing a simulation model based on the first-order filter smoothing control principle, which includes the dynamic changes of wind and solar power output, energy storage system power response, and state of charge (SOC); using the wind and solar power output data of scenario i as input, and employing the theoretical maximum output calculated in step 3. As initial power constraints, actual engineering constraints such as SOC operating range and charging / discharging efficiency are set; cycle-by-cycle simulation is performed on the complete time series of scenario i, and the energy storage power command is calculated and the SOC value is updated in each control cycle, while the actual output power of the energy storage system is recorded; after the simulation, the absolute value of the maximum energy storage output power recorded in the whole process is extracted, which is the actual maximum output power in this scenario.

[0082] Step 14, calculate each scene Sample values ​​of power margin coefficient .

[0083] This ratio reflects the amplification factor of the actual power required for scenario i relative to the theoretical power under actual engineering constraints.

[0084] Step 15: Perform statistical analysis on the sample values ​​of the power margin coefficient, and take the first target quantile at the confidence level as the power reserve margin. .

[0085] For all selected typical extreme fluctuation scenarios, a set of power margin coefficient sample values ​​was obtained. Statistical analysis was performed on this sample set, and the quantiles at a certain confidence level were taken as the power margin. For example, take the upper quantile at the 90% confidence level, i.e., the selected... It can cover 90% of extreme fluctuation scenarios. The quantile at the 90% confidence level is preferred as... .

[0086] There is an inherent relationship between the capacity requirements of energy storage systems and the smoothing time constant, as well as the scale of wind and solar power plants. The throughput power of the energy storage battery Expressed as an integral over time, the corresponding formula is:

[0087] Converting to the form of a difference equation, we get:

[0088] Solving for the results :

[0089] In the above formula, and This represents the electric energy storage system in The amount of electricity consumed at any given time. The capacity of the battery energy storage system can be configured according to the following formula:

[0090] The corresponding expression in the time domain is:

[0091] The total output power of a wind power station varies continuously with changes in wind speed and solar intensity, but its variation range is within a certain range, as follows:

[0092] In the above formula, This represents the total rated output power of the wind farm and photovoltaic power station. Therefore, we have:

[0093] If we treat the range limit in the above formula as the capacity of the energy storage system, then we will have

[0094] Therefore, it can be based on the maximum smoothing time constant. upper limit To determine the lower limit of the rated output power of the energy storage system. There is a power setpoint for the energy storage system. :

[0095] That is, the smoothing time constant reaches its maximum value. In this case, the minimum capacity theoretically required for an energy storage system is at least [amount missing]. The product of the total rated output power of the wind and solar power station.

[0096] In practical engineering applications, due to factors such as state of charge (SOC) constraints, charge / discharge efficiency losses, battery aging and degradation, and the superposition of extreme operating conditions, the actual required capacity of energy storage systems often exceeds the theoretical lower limit. To address these factors and ensure that energy storage systems can meet grid connection fluctuation constraints under various operating conditions, this embodiment introduces a capacity retention margin factor. The lower limit of the theoretical capacity is corrected.

[0097] S3.11, Pre-set capacity reserve margin .

[0098] S3.12, Lower limit of rated capacity configuration for energy storage systems Determined by the following formula:

[0099] in, This represents the total rated output power of the wind farm and photovoltaic power station.

[0100] This embodiment, through the configuration in step S3.12, achieves a direct mapping from grid connection standard parameters to the lower limit of energy storage capacity configuration, without the need for complex integral calculations or iterative optimization, making the calculation process simple and efficient. and , The proportional relationship characterizes the influence of each parameter on capacity configuration; the higher the smoothing requirement and the larger the site scale, the larger the required energy storage capacity. Furthermore, based on the same... Simultaneously defining both power and capacity configuration lower limits ensures their coordinated matching. Furthermore, a capacity reserve margin is introduced. This allows the configuration results to adapt to various constraints in actual engineering projects.

[0101] In some alternative implementations, a capacity reserve margin is preset. Specifically, it includes the following steps.

[0102] Step 21: Obtain a typical operating scenario library covering wind-solar-storage combined power generation systems under different climate types. The typical operating scenario library contains annual wind and solar power output time series data for different seasons and weather types.

[0103] The scenario database contains time-series data on annual wind and solar power output under different seasons (spring, summer, autumn, and winter) and different weather types (sunny, cloudy, overcast, rainy, snowy, etc.). The data sources for the scenario database can be photovoltaic energy storage empirical experimental datasets, publicly available meteorological and new energy power output datasets, or constructed from historical data accumulated over a long period of operation.

[0104] Step 22: Determine the theoretical maximum capacity requirement of the energy storage system. .

[0105] The theoretical maximum capacity requirement refers to the minimum capacity theoretically required for an energy storage system without considering actual operational constraints.

[0106] Step 23, for each typical operating scenario Using the lower limit of rated power configuration Simulations were performed, taking into account the state-of-charge constraints of the energy storage system, and statistics were compiled for each scenario. Minimum capacity actually required for energy storage .

[0107] The lower limit of the rated power configuration determined in step S3. As a power constraint for the energy storage system, operational simulations are performed for each typical operating scenario j. During the simulation, the state of charge (SOC) constraint of the energy storage system is considered, and the SOC change trajectory is recorded throughout the entire simulation period. By adjusting the trial capacity, the minimum capacity value that ensures the SOC remains within the allowable range is found, which is the actual minimum capacity required for that scenario.

[0108] Step 24: Calculate the sample value of the capacity margin coefficient for each scenario. .

[0109] This ratio reflects the magnification factor of the actual capacity required for scenario j relative to the theoretical capacity under actual operational constraints.

[0110] Step 25: Perform statistical analysis on the sample values ​​of the capacity margin coefficient, and take the second target quantile at the confidence level as the capacity retention margin. .

[0111] For all selected typical operating scenarios, a set of sample values ​​for capacity margin coefficients were obtained. Statistical analysis was performed on this sample set, and the quantiles at a certain confidence level were taken as the capacity retention margin. For example, taking the upper quantile at the 95% confidence level means that the selected... It can cover 95% of operating scenarios. In this embodiment, the 95% confidence level quantile is preferably used as... .

[0112] In this embodiment, a first-order filtered smoothing control model is used for simulation to obtain the results for each scenario. Minimum capacity actually required for energy storage Specifically, it includes the following steps.

[0113] Step 231: Set simulation parameters and initial conditions.

[0114] For typical operating scenarios Obtain its wind and solar power output time series data. ,in This represents the total number of sampling points for this scene, with a corresponding time span of [missing information]. (e.g., 24 hours or year-round). Set the lower limit of the rated power configuration for the energy storage system. Discretized control cycle Permissible range of state of charge Initial state of charge (0.5 can be taken as an example), and charge / discharge efficiency. .

[0115] Step 232: Set the capacity search range.

[0116] Set a lower bound for capacity Upper limit of capacity .

[0117] Step 233: Binary search iteration and SOC constraint verification.

[0118] In each iteration, perform the following sub-steps.

[0119] Step 233.1, set the test capacity .

[0120] Step 233.2, with As the rated capacity of energy storage As a power constraint, for the scenario A full-time simulation was performed. The simulation was based on a first-order filtered smoothing control model, with simulations conducted in each discretized control cycle. Perform the following calculations: Calculate the target power for grid connection ; Calculate energy storage power command and restrictions ; Update the SOC value based on the dynamic equation of the state of charge:

[0121] in To improve charging and discharging efficiency, the following parameters are taken during discharging: Take out while charging .

[0122] Step 233.3, record the entire simulation cycle (i.e. arrive The charged state trajectory of ) .

[0123] Step 233.4, verify the state of charge constraints.

[0124] If for all All If it is successful, it indicates the current test capacity. Feasible, let If any Make If the limit is exceeded, it indicates that the current test capacity is insufficient. .

[0125] Step 234: Convergence judgment.

[0126] Repeat step 233 until... Less than the preset accuracy threshold At this point, during convergence... That is, the scene Minimum capacity actually required for energy storage .

[0127] Repeat steps 231 to 234 above for all typical operating scenarios to obtain the minimum capacity requirement for each scenario. Used for capacity reserve margin The determination.

[0128] S4 configures the rated power and rated capacity of the energy storage system based on the lower limit of rated power configuration and the lower limit of rated capacity configuration.

[0129] The lower limit of the rated power configuration determined in step S3. This value represents the minimum power capability required by the energy storage system, ensuring that it meets grid connection fluctuation constraints under various operating conditions. It also utilizes the lower limit of the rated capacity configuration determined in step S3. As the rated capacity of the energy storage system, this value represents the minimum capacity required by the energy storage system. It ensures that the energy throughput demand can still be met after considering the SOC operating constraints, thereby ensuring that the output power fluctuation of the wind-solar-storage combined power generation system is well suppressed.

[0130] The effectiveness of the method in this embodiment is verified by using a wind-solar-storage new energy power station as a reference and performing simulation verification with the help of Matlab simulation software.

[0131] Select typical daily wind and light data from a certain location, and control the period. Smoothing time constant The maximum power fluctuation allowed for wind and solar power plants Total capacity of wind and solar power systems The specific parameters of the wind turbine, photovoltaic modules and energy storage unit are shown in Table 2. Based on this, the optimal configuration of the energy storage system is calculated to be 120MW / 50MWh, with an initial state of charge (SOC) of 0.5.

[0132] Table 2: Specific parameters of wind turbine generators, photovoltaic modules, and energy storage units

[0133] Using typical daily wind and solar data from a certain location, a simulation test was conducted in Matlab to simulate the smoothing power output of a wind-solar-storage renewable energy power station using an energy storage system over a 24-hour period. The test results are as follows. Figure 4 , Figure 5 , Figure 6 As shown.

[0134] Figure 4 The red curve represents the power generation capacity of wind and solar renewable energy, while the blue curve represents the total grid-connected power of wind, solar, and energy storage power stations. Figure 4It can be seen that the blue curve fluctuates less than the red curve, indicating that configuring energy storage units in wind, solar, and energy storage power plants can smooth out the volatility of renewable energy sources such as wind and solar. Meanwhile... Figure 5 and Figure 6 The values ​​of grid-connected power fluctuation of wind, solar and energy storage power stations before and after the configuration of energy storage units are given. It can be seen that the maximum power fluctuation of wind, solar and energy storage power stations in 1 minute before the configuration of energy storage is nearly 150MW, while the maximum grid-connected power fluctuation in 1 minute after the configuration of energy storage units is only 32MW. The grid-connected power fluctuation of wind, solar and energy storage power stations is greatly reduced and is lower than the 40MW required by relevant standards.

[0135] The foregoing has described in detail an embodiment of a method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants. Based on the above embodiment, this invention also provides a corresponding optimization configuration system for wind, solar, and energy storage power plants.

[0136] Figure 7 This is a schematic block diagram of an optimized configuration system structure for a wind-solar-storage energy storage power station, provided as an embodiment of the present invention. In this embodiment, the optimized configuration system 700 for the wind-solar-storage energy storage power station can be divided into multiple functional modules according to its functions. A module, as referred to in this invention, is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and is stored in memory.

[0137] The constraint parameter acquisition module 710 is used to acquire the grid-connected power fluctuation constraint parameters of wind farms and photovoltaic power plants connected to the grid. The constraint parameters include the maximum allowable power change within a unit discretized control period.

[0138] The upper limit determination module 720 for time constant is used to derive the upper limit value of the first-order filter smoothing time constant based on the maximum power change, the discretized control cycle, and the theoretical maximum output of the energy storage system, so that the grid-connected power fluctuation of the wind, solar and energy storage new energy power station meets the constraint of the maximum power change.

[0139] The rated power capacity lower limit determination module 730 is used to determine the rated power configuration lower limit of the energy storage system based on the ratio of the upper limit of the first-order filter smoothing time constant to the maximum power change and the discretized control cycle, and to determine the rated capacity configuration lower limit of the energy storage system based on the ratio of the upper limit of the first-order filter smoothing time constant to the total rated output power of the wind farm and photovoltaic power station.

[0140] The energy storage system configuration module 740 is used to configure the rated power and rated capacity of the energy storage system based on the lower limit of the rated power configuration and the lower limit of the rated capacity configuration.

[0141] The wind-solar-storage energy storage system optimization configuration system of this embodiment is used to implement the aforementioned wind-solar-storage energy storage system optimization configuration method. Therefore, the specific implementation of this system can be found in the embodiment section of the wind-solar-storage energy storage system optimization configuration method above. So, the specific implementation can be referred to the description of the corresponding embodiments, and will not be described in detail here.

[0142] Furthermore, since the wind-solar-storage energy storage system optimization configuration system of this embodiment is used to implement the aforementioned wind-solar-storage energy storage system optimization configuration method, its function corresponds to the function of the above method, and will not be repeated here.

[0143] Figure 8 This is a schematic diagram of a terminal 800 provided in an embodiment of the present invention, including: a processor 810, a memory 820, and a communication unit 830. The processor 810 is used to implement the process steps of the above-described embodiment of the wind-solar-storage energy storage system optimization configuration method when implementing the wind-solar-storage energy storage system optimization configuration program stored in the memory 820.

[0144] This invention also provides a computer storage medium, which may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The computer storage medium stores an optimization configuration program for a wind-solar-storage energy storage system at a renewable energy power station. When this program is executed by a processor, it implements the process steps of the aforementioned embodiment of the optimization configuration method for a wind-solar-storage energy storage system at a renewable energy power station.

[0145] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants, characterized in that, Includes the following steps: Obtain grid-connected power fluctuation constraint parameters for wind farms and photovoltaic power plants connected to the grid, wherein the constraint parameters include the maximum allowable power change within a unit discretized control cycle; Based on the maximum power change, the discretized control cycle, and the theoretical maximum output of the energy storage system, the upper limit of the first-order filter smoothing time constant is derived in reverse to ensure that the grid-connected power fluctuation of the wind, solar, and energy storage power station meets the constraint of the maximum power change. The lower limit of the rated power configuration of the energy storage system is determined based on the ratio of the upper limit of the first-order filter smoothing time constant to the maximum power change and the discretized control cycle. The lower limit of the rated capacity configuration of the energy storage system is determined based on the ratio of the upper limit of the first-order filter smoothing time constant to the total rated output power of the wind farm and the photovoltaic power station. The rated power and rated capacity of the energy storage system are configured based on the lower limit of the rated power configuration and the lower limit of the rated capacity configuration.

2. The method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power stations according to claim 1, characterized in that, Obtain the grid-connected power fluctuation constraint parameters for wind farms and photovoltaic power plants connected to the grid, specifically including: Obtain the data of wind farms and photovoltaic power plants per unit time Maximum permissible power variation ; Based on the discretization control requirements of the energy storage system, a discretization control cycle is set. ; Unit time Maximum permissible power variation Converted to unit discretized control cycle Maximum permissible power variation The conversion relationship is as follows: in, unit of time The number of discretized control cycles within the timeframe.

3. The method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants according to claim 2, characterized in that, The upper limit of the first-order filter smoothing time constant is derived in reverse, specifically including: Based on the first-order filter smoothing control principle, the relationship between the grid-connected power fluctuation of wind, solar, and energy storage power plants and the filter time constant is established, expressed as: in, and The first The and the first The target grid-connected power value for each discrete control cycle. For the first The charging and discharging power of the energy storage system in a discrete control cycle. The first-order filtering smoothing time constant; Based on the maximum power change Constraints: The derivation leads to: Based on the characteristics of the wind and solar resources of the wind-solar-storage new energy power station to be configured, determine the theoretical maximum output of the energy storage system. Then the upper limit of the first-order filter smoothing time constant. .

4. The method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants according to claim 3, characterized in that, The lower limit of the rated power configuration of the energy storage system is determined based on the proportional relationship between the upper limit of the first-order filter smoothing time constant, the maximum power change, and the discretized control cycle. Specifically, this includes: Preset power reserve margin ; The lower limit of rated power configuration Determined by the following formula: .

5. The method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants according to claim 4, characterized in that, Preset power reserve margin Specifically, it includes: Obtain a typical operating scenario library covering wind-solar-storage combined power generation systems with different climate types. The typical operating scenario library contains typical wind and solar power output scenario data under different climate zones, different seasons, and different weather types. Historical wind and solar power output data for several typical extreme fluctuation scenarios were selected from the typical operating scenario library. These typical extreme fluctuation scenarios refer to power fluctuation rates exceeding the maximum power change per unit time. The scene; For each typical extreme fluctuation scenario Based on the wind power output characteristics of this scenario, the theoretical maximum output of the energy storage system required to meet grid connection fluctuation constraints is calculated. And the actual maximum output of the energy storage system after considering the state of charge constraints and charge / discharge efficiency of the energy storage system. ; Calculate each scene Sample values ​​of power margin coefficient ; Statistical analysis was performed on the sample values ​​of the power margin coefficient, and the first target quantile at the confidence level was taken as the power reserve margin. .

6. The method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants according to claim 5, characterized in that, The lower limit of the rated capacity configuration of the energy storage system is determined based on the ratio of the upper limit of the first-order filter smoothing time constant to the total rated output power of the wind farm and photovoltaic power station. Specifically, this includes: Preset capacity reserve margin ; The lower limit of the rated capacity configuration of the energy storage system Determined by the following formula: in, This represents the total rated output power of the wind farm and photovoltaic power station.

7. The method for optimizing the configuration of energy storage systems in wind, solar, and energy storage power plants according to claim 6, characterized in that, Preset capacity reserve margin Specifically, it includes: Obtain a typical operating scenario library covering wind-solar-storage combined power generation systems with different climate types. The typical operating scenario library contains annual wind and solar power output time series data for different seasons and weather types. Determine the theoretical maximum capacity requirement of the energy storage system For each typical operating scenario Using the lower limit of rated power configuration Simulations were performed, taking into account the state-of-charge constraints of the energy storage system, and statistics were compiled for each scenario. Minimum capacity actually required for energy storage ; Calculate the sample value of the capacity margin coefficient for each scenario. ; Statistical analysis was performed on the sample values ​​of the capacity margin coefficient, and the second target quantile at the confidence level was taken as the capacity retention margin. .

8. An optimized configuration system for energy storage systems in wind, solar, and energy storage power plants, characterized in that, include: The constraint parameter acquisition module is used to acquire the grid-connected power fluctuation constraint parameters of wind farms and photovoltaic power plants connected to the grid. The constraint parameters include the maximum allowable power change within a unit discretized control period. The upper limit determination module for time constant is used to deduce the upper limit value of the first-order filter smoothing time constant based on the maximum power change, the discretized control cycle, and the theoretical maximum output of the energy storage system, so that the grid-connected power fluctuation of wind, solar and energy storage new energy power plants meets the constraint of the maximum power change. The rated power capacity lower limit determination module is used to determine the rated power configuration lower limit of the energy storage system based on the ratio of the upper limit of the first-order filter smoothing time constant to the maximum power change and the discretized control cycle, and to determine the rated capacity configuration lower limit of the energy storage system based on the ratio of the upper limit of the first-order filter smoothing time constant to the total rated output power of the wind farm and photovoltaic power station. The energy storage system configuration module is used to configure the rated power and rated capacity of the energy storage system based on the lower limit of the rated power configuration and the lower limit of the rated capacity configuration.

9. A terminal, characterized in that, include: The memory is used to store the optimization configuration program for the energy storage system of wind, solar and energy storage new energy power stations; The processor is used to implement the steps of the wind, solar, and energy storage power station energy storage system optimization configuration method as described in any one of claims 1 to 7 when executing the wind, solar, and energy storage power station energy storage system optimization configuration program.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores an optimization configuration program for the energy storage system of a wind, solar, and energy storage power station. When the optimization configuration program for the energy storage system of a wind, solar, and energy storage power station is executed by a processor, it implements the steps of the optimization configuration method for the energy storage system of a wind, solar, and energy storage power station as described in any one of claims 1 to 7.

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