Energy storage configuration method of optical storage system

Abstract

The invention discloses an energy storage configuration method for a light storage system, and the method comprises the steps: obtaining the temperature and radiation data of a photovoltaic power station, determining an original photovoltaic power generation power curve based on the obtained data, and carrying out the frequency decomposition of the original photovoltaic power generation power curve, and obtaining an original high-frequency power fluctuation curve and an original low-frequency power fluctuation curve; and constructing a constraint condition and a target function of energy storage optimization configuration, and solving the target function based on the original photovoltaic power generation power curve, the original high-frequency power fluctuation curve, the original low-frequency power fluctuation curve and the constraint condition to obtain optimal energy storage capacity configuration. The photovoltaic power generation power curve is decomposed into the low-frequency signal and the high-frequency signal, the objective function is solved based on the decomposed curve to obtain the optimal energy storage capacity configuration, the configuration method can smooth the output power of the photovoltaic power station, power fluctuation caused by natural factors such as weather changes is reduced, and the energy storage capacity configuration efficiency is improved. Therefore, the stability and reliability of the whole power system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and in particular relates to an energy storage configuration method for a photovoltaic storage system. Background Art

[0002] As energy storage technology becomes more mature, large-scale application of photovoltaic storage systems becomes increasingly possible, so the research on energy storage configuration strategies has great practical significance. There have been many studies on energy storage configuration strategies at home and abroad. At present, the mainstream method of pre-evaluation of energy storage configuration is to use production simulation to evaluate the improvement effect of energy storage on the consumption of new energy, as well as the evaluation of energy storage economy. Its main shortcoming is that its energy management does not pay enough attention to the volatility of photovoltaic power. The random fluctuation of photovoltaic power will pose potential safety hazards in the operation of electrolyzers, thus affecting the stability and reliability of the power system. Summary of the invention

[0003] The purpose of the present invention is to solve the deficiencies of the above-mentioned background technology and to provide a method for configuring energy storage in a photovoltaic storage system.

[0004] The technical solution adopted by the present invention is: a method for configuring energy storage of a photovoltaic storage system,

[0005] Acquire temperature and radiation data of the photovoltaic power station, determine an original photovoltaic power generation curve based on the acquired data, and perform frequency decomposition on the original photovoltaic power generation curve to obtain an original high-frequency power fluctuation curve and an original low-frequency power fluctuation curve;

[0006] The constraints and objective function of the energy storage optimization configuration are constructed, and the objective function is solved based on the original photovoltaic power generation power curve, the original high-frequency power fluctuation curve, the original low-frequency power fluctuation curve and the constraints to obtain the optimal energy storage capacity configuration.

[0007] Furthermore, the original photovoltaic power generation curve is:

[0008]

[0009] T m =T a +0.03×I g

[0010] Where P is the real-time power of the photovoltaic module, I g is the total irradiance, η is the efficiency of the photovoltaic module, P m is the power of the photovoltaic module in the standard test state, T m is the PV module temperature, T a is the ambient temperature.

[0011] Furthermore, the constraints of the energy storage optimization configuration include:

[0012] P pv +P uc +P bat =P L

[0013] E uc，min ≤E uc，i ≤E uc，max

[0014] E bat，min ≤E bat，i ≤E bat,max

[0015] P uc，min ≤P uc，i ≤P uc，max

[0016] P bat，min ≤P bat，i ≤P bat，max

[0017] S oc，min ≤S oc，i ≤S oc，max

[0018] V oc，min ≤V oc，i ≤V oc，max

[0019] P pv，min ≤P pv，i ≤P pv，max

[0020] Among them, P pv is the photovoltaic power generation power, P uc is the output power of the supercapacitor, P bat is the battery output power, P L is the load power; E uc，i 、E bat，i are the remaining capacity of the supercapacitor and the battery at the i-th moment, E uc，min 、E uc，max are the lower and upper limits of the remaining capacity of the supercapacitor, E bat，min 、E bat，max are the lower and upper limits of the remaining battery power, E uc，i and E bat，i are the operating power of the supercapacitor and the battery at the i-th moment, E uc，min and E uc，max are the lower and upper limits of the absolute value of the supercapacitor power, P bat，min and P bat，max are the lower and upper limits of the absolute value of battery power respectively; S oc，i is the battery state of charge at the i-th moment, Soc，min and S oc，max are the lower and upper limits of the battery state of charge, V oc，i is the voltage of the supercapacitor at the i-th moment, V oc，min and V oc，max are the lower and upper limits of the supercapacitor terminal voltage constraints, respectively, pv，i is the output power of the photovoltaic power station at the i-th moment, P pv，min and P pv，max They are the lower and upper limits of the output power of the photovoltaic power station respectively.

[0021] Furthermore, the objective function of the energy storage optimization configuration includes multiple optimization objective functions:

[0022] f=λ·f1′+(1-λ)f2′

[0023]

[0024] Among them, f is the multi-optimization target value of energy storage optimization configuration, f1 is the economic target value, f1' is the normalized value of the economic target, and f 1max 、f 1min are the maximum and minimum values ​​of the economic objective function, f2 is the power fluctuation target value, f2' is the power fluctuation target normalized value, and f 2max , f2min are the maximum and minimum values ​​of the power fluctuation objective function respectively, and λ is the weight coefficient of the economic objective.

[0025] Furthermore, the objective function of the energy storage optimization configuration includes an economic objective function:

[0026] f1=C iv +C om +C dc

[0027] C iv =C e1 E uc +C e2 E bat

[0028] C om =C m1 P uc +C m2 P bat

[0029] C dc =C d1 E uc +C d2 E bat

[0030] Among them, f1 is the economic target value, Civ is the purchase cost of the energy storage device, C om is the operation and maintenance cost of the energy storage system, C dc is the scrapping cost, E uc is the supercapacitor capacity, E bat is the battery capacity, C e1 and C e2 are the prices per unit capacity of supercapacitor and battery, C m1 and C m2 are the operation and maintenance costs per unit power of supercapacitor and battery, P uc and P bat are the operating power of the supercapacitor and the battery respectively, C d1 and C d2 are the scrapping cost coefficients per unit capacity of supercapacitors and batteries respectively.

[0031] Furthermore, the objective function of the energy storage optimization configuration includes a power fluctuation objective function:

[0032]

[0033] P DG，i =P dg，i +P bat，i +P uc，i

[0034] Where f2 is the power fluctuation target value, P DG，i is the output power after compensation by the composite energy storage system at the i-th moment, P DG，i-1 is the output power after compensation by the composite energy storage system at the i-1th moment, T is the total number of optimization calculation time nodes, P dg，i is the actual power generation without energy storage compensation at the i-th moment, P bat，i and P uc，i are the output powers of the battery and supercapacitor at the i-th moment respectively.

[0035] Furthermore, the solution process is:

[0036] Determine the capacity range of supercapacitors and batteries based on the original high-frequency power fluctuation curve;

[0037] Based on the capacity range, the capacity of the supercapacitor and the battery are controlled to increase in a set step size;

[0038] Based on the capacity of each group of supercapacitors and batteries, the economic objective function and the power fluctuation objective function are respectively solved to obtain the economic target value and the power fluctuation target value;

[0039] The economic target value and the power fluctuation target value are normalized respectively to obtain the economic target normalized value and the power fluctuation target normalized value;

[0040] Based on the normalized value of economic objective and the normalized value of power fluctuation objective, the multi-optimization objective function is solved to obtain the multi-optimization objective value;

[0041] Under the condition of satisfying the constraints, the capacity of a group of supercapacitors and batteries corresponding to the minimum multi-optimization objective value is the optimal energy storage capacity configuration.

[0042] Furthermore, the process of determining the capacity range of the supercapacitor and the battery according to the original high-frequency power fluctuation curve is as follows:

[0043] The area enclosed by the power fluctuation curve and the time axis in each continuous time period is calculated in sequence to obtain the power to be smoothed in each continuous time period;

[0044] The magnitude and frequency of the power to be smoothed within a certain period of time are counted to form a frequency distribution histogram of the power to be smoothed;

[0045] The capacity range of the supercapacitor and the battery is determined based on the size and frequency of the power to be smoothed as shown in the frequency distribution histogram.

[0046] Furthermore, the process of solving the volatility objective function is: performing power fluctuation smoothing processing on the original high-frequency power fluctuation curve to form a new high-frequency power fluctuation curve, synthesizing the new high-frequency power fluctuation curve with the original low-frequency power fluctuation curve to obtain a new photovoltaic power generation power curve, and solving the volatility objective function based on the new photovoltaic power generation power curve.

[0047] Furthermore, the process of performing power fluctuation smoothing is as follows:

[0048] Compare the power fluctuation to be smoothed on the original high-frequency power fluctuation curve with the capacity of the supercapacitor and the battery;

[0049] If the power fluctuation to be smoothed is smaller than the capacity of the supercapacitor, the power fluctuation to be smoothed is set to zero;

[0050] If the power fluctuation to be smoothed is greater than or equal to the capacity of the supercapacitor and less than twice the capacity of the supercapacitor, the ordinates of all the data points in this section of the original high-frequency power fluctuation curve are proportionally reduced so that the area of ​​the power fluctuation to be smoothed after reduction is equal to the capacity of the supercapacitor;

[0051] If the power fluctuation to be smoothed is greater than or equal to twice the capacity of the supercapacitor and less than the sum of the capacity of the supercapacitor and the capacity of the battery, the power fluctuation to be smoothed is set to zero;

[0052] If the power fluctuation to be smoothed is greater than or equal to the sum of the supercapacitor capacity and the battery capacity, the vertical coordinates of all data points in this section of the original high-frequency power fluctuation curve are proportionally reduced so that the area of ​​the power fluctuation to be smoothed after reduction is equal to the capacity of the supercapacitor.

[0053] The beneficial effects of the present invention are:

[0054] When configuring energy storage, the present invention first decomposes the photovoltaic power generation power curve into two parts: low-frequency signals and high-frequency signals. High-frequency fluctuations are usually related to fast-changing factors such as rapid movement of clouds and instantaneous weather changes, while low-frequency fluctuations may be related to slower-changing factors such as seasonal changes and sunshine duration. This subdivision helps to adopt more effective energy storage strategies for different fluctuation characteristics; based on the decomposed curve, the objective function is solved to obtain the optimal energy storage capacity configuration, which can smooth the output power of the photovoltaic power station and reduce power fluctuations caused by natural factors such as weather changes, thereby improving the stability and reliability of the entire power system.

[0055] The constraints constructed in the present invention limit the feasible solution space of energy storage configuration, ensuring that all possible configuration schemes meet the requirements of physics, economy, safety, etc. By setting reasonable constraints, unnecessary waste of resources can be avoided and the stability of the energy storage system and the power system in which it is located can be improved.

[0056] The objective function of energy storage optimization in the present invention is the core of the optimization problem, which quantifies the target that needs to be optimized for the energy storage configuration scheme. The objective function provides a clear optimization direction for the optimization algorithm. In complex optimization problems, multiple optimization targets may need to be considered simultaneously. In the optimization problem, the constraints and the objective function work together to determine the final effect of the energy storage configuration.

[0057] The process of solving the objective function of the present invention systematically considers the capacity configuration problem of supercapacitors and batteries, and comprehensively explores the performance under different capacity combinations through step-by-step increase and multi-objective optimization. It allows the weights of the economic objective function and the power volatility objective function, as well as the step size of the supercapacitor and battery capacity increase to be adjusted according to actual needs, so as to flexibly respond to different application scenarios and optimization needs. It can also be further extended to other types of energy storage devices and more complex optimization objectives, providing a general method and framework for the optimal configuration of energy storage systems.

[0058] The power fluctuation smoothing process performed by the present invention when solving the volatility objective function is an intervention in the random volatility of photovoltaic power from the perspective of energy management and allocation. The surplus power storage of supercapacitors and batteries is used to achieve "peak shaving and valley filling" of hydrogen production power to a certain extent, thereby reducing the volatility of the output power of photovoltaic power stations to a certain extent and helping the power system to achieve a balance between power supply and power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the present invention.

[0060] Figure 2 Schematic diagram of the original photovoltaic power generation curve of a photovoltaic power station for three consecutive days.

[0061] Figure 3 This is a schematic diagram of the original high-frequency power fluctuation curve for three consecutive days after decomposition by one-dimensional wavelet transform.

[0062] Figure 4 It is a schematic diagram of the original low-frequency power fluctuation curve for three consecutive days after decomposition by one-dimensional wavelet transform.

[0063] Figure 5 It is the distribution histogram of the power to be smoothed for three consecutive natural days.

[0064] Figure 6 It is the histogram of the power distribution to be smoothed for one consecutive year.

[0065] Figure 7 This is a schematic diagram of the relationship between the comprehensive energy storage performance index and the energy storage capacity of the supercapacitor after the configuration of the present invention. DETAILED DESCRIPTION

[0066] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0067] like Figure 1 As shown, the present invention provides a method for configuring energy storage of a photovoltaic storage system, comprising the following steps:

[0068] S1. Use software such as Meteonorm to obtain the time series of temperature and radiation data of the photovoltaic power station:

[0069] (1) Set the azimuth and inclination data of the photovoltaic module. The azimuth is the angle between the vertical plane of the solar cell array and the south direction. Under normal circumstances, the azimuth is facing south, that is, the azimuth is 0 degrees, which is the most suitable angle for solar panels with fixed azimuth. Sometimes, the choice of azimuth is not for the pursuit of maximum power generation, but for the sake of matching the load demand, and the azimuth will be changed according to the peak time of the load. The present invention takes the maximum total power generation as the principle and selects an azimuth of 0 degrees. The inclination angle is the angle between the plane of the solar cell array and the horizontal ground. The choice of the inclination angle should make the annual solar power generation as large as possible.

[29] , while minimizing the difference in power generation between winter and summer. The rules are as follows: when the latitude is between 26 and 40 degrees, the power generation angle is 5-10 degrees higher than this; when the latitude is between 41 and 55 degrees, the power generation angle is 10-15 degrees higher than this; when the latitude is greater than 55 degrees, the power generation angle is 15-20 degrees higher than this. According to this rule, the latitude of the site is 36.6 degrees north latitude, and 5 degrees is added to this, and the final tilt angle is 41.6 degrees. The albedo takes the default value of 0.2, the atmospheric turbidity is interpolated, and the altitude takes the actual altitude of 2261 meters.

[0070] (2) Calculation and output settings. Select the same period for the period, select Minute (Timeseries) for the radiation model, select the default for other options, select Standard minute for the output mode, and export to PVsyst file.

[0071] (3) Finally, we get the .DAT file of the temperature and radiation data of a certain location. This file format is not convenient to read directly using Matlab. Let's simply convert the data format: open it with Notepad and find that the data format is relatively standard. Select all the data to copy and paste it into Excel. Matlab can easily read the data for subsequent operations. Thus, the temperature and radiation data are successfully exported.

[0072] The calculation of real-time photovoltaic power generation power must take into account data such as solar radiation intensity and temperature. The present invention can obtain the active output time series of the photovoltaic power station, that is, the original photovoltaic power generation power curve, based on the existing radiation data and temperature data time series. The formula is as follows:

[0073]

[0074] T m =T a +0.03×I g

[0075] Where P is the real-time power of the photovoltaic module, I g is the total irradiance, η is the efficiency of the photovoltaic module, P m is the power of the photovoltaic module in the standard test state, T m is the PV module temperature, T a is the ambient temperature.

[0076] S2. Decompose the original photovoltaic power curve using one-dimensional wavelet transform in Matlab

[0077] The wavelet decomposition of a one-dimensional discrete signal can decompose the original discrete signal into two parts: a low-frequency signal and a high-frequency signal, which are respectively called the low-frequency signal obtained by the first decomposition and the high-frequency signal obtained by the first decomposition. For the low-frequency signal obtained by a one-dimensional wavelet transform, it can also be subjected to wavelet transform again to decompose the low-frequency signal obtained by the first decomposition into a low-frequency signal and a high-frequency signal, that is, the low-frequency signal obtained by the second decomposition and the high-frequency signal obtained by the second decomposition. The low-frequency signal obtained by the second decomposition can continue to be decomposed by wavelet transform, and this can be repeated until the problem is solved. The final decomposition result is the high-frequency signal obtained by each decomposition and the low-frequency signal obtained after the last decomposition. This multiple decomposition process can be regarded as a process of denoising the original signal one by one, and finally a low-frequency signal is obtained by the first decomposition.

[0078] Figure 2 The figure shows the original photovoltaic power curve of a photovoltaic power station with active output for three consecutive days. The minimum time interval is 1 minute. Figure 3 The figure shows the original high-frequency power fluctuation curve after decomposition for 3 days. Figure 4 The figure shows the original low-frequency power fluctuation curve after decomposition for 3 days. It can be seen from the figure that most of the high-frequency signals are small fluctuations, and a small part is large fluctuations. Supercapacitors can be used to smooth small fluctuations in high-frequency signals, and batteries can be used to smooth large fluctuations in high-frequency signals. If the configured energy storage capacity is large enough, the final smoothed curve should be the same as the low-frequency part obtained by wavelet decomposition. This curve is the ideal goal of smoothing fluctuations by configuring energy storage. Compared with the original photovoltaic power generation power curve, the power fluctuation of the original low-frequency power fluctuation curve is significantly smaller, and most of the random power fluctuations have been eliminated or weakened. These random power fluctuations are mainly caused by short-term cloud cover. If the energy storage configuration can achieve such an effect, it will greatly enhance the predictability and daily regularity of the photovoltaic power station power, and the power part can also be dispatched according to these improved photovoltaic power station active output curves. The energy storage device will be mainly used to smooth high-frequency power fluctuations. If the configured energy storage capacity is large enough, the high-frequency power fluctuations will be completely weakened, and the power curve finally output by the photovoltaic power station will be consistent with the low-frequency part obtained by frequency decomposition.

[0079] S3. Construct constraints and objective functions for optimal energy storage configuration.

[0080] 1. Constraints for optimal energy storage configuration

[0081] The photovoltaic storage system needs to meet certain constraints. The energy storage capacity is optimized under the premise of meeting the constraints. The constraints are real-time power balance constraints, composite energy storage system constraints, and photovoltaic power station power generation constraints. The real-time power balance constraint means that at any time, the total power of the active power supplier and active power consumer of the system is always equal. The constraint of the composite energy storage system is the energy storage constraint of the energy storage device. It is a constraint set to ensure that the energy storage device does not overcharge or over-discharge. This constraint is to avoid the energy storage device from shortening its service life and reducing its economic efficiency due to overcharging and over-discharging. In addition, for batteries, the state of charge constraint should also be set, and for supercapacitors, the terminal voltage constraint should also be set. From the relationship between the stored energy and the terminal voltage of the supercapacitor, it can be seen that the terminal voltage constraint of the supercapacitor is essentially the energy storage constraint of the supercapacitor. In the application, the common part of these two constraints is taken, that is, the state of charge constraint that satisfies both constraints is determined. In addition, the output power limit of the photovoltaic power station is also a constraint.

[0082] 1.1 Real-time power balance constraints

[0083] The first is the real-time power balance constraint, that is, at any moment, the sum of the photovoltaic power generation power, supercapacitor power, and battery power is equal to the load power.

[0084] The real-time power balance constraint can be expressed by the following equation:

[0085] P pv +P uc +P bat =P L

[0086] In the above formula, P pv is the photovoltaic power generation power, P uc is the supercapacitor output power, P bat is the battery output power, P L is the load power. The above powers are all output powers at the same time.

[0087] The above constraints can also be written as follows:

[0088] P pv，i +P uc，i +P bat，i =P L，i

[0089] The i in the subscript represents the corresponding power at the i-th moment. If the data is in minutes, the above formula represents the power balance constraint at the i-th minute. If the data is in hours, the above formula represents the real-time power balance constraint at the i-th hour.

[0090] 1.2 Constraints of Composite Energy Storage Systems

[0091] Energy storage systems, especially battery energy storage, have a limited service life. In order to extend the service life of the energy storage system as much as possible, it is necessary to keep the remaining power of the energy storage system within a reasonable range. Taking batteries as an example, overcharging and over-discharging will significantly reduce their service life. Therefore, in order to avoid overcharging and over-discharging of batteries, the remaining power of the energy storage system should be controlled within a range that is conducive to long-term use. Batteries should be used scientifically and reasonably to extend the service life of batteries as much as possible. In this way, the battery cost per unit time can be reduced after amortization, and economic benefits can be maximized.

[0092] E uc，min ≤E uc，i ≤E uc，max

[0093] E bat,min ≤E bat，i ≤E bat，max

[0094] Among them, E uc，i 、E bat，i are the remaining capacity of the supercapacitor and the battery at the i-th moment, E uc，min 、E uc，max are the lower and upper limits of the remaining capacity of the supercapacitor, E bat，min 、E bat，max They are the lower and upper limits of the remaining battery power respectively.

[0095] On the other hand, the charging and discharging power of the energy storage system also has a certain range. Whether it is a battery or a supercapacitor, it is limited by the physical parameters of the energy storage device and considering that the power of the energy storage device cannot exceed a certain range, otherwise it will cause excessive heat and damage the energy storage system structure. The discharge power of the battery and supercapacitor needs to meet the following constraints:

[0096] P uc，min ≤P uc，i ≤P uc，max

[0097] P bat，min ≤P bat，i ≤P bat,max

[0098] In the above formula, P uc，i and P bat，i are the operating power of the supercapacitor and the battery at the i-th moment, P uc，min and P uc，max are the lower and upper limits of the absolute value of the supercapacitor power, P bat，min and P bat，maxThey are the lower and upper limits of the absolute value of the battery power, respectively. The reason why they are absolute values ​​is that power exists in two states, input and output. Here, it is assumed that the upper and lower limits of power are equal in the input and output states.

[0099] For batteries, the state of charge S should also be added. oc constraint:

[0100] S oc，min ≤S oc，i ≤S oc，max

[0101] In the above formula, S oc，i is the battery state of charge at the i-th moment, S oc，min and S oc，max They are the lower and upper limits of the battery state of charge respectively.

[0102] For supercapacitors, in order to prevent them from being damaged, the terminal voltage V should also be increased. oc constraint:

[0103] V oc，min ≤V oc，i ≤V oc，max

[0104] In the above formula, V oc，i is the voltage of the supercapacitor at the i-th moment, V oc，min and V oc，max are the lower and upper limits of the supercapacitor terminal voltage constraints respectively.

[0105] 1.3. Power Constraints of Photovoltaic Power Plants

[0106] Due to the influence of multiple factors such as the number of photovoltaic modules, irradiation intensity, temperature and energy conversion efficiency, the output power of photovoltaic power stations must be limited to a certain extent. The power of photovoltaic power generation must meet the following constraints:

[0107] P pv，min ≤P pv，i ≤P pv，max

[0108] In the above formula, P pv，i is the output power of the photovoltaic power station at the i-th moment, P pv，min and P pv，max They are the lower and upper limits of the output power of the photovoltaic power station respectively.

[0109] 2. Objective function of energy storage optimization configuration

[0110] The present invention adopts two indicators, economic target and power volatility target, as the standard to measure whether the energy storage configuration is optimal. The optimal energy storage capacity takes into account both economic and volatility factors, that is, by configuring energy storage of appropriate capacity, the volatility of the power curve of the photovoltaic power station is significantly reduced. At the same time, the configured energy storage capacity cannot be too much, because this will make the cost very high. Therefore, the final objective function includes an economic objective function, a power volatility objective function, and a multi-optimization objective function combining the two.

[0111] 2.1 Economic Objective Function

[0112] The economic goal selected by the present invention is the total cost generated by the energy storage system during its entire life cycle, including construction cost, operation and maintenance cost, and scrapping cost. Construction cost refers to the cost of purchasing, installing and laying the energy storage device; operation and maintenance cost refers to the regular maintenance cost of the energy storage device during use and the operating cost of the supporting cooling system; scrapping cost refers to the excessive loss of charging and discharging efficiency of the energy storage device after long-term use, resulting in the loss of actual use value or even the complete loss of energy storage function. At this time, the laid energy storage device needs to be dismantled and recycled. The scrapping cost is generally the smallest of the three costs. If the recovery of the battery is taken into account, this cost may even be a positive value.

[0113] The life cycle cost of the composite energy storage system can be calculated according to the following formula:

[0114] f1=LCC hess =C iv +C om +C dc

[0115] In the above formula, f1 is the economic target value, LCC hess is the life cycle cost of the composite energy storage system, C iv is the purchase cost of the energy storage device, C om is the operation and maintenance cost of the energy storage system, C dc The cost of scrapping.

[0116] The construction cost, operation and maintenance cost, and scrapping cost of the composite energy storage system can be calculated according to the following formula:

[0117] C iv =C e1 E uc +C e2 E bat

[0118] C om =C m1 P uc +C m2 Pbat

[0119] C dc =C d1 E uc +C d2 E bat

[0120] In the above formula, E uc is the supercapacitor capacity, E bat is the battery capacity, C e1 is the price per unit capacity of supercapacitor, C e2 is the price of the battery per unit capacity, C m1 and C m2 The operation and maintenance cost per unit power of supercapacitor and battery, P uc and P bat are the operating powers of the supercapacitor and the battery, C d1 and C d2 are the scrapping cost coefficients per unit capacity of supercapacitors and batteries respectively.

[0121] 2.2 Power Fluctuation Objective Function

[0122] The main goal of configuring energy storage in photovoltaic power stations is to reduce the volatility of photovoltaic power station output power, making the photovoltaic output curve smoother, so as to facilitate prediction and scheduling, and help the power system achieve power supply and power consumption balance. Regarding power volatility, there are many measurement indicators, among which the absolute value of the difference in output power between two adjacent moments, or its square, can be used as a reference factor. In addition, the square of the difference in output power between two adjacent moments itself is not enough to objectively reflect the magnitude of volatility, because the annual output power base of each photovoltaic power station is different. Therefore, it is necessary to use the ratio of the former to the latter to measure the volatility of photovoltaic power station output power, so as to eliminate or weaken the impact of different bases of output power of different photovoltaic power stations.

[0123] The present invention uses the following formula to characterize the fluctuation of the output power of the photovoltaic power station:

[0124]

[0125] P DG，i =P dg，i +P bat，i +P uc，i

[0126] Where f2 is the power fluctuation target value, P DG，i is the output power after compensation by the composite energy storage system at the i-th moment, P DG，i-1is the output power after compensation by the composite energy storage system at the i-1th moment, T is the total number of optimization calculation time nodes, that is, the total duration of optimization calculation, P dg，i is the actual power generation without energy storage compensation at the i-th moment, P bat，i and P uc，i are the output powers of the battery and supercapacitor at the i-th moment respectively.

[0127] 2.3. Multiple Optimization Objective Functions

[0128] The configuration of energy storage should not only reduce costs as much as possible, but also achieve the effect of smoothing power fluctuations. However, these two indicators are negatively correlated. It is impossible to unilaterally reduce power fluctuations without increasing energy storage costs, and it is impossible to reduce energy storage costs without affecting the effect of smoothing power fluctuations. In the present invention, in order to take both into account, a suitable expression can be constructed to seek the optimal solution. At the same time, considering that there are obvious differences in dimensions and orders of magnitude between energy storage costs and power fluctuation smoothing effects, in order to scientifically eliminate the interference of such differences on multiple optimization objectives, the following formula can be used for normalization:

[0129]

[0130] In the formula, f1 is the economic target value, f1' is the normalized value of the economic target, and f 1max 、f 1min are the maximum and minimum values ​​of the economic objective function, f2 is the power fluctuation target value, f2' is the power fluctuation target normalized value, and f 2max 、f 2min The above formulas eliminate the influence of dimensions and units by comparing the two parameters of economy and volatility with the values ​​of the same units, and obtain the economy index and power fluctuation index without units.

[0131] At present, two evaluation indicators, the economic index and the volatility index, have been obtained. Neither of these two indicators can be used alone to evaluate whether the capacity of the energy storage system is reasonable. In order to comprehensively weigh these two indicators, the linear weighted method is used to combine the economic index and the power fluctuation smoothing index to obtain a multi-optimization objective function. The optimal capacity of the energy storage system will be solved based on this.

[0132] f=λ·f1'+(1-λ)f2'

[0133] In the above formula, f is the multi-optimization target value of energy storage optimization configuration, and λ is the weight coefficient of the economic target. It can take corresponding values ​​under the influence of the budget of energy storage configuration and the requirements of power fluctuation smoothing. This value is not fixed, but is adjusted by the specific requirements of the output power of the photovoltaic power station. If the requirements for power fluctuation are relatively high in the actual use scenario, but the price of energy storage is not very sensitive, the coefficient of the volatility index will be increased to adapt to the adjustment of this demand.

[0134] S4. Solution of optimal energy storage configuration

[0135] The composite energy storage system is composed of two or more types of energy storage, and the various types of energy storage that make up the composite energy storage system can often complement each other. In actual application scenarios, energy-type energy storage is often used to meet the needs of large energy storage capacity, while power-type energy storage is used to meet the requirements of large charging and discharging currents. The two work together to better realize the energy storage function.

[0136] Finally, the composite energy storage types selected by the present invention are power-type energy storage supercapacitors and energy-type energy storage lithium-ion batteries (i.e., the storage batteries described below). The power level of supercapacitors is relatively large, and the energy density of lithium-ion batteries is relatively large. The cost of unit energy storage is significantly higher for the former than for the latter, so the former should be as small as possible to smooth out small power fluctuations. However, in terms of service life, the former is longer than the latter. Most of the power fluctuations in the high-frequency power fluctuation curve are small-scale power fluctuations, and only a few power fluctuations are large. Therefore, supercapacitors are used to smooth out smaller power fluctuations, taking into account that the energy density of supercapacitors is relatively small, the energy transferred by a single charge and discharge is relatively small, and small power fluctuations on the power fluctuation curve appear very frequently, and supercapacitors can just withstand more charge and discharge times. In addition, lithium-ion batteries are used to smooth out larger power fluctuations, because the energy density of lithium-ion batteries is relatively large, and lithium-ion batteries per unit volume can charge and discharge more energy, which can smooth out larger power fluctuations. The frequency of these large power fluctuations is significantly less than that of smaller power fluctuations, which just coincides with the characteristic that lithium-ion batteries cannot withstand more charge and discharge times.

[0137] The optimal energy storage configuration takes into account both economy and reducing power fluctuations. Under this optimal energy storage capacity, the multi-optimization objective function will achieve the minimum value. The following will use the particle swarm algorithm to solve the optimal capacity configuration method based on the principle of minimizing the multi-optimization objective function. The particle swarm algorithm is an optimization tool based on iteration. The system initializes a set of random solutions and searches for the optimal value through iteration. It not only has global optimization capabilities, but also has strong local optimization capabilities.

[0138] When the energy storage capacity is large enough, all the peaks and valleys of the high-frequency fluctuation curve obtained by frequency decomposition can be completely eliminated. In this case, the index of power fluctuation smoothing is optimal, but in this case, a large capacity of energy storage needs to be configured, which will lead to a significant increase in energy storage costs, affecting economic efficiency, and the multi-optimization objective function is not optimal. When the energy storage capacity is 0, the energy storage cost is zero. At this time, the economy is of course optimal, but at this time the power fluctuation is the largest, and the power fluctuation objective function is also the largest. At this time, the multi-optimization objective function is not optimal. It can be determined that as the energy storage capacity gradually increases from zero, the economic curve rises, while the volatility curve gradually decreases. When the proportional coefficient of the economic function and the volatility function in the multi-optimization objective function is appropriate, as the configured energy storage capacity increases from zero, the objective function curve will show a trend of first decreasing and then increasing. The lowest point of the multi-optimization objective function corresponds to the optimal energy storage capacity configuration. Based on this principle, the optimal capacity configuration can be obtained.

[0139] The process of solving the objective function to obtain the optimal energy storage capacity configuration based on the original photovoltaic power generation power curve, the original high-frequency power fluctuation curve, the original low-frequency power fluctuation curve and the constraint conditions is as follows:

[0140] 4.1. Determine the capacity range of supercapacitors and batteries.

[0141] In the original high-frequency power fluctuation curve, most of the power fluctuations are small, and only a few power fluctuations have large amplitudes. In order to describe this difference more clearly, these power fluctuations are plotted into a frequency distribution histogram according to the size of the power fluctuations. Specifically as follows: First, calculate the size of each power fluctuation. The method used is to calculate the area surrounded by the power fluctuation curve of each part and the time axis. Starting from the time point when the power fluctuation is not zero, if the power fluctuation at this moment is a negative value, multiply it by -1 to calculate the area. Continue to judge the value of the next moment. If it is less than or equal to zero, still multiply the power at this moment by -1 and add it to the previous value to calculate the area. Judge the power value of each moment in turn until the power fluctuation value at this moment is a positive number, calculate the area of ​​the power curve below the time axis in the previous continuous moment and the time axis, and start calculating the sum of the power fluctuations in this continuous time period. Judge the power fluctuation value of the next moment. If it is greater than or equal to zero, add it to the area in the continuous time period until the sign is reversed again. In this way, calculate the area surrounded by the power fluctuation curve and the time axis in each continuous time period in turn. Finally, there are 338 power fluctuations to be smoothed in three consecutive natural days, and 53,778 power fluctuations to be smoothed in one consecutive year. After obtaining the power fluctuation size in each continuous time period (i.e., the power to be smoothed), they can be plotted into a frequency distribution histogram.

[0142] According to the above method, the frequency distribution histogram of the power to be smoothed within three consecutive natural days and the frequency distribution histogram within one consecutive year were drawn respectively. Figure 5 From the histogram distribution of the power to be smoothed for three consecutive natural days, we can see that there are 112 in the range of [0, 0.497] in the first group, accounting for 33.14% of the 338 power fluctuations to be smoothed in three consecutive natural days; there are 56 in the range of [0.497, 0.994] in the second group, and 30 in the range of [0.994, 1.491] in the third group. There are 198 in the range of the first three groups [0, 1.491], accounting for 58.58% of the total. From these data, we can see that small power fluctuations account for the majority of the power to be smoothed.

[0143] This feature is more obvious in the power fluctuations to be smoothed for one year, such as Figure 6 As shown in the figure, the number of the first group in the range of [0,0.497] is 20430, accounting for 37.99% of the total power to be stabilized in a continuous year, the second group in the interval of [0.497, 0.994] has 8026, the third group has 4918, and the first three groups have a total of 33374, accounting for 62.06% of the total power in a year. Due to the larger number of samples and the time span covering the span of the four seasons, the data in a continuous year is more universal and representative, and most of the power fluctuations to be stabilized are within a very small range. From the figure, the proportion of the power fluctuations to be stabilized exceeding 10MW·min is very small, and the frequency of the power fluctuations to be stabilized exceeding about 20MW·min (i.e. 333.33kW·h) is attenuated to a negligible level.

[0144] Based on the above analysis, the capacity range and ratio of lithium-ion batteries and supercapacitors can be determined. Power fluctuations within 0-10MW·min occur frequently, and the power fluctuations in this range are low, so supercapacitors can be used to smooth out power fluctuations. Power fluctuations of 10-40MW·min can be smoothed out by batteries. In order to make them correspond, the following formula can be used to determine their basic proportional relationship:

[0145] E bat =3E uc +10

[0146] 4.2. According to the above proportional relationship, determine the appropriate step length. The capacity ranges of supercapacitors and batteries are different, and their incremental step lengths are different. Based on the capacity range, the capacity of supercapacitors and batteries are controlled to increase with the set step length. That is, when the energy storage capacity of supercapacitors gradually increases from 0 to 10MW·min, the capacity of lithium-ion batteries gradually increases from 10MW·min to 40MW·min. After the step length is determined, under this relationship, the energy storage ratio of supercapacitors and lithium-ion batteries is not a fixed value, but a variable value.

[0147] The capacity of each group of supercapacitors and batteries is known, and the economic target value can be easily calculated based on the economic target function.

[0148] In order to obtain the power fluctuation objective function, it is necessary to perform power fluctuation smoothing processing on the original high-frequency power fluctuation curve to form a new high-frequency power fluctuation curve. The new high-frequency power fluctuation curve is synthesized with the original low-frequency power fluctuation curve to obtain a new photovoltaic power generation power curve, that is, the output power time series of the new photovoltaic storage system. The volatility objective function can be solved based on the new photovoltaic power generation power curve.

[0149] The process of smoothing the original high-frequency power fluctuation curve is as follows:

[0150] For each set of supercapacitors and lithium-ion batteries, the energy storage capacity;

[0151] When the power fluctuation to be smoothed on the high-frequency curve is smaller than the capacity of the supercapacitor, this part of the power fluctuation is completely eliminated, which is reflected in the high-frequency power fluctuation curve obtained by frequency decomposition, that is, this section of the power fluctuation to be smoothed is set to zero.

[0152] When the power fluctuation to be smoothed exceeds the energy storage capacity of the supercapacitor but is less than or equal to twice the energy storage capacity of the supercapacitor, the power fluctuation is incompletely smoothed. The specific operation is to proportionally reduce the vertical coordinates of all data points in this section on the high-frequency power fluctuation curve so that the area of ​​the power fluctuation to be smoothed is reduced by a value that is exactly equal to the energy storage capacity of the supercapacitor.

[0153] When the power fluctuation to be smoothed is greater than 2 times the capacity of the supercapacitor, but less than or equal to the sum of the supercapacitor capacity and the lithium-ion battery capacity, this part of the power fluctuation is completely eliminated. The reason why it is the sum of the supercapacitor capacity and the lithium-ion battery capacity is that the supercapacitor and the lithium-ion battery can work at the same time, making full use of the existing energy storage resources.

[0154] When the power fluctuation to be smoothed is greater than the sum of the supercapacitor capacity and the lithium-ion battery capacity, the above method of proportionally reducing the ordinate can be used to achieve incomplete smoothing. After the above power fluctuation smoothing, a new high-frequency power fluctuation curve is obtained.

[0155] 4.3. The economic target value and the power fluctuation target value are normalized respectively to obtain the economic target normalized value and the power fluctuation target normalized value.

[0156] In the process of calculating multiple optimization objective functions from the economic objective function and the volatility objective function, they need to be normalized first. During the normalization process, the maximum and minimum values ​​of the economic objective function and the maximum and minimum values ​​of the power volatility objective function are needed, so these values ​​need to be determined first.

[0157] The maximum and minimum values ​​of the economic objective function are easy to determine. As mentioned earlier, the capacity range of supercapacitors is 0-10MW·min, and the corresponding capacity range of lithium-ion batteries is 10-40MW·h. Therefore, we only need to use the maximum and minimum values ​​of the capacity of lithium-ion batteries and supercapacitors according to the economic objective function to respectively calculate the maximum and minimum values ​​of the economic objective function.

[0158] The maximum value of the power fluctuation objective function can be obtained by calculating the original photovoltaic power generation power curve and the power fluctuation objective function. This method calculates the maximum value of the power fluctuation objective function, because the original photovoltaic power generation power curve is not equipped with energy storage and the power fluctuation is the largest. bat，i and P uc，i The maximum value of the power fluctuation objective function can be calculated by taking the maximum value of the power fluctuation on the original photovoltaic power generation power curve and substituting it into the power fluctuation objective function.

[0159] The minimum value of the power fluctuation objective function can be set to 0, or it can be calculated using the original low-frequency power fluctuation curve obtained by frequency decomposition and the power fluctuation objective function. Because the original low-frequency power curve is obtained after the original photovoltaic power generation power curve eliminates the photovoltaic power generation power fluctuation caused by clouds blocking photovoltaic components, etc., this curve is the power generation power curve of the photovoltaic storage system when the configured energy storage capacity is large enough, and its power fluctuation is the smallest. The time series data of this curve can be used to calculate the minimum value of the power fluctuation objective function, and the final value is basically zero.

[0160] After determining the maximum and minimum values ​​of the economic objective function and the maximum and minimum values ​​of the power fluctuation objective function, the economic target values ​​and the power fluctuation target values ​​can be normalized to eliminate the differences caused by units, dimensions, etc., and obtain the normalized values ​​of the economic target and the power fluctuation target.

[0161] 4.4. Based on the normalized value of the economic target and the normalized value of the power fluctuation target, the multi-optimization objective function is solved to obtain the multi-optimization objective value. As the energy storage capacity gradually increases, the multi-optimization objective value will show a trend of first decreasing and then increasing, and it is easy to obtain the optimal energy storage capacity corresponding to its minimum value, that is, the optimal energy storage capacity configuration.

[0162] Taking λ = 0.1, and following the method mentioned in the above process, we can get the relationship between the comprehensive energy storage performance index and the supercapacitor capacity as follows: Figure 7 As can be seen from the figure, as the supercapacitor gradually increases, the comprehensive energy storage performance index (i.e., the multi-optimization target value) first decreases and then increases. When the supercapacitor capacity is 110kW·h, the comprehensive energy storage performance index reaches the minimum value. In this case, the optimal energy storage capacity configuration result is: the supercapacitor capacity is 110kW·h, and the lithium-ion battery capacity is 340kW·h.

[0163] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field.

Claims

1. A method for configuring energy storage in a photovoltaic energy storage system, characterized in that: Acquire temperature and radiation data of the photovoltaic power station, determine an original photovoltaic power generation curve based on the acquired data, and perform frequency decomposition on the original photovoltaic power generation curve to obtain an original high-frequency power fluctuation curve and an original low-frequency power fluctuation curve; The constraints and objective function of the energy storage optimization configuration are constructed, and the objective function is solved based on the original photovoltaic power generation power curve, the original high-frequency power fluctuation curve, the original low-frequency power fluctuation curve and the constraints to obtain the optimal energy storage capacity configuration.

2. The energy storage configuration method of the photovoltaic storage system according to claim 1, characterized in that: The original photovoltaic power generation curve is: T m =T a +0.03×I g Where P is the real-time power of the photovoltaic module, I g is the total irradiance, η is the efficiency of the photovoltaic module, P m is the power of the photovoltaic module in the standard test state, T m is the PV module temperature, T a is the ambient temperature.

3. The energy storage configuration method of the photovoltaic storage system according to claim 1, characterized in that: The constraints of the energy storage optimization configuration include: P pr +P uc +P bat =P L AND uc，min ≤E uc，i ≤E uc，max AND bat，min ≤E bat，i ≤E bat，max P uc，min ≤P uc，i ≤P uc，max P bat，min ≤P bat，i ≤P bat，max S oc，min ≤S oc，i ≤S oc，max In oc，min ≤V oc，i ≤V oc，max P pv，min ≤P pv，i ≤P pv，max Among them, P pv is the photovoltaic power generation power, P uc is the output power of the supercapacitor, P bat is the battery output power, P L is the load power; E uc，i 、E bat，i are the remaining capacity of the supercapacitor and the battery at the i-th moment, E uc，min、 E uc，max are the lower and upper limits of the remaining capacity of the supercapacitor, E bat，min 、E bat，max are the lower and upper limits of the remaining battery power, respectively. uc，i and P bat，i are the operating power of the supercapacitor and the battery at the i-th moment, P uc，min and P uc，max are the lower and upper limits of the absolute value of the supercapacitor power, P bat，min and P bat，max are the lower and upper limits of the absolute value of battery power respectively; S oc，i is the battery state of charge at the i-th moment, S oc，min and S oc，max are the lower and upper limits of the battery state of charge, V oc，i is the voltage of the supercapacitor at the i-th moment, V oc，min and V oc，max are the lower and upper limits of the supercapacitor terminal voltage constraints, respectively, pv，i is the output power of the photovoltaic power station at the i-th moment, P bv，min and P pv，max They are the lower and upper limits of the output power of the photovoltaic power station respectively.

4. The energy storage configuration method of the photovoltaic storage system according to claim 1, characterized in that: The objective function of the energy storage optimization configuration includes multiple optimization objective functions: f=λ·f1′+(1-λ)f2′ Among them, f is the multi-optimization target value of energy storage optimization configuration, f1 is the economic target value, f1′ is the normalized value of the economic target, and f 1max 、f 1min are the maximum and minimum values ​​of the economic objective function, f2 is the power fluctuation target value, f2′ is the power fluctuation target normalized value, and f 2max 、f 2min are the maximum and minimum values ​​of the power fluctuation objective function respectively, and λ is the weight coefficient of the economic objective.

5. The energy storage configuration method of the photovoltaic storage system according to claim 1, characterized in that: The objective function of the energy storage optimization configuration includes an economic objective function: f1=C iv +C om +C dc C iv =C e1 E uc +C e2 E bat C om =C m1 P uc +C m2 P bat C dc =C d1 E uc +C d2 E bat Among them, f1 is the economic target value, C iv is the purchase cost of the energy storage device, C om is the operation and maintenance cost of the energy storage system, C dc is the scrapping cost, E uc is the supercapacitor capacity, E bat is the battery capacity, C e1 and C e2 are the prices per unit capacity of supercapacitor and battery, C m1 and C m2 are the operation and maintenance costs per unit power of supercapacitor and battery, P uc and P bat are the operating power of the supercapacitor and the battery, respectively, C d1 and C d2 are the scrapping cost coefficients per unit capacity of supercapacitors and batteries respectively.

6. The energy storage configuration method of the photovoltaic storage system according to claim 1, characterized in that: The objective function of the energy storage optimization configuration includes the power fluctuation objective function: P DG，i =P dg，i +P bat，i +P uc，i Where f2 is the power fluctuation target value, P DG，i is the output power after compensation by the composite energy storage system at the i-th moment, P DG，i-1 is the output power after compensation by the composite energy storage system at the i-1th moment, T is the total number of optimization calculation time nodes, P dg，i is the actual power generation without energy storage compensation at the i-th moment, P bat，i and P uc，i are the output powers of the battery and supercapacitor at the i-th moment respectively.

7. The energy storage configuration method of the photovoltaic storage system according to claim 1, characterized in that: The solution process is: Determine the capacity range of supercapacitors and batteries based on the original high-frequency power fluctuation curve; Based on the capacity range, the capacity of the supercapacitor and the battery are controlled to increase in a set step size; Based on the capacity of each group of supercapacitors and batteries, the economic objective function and the power fluctuation objective function are respectively solved to obtain the economic target value and the power fluctuation target value; The economic target value and the power fluctuation target value are normalized respectively to obtain the economic target normalized value and the power fluctuation target normalized value; Based on the normalized value of economic objective and the normalized value of power fluctuation objective, the multi-optimization objective function is solved to obtain the multi-optimization objective value; Under the condition of satisfying the constraints, the capacity of a group of supercapacitors and batteries corresponding to the minimum multi-optimization objective value is the optimal energy storage capacity configuration.

8. The energy storage configuration method of the photovoltaic storage system according to claim 7, characterized in that: The process of determining the capacity range of supercapacitors and batteries based on the original high-frequency power fluctuation curve is: The area enclosed by the power fluctuation curve and the time axis in each continuous time period is calculated in sequence to obtain the power to be smoothed in each continuous time period; The magnitude and frequency of the power to be smoothed within a certain period of time are counted to form a frequency distribution histogram of the power to be smoothed; The capacity range of the supercapacitor and the battery is determined based on the size and frequency of the power to be smoothed as shown in the frequency distribution histogram.

9. The energy storage configuration method of the photovoltaic storage system according to claim 7, characterized in that: The process of solving the volatility objective function is: performing power fluctuation smoothing processing on the original high-frequency power fluctuation curve to form a new high-frequency power fluctuation curve, synthesizing the new high-frequency power fluctuation curve with the original low-frequency power fluctuation curve to obtain a new photovoltaic power generation power curve, and solving the volatility objective function based on the new photovoltaic power generation power curve.

10. The energy storage configuration method of the photovoltaic storage system according to claim 9, characterized in that: The process of performing power fluctuation smoothing is as follows: Compare the power fluctuation to be smoothed on the original high-frequency power fluctuation curve with the capacity of the supercapacitor and the battery; If the power fluctuation to be smoothed is smaller than the capacity of the supercapacitor, the power fluctuation to be smoothed is set to zero; If the power fluctuation to be smoothed is greater than or equal to the capacity of the supercapacitor and less than twice the capacity of the supercapacitor, the ordinates of all the data points in this section of the original high-frequency power fluctuation curve are proportionally reduced so that the area of ​​the power fluctuation to be smoothed after reduction is equal to the capacity of the supercapacitor; If the power fluctuation to be smoothed is greater than or equal to twice the capacity of the supercapacitor and less than the sum of the capacity of the supercapacitor and the capacity of the battery, the power fluctuation to be smoothed is set to zero; If the power fluctuation to be smoothed is greater than or equal to the sum of the supercapacitor capacity and the battery capacity, the vertical coordinates of all data points in this section of the original high-frequency power fluctuation curve are proportionally reduced so that the area of ​​the power fluctuation to be smoothed after reduction is equal to the capacity of the supercapacitor.

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