Energy storage facility sizing method and apparatus, electronic device, and storage medium

Abstract

An energy storage facility sizing method and apparatus, an electronic device, and a storage medium. The method comprises: determining energy storage data of a target energy storage facility (S110); determining a target average value on the basis of the energy storage data of the target energy storage facility, and determining a target demand on the basis of the target average value (S120); on the basis of the target demand, determining a target capacity by means of a first sizing model (S130); and generating a capacity distribution curve of the target energy storage facility on the basis of the target capacity (S140).

Description

Energy storage facility capacity determination method and device, electronic equipment and storage medium

[0001] The present application claims priority to the Chinese patent application No. 202410862165.3, filed on June 28, 2024, to the Chinese Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of energy storage, for example, to an energy storage facility capacity determination method and device, electronic equipment and storage medium. BACKGROUND

[0003] A microgrid is a small power generation and distribution system integrating distributed power sources, energy storage devices and power consumption loads, and can realize collaborative planning, scheduling and control among the parts. The microgrid system can maximize the use of distributed power output and reduce the adverse effects of intermittent distributed power on the distribution network, and is one of the effective ways of distributed new energy power generation grid connection. Scientific and reasonable energy storage facility capacity determination of the microgrid system can ensure sufficient energy storage capacity without wasting resources, maximize economic benefits while meeting new energy consumption and grid reliability requirements.

[0004] However, the current mainstream energy storage capacity determination method usually uses linear approximation to obtain energy storage capacity for the nonlinear microgrid system energy storage facility data, but the energy storage capacity data obtained by linear approximation cannot construct a detailed energy storage model to simulate the characteristics of the energy storage facility, and the capacity determination based thereon has low accuracy of the determined capacity data, thereby affecting the allocation of distribution network system resources. SUMMARY

[0005] The present application provides an energy storage facility capacity determination method and device, electronic equipment and storage medium to solve the problem of low accuracy of capacity determination data, thereby affecting the allocation of distribution network system resources.

[0006] According to an aspect of the present application, an energy storage facility capacity determination method is provided, comprising:

[0007] determining energy storage data of a target energy storage facility, the energy storage data of the target energy storage facility being used to represent power generation output and power consumption load of the target energy storage facility;

[0008] determining a target mean value according to the energy storage data of the target energy storage facility, and determining a target demand according to the target mean value, the target demand being the power consumption load and the power generation data corresponding to the power consumption load of the target energy storage facility;

[0009] The target capacity is determined by a first capacity model based on the target demand. The first capacity model consists of a second capacity model and a constraint function. The first capacity model is used to generate the capacity of the energy storage facility based on the demand information. The capacity of the energy storage facility is used to characterize the energy storage capacity that the energy storage facility can store. The second capacity model is used to determine the first capacity of the energy storage facility based on the demand information. The first capacity is used to characterize the maximum energy storage capacity that the energy storage facility can store within the target time period. The constraint function is used to correct the second capacity model when the first capacity does not meet the preset energy storage demand.

[0010] The target capacity distribution curve of the target energy storage facility is generated based on the target capacity.

[0011] According to another aspect of this application, a capacity stabilization device for an energy storage facility is provided, comprising:

[0012] The energy storage data determination module is configured to determine the energy storage data of the target energy storage facility, wherein the energy storage data of the target energy storage facility is used to characterize the power generation output and power load of the target energy storage facility;

[0013] The mean value determination module is configured to determine a target mean value based on the energy storage data of the target energy storage facility, and to determine a target demand based on the target mean value. The target demand is the electricity load of the target energy storage facility and the power generation data corresponding to the electricity load.

[0014] The target capacity determination module is configured to determine the target capacity based on the target demand using a first capacity model. The first capacity model consists of a second capacity model and a constraint function. The first capacity model is used to generate the capacity of the energy storage facility based on the demand information. The capacity of the energy storage facility is used to characterize the energy storage capacity that the energy storage facility can store. The second capacity model is used to determine the first capacity of the energy storage facility based on the demand information. The first capacity is used to characterize the maximum energy storage capacity that the energy storage facility can store within the target time period. The constraint function is used to correct the second capacity model when the first capacity does not meet the preset energy storage demand.

[0015] The distribution curve determination module is configured to generate a fixed-capacity distribution curve for the target energy storage facility based on the target fixed-capacity.

[0016] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the energy storage facility gradation method according to any embodiment of this application.

[0020] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the energy storage facility gradation method according to any embodiment of this application.

[0021] The technical solution of this application embodiment determines the energy storage data of the target energy storage facility; determines the target average value based on the energy storage data of the target energy storage facility, and determines the target demand based on the target average value. This can expand the sample size of the target demand, so that the obtained target demand can cover the actual demand as much as possible, providing a basis for subsequent capacity determination, and ensuring that the determined target capacity can cover all demands; determines the target capacity through a first capacity determination model based on the target demand. The first capacity determination model can effectively simulate the energy limitation, power limitation, and energy loss characteristics of the target energy storage facility, and can directly determine the target capacity based on the target demand. This achieves rapid and accurate determination of the optimal capacity of the target energy storage facility under the target demand. At the same time, the target energy storage facility stores energy according to the target capacity, which can maximize the new energy consumption effect and minimize load shedding; generates a capacity distribution curve of the target energy storage facility based on the target capacity. The capacity distribution curve of the target energy storage facility can more intuitively show the distribution of the target energy storage facility under different demands. The target energy storage facility is configured into a suitable microgrid network according to the capacity distribution curve, which can effectively meet the power load of the microgrid. Therefore, this method can accurately and effectively generate target capacity, and energy storage based on the target capacity can help promote the consumption of renewable energy while effectively meeting the power load of microgrids. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments 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.

[0023] Figure 1 is a flowchart of a method for calibrating an energy storage facility according to an embodiment of this application;

[0024] Figure 2 shows the unconstrained energy storage capacity change of an energy storage facility provided in an embodiment of this application over two cycles;

[0025] Figure 3 shows the energy storage capacity change of an energy storage facility provided in an embodiment of this application under two cycles, considering energy and power constraints.

[0026] Figure 4 shows the change in energy storage capacity of an energy storage facility provided in an embodiment of this application before and after considering energy loss;

[0027] Figure 5 shows the curves of power generation, power load, and energy storage capacity changing over time according to an embodiment of this application;

[0028] Figure 6 is a structural schematic diagram of a capacity-limiting device for an energy storage facility provided in an embodiment of this application;

[0029] Figure 7 is a schematic diagram of the structure of an electronic device for implementing the energy storage facility calibration method of the present application embodiment. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Figure 1 is a flowchart of a capacity determination method for energy storage facilities provided in an embodiment of this application. This embodiment is applicable to the case of capacity determination of energy storage facilities in a microgrid. This method can be executed by an energy storage facility capacity determination device, which can be implemented in hardware and / or software. The energy storage facility capacity determination device can be configured in any electronic device with network communication function. As shown in Figure 1, the method includes:

[0033] S110. Determine the energy storage data of the target energy storage facility.

[0034] Among them, the energy storage data of the target energy storage facility is used to characterize the power generation output and power load of the target energy storage facility.

[0035] For example, the power generation output data and power load data of the target energy storage facility can be obtained based on the operating status of the target energy storage facility.

[0036] S120. Determine the target average value based on the energy storage data of the target energy storage facility, and determine the target demand based on the target average value.

[0037] The target demand refers to the electricity load of the target energy storage facility and the corresponding power generation data.

[0038] For example, the average and standard deviation of power generation output are calculated based on power generation output data; the average and standard deviation of electricity load data are calculated based on electricity load data. Target demand is then generated using a random number algorithm based on the calculated average and standard deviation.

[0039] The basic idea of ​​random number generation algorithms is to first obtain random numbers that follow a uniform distribution, and then transform them into random numbers that follow a normal distribution through specific transformations. The Box-Muller algorithm can be used as a random number generation algorithm.

[0040] The above steps randomly generate target demand, which increases the sample size of the target energy storage facility's demand. A large number of demand samples can ensure that all the needs of the target energy storage facility during operation are covered, ensuring that the target energy storage facility can meet different needs when it is put into use.

[0041] S130. Determine the target volume using the first volume model based on the target requirements.

[0042] The first capacity model consists of a second capacity model and a constraint function. The first capacity model is used to generate the capacity of the energy storage facility based on the demand information. The capacity of the energy storage facility is used to characterize the energy storage capacity that the energy storage facility can store. The second capacity model is used to determine the first capacity of the energy storage facility based on the demand information. The first capacity is used to characterize the maximum energy storage capacity that the energy storage facility can store within the target time period. The constraint function is used to correct the second capacity model when the first capacity does not meet the preset energy storage demand.

[0043] The preset energy storage requirements refer to the long-term energy storage cycle operation requirements of the target energy storage facility. The target time period is the pre-selected short-term operating time of the target energy storage facility, where "short-term" can be either period A or year B, the specific definition of which needs to be determined based on the actual needs of the target energy storage facility. The target time period characterizes the capacity determination result of the target energy storage facility within a short period. The target time period can be 1 period or 2 periods, with 1 period being 12 months. The capacity determination result within the target time period, i.e., the first capacity determination, can only meet the energy storage requirements of the target energy storage facility within the target time period.

[0044] The constraint function is used to correct the energy storage capacity function in the second fixed-capacity model when the target time period does not meet the first preset time period.

[0045] The constraint functions include energy constraint functions and power constraint functions.

[0046] The process of determining the energy constraint function is as follows:

[0047] Energy constraints take into account the upper and lower limits of the depth of discharge of the target energy storage facility, expressed as a percentage as DD. max and DD min Then the total energy storage capacity E total It can be obtained from the first constant volume and the depth of discharge:

[0048] This allows us to determine the upper and lower limits S of the energy stored by the target energy storage facility. up and S low S up =E total (1-DD min ), S low =E total (1-DD max ),

[0049] If the constraints are strictly applied according to the upper and lower limits of the stored energy, the iterative solution obtained is usually not optimal. Therefore, a multiplier α between 0 and 1 is set, and the constraints are appropriately relaxed by using the difference between the stored energy capacities S(0) and S(T) at the start and end of the target time period. The final energy constraint function is expressed as: S low -α|S(T)-S(0)|≤S(t)≤S up +α|S(T)-S(0)|, 0≤α<1,

[0050] Where T is the target time period.

[0051] Based on the energy constraint function, the energy storage capacity function is modified to obtain the energy storage capacity at the beginning of the target time period for continuous cyclic charging and discharging after modification:

[0052] The process of determining the power constraint function is as follows:

[0053] The power limitation of energy storage will also affect the capacitive rating result, based on the ratio parameter C, which reflects the relationship between energy and power during energy storage charging and discharging. c and C d The charging and discharging power constraint can be calculated: P c =E total C c P d =E total C d ,

[0054] By appropriately relaxing the constraints by considering the difference between the energy storage capacities S(0) and S(T) at the start and end of the target time period, the final power constraint function is expressed as: -P d -α|S(T)-S(0)|≤G(t)-D(t)≤P c +α|S(T)-S(0)|, 0≤α<1,

[0055] The energy storage capacity model is modified according to the power constraint function, and a new round of fixed-capacity iteration is carried out until the difference between the energy storage capacity S(0) and S(T) at the start and end of the target time period is less than the allowable error ε. The iteration is considered to be converged, and the final target fixed capacity is obtained: |S(T)-S(0)|≤ε.

[0056] Where ε represents the error.

[0057] For example, the target requirement is input into the first capacity model. The first capacity is calculated based on the second capacity model in the first capacity model. If the target time period is greater than or equal to the first preset time period, the first capacity is used as the target capacity. If the target time period is less than the first preset time period, the second capacity model is modified by the constraint function, and the capacity generated by the modified model is used as the target capacity.

[0058] The first preset time period can be the expected operating time of the target energy storage facility, wherein the expected operating time is determined according to the actual operating needs of the target energy storage facility.

[0059] The reason for correcting the second capacity model through constraint functions is that the first capacity model, due to the limited number of factors considered, cannot meet the requirements for long-term energy storage cycles. It can only achieve preliminary capacity determination of the target energy storage facility and determine whether the target energy storage facility can meet the energy storage requirements.

[0060] For example, assuming the target energy storage facility is a distributed photovoltaic (PV) system, the target time period is 2 cycles, and the first capacities of the distributed PV system are 2125 MWh, as shown in Figure 2, the green and blue lines represent the changes in the energy storage capacity of the target energy storage facility over the two cycles. The unconstrained energy storage capacity change is determined based on the second capacities model. Since the charging and discharging of the lithium batteries included in the distributed PV system typically occurs on an annual basis...

[0061] Assuming the lithium battery's charge / discharge efficiency is 0.8 and the charge / discharge power ratio is 1C, the upper and lower limits of the storage capacity in the constraint function are calculated to be 2656 kWh and 531 kWh, respectively, with a maximum charge / discharge power of 2656 kW. The energy storage capacity function is corrected based on the energy and power constraint functions, resulting in the target energy storage capacity shown in Figure 3. The constrained energy storage capacity change is determined by the second constant-capacity model after the constraint function correction. As can be seen from the figure, the sustainable cyclic charge / discharge energy storage capacity of the distributed photovoltaic system is 1906 kWh. Assuming a 2% energy loss per month from the lithium battery, the impact of energy loss needs to be considered in the constant-capacity problem with an annual cycle. Therefore, the energy storage capacity function is iteratively solved under the constraints of the energy and power constraint functions, with a multiplier α of 0.1 and an allowable error ε of 0.01. After seven iterations, the energy storage capacity converges to 2115 kWh. The change in the distributed photovoltaic energy storage capacity is shown in Figure 4, indicating a reduction in the energy storage capacity constrained by the constraint function.

[0062] The above steps, based on the first capacity model and the target demand, generate the target capacity quickly and accurately according to the first prediction model, which is most suitable for the target energy storage facility's target demand. The obtained target capacity maximizes the energy storage utilization rate of the target energy storage facility. Furthermore, by adjusting the energy storage according to the target capacity, the effect of new energy consumption can be maximized while minimizing load shedding.

[0063] S140. Generate the capacity distribution curve of the target energy storage facility based on the target capacity.

[0064] Among them, the fixed-capacity distribution curve is used to characterize the distribution of the fixed-capacity of the target energy storage facility under different target requirements.

[0065] For example, based on the target capacity corresponding to different target needs, the capacity distribution curve of the target energy storage facility can be generated.

[0066] The generation process can be achieved through data fitting.

[0067] The above steps generate the target energy storage facility's capacity distribution curve based on the target capacity, which can intuitively obtain the distribution of the target energy storage facility's capacity under different demands. Based on the capacity distribution, the target energy storage facility can be configured into a suitable microgrid network, which can effectively meet the microgrid's power load.

[0068] Optionally, the target average is determined based on the energy storage data of the target energy storage facility, and the target demand is determined based on the target average, including steps A1-A2:

[0069] Step A1: Calculate the target mean value of the energy storage data of the target energy storage facility.

[0070] The target mean includes the average and standard deviation of power generation output data and the average and standard deviation of power load data.

[0071] For example, the average value of the energy storage data of the target energy storage facility is calculated using the average value calculation formula, and the standard deviation of the target energy storage facility is calculated based on the calculated average value and standard deviation calculation formula.

[0072] The mean and standard deviation can be expressed by the following formula:

[0073] Where n is the number of energy storage data of the target energy storage facility, and xi is the i-th sample value of the power generation output data or power load data.

[0074] Step A2: Obtain the target requirement using a random number algorithm based on the target mean.

[0075] The Box-Muller algorithm can be used as a random number algorithm.

[0076] The Box-Muller algorithm can be expressed as follows:

[0077] In the formula, r n1 and r n2 For the target demand, which is generated from a normally distributed random data set based on a target mean, the target demand's normal distribution has a specified mean and standard deviation, r. u1 and r u2 These are two random values ​​between 0 and 1 generated by a uniform distribution.

[0078] When calculating target requirements using the Box-Muller algorithm described above, either of the two formulas can be selected for calculation.

[0079] For example, firstly, random numbers between 0 and 1 are generated based on a uniform distribution. The obtained random numbers and the average and standard deviation of the power generation output data are then input into the Box-Muller algorithm formula for calculation, and the calculated result is used as the target power generation output. The obtained random numbers and the average and standard deviation of the electricity load data are then input into the Box-Muller algorithm formula for calculation, and the calculated result is used as the target electricity load.

[0080] The above steps calculate the target demand using a random number algorithm. In order to expand the sample data of the target demand, this solves the problem that the sample size is insufficient when the target demand is determined based on the actual needs of the actual target energy storage facilities, thus making it impossible to obtain a wider range of constant capacity distribution curves.

[0081] Optionally, the construction process of the first volumetric model includes steps B1-B3:

[0082] Step B1: Determine the energy storage data of the reference energy storage facility and construct the energy storage capacity function based on the energy storage data of the reference energy storage facility.

[0083] The reference energy storage facility and the target energy storage facility are of the same type. The energy storage capacity function is used to characterize the change of energy storage capacity over time.

[0084] The energy storage capacity function can be expressed by the following formula: S(t+Δt)=S(t)(1-σ)+(G(t)-D(t))η(t)Δt, σ=0, if S(t)<0,

[0085] Where σ is the energy loss rate of energy storage; G(t) is the function of the change of power generation output data with time; D(t) is the function of the change of electricity load data with time; and η(t) is the charging and discharging efficiency of energy storage facilities.

[0086] For example, based on the operating status of the reference energy storage facility, the power generation output data and power load data of the reference energy storage facility are obtained. The power generation output data and power load data of the reference energy storage facility are then used to generate a time-varying function of the power generation output data and power load data through data fitting. Based on the changing function, the energy storage capacity function is generated.

[0087] Step B2: Determine the second constant-capacity model based on the energy storage capacity function and the extreme value function.

[0088] The extreme value function is determined based on the energy storage capacity and reference constant capacity of the reference energy storage facility within the second preset time period. The reference constant capacity is the absolute value of the maximum or minimum value of the energy storage capacity of the reference energy storage facility.

[0089] The second preset time period is a pre-set time period used to generate the extreme point based on the energy storage capacity function. The second preset time period can be the same as the first preset time period.

[0090] The extremum function can be expressed by the following formula:

[0091] Where D is the energy storage capacity difference matrix.

[0092] For example, the energy storage capacity function and the extreme value function are fused to obtain the second constant capacity model.

[0093] The fusion process includes: calculating the extreme points within the second preset time period based on the energy storage capacity function; calculating the energy storage capacity difference matrix based on the extreme points; and performing corresponding calculations on the extreme function based on the obtained energy storage capacity difference matrix, thereby completing the fusion of the energy storage capacity function and the extreme function.

[0094] Step B3: Determine the first ductility model based on the second ductility model and the constraint function.

[0095] For example, the first ductility model is obtained by fusing the second ductility model with the constraint function.

[0096] The fusion process includes: if the reference time period corresponding to the reference first calibrated model obtained by the second calibrated model is greater than or equal to the first preset time period, then the second calibrated model is used as the first calibrated model; if the reference time period corresponding to the reference first calibrated model obtained by the second calibrated model is less than the first preset time period, then the second calibrated model modified by the constraint function is used as the first calibrated model. The reference time period is the same as the target time period.

[0097] Optionally, an energy storage capacity function is constructed based on the energy storage data of a reference energy storage facility, including steps C1-C5:

[0098] Step C1: Determine the charge and discharge efficiency of the reference energy storage facility.

[0099] For example, the charging and discharging efficiency of a reference energy storage facility can be determined based on the actual output or input energy of the reference energy storage facility during the charging and discharging process and the energy that should theoretically be output or input.

[0100] Step C2: Generate a function showing the change of the energy storage data of the reference energy storage facility over time based on the energy storage data of the reference energy storage facility.

[0101] For example, the power generation output data of the reference energy storage facility can be used to generate a function showing the change of the power generation output data of the reference energy storage facility over time through data fitting; the electricity load data of the reference energy storage facility can be used to generate a function showing the change of the electricity load data of the reference energy storage facility over time through data fitting.

[0102] Step C3: Determine the energy storage change rate of the reference energy storage facility based on the change function and the charging and discharging efficiency of the reference energy storage facility.

[0103] For example, by subtracting the time-varying function of the electricity load data and the time-varying function of the power generation output data of the reference energy storage facility, and multiplying the difference by the charging and discharging efficiency of the reference energy storage facility, the energy storage change rate of the reference energy storage facility can be obtained.

[0104] For example, given the time-varying functions G(t) and D(t) of power generation output and electricity load data, combined with the reference energy storage facility's charge / discharge efficiency η... c and η d The rate of change of the energy storage capacity S(t) of the reference energy storage facility over time can be obtained as follows:

[0105] Where, η c For reference, the charging efficiency of energy storage facilities; η d η(t) represents the discharge efficiency of the reference energy storage facility.

[0106] Step C4: Determine the energy storage capacity of the reference energy storage facility at the first moment based on the energy storage change rate of the reference energy storage facility.

[0107] For example, by integrating both sides of the rate of change of energy storage of the reference energy storage facility between (second time point and first time point), the energy storage capacity of the reference energy storage facility at the first time point can be obtained. For instance, given the energy storage capacity S(t) at time t, the energy storage capacity after a preset time Δt can be calculated, i.e., the energy storage capacity S(t+Δt) of the reference energy storage facility at the first time point, where t+Δt is the first time point and t is the second time point.

[0108] The calculation process for S(t+Δt) is shown below: S(t+Δt)-S(t)=(G(t)-D(t))n(t)Δt, S(t+Δt)=S(t)+(G(t)-D(t))n(t)Δt.

[0109] Step C5: Determine the energy storage capacity function based on the energy storage capacity of the reference energy storage facility at the first moment, the energy storage capacity of the reference energy storage facility at the second moment, and the energy loss rate.

[0110] The first moment is the moment preceding the second moment.

[0111] For example, based on the assumption that G(t) and D(t) remain constant over Δt, and considering the energy loss rate σ of the reference energy storage facility, the energy storage capacity function is: S(t+Δt)=S(t)(1-σ)+(G(t)-D(t))η(t)Δt, σ=0, if S(t)<0,

[0112] When the reference energy storage capacity level is negative, the loss rate is 0.

[0113] Optional, the extremum function construction process includes steps D1-D3:

[0114] Step D1: Determine the reference energy storage capacity of the reference energy storage facility within the second preset time period based on the energy storage capacity function.

[0115] The second preset time period is a pre-set time period used to determine the target extreme point. The second preset time period can be the same as or different from the first preset time period.

[0116] The reference energy storage capacity of the reference energy storage facility at each time point within the second preset time period is calculated based on the energy storage capacity function.

[0117] Step D2: Select the extreme points in the reference energy storage capacity and generate the energy storage capacity difference matrix based on the extreme points.

[0118] Extreme points are selected from the calculated reference energy storage capacity, and an energy storage capacity difference matrix is ​​generated based on the difference between the obtained extreme points and the difference between the energy storage capacity at the start and end times of the second preset time.

[0119] For example, based on the established energy storage capacity model, all extreme points of the energy storage capacity within a change period T, i.e., the second preset time period, are identified. Let the number of extreme points be n, and their times be t1, t2, ..., tn. n Then, based on the difference in energy storage capacity between extreme points, an energy storage capacity difference matrix D can be established:

[0120] Since the energy storage capacities S(0) and S(T) at the start and end of a cycle may not be equal, the extreme value difference is obtained by extending the lower left part of matrix D by another cycle T.

[0121] Step D3: Determine the extremum function based on the energy storage capacity difference matrix and the reference constant capacity.

[0122] For example, an extremum function is generated based on the values ​​of each difference in the energy storage capacity difference matrix and the size of the reference constant capacity.

[0123] The extremum function can be expressed by the following formula:

[0124] The meaning of the extreme value function is that if the energy storage capacity increases after one cycle, that is, the total power generation is higher than the total load, the energy storage facility should be sized according to the peak shaving demand as the cumulative decrease in energy storage capacity during discharge, and the excess part is curtailed power; if the energy storage capacity decreases after one cycle, that is, the total power generation is lower than the total load, the energy storage facility should be sized according to the valley filling demand as the cumulative increase in energy storage capacity during charging, and the insufficient part is the demand gap; if the energy storage capacity remains unchanged after one cycle, that is, the total power generation is equal to the total load, and the cumulative increase and decrease in energy storage capacity caused by charging and discharging are equal, the energy storage can be sized according to this range.

[0125] Optionally, the target volume is determined using the first volume determination model based on the target requirements, including steps E1-E3:

[0126] Step E1: Determine the first volume using the second volume model based on the target requirements.

[0127] For example, the target demand is input into the second fixed-capacity model. First, the energy storage capacity within the time period is generated according to the energy storage capacity function, and the extreme points in the energy storage capacity are obtained. The energy storage capacity difference matrix is ​​calculated based on the extreme points and the energy storage capacity at the start and end times of the time period. The first fixed capacity is determined by the extreme value function based on the energy storage capacity difference matrix.

[0128] Step E2: If the target time period is greater than or equal to the first preset time period, then the first volume is taken as the target volume.

[0129] For example, if the target time period is greater than or equal to the first preset time period, then the first fixed capacity is taken as the target fixed capacity. This indicates that the target energy storage facility currently only needs to store energy for a short period of time, so the first fixed capacity can be directly taken as the target fixed capacity. The length of the short period of time can be determined according to the actual needs of the specific target energy storage facility.

[0130] Step E3: If the target time period is less than the first preset time period, the energy storage capacity function is corrected by the constraint function, and the second fixed capacity is determined according to the corrected energy storage capacity function, and the second fixed capacity is used as the target fixed capacity.

[0131] For example, if the target time period is less than the first preset time period, it indicates that the target energy storage facility currently requires long-term energy storage. Therefore, it is necessary to correct the energy storage capacity function using a constraint function, calculate the energy storage capacity within the target time period using the corrected energy storage capacity function, select an extreme point based on the energy storage capacity, and calculate the energy storage capacity difference matrix based on the extreme point and the energy storage capacity at the start and end times of the corrected target time period. The target capacity is then determined using the extreme point function based on the energy storage capacity difference matrix. The length of the long period can be determined according to the actual needs of the specific target energy storage facility.

[0132] Optionally, the first volume determination is determined using the second volume determination model based on the target requirements, including steps F1-F3:

[0133] Step F1: Determine the energy storage capacity of the target energy storage facility using the energy storage capacity function based on the target requirements.

[0134] For example, a function is generated to show how the target demand changes over time, and this function is then input into the energy storage capacity function to calculate the energy storage capacity of the target energy storage facility within a first preset time period.

[0135] Step F2: Determine the energy storage capacity difference matrix of the target energy storage facility based on its energy storage capacity.

[0136] For example, the extreme points of the energy storage capacity can be screened based on the energy storage capacity of the target energy storage facility, and the energy storage capacity difference matrix of the target energy storage facility can be calculated based on the extreme points.

[0137] Step F3: Determine the first fixed capacity based on the energy storage capacity difference matrix of the target energy storage facility through the extreme value function.

[0138] For example, the first fixed capacity is determined based on the target energy storage facility capacity difference matrix according to the extreme value function. That is, if the difference between the energy storage capacity corresponding to the start time and the energy storage capacity corresponding to the end time of the first preset time is greater than 0, then the first fixed capacity is the absolute value of the minimum value of the elements in the target energy storage facility capacity difference matrix; if the difference between the energy storage capacity corresponding to the start time and the energy storage capacity corresponding to the end time of the first preset time is less than 0, then the first fixed capacity is the maximum value of the elements in the target energy storage facility capacity difference matrix; if the difference between the energy storage capacity corresponding to the start time and the energy storage capacity corresponding to the end time of the first preset time is equal to 0, then the first fixed capacity is the maximum value of the absolute value of the elements in the target energy storage facility capacity difference matrix.

[0139] For example, assuming the target energy storage facility is a distributed photovoltaic (PV) system with an annual power generation of 7.2 MWh and an annual load of 6 MWh for the microgrid, the monthly power generation and load of the distributed PV system are shown as the blue and red lines in Figure 5, respectively. Based on the energy storage capacity model in the second capacities model, the energy storage capacity change of the distributed PV system within one cycle can be obtained, as shown by the green line in Figure 5. Extending the target time period to two cycles yields the energy storage capacity change within two cycles, as shown by the blue line in Figure 2. From the figure, we can see that the extreme values ​​are S(0) = 0, S(t1) = -1375, S(T) = 195, S(t2) = 945, and S(t1') = -1180. Using the extreme value function, the first capacities can be determined to be 2125 MWh.

[0140] The technical solution of this embodiment determines the energy storage data of the target energy storage facility; determines the target average value based on the energy storage data of the target energy storage facility; determines the target demand based on the target average value, which can expand the sample size of the target demand, so that the obtained target demand can cover the actual demand as much as possible, providing a basis for subsequent capacity determination, and ensuring that the determined target capacity can cover all demands; determines the target capacity through a first capacity determination model based on the target demand. The first capacity determination model can effectively simulate the energy limitation, power limitation, and energy loss characteristics of the target energy storage facility, and can directly determine the target capacity based on the target demand, realizing the rapid and accurate determination of the optimal capacity of the target energy storage facility under the target demand. At the same time, the target energy storage facility stores energy according to the target capacity, which can maximize the new energy consumption effect and minimize load shedding; generates a capacity distribution curve of the target energy storage facility based on the target capacity, which can more intuitively see the distribution of the target energy storage facility under different demands. Based on the capacity distribution curve, the target energy storage facility is configured into a suitable microgrid network, which can effectively meet the power load of the microgrid. Therefore, this method can accurately and effectively generate target capacity, and energy storage based on the target capacity can help promote the consumption of renewable energy while effectively meeting the power load of microgrids.

[0141] Figure 6 is a schematic diagram of a capacity determination device for an energy storage facility provided in an embodiment of this application. This embodiment is applicable to the situation of capacity determination of energy storage facilities in a microgrid. The capacity determination device can be implemented in hardware and / or software and can be configured in any electronic device with network communication function. As shown in Figure 6, the device includes: an energy storage data determination module 210, an average value determination module 220, a target capacity determination module 230, and a distribution curve determination module 240, wherein:

[0142] Energy storage data determination module 210: used to determine the energy storage data of the target energy storage facility, which is used to characterize the power generation output and power load of the target energy storage facility;

[0143] Mean determination module 220: used to determine the target mean based on the energy storage data of the target energy storage facility, and to determine the target demand based on the target mean. The target demand is the power load of the target energy storage facility and the power generation data corresponding to the power load.

[0144] Target capacity determination module 230: used to determine the target capacity according to the target demand through a first capacity model. The first capacity model consists of a second capacity model and a constraint function. The first capacity model is used to generate the capacity of the energy storage facility according to the demand information. The capacity of the energy storage facility is used to characterize the energy storage capacity that the energy storage facility can store. The second capacity model is used to determine the first capacity of the energy storage facility according to the demand information. The first capacity is used to characterize the maximum energy storage capacity that the energy storage facility can store within the target time period. The constraint function is used to correct the second capacity model when the first capacity does not meet the preset energy storage demand.

[0145] Distribution curve determination module 240: used to generate the fixed-capacity distribution curve of the target energy storage facility based on the target fixed-capacity.

[0146] Optionally, the mean determination module 220 includes:

[0147] Mean determination unit: used to calculate the target mean value of the energy storage data of the target energy storage facility;

[0148] Target requirement determination unit: used to obtain target requirements based on the target mean using a random number algorithm.

[0149] Optionally, the target volume determination module 230 includes:

[0150] Energy storage capacity function determination unit: used to determine the energy storage data of the reference energy storage facility, and construct the energy storage capacity function based on the energy storage data of the reference energy storage facility. The reference energy storage facility and the target energy storage facility are the same type of energy storage facility.

[0151] The second fixed-capacity model determination unit is used to determine the second fixed-capacity model based on the energy storage capacity function and the extreme value function. The extreme value function is determined based on the energy storage capacity of the reference energy storage facility and the reference fixed capacity within the second preset time period. The reference fixed capacity is the absolute value of the maximum or minimum value of the energy storage capacity of the reference energy storage facility.

[0152] First volumetric model determination unit: used to determine the first volumetric model based on the second volumetric model and constraint functions.

[0153] Optionally, the energy storage capacity function determination unit includes:

[0154] Efficiency determination subunit: used to determine the charge and discharge efficiency of a reference energy storage facility;

[0155] The variation function determination sub-unit is used to generate a variation function of the energy storage data of the reference energy storage facility over time based on the energy storage data of the reference energy storage facility.

[0156] Energy storage change rate determination sub-unit: used to determine the energy storage change rate of the reference energy storage facility based on the change function and the charge / discharge efficiency of the reference energy storage facility;

[0157] Energy storage capacity determination sub-unit: used to determine the energy storage capacity of the reference energy storage facility at the first moment based on the energy storage change rate of the reference energy storage facility;

[0158] Energy storage capacity function determination sub-unit: used to determine the energy storage capacity function based on the energy storage capacity of the reference energy storage facility at the first moment, the energy storage capacity of the reference energy storage facility at the second moment, and the energy loss rate. The first moment is the moment before the second moment.

[0159] Optionally, the second volumetric model determining unit includes:

[0160] Reference energy storage capacity determination subunit: used to determine the reference energy storage capacity of the reference energy storage facility within a second preset time period based on the energy storage capacity function;

[0161] The difference matrix determines the sub-unit: it is used to select the extreme points in the reference energy storage capacity and generate the energy storage capacity difference matrix based on the extreme points;

[0162] Extremum function determination sub-unit: used to determine the extremum function based on the energy storage capacity difference matrix and the reference constant volume.

[0163] Optionally, the target volume determination module 230 includes:

[0164] First volume determination unit: used to determine the first volume according to the target requirements using the second volume determination model;

[0165] Target volume determination unit: used to determine the first volume as the target volume if the target time period is greater than or equal to the first preset time period;

[0166] Target capacity determination unit: If the target time period is greater than the first preset time period, the energy storage capacity function is corrected by the constraint function, and the second capacity is determined according to the corrected energy storage capacity function, and the second capacity is used as the target capacity.

[0167] Optionally, the first constant volume determination unit is specifically used for:

[0168] The energy storage capacity of the target energy storage facility is determined based on the target demand using an energy storage capacity function.

[0169] Determine the energy storage capacity difference matrix of the target energy storage facility based on its energy storage capacity;

[0170] The first capacity determination is determined based on the energy storage capacity difference matrix of the target energy storage facility through the extreme value function. The energy storage facility capacity determination device provided in this application embodiment can execute the energy storage facility capacity determination method provided in any of the above embodiments of this application, and has the corresponding functions and effects of executing the energy storage facility capacity determination method. For detailed process, please refer to the relevant operations of the energy storage facility capacity determination method in the foregoing embodiments.

[0171] Figure 7 is a schematic diagram of the structure of an electronic device for implementing the energy storage facility calibration method according to an embodiment of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0172] As shown in Figure 7, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0173] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0174] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as energy storage facility grading methods.

[0175] In some embodiments, the energy storage facility sizing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the energy storage facility sizing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the energy storage facility sizing method by any other suitable means (e.g., by means of firmware).

[0176] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0177] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0178] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0179] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0180] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0181] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0182] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

Claims

1. A method for determining the capacity of an energy storage facility, comprising: Determine the energy storage data of the target energy storage facility, which is used to characterize the power generation output and power load of the target energy storage facility; The target average is determined based on the energy storage data of the target energy storage facility, and the target demand is determined based on the target average. The target demand is the electricity load of the target energy storage facility and the power generation data corresponding to the electricity load. The target capacity is determined by a first capacity model based on the target demand. The first capacity model consists of a second capacity model and a constraint function. The first capacity model is used to generate the capacity of the energy storage facility based on the demand information. The capacity of the energy storage facility is used to characterize the energy storage capacity that the energy storage facility can store. The second capacity model is used to determine the first capacity of the energy storage facility based on the demand information. The first capacity is used to characterize the maximum energy storage capacity that the energy storage facility can store within the target time period. The constraint function is used to correct the second capacity model when the first capacity does not meet the preset energy storage demand. The target capacity distribution curve of the target energy storage facility is generated based on the target capacity.

2. The method according to claim 1, wherein, The step of determining the target average value based on the energy storage data of the target energy storage facility, and determining the target demand based on the target average value, includes: Calculate the target mean value from the energy storage data of the target energy storage facility; The target requirement is obtained using a random number algorithm based on the target mean.

3. The method according to claim 1, wherein, The construction process of the first volumetric model includes: Determine the energy storage data of the reference energy storage facility, and construct an energy storage capacity function based on the energy storage data of the reference energy storage facility. The reference energy storage facility and the target energy storage facility are of the same type. The second constant capacity model is determined based on the energy storage capacity function and the extreme value function. The extreme value function is determined based on the energy storage capacity and the reference constant capacity of the reference energy storage facility within the second preset time period. The reference constant capacity is the absolute value of the maximum or minimum value of the energy storage capacity of the reference energy storage facility. The first volumetric model is determined based on the second volumetric model and the constraint function.

4. The method according to claim 3, wherein, The step of constructing the energy storage capacity function based on the energy storage data of the reference energy storage facility includes: Determine the charge / discharge efficiency of a reference energy storage facility; Generate a function to show the change of the energy storage data of the reference energy storage facility over time based on the energy storage data of the reference energy storage facility; The energy storage change rate of the reference energy storage facility is determined based on the change function and the charge / discharge efficiency of the reference energy storage facility. The energy storage capacity of the reference energy storage facility at the first moment is determined based on the energy storage change rate of the reference energy storage facility. The energy storage capacity function is determined based on the energy storage capacity of the reference energy storage facility at the first moment, the energy storage capacity of the reference energy storage facility at the second moment, and the energy loss rate. The first moment is the moment before the second moment.

5. The method according to claim 3, wherein, The process of constructing the extremum function includes: The reference energy storage capacity of the reference energy storage facility in the second preset time period is determined according to the energy storage capacity function. Select the extreme points in the reference energy storage capacity, and generate an energy storage capacity difference matrix based on the extreme points; The extreme value function is determined based on the energy storage capacity difference matrix and the reference constant capacity.

6. The method according to claim 1, wherein, The step of determining the target volume using the first volume model based on the target requirements includes: The first volume is determined using the second volume model based on the target requirements. If the time period is greater than or equal to the first preset time period, then the first fixed volume is taken as the target fixed volume; If the time period is greater than the first preset time period, the energy storage capacity function is corrected by the constraint function, and the second capacity is determined based on the corrected energy storage capacity function, and the second capacity is taken as the target capacity.

7. The method according to claim 6, wherein, The step of determining the first capacity using the second capacity model based on the target requirement includes: The energy storage capacity of the target energy storage facility is determined using an energy storage capacity function based on the target demand. Determine the energy storage capacity difference matrix of the target energy storage facility based on the energy storage capacity of the target energy storage facility; The first fixed capacity is determined by the extreme value function of the target energy storage facility based on the energy storage capacity difference matrix of the target energy storage facility.

8. A capacity-limiting device for an energy storage facility, comprising: The energy storage data determination module is configured to determine the energy storage data of the target energy storage facility, wherein the energy storage data of the target energy storage facility is used to characterize the power generation output and power load of the target energy storage facility; The mean value determination module is configured to determine a target mean value based on the energy storage data of the target energy storage facility, and to determine a target demand based on the target mean value. The target demand is the electricity load of the target energy storage facility and the power generation data corresponding to the electricity load. The target capacity determination module is configured to determine the target capacity based on the target demand using a first capacity model. The first capacity model consists of a second capacity model and a constraint function. The first capacity model is used to generate the capacity of the energy storage facility based on the demand information. The capacity of the energy storage facility is used to characterize the energy storage capacity that the energy storage facility can store. The second capacity model is used to determine the first capacity of the energy storage facility based on the demand information. The first capacity is used to characterize the maximum energy storage capacity that the energy storage facility can store within the target time period. The constraint function is used to correct the second capacity model when the first capacity does not meet the preset energy storage demand. The distribution curve determination module is configured to generate a fixed-capacity distribution curve for the target energy storage facility based on the target fixed-capacity.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the energy storage facility gradation method according to any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions that, when executed by a processor, implement the energy storage facility gradation method according to any one of claims 1-7.

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