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

Abstract

The application discloses an energy storage facility constant volume method and device, electronic equipment and storage medium. The method comprises the following steps: determining energy storage data of a target energy storage facility; 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; determining a target constant volume through a first constant volume model according to the target demand; and generating a constant volume distribution curve of the target energy storage facility according to the target constant volume. The method can quickly and accurately realize the optimal constant volume of the target energy storage facility, which is helpful for promoting renewable energy consumption and effectively meeting the power load of a micro-grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage, and in particular to an energy storage facility capacity determination method and device, electronic equipment and a storage medium. BACKGROUND

[0002] A microgrid is a small power generation and distribution system integrating distributed power sources, energy storage devices and power 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 determination of the capacity of the energy storage facilities of the microgrid system can ensure sufficient energy storage capacity without wasting resources, maximize economic benefits while meeting the demand for new energy consumption and grid reliability.

[0003] However, the current mainstream energy storage capacity determination method usually uses linear approximation to obtain the energy storage capacity of 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 determined on this basis has low accuracy, thereby affecting the allocation of resources in the distribution network system. SUMMARY

[0004] The present application provides an energy storage facility capacity determination method, device, electronic equipment and storage medium to solve the problem of low accuracy of capacity data and affect the allocation of resources in the distribution network system.

[0005] According to an aspect of the present application, an energy storage facility capacity determination method is provided, comprising:

[0006] 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 load of the target energy storage facility;

[0007] 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 load of the target energy storage facility and the power generation data corresponding to the power load;

[0008] determining a target capacity by a first capacity determination model according to the target demand, the first capacity determination model being composed of a second capacity determination model and a constraint function, the first capacity determination model being used to generate the capacity of the energy storage facility according to the demand information, the capacity of the energy storage facility being used to represent the capacity of the energy storage facility that can store energy, the second capacity determination model being used to determine a first capacity of the energy storage facility according to the demand information, the first capacity being used to represent the maximum capacity of the energy storage facility that can store energy within a target time period, and the constraint function being used to modify the second capacity determination model when the first capacity does not meet a preset energy storage demand;

[0009] According to the target capacity, a target energy storage facility capacity distribution curve is generated.

[0010] According to another aspect of the present application, there is provided an energy storage facility capacity determination device, comprising:

[0011] An energy storage data determination module is configured to determine 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;

[0012] A mean value determination module is configured to determine a target mean value according to the energy storage data of the target energy storage facility, and determine a target demand according to the target mean value, the target demand being power consumption load and corresponding power generation data of the power consumption load of the target energy storage facility;

[0013] A target capacity determination module is configured to determine a target capacity according to the target demand through a first capacity model, the first capacity model being composed of a second capacity model and a constraint function, the first capacity model being used to generate a capacity of an energy storage facility according to demand information, the capacity of the energy storage facility being used to represent a capacity of the energy storage facility capable of storing energy, the second capacity model being used to determine a first capacity of the energy storage facility according to the demand information, the first capacity being used to represent a maximum capacity of the energy storage facility capable of storing energy within a target time period, and the constraint function being used to modify the second capacity model when the first capacity does not meet a preset energy storage demand;

[0014] A distribution curve determination module is configured to generate a target energy storage facility capacity distribution curve according to the target capacity.

[0015] According to another aspect of the present application, there is provided an electronic device, comprising:

[0016] at least one processor; and

[0017] a memory connected with the at least one processor in communication; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the energy storage facility capacity determination method according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions, the computer instructions being used to enable a processor to implement the energy storage facility capacity determination method according to any one of the embodiments of the present application when executed.

[0020] The technical scheme of the embodiment of the present application determines the energy storage data of the target energy storage facility; determines the target mean value according to the energy storage data of the target energy storage facility, and determines the target demand according to the target mean value, which can expand the sample quantity of the target demand, so that the obtained target demand can cover the actual demand as much as possible, thereby providing a basis for subsequent capacity determination, to ensure that the determined target capacity can cover all demands; the target capacity is determined through the first capacity determination model according to the target demand, the first capacity determination model can effectively simulate the energy limit, power limit and energy loss characteristics of the target energy storage facility, and the first capacity determination model can directly determine the target capacity according to the target demand, thereby realizing the rapid and accurate determination of the optimal capacity of the target energy storage facility under the target demand, and the target energy storage facility stores energy according to the target capacity, which can maximize the new energy consumption effect and minimize the load reduction; the capacity distribution curve of the target energy storage facility is generated according to the target capacity, the capacity distribution curve of the target energy storage facility can more intuitively see the distribution of the target energy storage facility under different demands, and the target energy storage facility is configured into a suitable micro-grid network according to the capacity distribution curve, which can effectively meet the micro-grid power load. Therefore, the method can accurately and effectively generate the target capacity, and store energy according to the target capacity, which can help to promote renewable energy consumption and effectively meet the micro-grid power load.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0023] Figure 1 A flow chart of a capacity determination method of an energy storage facility provided by the embodiment of the present application;

[0024] Figure 2 The capacity change of the energy storage facility under the 2-period without constraint is provided for the embodiment of the present application;

[0025] Figure 3 The capacity change of the energy storage facility under the 2-period considering energy and power constraints is provided for the embodiment of the present application;

[0026] Figure 4 The capacity change of the energy storage facility before and after considering energy loss is provided for the embodiment of the present application;

[0027] Figure 5 A curve of power generation force, power consumption load and energy storage capacity changing with time is provided for the embodiment of the present application.

[0028] Figure 6 A structural schematic diagram of the energy storage facility capacity setting device is provided for the embodiment of the present application.

[0029] Figure 7 A structural schematic diagram of the electronic device for implementing the energy storage facility capacity setting method is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0030] In order 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 described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0032] Figure 1 A flowchart of the energy storage facility capacity setting method is provided for the embodiment of the present application. The embodiment can be applicable to the case of setting the capacity of the energy storage facility in the micro-grid. The method can be executed by an energy storage facility capacity setting device, which can be realized in the form of hardware and / or software, and can be configured in any electronic device with network communication function. As shown in the figure, the method comprises: Figure 1

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

[0034] The energy storage data of the target energy storage facility is used to represent the power generation output and power consumption load of the target energy storage facility. ​

[0035] Specifically, the power generation output data and the power consumption load data of the target energy storage facility are obtained according to the operating state of the target energy storage facility.

[0036] S120, 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.

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

[0038] Specifically, the average value and the standard deviation of the power generation output are calculated according to the power generation output data, and the average value and the standard deviation of the power consumption load data are calculated according to the power consumption load data. The target demand is generated by a random number algorithm according to the calculated average value and standard deviation.

[0039] The basic idea of the random number algorithm is to first obtain a random number subject to a uniform distribution, and then convert the random number to a random number subject to a normal distribution through a specific transformation. The random number algorithm can use the Box-Muller algorithm.

[0040] The above steps randomly generate the target demand, which can increase the number of demand samples of the target energy storage facility. A large number of demand samples can ensure that all demands during the operation of the target energy storage facility are covered, and the target energy storage facility can meet different demands when it is put into use.

[0041] S130, determining a target fixed capacity according to the target demand through a first fixed capacity model.

[0042] The first fixed capacity model is composed of a second fixed capacity model and a constraint function. The first fixed capacity model is used to generate the fixed capacity of the energy storage facility according to the demand information. The fixed capacity of the energy storage facility is used to represent the capacity of the energy storage facility that can store energy. The second fixed capacity model is used to determine the first fixed capacity of the energy storage facility according to the demand information. The first fixed capacity is used to represent the maximum capacity of the energy storage facility that can store energy within a target time period. The constraint function is used to modify the second fixed capacity model when the first fixed capacity does not meet the preset energy storage demand.

[0043] The preset energy storage requirement is the requirement that the target energy storage facility needs to perform long-term energy storage cycle operation. The target time period is a pre-selected short time period of the operation of the target energy storage facility. The short time period can be A period or B years. The specific definition needs to be determined according to the actual demand of the target energy storage facility. The target time period is used to represent the fixed capacity result of the target energy storage facility within a short time period. The target time period can be 1 period or 2 periods. 1 period can be 12 months. The fixed capacity result within the target time period, i.e. the first fixed capacity, can only meet the energy storage requirement 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 constant volume model when the target time period does not meet the first preset time period.

[0045] Further, the constraint function comprises an energy constraint function and a power constraint function.

[0046] The determination process of the energy constraint function is as follows.

[0047] The energy limit considers the upper and lower limits of the discharge depth of the target energy storage facility, which is expressed as DD max and DD min , and the total energy storage capacity E total can be obtained according to the first constant volume and the discharge depth.

[0048]

[0049] Further, the upper and lower limits of the stored energy of the target energy storage facility S up and S low can be obtained.

[0050] S up = E total (1-DD min ),

[0051] S low = E total (1-DD max ),

[0052] If the upper and lower limits of the stored energy are strictly constrained, the iterative result is usually not the optimal solution, so a multiplier a between 0 and 1 is set, and the difference between the energy storage capacity S(0) at the start time and the energy storage capacity S(T) at the end time of the target time period is appropriately relaxed, and the final energy constraint function is represented as:

[0053] S low -α|S(T)-S(0)|≤S(t)≤S up +α|S(T)-S(0)|,

[0054] 0≤α<1,

[0055] wherein T is the target time period.

[0056] Further, according to the energy constraint function, the energy storage capacity at the start time of the target time period which can be continuously cycled is corrected:

[0057]

[0058] The determination process of the power constraint function is as follows.

[0059] The power limitation of the energy storage also affects the constant volume result, according to the ratio parameter C reflecting the relationship between energy and power during charging and discharging of the energy storage c and C d , the charging and discharging power constraint can be calculated:

[0060] P c =E total C c ,

[0061] P d =E total c d ,

[0062] By appropriately relaxing the constraint of the difference between the energy storage capacity S(0) and S(T) at the start and end time of the target time period, the final power constraint function is represented as:

[0063] -P d -α|S(T)-S(0)|≤G(t)-D(t)≤P c +α|S(T)-S(0)|,

[0064] 0≤α<1,

[0065] Further, according to the power constraint function, the energy storage capacity model is corrected, and a new round of constant volume iteration is performed, until the difference between the energy storage capacity S(0) and S(T) at the start and end time of the target time period is less than the allowable error ε, it is considered that the iteration converges, and the final target constant volume is obtained:

[0066] |S(T)-S(0)|≤ε.

[0067] Wherein, ε is an error.

[0068] Specifically, the target demand is input into the first constant volume model, and the first constant volume is calculated according to the second constant volume model in the first constant volume model. If the target time period is greater than or equal to the first preset time period, the first constant volume is taken as the target constant volume; if the target time period is less than the first preset time period, the second constant volume model is corrected by the constraint function, and the constant volume generated by the corrected model is taken as the target constant volume.

[0069] Wherein, the first preset time period can be the expected running time of the target energy storage facility, wherein the expected running time is determined according to the actual running demand of the target energy storage facility.

[0070] Further, the reason for correcting the second constant volume model by the constraint function is that the obtained first constant volume cannot meet the long-time energy storage cycle due to considering fewer factors, and can only realize the preliminary constant volume of the target energy storage facility and determine whether the target energy storage facility can meet the energy storage requirement.

[0071] For example, assuming that the target energy storage facility is distributed photovoltaic, the target time period is 2 cycles, and the first fixed capacity of distributed photovoltaic is 2125MWh, as shown in the following example: Figure 2 As shown in the figure, the green and blue lines show the changes in the target energy storage facility's energy storage capacity over two cycles, where the unconstrained energy storage capacity change is determined based on the second constant capacity model. The charging and discharging of lithium batteries in distributed photovoltaic systems is usually carried out on an annual basis.

[0072] Furthermore, assuming that the charge and discharge efficiency of the lithium battery is 0.8 and the charge and discharge energy power ratio is 1C, the upper and lower limits of the storage capacity in the constraint function are 2656kWh and 531kWh, and the maximum charge and discharge power is 2656kW. The energy storage capacity function is modified according to the energy constraint function and the power constraint function, so that the energy storage capacity of the target energy storage facility is obtained as follows: Figure 3 As shown, the change in constrained energy storage capacity is determined by the second constant capacity model after correction of the constraint function. It can be seen from the figure that the sustainable cyclic charging and discharging energy storage capacity of distributed photovoltaics is 1906kWh. Assuming that the lithium battery produces 2% energy loss per month, 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 constraint function and the power constraint function, the multiplier α is taken as 0.1, and the allowable error ε is taken as 0.01. After seven iterations, the energy storage capacity converges to 2115kWh. The change in the energy storage capacity of distributed photovoltaics is as follows: Figure 4 As shown in Figure 2, the energy storage capacity constrained by the constraint function is reduced.

[0073] The above steps generate a target capacity based on the target demand using the first capacity model. This allows for rapid and accurate generation of the capacity that best suits the target demand of the target energy storage facility based on the first prediction model. This target capacity maximizes the energy storage utilization rate of the target energy storage facility. Furthermore, energy storage based on the target capacity maximizes energy consumption while minimizing load shedding.

[0074] S140. Generate a constant capacity distribution curve of the target energy storage facility according to the target constant capacity.

[0075] Among them, the constant capacity distribution curve is used to characterize the distribution status of the constant capacity of the target energy storage facility under different target demands.

[0076] Specifically, a fixed capacity distribution curve of the target energy storage facility is generated according to the target fixed capacities corresponding to the different target demands.

[0077] Furthermore, the generation process may adopt a data fitting approach.

[0078] The step generates a capacity distribution curve of the target energy storage facility according to the target capacity, and can intuitively obtain the distribution of the target energy storage facility capacity under different demands, and can configure the target energy storage facility to a suitable micro-grid network according to the capacity distribution, and can effectively meet the power load of the micro-grid.

[0079] Optionally, the target mean is determined according to the energy storage data of the target energy storage facility, and the target demand is determined according to the target mean, including steps A1-A2:

[0080] Step A1, calculating the target mean of the energy storage data of the target energy storage facility.

[0081] The target mean includes: the average value and the standard deviation of the power generation output data, and the average value and the standard deviation of the power consumption load data.

[0082] Specifically, the average value of the energy storage data of the target energy storage facility is calculated by the average value calculation formula, and the standard deviation of the target energy storage facility is calculated according to the calculated average value and the standard deviation calculation formula.

[0083] Further, the average value and the standard deviation can be represented by the following formula:

[0084]

[0085] Wherein, n is the number of energy storage data of the target energy storage facility, x i is the i-th sample value of the power generation output data or the power consumption load data.

[0086] Step A2, obtaining the target demand by a random number algorithm according to the target mean.

[0087] The random number algorithm can use the Box-Muller algorithm.

[0088] Further, the Box-Muller algorithm can be represented by the following formula:

[0089]

[0090] In the formula, r n1 and r n2 are random data generated according to the target mean and following the normal distribution, i.e. the target demand, the normal distribution of the target demand has a specified average value and a standard deviation, r u1 and r u2 are two random values generated by uniform distribution and between 0 and 1.

[0091] Further, when calculating the target demand by the above Box-Muller algorithm, any one of the two formulas can be selected for calculation.

[0092] Specifically, first, a random number between 0 and 1 is generated according to uniform distribution, the obtained random number and the mean and standard deviation of the power generation output data are input into the Box-Muller algorithm formula for calculation, and the calculated result is taken as the target power generation output; the obtained random number and the mean and standard deviation of the power consumption load data are input into the Box-Muller algorithm formula for calculation, and the calculated result is taken as the target power consumption load.

[0093] The above step calculates the target demand through a random number algorithm, and in order to expand the sample data of the target demand, the actual demand of the target energy storage facility is determined according to the actual demand of the target energy storage facility, so as to solve the problem that the sample quantity is not enough, thereby the relatively wide constant volume distribution curve cannot be obtained.

[0094] Optionally, the construction process of the first constant volume model comprises steps B1-B3.

[0095] Step B1, determining the energy storage data of the reference energy storage facility, and constructing an energy storage capacity function according to the energy storage data of the reference energy storage facility.

[0096] The reference energy storage facility and the target energy storage facility are the same type of energy storage facility. The energy storage capacity function is used to represent the change of the energy storage capacity with time.

[0097] Further, the energy storage capacity function can be represented by the following formula:

[0098] S (t + Δt) = S (t) (1 - σ) + (G (t) - D (t)) η (t) Δt,

[0099] σ = 0, if S (t) < 0,

[0100] Wherein, σ is the energy storage energy loss rate; G(t) is a change function of the power generation output data changing with time; D(t) is a change function of the power consumption load data changing with time; η(t) is the charging and discharging efficiency of the energy storage facility.

[0101] Specifically, the power generation output data and the power consumption load data of the reference energy storage facility are obtained according to the operating state of the reference energy storage facility, the power generation output data and the power consumption load data of the reference energy storage facility are generated in the form of data fitting to generate the change function of the power generation output data and the power consumption load data changing with time, and the energy storage capacity function is generated according to the change function.

[0102] Step B2, determining a second constant volume model according to the energy storage capacity function and an extreme value function.

[0103] The extreme value function is used to determine the energy storage capacity of the reference energy storage facility and the reference constant volume in the second preset time period. The reference constant volume is the absolute value of the maximum or minimum value of the energy storage capacity of the reference energy storage facility.

[0104] The second preset time period is a time period preset for generating the extreme point according to the energy storage capacity function. The second preset time period can be the same as the first preset time period.

[0105] Further, the extreme function can be expressed by the following formula:

[0106]

[0107] D is the energy storage capacity difference matrix.

[0108] Specifically, the energy storage capacity function and the extreme function are fused to obtain the second constant capacity model.

[0109] Further, the fusion process includes: calculating the extreme point in the second preset time period according to the energy storage capacity function, calculating the energy storage capacity difference matrix according to the extreme point, and calculating the extreme function according to the obtained energy storage capacity difference matrix, so as to complete the fusion of the energy storage capacity function and the extreme function.

[0110] Step B3, determining the first constant capacity model according to the second constant capacity model and the constraint function.

[0111] Specifically, the first constant capacity model is obtained by fusing the second constant capacity model and the constraint function.

[0112] Further, the fusion process includes: if the reference time period corresponding to the reference first constant capacity obtained by the second constant capacity model is greater than or equal to the first preset time period, the second constant capacity model is taken as the first constant capacity model; if the reference time period corresponding to the reference first constant capacity obtained by the second constant capacity model is less than the first preset time period, the second constant capacity model modified by the constraint function is taken as the first constant capacity model. The reference time period is the same as the target time period.

[0113] Optionally, the energy storage capacity function is constructed according to the energy storage data of the reference energy storage facility, including steps C1-C5:

[0114] Step C1, determining the charging and discharging efficiency of the reference energy storage facility.

[0115] Specifically, the charging and discharging efficiency of the reference energy storage facility is determined according to the actual output or input energy of the reference energy storage facility in the charging and discharging process and the energy that should be theoretically output or input.

[0116] Step C2, generating a change function of the energy storage data of the reference energy storage facility with respect to time according to the energy storage data of the reference energy storage facility.

[0117] Specifically, the power generation output data of the reference energy storage facility is fitted to generate a change function of the power generation output data of the reference energy storage facility changing over time; and the power consumption load data of the reference energy storage facility is fitted to generate a change function of the power consumption load data of the reference energy storage facility changing over time.

[0118] Step C3, determining the energy storage change rate of the reference energy storage facility according to the change function and the charging and discharging efficiency of the reference energy storage facility.

[0119] Specifically, the change function of the power consumption load data of the reference energy storage facility changing over time and the change function of the power generation output data of the reference energy storage facility changing over time are subtracted, and the obtained difference value is multiplied by the charging and discharging efficiency of the reference energy storage facility to obtain the energy storage change rate of the reference energy storage facility.

[0120] For example, given the change functions G(t) and D(t) of the power generation output data and the power consumption load data changing over time, and the charging and discharging efficiency η of the reference energy storage facility, c and η d the change rate of the energy storage capacity S(t) of the reference energy storage facility changing over time can be obtained as:

[0121]

[0122]

[0123] wherein, η c is the charging efficiency of the reference energy storage facility; η d is the discharging efficiency of the reference energy storage facility; and η(t) is the efficiency of the reference energy storage facility.

[0124] Step C4, determining the energy storage capacity of the reference energy storage facility at the first time according to the energy storage change rate of the reference energy storage facility.

[0125] Specifically, the energy storage change rate of the reference energy storage facility is integrated on both sides between the second time and the first time to obtain the energy storage capacity of the reference energy storage facility at the first time. For example, the energy storage capacity at the preset time Δt after the energy storage capacity S(t) at the time t is obtained, i.e., the energy storage capacity S(t+Δt) of the reference energy storage facility at the first time, wherein t+Δt is the first time, and t is the second time.

[0126] Further, the calculation process of S(t+Δt) is as follows:

[0127]

[0128] S(t+Δt)-S(t)=(G(t)-D(t))η(t)Δt,

[0129] S(t+Δt) = S(t) + (G(t) - D(t))η(t)Δt.

[0130] Step C5, determining the energy storage capacity function according to the energy storage capacity of the reference energy storage facility at the first time, the energy storage capacity of the reference energy storage facility at the second time, and the energy loss rate.

[0131] The first time is the next time after the second time.

[0132] For example, based on the assumption that G(t) and D(t) remain unchanged within Δt, considering the energy loss rate σ of the reference energy storage facility, the energy storage capacity function is:

[0133] S(t+Δt) = S(t) + (G(t) - D(t))η(t)Δt.

[0134] σ = 0, if S(t) < 0,

[0135] Further, when the reference energy storage capacity level is negative, the loss rate is 0.

[0136] Optionally, the extreme value function construction process includes steps D1-D3:

[0137] Step D1, determining the reference energy storage capacity of the reference energy storage facility within a second preset time period according to the energy storage capacity function.

[0138] The second preset time period is a time period preset for determining the target extreme value point. The second preset time period can be the same as or different from the first preset time period.

[0139] The reference energy storage capacity of the reference energy storage facility at each time point within the second preset time period is calculated according to the energy storage capacity function.

[0140] Step D2, selecting extreme points in the reference energy storage capacity and generating an energy storage capacity difference matrix according to the extreme points.

[0141] The extreme points are selected from the calculated reference energy storage capacity, and the energy storage capacity difference matrix is generated according to the difference between the obtained extreme points and the difference between the energy storage capacities at the start and end times of the second preset time.

[0142] For example, according to the established energy storage capacity model, a change period T, i.e. all extreme points of the energy storage capacity within the second preset time period, is identified, and the number of extreme points is n, and the time points are t1, t2, …, tn respectively. n According to the difference in energy storage capacity between the extreme points, the energy storage capacity difference matrix D can be established:

[0143] Further, since the energy storage capacity S(0) and S(T) at the start and end of a cycle can not be equal, the extreme difference value after extending the matrix D by one cycle T is further obtained.

[0144] Step D3, determining the extreme value function according to the energy storage capacity difference value matrix and the reference capacity.

[0145] Specifically, the extreme value function is generated according to the size of each difference value in the energy storage capacity difference value matrix and the reference capacity.

[0146] Further, the extreme value function can be expressed by the following formula:

[0147]

[0148] Further, the meaning of the extreme value function is that if the energy storage capacity increases after one cycle, i.e., the total power generation is higher than the total load, at this time, the energy storage facility should be sized according to the peak shaving demand, which is the cumulative decrease of the energy storage capacity during discharging, and the excess part is the abandoned electricity; if the energy storage capacity decreases after one cycle, i.e., the total power generation is lower than the total load, at this time, the energy storage facility should be sized according to the valley filling demand, which is the cumulative increase of the energy storage capacity during charging, and the insufficient part is the demand gap; if the energy storage capacity remains unchanged after one cycle, i.e., the total power generation is equal to the total load, the cumulative increase and decrease of the energy storage capacity caused by charging and discharging are equal, and the energy storage can be sized according to the amplitude.

[0149] Optionally, the target sizing is determined by the first sizing model according to the target demand, including steps E1-E3:

[0150] Step E1, determining the first sizing by the second sizing model according to the target demand.

[0151] Specifically, the target demand is input into the second sizing model, first, the energy storage capacity in the time cycle is generated according to the energy storage capacity function, and the extreme point in the energy storage capacity is obtained, the energy storage capacity difference value matrix is calculated according to the extreme point and the energy storage capacity at the start and end of the time cycle, and the first sizing is determined by the extreme value function according to the energy storage capacity difference value matrix.

[0152] Step E2, if the target time cycle is greater than or equal to the first preset time cycle, the first sizing is taken as the target sizing.

[0153] Specifically, if the target time cycle is greater than or equal to the first preset time cycle, the first sizing is taken as the target sizing, which indicates that the target energy storage facility only needs to perform short-term energy storage, and therefore the first sizing can be directly taken as the target sizing. Wherein, the length of the short time can be determined according to the actual demand of the specific target energy storage facility.

[0154] Step E3, if the target time period is less than the first preset time period, the energy storage capacity function is modified by a constraint function, and a second fixed capacity is determined according to the modified energy storage capacity function, and the second fixed capacity is taken as the target fixed capacity.

[0155] Specifically, if the target time period is less than the first preset time period, it indicates that the target energy storage facility currently needs to store energy for a long time, so the energy storage capacity function needs to be modified by a constraint function, and the energy storage capacity in the target time period is calculated by modifying the energy storage capacity function. The extreme point is selected according to the energy storage capacity. The energy storage capacity difference matrix is calculated according to the extreme point and the energy storage capacity at the start and end time of the modified target time period. The target fixed capacity is determined by the extreme function according to the energy storage capacity difference matrix. Wherein, the length of the long time can be determined according to the actual demand of the specific target energy storage facility.

[0156] Optionally, the first fixed capacity is determined by the second fixed capacity model according to the target demand, including steps F1-F3:

[0157] Step F1, the energy storage capacity of the target energy storage facility is determined by the energy storage capacity function according to the target demand.

[0158] Specifically, the change function of the target demand changing with time is generated according to the target demand, and the change function is input into the energy storage capacity function to calculate the energy storage capacity of the target energy storage facility in the first preset time.

[0159] Step F2, the energy storage capacity difference matrix of the target energy storage facility is determined according to the energy storage capacity of the target energy storage facility.

[0160] Specifically, the extreme point of the energy storage capacity is selected according to the energy storage capacity of the target energy storage facility, and the energy storage capacity difference matrix of the target energy storage facility is calculated according to the extreme point.

[0161] Step F3, the first fixed capacity is determined by the extreme function according to the energy storage capacity difference matrix of the target energy storage facility.

[0162] Specifically, the first fixed capacity is determined according to the energy storage capacity difference matrix of the target energy storage facility according to the extreme 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, the first fixed capacity is the absolute value of the minimum value of the elements in the energy storage capacity difference matrix of the target energy storage facility. 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, the first fixed capacity is the maximum value of the elements in the energy storage capacity difference matrix of the target energy storage facility. 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, the first fixed capacity is the maximum absolute value of the elements in the energy storage capacity difference matrix of the target energy storage facility.

[0163] For example, assuming that the target energy storage facility is distributed photovoltaic, the annual power generation of distributed photovoltaic is 7.2MWh, the annual load of the microgrid is 6MWh, and the monthly power generation output and power load of distributed photovoltaic are Figure 5 According to the energy storage capacity model in the second constant capacity model, the energy storage capacity change of distributed photovoltaic in one cycle can be obtained, as shown in the following example: Figure 5 As shown by the green line. Extending the target time period to 2 cycles, the energy storage capacity change within 2 cycles can be obtained, as shown in Figure 2 As shown by the blue line, it can be seen from the figure that the extreme values ​​are S(0)=0, S(t1)=-1375, S(T)=195, S(t2)=945, S(t1')=-1180. Through the extreme value function, the first constant capacity can be obtained as 2125MWh.

[0164] The technical solution of this embodiment determines the energy storage data of the target energy storage facility; determines a target mean based on the energy storage data of the target energy storage facility; and determines the target demand based on the target mean. 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 sizing to ensure that the determined target sizing can cover all demands. The target sizing is determined based on the target demand using a first sizing model. The first sizing model can effectively simulate characteristics such as the energy limitation, power limitation, and energy loss of the target energy storage facility. The first sizing model can directly determine the target sizing based on the target demand, achieving rapid and accurate determination of the optimal sizing 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 sizing, maximizing the new energy consumption effect and minimizing load shedding. A sizing distribution curve for the target energy storage facility is generated based on the target sizing. The sizing distribution curve of the target energy storage facility more intuitively shows the distribution of the sizing of the target energy storage facility under different demands. The target energy storage facility is configured to an appropriate microgrid network based on the sizing distribution curve, effectively meeting the microgrid power load. Therefore, this method can accurately and effectively generate target capacity and store energy according to the target capacity, which can help promote the consumption of renewable energy while effectively meeting the power load of the microgrid.

[0165] Figure 6 This is a schematic diagram of the structure of an energy storage facility capacity determination device provided by an embodiment of the present invention. This embodiment is applicable to the case of determining the capacity of energy storage facilities in a microgrid. The energy storage facility capacity determination device can be implemented in the form of hardware and / or software, and can be configured in any electronic device with network communication capabilities. Figure 6 As shown, the device includes: an energy storage data determination module 210, a mean value determination module 220, a target constant capacity determination module 230 and a distribution curve determination module 240, wherein:

[0166] The energy storage data determination module 210 is configured to determine energy storage data of a target energy storage facility, and the energy storage data of the target energy storage facility is used to represent power generation output and power consumption of the target energy storage facility.

[0167] The mean value determination module 220 is configured to determine a target mean value according to the energy storage data of the target energy storage facility, and determine a target demand according to the target mean value, wherein the target demand is the power consumption of the target energy storage facility and power generation data corresponding to the power consumption.

[0168] The target capacity determination module 230 is configured to determine a target capacity by a first capacity determination model according to the target demand, wherein the first capacity determination model is composed of a second capacity determination model and a constraint function, the first capacity determination model is used to generate a capacity of an energy storage facility according to demand information, the capacity of the energy storage facility is used to represent a capacity of the energy storage facility that can store energy, the second capacity determination model is used to determine a first capacity of the energy storage facility according to the demand information, the first capacity is used to represent a maximum capacity of the energy storage facility that can store energy in a target time period, and the constraint function is used to modify the second capacity determination model when the first capacity does not meet a preset energy storage demand.

[0169] The distribution curve determination module 240 is configured to generate a capacity distribution curve of the target energy storage facility according to the target capacity.

[0170] Optionally, the mean value determination module 220 comprises:

[0171] The mean value determination unit is configured to calculate a target mean value from the energy storage data of the target energy storage facility.

[0172] The target demand determination unit is configured to obtain the target demand by a random number algorithm according to the target mean value.

[0173] Optionally, the target capacity determination module 230 comprises:

[0174] The energy storage capacity function determination unit is configured to determine energy storage data of a reference energy storage facility, and construct an energy storage capacity function according to the energy storage data of the reference energy storage facility, wherein the reference energy storage facility and the target energy storage facility are energy storage facilities of the same type.

[0175] The second capacity determination model determination unit is configured to determine the second capacity determination model according to the energy storage capacity function and an extreme value function, and the extreme value function is used to determine according to an energy storage capacity of the reference energy storage facility in a second preset time period and a reference capacity, wherein the reference capacity is an absolute value of a maximum value or a minimum value of the energy storage capacity of the reference energy storage facility.

[0176] The first capacity determination model determination unit is configured to determine the first capacity determination model according to the second capacity determination model and the constraint function.

[0177] Optionally, the energy storage capacity function determination unit comprises:

[0178] efficiency determining sub-unit: configured to determine the charging / discharging efficiency of the reference energy storage facility;

[0179] change function determining sub-unit: configured to generate a change function of the energy storage data of the reference energy storage facility according to the energy storage data of the reference energy storage facility;

[0180] energy storage change rate determining sub-unit: configured to determine the energy storage change rate of the reference energy storage facility according to the change function and the charging / discharging efficiency of the reference energy storage facility;

[0181] energy storage capacity determining sub-unit: configured to determine the energy storage capacity of the reference energy storage facility at the first time according to the energy storage change rate of the reference energy storage facility;

[0182] energy storage capacity function determining sub-unit: configured to determine an energy storage capacity function according to the energy storage capacity of the reference energy storage facility at the first time, the energy storage capacity of the reference energy storage facility at the second time and the energy loss rate, the first time being a next time after the second time.

[0183] Optionally, the second capacity determination model determining unit comprises:

[0184] reference energy storage capacity determining sub-unit: configured to determine the reference energy storage capacity of the reference energy storage facility within the second preset time period according to the energy storage capacity function;

[0185] difference matrix determining sub-unit: configured to select extreme points in the reference energy storage capacity, and generate an energy storage capacity difference matrix according to the extreme points;

[0186] extreme function determining sub-unit: configured to determine an extreme function according to the energy storage capacity difference matrix and the reference capacity.

[0187] Optionally, the target capacity determination module 230 comprises:

[0188] first capacity determination unit: configured to determine a first capacity according to the target demand through the second capacity determination model;

[0189] target capacity determination unit: configured to, if the target time period is greater than or equal to the first preset time period, take the first capacity as the target capacity;

[0190] target capacity determination unit: configured to, if the target time period is greater than the first preset time period, correct the energy storage capacity function through a constraint function, and determine a second capacity according to the corrected energy storage capacity function, and take the second capacity as the target capacity.

[0191] Optionally, the first capacity determination unit is specifically configured to:

[0192] determine the energy storage capacity of the target energy storage facility according to the target demand through the energy storage capacity function;

[0193] determining a target energy storage facility energy storage capacity difference matrix according to the target energy storage facility energy storage capacity;

[0194] determining a first capacity according to the target energy storage facility energy storage capacity difference matrix by an extreme value function. The energy storage facility capacity determination device provided in the embodiments of the present application can execute the energy storage facility capacity determination method provided in any of the embodiments of the present application, has the corresponding functions and advantages of executing the energy storage facility capacity determination method, and the detailed process is described in the foregoing energy storage facility capacity determination method.

[0195] Figure 7 A structural schematic diagram of an electronic device for implementing the energy storage facility capacity determination method of the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0196] 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, a random access memory (RAM) 13, etc., which is in communication with the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0197] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0198] The processor 11 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the energy storage facility sizing method.

[0199] In some embodiments, the energy storage facility sizing method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the energy storage facility sizing method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the energy storage facility sizing method by any other suitable means, such as by means of firmware.

[0200] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0201] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0202] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0203] To provide for interaction with a user, the systems and techniques described here 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; 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 acoustic, speech, or tactile input.

[0204] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0205] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0206] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, and this is not limited herein.

[0207] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining the capacity of an energy storage facility, characterized in that: The method comprises the following steps: determining the energy storage data of a target energy storage facility, wherein the energy storage data of the target energy storage facility is used to represent the power generation output and the power consumption load of the target energy storage facility; 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, wherein the target demand is the power consumption load of the target energy storage facility and the power generation data corresponding to the power consumption load; determining a target capacity according to the target demand through a first constant-volume model; wherein the step of determining the target capacity according to the target demand through the first constant-volume model comprises: determining a first constant capacity according to the target demand through the first constant-volume model, wherein the first constant capacity is used to represent the maximum capacity of the energy storage facility for storing energy within a target time period; if the target time period is greater than or equal to a first preset time period, then the first constant capacity is taken as the target capacity; if the target time period is less than the first preset time period, then a constraint function is used to modify an energy storage capacity function, and a second constant capacity is determined according to the modified energy storage capacity function, and the second constant capacity is taken as the target capacity; wherein the construction process of the first constant-volume model comprises: determining the energy storage data of a reference energy storage facility, and constructing an energy storage capacity function according to the energy storage data of the reference energy storage facility, wherein the reference energy storage facility and the target energy storage facility are energy storage facilities of the same type; determining a first constant-volume model according to the energy storage capacity function and an extreme value function, wherein the extreme value function is used to determine a reference constant capacity and a reference energy storage capacity within a second preset time period, and the reference constant capacity is the absolute value of the maximum value or the minimum value of the reference energy storage capacity; and generating a constant capacity distribution curve of the target energy storage facility according to the target constant capacity.

2. The method of claim 1, wherein, The step of determining the target mean value according to the energy storage data of the target energy storage facility, and determining the target demand according to the target mean value comprises: calculating the target mean value from the energy storage data of the target energy storage facility; obtaining the target demand through a random number algorithm according to the target mean value.

3. The method of claim 1, wherein, The step of constructing the energy storage capacity function according to the energy storage data of the reference energy storage facility comprises: determining the charging and discharging efficiency of the reference energy storage facility; generating a change function of the energy storage data of the reference energy storage facility with respect to time according to the energy storage data of the reference energy storage facility; determining the energy storage change rate of the reference energy storage facility according to the change function and the charging and discharging efficiency of the reference energy storage facility; determining the energy storage capacity of the reference energy storage facility at a first time according to the energy storage change rate of the reference energy storage facility; determining the energy storage capacity function according to the energy storage capacity of the reference energy storage facility at the first time, the energy storage capacity of the reference energy storage facility at a second time, and an energy loss rate, wherein the first time is the next time after the second time.

4. The method of claim 1, wherein, The construction process of the extreme value function comprises: determining the reference energy storage capacity of the reference energy storage facility within the second preset time period according to the energy storage capacity function; selecting extreme value points in the reference energy storage capacity, and generating an energy storage capacity difference matrix according to the extreme value points; determining the extreme value function according to the energy storage capacity difference matrix and the reference constant capacity.

5. The method of claim 1, wherein, The step of determining the first constant capacity according to the target demand through the first constant-volume model comprises: determining, according to the target demand, an energy storage capacity of the target energy storage facility by an energy storage capacity function; determining, according to the energy storage capacity of the target energy storage facility, a target energy storage capacity difference matrix of the target energy storage facility; determining, according to the target energy storage capacity difference matrix and the extreme value function, the first capacity.

6. An energy storage facility sizing device, comprising: comprising: an energy storage data determination module configured to determine 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; a mean value determination module configured to determine a target mean value according to the energy storage data of the target energy storage facility, and determine a target demand according to the target mean value, the target demand being the power consumption load and power generation data corresponding to the power consumption load of the target energy storage facility; a target capacity determination module configured to determine a target capacity according to the target demand by a first capacity determination model; wherein the target capacity determination module comprises: a first capacity determination unit configured to determine a first capacity according to the target demand by the first capacity determination model, the first capacity being used to represent a maximum capacity of the energy storage facility capable of storing energy within a target time period; a target capacity determination unit configured to, if the target time period is greater than or equal to a first preset time period, take the first capacity as the target capacity; a target capacity determination unit configured to, if the target time period is less than the first preset time period, correct an energy storage capacity function by a constraint function, and determine a second capacity according to the corrected energy storage capacity function, and take the second capacity as the target capacity; wherein the target capacity determination module comprises: an energy storage capacity function determination unit configured to determine energy storage data of a reference energy storage facility, and construct an energy storage capacity function according to the energy storage data of the reference energy storage facility, the reference energy storage facility and the target energy storage facility being energy storage facilities of the same type; a first capacity determination model determination unit configured to determine a first capacity determination model according to the energy storage capacity function and an extreme value function, the extreme value function being used to determine according to an energy storage capacity of the reference energy storage facility within a second preset time period and a reference capacity, the reference capacity being an absolute value of a maximum value or a minimum value of the energy storage capacity of the reference energy storage facility; a distribution curve determination module configured to generate a capacity distribution curve of the target energy storage facility according to the target capacity.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the energy storage facility capacity determination method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to implement the energy storage facility capacity determination method in any one of claims 1-5 when executed.

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