Capacity configuration and control method and device for battery energy storage power station participating in peak clipping and valley filling of power distribution network, and medium

Abstract

The invention discloses a capacity configuration and control method for a battery energy storage power station participating in peak clipping and valley filling of a power distribution network, and the method comprises the steps: firstly building an economical optimal capacity configuration model with the maximum income as a target, and carrying out the model solving through a genetic algorithm with implicit parallelism; by analyzing the annual historical data of the transformer substation, a basis is provided for the estimation range of the rated power, the rated capacity and the peak clipping rate. On the basis of obtaining an optimal capacity configuration result, a two-stage partition dynamic adjustment control strategy for the battery energy storage power station to participate in peak clipping and valley filling is provided. Wherein in the first stage, the predicted output of the load and the corresponding capacity allocation time period are calculated by taking the economical efficiency configuration result as the constraint, and in the second stage, the output required by the real-time load is dynamically regulated and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage power stations, and particularly to a capacity configuration and control method, device and medium for a battery energy storage power station to participate in peak shaving and valley filling of a distribution network. Background Art

[0002] As an important node in the power grid, a large peak-valley difference in the distribution network load is likely to reduce its power supply reliability. For example, for a substation in an industrial park, although the daily power consumption load does not change much, during holidays, especially during the Spring Festival, the load reaches the lowest level, and only a few enterprises retain security power consumption, so the peak-valley difference in power consumption is extremely large.

[0003] Due to the relatively early construction time of some substations in the industrial park, the technical standards for equipment selection are relatively low. With the increasing load, the substations are facing some problems that cannot be solved: (1) Currently, the substations are overloaded and cannot bear the increase in load in the future. The current maximum load carried is up to 90% of the capacity. With the economic development of the park and the growth of the load of some residential areas supplied, the existing capacity and equipment of the substations are difficult to undertake more loads. (2) Limited by the substation site and equipment selection, the substation cannot expand its capacity. If the main transformer of the substation is increased in capacity, increasing it to a 50MVA main transformer is of little significance for the load growth in the park, and if it is increased to a larger capacity, most equipment needs to be updated, and the investment cost performance is low. And if an expansion is carried out, although there is some space in the substation, it is not enough to arrange a transformer and supporting equipment. (3) The outgoing line intervals are limited, and the outgoing line corridors are limited. Affected by the overall planning of the park in that year, there is no sufficient reserved outgoing line channel in the road network around the substation. (4) The distribution network is mostly of a single-radiation structure, and the power supply reliability is poor. Since the distribution network is mostly of a single-radiation structure, it has gradually developed and formed with the access of user loads, and some lines use the original rural lines, so the power supply reliability is not high. And many industrial users in the park have very high requirements for power supply reliability due to the production of high-precision products. (5) The peak-valley difference in load is large, the peak time is short, its annual average load level is quite the same, but the load peak is very large, and it is found that the load peak time is short. Therefore, some substations cannot meet the load demand and there are no conditions for capacity increase. The peak-valley difference in load is large and the peak time is short. If forced to increase the capacity, the equipment utilization rate is low and it is extremely uneconomical. At the same time, the single-radiation distribution network structure results in low load power supply reliability, and the power supply for important loads cannot be guaranteed.

[0004] Currently, there are some substations in the distribution network that are overloaded but cannot be expanded or extended due to various reasons. Relieving the load pressure through an energy storage power station is the best choice. However, the cost of the energy storage power station is relatively high, and scientific and reasonable capacity configuration and operation control strategy design are required to ensure that the energy storage system can maximize its peak shaving ability on the basis of the best economy. Summary of the Invention

[0005] In order to solve the technical problems that existing substations currently have an overloaded load and are difficult to expand, and a energy storage power station is needed to relieve the load pressure, but the energy storage power station needs to maximize its peak shaving capacity on the basis of the optimal economy. The present invention provides a capacity configuration and control method, device and medium for a battery energy storage power station to participate in peak shaving and valley filling of a distribution network.

[0006] In order to achieve the above technical objectives, the technical solution of the present invention is as follows.

[0007] A capacity configuration and control method for a battery energy storage power station to participate in peak shaving and valley filling of a distribution network, comprising the following steps:

[0008] Step 1, establish an economic calculation model based on the benefits and costs of the battery energy storage power station applied to peak shaving and valley filling. The benefits include peak-valley arbitrage, delaying the cost of upgrading distribution network equipment, and reducing the environmental benefits of environmental governance. The costs include one-time investment installation costs and operation and maintenance costs.

[0009] Step 2, according to the historical power consumption load data of the substation supporting the battery energy storage power station, determine the estimated ranges of the rated power, rated capacity and peak shaving rate of the battery energy storage power station that meet the actual requirements.

[0010] Step 3, according to the results of Step 2, using the rated power, rated capacity and peak shaving rate of the battery energy storage power station as variables, and based on the constraint conditions, use a genetic algorithm with implicit parallelism to solve the economic calculation model within the estimated range, so as to obtain the economically optimal rated power, rated capacity and peak shaving rate.

[0011] Step 4, implement two-stage partition dynamic adjustment control for the battery energy storage power station:

[0012] The first stage, according to the results of Step 3, calculate the peak shaving line, valley filling line including the predicted load, and count the discharge power, charge power, peak shaving area, valley filling area and flat area.

[0013] The second stage, perform dynamic regulation and control, take the smaller of the reference peak shaving rate and the maximum predicted load peak shaving rate as the initial value of the peak shaving line, and then cycle to obtain the peak shaving line under the constraint conditions that the peak shaving line is greater than the mean line, the energy storage output does not exceed the rated power, and the discharge power does not exceed the rated capacity. At the same time, take the minimum load as the initial value of the valley filling line, and iterate to obtain the valley filling line according to the principle of power balance, that is, the constraint that the charge power is approximately equal to the discharge power under the condition that the energy storage output does not exceed the rated power. Then, according to the peak shaving line and the valley filling line, obtain the energy storage output and power conditions, and at the same time count the start and end times of the peak shaving and valley filling actions.

[0014] A capacity configuration and control method for a battery energy storage power station to participate in peak shaving and valley filling of a distribution network. In step 1 of the method, the expression of the economic calculation model is:

[0015] Max(C arbitrage +C delay +C env -C install -C opera )

[0016] Where Max represents taking the maximum value, C arbitrage represents peak-valley arbitrage, C delay represents the cost of delaying the upgrade of distribution network equipment, C env represents the environmental benefit of reducing environmental governance, C install represents the one-time investment and installation cost, C opera represents the operation and maintenance cost.

[0017] A capacity configuration and control method for a battery energy storage power station to participate in peak shaving and valley filling of a distribution network. In step 1 of the method, each item in the expression of the economic calculation model is calculated through the following steps:

[0018]

[0019] P i =P i d -P i c

[0020] Where P i refers to the operating power of the energy storage at time i, P i d and P i c refer to the discharge power and charge power of the energy storage respectively, λ ei is the time-of-use electricity price, DAY refers to the number of operating days for the energy storage to participate in peak shaving and valley filling in a year, ir and id refer to the inflation rate and discount rate respectively, N refers to the operating life of the energy storage, and t refers to the t-th year of the operating life.

[0021]

[0022] Where C inv represents the one-time investment required to upgrade the distribution network equipment.

[0023]

[0024] Where Q m and R pol,m refer to the emissions of the m-th pollutant and the unit treatment cost of the m-th pollutant respectively, and l is the total number of pollutants.

[0025] C install = C P ·P rate + C E ·E rate

[0026] Where C p and C E are the unit power cost and the unit capacity cost respectively. P rate and E rate are the rated power and the rated capacity of the energy storage of the battery energy storage power station respectively.

[0027]

[0028] Where C of is the unit price of the fixed cost, and C ov is the unit price of the variable cost.

[0029] For the capacity configuration and control method of a battery energy storage power station participating in peak shaving and valley filling of the distribution network, the step 2 includes:

[0030] According to the historical power consumption load data of the substation supporting the battery energy storage power station, first determine the estimated range of the rated power of the battery energy storage power station:

[0031] First, count the important load power, the power of the substation operating overloaded, and the peak shaving situation required by the maximum load of the substation, and obtain the minimum value P min of the estimated range of the rated power of the battery energy storage power station according to the following formula:

[0032] P min = max{P 重要负荷 . P 过载部分 . P max - P ol}

[0033] Where P 重要负荷 represents the important load power, P 过载部分 represents the power of the substation operating overloaded, P max represents the peak value of the daily load, and P ol represents the heavy load power value. is the power factor, and S N represents the transformer capacity of the substation.

[0034] Then, the minimum and maximum values of the peak-to-average difference for the whole year are statistically calculated and used as the lower and upper limits of the power configuration range for the battery energy storage power station to participate in peak shaving. Then, using the maximum load of the day as the minuend and multiple different powers from low to high within the power configuration range as the subtrahends, the differences are calculated respectively, and the multiple obtained differences are used as the peak shaving lines corresponding to different powers. Finally, the number of days when different peak shaving lines are greater than the average line throughout the year is statistically calculated, and the peak shaving line with a non-zero number of days and the largest corresponding power is selected, and the power of the selected peak shaving line is used as the maximum value of the estimated range of the rated power of the battery energy storage power station.

[0035] Next, determine the estimated range of the rated capacity of the battery energy storage power station:

[0036] Take the middle part surrounded by the maximum load of each day in the whole year and the peak shaving line as the energy storage capacity, and when the average line of the day is greater than the peak shaving line on that day, replace the peak shaving line with the average line of that day. The minimum and maximum energy storage capacities obtained through statistics are used as the minimum and maximum values of the estimated range of the rated capacity respectively.

[0037] Finally, determine the estimated range of the peak shaving rate:

[0038] Divide the minimum and maximum values of the estimated range of the rated power by the maximum load peak of the whole year respectively, and use the obtained quotients as the minimum and maximum values of the estimated range of the peak shaving rate.

[0039] For the method for capacity configuration and control of a battery energy storage power station participating in peak shaving and valley filling of a distribution network, step 3 includes:

[0040] Step 201: Based on the genetic algorithm, set the population size to M, and set the crossover probability, mutation probability, and maximum number of iterations. Then, use a set of rated power, rated capacity, and peak shaving rate as individuals in the population, and perform encoding within the estimated ranges of rated power, rated capacity, and peak shaving rate to generate a random initial population.

[0041] Step 202: Decode to obtain the actual values of the rated power, rated capacity, and peak shaving rate of the nth individual, that is, the nth group, and substitute them into the economic calculation model as the objective function, and solve the fitness value under the constraint conditions to measure the fitness level of the individuals in the population until the fitness values of M individuals are calculated.

[0042] Step 203: Based on the fitness value, use the roulette wheel method to select the next-generation individuals, and then perform crossover and mutation operations on the population with the probabilities set in step 201 to generate a new group of populations.

[0043] Step 204: Repeat steps 202 and 203 until the preset convergence condition is reached. When converging, the rated power, rated capacity, and peak shaving rate corresponding to the individual optimal solution are the economically optimal configurations, and use the economically optimal peak shaving rate as a reference.

[0044] For the capacity configuration and control method of a battery energy storage power station participating in peak shaving and valley filling of a distribution network, in step 202, the constraint conditions include:

[0045]

[0046] where η is the charging efficiency of the energy storage, P loadi refers to the load value at time i, P loadmax refers to the load peak, and α represents the peak shaving rate.

[0047] For the capacity configuration and control method of a battery energy storage power station participating in peak shaving and valley filling of a distribution network, in step 4, the first stage includes:

[0048] 1) Using the economically optimal rated capacity E rate , rated power P rate and reference peak shaving rate λ ref obtained in step 3, and the remaining capacity E rem of the energy storage read through the BMS of the battery energy storage power station as the initial values.

[0049] 2) Predicting the predicted load curve P fore (t) based on historical data, and simultaneously obtaining the predicted maximum load P foremax , predicted minimum load P foremin and predicted load average P foreave , and then calculating the predicted maximum peak shaving rate as λ fore = P rate / P foremax .

[0050] 3) If λ fore ≤λ ref , then the peak shaving rate λ peak = λ fore , otherwise λ peak = λ ref .

[0051] 4) Calculating the peak shaving line P liup , P liup = P foremax (1 - λ peak ), and setting the valley filling line P lid = P foremin .

[0052] 5) If P liup ≥P foreave , then P liup = P liup , otherwise P liup = P foreave .

[0053] 6) Starting from the first sampling point on the predicted load curve P fore (t), traverse all T sampling points on the predicted load curve P fore (t). If the predicted load at the sampling point is not less than the peak shaving line, then the energy storage discharge power P d (t) = P fore (t) - P liup , otherwise P d (t) = 0. When t = T, the traversal of the predicted load is completed, and calculate the discharge power E d = ∑∫P d (t)dt.

[0054] 7) If E d ≤ E rate , then P lid = P lid + Δl, where Δl represents the change step of the valley filling line moving up. Otherwise, P liup = P liup + Δl and return to step 6).

[0055] 8) If P fore (t) ≤ P lid , then the energy storage charging power is P c (t) = P fore (t) - P lid , otherwise P c (t) = 0.

[0056] 9) Calculate the charging power E c = ∑∫|P c (t)|dt.

[0057] 10) When E c + E rem - E d > δ, then return to step 7). Otherwise, determine the full-day predicted output P fore (t) of the energy storage on the predicted day, and the predicted charge and discharge power values E c (t) and E d (t), the valley filling area from the starting control point to the peak shaving starting point, the peak shaving area from the peak shaving starting point to the peak shaving end point, the flat area from the peak shaving starting point to the starting control point, i.e., the first sampling point, the peak shaving line P liup and the valley filling line P lid . Where δ is the preset capacity balance difference, and the value fluctuates within a predetermined range around 0.

[0058] In the described method for capacity configuration and control of a battery energy storage power station participating in peak shaving and valley filling of a distribution network, in step 4, the second stage includes:

[0059] When t ∈ the valley filling area, the real-time output P of the energy storagee P(t) = P real P(t) - P lid , and the real - time charging power is E ec E(t)=∑∫P(t)dt, compare the real - time power with the predicted power. If E e P(t)<E ec P(t), then P c =P lid +Δl, otherwise P lid =P lid -Δl. lid

[0060] When t belongs to the valley - filling period, P e P(t)=P real P(t)-P liup , E ed E(t)=∑∫P(t)dt. If the real - time discharging power E e P(t)<E ed P(t), then P d =P liup +Δl, otherwise P liup =P liup liup .

[0061] When t belongs to the flat period, if the real - time power E(t 平 )≤ξ, then end the dynamic regulation, otherwise P e P(t)=P liup -P real (t), where ξ represents the flag that the power in the flat period is exhausted, and its value is a predetermined power value.

[0062] On the other hand, the present invention also provides an electronic device, including:

[0063] One or more processors;

[0064] A storage device for storing one or more programs,

[0065] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0066] On the other hand, the present invention also provides a computer - readable medium, on which a computer program is stored. When the computer program is executed by a processor, the method as described above is implemented.

[0067] The technical effect of the present invention is that, taking economy as both the goal and the constraint, the present invention provides a complete set of solutions from energy storage configuration to operation, which not only ensures the economy of peak shaving and valley filling configuration but also avoids the occurrence of peak shaving failure. The present invention provides a practical and feasible solution for substations with heavy loads but where capacity expansion or reconstruction is not allowed or not economical, and has good practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 FIG. is a schematic diagram of energy storage participating in peak shaving and valley filling of the power grid.

[0069] Figure 2 FIG. is a flow chart of capacity configuration based on genetic algorithm.

[0070] Figure 3 FIG. is a flow chart of real-time control strategy for peak shaving and valley filling of battery energy storage by partition.

[0071] Figure 4 FIG. is the annual load curve of the substation.

[0072] Figure 5 FIG. is the probability distribution diagram of the load data of the substation in 2017.

[0073] Figure 6 FIG. is the 98.5th percentile diagram of the annual load of the substation.

[0074] Figure 7 FIG. is the curve diagram of each eigenvalue of the annual load of the substation.

[0075] Figure 8 FIG. is the load curve of the substation in which the daily load peak exceeds the heavy load line throughout the year.

[0076] Figure 9 FIG. is the comparison diagram of the peak shaving lines and the mean line with different powers of the substation every day throughout the year.

[0077] Figure 10 FIG. is the distribution diagram of the capacity required for peak shaving and valley filling of the substation every day throughout the year.

[0078] Figure 11 FIG. is the effect diagram of energy storage participating in peak shaving and valley filling on December 7 in the embodiment of the present invention, where (a) is the effect diagram of peak shaving and valley filling, and (b) is the curve diagram of the change of energy storage power.

[0079] Figure 12 FIG. is the effect diagram of energy storage participating in peak shaving and valley filling on November 20 in the embodiment of the present invention, where (a) is the effect diagram of peak shaving and valley filling, and (b) is the curve diagram of the change of energy storage power.

[0080] Figure 13 FIG. is the effect diagram of energy storage participating in peak shaving and valley filling on November 13 in the embodiment of the present invention, where (a) is the effect diagram of peak shaving and valley filling, and (b) is the curve diagram of the change of energy storage power.

[0081] Figure 14 This is the effect diagram of energy storage participating in peak shaving and valley filling on January 9 in the embodiment of the present invention. Among them, (a) is the effect diagram of peak shaving and valley filling, and (b) is the curve diagram of the change in energy storage power.

[0082] Figure 15 This is the comparison diagram of the predicted load curve and the actual load curve on December 17 in the embodiment of the present invention.

[0083] Figure 16 This is the effect diagram of peak shaving and valley filling without real-time adjustment.

[0084] Figure 17 This is the effect diagram of peak shaving and valley filling with dynamic adjustment by zone. Among them, (a) is the effect diagram of peak shaving and valley filling, and (b) is the curve diagram of the change in energy storage power. Specific implementation mode

[0085] See Figure 1 , for the role mode of battery energy storage in peak shaving and valley filling, specifically, when the original load curve is in the valley period of the load, the energy storage system absorbs electric energy from the power grid as a "load" to increase the minimum value of the load. During the peak period of the load, the energy storage acts as a "power source" to release electric energy instead of the power grid to reduce the maximum value of the load. Through the charging / discharging function of the energy storage system, the load curve after peak shaving and valley filling is within the range of the peak shaving line and the valley filling line (as shown by the dotted line in Figure 1 ) to achieve the purpose of reducing the peak-valley difference and smoothing the load.

[0086] The economic model for the capacity configuration of the energy storage system throughout its life cycle is established as shown in the following formula (1). The model includes the calculation of the benefits and costs of the battery energy storage system applied to peak shaving and valley filling. Among them, the profit of the energy storage includes peak-valley arbitrage, the cost of delaying the upgrade of distribution network equipment, and the environmental benefits of reducing environmental governance. The costs include the one-time investment installation cost and the operation and maintenance cost.

[0087] Mathematical model

[0088] Max(C arbitrage +C delay +C env -C install -C opera ) (1)

[0089] Specifically as follows:

[0090] 1) Peak-valley arbitrage C arbitrage Calculated by the following formula (2), which represents the direct benefit obtained by the energy storage system through low storage and high output under the time-of-use electricity price policy:

[0091]

[0092] P i =Pi d -P i c (3)

[0093] Among them, P i refers to the operating power of the energy storage at time i, and respectively refer to the discharge power and charge power of the energy storage. When the energy storage discharges, it is regarded as a power source and its value is positive; when charging, it is regarded as a load and its value is negative. λ ei is the time-of-use electricity price, DAY refers to the number of operating days for the energy storage to cut peaks and fill valleys in a year, ir and id respectively refer to the inflation rate and discount rate, and N refers to the operating life of the energy storage.

[0094] 2) When the energy storage system discharges during the peak load period, it can relieve the expansion pressure of the distribution network and delay the upgrade of equipment. C delay is calculated as the following formula (4), that is, the cost of delaying the upgrade of distribution network equipment

[0095]

[0096] In the formula, C inv represents the one-time investment required to upgrade the distribution network equipment.

[0097] 3) Environmental benefit C env is calculated through the following formula (5), which refers to the reduction in power generation emissions when the energy storage system replaces some traditional thermal power units for peak regulation. Among them, Q m (yuan / MWh) and R pol,m (yuan / kg) respectively refer to the emission amount of the mth pollutant and the unit treatment cost of the mth pollutant, and l is the total number of pollutants.

[0098]

[0099] 4) The cost includes the investment cost C install and the operation and maintenance cost C opera , which are calculated through the following formulas (6) and (7) respectively. The investment cost includes the power cost and the capacity cost. C p (yuan / kW) and C E (yuan / kWh) are the unit power cost and the unit capacity cost. The operation and maintenance cost includes the fixed cost and the variable cost. The fixed cost is proportional to the rated power, and the unit price of the fixed cost is C of (yuan / kW / year), and the variable cost is related to the annual power generation. The unit price of the variable cost is C ov (yuan / kWh).

[0100] C install =C P ·P rate +C E ·Erate (6)

[0101]

[0102] (2) Constraints

[0103] Using the rated power P rate , rated capacity E rate and peak shaving rate α as variables to obtain formula (1), and the related constraints are shown in formulas (8a - 8e), where η is the charging efficiency of the energy storage, P loadi refers to the load value at the i-th moment,

[0104] P loadmax refers to the peak load.

[0105]

[0106] The genetic algorithm is a highly robust intelligent bionic optimization algorithm with good inherent implicit parallelism and global search ability. By establishing an initial population and performing operations such as crossover, selection, and mutation generation by generation, the population evolves towards a region with a better fitness function, and finally finds the individual with the best fitness, that is, the optimal solution. In this embodiment, the genetic algorithm encodes the rated power P rate , rated capacity E rate and peak shaving rate α within the estimated range to generate a random initial population, decodes to obtain the values of P rate , E rate and α, substitutes them into formula (1) to calculate the fitness function value of each individual, and performs genetic operations based on the fitness function value of each individual to obtain the next generation population, and iterates until convergence to obtain the individual with the maximum fitness value, that is, the optimal solution. The calculation process is as shown in Figure 2 and the calculation steps are summarized as:

[0107] Initialization, set the population size to M, crossover probability, mutation probability, and maximum number of iterations, and generate a random initial population within the range of rated power, rated capacity, and peak shaving rate;

[0108] Decode to obtain the actual values of the rated power P rate , rated capacity E rate and peak shaving rate α of the n-th group, substitute them into formula (1) and solve, select formula (1) as the objective function, and its solution is the fitness value used to measure the fitness level of individuals in the population; until the fitness values of M individuals are calculated.

[0109] Select the next generation of individuals using the roulette wheel method based on the fitness value, and perform crossover and mutation operations on the population with a certain probability to generate a new group of population.

[0110] Repeat the steps iteratively until the convergence condition is reached. When the fitness value remains unchanged for 5 consecutive times or the maximum number of iterations is reached, it is considered that the global optimum has been achieved. When the program ends, the P corresponding to the optimal solution rate , E rate is the obtained capacity configuration plan, and α is the economically optimal peak shaving rate, which can be used as a reference peak shaving rate for the operation control strategy of the energy storage power station in the following text.

[0111] In this embodiment, a certain 110 kV substation in Hunan is taken as an example, and the 10 kV data on the low-voltage side of the No. 1 main transformer in 2017 is taken as Figure 4 shown, and the sampling period is 1 h. The specific situation is as follows: There are 8760 sampling points in 365 calendar days in 2017, 105 days on weekends, 11 legal holidays, and 249 working days. The load data of 8760 sampling points throughout 2017 is statistically analyzed in units of 1 MW, and the obtained probability distribution is as Figure 5 shown. It can be seen that the probability distribution of the annual load is approximately a normal distribution, that is, the probabilities of the load appearing at the maximum and minimum values are both very small. Figure 5 It can be seen that the probability of the annual maximum load value of 28.1967 MW appearing is only 0.0115%. Figure 6 is Figure 4 the sorting diagram of all the data in from large to small, Figure 6 the 98.5th percentile of the annual load in is about 25.41 MW (about 80% of the transformer capacity). According to the definition, the heavy-load power value P ol is:

[0112]

[0113] This means that 98.5% of the annual load is less than 25.41 MW, and only 1.5% of the time throughout the year is greater than 25.41 MW, that is, about 1.5% of the time throughout the year exceeds the heavy-load line. That is to say, as long as the capacity configuration of the energy storage power station can meet the peak shaving demand at 1.5% of the time throughout the year, the demand at other times is theoretically also met.

[0114] Indicators such as the daily maximum load, minimum load, average value, and peak-to-valley difference throughout the year are as Figure 7 shown. It can be seen that the number of days when the daily maximum load exceeds 50% of the transformer capacity is 308 days, the number of days exceeding 70% of the capacity is 49 days, and the number of days exceeding 80% of the capacity is 4 days, which are January 9th, November 13th, November 20th, and December 7th respectively as Figure 8 shown.

[0115] Figure 8On January 9, the maximum load was 25.5983 MW, the minimum load was 13.8649 MW, the average load was 20.1334 MW, and the peak-to-valley difference was 11.7334 MW. On November 13, the maximum load was 25.4562 MW, the minimum load was 13.4792 MW, the average load was 19.4889 MW, and the peak-to-valley difference was 11.977 MW. On November 20, the maximum load was 26.9178 MW, the minimum load was 12.992 MW, the average load was 20.0623 MW, and the peak-to-valley difference was 13.9258 MW. On December 7, the maximum load was 28.1967 MW, the minimum load was 14.7378 MW, the average load was 20.2221 MW, and the peak-to-valley difference was 13.4589 MW. Among them, the maximum load rate on December 7 was approximately 90%.

[0116] During the planning stage, it is assumed that the energy storage system participates in peak shaving every day throughout the year. Figure 7 The minimum peak-average difference is 1.0903 MW, and the maximum peak-average difference is 7.9745 MW. Then, considering the substation situation, the power configuration range is approximately from 1 MW to 8 MW. Figure 9 The figure shows the changes in the peak shaving line and the average line for each day of the year when the power configuration is from 1 MW to 8 MW. Among them, the peak shaving line for each day is obtained by subtracting the rated power of the energy storage from the maximum load of the day.

[0117] Figure 9 The black solid line is the average line for each day of the year, and the other curves from top to bottom are the peak shaving lines for each day when the energy storage outputs at the rated power (1 - 8 MW). Figure 9 By counting the number of days when the peak shaving line for the whole year is greater than the average line as shown in the figure, the following results can be obtained: when the power is 1 MW, the number of days is 365; when the power is 2 MW, the number of days is 340; when the power is 3 MW, the number of days is 193; when the power is 4 MW, the number of days is 57; when the power is 5 MW, the number of days is 15; when the power is 6 MW, the number of days is 2; when the power is 7 MW, the number of days is 1; when the power is 8 MW, the number of days is 0. The meaning that the peak shaving line is greater than the average line of the day is that when all the configured power is used without waste, it will not cause the phenomenon of reverse peak shaving. Therefore, the fact that the number of days when the peak shaving line is greater than the average line is 0 when the configuration is 8 MW means that from the single demand of peak shaving and valley filling for the whole-year load, there is not a single day in the whole year when the energy storage needs to output 8 MW of power for peak shaving and valley filling. This does not mean that the energy storage with a configured power of 8 MW cannot output 1 MW or 5 MW of power. It just means that part of the power in 8 MW is wasted and not fully utilized. On the contrary, when Prate = 1 MW, the number of days is 365, that is, the peak-average difference is greater than Prate, that is, the power utilization rate of the energy storage system is almost 100%. However, if the power configuration is too small, it may not be able to achieve the required peak shaving effect.

[0118] Users with high requirements for power supply reliability in the park enterprises are regarded as important loads, and the size of the important load is counted as 240 kW.

[0119] Select December 7th, the day with the largest annual load, as the typical day of the substation. From Figure 8 it can be seen that the difference between the load peak value and the heavy-load line on the typical day is 3.6 MW. Generally, the power configuration of the battery energy storage system needs to consider the power P of the important load 重要负荷， the power of the substation operating overloaded and the peak shaving situation required by the maximum load of the substation. Here, it is assumed that when the load peak occurs, the energy storage can reduce the peak to below the heavy-load line, as shown in the following formula (10).

[0120] P min = max{P 重要负荷 ; P 过载部分 ; P max - P ol} (10)

[0121] Among them, the size P of the important load 重要负荷 is 240 kW, and there is no overload phenomenon when the transformer is operating. According to formula (10), the minimum value P of the estimated range of the rated power of the battery energy storage power station is finally determined min to be 3.6 MW. Assuming that it is necessary to ensure that all peaks throughout the year do not exceed the heavy-load line after installing the energy storage power station, the estimated range of power is determined to be 3.6 MW - 7 MW based on the previous analysis.

[0122] The daily peak shaving line is the maximum load of the day minus the rated power. The part surrounded by the load curve and the peak shaving line is the electricity quantity that needs the energy storage to discharge. The energy storage capacity required for daily peak shaving under different power configurations is as Figure 10 shown. To prevent the load from being reverse peak-shaved by the energy storage system, it is stipulated that the daily peak shaving line is not less than the daily average line. If the peak shaving line is greater than the daily average value, this peak shaving line is retained; otherwise, the peak shaving line is replaced by the average value. Figure 10 As shown, with the increase of the power configuration of the energy storage system, the capacity configuration of the energy storage will also increase accordingly. Theoretically, in terms of the peak shaving and valley filling effect alone, it is better than when the power configuration is small. However, if the power and capacity are increased without restriction, for the situation of light load, there will be a waste of power capacity. Figure 10 The maximum annual capacity corresponding to the power from 1 MW to 8 MW in is: 8.6 MWh, 22.6 MWh, 30.12 MWh, 38.46 MWh, 44.52 MWh, 50.26 MWh, 51.63 MWh, 51.63 MWh; the corresponding annual average capacity is: 2.65 MWh, 9.49 MWh, 17.37 MWh, 21.24 MWh, 22.31 MWh, 22.54 MWh, 22.58 MWh, 22.61 MWh. The estimated range of the rated capacity is determined to be: 9 MWh - 50 MWh.

[0123] The peak shaving rate determines the effect of peak shaving and valley filling. As Figure 7 shown, the maximum peak-to-average difference throughout the year is 7.9745 MW on December 7, and the minimum peak average value is 1.0903 MW on October 2. Correspondingly, the maximum load on December 7 is 28.1967 MW, and the maximum load on October 2 is 11.0838 MW. According to the power configuration range of the substation energy storage system determined above, which is 3.6 MW - 7 MW, in principle, as long as the peak shaving demand on December 7 is met, the demand on October 2 will definitely be met. If the peak shaving rate is defined as the rated power of the energy storage / peak value, the estimated range of the peak shaving rate is 12.77% - 24.83%.

[0124] Taking the load on December 7 as a typical day, substitute the estimated range of the variables in the economic model shown in Equation (1) into the capacity configuration calculation method based on the genetic algorithm as Figure 3 shown. Among them, the time-of-use electricity price is: the peak period electricity price is 1.02992 yuan / kWh, the flat period electricity price is 0.6437 yuan / kWh, and the valley period electricity price is 0.25748 yuan / kWh. Based on the analysis of historical data, there are 249 working days. Excluding legal holidays, according to the current policy that most factories have three shifts and can take 4 days off per month, and the double weekends become single weekends, then take DAY as 302 days. The calculated capacity configuration plan is 5.0002 MW, 18.0044 MWh, 19.21%. Since the operation of the energy storage itself requires a certain output, which is about 15% of the rated power, the final configuration is a rated power of 5.75 MW and a rated capacity of 18 MWh.

[0125] Based on the above capacity configuration results and combined with the short-term daily load prediction data, a real-time optimization control strategy is proposed. This control strategy is generally divided into two parts. The first part is to obtain the peak shaving line, valley filling line of the predicted load, and count the discharge power, charge power, peak shaving area, valley filling area, and flat area. The second part is the real-time correction of the energy storage output. The initial value of the peak shaving line is taken as the smaller one between the reference peak shaving rate and the maximum peak shaving rate of the predicted load, and at the same time, while satisfying the constraint conditions that the peak shaving line is greater than the average line, the energy storage output does not exceed the rated power, and the discharge power does not exceed the rated capacity, the peak shaving line is obtained by cycling. The initial value of the valley filling line is taken as the minimum load value. Under the condition that the energy storage output does not exceed the rated power, according to the principle of power balance, that is, the constraint that the charge power is approximately equal to the discharge power, the valley filling line is obtained by iteration. Based on this, the output and power of the energy storage are obtained, and at the same time, the start and end times of the peak shaving and valley filling actions are counted, providing a basis for the real-time correction of the energy storage output in the future. The control strategy flow chart is as Figure 3 shown. The specific steps are as follows:

[0126] 1) Initialization: The rated capacity E of the energy storage rate and the rated power P rate, reference peak shaving rate λ ref , remaining energy storage capacity E rem .

[0127] 2) Obtain the predicted load curve P fore (t) based on historical data. At the same time, obtain the maximum predicted load P foremax , the minimum predicted load P foremin , the average predicted load P foreave , and the maximum peak shaving rate λ fore = P rate / P foremax .

[0128] 3) If λ fore ≤ λ ref , then the peak shaving rate λ peak = λ fore , otherwise λ peak = λ ref .

[0129] 4) Calculate the peak shaving line P liup , P liup = P foremax (1 - λ peak ), and let the valley filling line P lid = P foremin .

[0130] 5) If P liup ≥ P foreave , then P liup = P liup , otherwise P liup = P foreave .

[0131] 6) Let t = 1, traverse all T sampling points on the predicted load curve P fore (t). If P fore (t) ≥ P liup , then the energy storage discharge power is P d (t) = P fore (t) - P liup , otherwise P d (t) = 0. If t = T, the traversal of the predicted load is completed, and calculate the discharge power E d = ∑∫P d (t)dt.

[0132] 7) If E d ≤ E rate , then P lid = P lid + Δl, otherwise P liup = P liup + Δl and return to step 6).

[0133] 8) If P fore (t) ≤ P lid , then the energy storage charging power is P c (t) = P fore (t) - P lid , otherwise P c (t) = 0.

[0134] 9) Calculate the charging power E c = ∑∫|P c (t)|dt.

[0135] 10) If E c + E rem - E d ≤ δ, then obtain the full-day predicted output Pf ore (t) of the energy storage for the predicted day and the predicted charge and discharge power values E c (t) and E d (t), the valley filling area (from the starting control point to the peak shaving starting point), the peak shaving area (from the peak shaving starting point to the peak shaving end point), the flat area (from the peak shaving starting point to the starting control point, i.e., the first sampling point), the peak shaving line P liup and the valley filling line P lid . Otherwise, return to step 7) and continue the loop.

[0136] 11) Import the real-time load P rea l(t).

[0137] 12) The partition dynamic adjustment strategy, simply speaking, is to dynamically adjust the real-time energy storage output in chronological order from the starting control point, i.e., the valley filling area, to the end of the flat area. Specifically, when t ∈ the valley filling area, the real-time energy storage output P e (t) = P real (t) - P lid , the real-time charging power is E ec (t) = ∑∫P e (t)dt, judge the magnitude of the real-time power and the predicted power. If E ec (t) < E c (t) then P lid = P lid + Δl, otherwise P lid = P lid - Δl. When t ∈ the peak shaving area, P e (t) = P real (t) - P liup , E ed (t) = Σ∫P e (t)dt. If the real-time discharge power E ed (t) < E d (t) then P liup = P liup + Δl, otherwise Pliup =P liup When t ∈ the flat zone, if the real-time power consumption E(t 平 ) ≤ ξ, the program ends; otherwise, P e (t) = P liup -P real (t), where ξ is close to 0 or a pre-specified power consumption value. In this embodiment, ξ = 10 -2 .

[0138] Based on the real-time optimization control strategy of the present invention, the simulation results of peak shaving and valley filling for the historical data on December 7 are as follows Figure 11 shown.

[0139] The configured reference peak shaving rate is 19.21%. As described above, the available power for peak shaving and valley filling is 5.0002 MW. Since the maximum load on December 7 is 28.1967 MW, the calculated maximum peak shaving rate on December 7 is 17.73%. According to the proposed strategy, the smaller 17.73% should be taken as the peak shaving rate, and the initial value of the peak shaving line is 23.1974 MW. Assuming that the battery energy storage completes a full charge and discharge cycle within a day, based on the power balance criterion, that is, setting the power generated by the energy storage system for load peak shaving to be equal to the power absorbed for valley filling within the calculation period. Finally, after calculation, the peak shaving line is 23.1974 MW and the valley filling line is 17.9036 MW. The load curve after peak shaving and valley filling and the output curve of the energy storage are as Figure 11 (a) shown, and the power change of the energy storage is as Figure 11 (b) shown.

[0140] See Figure 12 , the load curve after peak shaving and valley filling and the output curve of the energy storage on November 20 are as Figure 12 (a) shown, and the power change of the energy storage is as Figure 12 (b) shown. The maximum load is 26.9178 MW, and the maximum peak shaving rate is 18.75%. According to the proposed strategy, the smaller 18.75% is still taken as the peak shaving rate, and the initial value of the peak shaving line is 21.8707 MW. The peak shaving and valley filling situation on November 20 is different from that on December 7. Calculated according to the initial peak shaving line, the required capacity on November 20 is larger than the rated capacity. Therefore, the peak shaving line needs to be raised. Finally, after iterative calculation, the peak shaving line is 23.3108 MW and the valley filling line is 16.3166 MW.

[0141] See Figure 13, the maximum load on November 13 was 25.4562 MW, and the maximum peak shaving rate was 19.64%. According to the proposed strategy, a smaller reference peak shaving rate of 19.21% was taken, and the initial value of the peak shaving line was 20.5661 MW. The same as the situation on November 20, when the peak shaving line was 20.5661 MW, the required capacity exceeded the configured rated capacity, and the peak shaving line needed to be raised to reduce the capacity demand. After calculation, the peak shaving line and valley filling line were 21.5869 MW and 16.3924 MW respectively. The load curve after peak shaving and valley filling and the output curve of the energy storage are as Figure 13 (a) shown, and the charge-discharge change of the energy storage is as Figure 13 (b) shown.

[0142] The load curve after peak shaving and valley filling and the output curve of the energy storage on January 9 are as Figure 14 (a) shown, and the charge-discharge change of the energy storage is as Figure 14 (b) shown. Its maximum load was 25.5983 MW, and the maximum peak shaving rate was 19.53%. According to the proposed strategy, a smaller reference peak shaving rate of 19.21% was taken, and the initial value of the peak shaving line was 20.6809 MW. Still, the required capacity exceeded the rated capacity due to the too large peak shaving rate. Therefore, after iterative calculation, the peak shaving line and valley filling line corresponding to the rated capacity were 22.0657 MW and 17.0643 MW.

[0143] Figures 11 - 14 They are respectively the simulation results of peak shaving and valley filling for 4 load curves where the daily maximum load exceeds 80% of the transformer capacity as Figure 8 shown. The results show that all four results have achieved good peak shaving and valley filling effects, without large valleys and peaks. At the same time Figures 11 to 14 the simulation results show that even with a relatively small capacity configuration, the large load peak shaving demand can be met through the control strategy proposed in this chapter.

[0144] However, it should be noted that Figures 11 to 14 the simulations are all based on the assumption that the real-time load is the same as the predicted load. However, in reality, there are often certain errors as Figure 15 shown.

[0145] Figure 15 In , the valleys and peaks of the actual load are higher than the predicted load, and the actual load peak arrives one hour earlier than the predicted load. Then, it is obviously unreasonable to arrange the output according to the predicted load. As Figure 16 (b) shown, if charging according to the valley filling line in Figure 11 , due to Figure 15There is a certain deviation between the predicted load and the actual load, resulting in insufficient charging power of the energy storage, and the stored energy is completely discharged just when the first peak of the actual load arrives, so that the peak shaving fails. However, through the zoning adjustment control strategy for energy storage proposed in this embodiment, the energy storage can still complete the task of peak shaving and valley filling, as Figure 17 shown, thus well solving Figure 16 the problem of insufficient peak shaving power shown in

[0146] Comparing Figure 11 (a)- Figure 14 the load curve after peak shaving and valley filling in (a), Figure 17 the load curve after peak shaving and valley filling in (a) is not strictly completed according to the established peak shaving line and valley filling line, but the peak load is effectively reduced, and the energy storage battery still plays an important role during the peak load period. The results of the above embodiments show that the strategy proposed by the present invention can complete the task of peak shaving and valley filling even when there is a large difference between the actual load and the predicted load, such as the peak load is larger and arrives earlier.

[0147] In an exemplary embodiment of the present invention, an electronic device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0148] In an exemplary embodiment of the present invention, a computer-readable storage medium is further provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0149] Those of ordinary skill in the art can understand that all or part of the processes in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed, it can include the processes of the above method embodiments.

[0150] The processors involved in the embodiments provided in this application can be a CPU, a GPU, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without limitation.

[0151] The technical features of the above embodiments can be combined arbitrarily. For the sake of brief description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0152] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. To sum up, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A capacity configuration and control method for a battery energy storage power station to participate in peak load shaving and valley filling in a distribution network, characterized in that: The following steps are involved: Step 1: Establish an economic calculation model based on the benefits and costs of using battery energy storage power stations to reduce peak loads and fill valleys; Step 2: Determine the estimated range of rated power, rated capacity and peak shaving rate of the battery energy storage station that meets actual needs based on the historical power load data of the substation supporting the battery energy storage station; Step 3: Based on the result of step 2, the rated power, rated capacity and peak shaving rate of the battery energy storage power station are used as variables, and based on the constraints, a genetic algorithm with implicit parallelism is used to solve the economic calculation model within the estimated range, so as to obtain the economically optimal rated power, rated capacity and peak shaving rate; Step 4: Implement two-stage partition dynamic adjustment control on the battery energy storage power station: In the first stage, according to the results of step 3, the peak shaving line and valley filling line of the predicted load are calculated, and the discharge power, charging power, peak shaving area, valley filling area and peace area are counted; In the second stage, dynamic control is performed, with the smaller of the reference peak shaving rate and the predicted maximum load peak shaving rate as the initial value of the peak shaving line. Then, the peak shaving line is obtained cyclically under the constraints that the peak shaving line is greater than the mean line, the energy storage output does not exceed the rated power, and the discharge capacity does not exceed the rated capacity. At the same time, the minimum load value is used as the initial value of the valley filling line, and the valley filling line is iteratively obtained under the constraint that the energy storage output does not exceed the rated power and according to the principle of power balance, that is, the charging capacity is approximately equal to the discharging capacity, under the condition that the energy storage output does not exceed the rated power. Then, the output and power of the energy storage are obtained based on the peak shaving line and the valley filling line, and the start and end time of the peak shaving and valley filling actions are calculated.

2. A capacity configuration and control method for a battery energy storage power station participating in peak load shifting of a distribution network according to claim 1, characterized in that: In step 1, the economic calculation model is expressed as: Max(C arbitrage +C delay +C env -C install -C opera ) Among them, Max means taking the maximum value, C arbitrage represents peak-to-valley arbitrage, C delay Indicates the delay in upgrading the distribution network equipment, C env Represents the environmental benefits of reducing environmental governance, C install Indicates the one-time investment and installation cost, C opera Indicates operation and maintenance cost.

3. A capacity configuration and control method for a battery energy storage power station participating in peak load shifting of a distribution network according to claim 2, characterized in that: In step 1, each term in the expression of the economic calculation model is calculated by the following steps: P i =P i d -P i c Where P i Refers to the operating power of the energy storage at time i, P i d and P i c Respectively refer to the discharge power and charging power of energy storage, λ ei is the time-of-use electricity price, DAY refers to the number of days in a year that the energy storage is used to smooth the peak and fill the valley, ir and id refer to the inflation rate and discount rate respectively, N refers to the operating life of the energy storage, and t refers to the tth year of the operating life; Among them C inv It represents the one-time investment required to upgrade the distribution network equipment; Where Q m and R pol,m They refer to the emission of the mth pollutant and the unit treatment cost of the mth pollutant, respectively, and l is the total number of pollutants; C install =C P ·P rate +C E ·E rate Among them C p and C E They are unit power cost and unit capacity cost respectively; P rate and E rate They are the rated power and rated capacity of the battery energy storage power station; Among them C of is the fixed cost unit price, C ov is the variable cost unit price.

4. A capacity configuration and control method for a battery energy storage power station participating in peak load shifting of a distribution network according to claim 1, characterized in that: The step 2 comprises: Based on the historical power load data of the substation supporting the battery energy storage power station, the estimated range of the rated power of the battery energy storage power station is first determined: First, count the power of important loads, the power of substation overload operation, and the peak shaving required by the substation maximum load, and then derive the minimum value P of the estimated range of the rated power of the battery energy storage station according to the following formula: min : P min =max{P 重要负荷 ;P 过载部分 ;P max -P ol } Among them, P 重要负荷 Indicates the important load power, P 过载部分 Indicates the power of substation overload operation, P max Represents the typical daily load peak, P ol Indicates the heavy load power value, is the power factor, S N Indicates the transformer capacity of the substation; Then, the minimum and maximum values ​​of the peak-to-average difference throughout the year are counted and used as the lower and upper limits of the power configuration range for the battery energy storage power station to participate in peak shaving. Then, the maximum load of the day is used as the minuend, and multiple different powers from low to high in the power configuration range are used as subtrahends to calculate the differences, and the obtained multiple differences are used as the peak shaving lines corresponding to different powers. Finally, the number of days in the year when different peak shaving lines are greater than the mean line is counted, and the peak shaving line with a non-zero number of days and the largest corresponding power is selected, and the power of the selected peak shaving line is used as the maximum value of the rated power estimation range of the battery energy storage power station. Next, determine the estimated range of the rated capacity of the battery energy storage power station: The middle part between the maximum load of each day in the whole year and the peak-shaving line is taken as the energy storage capacity, and when the daily average line is greater than the peak-shaving line, the peak-shaving line is replaced by the daily average line, and the minimum energy storage capacity and maximum energy storage capacity obtained by statistics are respectively taken as the minimum and maximum values ​​of the rated capacity estimation range; Finally, determine the estimated range of the peak shaving rate: The minimum and maximum values ​​of the estimated rated power range are divided by the annual maximum load peak, and the obtained quotients are used as the minimum and maximum values ​​of the estimated peak reduction rate range.

5. A capacity configuration and control method for a battery energy storage power station participating in peak load shifting of a distribution network according to claim 3, characterized in that: The step 3 comprises: Step 201, based on the genetic algorithm, the population size is set to M, and the crossover probability, mutation probability and maximum number of iterations are set, and then a set of rated power, rated capacity and peak shaving rate is used as individuals in the population, and encoded within the estimated range of the rated power, rated capacity and peak shaving rate to generate a random initial population; Step 202, decoding obtains the actual value of the rated power, rated capacity and peak shaving rate of the nth individual, i.e. the nth group, and substitutes it into the economic calculation model as the objective function, and solves the fitness value under the constraint condition to measure the fitness of the individuals in the population, until the fitness values ​​of M individuals are calculated; Step 203, based on the fitness value, the next generation of individuals is selected by roulette method, and then the population is crossover and mutation operations are performed with the probability set in step 201 to generate a new group of populations; Step 204, iterate steps 202 and 203 repeatedly until a preset convergence condition is reached. When convergence occurs, the rated power, rated capacity and peak shaving rate corresponding to the individual optimal solution are the economically optimal configurations, and the economically optimal peak shaving rate is used as a reference.

6. A capacity configuration and control method for a battery energy storage power station participating in peak load shifting of a distribution network according to claim 5, characterized in that: In step 202, the constraints include: Where η is the charging efficiency of the energy storage, P loadi Refers to the load value at time i, P loadmax Refers to the load peak value, and α represents the peak reduction rate.

7. A capacity configuration and control method for a battery energy storage power station participating in peak load shifting of a distribution network according to claim 1, characterized in that: In step 4, the first stage includes: 1) The most economical rated capacity E obtained in step 3 rate , Rated power P rate and reference peak clipping rate λ ref , and read the remaining energy storage capacity E through the BMS of the battery energy storage power station rem , as the initialization value; 2) Obtain the predicted load curve P based on historical data fore (t), and at the same time obtain the predicted maximum load P foremax , predicted load minimum value P foremin and the predicted load mean P foreave Then the predicted load maximum peak reduction rate is calculated as λ fore =P rate / P foremax ; 3) If λ fore ≤λ ref , then the peak clipping rate λ peak =λ fore , otherwise peak =λ ref ; 4) Calculate the peak clipping line P liup , P liup =P foremax (1-λ peak ), and let the valley filling line P lid =P foremin ; 5) If P liup ≥P foreave , then P liup =P liup , otherwise P liup =P foreave ; 6) From the predicted load curve P fore Starting from the first sampling point on (t), traverse the predicted load curve P fore (t) For all T sampling points on the graph, if the predicted load at the sampling point is not less than the peak shaving line, the energy storage discharge power P d (t) = P fore (t)-P liup , otherwise P d (t) = 0; when t = T, the predicted load traversal is completed and the discharge power E is calculated. d =∑∫P d (t)dt; 7) If E d ≤E rate , then P lid =P lid +Δl, Δl represents the change step length of the valley filling line moving up, otherwise P liup =P liup +Δl and return to step 6); 8) If P fore (t)≤P lid , then the energy storage charging power is P c (t) = P fore (t)-P lid , otherwise P c (t) = 0; 9) Calculate the charging capacity E c =∑∫|P c (t)|dt; 10) When E c +E rem -E d >δ, then return to step 7), otherwise determine the predicted output P of the energy storage for the whole day fore (t) and predicted charge and discharge capacity value E c (t) and E d (t), the valley filling area from the starting control point to the peak clipping starting point, the peak clipping area from the peak clipping starting point to the peak clipping ending point, the flat area from the peak clipping starting point to the starting control point, i.e. the first sampling point, the peak clipping line P liup and valley filling line P lid ; Wherein δ is the preset capacity balance difference, which fluctuates within a preset range above and below 0.

8. A capacity configuration and control method for a battery energy storage power station participating in peak load shifting of a distribution network according to claim 7, characterized in that: In step 4, the second stage includes: When t∈ is in the valley filling zone, the real-time output of energy storage is P e (t) = P real (t)-P lid , the real-time charging capacity is E ec (t) = ∑∫P e (t)dt, judge the size of real-time power and predicted power, if E ec (t) <E c (t) then P lid =P lid +Δl, otherwise P lid =P lid -Δl; When t∈the peak-cutting region, P e (t) = P real (t)-P liup , E ed (t) = ∑∫P e (t)dt, if the real-time discharge capacity E ed (t) <E d (t) then P liup =P liup +Δl, otherwise P liup =P liup ; When t∈ is in the flat zone, if the real-time power E(t 平 )≤ξ, then the dynamic control ends, otherwise P e (t) = P liup -P real (t), ξ represents the mark of the flat area power being fully discharged, and its value is a predetermined power value.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, It is characterized in that when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-8.

10. A computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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