Park energy storage optimal configuration method based on profit and loss equilibrium point electricity price

Abstract

A park energy storage optimal configuration method based on a profit and loss equilibrium point electricity price comprises the following steps: A, selecting a latest 1-2 year spot market day price curve, clustering the curve into K electricity price curves by using a K-means clustering algorithm, and solving an energy storage support production price-time table in combination with the park profit and loss equilibrium point electricity price; b, on the basis of the park production curve, the renewable energy output curve and the K electricity price curves obtained in the step A, a first-class daily production insufficiency curve, an original capacity excess curve and a second-class production insufficiency curve of the park are obtained; c, by taking the original excess capacity curve and the second type of production insufficiency curve as constraints, calculating the energy storage charge and discharge quantity under any given energy storage installation capacity / electric quantity, and solving the corrected second type of production insufficiency curve; and D, the energy storage discharge electricity price is calculated, the energy storage capacity step length is set to iteratively solve the energy storage optimal installation capacity, and the iteration termination condition is that the energy storage discharge electricity price is equal to the profit and loss balance point electricity price or the electricity quantity corresponding to the K corrected second type of production insufficiency curves is zero. The core of the method is that the technical (abandoned energy consumption) and economic (cost recovery) targets are closely combined through the profit and loss equilibrium point electricity price, and the solution of an optimal solution is realized by utilizing clustering analysis, curve construction, charge and discharge simulation and iterative calculation. The method aims to help a park investor to effectively utilize renewable energy sources to reduce waste and guarantee the economic feasibility of energy storage investment when deploying energy storage.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for energy storage planning in various parks (industrial parks, source network load storage parks, incremental distribution networks, and parks with fixed physical boundaries from public distribution networks), especially a planning method for solving renewable energy abandonment problems in parks and ensuring cost recovery of energy storage, and belongs to the field of power planning. BACKGROUND

[0002] Various parks have large-scale access to renewable energy such as wind power and photovoltaic power, but due to the mismatch between output fluctuation and load demand, the problem of abandoned wind and light is prominent. Energy storage systems can effectively consume excess new energy through energy time shifting and become a key energy regulation unit in parks. However, the existing energy storage planning method has the following limitations: 1) Lack of economic constraints: Traditional methods minimize abandoned energy rate as a single target, and do not establish a linkage model between energy storage investment cost and power spot market price, which is easy to cause energy storage investment redundancy due to excessive configuration; 2) Energy cost mechanism decoupling: Existing technologies ignore the dynamic price characteristics of the power spot market (such as peak-valley price difference and time sequence fluctuation), and do not use the breakeven point price as the energy storage charging and discharging threshold, which leads to the fact that the planning result cannot guarantee cost recovery; 3) Multi-objective decoupling: The current method separates and optimizes "renewable energy consumption" and "energy storage economy", and lacks a mechanism for solving the optimal capacity in a unified framework. Therefore, there is an urgent need for an energy storage configuration method that integrates price signals-cost constraints-abandoned energy control, which realizes efficient use of renewable energy in parks under the premise of ensuring sustainable investment economy.

[0003] The breakeven point price is also known as the zero-profit price point, the break-even price point, the breakeven point, the profit and loss divergence point, and the revenue turning point. The breakeven point is used as a limit, and when the energy cost is lower than the breakeven point price, the enterprise makes a profit, and vice versa, the enterprise incurs a loss. Similarly, for the breakeven point price, when the market price is lower than the breakeven point price, the enterprise will make a profit; when the market price is higher than the breakeven point price, the enterprise will incur a loss. Therefore, the breakeven point price, as an important economic indicator, plays an important role in measuring the cost recovery of energy storage in parks.

[0004] The term "K-Means" was first proposed by Professor James MacQueen in his paper "Some Methods for classification and Analysis of Multivariate Observations" in 1967, and since then, the K-Means algorithm has been widely promoted and applied. The K-means clustering algorithm is used to cluster the inter-line Euclidean geometric distance of the power spot market price curve, forming a limited category of price curves, which significantly reduces the computational complexity, and the occurrence probability of each category of curve is determined according to the proportion of the number of curves in the total number of curves as the data basis for subsequent energy storage planning. SUMMARY

[0005] The purpose of the present application is to provide a park energy storage optimal configuration method based on the break-even point price, to determine the optimal installation capacity of the park energy storage, to solve the problem of wind and light abandonment, to avoid excessive investment in energy storage, and to maximize the benefits of the park.

[0006] To achieve this purpose, the technical solution adopted by the present application is as follows.

[0007] A park energy storage optimal configuration method based on the break-even point price, the method comprising the steps of:

[0008] A. Selecting the recent 1-2 years of spot market intraday price curve, using K-means clustering algorithm to cluster it into K price curves, combining with the park break-even point price, to obtain the energy storage supporting production price-time table;

[0009] B. Based on the park production curve, the renewable energy output curve and the K price curves obtained in A, the first type of daily production deficiency curve, the original excess capacity curve and the second type of production deficiency curve of the park are obtained;

[0010] C. Taking the original excess capacity curve and the second type of production deficiency curve as constraints, the charging and discharging capacity of the energy storage under any given energy storage installation capacity / power is calculated, and the corrected second type of production deficiency curve is obtained;

[0011] D. Calculate the energy storage discharge price, set the energy storage capacity step, iterate to solve the optimal installation capacity of the energy storage, and the iteration termination condition is that the energy storage discharge price is equal to the break-even point price or the power corresponding to the K corrected second type of production deficiency curve is 0.

[0012] In particular, in step A, the breakeven point price of the park is defined as the electricity price that gradually increases under the premise that other production costs remain unchanged until the product production cost including electricity cost is equal to the product sales price; for any electricity price curve, the price occurrence time and specific price above the breakeven point price are counted and listed in a table, and this table is defined as the energy storage supporting production-time table, wherein the numbers obtained from each price curve occupy a row in the table, and there are K rows of numbers in total, and the last row of the table indicates the probability of the original electricity price curve.

[0013] In particular, in step B, the first type of daily production deficiency curve is defined as the curve generated after subtracting the daily average output curve of renewable energy from the park full-load production curve; the part of the first type of daily production deficiency curve with negative power indicates that there is surplus renewable energy output, and the curve formed by reversing this part alone is defined as the original excess production curve; the first type of production deficiency curve is modified by first correcting the negative values to 0, and then comparing with any electricity price curve, the time at which all the production power on the electricity price curve is less than or equal to the breakeven point price is counted, and the production power corresponding to the counted time on the first type of production deficiency curve is reduced to 0, and the modified curve is defined as the second type of production deficiency curve, and the second type of production deficiency curve corresponds to one of the K electricity price curves, and a total of K second type of production deficiency curves are formed, and it is considered that the occurrence probability of each production deficiency curve is equal to the occurrence probability of the electricity price curve.

[0014] In particular, in the iterative solution of the optimal installation capacity of the energy storage in step D, the capacity step method is used to gradually increase the capacity for evaluation, the main iteration calculation is designed to configure the capacity of the energy storage under the condition that the renewable energy curtailment is not completely consumed, the supplementary iteration calculation is designed to handle the situation that the energy storage needs to purchase additional electricity for charging, and the interpolation method is used to accurately determine the installed capacity / power of the energy storage between steps.

[0015] By using the optimal configuration method of the park energy storage based on the breakeven point price of the present application, the technical effects obtained are: taking the calculated excess production curve, the K types of electricity price curves obtained by clustering, and the corresponding K second type of production deficiency curves as the energy storage operation information, under the premise of ensuring full recovery of the energy storage cost, the most economical installed capacity / power of the energy storage is determined, and the renewable energy curtailment can be reduced.

[0016] Therefore, the present application proposes a systematic and refined method for determining the optimal capacity of energy storage systems in a park. The core is to closely combine technical (absorbing abandoned energy) and economic (cost recovery) targets through the breakeven point price, and to use clustering analysis, curve construction, charge and discharge simulation and iterative calculation to realize the solution of the optimal solution. The method aims to help park investors effectively utilize renewable energy and reduce waste when deploying energy storage, and ensure the economic feasibility of energy storage investment. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The specific operation process of the present application.

[0018] Figure 2 The example of the i-th price curve in the implementation method of the present application

[0019] Figure 3 The example of the daily production curve, the average daily curve of renewable energy within a year, the first type of daily production deficiency curve in the implementation method of the present application.

[0020] Figure 4 The example of the original capacity surplus curve in the implementation method of the present application.

[0021] Figure 5 The schematic diagram of the i-th second type of production deficiency curve in the implementation method of the present application.

[0022] Figure 6 The operation process of iterative solution of optimal installation capacity of energy storage in the implementation method of the present application. DETAILED DESCRIPTION

[0023] The present application will be further described in detail below with reference to the accompanying drawings.

[0024] The present application provides a technical solution: a park energy storage optimal configuration method based on an original capacity surplus curve, the method comprising the steps of:

[0025] A. Select the recent 1-2 years of spot market daily price curve, and use K-means clustering algorithm to cluster it into K price curves, and combine the breakeven point price of the park to obtain the energy storage support production price-time table;

[0026] B. Based on the park production curve, the renewable energy output curve and the K price curves obtained in A, the first type of daily production deficiency curve, the original capacity surplus curve and the second type of production deficiency curve of the park are obtained;

[0027] C. Taking the original capacity surplus curve and the second type of production deficiency curve as constraints, the charge and discharge capacity of the energy storage under any given energy storage installation capacity / power is calculated, and the corrected second type of production deficiency curve is obtained.

[0028] D. Calculate the energy storage discharge price, set the energy storage capacity step size, and solve the optimal energy storage capacity by iteration, and the iteration termination condition is that the energy storage discharge price is equal to the breakeven point price or the electricity corresponding to K modified second-type production deficiency curves is all 0.

[0029] The overall operation process of the method is shown in the accompanying Figure 1 .

[0030] For step A, first select the recent 1-2 years of intraday price curves of the spot market to form a set of power market price curves, use the K-Means clustering algorithm to form K types of price curves (K is an integer, generally not less than 3, and recommended not more than 10), and take the center value of each type of price curve to form a price curve. Then determine the occurrence probability of each type of price curve according to the proportion of the number of each type of price curve in the total number of curves, and finally obtain K price curves with specific probabilities.

[0031] The specific implementation method of the K-Means algorithm is as follows: STEP 1: Randomly select K curves from the set of power market price curves as initial cluster centers; STEP 2: Calculate the interline Euclidean distance of each curve to each cluster center, and distribute them to the nearest cluster one by one; STEP 3: After all the curves are distributed, define the class center as the mean curve of all curves in the cluster, and update the position of the K class centers; STEP 4: Compare the K cluster centers obtained in the previous calculation, if the cluster center changes, go to step 2, otherwise go to step 5; STEP 5: When the class center no longer changes, stop and output the clustering result, and use the mean curve corresponding to each class center to equalize each type of price curve, output the number of each type of price curve and the K mean curves, and then calculate the occurrence probability Q i of each mean curve, and finally obtain K price curves with specific probabilities. Q i The calculation formula is:

[0032] In the formula, K i is the number of the i-th type of price curve.

[0033] The breakeven point price Y refers to gradually increasing the electricity price under the premise that other production costs remain unchanged, until the product cost including the electricity cost is equal to the electricity value under the product sales price. For any type of price curve, the occurrence time and specific price of the price above the breakeven point are counted and listed in the table, which is called the energy storage supported production price-time table. Each price curve gets a row of numbers in the table, and there are K rows of numbers, and the last row of the table represents the probability Q i of the original price curve. The accompanying Figure 2As an example, if a day is divided into T time periods {t1, t2, t3…t T (T is usually linked to the electricity price type. That is, when the electricity price is hourly, T is 24; when the electricity price is 15-minute, T is 96; when T is 24, t1 is 00:00-1:00). When the electricity price is hourly, if the break-even price is 410 yuan / MWh, then the i-th row of the energy storage support production price schedule is as follows:

[0034] Unit: Yuan / MWh

[0035] For step B, first, the daily production curve of the park is obtained according to the full-load production mode of the park. The average daily output curve of renewable energy in the park is subtracted from this curve to obtain the first type of daily production shortage curve in the park. Figure 3 An example is given in .

[0036] The negative power portion of the daily underproduction curve indicates that the renewable energy output is greater than the park's maximum production load, which means that the renewable energy output is excessive and renewable energy will be wasted. The negative value portion of the power curve of the underproduction curve is inverted to form the original overproduction curve, as shown in the attached figure. Figure 4 shown.

[0037] The first type of underproduction curve is corrected by changing the negative part to 0, which becomes the corrected underproduction curve. For any one of the K electricity price curves, all the time periods on the curve that are less than or equal to the break-even price are counted, and the production power corresponding to this part of time is reduced to 0 on the corrected underproduction curve to obtain the second type of underproduction curve corresponding to the electricity price curve. A total of K second type underproduction curves are obtained, and the probability of each underproduction curve occurring is the same as the probability of the corresponding electricity price curve occurring. Figure 2 As an example, the break-even price is still 410 yuan / kWh. The second type of underproduction curve is shown in the attached figure. Figure 5 shown

[0038] For step C, the energy storage capacity is calculated based on the overcapacity curve. When the installed capacity of energy storage is P0MW / E0MWh, the charging capacity of energy storage is E c for:

[0039]

[0040] Where, P ci is the power of the overcapacity curve in period i; Δt is the length of a single period; in addition, the energy storage capacity constraint E c≤ E0; wherein, E0 = P0 x n0, n0 is the rated discharge hours of energy storage.

[0041] According to the energy storage supporting production price-time table, the discharge power of the energy storage is calculated according to the rule that the energy storage discharges at the time period with the highest electricity price. Taking the ith row of the energy storage supporting production price-time table as an example, all the elements in the first 24 columns of the row are sorted in descending order and represented as wherein N i represents the number of elements in the first 24 columns of the ith row of the energy storage supporting production price-time table, represents the column number represented by the largest element in the first 24 columns of the row, and its physical meaning is the number of time periods with the highest electricity price in the ith row of the energy storage supporting production price-time table. Similarly, represents the column number represented by the second largest element in the first 24 columns of the row, and so on. The sorted result of the ith row of the energy storage supporting production price-time table under this sorting rule is {11, 18, 10, 17, 20}.

[0042] The order of selecting the discharge time period of the energy storage is determined by the sorted result. Taking the ith row of the energy storage supporting production price-time table as an example, the energy storage first discharges at the 11th time period, and the discharge power of the energy storage at the time period is

[0043]

[0044] wherein, is the power of the ith second-type production deficiency curve at the 11th time period.

[0045] The discharge power E di (11) of the energy storage at the time period is:

[0046]

[0047] Then the energy storage discharges at the 18th time period, and the discharge power E and E di (18) are obtained according to the same principle as steps

[0042] -

[0046] , if the cumulative discharge power E di (11) + E di (18) is less than or equal to the charging power E c x η, wherein η is the charging and discharging efficiency, then the energy storage continues to discharge at the 10th time period, and the discharge power E and E di (10) are obtained according to the same principle as steps

[0042] -

[0046] , and then the size relationship between the cumulative discharge power E di (11) + E di (18) + E di (10) and E c x η is judged, if the cumulative discharge power is still less than or equal to E​c If the cumulative discharge capacity of the energy storage is greater than E c ×η after discharging in a certain period, the discharge power and discharge capacity of the period need to be corrected. Taking the case where the cumulative discharge capacity of the energy storage is greater than E di (10)=E c ×η-E di (11)-E di (18) after discharging in the 10th period, the discharge power of the 10th period is corrected as: For other periods without discharging, the discharge power and discharge capacity of the energy storage are both 0.

[0048] The total discharge capacity E di of the energy storage under the ith production deficiency curve of the second type is:

[0049]

[0050] According to the discharge power of the energy storage, K energy storage discharge power curves corresponding to the production deficiency curves of the second type are generated. The K corrected production deficiency curves of the second type are obtained by subtracting the corresponding energy storage discharge power curves from the production deficiency curves of the second type in each production deficiency curve of the second type. The power of the jth period of the ith production deficiency curve of the second type after correction is:

[0051]

[0052] When the installed capacity of the energy storage is P0MW / E0MWh, the calculation formula of the total discharge capacity E d of the energy storage is:

[0053]

[0054] If there is no discharge capacity under the ith production deficiency curve of the second type at this time, or the available discharge capacity is less than E c ·η, the actual charging capacity should also be corrected and reduced, and the corrected actual charging capacity is E c ', and the calculation formula is:

[0055]

[0056] For step D, first define the capacity step size of the energy storage device as ΔPMW, where ΔP is as small as possible, generally not greater than 10% of the maximum power value of the excess capacity curve, and the maximum power value of the excess capacity curve is an integer multiple of ΔP.

[0057] Start calculating the installed capacity of the energy storage, let n = 1.

[0058] According to step C, let P0 = n x ΔP, calculate the discharge capacity E of the energy storage at this time d , actual charging capacity E' c , and K modified second-type production curves.

[0059] If the actual total charging capacity E' c of the energy storage is less than or equal to the capacity E s contained in the original capacity surplus curve (i.e. the area of the figure enclosed by the original capacity surplus curve and the coordinate axes), the discharge price y d of the energy storage is:

[0060] In the formula, is the daily average investment cost of the energy storage when the installed capacity of the energy storage is P0 MW / E0 MWh.

[0061] If the discharge price y d of the energy storage is less than the break-even point price Y, calculate x = Y - y d If the capacities corresponding to the K modified second-type production deficiency curves are all 0, the whole process ends, P0 = n x ΔP, and the installed capacity of the energy storage is P0 MW / P0 x n0 MWh, otherwise continue to execute step

[0060] .

[0062] If when P0 = (n + 1) x ΔP, E' c = E s and when P0 = n x ΔP, E' c = E s , jump to step

[0064] , otherwise n = n + 1 and jump to step

[0058] .

[0063] If the discharge price y d of the energy storage is greater than or equal to the break-even point price Y, calculate y = y d - Y. If n = 1, n = 0, P0 = 0, the installed capacity of the energy storage is 0 MW / 0 MWh, and the whole process ends; if n > 1, P0 = (n - 1) x ΔP + x / (x + y) x ΔP, and the installed capacity of the energy storage is P0 MW / P0 x n0 MWh

[0064] If when P0 = n x ΔP, the total charging capacity E' c of the energy storage is equal to the capacity E s contained in the original capacity surplus curve, and when P0 = (n + 1) x ΔP, the total charging capacity E' c of the energy storage is still equal to the capacity E s contained in the original capacity surplus curve, it indicates that when P0 = n x ΔP, the energy storage has completely absorbed the abandoned capacity of the renewable energy, and if the capacity of the energy storage is continuously increased, the energy storage needs to be charged at a low price and discharged at a high price, so let m = 1.

[0065] On the basis of installing n x ΔP MW / n x ΔP x n0 MWh capacity of energy storage, additionally install m x ΔP MW / m x ΔP x n0 MWh capacity of energy storage, calculate the discharge power E of the newly added energy storage i .

[0066] Take the ith second type of production deficiency curve as an example, still according to the sorting result {11, 18, 10, 17, 20} of the ith row of the energy storage supporting production price-time table, select the discharge period of the energy storage in turn. First, select the 11th period for discharge, the newly added discharge power E of the energy storage in this period is:

[0067]

[0068] In the formula, is the power of the ith corrected second type of production deficiency curve in the 11th period.

[0069] The newly added discharge power E i (11) of the energy storage in this period is:

[0070]

[0071] Then the energy storage discharges in the 18th period, and the same as steps

[0066] -

[0070] , get and E i (18), if the cumulative newly added discharge power E i (11)+E i (18)≤m·ΔP·n0·η, then make the energy storage continue to discharge in the 10th period, and the same as steps

[0066] -

[0070] , get and E i (10), then continue to judge the size relationship between the cumulative newly added discharge power E i (11)+E i (18)+E i (10) and m·ΔP·n0·η, if the cumulative newly added discharge power is still less than or equal to m·ΔP·n0·η, then continue to select the next period according to the sorting result for discharge. If the cumulative newly added discharge power of the energy storage is greater than m·ΔP·n0·η after discharging in a period, the newly added discharge power and discharge power of the period need to be corrected. Taking the case that the energy storage discharges in the 10th period as an example, the newly added discharge power of the energy storage in the 10th period is corrected as: E i (10)=m·ΔP·n0·η-E i (11)-E i (18), and the newly added discharge power in the 10th period is corrected as: The discharge power and discharge capacity of the energy storage device are both 0 in other periods.

[0072] The discharge capacity E of the energy storage device in the i-th second-type production deficiency curve is: i

[0073]

[0074] According to the discharge capacity E i , the n1 lowest price periods are searched on the i-th price curve, and the charging electricity fee C i is calculated under the corresponding charging capacity. Wherein n1 is the rated charging hours of the energy storage device, and n1 is taken as an example. The i-th price curve is divided into T periods in a day, and the price of each period is Assuming that the price of the period with the lowest price is The price of the period with the second lowest price is The price of the period with the third lowest price is

[0075] When

[0076] When ,

[0077] When ,

[0078] The calculation formula of the total charging electricity fee C c is:

[0079]

[0080] The discharge price y d of the energy storage device at this time is:

[0081] Wherein C (n+m)·ΔP is the daily average investment cost of the energy storage device when P0=(n+m) x ΔP

[0082] Further, the second-type production deficiency curve obtained in step C is modified. The power P of the i-th second-type production deficiency curve in the j-th period after modification is:

[0083]

[0084] If y d <Y at this time, x=Y-y d ​If the electric quantity corresponding to the K modified second type of production deficiency curve is 0, the whole process ends, P0=(n+m) x ΔP, the energy storage installed capacity is P0 MW / P0 x n0 MWh, otherwise let m=m+1, jump to step

[0065] to step 1.

[0085] If y d ≥Y at this time, y=y d -Y, P0=(n+m-1) x ΔP+x / (x+y) x ΔP, the energy storage installed capacity is P0 MW / P0 x n0 MWh, and the calculation ends.

[0086] In the specific embodiment, for step D, the operation flow is as shown in the attached Figure 6 figure.

[0087] Finally, it should be noted that: the above embodiments are only the preferred embodiments of the present application, and the ordinary skilled in the art can still modify or replace the specific embodiments of the present application according to the above examples, and can appropriately select various parameters according to the actual experience, and any modification or equivalent replacement without departing from the spirit and scope of the present application is within the protection scope of the claims of the application.

Claims

1. A method for optimizing energy storage configuration in a park based on a break-even point electricity price, the method comprising the steps of: A. Select the intraday price curves of the spot market over the past 1-2 years and use the K-means clustering algorithm to cluster them into K electricity price curves. Combined with the park's break-even electricity price, calculate the energy storage support production price-timetable. B. Based on the park's production curve, renewable energy output curve, and the K electricity price curves obtained in A, calculate the park's first-category daily underproduction curve, original overcapacity curve, and second-category underproduction curve; C. Using the original overcapacity curve and the second-type underproduction curve as constraints, calculate the charge and discharge capacity of the energy storage at any given energy storage installation capacity / capacity, and obtain the corrected second-type underproduction curve; D. Calculate the energy storage discharge price, set the energy storage capacity step size, and iteratively solve the optimal energy storage installation capacity. The iteration termination condition is that the energy storage discharge price is equal to the break-even point price or the power corresponding to the K corrected second-type underproduction curves are all zero.

2. The method for optimizing energy storage configuration in a park based on the break-even point electricity price according to claim 1, characterized in that: In step A, the break-even point electricity price of the park is defined as gradually increasing the electricity price of the park under the premise that other production costs remain unchanged until the production cost of the product, including the electricity cost, is equal to the value of the electricity used in the product sales price. For any electricity price category curve, the time when the price above the break-even point electricity price appears and the specific price are counted and listed in a table. This table is defined as the energy storage supported production-timetable, in which the numbers obtained for each price curve occupy one row in the table, with a total of K rows of numbers. The last column of the table represents the probability of the original electricity price curve occurring.

3. The method for optimizing energy storage configuration in a park based on the break-even point electricity price according to claim 1, characterized in that: In the step B, a first-type daily production shortage curve is defined as a curve generated by subtracting the annual daily average output curve of the park's renewable energy from the park's full-load production curve; the negative power portion of the first-type daily production shortage curve indicates that there is a surplus in renewable energy output, and a curve formed by negating this portion is defined as the original overcapacity curve; the first-type production shortage curve is corrected by first correcting the negative values ​​to 0, then comparing it with any electricity price curve, and counting the time when all electricity prices less than or equal to the break-even point appear on the electricity price curve, reducing the production power corresponding to the statistically included time on the first-type production shortage curve to 0, and the corrected curve is defined as a second-type production shortage curve, and the second-type production shortage curve corresponds to K electricity price curves one-to-one, forming a total of K second-type production shortage curves, and it is assumed that the probability of occurrence of each production shortage curve is equal to the probability of occurrence of the electricity price curve.

4. The method for optimizing energy storage configuration in a park based on the break-even point electricity price according to claim 1, characterized in that: In the iterative solution of the optimal energy storage installation capacity in step D, the capacity step method is used to gradually increase the capacity for evaluation. A main iterative calculation is designed to configure the energy storage capacity when the renewable energy curtailment power is not fully absorbed. A supplementary iterative calculation is designed to handle the situation where additional electricity purchase and charging are required for energy storage. The interpolation method is used to accurately determine the installed capacity / power of the energy storage between steps.

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