A power grid energy storage configuration method and device, a terminal device and a storage medium

Abstract

The application discloses a power grid energy storage configuration method and device, terminal equipment and storage medium, through acquiring the demand side load actual power, new energy power generation actual power, demand side load predicted power, new energy power generation predicted power, each adjustable unit power generation power upper and lower limit value and new power system active power distribution system power supply reliability confidence of a to-be-detected power grid within a preset time, and generating a demand side load power probability statistical model and a new energy power generation power probability statistical model based on operation data, constructing an energy storage configuration random robust optimization model according to the demand side load power probability statistical model and the new energy power generation power probability statistical model, calculating the energy storage configuration random robust optimization model through a convex optimization method, obtaining a target energy storage configuration capacity vector, and thus generating an energy storage configuration scheme of the to-be-detected power grid.

Description

Technical Field

[0001] This invention relates to the field of new power system energy storage configuration optimization technology, and in particular to a power grid energy storage configuration method, device, terminal equipment and storage medium. Background Technology

[0002] In new power systems dominated by new energy sources, these new energy sources play a leading role. However, the randomness and volatility of these new energy sources (wind power and photovoltaic power) threaten the safe and stable operation of the power grid. The flexible charging and discharging capabilities of energy storage devices can smooth out fluctuations in new energy power generation. Energy storage devices installed at wind farms can maintain a smooth and stable power generation curve. Reasonable configuration of energy storage can improve the reliability of the active distribution network in new power systems. However, the unit cost of energy storage devices is currently very high. Therefore, it is necessary to study the optimal configuration method of energy storage in active distribution networks with new energy integration under new power systems to minimize the configuration cost of energy storage devices.

[0003] Currently, there are no effective measures for energy storage configuration schemes, and the generated schemes suffer from low accuracy. Therefore, there is an urgent need for a grid-based energy storage configuration strategy to address the problem of low accuracy in energy storage configuration schemes. Summary of the Invention

[0004] This invention provides a grid energy storage configuration method, device, terminal equipment, and storage medium to improve the accuracy of grid energy storage configuration schemes.

[0005] To address the above problems, one embodiment of the present invention provides a grid energy storage configuration method, comprising:

[0006] The system acquires operational data of the power grid under test within a preset time period. The operational data includes: actual demand-side load power, actual power of renewable energy generation, predicted demand-side load power, predicted power of renewable energy generation, upper and lower limits of power generation of each adjustable unit, and confidence level of power supply reliability of the active distribution system of the new power system.

[0007] Based on the actual demand-side load power, the actual power of renewable energy generation, the predicted demand-side load power, and the predicted renewable energy generation power, a probabilistic statistical model of the demand-side load power and a probabilistic statistical model of renewable energy generation power are generated for the power grid under test.

[0008] Based on the demand-side load power probability statistical model and the new energy power generation probability statistical model, and based on the upper and lower limits of the power generation of each adjustable unit and the confidence level of the power supply reliability of the active distribution system of the new power system, a stochastic robust optimization model for energy storage configuration is constructed.

[0009] The energy storage configuration stochastic robust optimization model is calculated based on the convex optimization method to obtain the target energy storage configuration capacity vector, and the energy storage configuration scheme of the power grid to be tested is generated based on the target energy storage configuration capacity vector.

[0010] As an improvement to the above scheme, the step of generating a probabilistic statistical model of the demand-side load power and a probabilistic statistical model of the new energy power of the power grid under test based on the actual demand-side load power, the actual power of the new energy power generation, the predicted demand-side load power, and the predicted new energy power generation includes:

[0011] Based on the actual demand-side load power, the actual power of renewable energy generation, the predicted demand-side load power, and the predicted renewable energy generation power, calculate the demand-side load power prediction error data corresponding to the predicted demand-side load power, and the renewable energy generation power prediction error data corresponding to the predicted renewable energy generation power; wherein, the demand-side load power prediction error data includes: the expectation, variance, and skewness of the demand-side load power prediction error, and the renewable energy generation power prediction error data includes: the expectation, variance, and skewness of the renewable energy generation power prediction error;

[0012] Based on the demand-side load power prediction error data and the new energy power generation prediction error data, a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data and a new energy power generation probabilistic statistical model corresponding to the new energy power generation prediction error data are generated.

[0013] As an improvement to the above scheme, the step of calculating the demand-side load power prediction error data corresponding to the demand-side load prediction power and the new energy power prediction error data corresponding to the new energy power prediction power, based on the actual demand-side load power, the actual power of new energy power generation, the predicted demand-side load power, and the predicted new energy power generation power, includes:

[0014] Based on the actual demand-side load power and the predicted demand-side load power, the expected value, variance, and skewness of the demand-side load power prediction error are obtained by substituting them into a preset formula for calculating the demand-side load power prediction error. Specifically, the formula for calculating the demand-side load power prediction error is as follows:

[0015] ΔP L =P L,A —P L,F

[0016]

[0017]

[0018]

[0019] In the formula, N T P represents the total number of months over the years. L,A P represents the actual value of the demand-side load power. L,F The predicted power value for demand-side load is ΔP. L For demand-side load power forecasting error, Let E{ΔP} be the demand-side load power forecast error for month k. L}、D{ΔP L}、K(ΔP L These represent the expected value, variance, and skewness of the demand-side load power forecasting error, respectively.

[0020] Based on the actual power output and the predicted power output of the new energy power generation, the expected value, variance, and skewness of the new energy power generation prediction error are obtained by substituting them into a preset formula for calculating the new energy power generation prediction error. Specifically, the formula for calculating the new energy power generation prediction error is as follows:

[0021] ΔP D =P D,A —P D,F

[0022]

[0023]

[0024]

[0025] In the formula, P D,A P represents the actual power output of new energy power generation. D,F ΔP represents the predicted power output of new energy generation. D For the prediction error of new energy power generation, Let E{ΔP} be the prediction error of new energy power generation in month k. D}、D{ΔP D}、K(ΔP D ) represent the expectation, variance, and skewness of the prediction error for new energy power generation, respectively.

[0026] As an improvement to the above scheme, the step of generating a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data and a new energy power generation probabilistic statistical model corresponding to the new energy power generation prediction error data based on the demand-side load power prediction error data and the new energy power generation prediction error data includes:

[0027] Based on the demand-side load power prediction error data, a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data is generated; wherein, the demand-side load power probabilistic statistical model is specifically as follows:

[0028] P′ L,A =P′ L,F +ΔP′ L

[0029] In the formula, P′ L,A Let P′ be the actual value of the demand-side load power to be solved. L,F Let ΔP′ be the predicted power value of the demand-side load to be solved. L For a random variable, the expected value, variance, and skewness of the demand-side load power forecasting error are known.

[0030] Based on the new energy power generation prediction error data, a new energy power generation probability statistical model corresponding to the new energy power generation prediction error data is generated; wherein, the new energy power generation probability statistical model is specifically as follows:

[0031] P′ D,A =P′ D,F +ΔP′ D

[0032] In the formula, P′ D,A Let P′ be the actual power value of the new energy power generation to be solved. D,F Let ΔP′ be the predicted power value of the new energy power generation to be solved. D The random variable is the prediction error of new energy power generation, with known expectation, variance, and skewness.

[0033] As an improvement to the above scheme, the energy storage configuration stochastic robust optimization model includes:

[0034] min{CESS}

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, CESS is the energy storage configuration capacity vector for 12 months, and t represents the t-th month of the year. This represents the actual demand-side load power value for month t. Let t be the actual power output of new energy generation in month t. Let t be the net load forecast power value for month t. Let be the net load power prediction error for month t. Let be the demand-side load power forecast error for month t. Let be the prediction error of new energy power generation in month t. Let be the relative prediction error of net load power in month t. This represents the upper limit of the power generation capacity of the i-th adjustable generating unit. N represents the lower limit of the generating capacity of the i-th adjustable unit. G Let be the total number of adjustable generating units in the regional power grid, inf Pr{} be the probability underfimum function, and Φ be the function that satisfies the given expectation, variance, and skewness. The set of values ​​for α is the confidence level of power supply reliability of the active distribution system in the new power system.

[0042] As an improvement to the above scheme, the calculation of the stochastic robust optimization model of the energy storage configuration based on the convex optimization method to obtain the target energy storage configuration capacity vector includes:

[0043] Based on the probabilistic lower bound function in the stochastic robust optimization model for energy storage configuration, a probabilistic upper bound function is determined; wherein, the probabilistic upper bound function is specifically:

[0044]

[0045]

[0046] In the formula, sup Pr{} is the supremum function of probability, and β=1-α;

[0047] Based on the convex optimization method and the probabilistic supremum function, the boundary conditions corresponding to the probabilistic supremum function are calculated; wherein, the boundary conditions are specifically:

[0048]

[0049]

[0050] δ1=a1 / M1―1

[0051] δ2=a2 / M1―1

[0052] In the formula, M1 is;

[0053] Based on the duality theory of convex optimization, the probabilistic supremum function, and the boundary conditions, the target energy storage configuration capacity vector is calculated.

[0054] Accordingly, one embodiment of the present invention also provides a power grid energy storage configuration device, including: a data acquisition module, a first model generation module, a second model generation module, and a result generation module;

[0055] The data acquisition module is used to acquire the operating data of the power grid to be tested within a preset time period; wherein, the operating data includes: actual power of demand-side load, actual power of new energy power generation, predicted power of demand-side load, predicted power of new energy power generation, upper and lower limits of power generation of each adjustable unit, and confidence level of power supply reliability of the active distribution system of the new power system.

[0056] The first model generation module is used to generate a probabilistic statistical model of the demand-side load power and a probabilistic statistical model of the new energy power generation of the power grid to be tested, based on the actual power of the demand-side load, the actual power of the new energy power generation, the predicted power of the demand-side load, and the predicted power of the new energy power generation.

[0057] The second model generation module is used to construct a stochastic robust optimization model for energy storage configuration based on the demand-side load power probabilistic statistical model and the new energy power generation probabilistic statistical model, and based on the upper and lower limits of the power generation of each adjustable unit and the confidence level of the power supply reliability of the active distribution system of the new power system.

[0058] The result generation module is used to calculate the stochastic robust optimization model of the energy storage configuration based on the convex optimization method, obtain the target energy storage configuration capacity vector, and generate the energy storage configuration scheme of the power grid to be tested based on the target energy storage configuration capacity vector.

[0059] Accordingly, one embodiment of the present invention also provides a computer terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a grid energy storage configuration method as described in the present invention.

[0060] Accordingly, one embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a grid energy storage configuration method as described in the present invention.

[0061] As can be seen from the above, the present invention has the following beneficial effects:

[0062] This invention provides a method for configuring energy storage in a power grid. It involves acquiring the actual demand-side load power, actual renewable energy generation power, predicted demand-side load power, predicted renewable energy generation power, upper and lower limits of power generation from each adjustable generating unit, and the reliability confidence level of the active distribution system in a new power system within a preset time period. Based on the operational data, it generates probabilistic statistical models of demand-side load power and renewable energy generation power. Then, it constructs a stochastic robust optimization model for energy storage configuration based on these models. The model is then calculated using a convex optimization method to obtain the target energy storage configuration capacity vector, thereby generating an energy storage configuration scheme for the power grid under test. This invention, by constructing and solving a stochastic robust optimization model for energy storage configuration, generates energy storage configuration schemes, enabling the analysis and judgment of power grid energy storage scheme configurations. This significantly reduces errors in power grid energy storage scheme configuration and improves the accuracy of power grid energy storage configuration schemes.

[0063] Furthermore, this invention transforms the stochastic optimization model into a deterministic model using a convex optimization method. This transformation process does not require generating a large number of samples, avoids the "curse of dimensionality" problem, and can obtain the globally optimal solution. This significantly improves the efficiency of grid energy storage configuration. Attached Figure Description

[0064] Figure 1 This is a schematic flowchart of a power grid energy storage configuration method provided in an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of the structure of a power grid energy storage configuration device provided in an embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram of the energy storage and high / low load backup required for a power distribution system provided in an embodiment of the present invention;

[0067] Figure 4 This is a diagram showing the relationship between reserve coefficients under different new energy penetration rates, provided by an embodiment of the present invention.

[0068] Figure 5 This is a graph showing the relationship between energy storage configuration coefficients under different new energy penetration rates, provided by an embodiment of the present invention.

[0069] Figure 6 This is a schematic diagram of a terminal device structure provided in an embodiment of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Example 1

[0072] See Figure 1 , Figure 1 This is a flowchart illustrating a grid energy storage configuration method according to an embodiment of the present invention, as shown below. Figure 1 As shown, this embodiment includes steps 101 to 104, and the specific steps are as follows:

[0073] Step 101: Obtain the operating data of the power grid to be tested within a preset time period; wherein, the operating data includes: actual power of demand-side load, actual power of new energy power generation, predicted power of demand-side load, predicted power of new energy power generation, upper and lower limits of power generation of each adjustable unit, and confidence level of power supply reliability of the new power system active distribution system.

[0074] In this embodiment, the preset time can be twelve months.

[0075] Step 102: Based on the actual demand-side load power, the actual power of renewable energy generation, the predicted demand-side load power, and the predicted renewable energy generation power, generate a probabilistic statistical model of the demand-side load power and a probabilistic statistical model of the renewable energy generation power of the power grid to be tested.

[0076] In this embodiment, generating a probabilistic statistical model of the demand-side load power and a probabilistic statistical model of the new energy power generation of the power grid under test based on the actual demand-side load power, the actual power of the new energy power generation, the predicted demand-side load power, and the predicted new energy power generation includes:

[0077] Based on the actual demand-side load power, the actual power of renewable energy generation, the predicted demand-side load power, and the predicted renewable energy generation power, calculate the demand-side load power prediction error data corresponding to the predicted load power, and the renewable energy generation power prediction error data corresponding to the predicted renewable energy generation power; wherein, the demand-side load power prediction error data includes: the expected value, variance, and skewness of the demand-side load power prediction error, and the renewable energy generation power prediction error data includes: the expected value, variance, and skewness of the renewable energy generation power prediction error;

[0078] Based on the demand-side load power prediction error data and the new energy power generation prediction error data, a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data and a new energy power generation probabilistic statistical model corresponding to the new energy power generation prediction error data are generated.

[0079] In this embodiment, the step of calculating the demand-side load power prediction error data corresponding to the demand-side load prediction power and the new energy power prediction error data corresponding to the new energy power prediction power based on the actual demand-side load power, the actual power of new energy power generation, the predicted demand-side load power, and the predicted new energy power generation power includes:

[0080] Based on the actual demand-side load power and the predicted demand-side load power, the expected value, variance, and skewness of the demand-side load power prediction error are obtained by substituting them into a preset formula for calculating the demand-side load power prediction error. Specifically, the formula for calculating the demand-side load power prediction error is as follows:

[0081] ΔP L =P L,A —P L,F

[0082]

[0083]

[0084]

[0085] In the formula, N T P represents the total number of months over the years. L,A P represents the actual value of the demand-side load power. L,F The predicted power value for demand-side load is ΔP. L For demand-side load power forecasting error, Let E{ΔP} be the demand-side load power forecast error for month k. L}、D{ΔP L}、K(ΔP L These represent the expected value, variance, and skewness of the demand-side load power forecasting error, respectively.

[0086] Based on the actual power output and the predicted power output of the new energy power generation, the expected value, variance, and skewness of the new energy power generation prediction error are obtained by substituting them into a preset formula for calculating the new energy power generation prediction error. Specifically, the formula for calculating the new energy power generation prediction error is as follows:

[0087] ΔP D =P D,A —P D,F

[0088]

[0089]

[0090]

[0091] In the formula, P D,A P represents the actual power output of new energy power generation. D,F ΔP represents the predicted power output of new energy generation. D For the prediction error of new energy power generation, Let E{ΔP} be the prediction error of new energy power generation in month k. D}、D{ΔP D}、K(ΔP D ) represent the expectation, variance, and skewness of the prediction error for new energy power generation, respectively.

[0092] In this embodiment, generating a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data and a new energy power generation probabilistic statistical model corresponding to the new energy power generation prediction error data based on the demand-side load power prediction error data and the new energy power generation prediction error data includes:

[0093] Based on the demand-side load power prediction error data, a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data is generated; wherein, the demand-side load power probabilistic statistical model is specifically as follows:

[0094] P′ L,A =P′ L,F +ΔP′ L

[0095] In the formula, P′ L,A Let P′ be the actual value of the demand-side load power to be solved. L,F Let ΔP′ be the predicted power value of the demand-side load to be solved. L For a random variable, the expected value, variance, and skewness of the demand-side load power forecasting error are known.

[0096] Based on the new energy power generation prediction error data, a new energy power generation probability statistical model corresponding to the new energy power generation prediction error data is generated; wherein, the new energy power generation probability statistical model is specifically as follows:

[0097] P′ D,A =P′ D,F +ΔP′ D

[0098] In the formula, P′ D,ALet P′ be the actual power value of the new energy power generation to be solved. D,F Let ΔP′ be the predicted power value of the new energy power generation to be solved. D The random variable is the prediction error of new energy power generation, with known expectation, variance, and skewness.

[0099] Step 103: Based on the demand-side load power probabilistic statistical model and the new energy power generation probabilistic statistical model, and based on the upper and lower limits of the power generation of each adjustable unit and the confidence level of the power supply reliability of the new power system active distribution system, construct a stochastic robust optimization model for energy storage configuration.

[0100] In this embodiment, the stochastic robust optimization model for energy storage configuration includes:

[0101] min{CESS}

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, CESS is the energy storage configuration capacity vector for 12 months, and t represents the t-th month of the year. This represents the actual demand-side load power value for month t. Let t be the actual power output of new energy generation in month t. Let t be the net load forecast power value for month t. Let be the net load power prediction error for month t. Let be the demand-side load power forecast error for month t. Let be the prediction error of new energy power generation in month t. Let be the relative prediction error of net load power in month t. This represents the upper limit of the power generation capacity of the i-th adjustable generating unit. N represents the lower limit of the generating capacity of the i-th adjustable unit. G Let be the total number of adjustable generating units in the regional power grid, inf Pr{} be the probability underfimum function, and Φ be the function that satisfies the given expectation, variance, and skewness. The set of values ​​for α is the confidence level of power supply reliability of the active distribution system in the new power system.

[0109] Step 104: Calculate the stochastic robust optimization model of the energy storage configuration based on the convex optimization method to obtain the target energy storage configuration capacity vector, and generate the energy storage configuration scheme of the power grid to be tested based on the target energy storage configuration capacity vector.

[0110] In this embodiment, the calculation of the stochastic robust optimization model of the energy storage configuration based on the convex optimization method to obtain the target energy storage configuration capacity vector includes:

[0111] Based on the probabilistic lower bound function in the stochastic robust optimization model for energy storage configuration, a probabilistic upper bound function is determined; wherein, the probabilistic upper bound function is specifically:

[0112]

[0113]

[0114] In the formula, sup Pr{} is the supremum function of probability, and β=1-α;

[0115] Based on the convex optimization method and the probabilistic supremum function, the boundary conditions corresponding to the probabilistic supremum function are calculated; wherein, the boundary conditions are specifically:

[0116]

[0117]

[0118] δ1=a1 / M1―1

[0119] δ2=a2 / M1―1

[0120] In the formula, M1 is the expectation of the random vector, and the specific calculation formula is as follows: M1=E[X];

[0121] Based on the duality theory of convex optimization, the probabilistic supremum function, and the boundary conditions, the target energy storage configuration capacity vector is calculated.

[0122] In a specific embodiment, the convex optimization duality theory is as follows: From the above boundary conditions, it can be seen that δ1 and δ2 are both linear functions of CESS(t), and simultaneously, according to:

[0123]

[0124] Wherein, f1 is known from the duality theory of convex optimization to be the optimal value of the above probabilistic supremacy function (see the explanation of step 104 in the following embodiment), so f1≤β;

[0125] The expressions for g1, g2, and g3 are given in the following formulas;

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] In the formula, These are the squared coefficient of variation and the third-order coefficient of variation, respectively, and M1 is the expectation of the random vector. The specific calculation formula is as follows:

[0132] M1 = E[X];

[0133] M² is the variance of the random vector, and the specific calculation formula is as follows:

[0134] M2 = E[(XE[X])2]

[0135] M3 is the skewness of the random vector, and the specific calculation formula is as follows:

[0136] M3 = E[(XE[X])3]

[0137] It can be concluded that f1 is an inverse proportional function of CESS(t), i.e., f1∝1 / CESS(t). The minimum value of CESS(t) can be obtained from f1≤β, and the target energy storage configuration power vector can be obtained from this.

[0138] The target energy storage configuration capacity vector is obtained by: based on the predicted load power, the predicted new energy power generation power, and the target energy storage configuration capacity optimization model, using duality theory to transform the energy storage optimization model containing random variables (see steps 5 and 6) into a deterministic optimization model. The minimum energy storage power can be obtained by writing a calculation program in MATLAB, and the energy storage configuration scheme of the power grid to be tested can be generated.

[0139] In one specific embodiment, a convex optimization method is used to transform the stochastic robust optimization model into a conventional deterministic optimization problem without random variables;

[0140] The transformation process of converting the stochastic robust optimization model of energy storage configuration into a conventional deterministic optimization problem without random variables using convex optimization methods is as follows:

[0141] First, let's introduce a class of optimization inequality problems in probability theory:

[0142]

[0143]

[0144] Where z is a random variable, S is a given set of values ​​for z, σj is the j-th probability moment of z, μ is the probability of z taking a certain value, and Ω is the set of probability distributions of z that satisfy the given probability moments.

[0145] Using probability theory, the above optimization problem can be transformed into the following convex optimization problem:

[0146]

[0147]

[0148] Among them, y r M is an intermediate variable introduced. r The r-th moment corresponding to z.

[0149] When S = {x >= a}, the optimal solution to the above convex optimization problem is as follows:

[0150]

[0151] Where δ=a / M1-1, g1, g2, and g3 are obtained according to the following formula:

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] In a specific embodiment, the stochastic robust optimization model for energy storage configuration can be transformed into the following model for solution:

[0158] min{CESS}

[0159] X opt (δ1)≤β

[0160] X opt (δ2)≤β

[0161] In a specific embodiment, the IEEE 14-node system was used for simulation verification, and the system data is shown in Table 1. The confidence level for the reliability of the active distribution system was set to 0.98. The probability statistical moments of the net load obtained from S4, S5, and S8 were: expectation 0.044, variance 0.548, and skewness 3.013, as shown in Table 2. The upper and lower limits of the adjustable generating capacity provided by the regional power grid were 13MW and 8.5MW, respectively, as shown in Table 3. The renewable energy penetration rate was defined as the ratio of the maximum renewable energy generation capacity to the maximum demand-side load capacity within the year. The energy storage coefficient ESC was defined as the ratio of the maximum configured energy storage capacity to the predicted maximum demand-side load capacity within the year. The reserve coefficient LRC was defined as the system reserve (i.e., (1+(1+δ1)M1)P). net The ratio of the energy storage capacity to the projected maximum load power on the demand side within the year can be used to calculate the renewable energy penetration rate of 31.25% from the data in Table 1. Correspondingly, based on this plan, the required energy storage and high / low load reserves for the active distribution system can be calculated. Figure 3 As shown. By changing the new energy penetration rate p, i.e., p is set to 60%, 50%, 40%, 30%, 20%, and 10%, the corresponding initial system loads are set to 0.52, 0.63, 0.78, 1.04, 1.5625, and 3.125 times the initial load. Based on this scheme, the optimal energy storage configuration power for 12 months of the year is obtained. Taking the maximum energy storage configuration power during the 12 months of the year, the energy storage coefficient ESC (the ratio of the maximum energy storage configuration power in the year to the predicted maximum demand-side load power) and reserve coefficient LRC (the system reserve coefficient LRC is the system reserve (i.e., (1+(1+δ1)M1)P)) can be calculated for various penetration rates. net The ratio of ( ) to the predicted maximum demand-side load power for the year (e.g.) Figure 4 , Figure 5 As shown. Figure 4 This indicates that as the penetration rate of new energy sources increases, the backup capacity of the system needs to be increased to cope with the increasing fluctuations in new energy sources; from Figure 5 The results show that the energy storage configuration coefficient increases or decreases with increasing penetration rate. This is because the system has different unit scheduling schemes under different penetration rates, resulting in different reserve capacities. Therefore, the energy storage demand does not necessarily increase, which proves the feasibility and innovation of this scheme.

[0162]

[0163] Table 1

[0164]

[0165] Table 2

[0166]

[0167] Table 3

[0168] See Figure 2 , Figure 2 This is a schematic diagram of the structure of a power grid energy storage configuration device according to an embodiment of the present invention, including: a data acquisition module 201, a first model generation module 202, a second model generation module 203, and a result generation module 204;

[0169] The data acquisition module is used to acquire the operating data of the power grid to be tested within a preset time period; wherein, the operating data includes: actual power of demand-side load, actual power of new energy power generation, predicted power of demand-side load, predicted power of new energy power generation, upper and lower limits of power generation of each adjustable unit, and confidence level of power supply reliability of the active distribution system of the new power system.

[0170] The first model generation module is used to generate a probabilistic statistical model of the demand-side load power and a probabilistic statistical model of the new energy power generation of the power grid to be tested, based on the actual power of the demand-side load, the actual power of the new energy power generation, the predicted power of the demand-side load, and the predicted power of the new energy power generation.

[0171] The second model generation module is used to construct a stochastic robust optimization model for energy storage configuration based on the demand-side load power probabilistic statistical model and the new energy power generation probabilistic statistical model, and based on the upper and lower limits of the power generation of each adjustable unit and the confidence level of the power supply reliability of the active distribution system of the new power system.

[0172] The result generation module is used to calculate the stochastic robust optimization model of the energy storage configuration based on the convex optimization method, obtain the target energy storage configuration capacity vector, and generate the energy storage configuration scheme of the power grid to be tested based on the target energy storage configuration capacity vector.

[0173] It is understood that the above system item embodiments correspond to the method item embodiments of the present invention, and can implement the grid energy storage configuration method provided by any of the above method item embodiments of the present invention.

[0174] This embodiment acquires the actual demand-side load power, actual renewable energy generation power, predicted demand-side load power, predicted renewable energy generation power, upper and lower limits of power generation of each adjustable unit, and the power supply reliability confidence of the active distribution system of the new power system within a preset time period for the power grid under test. Based on the operational data, it generates a probabilistic statistical model of demand-side load power and a probabilistic statistical model of renewable energy generation power. A stochastic robust optimization model for energy storage configuration is then constructed based on these models. A convex optimization method is used to calculate this model, obtaining the target load power, target renewable energy generation power, and target energy storage configuration capacity vector, thereby generating the energy storage configuration scheme for the power grid under test. This invention, by constructing and solving a stochastic robust optimization model for energy storage configuration, utilizes the probabilistic statistical moment information of random variables instead of a single probability distribution to model random variables. This makes it unaffected by the probability distribution of renewable energy generation power, resulting in a wider range of applications, especially suitable for regions with highly discrete renewable energy generation power. Furthermore, this invention utilizes the theory of probabilistic optimization inequalities to transform the stochastic optimization model into a deterministic optimization model. This transformation process does not require generating a large number of samples, avoids the "curse of dimensionality" problem, and can obtain a globally optimal solution.

[0175] Example 2

[0176] See Figure 6 , Figure 6 This is a schematic diagram of the terminal device structure provided in an embodiment of the present invention.

[0177] One terminal device in this embodiment includes: a processor 601, a memory 602, and a computer program stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program, it implements the steps of the various grid energy storage configuration methods described above in the embodiments, for example... Figure 1 All steps of the grid energy storage configuration method shown. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments, for example: Figure 2 All modules of the grid energy storage configuration device shown.

[0178] In addition, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the grid energy storage configuration method as described in any of the above embodiments.

[0179] Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the diagram, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0180] The processor 601 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor 601 is the control center of the terminal device, connecting various parts of the terminal device through various interfaces and lines.

[0181] The memory 602 can be used to store the computer programs and / or modules. The processor 601 implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0182] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0183] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0184] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for configuring grid energy storage, characterized in that, include: The system acquires operational data of the power grid under test within a preset time period. The operational data includes: actual demand-side load power, actual power of renewable energy generation, predicted demand-side load power, predicted power of renewable energy generation, upper and lower limits of power generation of each adjustable unit, and confidence level of power supply reliability of the active distribution system of the new power system. Based on the actual demand-side load power and the predicted demand-side load power, the expected value, variance, and skewness of the demand-side load power prediction error are obtained by substituting them into a preset formula for calculating the demand-side load power prediction error. Specifically, the formula for calculating the demand-side load power prediction error is as follows: In the formula, This is the total number of months over the years. This represents the actual value of demand-side load power. Forecast power values ​​for demand-side load. For demand-side load power forecasting error, The demand-side load power forecast error for month k is... , , These are the expected value, variance, and skewness of the demand-side load power forecasting error, respectively. Based on the actual power output and the predicted power output of the new energy power generation, the expected value, variance, and skewness of the new energy power generation prediction error are obtained by substituting them into a preset formula for calculating the new energy power generation prediction error. Specifically, the formula for calculating the new energy power generation prediction error is as follows: In the formula, This represents the actual power output of new energy power generation. For the predicted power output of new energy generation, For the prediction error of new energy power generation, Let be the prediction error of new energy power generation in month k. , , These are the expected value, variance, and skewness of the prediction error for new energy power generation, respectively; wherein, the prediction error data for demand-side load power includes: the expected value, variance, and skewness of the prediction error for demand-side load power, and the prediction error data for new energy power generation includes: the expected value, variance, and skewness of the prediction error for new energy power generation. Based on the demand-side load power prediction error data and the new energy power generation prediction error data, generate a demand-side load power probability statistical model corresponding to the demand-side load power prediction error data and a new energy power generation probability statistical model corresponding to the new energy power generation prediction error data. Based on the demand-side load power probability statistical model and the new energy power generation probability statistical model, and based on the upper and lower limits of the power generation of each adjustable unit and the confidence level of the power supply reliability of the active distribution system of the new power system, a stochastic robust optimization model for energy storage configuration is constructed. The energy storage configuration stochastic robust optimization model is calculated based on the convex optimization method to obtain the target energy storage configuration capacity vector, and the energy storage configuration scheme of the power grid to be tested is generated based on the target energy storage configuration capacity vector.

2. The grid energy storage configuration method according to claim 1, characterized in that, The step of generating a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data and a new energy power generation probabilistic statistical model corresponding to the new energy power generation prediction error data based on the demand-side load power prediction error data and the new energy power generation prediction error data includes: Based on the demand-side load power prediction error data, a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data is generated; wherein, the demand-side load power probabilistic statistical model is specifically as follows: In the formula, The actual value of the demand-side load power to be solved. The demand-side load forecast power value is to be solved. For a random variable, the expected value, variance, and skewness of the demand-side load power forecasting error are known. Based on the new energy power generation prediction error data, a new energy power generation probability statistical model corresponding to the new energy power generation prediction error data is generated; wherein, the new energy power generation probability statistical model is specifically as follows: In the formula, The actual power output of the new energy power generation to be solved. The predicted power output of the new energy generation to be solved is... The random variable is the prediction error of new energy power generation, with known expectation, variance, and skewness.

3. The grid energy storage configuration method according to claim 2, characterized in that, The energy storage configuration stochastic robust optimization model includes: In the formula, CESS is the energy storage configuration capacity vector for 12 months, and t represents the t-th month of the year. This represents the actual demand-side load power value for month t. Let t be the actual power output of new energy generation in month t. Let t be the predicted power value of the demand-side load for month t. Let t be the predicted power output of new energy generation in month t. Let t be the net load forecast power value for month t. Let be the net load power prediction error for month t. Let be the demand-side load power forecast error for month t. Let be the prediction error of new energy power generation in month t. Let be the relative prediction error of net load power in month t. This represents the upper limit of the power generation capacity of the i-th adjustable generating unit. This represents the lower limit of the power generation capacity of the i-th adjustable generating unit. Let be the total number of adjustable generating units in the regional power grid, inf Pr{} be the probability underfimum function, and Φ be the function that satisfies the given expectation, variance, and skewness. The set of values ​​for α is the confidence level of power supply reliability of the active distribution system in the new power system.

4. The grid energy storage configuration method according to claim 3, characterized in that, The calculation of the stochastic robust optimization model for the energy storage configuration based on the convex optimization method to obtain the target energy storage configuration capacity vector includes: Based on the probabilistic lower bound function in the stochastic robust optimization model for energy storage configuration, a probabilistic upper bound function is determined; wherein, the probabilistic upper bound function is specifically: In the formula, Let be the probabilistic supremum function. ; Based on the convex optimization method and the probabilistic supremum function, the boundary conditions corresponding to the probabilistic supremum function are calculated; wherein, the boundary conditions are specifically: In the formula, The expected value of a random vector is calculated using the following formula: ; Based on the duality theory of convex optimization, the probabilistic supremum function, and the boundary conditions, the target energy storage configuration capacity vector is calculated.

5. A grid energy storage configuration device, characterized in that, include: The system comprises a data acquisition module, a first model generation module, a second model generation module, and a result generation module. The data acquisition module is used to acquire the operating data of the power grid to be tested within a preset time period; wherein, the operating data includes: actual power of demand-side load, actual power of new energy power generation, predicted power of demand-side load, predicted power of new energy power generation, upper and lower limits of power generation of each adjustable unit, and confidence level of power supply reliability of the active distribution system of the new power system. The first model generation module is used to obtain the expectation, variance, and skewness of the demand-side load power prediction error by substituting the actual demand-side load power and the predicted demand-side load power into a preset demand-side load power prediction error data calculation formula; wherein, the specific formula for calculating the demand-side load power prediction error data is as follows: In the formula, This is the total number of months over the years. This represents the actual value of demand-side load power. Forecast power values ​​for demand-side load. For demand-side load power forecasting error, The demand-side load power forecast error for month k is... , , These are the expected value, variance, and skewness of the demand-side load power prediction error, respectively. Based on the actual power of renewable energy generation and the predicted power of renewable energy generation, the expected value, variance, and skewness of the renewable energy power prediction error are obtained by substituting them into a preset formula for calculating the renewable energy power prediction error data. Specifically, the formula for calculating the renewable energy power prediction error data is as follows: In the formula, This represents the actual power output of new energy power generation. For the predicted power output of new energy generation, For the prediction error of new energy power generation, Let be the prediction error of new energy power generation in month k. , , These are the expected value, variance, and skewness of the prediction error for renewable energy power generation; wherein, the demand-side load power prediction error data includes: the expected value, variance, and skewness of the demand-side load power prediction error, and the renewable energy power generation prediction error data includes: the expected value, variance, and skewness of the renewable energy power generation prediction error; based on the demand-side load power prediction error data and the renewable energy power generation prediction error data, a demand-side load power probabilistic statistical model corresponding to the demand-side load power prediction error data and a renewable energy power generation probabilistic statistical model corresponding to the renewable energy power generation prediction error data are generated; The second model generation module is used to construct a stochastic robust optimization model for energy storage configuration based on the demand-side load power probabilistic statistical model and the new energy power generation probabilistic statistical model, and based on the upper and lower limits of the power generation of each adjustable unit and the confidence level of the power supply reliability of the active distribution system of the new power system. The result generation module is used to calculate the stochastic robust optimization model of the energy storage configuration based on the convex optimization method, obtain the target energy storage configuration capacity vector, and generate the energy storage configuration scheme of the power grid to be tested based on the target energy storage configuration capacity vector.

6. A computer terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a grid energy storage configuration method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a grid energy storage configuration method as described in any one of claims 1 to 4.

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