Energy storage planning method and system in novel power system

Abstract

The invention discloses an energy storage planning method and system in a novel power system, and the method employs a linear regression method based on historical meteorological scene data and historical photovoltaic output data of an area where the novel power system is located, solves a problem that the conventional photovoltaic output prediction is low in precision, achieves a higher-precision photovoltaic output prediction value, and improves the prediction precision. And a reliable data basis is provided for subsequent energy storage planning. According to the method, the problem of unreasonable energy storage access point selection is solved based on the photovoltaic output predicted value by constructing the energy storage access point stability evaluation system, an energy storage site selection scheme for maximizing the system stability is realized, and the overall stability of a power system is effectively improved. According to the method, the energy storage capacity optimal configuration model taking the energy storage capacity configuration scheme as a decision variable is constructed, the problem of unreasonable energy storage capacity configuration is solved, the annual investment and operation cost of the energy storage equipment is minimized, and meanwhile, the safety and stability of the system are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage planning, and in particular relates to a new energy storage planning method and system in a power system. Background Art

[0002] With the rapid development of renewable energy, especially photovoltaic power generation, the volatility and uncertainty in the power system have increased significantly, posing challenges to the stability and reliability of the system. As an important means of regulating and stabilizing the power system, energy storage systems have received extensive attention. However, the current energy storage planning methods have many problems and defects and need to be improved and optimized.

[0003] At present, the energy storage planning methods for photovoltaic stations mainly include static planning methods and dynamic planning methods. Static planning ignores the dynamic changes of the system and is difficult to cope with the volatility of photovoltaic power generation. Although dynamic planning takes into account the dynamic characteristics of the system, it has high computational complexity and is prone to falling into local optimal solutions.

[0004] Although these methods have solved the energy storage planning problem to a certain extent, they still have significant defects. First, current research mainly focuses on configuring energy storage at photovoltaic access points, but under certain conditions, configuring energy storage at other nodes of the system may have better effects on system operation and economy. Second, existing methods do not fully consider the impact of photovoltaic output volatility on system stability and energy storage capacity. The output power of photovoltaic power generation is greatly affected by factors such as weather. Ignoring the volatility of photovoltaic output power will lead to unreasonable configuration of energy storage system.

[0005] The above defects lead to reduced system stability and low economic benefits. The unreasonable configuration of the energy storage system makes it difficult to cope with the fluctuations in photovoltaic power generation, which increases the frequency and voltage fluctuations of the power system, and also leads to an increase in investment and operating costs.

[0006] In summary, proposing an energy storage planning method based on photovoltaic power prediction and optimization algorithm is an urgent problem to be solved by technical personnel in this field. Summary of the invention:

[0007] In order to solve the problems existing in the prior art, the present invention proposes a novel energy storage planning method and system in a power system.

[0008] The technical solution of the present invention is as follows:

[0009] A novel energy storage planning method in a power system, the method comprising:

[0010] Based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system, the linear regression method is used to obtain the photovoltaic output prediction value;

[0011] Based on the photovoltaic output prediction value, under the pre-constructed energy storage access point stability evaluation system, the stability evaluation values ​​corresponding to different energy storage access points are obtained, and the energy storage access point corresponding to the maximum stability evaluation value is used as the energy storage site selection plan;

[0012] Based on the energy storage site selection plan, an energy storage capacity optimization configuration model with the energy storage capacity configuration plan as the decision variable is constructed. The energy storage capacity optimization configuration model takes the minimum total annualized investment and operation cost of the connected energy storage equipment as the objective function, and takes system safety and stability constraints, energy storage operation constraints, power flow constraints and wind abandonment constraints as constraints;

[0013] Solve the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration plan.

[0014] Furthermore, the specific method of using the linear regression method to obtain the photovoltaic output prediction value based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system includes:

[0015] Construct a multivariate linear regression model, the matrix representation of which is:

[0016]

[0017] Where X is the explanatory variable, corresponding to the meteorological scene data, X is an i×(j+1) matrix; Y is the dependent variable, corresponding to the photovoltaic output data, Y is an i×1 vector; β is the regression coefficient, β is a (j+1)×1 vector; ε is the model residual, ε is an i×1 vector.

[0018] The historical meteorological scene data and the historical photovoltaic output data are substituted into the multivariate linear regression model, and the least squares method is used to determine the regression coefficient by calculating the method of minimizing the residual sum of squares of the multivariate linear regression model, and the training of the multivariate linear regression model is completed; wherein, the residual sum of squares of the multivariate linear regression model is:

[0019] S=ε T ε=(Y-Xβ) T (Y-Xβ)

[0020] In the formula, S is a non-negative quadratic function with a minimum value;

[0021] According to the matrix derivation rule, let have:

[0022]

[0023] The least squares estimate of the regression coefficient of the multivariate linear regression model is further obtained as:

[0024]

[0025] Based on the current meteorological scenario data, the trained multivariate linear regression model is used to obtain the photovoltaic output prediction value.

[0026] Furthermore, the meteorological scene data includes temperature, relative humidity, cloud cover, wind speed and dew point temperature.

[0027] Furthermore, the primary indicator of the energy storage access point stability assessment system is a system voltage stability evaluation index, and the secondary indicators under the primary indicator include a voltage stability margin index, a node voltage static sensitivity index, a voltage deviation index, and a voltage stability index.

[0028] Among them, the voltage stability margin index U at the jth selected access point is marj The expression is:

[0029]

[0030] U marj is the voltage stability margin index of the jth candidate access point, U crj is the critical voltage value of the jth candidate access point, U j is the node voltage of the jth access point to be selected.

[0031] The voltage deviation index U at the jth selected access point DIj The expression is:

[0032]

[0033] U DIj is the voltage deviation index of the jth access point to be selected; U ratedj is the rated voltage of the jth access point to be selected; U j is the node voltage of the jth access point to be selected;

[0034] The node voltage static sensitivity index of the jth candidate access point is represented by Z j for:

[0035]

[0036]

[0037] Z j Characterizes the static sensitivity of the injected power change of the jth candidate access point to the voltage of other nodes in the system, λ js When the power injected into the jth candidate access point changes, the voltage-active sensitivity value of node s in the system, U s is the node voltage of node s, P j is the active power of the jth access point to be selected, ωj is the weight factor of the jth candidate access point, and its value is measured by the deviation of the node voltage from the node expected voltage; U j is the node voltage of the jth candidate access point, U refj is the expected voltage of the jth candidate access point.

[0038] The voltage stability index F of the jth candidate access point j for:

[0039]

[0040] Among them, U j is the node voltage value of the jth access point to be selected; U j0 The node voltage value of the jth candidate access point under no-load state.

[0041] Furthermore, the specific method of obtaining stability evaluation values ​​corresponding to different energy storage access points based on the photovoltaic output prediction value under the pre-constructed energy storage access point stability evaluation system, and using the energy storage access point corresponding to the maximum stability evaluation value as the energy storage site selection scheme includes:

[0042] Under the pre-built energy storage access point stability evaluation system, the secondary index values ​​of different secondary indicators corresponding to each energy storage access point are obtained; for the jth access point to be selected, its secondary indicators are recorded as: voltage stability margin index O 1j , node voltage static sensitivity index O 2j , voltage deviation index O 3j , voltage stability index O 4j ;

[0043] The original data matrix of the secondary index values ​​related to different energy storage access points is constructed, and its expression is:

[0044]

[0045] In the formula, O ij is the value of the i-th secondary indicator under the j-th access point to be selected;

[0046] The elements in the original data matrix are dimensionless processed, and the weight ratio of each element is further calculated. The dimensionless processing result of the i-th secondary index under the j-th access point to be selected is defined as K ij , the weight ratio of the i-th secondary indicator under the j-th candidate access point is defined as G ij :

[0047]

[0048]

[0049] In the formula, K ij and G ij The ranges are: 0≤K ij ≤1,0≤G ij ≤1;

[0050] Based on the calculated weight ratio G of the i-th secondary indicator under the j-th candidate access point ij , the information entropy value A of the jth candidate access point j , the difference coefficient B of the jth candidate access point j and the entropy weight D of the jth candidate access point j The calculation method is as follows:

[0051]

[0052] B j =1-A j

[0053]

[0054] In the formula, n is the number of secondary indicators corresponding to the jth access point;

[0055] After obtaining the entropy weight of each indicator, the TOPSIS algorithm is used to realize the comprehensive evaluation of multidimensional indicators. First, the normalized decision matrix H is constructed:

[0056]

[0057] Among them, the elements in the matrix H ij It can be expressed as:

[0058]

[0059] In the formula, G ij is the weight ratio of the i-th secondary indicator under the j-th access point to be selected;

[0060] The next step is to construct the weighted normalized decision matrix U,

[0061]

[0062] Among them, the element U in the matrix ij It can be expressed as:

[0063] U ij =D j ·H ij

[0064] Where D j is the entropy weight of the jth candidate access point.

[0065] In the matrix U, under the jth candidate access point, the decision elements U of all secondary indicators ij The maximum value of is the positive ideal solution under the jth candidate access point, U ij The minimum value of is the negative ideal solution under the jth candidate access point, that is:

[0066]

[0067]

[0068] On this basis, the distance of the positive ideal solution under the jth candidate access point is calculated and the distance to the negative ideal solution

[0069]

[0070]

[0071] Finally, the relative proximity index F of the jth candidate access point is calculated respectively. dj :

[0072]

[0073] F dj The larger the value, the better the comprehensive evaluation. dj Maximum F dmax The corresponding access point is the weak node of the system, that is, the optimal energy storage access point, namely:

[0074] F dmax ={max(F dj )j=1,2,…,n}

[0075] Furthermore, the objective function C of the energy storage capacity optimization configuration model is s for:

[0076]

[0077] Where E1 is the annualized investment cost per unit capacity of the energy storage device, E2 is the annualized operation and maintenance cost per unit capacity of the energy storage device, and N k The number of energy storage access points in the table system, Q sk represents the capacity of the energy storage device at the kth access point, so that the objective function C s The energy storage device capacity corresponding to the minimum value is Q sk , which is the result of optimal configuration of energy storage capacity.

[0078] The system safety and stability constraints are:

[0079] P hy,xy (k)+Pth,xy (k)+P ess,xy (k)-P load,xy (k) = P xy (k)-P loss,xy (k)

[0080]

[0081] i xy,min ≤I xy (k)≤i xy,max

[0082] Where P x j(k) represents the power transmitted from node x to node y in the kth time period; P loss xy(k) represents the transmission line loss between node x and node y; UxN is the rated value of the operating voltage at node x; Uq is the voltage operating value at node x; I xy (k) represents the current of the line between nodes x and y; i xy,min andi xy,max Respectively represent the maximum and minimum values ​​of the line current;

[0083] The energy storage operation constraints are:

[0084]

[0085]

[0086]

[0087] Where P ESS is the charging and discharging power of the energy storage system during period t; is the initial charge state of the energy storage system; is the state of charge of the energy storage system during period t; The upper and lower limits of the SOC operating range of the energy storage system;

[0088] The power flow constraint is

[0089] Where P gx , P wx , P sx and P Lx are respectively the active output of the conventional power supply, wind turbine (field), energy storage and load of the x-th node at the current time section; (If the x-th node is not equipped with an energy storage device, then P sx =0;)

[0090] Q gx , Q wx , Q sx and Q Lxare the reactive outputs of conventional power supply, wind turbine (field), energy storage and load of the x-th node at the current time section;

[0091] U x and U y are the voltage amplitudes at nodes x and y respectively;

[0092] G xy and B xy are the real and imaginary parts of the branch node admittance respectively;

[0093] θ xy is the phase angle difference between node x and node y;

[0094] and are the maximum and minimum voltage amplitudes respectively;

[0095] The wind curtailment constraint is

[0096]

[0097] In the formula, R wab is the annual power abandonment rate of new energy power stations; is the total annual electricity generation from wind power; is the total annual grid-connected electricity of the wind farm; ε% is the annual wind curtailment rate limit.

[0098] Furthermore, the specific method of solving the energy storage capacity optimization configuration model and obtaining the energy storage capacity configuration scheme includes:

[0099] The particle swarm algorithm is used to perform power flow optimization calculations to determine the optimal capacity of regional power grid energy storage configuration under high wind power access. Each particle is regarded as a potential solution, and the global optimal solution is found through particle swarm algorithm iteration. The update speed and position of each particle in the iteration are defined as:

[0100]

[0101]

[0102] In the formula, c1 is the self-learning factor; c2 is the global learning factor; r1 and r2 are random constants between [0,1]; ω is the inertia weight coefficient; the subscript k is the current iteration number of the particle swarm algorithm; is the position vector of the i-th particle in the k-th iteration; is the velocity vector of the i-th particle in the k-th iteration.

[0103] A new energy storage planning system in a power system includes a prediction module, a site selection module, a modeling module and a model solving module; wherein,

[0104] The prediction module is used to obtain the photovoltaic output prediction value by using a linear regression method based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system;

[0105] The site selection module is used to obtain stability evaluation values ​​corresponding to different energy storage access points under a pre-constructed energy storage access point stability evaluation system based on the photovoltaic output prediction value, and use the energy storage access point corresponding to the maximum stability evaluation value as the energy storage site selection plan;

[0106] The modeling module is used to construct an energy storage capacity optimization configuration model with the energy storage capacity configuration plan as a decision variable based on the energy storage site selection plan, and the energy storage capacity optimization configuration model takes the minimum total annualized investment and operation cost of the connected energy storage equipment as the objective function, and takes system safety and stability constraints, energy storage operation constraints, power flow constraints and wind abandonment constraints as constraints;

[0107] The model solving module is used to solve the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration solution.

[0108] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0109] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0110] Compared with the prior art, the present invention has the following beneficial effects:

[0111] The present invention proposes a method and system for energy storage planning in a new power system. The method adopts a linear regression method based on historical meteorological scene data and historical photovoltaic output data of the area where the new power system is located, which solves the problem of low accuracy of traditional photovoltaic output prediction and achieves a higher accuracy photovoltaic output prediction value, providing a reliable data basis for subsequent energy storage planning.

[0112] The method of the present invention solves the problem of unreasonable selection of energy storage access points by constructing an energy storage access point stability evaluation system based on photovoltaic output prediction values, realizes an energy storage site selection scheme that maximizes system stability, and effectively improves the overall stability of the power system.

[0113] The method of the present invention constructs an energy storage capacity optimization configuration model with the energy storage capacity configuration scheme as the decision variable, solves the problem of unreasonable energy storage capacity configuration, minimizes the annualized investment and operation cost of the energy storage equipment, and ensures the safety and stability of the system.

[0114] The method of the present invention adopts the TOPSIS method to comprehensively evaluate the multi-dimensional indicators of different energy storage access points, solves the problem of unreasonable weight distribution of each indicator in multi-indicator decision-making, realizes the optimization of energy storage site selection scheme, and improves the reliability and stability of system operation.

[0115] The method of the present invention adopts a particle swarm optimization (PSO) algorithm to solve the energy storage capacity optimization configuration model, which solves the problems of difficult capacity configuration and low computational efficiency in large-scale energy storage systems, realizes efficient and accurate energy storage capacity configuration, and optimizes the energy utilization efficiency of the power system.

[0116] The method of the present invention solves the system stability risks that may occur in the planning process of the energy storage system by considering multi-dimensional factors such as system safety and stability, energy storage operation constraints, power flow constraints and wind abandonment constraints, and ensures the safety and feasibility of the energy storage planning scheme in actual operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] Figure 1 It is a flow chart of the energy storage planning method in the novel power system in the embodiment;

[0118] Figure 2 Flowchart of the particle swarm algorithm in the embodiment. DETAILED DESCRIPTION

[0119] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0120] Embodiment 1:

[0121] A novel energy storage planning method in a power system of the present invention is as follows: Figure 1 As shown, the method includes:

[0122] S1. Based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system, a linear regression method is used to obtain the photovoltaic output prediction value;

[0123] S2. Based on the photovoltaic output forecast value, obtain the stability evaluation values ​​corresponding to different energy storage access points under the pre-built energy storage access point stability evaluation system, and use the energy storage access point corresponding to the maximum stability evaluation value as the energy storage site selection plan;

[0124] S3. Based on the energy storage site selection plan, an energy storage capacity optimization configuration model with the energy storage capacity configuration plan as the decision variable is constructed. The energy storage capacity optimization configuration model takes the minimum total annual investment and operation cost of the connected energy storage equipment as the objective function, and takes system safety and stability constraints, energy storage operation constraints, power flow constraints and wind curtailment constraints as constraints;

[0125] S4. Solve the energy storage capacity optimization configuration model to obtain the energy storage capacity configuration plan.

[0126] Embodiment 2:

[0127] This embodiment is further designed on the basis of the first embodiment in that, in this embodiment, based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system, a specific method for obtaining the photovoltaic output prediction value by using the linear regression method includes:

[0128] S11) Construct a multiple linear regression model, the matrix representation of the model is:

[0129]

[0130] Where X is the explanatory variable, corresponding to the meteorological scene data, X is an i×(j+1) matrix; Y is the dependent variable, corresponding to the photovoltaic output data, Y is an i×1 vector; β is the regression coefficient, β is a (j+1)×1 vector; ε is the model residual, ε is an i×1 vector.

[0131] S12) Substitute the historical meteorological scene data and the historical photovoltaic output data into the multiple linear regression model, and use the least squares method to determine the regression coefficient by calculating the method of minimizing the residual sum of squares of the multiple linear regression model, and complete the training of the multiple linear regression model; wherein the residual sum of squares of the multiple linear regression model is:

[0132] S=ε T ε=(Y-Xβ) T (Y-Xβ)

[0133] In the formula, S is a non-negative quadratic function with a minimum value;

[0134] According to the matrix derivation rule, let have:

[0135]

[0136] S13) The least squares estimate of the regression coefficient of the multivariate linear regression model is further obtained as:

[0137]

[0138] S14) Based on the current meteorological scene data, the photovoltaic output prediction value is obtained using the trained multivariate linear regression model.

[0139] Among them, the current meteorological scene data can adopt the typical value of meteorological scene data representing a typical meteorological period (such as the plum rain season), or it can be the meteorological data of a typical day in the season reflecting the regional climate type (such as temperate monsoon climate) of the area where the new power system is located.

[0140]

[0141] Embodiment three:

[0142] This embodiment is further designed on the basis of the second embodiment in that the meteorological scene data in this example includes temperature, relative humidity, cloud cover, wind speed and dew point temperature.

[0143] Embodiment 4:

[0144] This embodiment is further designed on the basis of the third embodiment in that the primary indicator of the energy storage access point stability evaluation system in this example is a system voltage stability evaluation index, and the secondary indicators under the primary indicator include a voltage stability margin index, a node voltage static sensitivity index, a voltage deviation index and a voltage stability index.

[0145] Among them, the voltage stability margin index U at the jth candidate access point is marj The expression is:

[0146]

[0147] U marj is the voltage stability margin index of the jth candidate access point, U crj is the critical voltage value of the jth candidate access point, U j is the node voltage of the jth access point to be selected.

[0148] Voltage deviation index U at the jth candidate access point DIj The expression is:

[0149]

[0150] U DIj is the voltage deviation index of the jth access point to be selected; U ratedj is the rated voltage of the jth access point to be selected; U j is the node voltage of the jth candidate access point. Similar to the voltage stability index, whether photovoltaic power prediction is considered for the access point is similar to the voltage stability index U. marj Have a greater impact;

[0151] The static sensitivity index of node voltage at the jth candidate access point is expressed as Z j for:

[0152]

[0153]

[0154] Z j Characterizes the static sensitivity of the injected power change of the jth candidate access point to the voltage of other nodes in the system, λ js When the power injected into the jth candidate access point changes, the voltage-active sensitivity value of node s in the system, U s is the node voltage of node s, P j is the active power of the jth access point to be selected, ω j is the weight factor of the jth candidate access point, and its value is measured by the deviation of the node voltage from the node expected voltage; U j is the node voltage of the jth candidate access point, U refj is the expected voltage of the jth candidate access point.

[0155] The voltage stability index F of the jth candidate access point j for:

[0156]

[0157] Among them, U j is the node voltage value of the jth access point to be selected; U j0 The node voltage value of the jth candidate access point under no-load state.

[0158] Since the node voltage U of the jth candidate access point j Affected by the fluctuation of photovoltaic output power, whether photovoltaic power prediction is considered has a great impact on the voltage stability margin index of the access point, the node voltage static sensitivity index, the voltage deviation index index and the voltage stability index index.

[0159] Embodiment five:

[0160] This embodiment is further designed on the basis of the fourth embodiment. In this embodiment, based on the photovoltaic output prediction value, under the pre-built energy storage access point stability evaluation system, the stability evaluation values ​​corresponding to different energy storage access points are obtained, and the energy storage access point corresponding to the maximum stability evaluation value is used as the specific method of the energy storage site selection scheme includes:

[0161] The Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) is used to achieve a comprehensive evaluation of multi-dimensional indicators of different energy storage access points. By setting positive and negative ideal solutions and calculating the weighted Euclidean distance of the ideal solution, the degree to which the solution is close to the optimal solution is obtained. The specific steps include:

[0162] (1) Data preprocessing based on entropy weight method

[0163] Under the pre-built energy storage access point stability evaluation system, the secondary index values ​​of different secondary indicators corresponding to each energy storage access point are obtained; for the rth access point to be selected, its secondary indicators are recorded as: voltage stability margin index O 1r , node voltage static sensitivity index O 2r , voltage deviation index O 3r , voltage stability index O 4r ;

[0164] The original data matrix of the secondary index values ​​related to different energy storage access points is constructed, and its expression is:

[0165]

[0166] In the formula, O ij is the value of the ith secondary indicator under the jth candidate access point; since the solution of various economic parameters in actual measurement is based on dimensional numerical calculation, and the charging and discharging efficiency and various voltage stability indicators are calculated using per-unit values ​​or percentages, the indicators in the dimensional layer differ in physical meaning, measurement unit and order of magnitude. Before calculating the comprehensive weight, standardization preprocessing is required, that is, the absolute data of each indicator is converted into relative values, the dimensional influence of different indicators is eliminated, and the indicators are ensured to be at the same order of magnitude to achieve homogenization of heterogeneous indicators.

[0167] The elements in the original data matrix are dimensionless processed. The dimensionless processing can be carried out by using the dimensionless standardization preprocessing of the larger the better type and the smaller the better type to unify the positive and negative indicators, and further calculate the weight proportion of each element. Among them, the dimensionless processing result of the i-th secondary indicator under the j-th access point to be selected is defined as K ij , the weight ratio of the i-th secondary indicator under the j-th candidate access point is defined as G ij :

[0168]

[0169]

[0170] In the formula, K ij and G ijThe ranges are: 0≤K ij ≤1,0≤G ij ≤1;

[0171] Based on the calculated weight ratio G of the i-th secondary indicator under the j-th candidate access point ij , the information entropy value A of the jth candidate access point j , the difference coefficient B of the jth candidate access point j and the entropy weight D of the jth candidate access point j The calculation method is as follows:

[0172]

[0173] B j =1-A j

[0174]

[0175] Wherein, n is the number of secondary indicators corresponding to the jth access point, and in this embodiment, n=4;

[0176] (2) TOPSIS decision analysis model

[0177] After obtaining the entropy weight of each indicator, the TOPSIS algorithm is used to realize the comprehensive evaluation of multidimensional indicators. First, the normalized decision matrix H is constructed:

[0178]

[0179] Among them, the elements in the matrix H ij It can be expressed as:

[0180]

[0181] In the formula, G ij is the weight ratio of the i-th secondary indicator under the j-th access point to be selected;

[0182] The next step is to construct the weighted normalized decision matrix U,

[0183]

[0184] Among them, the element U in the matrix ij It can be expressed as:

[0185] U ij =D j ·H ij

[0186] Where D j is the entropy weight of the jth candidate access point.

[0187] In the matrix U, under the jth candidate access point, the decision elements U of all secondary indicators ij The maximum value of is the positive ideal solution under the jth candidate access point, U ij The minimum value of is the negative ideal solution under the jth candidate access point, that is:

[0188]

[0189]

[0190] On this basis, the distance of the positive ideal solution under the jth candidate access point is calculated and the distance to the negative ideal solution

[0191]

[0192]

[0193] Finally, the relative proximity index F of the jth candidate access point is calculated respectively. dj :

[0194]

[0195] F dj The larger the value, the better the comprehensive evaluation. dj Maximum F dmax The corresponding access point is the weak node of the system, that is, the optimal energy storage access point, namely:

[0196] F dmax ={max(F dj )|j=1,2,…,n}

[0197] In particular, if the relative proximity index F of multiple access points dj If they are the same and both are maximum values, there are multiple energy storage access points in the system, which can be further screened according to other methods.

[0198] Embodiment six:

[0199] This embodiment is further designed on the basis of the fifth embodiment in that the objective function C of the energy storage capacity optimization configuration model in this embodiment is s for:

[0200]

[0201] Where E1 is the annualized investment cost per unit capacity of the energy storage device, E2 is the annualized operation and maintenance cost per unit capacity of the energy storage device, and N k The number of energy storage access points in the table system, Q sk represents the capacity of the energy storage device at the kth access point, so that the objective function Cs The energy storage device capacity corresponding to the minimum value is Q sk , which is the result of optimal configuration of energy storage capacity.

[0202] In order to keep the system running safely and stably, the constraints include satisfying power balance constraints, voltage and line current constraints. The power generation and power consumption of the system lines are kept in balance; the node voltage fluctuation and line current do not exceed the threshold. The system safety and stability constraints are:

[0203] P hy,xy (k)+P th,xy (k)+P ess,xy (k)-P load,xy (k) = P xy (k)-P loss,xy (k)

[0204]

[0205] i xy,min ≤I xy (k)≤i xy,max

[0206] Where P x j(k) represents the power transmitted from node x to node y in the kth time period; P loss xy(k) represents the transmission line loss between node x and node y; UxN is the rated value of the operating voltage at node x; Uq is the voltage operating value at node x; I xy (k) represents the current of the line between nodes x and y; i xy,min andi xy,max Respectively represent the maximum and minimum values ​​of the line current;

[0207] The energy storage operation constraints are:

[0208]

[0209]

[0210]

[0211] Where P ESS is the charging and discharging power of the energy storage system during period t; is the initial charge state of the energy storage system; is the state of charge of the energy storage system during period t; The upper and lower limits of the SOC operating range of the energy storage system;

[0212] The power flow constraint is

[0213] Where Pgx , P wx , P sx and P Lx are respectively the active output of conventional power supply, wind turbine (field), energy storage and load of the x-th node at the current time section; if the x-th node is not equipped with energy storage device, then P sx =0;

[0214] Q gx , Q wx , Q sx and Q Lx are the reactive outputs of conventional power supply, wind turbine (field), energy storage and load of the x-th node at the current time section;

[0215] U x and U y are the voltage amplitudes at nodes x and y respectively;

[0216] G xy and B xy are the real and imaginary parts of the branch node admittance respectively;

[0217] θ xy is the phase angle difference between node x and node y;

[0218] and are the maximum and minimum voltage amplitudes respectively;

[0219] The wind curtailment constraint is

[0220]

[0221] In the formula, R wab is the annual power abandonment rate of new energy power stations; is the total annual electricity generation from wind power; is the total annual grid-connected electricity of the wind farm; ε% is the annual wind power abandonment rate limit, which is generally taken as 5% with reference to China's new energy abandonment rate assessment indicators.

[0222] Embodiment seven:

[0223] This embodiment is further designed on the basis of the sixth embodiment in that the specific method for solving the energy storage capacity optimization configuration model and obtaining the energy storage capacity configuration scheme in this embodiment includes:

[0224] The particle swarm optimization (PSO) algorithm is used to perform power flow optimization calculations to determine the optimal capacity of regional power grid energy storage configuration under high wind power access. Each particle is regarded as a potential solution, and the global optimal solution is found through the iteration of the particle swarm algorithm. The update speed and position of each particle in the iteration are defined as:

[0225]

[0226]

[0227] In the formula, c1 is the self-learning factor, which indicates the weight of the particle's next action coming from its own experience; c2 is the global learning factor, which indicates the weight of the particle's next action coming from other particles' experience; r1 and r2 are random constants between [0,1], which increase the randomness of the search; ω is the inertia weight coefficient; and the subscript k is the current number of iterations of the particle swarm algorithm. is the position vector of the i-th particle in the k-th iteration; is the velocity vector of the i-th particle in the k-th iteration.

[0228] like Figure 2 As shown, the specific steps include:

[0229] 1) Input the energy storage capacity optimization configuration model and optimization constraints;

[0230] 2) Generate a PSO population and initialize the speed and position of the particles;

[0231] 3) Update the speed and position of each particle and calculate the fitness value;

[0232] 4) Update the individual historical optimal fitness and position of each particle;

[0233] 5) Update the historical optimal fitness and position of the group;

[0234] 6) Update parameters such as the number of iterations;

[0235] 7) Determine whether the termination condition is met. If so, jump to step 8). If not, return to step 3).

[0236] 8) Output the optimal configuration capacity of energy storage.

[0237] Embodiment eight:

[0238] A novel energy storage planning system in a power system of the present invention comprises a prediction module, a site selection module, a modeling module and a model solving module; wherein,

[0239] A prediction module is used to obtain a photovoltaic output prediction value by using a linear regression method based on historical meteorological scene data of the area where the new power system is located and historical photovoltaic output data of the new power system;

[0240] A site selection module is used to obtain stability evaluation values ​​corresponding to different energy storage access points based on the predicted value of photovoltaic output under the pre-built energy storage access point stability evaluation system, and use the energy storage access point corresponding to the maximum stability evaluation value as the energy storage site selection plan;

[0241] A modeling module is used to construct an energy storage capacity optimization configuration model based on the energy storage site selection plan, with the energy storage capacity configuration plan as the decision variable. The energy storage capacity optimization configuration model takes the minimum total annual investment and operation cost of the connected energy storage equipment as the objective function, and takes system safety and stability constraints, energy storage operation constraints, power flow constraints and wind curtailment constraints as constraints;

[0242] The model solving module is used to solve the energy storage capacity optimization configuration model and obtain the energy storage capacity configuration plan.

[0243] Embodiment nine:

[0244] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0245] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0246] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A novel energy storage planning method in a power system, characterized in that: The method comprises: Based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system, the linear regression method is used to obtain the photovoltaic output prediction value; Based on the photovoltaic output prediction value, under the pre-constructed energy storage access point stability evaluation system, the stability evaluation values ​​corresponding to different energy storage access points are obtained, and the energy storage access point corresponding to the maximum stability evaluation value is used as the energy storage site selection plan; Based on the energy storage site selection plan, an energy storage capacity optimization configuration model with the energy storage capacity configuration plan as the decision variable is constructed. The energy storage capacity optimization configuration model takes the minimum total annualized investment and operation cost of the connected energy storage equipment as the objective function, and takes system safety and stability constraints, energy storage operation constraints, power flow constraints and wind abandonment constraints as constraints; Solve the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration plan.

2. The energy storage planning method in the new power system according to claim 1 is characterized in that: The specific method of using the linear regression method to obtain the photovoltaic output prediction value based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system includes: Construct a multivariate linear regression model, the matrix representation of which is: Where X is the explanatory variable, corresponding to the meteorological scene data, X is an i×(j+1) matrix; Y is the dependent variable, corresponding to the photovoltaic output data, Y is an i×1 vector; β is the regression coefficient, β is a (j+1)×1 vector; ε is the model residual, ε is an i×1 vector. The historical meteorological scene data and the historical photovoltaic output data are substituted into the multivariate linear regression model, and the least squares method is used to determine the regression coefficient by calculating the method of minimizing the residual sum of squares of the multivariate linear regression model, and the training of the multivariate linear regression model is completed; wherein, the residual sum of squares of the multivariate linear regression model is: S=ε T ε=(Y-Xβ) T (Y-Xβ) Where S is a non-negative quadratic function with a minimum value; According to the matrix derivation rule, let have: The least squares estimate of the regression coefficient of the multivariate linear regression model is further obtained as: Based on the current meteorological scenario data, the trained multivariate linear regression model is used to obtain the photovoltaic output prediction value.

3. The energy storage planning method in the new power system according to claim 2 is characterized in that: The meteorological scene data includes temperature, relative humidity, cloud cover, wind speed and dew point temperature.

4. The method for energy storage planning in a novel power system according to claim 3 is characterized in that: The primary indicator of the energy storage access point stability evaluation system is the system voltage stability evaluation index, and the secondary indicators under the primary indicator include the voltage stability margin index, the node voltage static sensitivity index, the voltage deviation index and the voltage stability index. Among them, the voltage stability margin index U under the jth selected access point marj The expression is: U marj is the voltage stability margin index of the jth candidate access point, U crj is the critical voltage value of the jth candidate access point, U j is the node voltage of the jth access point to be selected. The voltage deviation index U at the jth selected access point DIj The expression is: U DIj is the voltage deviation index of the jth access point to be selected; U ratedj is the rated voltage of the jth access point to be selected; U j is the node voltage of the jth access point to be selected; The node voltage static sensitivity index of the jth candidate access point is represented by Z j for: Z j Characterizes the static sensitivity of the injected power change of the jth candidate access point to the voltage of other nodes in the system, λ js When the power injected into the jth candidate access point changes, the voltage-active sensitivity value of node s in the system, U s is the node voltage of node s, P j is the active power of the jth access point to be selected, ω j is the weight factor of the jth candidate access point, and its value is measured by the deviation of the node voltage from the node expected voltage; U j is the node voltage of the jth candidate access point, U refj is the expected voltage of the jth candidate access point. The voltage stability index F of the jth candidate access point j for: Among them, U j is the node voltage value of the jth access point to be selected; U j0 The node voltage value of the jth candidate access point under no-load state.

5. The method for energy storage planning in a novel power system according to claim 4, characterized in that: The specific method of obtaining stability evaluation values ​​corresponding to different energy storage access points based on the photovoltaic output prediction value and using the energy storage access point corresponding to the maximum stability evaluation value as the energy storage site selection scheme under the pre-constructed energy storage access point stability evaluation system includes: Under the pre-built energy storage access point stability evaluation system, the secondary index values ​​of different secondary indicators corresponding to each energy storage access point are obtained; for the jth access point to be selected, its secondary indicators are recorded as: voltage stability margin index O 1j , node voltage static sensitivity index O 2j , voltage deviation index O 3j , voltage stability index O 4j ; The original data matrix of the secondary index values ​​related to different energy storage access points is constructed, and its expression is: In the formula, O ij is the value of the i-th secondary indicator under the j-th access point to be selected; The elements in the original data matrix are dimensionless processed, and the weight proportion of each element is further calculated. The dimensionless processing result of the i-th secondary index under the j-th access point to be selected is defined as K ij , the weight ratio of the i-th secondary indicator under the j-th candidate access point is defined as G ij : In the formula, K ij and G ij The ranges are: 0≤K ij ≤1,0≤G ij ≤1; Based on the calculated weight ratio G of the i-th secondary indicator under the j-th candidate access point ij , the information entropy value A of the jth candidate access point j , the difference coefficient B of the jth candidate access point j and the entropy weight D of the jth candidate access point j The calculation method is as follows: B j =1-A j In the formula, n is the number of secondary indicators corresponding to the jth access point; After obtaining the entropy weight of each indicator, the TOPSIS algorithm is used to realize the comprehensive evaluation of multidimensional indicators. First, the normalized decision matrix H is constructed: Among them, the elements in the matrix H ij It can be expressed as: In the formula, G ij is the weight ratio of the i-th secondary indicator under the j-th access point to be selected; The next step is to construct the weighted normalized decision matrix U, Among them, the element U in the matrix ij It can be expressed as: U ij =D j ·H ij Where D j is the entropy weight of the jth candidate access point. In the matrix U, under the jth candidate access point, the decision elements U of all secondary indicators ij The maximum value of is the positive ideal solution under the jth candidate access point, U ij The minimum value of is the negative ideal solution under the jth candidate access point, that is: On this basis, the distance of the positive ideal solution under the jth candidate access point is calculated and the distance to the negative ideal solution Finally, the relative proximity index F of the jth candidate access point is calculated respectively. dj : F dj The larger the value, the better the comprehensive evaluation. dj Maximum F dmax The corresponding access point is the weak node of the system, that is, the optimal energy storage access point, namely: F dmax ={max(F dj )|j=1,2,…,n}。 6. The method for energy storage planning in a novel power system according to claim 5, characterized in that: The objective function C of the energy storage capacity optimization configuration model s for: Where E1 is the annualized investment cost per unit capacity of the energy storage device, E2 is the annualized operation and maintenance cost per unit capacity of the energy storage device, and N k The number of energy storage access points in the table system, Q sk represents the capacity of the energy storage device at the kth access point, so that the objective function C s The energy storage device capacity corresponding to the minimum value is Q sk , which is the result of optimal configuration of energy storage capacity. The system safety and stability constraints are: P hy,xy (k)+P th,xy (k)+P ess,xy (k)-P load,xy (k)=P xy (k)-P loss,xy (k) i xy,min ≤I xy (k)≤i xy,max Where P x j(k) represents the power transmitted from node x to node y in the kth time period; P loss xy(k) represents the transmission line loss between node x and node y; UxN is the rated value of the operating voltage of node x; Uq is the voltage operating value of node x; I xy (k) represents the current of the line between nodes x and y; i xy,min andi xy,max Respectively represent the maximum and minimum values ​​of the line current; The energy storage operation constraints are: Where P ESS is the charging and discharging power of the energy storage system during period t; is the initial charge state of the energy storage system; is the state of charge of the energy storage system during period t; The upper and lower limits of the SOC operating range of the energy storage system; The power flow constraint is Where P gx , P wx , P sx and P Lx are respectively the active output of the conventional power supply, wind turbine (field), energy storage and load of the x-th node at the current time section; (If the x-th node is not equipped with an energy storage device, then P sx =0;) Q gx , Q wx , Q sx and Q Lx are the reactive outputs of conventional power supply, wind turbine (field), energy storage and load of the x-th node at the current time section; U x and U y are the voltage amplitudes at nodes x and y respectively; G xy and B xy are the real and imaginary parts of the branch node admittance respectively; θ xy is the phase angle difference between node x and node y; and are the maximum and minimum voltage amplitudes respectively; The wind curtailment constraint is In the formula, R wab is the annual power abandonment rate of new energy power stations; is the total annual electricity generation of wind power; is the total annual grid-connected electricity of the wind farm; ε% is the annual wind curtailment rate limit.

7. The method for energy storage planning in a novel power system according to claim 6, characterized in that: The specific method of solving the energy storage capacity optimization configuration model and obtaining the energy storage capacity configuration solution includes: The particle swarm algorithm is used to perform power flow optimization calculations to determine the optimal capacity of regional power grid energy storage configuration under high wind power access. Each particle is regarded as a potential solution, and the global optimal solution is found through particle swarm algorithm iteration. The update speed and position of each particle in the iteration are defined as: In the formula, c1 is the self-learning factor; c2 is the global learning factor; r1 and r2 are random constants between [0,1]; ω is the inertia weight coefficient; the subscript k is the current iteration number of the particle swarm algorithm; is the position vector of the i-th particle in the k-th iteration; V i k is the velocity vector of the i-th particle in the k-th iteration.

8. A new energy storage planning system in a power system, characterized in that: include: Prediction module, site selection module, modeling module and model solving module; among them, The prediction module is used to obtain the photovoltaic output prediction value by using a linear regression method based on the historical meteorological scene data of the area where the new power system is located and the historical photovoltaic output data of the new power system; The site selection module is used to obtain stability evaluation values ​​corresponding to different energy storage access points under a pre-constructed energy storage access point stability evaluation system based on the photovoltaic output prediction value, and use the energy storage access point corresponding to the maximum stability evaluation value as the energy storage site selection plan; The modeling module is used to construct an energy storage capacity optimization configuration model with the energy storage capacity configuration plan as a decision variable based on the energy storage site selection plan, and the energy storage capacity optimization configuration model takes the minimum total annualized investment and operation cost of the connected energy storage equipment as the objective function, and takes system safety and stability constraints, energy storage operation constraints, power flow constraints and wind abandonment constraints as constraints; The model solving module is used to solve the energy storage capacity optimization configuration model to obtain an energy storage capacity configuration solution.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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