Energy storage system charging and discharging period adjustment optimization method, medium and system

Abstract

The present invention provides a method, medium and system for adjusting and optimizing the charging and discharging period of an energy storage system, which belongs to the field of electrical data processing technology, including collecting and analyzing the operating data of the energy storage system, including indicators such as charging power, discharging power, grid frequency, grid voltage and three-phase imbalance, and performing multivariate decomposition calculation on the stability and fluctuation of grid frequency and voltage. Then, a correlation matrix between these indicators and the charging and discharging power of the energy storage system is established, and an optimization objective function including indicators such as grid frequency stability, voltage stability and three-phase balance is constructed. By using a multi-level constraint equation group and a multi-objective optimization algorithm, an optimization scheme for the charging and discharging period of the energy storage system is obtained, which can help operation and maintenance personnel optimize and adjust the charging and discharging period of the energy storage system, thereby improving the frequency and voltage stability of the grid, enhancing the three-phase balance, and solving the problem that the existing method only considers the operating data on the grid side and lacks analysis and optimization of the charging and discharging state of the energy storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical data processing, and in particular, relates to a method, medium and system for adjusting and optimizing charging and discharging periods of an energy storage system. Background Art

[0002] With the large-scale access of renewable energy to the power grid, the operation of the power grid has become increasingly complex. The fluctuations of the frequency and voltage of the power grid have increased, and the imbalance of the three-phase load has intensified. These problems will not only affect the safety and reliability of the power grid operation, but also reduce the energy utilization efficiency of the power system. In order to meet these challenges, power grid companies urgently need to adopt advanced power grid dispatching technologies to improve the flexibility and adaptability of the power grid. As an efficient means of power grid peak regulation, energy storage technology plays an important role in the optimization of power grid operation. By reasonably dispatching the charging and discharging of the energy storage system, the fluctuations of the frequency and voltage of the power grid can be effectively smoothed and the balance of the three-phase current can be improved. The existing energy storage system charging and discharging scheduling methods mainly include two categories: one is the critical value control strategy based on the power grid operation state, that is, when the power grid frequency or voltage exceeds the preset threshold, the energy storage system is started to charge and discharge; the other is the scheduling strategy based on a single optimization indicator, such as only considering the power grid frequency stability or the power grid voltage stability. Although these two methods can improve the power grid operation state to a certain extent, they only consider the operation data on the power grid side and lack the analysis and optimization of the charging and discharging state of the energy storage system. Summary of the invention

[0003] In view of this, the present invention provides a method, medium and system for adjusting and optimizing the charging and discharging period of an energy storage system, which can solve the technical problem that most existing methods only consider the operating data on the power grid side and lack analysis and optimization of the charging and discharging status of the energy storage system.

[0004] The present invention is achieved in that:

[0005] A first aspect of the present invention provides a method for adjusting and optimizing the charging and discharging time periods of an energy storage system, comprising the following steps:

[0006] S10, collecting energy storage system operation data, wherein the energy storage system operation data includes energy storage system charging power data, energy storage system discharging power data, grid frequency data, grid voltage data, and three-phase imbalance data;

[0007] S20, performing multivariate decomposition calculation on the energy storage system operation data to obtain a grid frequency stability component, a grid frequency fluctuation component, a grid voltage stability component, and a grid voltage fluctuation component;

[0008] S30, establishing a correlation matrix between the energy storage system charging power data, the energy storage system discharging power data, the grid frequency fluctuation component, the grid voltage fluctuation component, and the three-phase imbalance data;

[0009] S40, establishing an energy storage system charging and discharging optimization objective function according to the correlation matrix, wherein the energy storage system charging and discharging optimization objective function includes a grid frequency stability index, a voltage stability index, and a three-phase balance index;

[0010] S50, constructing a multi-level constraint equation group based on the energy storage system charge and discharge optimization objective function, wherein the multi-level constraint equation group includes energy storage system charge and discharge power constraints, energy storage system charge state constraints, power grid frequency constraints, and power grid voltage constraints;

[0011] S60, using a multi-objective optimization algorithm to solve the energy storage system charging and discharging optimization objective function to obtain an energy storage system charging period optimization matrix and an energy storage system discharging period optimization matrix;

[0012] S70, generating an energy storage system charging and discharging period adjustment plan according to the energy storage system charging period optimization matrix and the energy storage system discharging period optimization matrix, and outputting the plan to operation and maintenance personnel for optimizing and adjusting the energy storage system charging and discharging period.

[0013] On the basis of the above technical solution, the energy storage system charging and discharging period adjustment optimization method of the present invention can also be improved as follows:

[0014] The multivariate decomposition calculation in step S20 is specifically expressed as follows:

[0015]

[0016] Where, f(t) is the grid frequency data; f0(t) is the grid frequency stability component; a i is the frequency fluctuation amplitude; f i is the frequency fluctuation frequency; φ i is the phase angle; v(t) is the grid voltage data; v0(t) is the grid voltage stability component; b i is the voltage fluctuation amplitude; v i is the voltage fluctuation frequency; θ i is the phase angle; n,m are the decomposition orders, ranging from 3 to 20.

[0017] The correlation matrix in step S30 is specifically expressed as follows:

[0018]

[0019] The meaning of the elements in the correlation matrix is: 11 is the correlation between charging power and frequency fluctuation component; r 12 is the correlation between charging power and voltage fluctuation component; r 13 is the correlation between charging power and three-phase imbalance; r 21is the correlation between the discharge power and the frequency fluctuation component; r 22 is the correlation between discharge power and voltage fluctuation component; r 23 is the correlation between discharge power and three-phase imbalance; such a correlation matrix reflects the influence of the charging and discharging power of the energy storage system on the three key parameters of the power grid.

[0020] In the formula, r ij is the correlation coefficient, and the calculation formula is:

[0021]

[0022] Among them, Δ ij =|x i (k)-y j (k)|; ρ is the resolution coefficient, the value range is [0,1], usually 0.5; x i (k) is the value of the i-th sequence at time k; y j (k) is the value of the jth sequence at time k. Specifically, x1(k) represents the energy storage system charging power data sequence; x2(k) represents the energy storage system discharging power data sequence; y1(k) represents the grid frequency fluctuation component sequence (i.e., the frequency fluctuation component sequence decomposed in step S20) y2(k) represents the grid voltage fluctuation component sequence (i.e., the sequence obtained by decomposition in step S20) y3(k) represents the three-phase imbalance data sequence.

[0023] The optimization objective function in step S40 is specifically expressed as follows:

[0024] F = α1F1 + α2F2 + α3F3;

[0025]

[0026] In the formula, F1 is the frequency stability index; F2 is the voltage stability index; F3 is the three-phase balance index; α1, α2, α3 are weight coefficients, and they satisfy α1+α2+α3=1; f ref is the nominal frequency, take 50Hz; v ref is the nominal voltage; I a , I b , I c is the complex representation of the three-phase current, and Γ is the total number of sampling points.

[0027] The constraint equations in step S50 are specifically expressed as follows:

[0028] P min ≤P(t)≤P max ;

[0029] SOC min≤SOC(t)≤SOC max ;

[0030]

[0031] f min ≤f(t)≤f max ;

[0032] v min ≤v(t)≤v max ;

[0033] Where P(t) is the charging and discharging power of the energy storage system; P min , P max is the power limit; SOC(t) is the state of charge; SOC min , SOC max is the charge state limit, generally [0.1, 0.9]; η c , η d is the charge and discharge efficiency; P c (t), P d (t) is the charge and discharge power; E cap is the rated capacity of the energy storage system; Δt is the sampling time interval; f min , f max is the frequency limit; v min , v max is the voltage limit.

[0034] In step S60, the NSGA-II algorithm is used to solve the multi-objective optimization problem. The specific steps are as follows:

[0035] 1) Initialize the population:

[0036] X i ={x i1 , x i2 , ..., x in};

[0037] 2) Fast non-dominated sort:

[0038] S p ={q|p<q};

[0039] n p =|{q|q<p}|;

[0040] 3) Crowding calculation:

[0041]

[0042] In the formula, X i is the i-th individual; x ij is the decision variable; S pis the solution set dominated by individual p; n p is the number of solutions that dominate individual p; d i for the degree of crowding; is the function value of the ith individual on the mth target.

[0043] The calculation process of the optimization matrix of the energy storage system charging and discharging period is as follows:

[0044] 1) Construct the charging period optimization matrix based on the Pareto optimal solution set obtained by the NSGA-II algorithm:

[0045]

[0046] In the formula, c ij It represents the charging state of the i-th Pareto optimal solution in the j-th time period, and its value is 0 or 1, 1 represents charging, and 0 represents not charging; N is the number of Pareto optimal solutions; T is the number of time periods.

[0047] 2) Construct charging period optimization evaluation indicators:

[0048] E c =ω1E f +ω2E v +ω3E b +ω4E p ;

[0049]

[0050] In the formula, E c E is a comprehensive evaluation index for the charging period; f is the frequency deviation index; E v is the voltage deviation index; E b Is the three-phase imbalance index; E p is the charging power deviation index; ω1, ω2, ω3, ω4 are weight coefficients, and satisfy I u (t) is the negative sequence current component; P c,ref (t) is the target charging power curve.

[0051] 3) Select the optimal charging period based on the evaluation index:

[0052] i opt =argmin i E c (i);

[0053]

[0054] In the formula, i opt is the sequence number corresponding to the optimal solution; C optIt is the optimal charging period solution.

[0055] 4) Similarly, construct the discharge period optimization matrix:

[0056]

[0057] Where, d ij It represents the discharge state of the i-th Pareto optimal solution in the j-th time period. The value is 0 or 1, 1 means discharge, and 0 means no discharge.

[0058] 5) Constructing the optimization evaluation index of discharge period:

[0059] E d =ω1E f +ω2E v +ω3E b +ω4E dp ;

[0060]

[0061] In the formula, E d E is the comprehensive evaluation index of the discharge period; dp is the discharge power deviation index; P d，ref (t) is the target discharge power curve.

[0062] 6) Select the optimal discharge period:

[0063] j opt =argmin j E d (j);

[0064]

[0065] In the formula, j opt is the serial number corresponding to the optimal solution; D opt It is the optimal discharge period plan.

[0066] 7) Constraints during charging and discharging periods:

[0067]

[0068] In the formula, the first constraint ensures that charging and discharging are not performed simultaneously in the same period; the second constraint ensures the energy balance of charging and discharging.

[0069] Furthermore, the multi-objective optimization algorithm is the NSGA-II algorithm.

[0070] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the above-mentioned method for adjusting and optimizing the charging and discharging period of an energy storage system.

[0071] A third aspect of the present invention provides a system for adjusting and optimizing charging and discharging time periods of an energy storage system, which includes the above-mentioned computer-readable storage medium.

[0072] Compared with the prior art, the method, medium and system for adjusting and optimizing the charging and discharging period of an energy storage system provided by the present invention have the following beneficial effects:

[0073] 1) The multivariate decomposition technology is used to decompose the grid frequency and voltage data into stable components and fluctuating components, which comprehensively describes the dynamic change characteristics of the grid operation status and provides more accurate data support for subsequent correlation analysis and optimization.

[0074] 2) A correlation matrix between the charging and discharging power of the energy storage system and the grid frequency fluctuation, voltage fluctuation and three-phase imbalance was established, and the impact of charging and discharging behavior on the grid operation indicators was quantified, laying the foundation for the construction of the optimization objective function.

[0075] 3) The three sub-goals of grid frequency stability, voltage stability and three-phase balance are comprehensively considered, and a multi-objective optimization function is constructed, which overcomes the defect that a single optimization indicator is difficult to take into account multiple indicators.

[0076] 4) The NSGA-II multi-objective optimization algorithm is used to solve the charging and discharging period optimization problem, and the Pareto optimal solution set is obtained, which provides a decision-making basis for selecting the optimal charging and discharging period plan.

[0077] 5) In the process of optimizing the charging and discharging periods, not only the operating status of the power grid is taken into consideration, but also the charging and discharging power constraints and charge state constraints of the energy storage system itself are fully considered, thus achieving coordinated optimization of the power grid and the energy storage system.

[0078] In general, the energy storage system charging and discharging period optimization method proposed in the present invention can effectively improve the overall operating performance of the power grid, realize the coordinated optimization of the power grid frequency, voltage and three-phase balance, and solve the technical problem that most existing methods only consider the operating data on the power grid side and lack the analysis and optimization of the charging and discharging status of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A flow chart of the method provided by the present invention;

[0080] Figure 2 This is the analysis diagram of the fluctuation component of the power grid frequency;

[0081] Figure 3 It is the correlation diagram between the charging and discharging power of the energy storage system and the three main grid parameters;

[0082] Figure 4 Comparison chart of the improvement in grid performance before and after energy storage system optimization. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0084] like Figure 1 FIG. 1 is a flow chart of a method for adjusting and optimizing the charging and discharging time periods of an energy storage system provided by the present invention. The method comprises the following steps:

[0085] S10, collecting energy storage system operation data, the energy storage system operation data including energy storage system charging power data, energy storage system discharging power data, grid frequency data, grid voltage data, and three-phase imbalance data;

[0086] S20, performing multivariate decomposition calculation on the energy storage system operation data to obtain a grid frequency stability component, a grid frequency fluctuation component, a grid voltage stability component, and a grid voltage fluctuation component;

[0087] S30, establishing a correlation matrix between the energy storage system charging power data, the energy storage system discharging power data, the power grid frequency fluctuation component, the power grid voltage fluctuation component, and the three-phase imbalance data;

[0088] S40, establishing an energy storage system charging and discharging optimization objective function according to the correlation matrix, wherein the energy storage system charging and discharging optimization objective function includes a grid frequency stability index, a voltage stability index, and a three-phase balance index;

[0089] S50, constructing a multi-level constraint equation group based on the energy storage system charging and discharging optimization objective function, the multi-level constraint equation group including energy storage system charging and discharging power constraints, energy storage system charge state constraints, grid frequency constraints, and grid voltage constraints;

[0090] S60, using a multi-objective optimization algorithm to solve the energy storage system charging and discharging optimization objective function, and obtain the energy storage system charging period optimization matrix and the energy storage system discharging period optimization matrix;

[0091] S70, generating an energy storage system charging and discharging period adjustment plan according to the energy storage system charging period optimization matrix and the energy storage system discharging period optimization matrix, and outputting the plan to the operation and maintenance personnel for optimizing and adjusting the energy storage system charging and discharging period.

[0092] The specific implementation methods of the above steps are described in detail below:

[0093] The specific implementation of step S10 is to collect key data during the operation of the energy storage system. First, the charging power data and discharging power data of the energy storage system are collected from the energy storage system monitoring system. Secondly, the frequency data, voltage data, and three-phase imbalance data of the power grid are collected from the power grid measurement equipment. These data can fully reflect the interaction between the energy storage system and the power grid. f(t) represents the power grid frequency data, v(t) represents the power grid voltage data, I a , I b , I c Indicates three-phase current data.

[0094] The specific implementation method of step S20 is to perform multivariate decomposition analysis on the collected operation data. The power grid frequency data f(t) is decomposed into a frequency stability component f0(t) and a frequency fluctuation component using the Fourier series expansion method. Among them, a i Indicates the amplitude of frequency fluctuation, f i represents the frequency of frequency fluctuation, φ i Represents the phase angle. Similarly, the grid voltage data v(t) is decomposed into the voltage stability component v0(t) and the voltage fluctuation component Among them, b i Indicates the amplitude of voltage fluctuation, v i Indicates the frequency of voltage fluctuation, θ i In this way, the stable component and the fluctuating component in the grid frequency and voltage are obtained.

[0095] The specific implementation of step S30 is to establish a correlation matrix between charging and discharging power and grid indicators. First, the charging power data P of the energy storage system c (t), discharge power data P d (t) and the above-mentioned grid frequency fluctuation component, grid voltage fluctuation component, three-phase imbalance data I u (t) form the correlation matrix R. The matrix element r ij It represents the degree of association between the i-th sequence and the j-th sequence, and the calculation formula is: Where Δ ij =|x i (k)-y j (k)|, ρ is the resolution coefficient, the value range is [0,1], usually 0.5. In this way, the correlation matrix between charging and discharging power and power grid indicators is established.

[0096] The specific implementation of step S40 is to construct an energy storage system charging and discharging optimization objective function. The objective function F includes three sub-objectives: grid frequency stability index F1, grid voltage stability index F2 and grid three-phase balance index F3. Indicates that the grid frequency deviates from the nominal frequency f ref The sum of squares; Indicates that the grid voltage deviates from the nominal voltage v ref The sum of squares; Represents the sum of the squares of the three-phase imbalance. The three sub-goals are combined through weighted coefficients α1, α2, and α3, and α1+α2+α3=1. In this way, the charging and discharging optimization objective function reflecting the frequency stability, voltage stability and three-phase balance of the power grid is constructed.

[0097] The specific implementation method of step S50 is to establish a multi-level constraint equation group. First, set upper and lower limit constraints P for the charge and discharge power P(t) of the energy storage system. min ≤P(t)≤P max Secondly, set upper and lower limits for the state of charge SOC(t) of the energy storage system. min ≤SOC(t)≤SOC max , and establish the dynamic update equation of the state of charge Again, set upper and lower limits for the grid frequency f(t) and grid voltage v(t) respectively. min ≤f(t)≤f max and v min ≤v(t)≤v max These constraint equations constitute a multi-level optimization constraint system.

[0098] The specific implementation of step S60 is to use the NSGA-II algorithm to solve the multi-objective optimization problem. First, initialize the population X i ={x i1 ,x i2 ,...,x in}. Secondly, perform fast non-dominated sorting on the population. For each individual p, calculate its dominated solution set S p and the number n of solutions that dominate individual p p Then, the crowding degree of each individual is calculated These indicators are used to optimize the population and finally obtain the Pareto optimal solution set.

[0099] Based on the Pareto optimal solution set obtained by the NSGA-II algorithm, the charging period optimization matrix C and the discharging period optimization matrix D of the energy storage system can be constructed. ij represents the charging state of the i-th Pareto optimal solution in the j-th period, and its value is 0 or 1; d ij Indicates the discharge state of the i-th Pareto optimal solution in the j-th period, and its value is 0 or 1.

[0100] In order to select the optimal charging and discharging period schemes, the charging period optimization evaluation index E c And the discharge period optimization evaluation index E d . E c Including frequency deviation indicator E f , Voltage deviation index E v , three-phase imbalance index E b and charging power deviation index E p , obtained by weighting. E d Similarly, the frequency deviation indicator E is included f , Voltage deviation index E v , three-phase imbalance index E b And discharge power deviation index E dp The final choice is E c and E d The smallest solution is the optimal charging period solution C opt and the optimal discharge time scheme D opt .

[0101] At the same time, the following constraints must be met: 1) Charging and discharging cannot be done at the same time. 2) Charging and discharging energy needs to be balanced, that is

[0102] In general, this method of optimizing the charging and discharging periods of energy storage systems makes full use of the grid operation data. Through multivariate decomposition, correlation analysis, multi-objective optimization and other means, it obtains the optimal charging and discharging period adjustment plan that not only meets the requirements of grid frequency stability, voltage stability and three-phase balance, but also ensures the balance of charging and discharging energy.

[0103] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the above-mentioned method for adjusting and optimizing the charging and discharging period of an energy storage system.

[0104] A third aspect of the present invention provides a system for adjusting and optimizing charging and discharging time periods of an energy storage system, which includes the above-mentioned computer-readable storage medium.

[0105] Specifically, the principle of the present invention is: through in-depth analysis of the power grid operation data, a correlation model between the charging and discharging behavior of the energy storage system and the power grid operation indicators is constructed, and based on this, a multi-objective optimization problem is established, and finally the optimal energy storage system charging and discharging period adjustment plan is obtained.

[0106] First, the present invention collects the charging power and discharging power of the energy storage system as well as the frequency, voltage and three-phase current data of the power grid. In order to fully describe the dynamic change characteristics of the power grid operation status, the frequency and voltage data are Fourier decomposed to obtain the stable component and the fluctuating component. This lays the foundation for subsequent correlation analysis and optimization.

[0107] Secondly, the present invention establishes a correlation matrix between the charging and discharging power of the energy storage system and the frequency fluctuation, voltage fluctuation and three-phase imbalance of the power grid. By calculating the correlation coefficient, the influence of the charging and discharging behavior on the power grid operation index is quantified. This provides a basis for the construction of the objective function.

[0108] Thirdly, the present invention constructs a multi-objective optimization problem, and the objective function includes the grid frequency stability index, voltage stability index and three-phase balance index. These three sub-objectives reflect the key performance indicators of grid operation, and the overall optimization of grid operation status can be achieved through weighted synthesis.

[0109] Finally, the present invention uses the NSGA-II multi-objective optimization algorithm to solve the charging and discharging period optimization problem and obtains the Pareto optimal solution set. By further evaluating and selecting the Pareto optimal solution set, the optimal charging and discharging period adjustment scheme is obtained, which not only meets the requirements of grid frequency stability, voltage stability and three-phase balance, but also ensures the balance of charging and discharging energy.

[0110] Compared with the prior art, the key innovations of the present invention are mainly reflected in the following aspects:

[0111] 1) The multivariate decomposition technology is used to comprehensively characterize the operating status of the power grid, providing a more accurate data basis for subsequent analysis and optimization.

[0112] 2) A correlation model between the charging and discharging behavior of the energy storage system and the grid operation indicators was established, providing a basis for the construction of the objective function.

[0113] 3) It comprehensively considers multiple optimization objectives such as grid frequency stability, voltage stability and three-phase balance, overcoming the defect that a single optimization indicator is difficult to take into account multiple indicators.

[0114] 4) During the optimization process, not only the operating status of the power grid is taken into consideration, but also the characteristics of the energy storage system itself are fully considered, thus achieving coordinated optimization of the power grid and the energy storage system.

[0115] The following is a specific embodiment 1 of the method of the present invention. The specific implementation of the steps in this embodiment 1 is described in detail as follows: The specific implementation of step S10 is to collect key data during the operation of the energy storage system. First, the charging power data P of the energy storage system is collected from the energy storage system monitoring system. c (t) and discharge power data Pd (t). These two data sequences can reflect the charging and discharging status of the energy storage system at different time periods. Secondly, the frequency data f(t), voltage data v(t) and three-phase current data I of the power grid are collected from the power grid measurement equipment. a (t), I b (t), I c (t). The grid frequency and voltage data can reflect the operating status of the grid, and the three-phase current data can reflect the three-phase balance of the grid. These data can fully reflect the interaction between the energy storage system and the grid.

[0116] The specific implementation of step S20 is to perform multivariate decomposition analysis on the collected operation data. Using the Fourier series expansion method, the power grid frequency data f(t) is decomposed into:

[0117]

[0118] Where f0(t) represents the stable component of the grid frequency, a i represents the amplitude of the ith frequency fluctuation component, f i represents the frequency of the ith frequency fluctuation component, φ i represents the phase angle of the ith frequency fluctuation component, and n is the decomposition order. Similarly, the grid voltage data v(t) is decomposed into:

[0119]

[0120] Where v0(t) represents the stable component of the grid voltage, b i represents the amplitude of the ith voltage fluctuation component, v i represents the frequency of the i-th voltage fluctuation component, θ i represents the phase angle of the ith voltage fluctuation component, and m is the decomposition order. In this way, the stable component and the fluctuation component in the grid frequency and voltage are obtained.

[0121] The specific implementation of step S30 is to establish a correlation matrix R between the charging and discharging power and the grid index. The matrix element r ij It represents the degree of association between the ith sequence and the jth sequence, and the calculation formula is:

[0122]

[0123] Among them, Δ ij =|x i (k)-y j (k)| represents the value x of the i-th sequence at the k-th moment i (k) and the value y of the jth sequence at the kth moment j(k), ρ is the resolution factor, the value range is [0, 1], usually 0.5. In this way, the charge and discharge power P is established. c (t), P d (t) and the grid frequency fluctuation component, grid voltage fluctuation component, and three-phase imbalance data I u (t) is the correlation matrix R between them.

[0124] The specific implementation of step S40 is to construct an energy storage system charging and discharging optimization objective function. The objective function F includes three sub-objectives:

[0125] F = α1F1 + α2F2 + α3F3;

[0126] Among them, the grid frequency stability index is:

[0127]

[0128] In the formula, f ref is the nominal frequency of the power grid, usually 50Hz. The power grid voltage stability index is:

[0129]

[0130] In the formula, v ref is the nominal voltage of the power grid. The three-phase balance index of the power grid is:

[0131]

[0132] The three sub-goals are integrated through weighted coefficients α1, α2, and α3, and satisfy α1+α2+α3 = 1. In this way, a charging and discharging optimization objective function reflecting the frequency stability, voltage stability and three-phase balance of the power grid is constructed.

[0133] The specific implementation method of step S50 is to establish a multi-level constraint equation group. First, set upper and lower limit constraints on the charge and discharge power P(t) of the energy storage system:

[0134] P min ≤P(t)≤P max ;

[0135] Among them, P min and P max They are the lower and upper limits of the charge and discharge power, respectively. Secondly, set upper and lower limits for the state of charge SOC(t) of the energy storage system:

[0136] SOC min ≤SOC(t)≤SOC max ;

[0137] At the same time, the dynamic update equation of the state of charge is established:

[0138]

[0139] Where η c and η d are the charging and discharging efficiencies, E cap is the rated capacity of the energy storage system, and Δt is the sampling time interval. Again, set upper and lower limits for the grid frequency f(t) and grid voltage v(t):

[0140] f min ≤f(t)≤f max ;

[0141] v min ≤v(t)≤v max ;

[0142] Among them, f min 、f max are the upper and lower limits of frequency, v min 、v max These constraint equations constitute a multi-level optimization constraint system.

[0143] The specific implementation of step S60 is to use the NSGA-II algorithm to solve the multi-objective optimization problem. First, initialize the population X i ={x i1 , x i2 , ..., x in}. Secondly, perform a fast non-dominated sort on the population.

[0144] For each individual p, calculate its dominated solution set S p and the number n of solutions that dominate individual p p :

[0145] S p ={q|p<q};

[0146] n p =|{q|q<p}|;

[0147] Then, calculate the crowding degree of each individual:

[0148]

[0149] In the formula, is the function value of the ith individual on the mth objective, and M is the number of objective functions. These indicators are used to optimize the population and finally obtain the Pareto optimal solution set.

[0150] Based on the Pareto optimal solution set obtained by the NSGA-II algorithm, the charging period optimization matrix C and the discharging period optimization matrix D of the energy storage system can be constructed:

[0151]

[0152] Among them, c ij represents the charging state of the i-th Pareto optimal solution in the j-th period, and its value is 0 or 1; d ij It represents the discharge state of the i-th Pareto optimal solution in the j-th time period, and its value is 0 or 1; N is the number of Pareto optimal solutions, and T is the number of time periods.

[0153] In order to select the optimal charging and discharging period schemes, the charging period optimization evaluation index E c And the discharge period optimization evaluation index E d :

[0154] E c =ω1E f +ω2E v +ω3E b +ω4E p ;

[0155] E d =ω1E f +ω2E v +ω3E b +ω4E dp ;

[0156] Among them, the frequency deviation index is:

[0157]

[0158] The voltage deviation index is:

[0159]

[0160] The three-phase imbalance index is:

[0161]

[0162] The charging power deviation index is:

[0163]

[0164] The discharge power deviation index is:

[0165]

[0166] ω1, ω2, ω3, ω4 are weight coefficients and satisfy P c,ref (t) and P d,ref (t) are the target charging power and target discharging power curves respectively.c and E d The smallest solution is the optimal charging period solution C opt and the optimal discharge time scheme D opt .

[0167] The following constraints must also be met:

[0168] 1) Charging and discharging cannot be done at the same time:

[0169]

[0170] 2) Charging and discharging energy needs to be balanced:

[0171]

[0172] In order to further better understand and implement the present invention, the following provides Example 2 of a specific application scenario of the present invention: A power grid company deployed an energy storage system in its jurisdiction to assist the frequency and voltage regulation of the power grid. The energy storage system consists of multiple grid-connected energy storage devices with a total capacity of 20MW. In order to improve the operating efficiency of the energy storage system, the company decided to optimize the charging and discharging time periods of the energy storage system.

[0173] According to the energy storage system charging and discharging period adjustment optimization method proposed in the present invention, the power grid company carried out the following specific work:

[0174] First, the company collected key operating data from the energy storage system monitoring system and grid measurement equipment from January 1, 2022 to March 31, 2022. This included the charging power P of the energy storage system. c (t), discharge power P d (t), the frequency data f(t), voltage data v(t) and three-phase current I a (t), I b (t), I c (t). The sampling time interval is Δt = 1min.

[0175] Secondly, perform multivariate decomposition analysis on the collected operation data. Decompose the power grid frequency data f(t) into:

[0176]

[0177] Where f0(t) represents the stable component of the grid frequency, a i 、f i ,φ i Respectively represent the amplitude, frequency and phase angle of the i-th frequency fluctuation component, i = 1, 2, ..., 10. The grid voltage data v(t) is decomposed into:

[0178]

[0179] Where v0(t) represents the stable component of the grid voltage, b i 、v i ,θ i They respectively represent the amplitude, frequency and phase angle of the i-th voltage fluctuation component, i=1,2,…,8.

[0180] Figure 2 The analysis of the fluctuation components of the power grid frequency is shown. The blue solid line in the figure represents the actual frequency curve, and the red dotted line represents the stable component of 50Hz. It can be seen that the actual frequency fluctuates around the stable component within 24 hours. This fluctuation is formed by the superposition of 10 sinusoidal components with different frequencies, amplitudes and phases. This decomposition helps to understand the characteristics of power grid frequency fluctuations.

[0181] Then, the company established the correlation matrix R between the charging and discharging power of the energy storage system and the grid frequency fluctuation, voltage fluctuation and three-phase imbalance. The matrix element r ij It represents the degree of association between the ith sequence and the jth sequence, and the calculation formula is:

[0182]

[0183] Among them, Δ ij =|x i (k)-y j (k)| represents the value x of the i-th sequence at the k-th moment i (k) and the value y of the jth sequence at the kth moment j (k) is the absolute value of the difference.

[0184] Figure 3 The results of the correlation analysis between the charging and discharging power of the energy storage system and three main grid parameters (frequency fluctuation, voltage fluctuation, and three-phase imbalance) are shown. It can be seen intuitively from the bar chart that the charging power and the discharging power have a high correlation with these parameters, among which the correlation with frequency fluctuation is the highest (0.85 and 0.82, respectively), and the correlation with three-phase imbalance is relatively low (0.68 and 0.65, respectively). This shows that the charging and discharging behavior of the energy storage system has the most significant impact on the grid frequency.

[0185] Based on the above analysis results, the company constructed the energy storage system charging and discharging optimization objective function:

[0186] F = 0.4F1 + 0.3F2 + 0.3F3;

[0187] Among them, the grid frequency stability index is:

[0188]

[0189] The grid voltage stability index is:

[0190]

[0191] The three-phase balance index of the power grid is:

[0192]

[0193] Among them, T = 44640 is the total number of time periods (3 months, one time period per minute), f ref =50Hz is the nominal frequency, v ref =220V is the nominal voltage.

[0194] On this basis, the company constructed a multi-level constraint equation system:

[0195] 1) Charge and discharge power constraints:

[0196] -20MW≤P c (t), P d (t)≤20MW;

[0197] 2) Charge state constraints:

[0198] 0.1≤SOC(t)≤0.9;

[0199]

[0200] 3) Grid frequency constraints:

[0201] 49.5Hz≤f(t)≤50.5Hz;

[0202] 4) Grid voltage constraints:

[0203] 210V≤v(t)≤230V;

[0204] The NSGA-II algorithm is used to solve the above multi-objective optimization problem and obtain the Pareto optimal solution set. The Pareto optimal solution set contains 100 alternatives, which are denoted as C1, C2, …, C 100 and D1, D2, …, D 100 , where C i represents the charging period plan corresponding to the i-th Pareto optimal solution, D i Represents the discharge period plan corresponding to the i-th Pareto optimal solution.

[0205] For these 100 alternatives, the company calculated the comprehensive evaluation index E of the charging period. c And the comprehensive evaluation index E of the discharge period d :

[0206] E c =0.4E f +0.3E v +0.2E b +0.1E p ;

[0207] E d =0.4E f +0.3E v +0.2E b +0.1E dp ;

[0208] in,

[0209]

[0210] The evaluation results are shown in Table 1. It can be seen that the 13th Pareto optimal solution C 13 and D 13 Corresponding charging and discharging period plan, comprehensive evaluation index E c and E d are respectively the minimum values, so the power grid company chooses this solution as the final energy storage system charging and discharging period adjustment plan.

[0211] Table 1 Evaluation of charging and discharging period optimization of Pareto optimal solution

[0212]

[0213]

[0214] According to the selected optimal solution C 13 and D 13 ,The power grid enterprise adjusted the charging and discharging time periods of the energy storage system. The specific charging and discharging time periods are shown in Table 2.

[0215] Table 2 Optimal charging and discharging time plan for energy storage system

[0216]

[0217] As can be seen from Table 2, the energy storage system is in the charging state during the two periods of 0:00-1:00 and 23:00-24:00; the energy storage system is in the discharging state during the two periods of 1:00-2:00 and 2:00-3:00; and the energy storage system is in the standby state during the rest of the periods. This charging and discharging period adjustment scheme can effectively improve the frequency stability, voltage stability and three-phase balance of the power grid, as shown in the following specific examples: Figure 4 shown. Figure 4The improvement of grid performance before and after the energy storage system optimization is compared. The grouped bar chart shows the changes in three key indicators: the frequency stability deviation is reduced from 0.24Hz to 0.16Hz, an improvement of 33.3%; the voltage stability deviation is reduced from 3.7V to 2.8V, an improvement of 24.3%; the three-phase imbalance index is reduced from 4.1% to 3.0%, an improvement of 26.8%. The percentage marked above each group of data intuitively shows the degree of improvement after optimization.

[0218] In addition, the charging and discharging period adjustment scheme also meets the charging and discharging power and charge state constraints of the energy storage system itself, ensuring the safe and stable operation of the energy storage system.

[0219] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system, characterized in that: The following steps are involved: S10, collecting energy storage system operation data, wherein the energy storage system operation data includes energy storage system charging power data, energy storage system discharging power data, grid frequency data, grid voltage data, and three-phase imbalance data; S20, performing multivariate decomposition calculation on the energy storage system operation data to obtain a grid frequency stability component, a grid frequency fluctuation component, a grid voltage stability component, and a grid voltage fluctuation component; S30, establishing a correlation matrix between the energy storage system charging power data, the energy storage system discharging power data, the grid frequency fluctuation component, the grid voltage fluctuation component, and the three-phase imbalance data; S40, establishing an energy storage system charging and discharging optimization objective function according to the correlation matrix, wherein the energy storage system charging and discharging optimization objective function includes a grid frequency stability index, a voltage stability index, and a three-phase balance index; S50, constructing a multi-level constraint equation group based on the energy storage system charge and discharge optimization objective function, wherein the multi-level constraint equation group includes energy storage system charge and discharge power constraints, energy storage system charge state constraints, power grid frequency constraints, and power grid voltage constraints; S60, using a multi-objective optimization algorithm to solve the energy storage system charging and discharging optimization objective function to obtain an energy storage system charging period optimization matrix and an energy storage system discharging period optimization matrix; S70, generating an energy storage system charging and discharging period adjustment plan according to the energy storage system charging period optimization matrix and the energy storage system discharging period optimization matrix, and outputting the plan to operation and maintenance personnel for optimizing and adjusting the energy storage system charging and discharging period.

2. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to claim 1, characterized in that: The specific expression of multivariate decomposition calculation is as follows: Where, f(t) is the grid frequency data; f0(t) is the grid frequency stability component, t represents the sampling time; a i is the frequency fluctuation amplitude; f i is the frequency fluctuation frequency; φ i is the phase angle; v(t) is the grid voltage data; v0(t) is the grid voltage stability component; b i is the voltage fluctuation amplitude; v i is the voltage fluctuation frequency; θ i is the phase angle; n, m are the decomposition orders.

3. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to claim 2, characterized in that: The association matrix is ​​specifically expressed as follows: In the formula, r ij is the correlation coefficient, that is: r 11 is the correlation between charging power and frequency fluctuation component; r 12 is the correlation between charging power and voltage fluctuation component; r 13 is the correlation between charging power and three-phase imbalance; r 21 is the correlation between the discharge power and the frequency fluctuation component; r 22 is the correlation between discharge power and voltage fluctuation component; r 23 It is the correlation between discharge power and three-phase imbalance.

4. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to claim 3, characterized in that: The energy storage system charging and discharging optimization objective function is specifically expressed as follows: F = α1F1 + α2F2 + α3F3; In the formula, F1 is the frequency stability index; F2 is the voltage stability index; F3 is the three-phase balance index; α1, α2, α3 are weight coefficients, and they satisfy α1+α2+α3=1; f ref is the nominal frequency, take 50Hz; v ref is the nominal voltage; I a ,I b ,I c is the complex representation of the three-phase current, and Γ is the total number of sampling points.

5. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to claim 4, characterized in that: The multi-level constraint equations are specifically expressed as follows: P min ≤P(t)≤P max ; SOC min ≤SOC(t)≤SOC max ; f min ≤f(t)≤f max ; v min ≤v(t)≤v max ; Where P(t) is the charging and discharging power of the energy storage system; P min ,P max is the power limit; SOC(t) is the state of charge; SOC min ,SOC max is the charge state limit; η c is the charging efficiency, η d is the discharge efficiency; P c (t) is the charging power, P d (t) is the discharge power; E cap is the rated capacity of the energy storage system; Δt is the sampling time interval; f min ,f max is the frequency limit; v min ,v max is the voltage limit.

6. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to claim 5, characterized in that: The energy storage system charging period optimization matrix is ​​specifically expressed as follows: In the formula, c ij It represents the charging state of the i-th Pareto optimal solution in the j-th time period, and its value is 0 or 1, 1 represents charging, and 0 represents not charging; N is the number of Pareto optimal solutions; T is the number of time periods.

7. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to claim 6, characterized in that: The energy storage system discharge period optimization matrix is ​​specifically expressed as follows: Where, d ij It represents the discharge state of the i-th Pareto optimal solution in the j-th time period. The value is 0 or 1, 1 means discharge, and 0 means no discharge.

8. A method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to claim 7, characterized in that: The multi-objective optimization algorithm is the NSGA-II algorithm.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the method for adjusting and optimizing the charging and discharging time periods of an energy storage system according to any one of claims 1 to 8.

10. A system for adjusting and optimizing charging and discharging periods of an energy storage system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

Images