Calculation method for charging pile load elasticity curve

Abstract

The invention discloses a calculation method for a charging pile load elastic curve, and the method comprises the following steps: giving a group of charging pile load elastic curves beta i and an initial iteration point beta (0), and calculating expressions q (0) and qi corresponding to beta (0) and beta i in ln; calculating a shortest curve alpha i based on a Riemann optimization method, and obtaining a value and a gradient of a cost function; determining a step length lambda k by using a backtracking linear search algorithm, and calculating a next iteration point; and checking whether a preset stop criterion is met, if so, stopping iteration, and outputting a current result as Karcher Mean, and if not, letting k = k + 1, and returning to the step of calculating the shortest curve to continue iterative calculation. The method has the beneficial effects of improving calculation accuracy, reducing calculation cost and enhancing scene adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile load data analysis, and in particular to a method for calculating a charging pile load elasticity curve. Background Art

[0002] With the increasing global emphasis on environmental protection and sustainable development, electric vehicles (EVs) have been widely promoted and applied as a clean and efficient means of transportation. The popularity of EVs has led to a rapid increase in the number of charging stations, making them a vital component of urban infrastructure. However, the load characteristics of charging stations are complex and variable, influenced by a variety of factors. Accurately analyzing the load elasticity curves of charging stations is crucial for the stable operation and rational planning of power systems.

[0003] The load on charging piles is significantly affected by external factors such as sunlight and weather. For example, during sunny days, some charging piles equipped with photovoltaic charging facilities may reduce their demand for grid power. In hot or cold weather, electric vehicle battery performance and charging requirements can change, further impacting the load on the charging piles. Furthermore, the frequency of charging pile usage varies significantly across regions and time periods. For example, charging demand is often higher in commercial areas during the daytime on weekdays and in residential areas at night. These complex influencing factors contribute to a high degree of uncertainty and volatility in the load elasticity curve for charging piles.

[0004] In terms of grid dispatching, accurately understanding the load elasticity curve of charging piles helps rationally manage grid loads during peak periods and alleviate power supply pressure. When a large number of electric vehicles are charging simultaneously, without effective load management, local grid overloads can occur, impacting the stability and reliability of power supply. By analyzing the load elasticity curve of charging piles, grid dispatchers can formulate strategies in advance, such as guiding vehicle owners to charge during off-peak periods or implementing intelligent control of charging piles to optimize the allocation of power resources.

[0005] The analysis of charging pile load elasticity curves provides a scientific basis for charging station construction planning. Based on regional load demands and changing trends, the location and number of charging stations can be rationally allocated to avoid over- or under-construction. Furthermore, it can guide the configuration of appropriate charging equipment and capacity at charging stations, improving resource utilization efficiency and reducing construction and operating costs.

[0006] The field of elastic shape analysis of curves plays a vital role in numerous scenarios, including imaging applications and object contour analysis. Accurately calculating the distance between two curve shapes is a fundamental operation in elastic shape analysis, and the calculation of the Karcher Mean relies on this operation. Currently, most elastic shape analyses use coordinate descent algorithms to calculate closed curves. However, this algorithm has significant flaws. When evaluating reparameterized curves, the points on which the interpolation parameterized polynomial is based change over multiple iterations, causing the parametric polynomial to change, and thus the shape of the curve to change with each reparameterization. This not only severely impacts the accuracy of the calculations, making the analysis results incapable of truly reflecting the characteristics of the curve, but also incurs high costs during the calculation process, including significant consumption of computing resources and increased computation time. These flaws are particularly prominent when processing large-scale charging pile load elastic curve data, making it impossible to meet the high-precision and high-efficiency computational requirements of practical applications.

[0007] In summary, the existing calculation methods have shortcomings in processing the Karcher Mean of the charging pile load elasticity curve. A new and more effective calculation method is urgently needed to improve the accuracy and efficiency of the calculation and meet the needs of practical applications such as power grid scheduling and charging station planning. Summary of the Invention

[0008] In order to solve the above problems, especially to address the deficiencies in the prior art, the present invention provides a method for calculating the load elasticity curve of a charging pile, which can solve the above problems.

[0009] To achieve the above objectives, the present invention adopts the following technical means:

[0010] A method for calculating a charging pile load elasticity curve comprises the following steps:

[0011] Given a set of charging pile load elasticity curves β i (I=1,2,···,N) and the initial iteration point β (0) , calculate l n Medium Beta (0) and β i The corresponding representation q (0) and q i (i=1,2,···,N), and let the number of iterations k=0;

[0012] Calculation of the shortest curve α based on the Riemann optimization method i , so that α i (1) = q i and Get the value and gradient of the cost function;

[0013] Use the backtracking linear search algorithm to determine the step size λ k , and calculate the next iteration point Among them k is the gradient of the cost function;

[0014] Check whether the preset stopping criteria are met. If so, stop the iteration and output the current result as KarcherMean. If not, set k=k+1 and return to the shortest curve calculation step to continue the iterative calculation.

[0015] A further solution of the present invention is that when the shortest curve is calculated based on the Riemann optimization method, the Riemann manifold The cost function H(γ) on is defined as:

[0016]

[0017] Among them, for γ∈Γ o , γ(0)=0,γ(1=1) is satisfied almost everywhere,

[0018] A further solution of the present invention is to make but get for:

[0019]

[0020] A further solution of the present invention is to ensure that 2 (s)≠0, introduce the barrier function B(γ):

[0021]

[0022] A further embodiment of the present invention is that к=γ -1 , making

[0023]

[0024] A further solution of the present invention is to construct a new function L(l):

[0025]

[0026] Here, ω is a constant used to control the influence of the additional term.

[0027] A further solution of the present invention is that the gradient gradL(l) of the cost function L(l) is:

[0028]

[0029] in, X(t)=<q1(t),q2(p1(t))> 2, y(t) is y'(t) =<q1(t),2l(t)q'2(p1(t))> The indefinite integral of 2,

[0030] A further solution of the present invention is to process q2 using the interpolation cubic spline of the point set on it to satisfy the Riemann Quasi-Newton optimization algorithm for C 2 Convergence requirements for the cost function.

[0031] A further solution of the present invention is that all integrals in the algorithm are approximated by the composite trapezoidal criterion.

[0032] Beneficial effects of the present invention:

[0033] 1. The present invention improves calculation accuracy

[0034] Avoid the defects of existing algorithms: When evaluating the reparameterized curve, the existing coordinate descent algorithm causes the shape of the curve to change due to interpolation problems, affecting the accuracy of the calculation. The present invention avoids such problems through a unique calculation method based on Riemann optimization and a carefully designed cost function. When calculating the Karcher Mean of the charging pile load elasticity curve, the curve data can be processed more accurately, so that the results truly reflect the characteristics of the curve, providing reliable data support for subsequent load analysis and power grid scheduling. For example, in complex load scenarios such as different weather and time periods, the curve mean can be accurately calculated to help the power grid arrange the load reasonably.

[0035] Meet the convergence requirements: For the cost function q2 part, use the interpolation cubic spline to process and meet the Riemann quasi-Newton optimization algorithm for C 2 The convergence requirement of the cost function ensures the stability of the calculation process and the accuracy of the results from the algorithm principle level.

[0036] 2. The present invention reduces the computational cost

[0037] Reducing unnecessary calculations: Optimizing algorithmic processes and logic avoids the numerous ineffective calculations often associated with frequent curve shape changes in existing technologies. The use of optimization techniques such as the backtracking linear search algorithm streamlines the calculation steps, reduces computing resource consumption, and lowers computational costs. This approach is particularly suitable for real-time processing of large-scale charging pile load data.

[0038] Improved Computing Efficiency: While ensuring accuracy, the system significantly reduces computation time. For example, when processing massive amounts of historical charging pile load elasticity curve data to provide real-time decision-making for grid dispatch, it can quickly output Karcher Mean results, meeting the timeliness requirements of practical applications and improving overall work efficiency.

[0039] 3. The present invention has enhanced scene adaptability

[0040] Responding to diverse data characteristics: The system is highly adaptable to load elasticity curves for charging piles of varying types and sizes. Whether the data fluctuates significantly or remains relatively stable, the Karcher Mean can be stably and accurately calculated.

[0041] Supporting Multi-Scenario Applications: This tool provides effective analysis tools for diverse charging pile load scenarios. In grid dispatching, it can rationally schedule peak loads based on the accurate Karcher Mean, alleviating power supply pressure. In charging station planning, it can guide scientific layout and equipment configuration, improve resource utilization efficiency, and achieve comprehensive and refined management of charging pile loads. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be described clearly and completely below. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] A method for calculating a charging pile load elasticity curve comprises the following steps:

[0045] Initial setting: Given a set of charging pile load elasticity curves β i (i=1,2,···,N) and the initial iteration point β (0) , calculate l n Medium Beta (0) and β i The corresponding representation q (0) and q i (i=1,2,···,N), and let the number of iterations k=0.

[0046] Shortest curve calculation: Calculate the shortest curve α based on the Riemann optimization method i , so that α i (1) = q i and Get the value and gradient of the cost function. The details are as follows:

[0047] When calculating the shortest curve based on the Riemann optimization method, the Riemann manifold The cost function H(γ) on is defined as:

[0048]

[0049] Among them, for γ∈Γo , γ(0)=0,γ(1=1) is satisfied almost everywhere,

[0050] make but get for:

[0051]

[0052] To ensure l 2 (s)≠0, introduce the barrier function B(γ):

[0053]

[0054] к=γ -1 , making

[0055] Construct a new function L(l):

[0056]

[0057] Here, ω is a constant used to control the influence of the additional term.

[0058] The gradient gradl(l) of the cost function L(l) is:

[0059]

[0060] in, X(t)=<q1(t),q2(p1(t))> 2, y(t) is y'(t) =<q1(t),2l(t)q'2(p1(t))> The indefinite integral of 2, Considering that q2 is composed of multiple functions, in order to satisfy the Riemann Quasi-Newton optimization algorithm for C 2 The convergence requirement of the cost function is handled by using the interpolating cubic spline of the point set on q2. At the same time, all integrals in the algorithm are approximated by the composite trapezoidal criterion.

[0061] Iterative update: Use the backtracking linear search algorithm to determine the step size λ k , and calculate the next iteration point Among them k is the gradient of the cost function.

[0062] Termination judgment: Check whether the preset stopping criteria are met. If so, stop the iteration and output the current result as Karcher Mean. If not, set k = k + 1 and return to the shortest curve calculation step to continue the iterative calculation.

[0063] Example 2

[0064] Calculation based on simulated data

[0065] Assume that in a certain virtual area, there are five simulated charging pile load elasticity curves β1, β2, β3, β4, and β5. These curves are generated by mathematical models to simulate the load changes under different time periods and different charging demands. Given the initial iteration point β (0) , the initial iteration point can be preliminarily set based on experience or simple mean calculation.

[0066] Initial settings:

[0067] First, according to the set curve data, calculate l n Medium Beta (0) and β i (i=1,2,3,4,5) corresponds to q (0) and q i In the calculation process, according to the relevant mathematical conversion formula, the curve β i Convert to q i After the conversion is completed, the number of iterations k is set to 0.

[0068] Shortest curve calculation:

[0069] Calculation of the shortest curve α based on the Riemann optimization method i (i=1,2,3,4,5). When calculating, first determine the Riemann manifold The cost function H(γ) on :

[0070]

[0071] make Get further

[0072]

[0073] To ensure l 2 (s)≠0, introduce the barrier function B(γ):

[0074]

[0075] Construct a new function L(l):

[0076]

[0077] Among them, ω is 0.5, and the gradient gradL(l) of the cost function L(l) is calculated. During the calculation process, for q2, the interpolation cubic spline of the point set on it is used to process it to meet the Riemann Quasi-Newton optimization algorithm for C 2Convergence requirements of the cost function. All integrals are approximated by the composite trapezoidal criterion to obtain the value and gradient of the cost function and obtain the shortest curve α i .

[0078] Iterative updates:

[0079] Use the backtracking linear search algorithm to determine the step size λ0 based on the calculated cost function gradient ζ0 and calculate the next iteration point

[0080] Termination judgment:

[0081] Check whether the preset stopping criteria are met. In this case, the stopping criteria are set to 10 iterations or the change in the cost function value is less than 0.001. If not, set k = 1 and return to the shortest curve calculation step to continue iteration. If satisfied, stop the iteration and output the current result as the Karcher Mean.

[0082] Example 3

[0083] Calculation based on actual data

[0084] The actual load data of 10 charging piles distributed in different areas of a city within one month are selected to form 10 charging pile load elasticity curves β1, β2, ···, β 10 Through preliminary analysis of historical data, the initial iteration point β is determined (0) .

[0085] Initial settings:

[0086] Preprocess the actual collected curve data, remove abnormal data points, and then calculate l n Medium Beta (0) and β i (i=1,2,···,10) corresponds to q (0) and q i After the conversion is completed, set the number of iterations k = 0.

[0087] Shortest curve calculation:

[0088] Also based on the Riemann optimization method to calculate the shortest curve α i (i=1,2,···,10). Determine the cost function H(γ), B(γ) and L(l), calculate the gradient gradL(l) of the cost function L(l). When processing q2, use interpolation cubic spline, and perform approximate calculation integral through composite trapezoidal criterion to obtain the value and gradient of the cost function, and obtain the shortest curve α i .

[0089] Iterative updates:

[0090] Use the backtracking linear search algorithm to determine the step size λ0 according to the cost function gradient ζ0 and calculate the next iteration point

[0091] Termination judgment:

[0092] The stopping criteria set in this paper are 15 iterations or a change in the cost function value of less than 0.0005. If this condition is not met, k is set to 1 and the iteration returns to the shortest curve calculation step. If it is met, the iteration stops and the current result is output as the Karcher Mean. This result can be used to analyze the overall load elasticity trend of charging piles in the city, providing a basis for grid scheduling and new charging station planning.

[0093] The present invention is provided as an example, not as a limitation of the embodiments. Those skilled in the art will appreciate that other variations or modifications may be made based on the above description. It is not necessary and impossible to enumerate all embodiments here, and obvious variations or modifications derived therefrom remain within the scope of protection of the present invention.

Claims

1. A method for calculating the load elasticity curve of a charging pile, characterized in that: The following steps are involved: Given a set of charging pile load elasticity curves β i (i=1,2,···,N) and the initial iteration point β (0) , calculate l n Medium Beta (0) and β i The corresponding representation q (0) and q i (i=1, 2, ···, N), and let the number of iterations k=0; Calculation of the shortest curve α based on the Riemann optimization method i , so that α i (1) = q i and , i=1,2,···,N, obtain the value and gradient of the cost function; Use the backtracking linear search algorithm to determine the step size λ k , and calculate the next iteration point Among them k is the gradient of the cost function; Check whether the preset stopping criteria are met. If so, stop the iteration and output the current result as KarcherMean. If not, set k=k+1 and return to the shortest curve calculation step to continue the iterative calculation.

2. The method for calculating the load elasticity curve of a charging pile according to claim 1, characterized in that: When calculating the shortest curve based on the Riemann optimization method, the Riemann manifold The cost function H(γ) on is defined as: Among them, for γ∈Γ o , γ(0)=0,γ(1=1) is satisfied almost everywhere, 3. The method for calculating the load elasticity curve of a charging pile according to claim 2, characterized in that: make but get for:

4. The method for calculating the load elasticity curve of a charging pile according to claim 3, characterized in that: To ensure l 2 (s)≠0, introduce the barrier function B(γ):

5. The method for calculating the load elasticity curve of a charging pile according to claim 4, characterized in that: к=γ -1 , making 6. The method for calculating the load elasticity curve of a charging pile according to claim 5, characterized in that: Construct a new function L(l): Here, ω is a constant used to control the influence of the additional term.

7. The method for calculating the load elasticity curve of a charging pile according to claim 6, characterized in that: The gradient gradL(l) of the cost function L(l) is: in, x(t)=<q1(t),q2(p1(t))> 2, y(t) is y'(t)=<q1(t),2l(t)q'2(p1(t))> The indefinite integral of 2, 8. The method for calculating the load elasticity curve of a charging pile according to claim 7, characterized in that: For q2, the interpolation cubic spline of the point set is used to process it to meet the Riemann Quasi-Newton optimization algorithm for C 2 Convergence requirements for the cost function.

9. A method for calculating a charging pile load elasticity curve according to any one of claims 1 to 8, characterized in that: All integrals in the algorithm are approximated by the composite trapezoidal criterion.

Images