Method for aggregating monomer small load into large load by considering uncertainty

Abstract

The invention discloses a method for aggregating single small loads into large loads by considering uncertainty, and relates to the technical field of electric power systems, and the method comprises the following steps: dividing the single small loads into the following classes according to the uncertainty: the first class is that only the upper and lower bounds of the power of the single small loads are known; the second class is that the mode is known on the basis of knowing the upper and lower bounds of the power of the single small load; the third type is the distribution condition of most of power of the known single small load, that is, the power data set has been obtained; acquiring information of a series of single small loads, and classifying the information according to the information adequacy; establishing a probability density function for each single small load according to the classification; and discretizing different monomer small-load probability density functions in sequence and carrying out numerical convolution on every two of the different monomer small-load probability density functions to obtain a new probability density function, continuously carrying out convolution until only one probability density function is left, and taking the left probability density function as a probability density function of a large load formed after all small loads are aggregated.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for aggregating single small loads into a large load in consideration of uncertainty. Background Art

[0002] The load of electric vehicle charging stations and electric boiler clusters are typical aggregated large loads (referred to as "large loads"), which are aggregated from multiple single small loads (individual charging piles, individual electric boilers). The growth of the load of electric vehicle charging stations and electric boiler clusters is increasingly affecting the safe and stable operation of the power system. On the one hand, these aggregated large loads will bring significant load peaks, bring challenges to the frequency regulation and peak regulation of the power system and aggravate the congestion of the power grid; on the other hand, the large load aggregated from multiple single small loads has potential flexibility and can provide support for the frequency regulation, peak regulation and congestion reduction of the power system. To tap this flexibility, it is first necessary to develop an aggregation method and system for aggregating single small loads (such as a single charging pile) into a large load. Since human behavior is uncertain, single small loads are also uncertain, and the aggregation process must take this uncertainty into account.

[0003] In addition, due to different degrees of data accumulation, there are different categories of descriptions of the uncertainty of single small loads: when the data accumulation is small, the description of the uncertainty of single small loads is rough, for example, only the upper and lower bounds of the interval of single small loads are known; as the data accumulation increases, the description of the uncertainty of single small loads becomes more and more specific, for example, in addition to knowing the upper and lower bounds, the mode is also known, and even the complete probability distribution is known.

[0004] The aggregation method of small monomer loads into large loads needs to adapt to the above-mentioned multiple descriptions of the uncertainty of small monomer loads in order to adapt to the complex and diverse conditions of the engineering site, so as to be both innovative and practical. Summary of the invention

[0005] The purpose of the present invention is to provide a monomer small load polymerization system taking uncertainty into consideration.

[0006] The technical solution of the present invention is to provide a method for aggregating small monomer loads into a large load taking into account uncertainty, the method comprising:

[0007] S1. Divide single small loads into the following categories according to the difference in uncertainty:

[0008] The first category is that only the upper and lower bounds of the power of a single small load are known;

[0009] The second type is that based on knowing the upper and lower bounds of the power of a single small load, its mode is also known;

[0010] The third category is the distribution of most of the power of known single small loads, that is, their power data sets have been obtained;

[0011] S2, obtaining a series of information of single small loads, and classifying them into one of the classifications in step S1 according to their information sufficiency, where the information sufficiency index is the number of measurement data;

[0012] S3. After classifying the single small loads, a probability density function is established for each single small load according to its classification;

[0013] S4. Discretize the probability density functions of different single small loads in turn and perform numerical convolution on them two by two to obtain a new probability density function. Continue the convolution until only one probability density function remains. The remaining probability density function is the probability density function of the large load formed by the aggregation of all small loads.

[0014] In any of the above technical solutions, further, in step S2, the information sufficiency index specifically includes:

[0015] For single small loads whose upper and lower bounds have been determined but for which actual measurement data are not yet available, they are classified into the first category;

[0016] For single small loads with no more than N sample data, where N is a preset positive integer, the upper and lower bounds and mode of the load power are calculated from the measured data and classified into the second category;

[0017] For a single small load with more than N measured sample data, it is classified into the third category.

[0018] In any of the above technical solutions, further, the step of establishing a probability density function for the first type of single small load in step S3 includes:

[0019] Calculate the upper and lower bounds of each single small load power in turn Standard Deviation Then establish the probability density function of the bilateral truncated Gaussian distribution:

[0020]

[0021] Among them, P1 and P2 are the lower and upper bound power respectively, x is the power of a single small load, which is a random variable; ψ, φ, and Φ represent the probability density function of the bilateral truncated Gaussian distribution, the probability density function of the unbounded Gaussian distribution, and the cumulative distribution function of the unbounded Gaussian distribution respectively.

[0022] In any of the above technical solutions, further, the step of establishing a probability density function for the second type of single small load in step S3 includes:

[0023] The probability density function of the triangular distribution is established based on the lower limit P1, upper limit P2 and mode m of the single small load:

[0024]

[0025] Among them, x is the power of a single small load, which is a random variable; f is the probability density function of the triangular distribution.

[0026] In any of the above technical solutions, further, the step of establishing a probability density function for the third type of single small load in step S3 includes:

[0027] According to the known power data set {x1,x2,…,x n}, n is the number of data, construct the Gaussian kernel K(x):

[0028]

[0029] Calculate the mean Standard Deviation Bandwidth h = 1.06·σ·n -1 / 5 ;

[0030] Get the probability density function g(x) of the kernel density estimate:

[0031]

[0032] In any of the above technical solutions, further, step S4 specifically includes:

[0033] Select a number of discrete points at equal intervals within the upper and lower bounds [P1, P2] of each probability density function, calculate the value of the probability density function at each discrete point, and compose the obtained values ​​into a discretized function in order to complete the discretization of all probability density functions;

[0034] Convolve the discretized probability density functions two by two, and the convolution formula is:

[0035]

[0036] Among them, f1(n) and f2(n) are the discretization results of two different probability density functions, z is the load power value after discretization; b is the intermediate variable; * is the convolution operator.

[0037] The beneficial effects of the present invention are:

[0038] The present invention aggregates a series of single small loads into a large load. The aggregated large load result can effectively help the power grid dispatching platform optimize the dispatching strategy, thereby alleviating the pressure caused by load peaks during the peak period of power demand, reducing power grid congestion, and improving the safety and stability of power grid operation.

[0039] The aggregation method proposed in the present invention is adaptable to the description of various uncertainties of single small loads. It is applicable to planning scenarios where there is no real measurement data, trial operation scenarios where there is a small amount of measurement data, and operation scenarios where there is relatively sufficient measurement data. It has strong universality. It is applicable to the aggregation of any number of single small loads. Taking the charging pile load as an example, it is applicable to two or three charging piles on the roadside, or more than one hundred charging piles in a parking lot, or even hundreds of thousands of charging piles in a city. It can meet a wide range of actual needs and support the selection of a suitable range for aggregation according to actual needs.

[0040] This patent has a wide range of application scenarios and is suitable for photovoltaic storage and charging microgrid systems, power systems, virtual power plant systems, and integrated energy systems. It is suitable for the optimized operation, optimized planning, and optimized scheduling scenarios of each system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The advantages of the above and additional aspects of the present invention will become apparent and easily understood in the description of the embodiments in conjunction with the following drawings, in which:

[0042] Figure 1 It is a schematic flow chart of a method for aggregating small monomer loads into a large load in consideration of uncertainty according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0045] like Figure 1 As shown, this embodiment provides a method for aggregating small monomer loads into a large load taking into account uncertainty, and the method includes:

[0046] S1. Divide single small loads into the following categories according to the difference in uncertainty:

[0047] The first category is that only the upper and lower bounds of the power of a single small load are known, that is, the power P∈[P1,P2], where P1 and P2 are the lower bound power and the upper bound power respectively;

[0048] The second type is based on knowing the upper and lower bounds of the power of a single small load, and also knowing the mode m;

[0049] The third category is the distribution of most of the power of known single small loads, that is, their power data sets have been obtained.

[0050] S2. Obtain information on a series of single small loads, and classify them into one of the classifications in step S1 according to the degree of information sufficiency.

[0051] Specifically, in the order of the first to the third category, the information sufficiency of single small loads increases successively, and the information sufficiency is determined by the actual situation of the project: for projects that are still in the planning stage, there is no actual measurement data for single small loads, but the upper and lower bounds of single small loads can be estimated through experience, so they can be classified into the first category; for single small loads in the trial operation stage of the project, there are some real measurement data, and the number of sample data generally does not exceed N (N = 200), from which the upper and lower bounds and mode of load power can be calculated, but it is not enough to determine its probability density function, so it can be classified into the second category; for single small loads that have been in operation for a considerable period of time, the accumulated measurement data generally exceeds N (N = 200), which is sufficient to support the establishment of its probability density function, so the single small load can be classified into the third category for modeling. The above classification method is a general method, and the number of samples of measurement data N can be determined according to the actual situation.

[0052] S3. After classifying the single small loads, execute the following process to establish a probability density function for each single small load:

[0053] S31. For the first type of single small load, calculate the upper and lower bounds in turn. Standard Deviation Then establish the probability density function of the bilateral truncated Gaussian distribution:

[0054]

[0055] In the above formula, x is the power of a single small load, which is a random variable; ψ, φ, and Φ represent the probability density function of the bilateral truncated Gaussian distribution, the probability density function of the unbounded Gaussian distribution, and the cumulative distribution function of the unbounded Gaussian distribution, respectively.

[0056] S32. For the second type of single small load, a probability density function of a triangular distribution is established based on its lower bound P1, upper bound P2, and mode m:

[0057]

[0058] In the above formula, x is the power of a single small load, which is a random variable; f is the probability density function of the triangular distribution.

[0059] S33. For the third type of single small load, its power data set {x1, x2, …, x n}, n is the number of data, construct the Gaussian kernel K(x):

[0060]

[0061] Calculate the mean Standard Deviation Bandwidth h = 1.06·σ·n -1 / 5 .

[0062] Get the probability density function g(x) of the kernel density estimate:

[0063]

[0064] S4. Discretize the probability density functions of different single small loads in turn and perform numerical convolution on them two by two to obtain a new probability density function. Continue the convolution until only one probability density function remains.

[0065] Specifically, the steps of discretizing the probability density function include:

[0066] Select a number of discrete points at equal intervals between the upper and lower bounds [P1, P2], calculate the value of the probability density function at each discrete point, and organize the obtained values ​​into a discretized function in order.

[0067] The convolution formula is as follows:

[0068]

[0069] In the above formula, f1(n) and f2(n) are the discretization results of two different probability density functions, y(z) is the convolution result, z is the load power value after discretization; b is the intermediate variable; * is the convolution operator.

[0070] The remaining probability density function is the probability density function of the large load formed by the aggregation of all small loads.

[0071] The aggregated large load results can effectively help the power grid dispatching platform optimize the dispatching strategy, thereby alleviating the pressure caused by load peaks during peak electricity demand periods, reducing grid congestion, and improving the safety and stability of grid operation.

[0072] The aggregated large load can be used in the following application scenarios: The aggregated electric vehicle charging station load results can be output to the energy management platform of the photovoltaic + energy storage + charging station system to guide the optimized operation of the photovoltaic + energy storage + charging station system; can be output to the management and control platform of the virtual power plant system including the charging station to guide the optimized operation of the virtual power plant; can be output to the planning system of the photovoltaic storage charging station to guide the planning of the photovoltaic storage charging station; can be output to the power grid dispatching platform to guide the power grid dispatching to perform dispatching operations to maintain the safe and stable operation of the power grid. The aggregated electric boiler cluster load results can be output to the management and control platform of the virtual power plant system including the electric boiler cluster to guide the optimized operation of the virtual power plant; can be output to the energy management platform of the comprehensive energy system including the electric boiler cluster to guide the optimized operation of the system; can be output to the power grid dispatching platform to guide the power grid dispatching to perform dispatching operations to maintain the safe and stable operation of the power grid. The above are typical application scenarios for the aggregation of large load results, and other application scenarios that are not listed can also be included.

[0073] In summary, the present invention provides a method for aggregating small monomer loads into a large load taking into account uncertainty, the method comprising:

[0074] S1. Divide single small loads into the following categories according to the difference in uncertainty:

[0075] The first category is that only the upper and lower bounds of the power of a single small load are known;

[0076] The second type is that based on knowing the upper and lower bounds of the power of a single small load, its mode is also known;

[0077] The third category is the distribution of most of the power of known single small loads, that is, their power data sets have been obtained;

[0078] S2, obtaining a series of information of single small loads, and classifying them into one of the classifications in step S1 according to their information sufficiency, where the information sufficiency index is the number of measurement data;

[0079] S3. After classifying the single small loads, a probability density function is established for each single small load according to its classification;

[0080] S4. Discretize the probability density functions of different single small loads in turn and perform numerical convolution on them two by two to obtain a new probability density function. Continue the convolution until only one probability density function remains. The remaining probability density function is the probability density function of the large load formed by the aggregation of all small loads.

[0081] The steps in the present invention can be adjusted in order, combined or deleted according to actual needs.

[0082] The units in the device of the present invention can be combined, divided and deleted according to actual needs.

[0083] Although the present invention is disclosed in detail with reference to the accompanying drawings, it should be understood that these descriptions are merely exemplary and are not intended to limit the application of the present invention. The scope of protection of the present invention is defined by the appended claims and may include various modifications, alterations and equivalents made to the invention without departing from the scope and spirit of the present invention.

Claims

1. A method for aggregating small monomer loads into a large load taking into account uncertainty, characterized in that: The method comprises: S1. Divide single small loads into the following categories according to the difference in uncertainty: The first category is that only the upper and lower bounds of the power of a single small load are known; The second type is that based on knowing the upper and lower bounds of the power of a single small load, its mode is also known; The third category is the distribution of most of the power of known single small loads, that is, their power data sets have been obtained; S2. Obtain information of a series of single small loads, and classify them into one of the classifications in step S1 according to their information sufficiency, wherein the information sufficiency index is the number of measurement data; S3. After classifying the single small loads, a probability density function is established for each single small load according to its classification; S4. Discretize the probability density functions of different single small loads in turn and perform numerical convolution on them two by two to obtain a new probability density function. Continue the convolution until only one probability density function remains. The remaining probability density function is the probability density function of the large load formed by the aggregation of all small loads.

2. The method for aggregating small monomer loads into a large load considering uncertainty as claimed in claim 1, characterized in that: In step S2, the information sufficiency index specifically includes: For single small loads whose upper and lower bounds have been determined but for which actual measurement data are not yet available, they are classified into the first category; For single small loads with no more than N sample data, where N is a preset positive integer, the upper and lower bounds and mode of the load power are calculated from the measured data and classified into the second category; For a single small load with more than N measured sample data, it is classified into the third category.

3. The method for aggregating small monomer loads into a large load considering uncertainty as claimed in claim 1, characterized in that: The step of establishing a probability density function for the first type of single-unit small load in step S3 includes: Calculate the upper and lower bounds of each single small load power in turn Standard Deviation Then establish the probability density function of the bilateral truncated Gaussian distribution: Among them, P1 and P2 are the lower and upper bound power respectively, x is the power of a single small load, which is a random variable; ψ, φ, and Φ represent the probability density function of the bilateral truncated Gaussian distribution, the probability density function of the unbounded Gaussian distribution, and the cumulative distribution function of the unbounded Gaussian distribution respectively.

4. The method for aggregating small monomer loads into a large load considering uncertainty as claimed in claim 1, characterized in that: The step of establishing a probability density function for the second type of single-unit small load in step S3 includes: The probability density function of the triangular distribution is established based on the lower limit P1, upper limit P2 and mode m of the single small load: Among them, x is the power of a single small load, which is a random variable; f is the probability density function of the triangular distribution.

5. The method for aggregating small monomer loads into a large load considering uncertainty as claimed in claim 1, characterized in that: The step of establishing a probability density function for the third type of single-unit small load in step S3 includes: According to the known power data set {x1,x2,…,x n }, n is the number of data, construct the Gaussian kernel K(x): Calculate the mean Standard Deviation Bandwidth h = 1.06·σ·n -1 / 5 ; Get the probability density function g(x) of the kernel density estimate:

6. The method for aggregating small monomer loads into a large load considering uncertainty as claimed in claim 1, characterized in that: The step S4 specifically includes: Select a number of discrete points at equal intervals within the upper and lower bounds [P1, P2] of each probability density function, calculate the value of the probability density function at each discrete point, and compose the obtained values ​​into a discretized function in order to complete the discretization of all probability density functions; Convolve the discretized probability density functions two by two, and the convolution formula is: Among them, f1(n) and f2(n) are the discretization results of two different probability density functions, z is the load power value after discretization; b is the intermediate variable; * is the convolution operator.

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