A user reliability demand analysis method and terminal considering source-load interaction

Abstract

The application discloses a kind of user reliability demand analysis method and terminal considering source-load interaction, based on illumination intensity historical data and user load historical data determine the load node of user with illumination intensity strong correlation, according to load node establishes source-load interaction correlation model, according to source-load interaction correlation model obtains net load characteristic curve, utilizes net load characteristic curve to carry out user reliability demand analysis, can combine closely, source, load real-time operation, on the basis of efficient interaction of the two, analyze user reliability demand, so as to improve the fine management level of power supply reliability from demand side, can reduce power grid investment cost, further improves power supply reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network reliability planning, and in particular to a user reliability demand analysis method and terminal taking into account source-load interaction. Background Art

[0002] With the construction of new power systems, the factors affecting the reliability level of distribution networks are gradually increasing. At present, domestic scholars and experts are studying the reliability of distribution network power supply from different perspectives, mainly covering the factors affecting power supply reliability, power supply reliability assessment methods and power supply reliability indicators. In particular, considering the rapid development of informatization and economy, users' demand for power supply reliability has gradually increased, resulting in a significant impact of user demand-side response on power supply reliability. Some scholars and power companies have also studied the power supply reliability assessment under the differentiation of user reliability and have achieved certain research results. However, the analysis of differentiated user reliability needs is limited to the traditional reliability index decomposition and its consideration of user reliability assessment research, and has not comprehensively and holistically considered the user reliability needs under the "source-load" interaction. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a user reliability demand analysis method and terminal taking into account source-load interaction, which can improve the level of refined management of power supply reliability from the demand side.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A user reliability demand analysis method taking into account source-load interaction comprises the following steps:

[0006] Determine the load nodes of users that are strongly correlated with the light intensity based on the historical light intensity data and the historical user load data, and establish a source-load interaction correlation model based on the load nodes;

[0007] Obtaining photovoltaic and load correlation data at the load node according to the source-load interaction correlation model, and obtaining a net load characteristic curve based on the photovoltaic and load correlation data;

[0008] The net load characteristic curve is divided into power consumption periods, and power outage losses are fitted based on the divided power consumption periods to obtain comprehensive user reliability requirements.

[0009] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0010] A user reliability demand analysis terminal taking into account source-load interaction includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0011] Determine the load nodes of users that are strongly correlated with the light intensity based on the historical light intensity data and the historical user load data, and establish a source-load interaction correlation model based on the load nodes;

[0012] Obtaining photovoltaic and load correlation data at the load node according to the source-load interaction correlation model, and obtaining a net load characteristic curve based on the photovoltaic and load correlation data;

[0013] The net load characteristic curve is divided into power consumption periods, and power outage losses are fitted based on the divided power consumption periods to obtain comprehensive user reliability requirements.

[0014] The beneficial effects of the present invention are: based on the historical data of light intensity and the historical data of user load, the load nodes of users that are strongly correlated with light intensity are determined, a source-load interaction correlation model is established according to the load nodes, and a net load characteristic curve is obtained according to the source-load interaction correlation model. The net load characteristic curve is used to analyze the user reliability demand, which can closely combine the real-time operation of load and source, analyze the user reliability demand on the basis of the efficient interaction between the two, thereby improving the level of refined management of power supply reliability from the demand side, reducing the investment cost of the power grid, and further improving the power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of the steps of a method for analyzing user reliability requirements taking into account source-load interaction according to an embodiment of the present invention;

[0016] Figure 2 2 is a schematic structural diagram of a user reliability demand analysis terminal taking source-load interaction into consideration according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0018] Please refer to Figure 1 A user reliability demand analysis method considering source-load interaction includes the following steps:

[0019] Determine the load nodes of users that are strongly correlated with the light intensity based on the historical light intensity data and the historical user load data, and establish a source-load interaction correlation model based on the load nodes;

[0020] Obtaining photovoltaic and load correlation data at the load node according to the source-load interaction correlation model, and obtaining a net load characteristic curve based on the photovoltaic and load correlation data;

[0021] The net load characteristic curve is divided into power consumption periods, and power outage losses are fitted based on the divided power consumption periods to obtain comprehensive user reliability requirements.

[0022] From the above description, it can be seen that the beneficial effects of the present invention are: based on the historical data of light intensity and the historical data of user load, the load nodes of users that are strongly correlated with the light intensity are determined, a source-load interaction correlation model is established according to the load nodes, and a net load characteristic curve is obtained according to the source-load interaction correlation model. The net load characteristic curve is used to analyze the user reliability demand, which can closely combine the real-time operation of load and source, and analyze the user reliability demand on the basis of the efficient interaction between the two, thereby improving the level of refined management of power supply reliability from the demand side, reducing the investment cost of the power grid, and further improving the power supply reliability.

[0023] Furthermore, the determining of the load node of the user that is strongly correlated with the light intensity based on the light intensity historical data and the user load historical data includes:

[0024] Calculate the Kendall rank correlation coefficient between historical data on light intensity and historical data on user load;

[0025] Sorting the Kendall rank correlation coefficients in descending order to obtain sorted Kendall rank correlation coefficients;

[0026] The load nodes of users that are strongly correlated with the light intensity are determined according to the sorted Kendall rank correlation coefficient.

[0027] From the above description, it can be seen that it is more accurate and efficient to screen out load nodes of users that are strongly correlated with light intensity by calculating the Kendall rank correlation coefficient between light intensity history data and user load history data.

[0028] Furthermore, establishing a source-load interaction correlation model according to the load node includes:

[0029] A source-load interaction correlation model is established using a Copula function according to the load nodes.

[0030] From the above description, we can see that the Copula function is not only applicable when solving complex high-dimensional joint distribution problems, but can also be used to describe nonlinear and asymmetric correlations between variables. Using the Copula function to establish a source-load interaction correlation model based on load nodes ensures the effectiveness of subsequent reliability demand analysis.

[0031] Furthermore, obtaining a net load characteristic curve based on the photovoltaic and load correlation data includes:

[0032] Constructing a maximum load vector group and a minimum load vector group of the load node according to the photovoltaic and load correlation data;

[0033] Determining a load node fluctuation characteristic according to the maximum load value vector group and the minimum load value vector group;

[0034] Acquiring light intensity data, and calculating photovoltaic output power characteristics based on the light intensity data;

[0035] generating a load characteristic curve according to the load node fluctuation characteristics, and generating a photovoltaic power station output power curve according to the photovoltaic output power characteristics;

[0036] The load characteristic curve is coupled with the photovoltaic power station output power curve to obtain a net load characteristic curve.

[0037] From the above description, it can be seen that the net load characteristic curve represents the net load characteristics under source-load interaction, which facilitates subsequent reliability demand analysis.

[0038] Furthermore, determining the load node fluctuation characteristics according to the maximum load value vector group and the minimum load value vector group includes:

[0039] Determine a load peak point and a load valley point according to the maximum load value vector group and the minimum load value vector group;

[0040] The load fluctuation interval is divided according to the load peak point and the valley point to obtain the load node fluctuation characteristics.

[0041] From the above description, it can be seen that the load node fluctuation characteristics are obtained according to the maximum load vector group and the minimum load vector group, and the user's electricity consumption situation can be understood.

[0042] Furthermore, the calculating of photovoltaic output power characteristics based on the light intensity data includes:

[0043] P = I × A × τ;

[0044] Where P represents the photovoltaic output power of the photovoltaic power station at a certain moment, I represents the light intensity of the photovoltaic panel, A represents the area of ​​a single photovoltaic module, and τ represents the photoelectric conversion efficiency of the photovoltaic cell.

[0045] From the above description, it can be seen that the photovoltaic output power characteristics calculated based on light intensity data can be subsequently combined with the load node fluctuation characteristics to obtain the net load curve, which improves the comprehensiveness and integrity of the user reliability demand analysis.

[0046] Furthermore, dividing the net load characteristic curve into power consumption time periods includes:

[0047] The net load characteristic curve is divided into power consumption time periods using a fuzzy semi-trapezoidal membership function method.

[0048] Furthermore, the use of the fuzzy semi-trapezoidal membership function method to divide the net load characteristic curve into power consumption periods includes:

[0049]

[0050] Wherein, u(x) represents the membership value, b represents the peak point of the net load characteristic curve, a represents the valley point of the net load characteristic curve, and x represents the characteristic function of the net load changing with time t.

[0051] From the above description, it can be seen that using the fuzzy semi-trapezoidal membership function method to divide electricity consumption time periods is faster and more convenient.

[0052] Furthermore, the power outage loss is fitted based on the divided power consumption periods to obtain the comprehensive user reliability requirements, including:

[0053] f=p1d1+p2d2;

[0054] Where f represents the comprehensive user reliability demand, p1 represents the peak load, p2 represents the valley load, d1 represents the peak electricity price, and d2 represents the valley electricity price.

[0055] From the above description, we can see that, taking into account the different electricity prices for users in different time periods, the power outage losses are fitted according to the divided electricity consumption periods to obtain the power outage losses of users in each time period, that is, the comprehensive user reliability requirements. Subsequently, the comprehensive user reliability requirements can provide a reference for planners to select photovoltaic and other new energy grid connection points when conducting reliability planning, and also provide guarantees for the safe and stable operation of the power grid under the construction of new power systems.

[0056] Please refer to Figure 2 Another embodiment of the present invention provides a user reliability demand analysis terminal taking into account source-load interaction, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned user reliability demand analysis method taking into account source-load interaction is implemented.

[0057] The above-mentioned user reliability demand analysis method and terminal considering source-load interaction of the present invention can be applied to distribution network reliability planning scenarios, and are described below through specific implementation methods:

[0058] Please refer to Figure 1 , embodiment 1 of the present invention is:

[0059] A user reliability demand analysis method taking into account source-load interaction comprises the following steps:

[0060] S1. Determine the load nodes of users that are strongly correlated with the light intensity based on the historical light intensity data and the historical user load data, and establish a source-load interaction correlation model based on the load nodes, specifically including S11-S14:

[0061] S11. Calculate the Kendall rank correlation coefficient between the historical data of light intensity and the historical data of user load.

[0062] S12. Sort the Kendall rank correlation coefficients in descending order to obtain sorted Kendall rank correlation coefficients.

[0063] S13. Determine the load node of the user that is strongly correlated with the light intensity according to the sorted Kendall rank correlation coefficient.

[0064] Specifically, a Kendall rank correlation coefficient reaching a preset threshold is selected from the sorted Kendall rank correlation coefficients, and the load node of the user corresponding to the Kendall rank correlation coefficient reaching the preset threshold is determined as the load node of the user with strong correlation with light intensity.

[0065] S14. Establishing a source-load interaction correlation model using a Copula function according to the load nodes.

[0066] S2. Obtaining photovoltaic and load correlation data at the load node according to the source-load interaction correlation model, and obtaining a net load characteristic curve based on the photovoltaic and load correlation data, specifically including S21-S26:

[0067] S21. Obtaining photovoltaic and load correlation data at the load node according to the source-load interaction correlation model.

[0068] S22: Construct the maximum load vector group of the load node according to the photovoltaic and load correlation data and load minimum vector group

[0069] S23, determining the load node fluctuation characteristics according to the maximum load vector group and the minimum load vector group, specifically including S231-S232:

[0070] S231. Determine a load peak point and a load valley point according to the maximum load value vector group and the minimum load value vector group.

[0071] S232. Divide the load fluctuation interval according to the load peak points and valley points to obtain load node fluctuation characteristics.

[0072] S24. Obtain light intensity data, and calculate photovoltaic output power characteristics based on the light intensity data, specifically:

[0073] P = I × A × τ;

[0074] Where P represents the photovoltaic output power of the photovoltaic power station at a certain moment, I represents the light intensity of the photovoltaic panel, A represents the area of ​​a single photovoltaic module, and τ represents the photoelectric conversion efficiency of the photovoltaic cell.

[0075] S25. Generate a load characteristic curve according to the load node fluctuation characteristics, and generate a photovoltaic power station output power curve according to the photovoltaic output power characteristics.

[0076] S26. Couple the load characteristic curve with the photovoltaic power station output power curve to obtain a net load characteristic curve.

[0077] S3. Divide the net load characteristic curve into power consumption periods, and perform power outage loss fitting based on the divided power consumption periods to obtain comprehensive user reliability requirements.

[0078] The dividing of the net load characteristic curve into power consumption time periods includes:

[0079] The net load characteristic curve is divided into power consumption periods using the fuzzy semi-trapezoidal membership function method, specifically:

[0080]

[0081] Wherein, u(x) represents the membership value, b represents the peak point of the net load characteristic curve, a represents the valley point of the net load characteristic curve, and x represents the characteristic function of the net load changing with time t, that is, the net load characteristic curve.

[0082] The electricity consumption period includes valley period and peak period. The period when the membership value reaches 0.7 is divided into valley period, and the period when the membership value reaches 0.3 is divided into peak period.

[0083] The method of fitting the power outage loss based on the divided power consumption periods to obtain the comprehensive user reliability requirements includes:

[0084] f=p1d1+p2d2;

[0085] Where f represents the comprehensive user reliability demand, p1 represents the peak load, p2 represents the valley load, d1 represents the peak electricity price, and d2 represents the valley electricity price.

[0086] Please refer to Figure 2 , the second embodiment of the present invention is:

[0087] A user reliability demand analysis terminal taking into account source-load interaction includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the user reliability demand analysis method taking into account source-load interaction in embodiment 1 is implemented.

[0088] In summary, the present invention provides a user reliability demand analysis method and terminal taking into account source-load interaction, which determines the load nodes of users who are strongly correlated with light intensity based on light intensity historical data and user load historical data, establishes a source-load interaction correlation model according to the load nodes, obtains a net load characteristic curve according to the source-load interaction correlation model, and uses the net load characteristic curve to perform user reliability demand analysis, which can closely combine the real-time operation of load and source, analyze user reliability needs based on the efficient interaction between the two, thereby improving the level of refined management of power supply reliability from the demand side, reducing the investment cost of the power grid, and further improving the power supply reliability; in addition, it is more accurate and efficient to screen the load nodes of users who are strongly correlated with light intensity by calculating the Kendall rank correlation coefficient between the light intensity historical data and the user load historical data.

[0089] The above descriptions are merely embodiments of the present invention and are not intended to limit the scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the scope of the present invention's patent protection.

Claims

1. A user reliability demand analysis method taking into account source-load interaction, characterized in that: Including steps: Determine the load nodes of users that are strongly correlated with the light intensity based on the historical light intensity data and the historical user load data, and establish a source-load interaction correlation model based on the load nodes; Obtaining photovoltaic and load correlation data at the load node according to the source-load interaction correlation model, and obtaining a net load characteristic curve based on the photovoltaic and load correlation data; Dividing the net load characteristic curve into power consumption periods, and fitting power outage losses based on the divided power consumption periods to obtain comprehensive user reliability requirements; The method of determining the load node of a user that is strongly correlated with the light intensity based on the light intensity historical data and the user load historical data includes: Calculate the Kendall rank correlation coefficient between historical data on light intensity and historical data on user load; Sorting the Kendall rank correlation coefficients in descending order to obtain sorted Kendall rank correlation coefficients; Determine the load node of the user that is strongly correlated with the light intensity according to the sorted Kendall rank correlation coefficient; The obtaining of a net load characteristic curve based on the photovoltaic and load correlation data includes: Constructing a maximum load vector group and a minimum load vector group of the load node according to the photovoltaic and load correlation data; Determining a load node fluctuation characteristic according to the maximum load value vector group and the minimum load value vector group; Acquiring light intensity data, and calculating photovoltaic output power characteristics based on the light intensity data; generating a load characteristic curve according to the load node fluctuation characteristics, and generating a photovoltaic power station output power curve according to the photovoltaic output power characteristics; Coupling the load characteristic curve with the photovoltaic power station output power curve to obtain a net load characteristic curve; The determining of the load node fluctuation characteristics according to the maximum load value vector group and the minimum load value vector group includes: Determine a load peak point and a load valley point according to the maximum load value vector group and the minimum load value vector group; Divide the load fluctuation interval according to the load peak point and the valley point to obtain the load node fluctuation characteristics; Calculating photovoltaic output power characteristics based on the light intensity data includes: ； In the formula, P represents the photovoltaic output power of the photovoltaic power station at a certain moment, I represents the light intensity of the photovoltaic panel, and A represents the area of ​​a single photovoltaic module. Represents the photoelectric conversion efficiency of photovoltaic cells.

2. The user reliability demand analysis method considering source-load interaction according to claim 1, characterized in that: The establishing of the source-load interaction correlation model according to the load node includes: A source-load interaction correlation model is established using a Copula function according to the load nodes.

3. The user reliability demand analysis method considering source-load interaction according to claim 1, characterized in that: The dividing the net load characteristic curve into power consumption time periods includes: The net load characteristic curve is divided into power consumption time periods using a fuzzy semi-trapezoidal membership function method.

4. The user reliability demand analysis method considering source-load interaction according to claim 3, characterized in that: The method of dividing the net load characteristic curve into power consumption periods using the fuzzy semi-trapezoidal membership function method includes: ； Wherein, u(x) represents the membership value, b represents the peak point of the net load characteristic curve, a represents the valley point of the net load characteristic curve, and x represents the characteristic function of the net load changing with time t.

5. The user reliability demand analysis method considering source-load interaction according to claim 3, characterized in that: The power outage loss is fitted based on the divided power consumption period to obtain the comprehensive user reliability requirements, including: ； Where f represents the comprehensive user reliability demand, p1 represents the peak load, p2 represents the valley load, d1 represents the peak electricity price, and d2 represents the valley electricity price.

6. A user reliability demand analysis terminal taking into account source-load interaction, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for analyzing user reliability requirements taking into account source-load interaction according to any one of claims 1 to 5 is implemented.

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