Distributed photovoltaic power quality regulation capability evaluation method based on set operation

Abstract

The invention provides a distributed photovoltaic power quality regulation capability evaluation method based on set operation, which comprises the following steps: firstly, obtaining an active power point estimation result according to prediction data, counting prediction errors, obtaining an error probability density function, further calculating a confidence interval, and constructing an active power reachable set; secondly, statistics is conducted on historical data of the power grid voltage, a voltage reachable set is obtained, a mechanism model calculation formula is converted into a reachable set formula, and through a method of center point addition and generation element superposition, a method of center point multiplication and a method of generation element and center point cross multiplication and then superposition, a mechanism model is obtained; and an estimated value of reactive power is obtained at one time through a set operation method. According to the method, the speed and credibility of photovoltaic active and reactive power assessment calculation are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of distributed photovoltaic power quality regulation capability evaluation, and in particular relates to a distributed photovoltaic power quality regulation capability evaluation method based on set operations. Background Art

[0002] In the process of new urbanization and urban industrial transformation in my country, the large-scale access of high-proportion renewable energy, new flexible loads and high-end industrial clusters has aggravated the power quality problems such as voltage over-limit and harmonic over-standard in the power distribution system. At the same time, the disturbance emission and regulation potential of power electronic converters are both combined, making energy storage, electric vehicle charging piles, photovoltaics, etc. all power quality regulation resources. However, it is difficult to achieve reliable assessment of regulation capacity for uncertain regulation resources such as distributed photovoltaics with multiple influences such as random changes in output power and random fluctuations in grid voltage. In order to manage the power quality problems of power distribution systems with high proportions of renewable energy, it is necessary to conduct a comprehensive assessment of the regulation capacity of distributed photovoltaics and other power quality regulation resources to support the effective management of power quality problems such as voltage over-limit and harmonic over-standard. Traditional distributed photovoltaics usually use point estimates for output forecasts, which makes it difficult to include multiple uncertain factors such as grid voltage in the evaluation scope, and are often limited to active power forecasts. The reactive power of distributed photovoltaics is rarely explored for its regulation potential, resulting in low credibility of capacity analysis results and failure to provide a reliable, comprehensive and quantifiable regulation capacity range for power quality management, which in turn seriously affects the effectiveness of power quality management. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a distributed photovoltaic power quality regulation capacity evaluation method based on set operations. For distributed photovoltaic regulation resources with output uncertainty, the point estimation result of active power is first obtained according to the prediction data, and the prediction error is statistically analyzed to obtain the probability density function of the prediction error, and then the confidence interval of the estimation result is calculated to construct the reachable set of active power. Secondly, the historical data of the grid voltage is statistically analyzed to obtain the voltage reachable set; further combined with the mathematical mechanism model of distributed photovoltaics, the corresponding capacity evaluation reachable set model is constructed, and the reactive power regulation range is obtained at one time through set operations by the method of adding center points and superimposing generators, and the method of multiplying center points and cross-multiplying generators and center points and then superimposing.

[0004] In order to achieve the above object, the present invention adopts the following technical solution: A distributed photovoltaic power quality regulation capability evaluation method based on set operation includes the following steps: Step 1, calculating a distributed photovoltaic power-voltage coupling mechanism model, wherein the distributed photovoltaic power-voltage coupling mechanism model is a mechanism model between the active power, reactive power and voltage of the distributed photovoltaic obtained in a maximum power point tracking operation mode; Step 2: obtaining a power quality regulation capability evaluation model according to the distributed photovoltaic power-voltage coupling mechanism model of step 1; Step 3: Statistic the active power prediction data of distributed photovoltaics, combine it with the actual power data analysis, statistically analyze the prediction deviation, obtain the distribution model and probability density function of the active power deviation of distributed photovoltaics in each period under the confidence interval, and thus obtain the active power reachable set model of distributed photovoltaics; Step 4: Count the historical data of the grid voltage to obtain a grid voltage reachable set; Step 5: In the power quality regulation capability evaluation model, a subtraction operation is performed between existing sets, wherein the subtraction operation is subtraction of reachable sets; Step 6: Obtain the reachable set of reactive power using a reachability analysis method, wherein the reachability analysis method includes a convex hull operation.

[0005] Furthermore, the step 1 comprises: In the maximum power point tracking operation mode, the mechanism model between the active power, reactive power and voltage of distributed photovoltaics satisfies: (1) in, is the upper boundary of reactive power of distributed photovoltaic; U g The grid voltage at the distributed photovoltaic grid connection point; is the AC side output voltage of the distributed photovoltaic inverter, which is a rated value; is the system angular frequency; It is the grid-connected inductor of distributed photovoltaic inverter; is the distributed photovoltaic active power, is the rated capacity of distributed photovoltaics; is the power factor value of distributed photovoltaics; (2) in, is the lower boundary of reactive power of distributed photovoltaic; U g The grid voltage at the distributed photovoltaic grid connection point; is the AC side output voltage of the distributed photovoltaic inverter, which is a rated value; is the system angular frequency; It is the grid-connected inductor of distributed photovoltaic inverter; is the distributed photovoltaic active power, is the rated capacity of distributed photovoltaics; is the power factor value of distributed photovoltaics.

[0006] Furthermore, the step 2 comprises: According to the mechanism model between the active power, reactive power and voltage of distributed photovoltaic in step 1, the power quality regulation capability evaluation model is obtained as follows: (3) in, is the reachable set of distributed photovoltaic reactive power at a certain time section; is the reachable set of grid voltage at a certain time section; It is the reachable set of distributed photovoltaic active power in a certain time section.

[0007] Furthermore, the step 3 comprises: The active power prediction data of distributed photovoltaics is statistically analyzed, combined with the actual power data analysis, and the prediction deviation is statistically analyzed to obtain the distribution model and probability density function of the active power deviation of distributed photovoltaics in each period. Under a certain confidence interval, the probability density function calculation method is as follows: (4) in, Represents the probability density function of distributed photovoltaic active power deviation, is the scale parameter of distributed photovoltaic active power deviation distribution, is the location parameter of the expected value of distributed photovoltaic active power. , This satisfies the 95% confidence interval.

[0008] On this basis, the active power reachable set of distributed photovoltaic is modeled as follows: (5) (6) (7) in, is the reachable active power set of distributed photovoltaics at a certain time section; is the expected value of distributed photovoltaic active power at a certain time section, as The center point of ; is the upper deviation range of distributed photovoltaic active power, is the lower deviation range of distributed photovoltaic active power, and As The generator of ; In formula (3), there exists The set operation can achieve set multiplication, and the corresponding process satisfies: (8) (9).

[0009] Furthermore, the step 4 comprises: The historical data of grid voltage are counted to obtain the reachable set of grid voltage: (10) in, is the grid voltage reachable set at a certain time section, is the expected value of the grid voltage at a certain time section, To meet the scale parameters of the voltage 0.95-1.05pu distribution (pu is the per unit value) under the 95% confidence interval, It means that it satisfies the normal distribution.

[0010] On this basis, the grid voltage reachable set is modeled as follows: (11) (12) (13) in, As The center point of ; is the upper deviation range of the grid voltage, is the lower deviation range of the grid voltage, select As The generator of ; In formula (3), there is The set operation can achieve set multiplication, and the corresponding process satisfies: (14) (15).

[0011] Furthermore, the step 5 comprises: In formula (3), there is a subtraction operation between sets, which can achieve set subtraction. The corresponding process satisfies: (16) (17).

[0012] Further, the step 6 comprises: 1) Based on the reachable set of the system at the current moment, calculate the reachable set formed by the solution corresponding to equation (3) in the next stage.

[0013] 2) Perform convex hull operation on the reachable set in the previous state and the reachable set in the final state to obtain the overall reachable set running trajectory.

[0014] 3) Through a finite number of Taylor series expansions and using matrices to perform equivalent transformations on the remainders in the series, special solutions for different equations are further added and the reachable set is modified to obtain a more reliable operating range.

[0015] Beneficial effects: Compared with the traditional photovoltaic power prediction and evaluation method, the present invention takes into account factors such as the probability of power prediction error of distributed photovoltaics, as well as the influence of multiple uncertain factors such as random changes in output power and random fluctuations in grid voltage, and defines the uncertainty of distributed photovoltaic output by introducing reachable sets and set operations. Reachable set operations are a formal analysis method that obtains all possible trajectory sets of key parameters in the capacity evaluation model from the initial state through a one-time calculation, and can quickly calculate the power quality regulation capacity range of distributed photovoltaics, greatly improving the speed of photovoltaic regulation capacity evaluation calculations, providing a reliable, comprehensive, and quantifiable capacity boundary for power quality management, and effectively supporting efficient power quality management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the distributed photovoltaic power quality regulation capability evaluation method based on set operation of the present invention; Figure 2 Schematic diagram of distributed photovoltaic active power under 95% confidence interval; Figure 3 The diagram is a schematic diagram of the reachable active power set of distributed photovoltaics; Figure 4 Schematic diagram of the grid voltage under the 95% confidence interval; Figure 5 This is a schematic diagram of the grid voltage reachable set. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0018] like Figure 1 As shown, the distributed photovoltaic power quality regulation capability evaluation method based on set operation of the present invention includes the following steps: Step 1: Calculate the mechanism model of distributed photovoltaic power-voltage coupling. In the MPPT (Maximum power point tracking) operation mode, the mechanism model between the active power, reactive power and voltage of distributed photovoltaics satisfies: (1) in, is the upper boundary of reactive power of distributed photovoltaic; U g The grid voltage at the distributed photovoltaic grid connection point; is the AC side output voltage of the distributed photovoltaic inverter, which is a rated value; is the system angular frequency; It is the grid-connected inductor of distributed photovoltaic inverter; is the distributed photovoltaic active power, is the rated capacity of distributed photovoltaics; is the power factor value of distributed photovoltaics.

[0019] (2) in, is the lower boundary of reactive power of distributed photovoltaic; U g The grid voltage at the distributed photovoltaic grid connection point; is the AC side output voltage of the distributed photovoltaic inverter, which is a rated value; is the system angular frequency; It is the grid-connected inductor of distributed photovoltaic inverter; is the distributed photovoltaic active power, is the rated capacity of distributed photovoltaics; is the power factor value of distributed photovoltaics.

[0020] Step 2: Based on the mechanism model between the active power, reactive power and voltage of distributed photovoltaic in step 1, the power quality regulation capability evaluation model can be obtained as follows: (3) in, is the reachable set of distributed photovoltaic reactive power at a certain time section; is the reachable set of grid voltage at a certain time section; It is the reachable set of distributed photovoltaic active power in a certain time section.

[0021] Step 3: Statistic the active power prediction data of distributed photovoltaics, combine it with the actual power data analysis, statistically analyze the prediction deviation, and obtain the distribution model and probability density function of the active power deviation of distributed photovoltaics in each period. Under a certain confidence interval, the probability density function calculation method is as follows: (4) in, Represents the probability density function of distributed photovoltaic active power deviation, is the scale parameter of distributed photovoltaic active power deviation distribution, is the location parameter of the expected value of distributed photovoltaic active power, usually selected , The 95% confidence interval can be satisfied if Figure 2 shown.

[0022] On this basis, the active power reachable set of distributed photovoltaic is modeled as follows: (5) (6) (7) in, is the reachable active power set of distributed photovoltaics at a certain time section; is the expected value of distributed photovoltaic active power at a certain time section, as The center point of ; is the upper deviation range of distributed photovoltaic active power, is the lower deviation range of distributed photovoltaic active power, and the interval is selected As The generator of .

[0023] by Figure 3 For example, there are 24 hours a day, 15 minutes per point, that is, the reachable set around 12 o'clock satisfies , .

[0024] In formula (3), there exists The set operation can achieve set multiplication, and the corresponding process satisfies: (8) (9) Step 4: Count the historical data of the grid voltage to obtain a grid voltage reachable set.

[0025] The grid voltage reachable set satisfies the formula: (10) in, is the grid voltage reachable set at a certain time section, is the expected value of the grid voltage at a certain time section, To meet the scale parameters of the voltage 0.95-1.05pu distribution (pu is the per unit value) under the 95% confidence interval, It means that it satisfies the normal distribution, such as Figure 4 shown.

[0026] On this basis, the grid voltage reachable set is modeled as follows: (11) (12) (13) in, As The center point of ; is the upper deviation range of the grid voltage, is the lower deviation range of the grid voltage, select As The generator of .

[0027] by Figure 5 For example, there are 24 hours a day, 15 minutes per point, that is, the reachable set around 12 o'clock satisfies , .

[0028] In formula (3), there exists The set operation can achieve set multiplication, and the corresponding process satisfies: (14) (15) Step 5: In formula (3), there is a subtraction operation between sets, which can achieve set subtraction. The corresponding process satisfies: (16) (17) Step 6: To obtain the reachable set of reactive power, a reachability analysis method such as convex hull calculation is required. The specific process is as follows: 1) Based on the reachable set of the system at the current moment, calculate the reachable set formed by the solution corresponding to formula (3) in the next stage.

[0029] 2) Perform convex hull operation on the reachable set in the previous state and the reachable set in the final state to obtain the overall reachable set running trajectory.

[0030] 3) Through a finite number of Taylor series expansions and using matrices to perform equivalent transformations on the remainders in the series, special solutions for different equations are further added to modify the reachable set in order to obtain a more reliable operating range.

Claims

1. A distributed photovoltaic power quality regulation capability evaluation method based on set operations, characterized in that: The steps include: Step 1, calculating a distributed photovoltaic power-voltage coupling mechanism model, wherein the distributed photovoltaic power-voltage coupling mechanism model is a mechanism model between the active power, reactive power and voltage of the distributed photovoltaic obtained in a maximum power point tracking operation mode; Step 2: obtaining a power quality regulation capability evaluation model according to the distributed photovoltaic power-voltage coupling mechanism model of step 1; Step 3: Statistic the active power prediction data of distributed photovoltaics, combine it with the actual power data analysis, statistically analyze the prediction deviation, obtain the distribution model and probability density function of the active power deviation of distributed photovoltaics in each period under the confidence interval, and thus obtain the active power reachable set model of distributed photovoltaics; Step 4: Count the historical data of the grid voltage to obtain a grid voltage reachable set; Step 5: In the power quality regulation capability evaluation model, a subtraction operation is performed between existing sets, wherein the subtraction operation is subtraction of reachable sets; Step 6: Use a reachability analysis method to obtain a reachable set of reactive power, wherein the reachability analysis method includes a convex hull operation.

2. According to claim 1, a distributed photovoltaic power quality regulation capability evaluation method based on set operation is characterized in that: The step 1 comprises: In the maximum power point tracking operation mode, the mechanism model between the active power, reactive power and voltage of distributed photovoltaics satisfies: （1） in, is the upper boundary of reactive power of distributed photovoltaic; U g The grid voltage at the distributed photovoltaic grid connection point; is the AC side output voltage of the distributed photovoltaic inverter, which is a rated value; is the system angular frequency; It is the grid-connected inductor of distributed photovoltaic inverter; is the distributed photovoltaic active power, is the rated capacity of distributed photovoltaics; is the power factor value of distributed photovoltaics; （2） in, is the lower boundary of reactive power of distributed photovoltaic; U g The grid voltage at the distributed photovoltaic grid connection point; is the AC side output voltage of the distributed photovoltaic inverter, which is a rated value; is the system angular frequency; It is the grid-connected inductor of distributed photovoltaic inverter; is the distributed photovoltaic active power, is the rated capacity of distributed photovoltaics; is the power factor value of distributed photovoltaics.

3. The method for evaluating distributed photovoltaic power quality regulation capability based on set operations according to claim 1, characterized in that: The step 2 comprises: According to the mechanism model between the active power, reactive power and voltage of distributed photovoltaic in step 1, the power quality regulation capability evaluation model is obtained as follows: （3） in, is the reachable set of distributed photovoltaic reactive power at a certain time section; is the reachable set of grid voltage at a certain time section; It is the reachable set of distributed photovoltaic active power in a certain time section.

4. The method for evaluating distributed photovoltaic power quality regulation capability based on set operations according to claim 3 is characterized in that: The step 3 comprises: The active power prediction data of distributed photovoltaics is statistically analyzed, combined with the actual power data analysis, and the prediction deviation is statistically analyzed to obtain the distribution model and probability density function of the active power deviation of distributed photovoltaics in each period. Under the confidence interval, the probability density function calculation method is as follows: （4） in, Represents the probability density function of distributed photovoltaic active power deviation, is the scale parameter of distributed photovoltaic active power deviation distribution, is the location parameter of the expected value of distributed photovoltaic active power; , Satisfy 95% confidence interval; On this basis, the active power reachable set of distributed photovoltaic is modeled as follows: （5） （6） （7） in, is the reachable active power set of distributed photovoltaics at a certain time section; is the expected value of distributed photovoltaic active power at a certain time section, as The center point of ; is the upper deviation range of distributed photovoltaic active power, is the lower deviation range of distributed photovoltaic active power, and As The generator of ; In formula (3), there exists The set operation can achieve set multiplication, and the corresponding process satisfies: （8） （9）。 5. The method for evaluating distributed photovoltaic power quality regulation capability based on set operations according to claim 4 is characterized in that: The step 4 comprises: The historical data of grid voltage are counted to obtain the reachable set of grid voltage: （10） in, is the grid voltage reachable set at a certain time section, is the expected value of the grid voltage at a certain time section, To meet the scale parameters of the voltage 0.95-1.05pu distribution (pu is the per unit value) under the 95% confidence interval, It means that it satisfies the normal distribution; On this basis, the grid voltage reachable set is modeled as follows: （11） （12） （13） in, As The center point of ; is the upper deviation range of the grid voltage, is the lower deviation range of the grid voltage, select As The generator of ; In formula (3), there exists The set operation can achieve set multiplication, and the corresponding process satisfies: （14） （15）。 6. A distributed photovoltaic power quality regulation capability evaluation method based on set operation according to claim 5, characterized in that: The step 5 comprises: In formula (3), there is a subtraction operation between sets, which can achieve set subtraction. The corresponding process satisfies: (16) （17）。 7. The method for evaluating distributed photovoltaic power quality regulation capability based on set operations according to claim 2, characterized in that: The step 6 comprises: 1) Based on the reachable set of the system at the current moment, calculate the reachable set formed by the solution corresponding to equation (3) in the next stage; 2) Perform convex hull operation on the reachable set in the previous state and the reachable set in the final state to obtain the overall reachable set running trajectory; 3) Through a finite number of Taylor series expansions and using matrices to perform equivalent transformations on the remainders in the series, special solutions for different equations are further added and the reachable set is modified to obtain a more reliable operating range.

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