Method and system for calculating actual power load of line based on active power

Abstract

The invention discloses a method and a system for calculating an actual power load of a line based on active power. The method comprises the following steps: collecting a distributed photovoltaic data set of a current round; calculating probability density distribution based on the last round of distributed photovoltaic historical data set; constructing a cumulative distribution function, and generating a corresponding particle set; updating the particle state; updating the weight of the particles; whether resampling needs to be carried out or not is judged by combining the effectiveness of the distributed photovoltaic active power particle set after weight updating; calculating an active power estimation value; and calculating the actual power load of the line based on the active power estimation value and the active output particle set of the single distributed power supply. According to the technical scheme provided by the invention, not only can the statistical accuracy of the actual electrical load be improved, but also the volatility of the distributed power supply and the influence of the volatility on the line load can be dealt with, so that accurate data support is provided for scheduling, management and optimization of the power system, and the method has relatively high application value.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity load calculation, and particularly relates to a method and system for calculating the actual electricity load of a line based on active power. Background Art

[0002] For the case where a line is connected to a distributed photovoltaic system, currently the industry does not have technical means to statistically calculate the actual electricity load of the line. The active power value observed at the line outlet through the dispatching automation system is the value after the "offset" between the actual electricity load of the line and the active power output of the distributed photovoltaic system. In addition, technologies such as N-1 checking and equipment overload analysis based on the above values are also difficult to reflect the actual operation of the power grid.

[0003] In view of the above, there is an urgent need to design a technical means capable of calculating the actual electricity load of a line to meet the user's usage requirements. Summary of the Invention

[0004] To meet the user's usage requirements, the object of the present invention is to provide a method and system for calculating the actual electricity load of a line based on active power.

[0005] To achieve the object of the present invention, the technical solutions provided by the present invention are as follows: First Aspect The present application provides a method for calculating the actual electricity load of a line based on active power, including the following steps: Step S1: Collect the distributed photovoltaic data set of the current round, where the distributed photovoltaic data set includes distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active power output data of a single distributed power source; Step S2: Based on the distributed photovoltaic historical data set of the previous round, use the kernel density formula to preliminarily calculate the probability density distribution; Step S3: Based on the probability density distribution, implicitly construct a cumulative distribution function in the form of rejection sampling, perform particle randomization screening on the distributed photovoltaic data set, and generate corresponding distributed photovoltaic active power particle sets, distributed photovoltaic irradiance particle sets, distributed photovoltaic inverter efficiency particle sets, distributed photovoltaic line loss rate particle sets, distributed photovoltaic module efficiency particle sets, and active power output particle sets of a single distributed power source; Step S4: Update the particle states in the distributed photovoltaic active power particle set; Step S5: Update the weights of the particles in the distributed photovoltaic active power particle set after the particle states are updated in the form of an observation likelihood function; Step S6: Determine whether resampling is required by combining the effectiveness of the distributed PV active power particle set after weight update; if resampling is required, obtain the final distributed PV active power particle set through resampling; if resampling is not required, directly obtain the final distributed PV active power particle set; Step S7: Calculate the estimated value of active power based on the final distributed PV active power particle set; Step S8: Calculate the actual line power consumption load based on the estimated value of active power and the active power output particle set of a single distributed power source.

[0006] Second aspect Corresponding to the above method, the present application provides a system for calculating the actual line power consumption load based on active power, including a current round data set acquisition unit, a probability density distribution calculation unit, a particle set generation unit, a particle state update unit, a particle weight update unit, a resampling unit, an active power estimated value calculation unit, and a line actual power consumption load calculation unit; The current round data set acquisition unit is configured to acquire the distributed PV data set of the current round, and the distributed PV data set includes distributed PV active power data, distributed PV irradiance data, distributed PV inverter efficiency data, distributed PV line loss rate data, distributed PV module efficiency data, and the active power output data of a single distributed power source; The probability density distribution calculation unit is configured to preliminarily calculate the probability density distribution based on the historical distributed PV data set of the previous round using the kernel density formula; The particle set generation unit is configured to implicitly construct a cumulative distribution function in the form of rejection sampling based on the probability density distribution, perform particle random screening on the distributed PV data set, and generate corresponding distributed PV active power particle sets, distributed PV irradiance particle sets, distributed PV inverter efficiency particle sets, distributed PV line loss rate particle sets, distributed PV module efficiency particle sets, and active power output particle sets of a single distributed power source; The particle state update unit is configured to update the particle state in the distributed PV active power particle set; The particle weight update unit is configured to update the weights of the particles in the distributed PV active power particle set after particle state update in the form of an observation likelihood function; The resampling unit is configured to determine whether resampling is required by combining the effectiveness of the distributed PV active power particle set after weight update; if resampling is required, obtain the final distributed PV active power particle set through resampling; if resampling is not required, directly obtain the final distributed PV active power particle set; The active power estimation value calculation unit is configured to calculate an active power estimation value based on the final distributed PV active power particle set; The actual line power consumption load calculation unit is configured to calculate the actual line power consumption load based on the active power estimation value and the active power output particle set of a single distributed power source.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: The technical solution provided by the present invention can not only improve the statistical accuracy of the actual power consumption load, but also cope with the volatility of distributed power sources and their impact on the line load, thereby providing accurate data support for the dispatching, management, and optimization of the power system, and having high application value. Description of the Drawings

[0008] Figure 1 It is a schematic flowchart of the method provided by an embodiment of the present invention. Detailed Embodiments

[0009] Next, with reference to the drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0010] As Figure 1 shown, a method for calculating the actual line power consumption load based on active power provided by this embodiment includes the following steps: Step S1: Collect the distributed PV data set of the current round. The distributed PV data set includes distributed PV active power data, distributed PV irradiance data, distributed PV inverter efficiency data, distributed PV line loss rate data, distributed PV module efficiency data, and active power output data of a single distributed power source; It should be noted that the distributed PV active power data, distributed PV irradiance data, distributed PV inverter efficiency data, distributed PV line loss rate data, and distributed PV module efficiency data are collected from the automation system of the control center, and the active power output data of a single distributed power source is collected from the marketing and metering system.

[0011] Step S2: Based on the distributed PV historical data set of the previous round, use the kernel density formula to preliminarily calculate the probability density distribution; It should be noted that in the previous round of historical data set, a data set corresponding to the type and content of the distributed PV data set of the current round is covered.

[0012] Among them, in step S2, the calculation formula of the probability density distribution is as follows: ; In the formula, is the probability density distribution; n represents the number of results in the historical dataset of distributed PV in the previous round; h represents the kernel bandwidth; k represents the index number of the results in the historical dataset of distributed PV in the previous round; M represents the result of the historical dataset of distributed PV in the previous round corresponding to the current index number; represents the kernel function, and usually the Gaussian kernel or the uniform kernel is selected according to the data distribution form; M k represents the k-th result of the historical dataset of distributed PV in the previous round.

[0013] Among them, the kernel bandwidth h can be implemented by minimizing the negative log-likelihood form, specifically: h = arg min h [ − ∑ k = 1 n log f ^ − k ( M k ) ] ; In the formula, represents the kernel density calculated after removing the k-th sample from all the data used.

[0014] Step S3: Based on the probability density distribution, implicitly construct the cumulative distribution function in the form of rejection sampling, randomly screen the distributed PV dataset, and generate the corresponding active power particle set of distributed PV, irradiance particle set of distributed PV, inverter efficiency particle set of distributed PV, line loss rate particle set of distributed PV, component efficiency particle set of distributed PV, and active power output particle set of a single distributed power source; Among them, the step S3 specifically includes: Step S31: Based on the probability density distribution, implicitly construct the cumulative distribution function in the form of rejection sampling, specifically: ; In the formula, represents the envelope distribution function, and usually the Gaussian distribution or the uniform distribution form is selected empirically; c represents the sampling ratio constant; among them, the sampling ratio constant is specifically: c = arg max M [ f ^ ( M ) g ( M ) ] .

[0015] Step S32: Generate a candidate point from the envelope distribution function, ; For the candidate point , calculate its acceptance probability : ; In the formula, is the envelope distribution function of the candidate point ; Step S33: Generate a random probability distribution, ; If it meets the condition: , then accept as a particle. Otherwise, regenerate candidate points until the distributed photovoltaic dataset is initialized, obtaining a distributed photovoltaic active power particle set, a distributed photovoltaic irradiance particle set, a distributed photovoltaic inverter efficiency particle set, a distributed photovoltaic line loss rate particle set, a distributed photovoltaic module efficiency particle set, and an active power output particle set of a single distributed power source.

[0016] Step S4: Update the particle state in the distributed photovoltaic active power particle set; Among them, in step S4, when updating the particle state in the distributed photovoltaic active power particle set, the following form is adopted: ; In the formula, i represents the particle number, , N represents the total number of particles; , represents the active power of the i-th particle at time t and time t - 1 respectively; represents the process noise, that is, the quantitative manifestation of system uncertainty, and its form is usually zero-mean Gaussian noise, which is calculated by combining the distributed photovoltaic irradiance particle set, the distributed photovoltaic inverter efficiency particle set, the distributed photovoltaic line loss rate particle set, and the distributed photovoltaic module efficiency particle set; represents the particle state update function; represents the uncertainty of the i-th particle in the distributed photovoltaic active power particle set at time t.

[0017] Among them, the process noise is specifically: ; In the formula, represents the covariance matrix of the process noise, specifically: In the formula, , , , represent the noise intensities of the irradiance, inverter efficiency, line loss rate, and module efficiency of the i-th particle at time t respectively; Furthermore, the noise intensity is specifically: In the formula, , , , respectively represent the noise intensity proportionality factor required for particle quantization of the acquisition variables of the automation system part of the control center corresponding to the i-th particle at time t, and take values from 0.01 to 0.1 according to actual needs; represents t the irradiance corresponding to the i -th particle at time ; t represents i the irradiance corresponding to the -th particle at time t t-1; i represents the inverter efficiency corresponding to the t -th particle at time i t; represents t the inverter efficiency corresponding to the i -th particle at time t-1; t represents i the line loss rate corresponding to the -th particle at time t t; i represents the line loss rate corresponding to the t -th particle at time i t-1;

[0018] The state update function in step S4 is specifically: y ( Δ P t ( i ) , w t ( i ) ) = G t ( i ) ( 1 + w tG ( i ) ) A ⋅ [ 1 + γ G t ( i ) ( 1 + w tG ( i ) ) G t T n ] ⋅ G t ( i ) ( 1 + w tG ( i ) ) G stc ⋅ η inv ( t ) ( i ) ( 1 + w tinv ( i ) ) ⋅ [ 1 − p l ( t ) ( i ) ⋅ ( 1 + w tl ( i ) ) ] ⋅ η m ( t ) ( i ) ( 1 + w tm ( i ) ) In the formula, , , , respectively represent the zero-mean Gaussian noise generation results after particle quantization of the acquisition variables of the automation system part of the control center corresponding to the i-th particle at time t; represents the effective area of the photovoltaic array in the photovoltaic system; represents the temperature power coefficient; represents the nominal operating temperature of the photovoltaic module.

[0019] Step S5: Update the weights of the particles in the distributed photovoltaic active power particle set after the particle state is updated in the form of an observation likelihood function; after the weights are updated, the distribution of the particles will concentrate near the measured value, and the low-weight particles will be gradually eliminated, and the high-weight particles will have a greater impact on the estimation result.

[0020] Among them, step S5 specifically includes: Step S51. Calculate the estimated weight of the particle, with the formula as follows: ; In the formula, represents the normalized weight of the i-th particle at time t-1; represents the measured active power value at time t; represents the estimated weight of the i-th particle at time t; represents the observation likelihood function, usually expressed in the form of a Gaussian distribution: ; In the formula, represents the measurement error variance at time t, which can be updated through the following formula: ; In the formula, represents the measurement error variance at the previous moment; represents the estimated active power value at the previous moment; represents the update smoothing factor, used to balance the contributions of historical estimates and current errors, with an initial value set to 0.9. For a dynamic system, generally takes a value between 0.8 and 0.9; for a steady-state system, generally takes a value between 0.95 and 0.99.

[0021] Specifically, the initial value of the measurement error variance can be determined from historical measurement results, specifically as: ; In the formula, represents the k-th historical estimation result regarding the active power; <> Step S52. Calculate the normalized weight of the particle, with the formula as follows: ; In the formula, v represents the index variable, used to unify the particle numbers; represents the estimated weight of the v-th particle at time t, represents the normalized weight of the i-th particle at time t.

[0022] Specifically, the initial value of the weight is uniformly distributed, that is, there is no preference for the state of each particle at the initial moment: ; In the formula, represents the weight of the i-th particle in the initial state; Step S6: Based on the effectiveness of the distributed PV active power particle set after weight update, determine whether resampling is required. If resampling is required, obtain the final distributed PV active power particle set through resampling; if resampling is not required, directly obtain the final distributed PV active power particle set. Among them, in step S6, the method for determining whether resampling is required is as follows: E s = 1 ∑ i = 1 N [ ω t ( i ) ] 2 < N 2 ; In the formula, represents the number of effective samples; If the above formula holds, it indicates that the particle set has degenerated and resampling is required; otherwise, it shows that the effectiveness of the particle set is relatively high and the state estimation of the filter is still relatively accurate, so resampling is not required.

[0023] Among them, in step S6, the method for resampling is as follows: Step S61: Calculate the cumulative weight of the particles, specifically: ; In the formula, represents the cumulative weight of the i-th particle at time t; j represents the particle index number; represents the normalized weight of the j-th particle at time t; Step S62: To determine the resampling position of the particles, generate a uniformly distributed random number sequence: ; In the formula, and are both random numbers; for each random number , find its corresponding particle index , specifically as follows: ; According to the particle index , resampling can be achieved.

[0024] Resampling can be achieved by the newly obtained particle index sequence. In particular, after obtaining the new particle set, the weight of each particle is reset to be equal, that is, each resampled particle has the same weight.

[0025] Step S7: Based on the final distributed PV active power particle set, calculate the active power estimation value; Among them, in step S7, the method for calculating the active power estimation value is as follows: ; In the formula, represents the active power estimation value at time t.

[0026] Step S8: Calculate the actual line power consumption load based on the estimated active power value and the active power output particle set of a single distributed power source.

[0027] Among them, in step S8, the actual line power consumption load is calculated as follows: ; In the formula, represents the active power output value of the s-th distributed power source connected to this line; represents the number of distributed power sources connected to this line; represents the actual line power consumption load.

[0028] In addition, corresponding to the above method, this embodiment also provides a system for calculating the actual line power consumption load based on active power, including a current round data set acquisition unit, a probability density distribution calculation unit, a particle set generation unit, a particle state update unit, a particle weight update unit, a resampling unit, an estimated active power value calculation unit, and an actual line power consumption load calculation unit; The current round data set acquisition unit is used to acquire the distributed photovoltaic data set of the current round, and the distributed photovoltaic data set includes distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active power output data of a single distributed power source; The probability density distribution calculation unit is used to preliminarily calculate the probability density distribution based on the historical distributed photovoltaic data set of the previous round by using the kernel density formula; The particle set generation unit is used to implicitly construct the cumulative distribution function in the form of rejection sampling based on the probability density distribution, perform particle random screening on the distributed photovoltaic data set, and generate corresponding distributed photovoltaic active power particle sets, distributed photovoltaic irradiance particle sets, distributed photovoltaic inverter efficiency particle sets, distributed photovoltaic line loss rate particle sets, distributed photovoltaic module efficiency particle sets, and active power output particle sets of a single distributed power source; The particle state update unit is used to update the particle state in the distributed photovoltaic active power particle set; The particle weight update unit is used to update the weights of the particles in the distributed photovoltaic active power particle set after the particle state is updated in the form of an observation likelihood function; The resampling unit is used to combine the effectiveness of the distributed photovoltaic active power particle set after weight update to determine whether resampling is required; if resampling is required, the final distributed photovoltaic active power particle set is obtained through resampling; if resampling is not required, the final distributed photovoltaic active power particle set is directly obtained; The active power estimation value calculation unit is configured to calculate an active power estimation value based on the final distributed PV active power particle set; The actual line power consumption load calculation unit is configured to calculate the actual line power consumption load based on the active power estimation value and the active power output particle set of a single distributed power source.

[0029] Finally, it should be noted that the above embodiments are only used for exemplifying and explaining the present invention, and are not intended to limit the present invention to the scope of the described embodiments. In addition, those skilled in the art can understand that the present invention is not limited to the above embodiments, and more variations and modifications can be made according to the teachings of the present invention, and these variations and modifications all fall within the scope claimed by the present invention.

Claims

1. A method for calculating the actual power consumption load of a line based on active power, characterized in that It includes the following steps: Step S1: Collect the distributed photovoltaic dataset of the current round. The distributed photovoltaic dataset includes distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active power output data of a single distributed power source; Step S2: Based on the distributed photovoltaic historical dataset of the previous round, preliminarily calculate the probability density distribution using the kernel density formula; Step S3: Based on the probability density distribution, implicitly construct the cumulative distribution function in the form of rejection sampling, randomly screen the distributed photovoltaic dataset, and generate corresponding distributed photovoltaic active power particle sets, distributed photovoltaic irradiance particle sets, distributed photovoltaic inverter efficiency particle sets, distributed photovoltaic line loss rate particle sets, distributed photovoltaic module efficiency particle sets, and active power output particle sets of a single distributed power source; Step S4: Update the particle states in the distributed photovoltaic active power particle set; Step S5: Update the weights of the particles in the distributed photovoltaic active power particle set after the particle state update in the form of an observation likelihood function; Step S6: Combine the effectiveness of the distributed photovoltaic active power particle set after weight update to determine whether resampling is required; if resampling is required, obtain the final distributed photovoltaic active power particle set through resampling; if resampling is not required, directly obtain the final distributed photovoltaic active power particle set; Step S7: Calculate the active power estimate based on the final distributed photovoltaic active power particle set; Step S8: Calculate the actual line power consumption based on the active power estimate and the active power output particle set of a single distributed power source.

2. The method for calculating the actual power consumption load of a line based on active power according to claim 1, wherein In step S2, the calculation formula of the probability density distribution is as follows: ； In the formula, is the probability density distribution; n represents the number of results in the historical dataset of distributed PV in the previous round; h represents the kernel bandwidth; k represents the index number of the results in the historical dataset of distributed PV in the previous round; M represents the result of the historical dataset of distributed PV in the previous round corresponding to the current index number; represents the kernel function, and usually the Gaussian kernel or the uniform kernel is selected according to the data distribution form; M k represents the k-th result of the historical dataset of distributed PV in the previous round.

3. The method for calculating the actual power consumption load of a line based on active power according to claim 2, wherein The specific content of step S3 includes: Step S31: Based on the probability density distribution, implicitly construct the cumulative distribution function in the form of rejection sampling, specifically: ； wherein represents the envelope distribution function, and a Gaussian distribution or a uniform distribution form is selected; c represents the sampling proportionality constant; Step S32: Generate a candidate point from the envelope distribution function, ; For the candidate point , calculate its acceptance probability : ； In the formula, is the candidate point 's envelope distribution function; Step S33: Generate a random probability distribution, ; if it satisfies the condition: , then accept as a particle, otherwise regenerate the candidate points until the distributed photovoltaic dataset is initialized completely, obtaining a distributed photovoltaic active power particle set, a distributed photovoltaic irradiance particle set, a distributed photovoltaic inverter efficiency particle set, a distributed photovoltaic line loss rate particle set, a distributed photovoltaic module efficiency particle set, and an active power output particle set of a single distributed power source.

4. A method for calculating the actual power consumption load of a line based on active power according to claim 3, characterized in that, In step S4, the particle states in the distributed photovoltaic active power particle set are updated in the following form: ； Wherein, i represents the particle number, , and N represents the total number of particles; , represents the active power of the i-th particle at time t and t-1 respectively; represents the process noise, which is calculated by combining the distributed photovoltaic irradiance particle set, the distributed photovoltaic inverter efficiency particle set, the distributed photovoltaic line loss rate particle set, and the distributed photovoltaic module efficiency particle set; represents the state update function of the particle; represents the uncertainty of the i-th particle in the distributed photovoltaic active power particle set at time t.

5. A method for calculating the actual power consumption load of a line based on active power according to claim 4, characterized in that, The specific content of step S5 includes: Step S51. Calculate the weight estimate value of the particle, and the formula is as follows: ； wherein, represents the normalized weight of the i-th particle at the (t-1)th moment; represents the measured value of active power at the t-th moment; represents the observation likelihood function; represents the estimated value of the weight of the i-th particle at the t-th moment; Step S52. Calculate the normalized weight of the particle, and the formula is as follows: ； Wherein, v represents an index variable for uniformly numbering particles; represents the estimated weight value of the v-th particle at time t, represents the normalized weight of the i-th particle at time t.

6. A method for calculating the actual power consumption load of a line based on active power according to claim 5, characterized in that, In step S6, the method for determining whether resampling is required is as follows: ； Wherein, represents the number of effective samples; if the above formula holds, it means that the particle set has degenerated and resampling is required; On the contrary, resampling is not required.

7. A method for calculating the actual electricity load of a line based on active power according to claim 6, characterized in that, In step S6, the method for performing resampling is as follows: Step S61: Calculate the cumulative weight of the particle, specifically: ； wherein, represents the cumulative weight of the i-th particle at time t; j represents the particle index number; represents the normalized weight of the j-th particle at time t; Step S62: To determine the resampling position of the particle, generate a uniformly distributed random number sequence: ； In the formula, and are both random numbers; for each random number , find out its corresponding particle index as follows: ； According to the particle index , resampling can be achieved.

8. A method for calculating the actual power consumption load of a line based on active power according to claim 7, characterized in that In step S7, the method for calculating the active power estimate is as follows: ； Wherein, represents the estimated active power value at time t.

9. A method for calculating the actual power consumption load of a line based on active power according to claim 8, characterized in that In step S8, the method for calculating the actual line power consumption is as follows: ； Wherein, represents the active power output value of the s-th distributed power source connected to this line; represents the number of distributed power sources connected to this line; represents the actual power consumption load of the line.

10. A system for calculating the actual power consumption load of a line based on active power, characterized in that, It includes a current round dataset acquisition unit, a probability density distribution calculation unit, a particle set generation unit, a particle state update unit, a particle weight update unit, a resampling unit, an active power estimate calculation unit, and an actual line power consumption calculation unit; The current round dataset acquisition unit is used to acquire the distributed photovoltaic dataset of the current round. The distributed photovoltaic dataset includes distributed photovoltaic active power data, distributed photovoltaic irradiance data, distributed photovoltaic inverter efficiency data, distributed photovoltaic line loss rate data, distributed photovoltaic module efficiency data, and active power output data of a single distributed power source; The probability density distribution calculation unit is used to preliminarily calculate the probability density distribution based on the distributed photovoltaic historical dataset of the previous round by using the kernel density formula; The particle set generation unit is used to implicitly construct the cumulative distribution function in the form of rejection sampling based on the probability density distribution, randomly screen the distributed photovoltaic dataset by particles, and generate corresponding distributed photovoltaic active power particle sets, distributed photovoltaic irradiance particle sets, distributed photovoltaic inverter efficiency particle sets, distributed photovoltaic line loss rate particle sets, distributed photovoltaic module efficiency particle sets, and active power output particle sets of a single distributed power source; The particle state update unit is used to update the particle state in the distributed photovoltaic active power particle set; The particle weight update unit is used to update the weights of the particles in the distributed photovoltaic active power particle set after the particle state is updated in the form of an observation likelihood function; The resampling unit is used to determine whether resampling is required in combination with the validity of the distributed photovoltaic active power particle set after weight update; if resampling is required, the final distributed photovoltaic active power particle set is obtained through resampling; if resampling is not required, the final distributed photovoltaic active power particle set is directly obtained; The active power estimation value calculation unit is used to calculate the active power estimation value based on the final distributed photovoltaic active power particle set; The actual line power consumption calculation unit is used to calculate the actual line power consumption based on the active power estimation value and the active power output particle set of a single distributed power source.

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