CN115800385A - Electric energy quality regulation and control method based on adjustable and controllable capacity of photovoltaic inverter and charging pile - Google Patents
Electric energy quality regulation and control method based on adjustable and controllable capacity of photovoltaic inverter and charging pile Download PDFInfo
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Abstract
The invention relates to an electric energy quality regulation and control method based on the adjustable and controllable capacity of a photovoltaic inverter and a charging pile. The invention comprises the following steps: calculating the adjustable capacity of the photovoltaic inverter and the charging pile; determining a power quality area to be regulated; regulating and controlling the quality of electric energy; and (5) evaluating the power quality index. According to the method, the electric energy quality of the alternating current-direct current power distribution network is improved by utilizing the electric energy quality area active limitation method of the adjustable capacity of the photovoltaic inverter and the charging pile, so that the residual capacity of the photovoltaic inverter and the charging pile of the electric automobile is fully utilized, and the electric energy quality of the alternating current-direct current power distribution network is improved in an area limitation mode.
Description
Technical Field
The invention relates to the technical field of power quality regulation and control of a power distribution network, in particular to a power quality regulation and control method based on the adjustable and controllable capacity of a photovoltaic inverter and a charging pile.
Background
Photovoltaic inverter, electric automobile fill electric pile etc. and use increasingly extensively in the distribution network based on the device of power electronics technique, and the electric energy quality problem that is brought by high proportion new forms of energy consumption, many unit load power consumption is obvious gradually. The grid connection of the photovoltaic inverter and the electric vehicle charging pile can generate a series of influences on the power quality of a power grid, wherein the influences mainly comprise voltage deviation, harmonic waves and three-phase voltage unbalance, and the safe and stable operation of the power grid is influenced.
Most of the existing methods for improving the power quality include an active power filter, a static synchronous compensator, a dynamic voltage restorer and other power quality control devices. However, one type of regulation and control equipment mainly aims at the problem of one type of electric energy quality, and for the problem of multiple types of electric energy quality, multiple types of regulation and control equipment need to be purchased, the purchase and maintenance cost of the equipment needs to be considered, and extra physical space is occupied, so that the layout and wiring in a power distribution network are more complicated.
Meanwhile, the power quality control method in the power distribution network can be classified into two types, the first type is that the traditional power quality control equipment is added, but the economic cost and the physical space are limited. Considering that the inverter is not always in a full power state, the method for regulating and controlling the power quality by utilizing the residual capacity of the inverter is a second type of regulation and control method, the inverter equipment is similar to the topological structures of APF, SVC and the like, the regulation and control of the power quality can be realized by upgrading a software algorithm or adding a corresponding sensor on the basis of the existing hardware, and when a plurality of inverters are cooperatively controlled to participate in the regulation and control of the power quality, harmonic wave, reactive power and unbalanced components in a power grid can be effectively compensated.
Then, how to consider photovoltaic inverter and electric automobile fill electric pile comprehensive factor, adjust and control the processing of electric energy quality has become the technical problem that needs to solve badly.
Disclosure of Invention
The invention aims to solve the defect that influence factors of a photovoltaic inverter and an electric vehicle charging pile are not fully considered in the electric energy quality regulation and control method in the prior art, and provides an electric energy quality regulation and control method based on the adjustable and controllable capacity of the photovoltaic inverter and the charging pile to solve the problems.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a power quality regulation and control method based on the adjustable and controllable capacity of a photovoltaic inverter and a charging pile comprises the following steps:
11 Calculation of the controllable capacity of the photovoltaic inverter and the charging pile: the method comprises the steps of utilizing the residual capacity of an inverter to adjust the power quality index of a power grid and calculating the controllable capacity Q of the inverter and the power quality index of the power grid, wherein the inverter comprises a photovoltaic inverter and an electric automobile charging pile, and the controllable capacity Q comprises the capacity constraint Q of the photovoltaic inverter pv And the electric automobile fills the controllability Q of electric pile EV (t,x)；
12 Determination of the power quality region to be regulated): according to the severity of the power quality index, selecting the most severe power quality index (in practical application, the power quality index can comprise a harmonic quality index, a voltage deviation index and a three-phase imbalance index) as the power quality index of the first regulation, performing region division on a region needing power quality regulation based on the selected power quality index, and sequentially dividing a plurality of nodes with high correlation degrees into the same region according to the sequence of the correlation degrees, thereby completing region division and obtaining a plurality of regions of power quality to be regulated;
13 Control of power quality): carrying out power quality regulation and control on a plurality of regions of which the power quality is to be regulated and controlled;
14 Evaluation of power quality indicators: and (3) after regulation, using the power quality monitor to evaluate each power quality index to obtain an evaluation result, if the power quality indexes do not meet the requirements, selecting the power quality indexes which do not meet the requirements to carry out regulation and control again, and repeating the steps 12-14) until all the power quality indexes meet the requirements.
Namely, regulation and control are carried out and evaluation are carried out according to regional division, and the specific process of repeatedly regulating and controlling until the power quality reaches the standard is as follows: firstly, performing regional division on the most serious power quality index according to grey correlation analysis, regulating and controlling the power quality index by using a controllable inverter, and not performing decoupling regulation and control on the power quality index; and evaluating the power quality after regulation, selecting the second serious index for regional division and regulation, and repeatedly regulating and controlling until the power quality reaches the standard.
The calculation of the controllable capacity of the photovoltaic inverter and the charging pile comprises the following steps:
21 Let the photovoltaic array output power Ps be expressed as:
P S ＝ηSI[1-0.005(t 0 +25)]， (1)
wherein S is the area of the photovoltaic array, I is the irradiation intensity on the photovoltaic array, t 0 The ambient temperature is used, and eta is the photoelectric conversion efficiency of the photovoltaic array;
the influence factors on the photovoltaic array, namely the irradiation intensity and the ambient temperature in the formula (1), are used as the input quantity of the prediction model F to obtain the predicted value P of the output power of the photovoltaic array pv Expression (2) of (c):
P pv ＝F prediction (I,t 0 )； (2)
22 Calculate a capacity constraint Q for a photovoltaic inverter pv : calculating the maximum reactive output Q of the photovoltaic inverter which can be used for voltage control by using the following formula (3) pv.max With its capacity constraint Q pv ：
Wherein Q pv.max For maximum reactive output of the photovoltaic inverter, S pv Rated capacity, P, of photovoltaic inverter pv Outputting a predicted value of the power for the photovoltaic array; q pv Is a capacity constraint of the photovoltaic inverter;
23 Calculation of the controllability of the electric vehicle charging pile: time-space characteristics of combining electric automobile charging pile are combined to calculate controllability Q of electric automobile charging pile EV (t,x)；
24 To the initial photovoltaic array output power predicted value P pv And correcting the total load G (t, x) of the electric automobile, which specifically comprises the following substeps:
241 Controllable capacity Q of the photovoltaic inverter calculated according to the above steps pv And the adjustable capacity Q of the charging pile of the electric automobile EV (t, x) and calculating a predicted relative error δ, respectively, as a calculation formula shown in the following equation (8):
wherein, P F Respectively calculating residual capacity prediction values P for the photovoltaic inverter and the electric vehicle charging pile R Respectively counting the actual values of the residual capacity of the photovoltaic inverter and the charging pile of the electric automobile;
242 Determining a state probability transition matrix, taking an upper threshold value and a lower threshold value of respective relative error delta of the photovoltaic inverter and the electric vehicle charging pile as a state division range, and calculating a state transition probability matrix P according to the state of the relative error (k) As shown in the following formula (9):
wherein the content of the first and second substances,
number of transitions from i to j of sample state, A i Is the total number of occurrences of the state;
according to the calculated relative error delta and the state transition probability matrix P (k) Determining an initial state vector X (0), and calculating the state transition result of the k step by using a state transition formula (10):
X(k)＝X(0)P (k) ， (10)
the elements in the matrix X (k) are the probabilities that the relative errors are located in different states, the relative error delta' is predicted according to Markov obtained by an upper threshold and a lower threshold of an error state interval, and the corrected power value P of the photovoltaic and electric vehicles is calculated according to a formula (11) T ：
Wherein, delta h 、δ l Respectively an upper threshold and a lower threshold, P, of the interval of the error state T Is the corrected power value;
243 Respectively substituting the corrected power values into the calculation formulas (3) and (7) of the controllable capacities of the photovoltaic inverter and the electric vehicle and respectively replacing P pv And G (t, x) are calculated again to obtain the final controllable capacity Q.
The determination of the electric energy quality area to be regulated comprises the following steps:
31 Carrying out correlation analysis on the monitoring data of the power quality index, and calculating a correlation coefficient of a comparison sequence and a reference sequence to realize the partition of the power quality;
assuming that m monitoring points are arranged in a power quality monitoring network of a certain system, the time dimension of the same power quality index data of each monitored node after data preprocessing is n, and sequencing is performed according to the initial number sequence of the nodes, so that the whole power quality time sequence group is represented as follows:
determining a reference sequence and a comparison sequence, and for m-row time sequences, firstly selecting a first row sequence as the reference sequence, recording as i =1, and taking the rest row sequences as the comparison sequence; and normalizing the data to a [0,1] interval;
wherein x is max Representing the maximum value, x, within a data sequence min Representing a minimum value within the data sequence;
32 Calculate the gray correlation coefficient:
therein, ζ k (i) Denotes the k-th row comparison sequence X k With reference sequence X 1 The corresponding correlation coefficient on the ith element;
representing the two-level minimum difference, namely the minimum value in the absolute values of the differences of the n elements corresponding to the m-1 row comparison sequence and the reference sequence;
representing the maximum difference of two levels, namely the maximum value in the absolute values of the differences of the n elements corresponding to the m-1 row comparison sequence and the reference sequence; rho represents a resolution coefficient, and the value range is generally between 0 and 1;
33 Processing by using an averaging method to obtain the degree of association between two sequences:
wherein r is k I.e. the k-th row comparison sequence X k With reference sequence X 1 The gray correlation degree of (a);
wherein r is ka Representing the comprehensive relevance of the node k to a certain area; v represents the number of nodes unique to the region; r is a radical of hydrogen kj Representing the association degree of the jth node and the node k in the area;
herein, aiming at a certain power quality index, when each node in the monitoring network is used as a reference node, the gray correlation degree of other nodes and the node is calculated through gray correlation analysis; sequencing the relevance of each node and a reference node from large to small, and reasonably dividing the relevance area of the network according to the relevance sequencing when each node is taken as the reference node, so that the nodes with higher power quality relevance coupling degrees are divided into the same area;
aiming at the relevance ranking table when each node is used as a reference node, firstly, the nodes with the top 50% of relevance ranking in each row in the table are extracted, and then, for the nodes contained in the table together, the comprehensive relevance of the unique nodes and each same node in each region is contrastively analyzed to determine the region to which the nodes belong.
34 According to the sequence of the relevance degrees, a plurality of nodes with high relevance degrees are sequentially divided into the same area, and area division is sequentially completed;
35 The node with the maximum comprehensive relevance degree obtained by calculation is selected as the leading node of the area by comparing the comprehensive relevance degree of each node in the area.
The specific process of regulating and controlling the quality of the electric energy is as follows:
based on a plurality of regions of electric energy quality to be regulated and controlled, when the inverters are distributed, the controllable capacity of inverters in the regions is preferentially utilized, if the controllable capacity of the inverter in a certain region is not regulated and controlled in the electric energy quality, the inverters in other regions are distributed according to a formula (20), the controllable capacity of the inverter is distributed to each region of the electric energy quality to be regulated and controlled by taking the line distance between the inverter and a leading node in the region of the electric energy quality to be regulated and controlled as a constraint condition, and the electric energy quality regulation and control are completed, wherein the specific method comprises the following steps:
and expressing the numerical value of the power quality data exceeding the power quality standard value as follows:
in the formula, x i A certain power quality index data, x, for the i node b Is a certain power quality index standard, sigma, of a node i The value of a certain index of the i node exceeding the standard value of the power quality;
calculating and sequencing shortest line distances between the inverters with the controllable capacity in the adjacent areas and the leading nodes of the areas, determining the shortest distance between the inverters with the controllable capacity in the adjacent areas and the leading nodes according to the formulas (18) and (19), and distributing the inverters with the controllable capacity in the adjacent areas to the areas according to the distances for power quality control;
D(u)+W(u,v i )＜D(v i )(18)
wherein D (u) is the shortest path from the leading node s to the node u, and W (u, v) i ) For node u to the next node v i Shortest path of (c), D (v) i ) Is the leading node s to the node v i The shortest path of (2);
if the formula (18) is satisfied, the formula (19) is iteratively updated to obtain the shortest path from the leading node to each residual capacity, and the nodes D (v) of other areas are calculated i ) Setting to be infinite so as to separate each area and avoid bringing nodes in other areas into the shortest path;
the method comprises the steps that an inverter with adjustable capacity is distributed into a region nearby according to a limiting demand, and then the minimum sum of the degrees that a certain power quality index of all nodes in the region exceeds a power quality standard value is taken as an optimization target to be solved;
wherein, Δ σ is the sum of the degree that a certain power quality index of all nodes in the region exceeds the power quality standard value, and α isThe unit potential of the same power quality index improves the contribution rate,
for the area i to actually utilize the capacity,
the capacity is required for the area i,
in order to allocate the controllable capacity into a region,
for the jth inverter to use the capacity,
is the maximum value of the controllable capacity of the ith inverter, X L 、X H The function F is the improvement degree of the actual contribution capacity of the area i to exceeding the standard value of the power quality under different indexes, namely the upper limit value and the lower limit value of the exceeding range of the power quality index.
For different power quality indexes, the unit potential improvement contribution rate alpha is different, and the use capacity of each inverter does not exceed the maximum value of the adjustable capacity; and aiming at different unit potential improvement contribution rates of different indexes, calculating to obtain a specific distribution scheme through a formula (18) when capacity distribution is carried out according to the regulated capacity constraint.
The calculation of the adjustable and controllable capacity of the electric automobile charging pile comprises the following steps:
51 Determine the time distribution of the trip chain: in a travel activity, the characteristic quantities of the start time, the travel time, the stop time and the end time need to satisfy the following matrix in the travel activity:
wherein, T a-i 、T a-j Respectively, when arriving at i destination and j destinationEngraving; t is d-i 、T d-j The time when the user leaves the destination i and the destination j respectively; t is p-i 、T p-j Parking time of i destination and j destination;
calculating the arrival time and the departure time of the electric automobile according to the formula (4), thereby determining the time distribution T of the trip chain;
52 Determine the spatial distribution of the trip chain: determining the space transfer distribution condition of the travel chain by utilizing a Markov process, wherein the place where the electric automobile runs to the destination i is an event E i Wherein i is any one of the office area W, the residential area H and the entertainment area E, when the electric vehicle travels from the destination i to the next destination j, the state transition probability between the two destinations is determined by using the matrix P ij The matrix P is specifically represented as:
53 Determining the total load of the electric automobile according to the time distribution and the space distribution of the trip chain:
wherein G (T, P) is an electric vehicle load prediction model, and G (T, x) is the total electric vehicle load obtained by prediction;
54 Combine electric automobile to fill electric pile's space-time characteristic and calculate electric automobile fills electric pile's regulatable ability Q EV (t, x): calculating the adjustable and controllable capacity Q of the charging pile of the electric automobile by using the following formula (7) EV (t,x)：
Wherein q is an individual element capable of regulating and controlling capacity, W (t, x) is the initial space-time distribution total capacity of the electric automobile charging pile, and G (t, x) is the total load of the electric automobile.
Advantageous effects
Compared with the prior art, the electric energy quality area active limiting method based on the adjustable and controllable capacity of the photovoltaic inverter and the charging pile is utilized, so that the residual capacity of the photovoltaic inverter and the charging pile of the electric automobile is fully utilized, and the electric energy quality of the AC/DC distribution network is improved in an area limiting mode.
The invention also has the following advantages:
(1) The zoning treatment strategy can pertinently carry out regional treatment on the complex network with the power quality problem, does not need to decouple power quality indexes and consider the complex operation characteristics of pollution sources, and is simple and effective in method and free from additional burden. The photovoltaic inverter and the electric vehicle charging pile are used for regulating and controlling the power quality problem on site, and the surplus capacities of the photovoltaic inverter and the electric vehicle charging pile are reasonably configured so as to realize full utilization, so that the current power quality regulation and control dilemma is solved, and the intelligent power grid system has wide application prospect in building of the intelligent power grid.
(2) The method includes the steps of respectively predicting photovoltaic inverter power and electric vehicle load to obtain the adjustable capacity of the photovoltaic inverter and the electric vehicle charging pile under different time and space, performing correlation analysis on the same electric energy quality time sequence of different nodes in a power distribution network, dividing the network into a plurality of areas by utilizing the degree of correlation of the nodes, determining an electric energy quality leading node of each area, preferentially utilizing the adjustable photovoltaic inverter and the charging pile in the area to adjust and control the electric energy quality of the leading node, calling the adjustable residual capacity of the photovoltaic inverter and the charging pile in the rest areas according to the limiting conditions, adjusting and controlling the electric energy quality of the leading node to realize the electric energy quality adjustment and control of the whole area, sequentially completing the electric energy quality adjustment and control of the plurality of areas, and having important theoretical and practical application value for maintaining the electric energy stability of the power grid.
(3) On the basis of outputting active power, the photovoltaic and charging pile inverters are used as hardware bases of the electric energy quality control equipment, so that the required capacity of the traditional electric energy quality control equipment is reduced, and the redundancy of a system is reduced. The problem of the power quality area is limited on the spot through regional regulation, and the pollution problem is solved more accurately, so that the overall regulation and control of the power quality pollution are realized through the power quality regulation and control of the leading node.
Drawings
FIG. 1 is a sequence diagram of the method of the present invention;
FIG. 2 is a schematic block diagram of the overall process of the power quality control method of the present invention;
FIG. 3 is a schematic diagram of an electric vehicle trip chain according to a preferred embodiment of the present invention;
FIG. 4 is a sectional view of an AC/DC distribution network according to another preferred embodiment of the present invention;
fig. 5 is a schematic diagram illustrating the division of the regions according to the voltage quality index in another preferred embodiment of the present invention.
Detailed Description
So that the manner in which the above recited features of the present invention can be understood and readily understood, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings, wherein:
the method includes the steps that photovoltaic output and electric vehicle load are predicted through weather prediction and similar days, the adjustable capacity of a photovoltaic inverter and the adjustable capacity of an electric vehicle charging pile under different time and space are obtained, correlation analysis is conducted on the same electric energy quality time sequence of different nodes in a power distribution network, the network is divided into a plurality of control areas through the mutual correlation degree of the nodes, electric energy quality leading nodes of all the areas are determined, the adjustable photovoltaic inverter and the charging pile are respectively brought into different areas according to the limitation requirements and the electric distances, and the electric energy quality of the leading nodes is adjusted and controlled, so that the area adjustment and control of the electric energy quality are achieved.
Specifically, the invention provides an electric energy quality regulation and control method based on the adjustable and controllable capacity of a photovoltaic inverter and a charging pile, as shown in fig. 1 and fig. 2, the method comprises the following steps:
s1, adjusting the power quality index of a power grid by using the residual capacities of a photovoltaic inverter and an electric vehicle charging pile, and calculating the adjustable capacity Q of the photovoltaic inverter and the adjustable capacity Q of the electric vehicle charging pile, wherein the adjustable capacity Q comprises the adjustable capacity of the photovoltaic inverterQuantity Q pv And electric automobile fills controllability Q of electric pile EV (t, x); the method specifically comprises the following steps:
s11, calculating the controllable capacity Q of the photovoltaic inverter pv First, the photovoltaic array output power Ps is expressed as
P S ＝ηSI[1-0.005(t 0 +25)] (1)
Wherein S is the area of the photovoltaic array, I is the irradiation intensity on the photovoltaic array, t 0 And eta is the ambient temperature, and eta is the photoelectric conversion efficiency of the photovoltaic array.
The influence factors on the photovoltaic array, namely the irradiation intensity and the ambient temperature in the formula (1), are used as the input quantity of a prediction model F, the model F can be an Elman neural network prediction model based on ant colony optimization, the photovoltaic output power short-term prediction is carried out based on the optimized BP neural network, and the predicted value P of the photovoltaic array output power is obtained pv Expression (2) of (a):
P pv ＝F prediction (I,t 0 ) (2)
The prediction model F can be selected as needed. The specifications, physical characteristics and installation modes of the solar panels in the photovoltaic system are determined, so that correlation analysis is carried out on meteorological factors during photovoltaic power generation power prediction, and short-term power generation power is predicted. In the analysis of the influence factors of the photovoltaic power generation power, the weather factors of the environment where the photovoltaic array is located are analyzed.
Calculating the controllable capacity Q of the photovoltaic inverter pv : calculating the maximum reactive output Q that the photovoltaic inverter can use for voltage control using the following formula (3) pv Max and its capacity constraint Q pv ：
Wherein Q is pv Max is the maximum reactive output of the photovoltaic inverter, S pv Rated capacity, P, of photovoltaic inverter pv Outputting a predicted value of the power for the photovoltaic array; q pv For capacity constraints of photovoltaic inverters, i.e.The reactive power that a photovoltaic inverter can generate is limited by the rated capacity.
S12, calculating the adjustable and controllable capacity Q of the electric automobile charging pile based on a trip chain by combining the time-space characteristics of the electric automobile charging pile EV (t, x), comprising the following substeps:
s121, determining time distribution of a trip chain: each time a user finishes a trip activity, a complete trip chain is formed, and in one trip activity, the characteristic quantities of the starting time, the traveling time, the parking time and the ending time need to satisfy the following matrixes in the trip activity:
wherein, T a-i 、T a-j Respectively reaching the i destination and the j destination; t is a unit of d-i 、T d-j The time when the user leaves the destination i and the destination j respectively; t is p-i 、T p-j I and j are parking times of the destination. The vehicle runs between two destinations, the distance of the vehicle shows a certain regularity, and load prediction is carried out by using a plurality of historical load days with higher similarity in week type, environment, weather and the like with the predicted day in the historical sample load days of the similar days. The entire trip chain is shown in fig. 3.
And (5) calculating the arrival time and the departure time of the vehicle according to the formula (4) so as to determine the time distribution T of the trip chain.
S122, determining the spatial distribution of the trip chain: determining the spatial transfer distribution condition of a travel chain by utilizing a Markov process, wherein the place where the electric automobile runs to the destination i is an event E i Where i is any one of the office area W, the residential area H and the entertainment area E, the state transition probability between the two destinations is utilized by the matrix P when the electric vehicle travels from the destination i to the next destination j ij A representation, in matrix form:
s123, determining the total load of the electric automobile according to the time distribution and the space distribution of the trip chain,
wherein g (T, P) is a load prediction model of the electric vehicle, and in practical application, the model can be a Monte Carlo method.
S124, calculating the adjustable and controllable capacity Q of the electric automobile charging pile by using the following formula (7) EV (t,x)：
Wherein q is an individual element with adjustable and controllable capacity, W (t, x) is the initial empty distribution total capacity of the electric automobile charging pile, and G (t, x) is the total load of the electric automobile.
S13, correcting the initial power predicted value, which specifically comprises the following substeps:
s131, and the controllable capacity Q of the photovoltaic inverter calculated according to the step S11 and the step S12 pv And the electric automobile fills the controllability Q of electric pile EV And (t, x) respectively calculating the predicted value of the photovoltaic inverter and the predicted relative error delta of the residual capacity of the charging pile of the electric automobile, wherein the calculation formula is as follows:
wherein, P F The residual capacity prediction values P obtained by respective calculation of the photovoltaic inverter and the electric vehicle charging pile R Respectively counting the actual residual capacity values of the photovoltaic inverter and the electric vehicle charging pile; in a specific calculation process, the predicted relative errors delta of the photovoltaic inverter and the electric vehicle are respectively calculated according to the respective predicted values and the actual residual capacities of the photovoltaic inverter and the electric vehicle.
S132, determining the formA state probability transition matrix, wherein an upper threshold value and a lower threshold value of respective relative error delta of the photovoltaic inverter and the electric vehicle charging pile are used as ranges for state division, and a state transition probability matrix P is calculated according to the states of the relative errors (k) ：
Wherein the content of the first and second substances,
number of transitions from i to j of sample state, A i Is the total number of occurrences of the state;
determining an initial state vector X (0) according to the error data predicted by the prediction model and the obtained state transition matrix, and calculating the state transition result of the k step by using a state transition formula (10):
X(k)＝X(0)P (k) (10)。
in the specific calculation process, the initial state vector X (0) is determined according to the error data predicted by the prediction model and the state transition matrix obtained above.
The elements in the matrix X (k) are probabilities that the relative errors are located in different states, and power values P after photovoltaic and electric automobile correction are respectively calculated according to Markov predicted relative errors delta' obtained by the state probabilities and upper and lower threshold values of the relative errors T ：
Wherein, delta h And delta l Respectively an upper threshold and a lower threshold, P, of the interval of the error state T Is the corrected power value. Respectively calculating the corrected photovoltaic array output power predicted values P of the photovoltaic inverters according to a formula (11) pv And electric automobileTotal load G (t, x).
S133, the corrected power value is respectively brought into a calculation formula (3) and a calculation formula (7) of the adjustable capacity of the photovoltaic and the electric automobile to respectively replace P in the calculation formula of the photovoltaic inverter pv And predicting the value of G (t, x) in the calculation formula of the electric vehicle charging pile again to obtain the final adjustable capacity Q.
And S2, determining the power quality index, wherein the determining method specifically comprises the step of selecting one of the most serious power quality indexes as the power quality index for first regulation and control. And performing region division on the region needing power quality control, as shown in fig. 4. The method comprises the following substeps:
s21, performing correlation analysis on the monitoring data of the power quality index, and calculating a correlation coefficient of the comparison sequence and the reference sequence to realize the partition of the power quality; assuming that m monitoring points are shared in a power quality monitoring network of a certain system, the time dimension of the same power quality index data of each monitored node after data preprocessing is n, and sequencing is performed according to the size sequence of the initial numbers of the nodes, so that the whole power quality time sequence group can be expressed as follows:
determining a reference sequence and a comparison sequence, and for m-row time sequences, firstly selecting a first row sequence as the reference sequence, marking as i =1, and taking the rest row sequences as the comparison sequences; normalizing the data in the array to a [0,1] interval;
wherein x is max Representing the maximum value, x, within a data sequence min Representing a minimum value within the data sequence;
s22, calculating a gray correlation coefficient:
therein, ζ k (i) Denotes the k-th row comparison sequence X k With reference sequence X 1 The corresponding correlation coefficient on the ith element;
representing the two-level minimum difference, namely the minimum value in the absolute values of the differences of the n elements corresponding to the m-1 row comparison sequence and the reference sequence;
representing the two-level maximum difference, namely the maximum value in the absolute values of the differences of the n elements corresponding to the m-1 row comparison sequence and the reference sequence; rho represents a resolution coefficient, and the value range is generally between 0 and 1;
s23, processing by adopting an averaging method to obtain the association degree between the two sequences:
wherein r is k I.e. the k-th row comparison sequence X k With reference sequence X 1 The gray correlation degree of (a);
wherein r is ka Representing the comprehensive relevance of the node k to a certain area; v represents the number of nodes unique to the region; r is kj Representing the association degree of the jth node and the node k in the area;
s24, sequentially dividing a plurality of nodes with high association degrees into a plurality of areas according to the association degree sequence;
and S25, selecting the node with the maximum comprehensive relevance degree obtained by calculation as a leading node of the region by comparing the comprehensive relevance degree of each node in the region, and performing power quality regulation and control on the leading node by preferentially utilizing the controllable capacity Q during subsequent regulation and control so as to complete the power quality regulation and control of the whole region.
S3, carrying out electric energy quality regulation and control on a plurality of areas of electric energy quality to be regulated and controlled, specifically comprising the following steps:
based on the plurality of regions of the power quality to be regulated and controlled obtained by dividing in the step S2, when the inverters are distributed, the controllable capacity of the inverters in the regions is preferentially utilized, if the controllable capacity of the inverter in a certain region does not complete the power quality regulation and control, the inverters in the other regions are distributed according to the formula (20), and the controllable capacity of the inverter is distributed to each region of the power quality to be regulated and controlled by taking the line distance between the inverter and the main guide node in the region of the power quality to be regulated and controlled as a constraint condition, so as to complete the power quality regulation and control, and the specific method comprises the following steps:
and expressing the numerical value of the electric energy quality data exceeding the electric energy quality standard value as follows:
in the formula, x i A certain power quality index data, x, for the i node b Is a certain power quality index standard, sigma, of a node i The value of a certain index of the i node exceeding the standard value of the power quality;
calculating and sequencing shortest line distances between inverters with controllable capacity in adjacent areas and a leading node of the areas, determining the shortest distance between the inverters with controllable capacity in the adjacent areas and the leading node according to equations (18) and (19), and distributing inverters with controllable capacity in the adjacent areas to the areas according to the distances to control the power quality;
D(u)+W(u,v i )＜D(v i )(18)
wherein D (u) is the shortest path from the leading node s to the node u, and W (u, v) i ) For node u to the next node v i Shortest path of (1), D (v) i ) Is the leading node s to the node v i The shortest path of (2);
if the formula (18) is satisfied, the formula (19) is iteratively updated to obtain the shortest path from the leading node to each residual capacity, and the nodes D (v) of other areas are calculated i ) Setting to be infinite so as to separate each area and avoid bringing nodes in other areas into the shortest path;
distributing an inverter with adjustable capacity to the area according to the limit requirement, and then solving by taking the minimum sum of the degrees that a certain power quality index of all nodes in the area exceeds a power quality standard value as an optimization target;
wherein, delta sigma is the sum of the degree that a certain power quality index of all nodes in the region exceeds the power quality standard value, alpha is the unit potential improvement contribution rate of different power quality indexes,
for the area i to actually utilize the capacity,
a capacity is required for the area i,
in order to allocate the controllable capacity into a region,
for the jth inverter to use the capacity,
is the maximum value of the controllable capacity of the ith inverter, X L 、 X H For the upper and lower limit values of the exceeding range of the power quality index, the function F is the actual contribution capacity pair of the region i under different index conditionsAnd exceeding the standard value of the power quality.
And S4, after regulation and control, the power quality monitor is used for evaluating each power quality index to obtain an evaluation result, if the power quality index does not meet the requirement, the power quality index which does not meet the requirement is selected for regulation and control again, and the steps S2 to S4 are repeated until all the power quality indexes meet the requirement.
Preferably, the step S4 of regulating and evaluating according to the region division, and the specific process of repeatedly regulating and controlling until the quality of the electric energy reaches the standard is as follows: firstly, carrying out regional division according to grey correlation analysis aiming at the most serious power quality index, regulating and controlling the power quality index by utilizing the controllable capacity, and not carrying out decoupling regulation and control on the power quality index; and evaluating the power quality after regulation, selecting the second serious index for regional division and regulation, and repeatedly regulating and controlling until the power quality reaches the standard.
Preferably, in step S3, for indexes of different power quality, the unit potential improvement contribution rates α are different, and the use capacity of each photovoltaic inverter does not exceed the maximum value of the controllable capacity; and aiming at different unit potential improvement contribution rates of different indexes, calculating to obtain a specific distribution scheme through an equation (18) when capacity distribution is carried out according to the regulated capacity constraint.
Preferably, in step S23, for a certain power quality index, when each node in the monitoring network is used as a reference node, the gray correlation degree between other nodes and the node is calculated through gray correlation analysis; sequencing the relevance of each node and a reference node from large to small, and reasonably dividing the relevance area of the network according to the relevance sequencing of each node as the reference node, so that the nodes with higher power quality relevance coupling degrees are divided into the same area;
aiming at the relevance ranking table when each node is used as a reference node, firstly, the nodes with the top 50% of relevance ranking in each row of the table are extracted, and then, for the nodes contained in the table together, the comprehensive relevance of the unique node and each same node in each area is contrastively analyzed to determine the area to which the nodes belong.
The technical scheme of the invention is described by the following specific embodiments: as shown in fig. 5, the node network is used as a main network, and a photovoltaic inverter and an electric vehicle charging pile are added as topologies of an embodiment, and when the node network is specifically implemented, the node network comprises the following steps:
s1, calculating the controllable capacity Q of the photovoltaic inverter and the electric automobile charging pile:
adjustable capacity Q obtained by photovoltaic inverter through weather prediction pv And the electric automobile charging pile can regulate and control the capacity Q determined by means of the trip chain EV (t,x)。
S2, in the embodiment, the voltage deviation index cannot meet the requirement, so that the voltage deviation index is selected as the power quality index, and the region needing power quality regulation is divided into regions.
And S3, performing power quality regulation and control on a plurality of regions to be regulated and controlled.
For the voltage deviation index, the network is divided into the nodes in the divided regions as shown in fig. 5 according to the voltage deviation data, the nodes in the divided regions are adjacent to each other, the correlation coupling degree of the nodes in the regions is relatively high, the voltage deviation indexes between the nodes are tightly influenced with each other, the node with the maximum comprehensive correlation degree is selected as the leading node of the region by comparing the comprehensive correlation degree of each node in the region under the index, and finally the leading node of each region is respectively the node marked by the dotted square shown in fig. 5.
In area 1, the power quality of each node in the initial area is shown in table 1 below:
TABLE 1
| Node name | Leading node | Node 2 | Node 3 | Node 4 |
| Voltage deviation index | 0 | 1 | 0.5 | 1 |
In table 1, a voltage deviation index 1 represents that the power quality of the node is good and does not need to be regulated, a voltage deviation index 0 represents that the power quality of the node is very poor, a voltage deviation index is between 0 and 1, the voltage deviation is judged according to the digital magnitude, the closer to 0, the larger the voltage deviation is represented, and the closer to 1, the smaller the voltage deviation is represented. It can be seen from table 1 that the voltage deviation of the dominant node in the region 1 is very large, and needs to be regulated.
When the voltage deviation is regulated, the residual capacity of the photovoltaic inverter and the electric automobile charging pile in the area is considered, and the leading node is regulated and controlled: the regulation and control of the leading node need 80KW, the controllable capacities of the photovoltaic inverter and the charging pile of the electric automobile in the region 1 are 50KW and 40KW respectively, and the photovoltaic inverter is closer to the leading node, so that 50KW in the controllable capacities of the photovoltaic inverter in the region is distributed to regulate and control the leading node. And if the regulation and control meet the requirements, the regulation and control are not continued. If the regulation and control can not reach the demand, then continue to bring remaining adjustable and control capacity 30KW in the electric automobile fills electric pile into and carries out regional regulation and control.
In this embodiment, the voltage deviation indexes of each node in the region after the regulation and control of the photovoltaic inverter are completed are as shown in table 2 below:
TABLE 2
| Node name | Leading node | Node 2 | Node 3 | Node 4 |
| Voltage deviation index | 0.8 | 1 | 1 | 1 |
It can be seen from table 2 that the voltage deviation of the dominant node in the region 1 has been improved a lot after the regulation, but still the requirement that all nodes do not need to be regulated is not met, and further regulation is still needed. Therefore, the residual adjustable capacity 30KW in the electric automobile charging pile is continuously brought into regional adjustment and control.
In this embodiment, the voltage deviation indexes of each node in the region after the regulation and control of the electric vehicle charging pile are as shown in table 3 below:
TABLE 3
| Node name | Leading node | Node 2 | Node 3 | Node 4 |
| Voltage deviation index | 1 | 1 | 1 | 1 |
As can be seen from Table 3, after the regulation, all nodes in the region 1 do not need to be regulated continuously, so far, the regulation of the region 1 is completed.
And in the area 2, regulating and controlling according to the steps, and if the photovoltaic inverter in the calling area is insufficient in regulating and controlling, distributing the adjacent electric automobile charging piles into the area 2 for regulating and controlling according to the limiting conditions of distance and capacity.
In the area 2 of the present embodiment, the voltage deviation indexes of the respective nodes in the initial area are as shown in table 4 below:
TABLE 4
| Node name | Leading node | Node 2 | Node 3 | Node 4 |
| Voltage deviation index | 0 | 0.2 | 0.5 | 1 |
When the voltage deviation is regulated, the residual capacity of the photovoltaic inverter and the electric automobile charging pile in the area is considered, and the leading node is regulated and controlled: the regulation and control of the leading node needs 95KW, the adjustable and controllable capacities of the photovoltaic inverter in the region 2 and the electric automobile charging pile in the adjacent region are 60KW and 50KW respectively, and the photovoltaic inverter is located in the region 2, so that 60KW in the adjustable and controllable capacity of the photovoltaic inverter in the region is distributed to regulate and control the leading node. And if the regulation and control meet the requirements, the regulation and control are not continued. If regulation and control can not reach the demand, then continue to bring into the regional regulation and control of carrying out with remaining adjustable and controllable capacity 35KW in the electric automobile fills electric pile.
In this embodiment, the voltage deviation indexes of each node in the region after the regulation and control of the photovoltaic inverter are completed are as shown in table 5 below:
TABLE 5
| Node name | Leading node | Node 2 | Node 3 | Node 4 |
| Voltage deviation index | 0.6 | 0.8 | 1 | 1 |
It can be seen from table 5 that the voltage deviation of the dominant node in the region 2 has been improved a lot after the regulation, but still the requirement that all nodes do not need to be regulated is not met, and further regulation is still needed. However, no inverter with adjustable capacity can be used in the region, so that 35KW of the remaining adjustable capacity in the adjacent charging pile of the electric vehicle is included for regional regulation.
In this embodiment, the voltage deviation indexes of each node in the region after the regulation and control of the electric vehicle charging pile are as shown in table 6 below:
TABLE 6
| Node name | Leading node | Node 2 | Node 3 | Node 4 |
| Voltage deviation index | 1 | 1 | 1 | 1 |
As can be seen from Table 6, after the regulation, all nodes in the region 2 do not need to be regulated continuously, so far, the regulation of the region 2 is completed. In this embodiment, the pv inverters in the area 3 meet the requirements, so that part of the pv inverters in the area 3 is called to perform regulation and control, so that all the nodes meet the requirement of voltage deviation, thereby meeting the requirement.
And for the rest areas, regulating and controlling according to the steps, in the area 3, regulating and controlling according to the steps, and if the regulation and control of the photovoltaic inverter in the calling area are insufficient, distributing the electric automobile charging piles of the adjacent area 4 into the area 3 for regulation and control according to the limiting conditions of distance and capacity. The regulation of all regions is completed in turn according to the above method. And evaluating the power quality after the regulation and control are finished, if a certain power quality index cannot reach the standard, continuing to select the power quality index which does not reach the standard, such as a harmonic index, to perform regional division and regulation again, namely repeating the steps S2-S3 to perform regulation and control again until the voltage deviation reaches the standard. In this embodiment, the area to be regulated does not meet the requirement only on the voltage deviation index, and therefore, the regulation of the remaining power quality indexes is not continued. If in other embodiments, each power quality index does not reach the standard, the steps S2-S3 are repeated in sequence according to the severity of each power quality index until the power quality reaches the standard.
Compared with the existing power quality management method, the zoning management strategy of the invention can carry out regional management on the complex network with the power quality problem in a targeted manner, does not need to decouple the power quality index and consider the complex operation characteristic of a pollution source, and has the advantages of simplicity, effectiveness and no need of increasing extra burden. The photovoltaic inverter and the electric vehicle charging pile are used for limiting the problem of the electric energy quality on the spot, the residual capacities of the photovoltaic inverter and the electric vehicle charging pile are scientifically and reasonably configured to realize full utilization, the current situation of electric energy quality regulation is solved, and the intelligent power grid is wide in application prospect.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. A power quality regulation and control method based on the adjustable and controllable capacity of a photovoltaic inverter and a charging pile is characterized by comprising the following steps:
11 Calculation of the controllable capacity of the photovoltaic inverter and the charging pile: the method comprises the steps of utilizing the residual capacity of an inverter to adjust the power quality index of a power grid and calculating the controllable capacity Q of the inverter and the power quality index of the power grid, wherein the inverter comprises a photovoltaic inverter and an electric automobile charging pile, and the controllable capacity Q comprises the capacity constraint Q of the photovoltaic inverter pv And the adjustable capacity Q of the electric automobile charging pile EV (t,x)；
12 Determination of the power quality region to be regulated): according to the severity of the power quality index, selecting the most severe power quality index as the power quality index for the first regulation, performing region division on the region needing power quality regulation based on the selected power quality index, and sequentially dividing a plurality of nodes with high correlation degrees into the same region according to the sequence of the correlation degrees, thereby completing the region division and obtaining a plurality of regions of power quality to be regulated;
13 Control of power quality): carrying out power quality regulation and control on a plurality of regions of which the power quality is to be regulated and controlled;
14 Evaluation of power quality indicators: and after regulation, the power quality monitor is used for evaluating each power quality index to obtain an evaluation result, if the power quality index does not meet the requirement, the power quality index which does not meet the requirement is selected for regulation again, and the steps 12-14 are repeated until all the power quality indexes meet the requirement.
2. The method for regulating and controlling the electric energy quality based on the controllable capacity of the photovoltaic inverter and the charging pile according to claim 1, wherein the calculation of the controllable capacity of the photovoltaic inverter and the charging pile comprises the following steps:
21 Let the photovoltaic array output power Ps be expressed as:
P S ＝ηSI[1-0.005(t 0 +25)]， (1)
wherein S is the area of the photovoltaic array, I is the irradiation intensity on the photovoltaic array, t 0 Taking the ambient temperature as the reference, and eta is the photoelectric conversion efficiency of the photovoltaic array;
the influence factors on the photovoltaic array, namely the irradiation intensity and the ambient temperature in the formula (1), are used as the input quantity of a prediction model F to obtain a predicted value P of the output power of the photovoltaic array pv Expression (2) of (a):
P pv ＝F prediction (I,t 0 )； (2)
22 Calculate a capacity constraint Q for a photovoltaic inverter pv : calculating the maximum reactive output Q of the photovoltaic inverter which can be used for voltage control by using the following formula (3) pv.max With its capacity constraint Q pv ：
Wherein Q is pv.max For maximum reactive output of the photovoltaic inverter, S pv Rated capacity, P, for photovoltaic inverters pv Outputting a predicted value of the power for the photovoltaic array; q pv Is a capacity constraint of the photovoltaic inverter;
23 Calculation of the controllability of the charging pile of the electric vehicle: calculating the controllability Q of the charging pile of the electric automobile by combining the time-space characteristics of the charging pile of the electric automobile EV (t,x)；
24 To the initial photovoltaic array output power predicted value P pv And the total load G (t, x) of the electric vehicle, whichThe method specifically comprises the following substeps:
241 The regulated capacity Q of the photovoltaic inverter calculated according to the above steps pv And the electric automobile fills the controllability Q of electric pile EV The predicted values of (t, x) are calculated as the predicted relative errors δ, respectively, as shown in the following equation (8):
wherein, P F Respectively calculating residual capacity predicted values P for the photovoltaic inverter and the electric vehicle charging pile R Respectively counting the actual residual capacity values of the photovoltaic inverter and the electric vehicle charging pile;
242 Determining a state probability transition matrix, taking an upper threshold value and a lower threshold value of respective relative errors delta of the photovoltaic inverter and the electric vehicle charging pile as a state division range, and calculating a state transition probability matrix P according to the states of the relative errors (k) As shown in the following formula (9):
wherein the content of the first and second substances,
the number of transitions from i to j of the sample state, A i Is the total number of occurrences of the state;
according to the calculated relative error delta and the state transition probability matrix P (k) Determining an initial state vector X (0), and calculating the state transition result of the k step by using a state transition formula (10):
X(k)＝X(0)P (k) ， (10)
the elements in the matrix X (k) are the probabilities that the relative error is in different states, the rootAccording to the relative error delta' of Markov prediction obtained from the upper threshold and the lower threshold of the error state interval, respectively calculating the corrected power value P of the photovoltaic and the electric automobile according to a formula (11) T ：
P T ＝P R (1-δ')，
Wherein, delta h 、δ l Respectively an upper threshold and a lower threshold, P, of the interval of the error state T Is the corrected power value;
243 Respectively substituting the corrected power values into the calculation formulas (3) and (7) of the controllable capacities of the photovoltaic inverter and the electric vehicle and respectively replacing P pv And G (t, x) are calculated again to obtain the final adjustable capacity Q.
3. The method for regulating and controlling the power quality based on the controllable capacity of the photovoltaic inverter and the charging pile according to claim 1, wherein the step of determining the power quality area to be regulated and controlled comprises the following steps:
31 Carrying out correlation analysis on the monitoring data of the power quality index, and calculating a correlation coefficient of a comparison sequence and a reference sequence to realize the partition of the power quality;
assuming that m monitoring points are arranged in a power quality monitoring network of a certain system, the time dimension of the same power quality index data of each monitored node after data preprocessing is n, and sequencing is performed according to the size sequence of the initial numbers of the nodes, so that the whole power quality time sequence group is represented as follows:
determining a reference sequence and a comparison sequence, and for m-row time sequences, firstly selecting a first row sequence as the reference sequence, recording as i =1, and taking the rest row sequences as the comparison sequence; and normalizing the data to a [0,1] interval;
wherein x is max Representing the maximum value, x, within a data sequence min Representing a minimum value within the data sequence;
32 Calculate the gray correlation coefficient:
therein, ζ k (i) Denotes the k-th row comparison sequence X k With reference sequence X 1 The corresponding correlation coefficient on the ith element;
representing the two-stage minimum difference, namely the minimum value in the absolute value of the difference of the n elements corresponding to the m-1 row comparison sequence and the reference sequence;
representing the maximum difference of two levels, namely the maximum value in the absolute values of the differences of the n elements corresponding to the m-1 row comparison sequence and the reference sequence; rho represents a resolution coefficient, and the value range is generally between 0 and 1;
33 Processing by using an averaging method to obtain the degree of association between two sequences:
wherein r is k I.e. the k-th row comparison sequence X k With reference sequence X 1 The grey correlation degree of (c);
wherein r is ka Representing the comprehensive relevance of the node k to a certain area; v represents the number of nodes unique to the region; r is kj Representing the degree of association between the jth node and the node k in the area;
34 A plurality of nodes with high association degree are sequentially divided into the same area according to the association degree sequence, and area division is sequentially completed;
35 The node with the maximum comprehensive relevance degree obtained by calculation is selected as the leading node of the region by comparing the comprehensive relevance degree of each node in the region.
4. The method for regulating and controlling the electric energy quality based on the adjustable and controllable capacity of the photovoltaic inverter and the charging pile according to claim 1, characterized in that the specific process of regulating and controlling the electric energy quality is as follows:
based on a plurality of areas of power quality to be regulated and controlled, when the inverters are distributed, the controllable capacity of the inverters in the areas is preferentially utilized, if the controllable capacity of the inverter in a certain area does not finish power quality regulation and control, the inverters in other areas are distributed according to a formula (20), the controllable capacity of the inverter is distributed to each area of the power quality to be regulated and controlled by taking the line distance between the inverter and a main guide node in the area of the power quality to be regulated and controlled as a constraint condition, and the power quality regulation and control are completed, wherein the specific method comprises the following steps:
and expressing the numerical value of the power quality data exceeding the power quality standard value as follows:
in the formula, x i A certain power quality index data, x, for the i node b Is a certain power quality index standard, sigma, of a node i The value of a certain index of the i node exceeding the standard value of the power quality;
calculating and sequencing shortest line distances between the inverters with the controllable capacity in the adjacent areas and the leading nodes of the areas, determining the shortest distance between the inverters with the controllable capacity in the adjacent areas and the leading nodes according to the formulas (18) and (19), and distributing the inverters with the controllable capacity in the adjacent areas to the areas according to the distances for power quality control;
D(u)+W(u,v i )＜D(v i ) (18)
wherein D (u) is the shortest path from the leading node s to the leading node u, and W (u, v) i ) For node u to the next node v i Shortest path of (1), D (v) i ) Is the leading node s to the node v i The shortest path of (2);
if the formula (18) is satisfied, the formula (19) is iteratively updated to obtain the shortest path from the leading node to each residual capacity, and the nodes D (v) of other areas are calculated i ) Setting the distance to infinity so as to separate each area and avoid incorporating nodes in other areas when seeking the shortest path;
the method comprises the steps that an inverter with adjustable capacity is distributed into a region nearby according to a limiting demand, and then the minimum sum of the degrees that a certain power quality index of all nodes in the region exceeds a power quality standard value is taken as an optimization target to be solved;
wherein, delta sigma is the sum of the degree that a certain power quality index of all nodes in the region exceeds the power quality standard value, alpha is the unit potential improvement contribution rate of different power quality indexes,
for the actual utilization of the region iThe capacity of the electric power transmission device is,
the capacity is required for the area i,
in order to allocate the controllable capacity into a region,
for the jth inverter to use the capacity,
is the maximum value of the controllable capacity of the ith inverter, X L 、X H The function F is the improvement degree of the actual contribution capacity of the area i to exceeding the standard value of the power quality under different indexes, namely the upper limit value and the lower limit value of the exceeding range of the power quality index.
5. The method for regulating and controlling the electric energy quality based on the controllable capacity of the photovoltaic inverter and the charging pile according to claim 2, wherein the calculation of the controllable capacity of the charging pile of the electric automobile comprises the following steps:
51 Determine the time distribution of the trip chain: in a travel activity, the characteristic quantities of the starting time, the traveling time, the parking time and the ending time need to satisfy the following matrix in the travel activity:
wherein, T a-i 、T a-j Respectively reaching the i destination and the j destination; t is d-i 、T d-j The time when the user leaves the destination i and the destination j respectively; t is p-i 、T p-j Parking time of i destination and j destination;
calculating the arrival time and the departure time of the electric automobile according to the formula (4), thereby determining the time distribution T of the trip chain;
52 Determine the spatial distribution of the trip chain: determining the spatial transfer distribution condition of a travel chain by utilizing a Markov process, wherein the place where the electric automobile runs to the destination i is an event E i Wherein i is any one of the office area W, the residential area H and the entertainment area E, when the electric vehicle travels from the destination i to the next destination j, the state transition probability between the two destinations is utilized by the matrix P ij The matrix P is specifically represented as:
53 Determining the total load of the electric automobile according to the time distribution and the space distribution of the trip chain:
G(t,x)＝{g|g∈g(T,P)}
wherein G (T, P) is an electric vehicle load prediction model, and G (T, x) is the total electric vehicle load obtained by prediction;
54 Combine electric automobile to fill electric pile's space-time characteristic and calculate electric automobile fills electric pile's regulatable ability Q EV (t, x): calculating the adjustable and controllable capacity Q of the charging pile of the electric automobile by using the following formula (7) EV (t,x)：
Wherein q is an individual element capable of regulating and controlling capacity, W (t, x) is the initial space-time distribution total capacity of the electric automobile charging pile, and G (t, x) is the total load of the electric automobile.
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| CN116722608A (en) * | 2023-04-28 | 2023-09-08 | 国网河北省电力有限公司电力科学研究院 | Reactive power compensation system based on photovoltaic inverter |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107392466A (en) * | 2017-07-21 | 2017-11-24 | 浙江群力电气有限公司 | A kind of power network quality of power supply Dynamic Comprehensive Evaluation method and device |
| CN108171425A (en) * | 2017-12-28 | 2018-06-15 | 国网冀北电力有限公司秦皇岛供电公司 | Power quality partition method, device and storage medium |
| CN109066703A (en) * | 2018-09-20 | 2018-12-21 | 山东理工大学 | A kind of distribution network voltage regulation method counted and source lotus is coordinated |
| CN109165774A (en) * | 2018-08-07 | 2019-01-08 | 燕山大学 | A kind of short-term photovoltaic power prediction technique |
| CN111224418A (en) * | 2018-11-27 | 2020-06-02 | 平高集团有限公司 | Micro-grid scheduling method and system based on electric vehicle energy storage |
| WO2021098352A1 (en) * | 2019-11-22 | 2021-05-27 | 国网福建省电力有限公司 | Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations |
| CN113381451A (en) * | 2021-07-22 | 2021-09-10 | 南方电网科学研究院有限责任公司 | Photovoltaic power station inverter cluster regulation and control method and device |
| WO2022001262A1 (en) * | 2020-07-03 | 2022-01-06 | 石家庄科林电气股份有限公司 | Photovoltaic inverter-based power quality optimization method for distribution transformer area |
| CN114069730A (en) * | 2021-11-15 | 2022-02-18 | 广东电网有限责任公司 | Reactive power coordination method and device for regional power distribution network |
| CN114400657A (en) * | 2021-12-28 | 2022-04-26 | 广东电网有限责任公司广州供电局 | Power distribution network cooperative regulation and control method and system considering electric vehicle access |
| CN114565153A (en) * | 2022-02-24 | 2022-05-31 | 中冶赛迪工程技术股份有限公司 | Regional wind, light and vehicle combination optimization method based on dispatchable capacity of electric vehicle |
-
2022
- 2022-08-15 CN CN202210973159.6A patent/CN115800385B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107392466A (en) * | 2017-07-21 | 2017-11-24 | 浙江群力电气有限公司 | A kind of power network quality of power supply Dynamic Comprehensive Evaluation method and device |
| CN108171425A (en) * | 2017-12-28 | 2018-06-15 | 国网冀北电力有限公司秦皇岛供电公司 | Power quality partition method, device and storage medium |
| CN109165774A (en) * | 2018-08-07 | 2019-01-08 | 燕山大学 | A kind of short-term photovoltaic power prediction technique |
| CN109066703A (en) * | 2018-09-20 | 2018-12-21 | 山东理工大学 | A kind of distribution network voltage regulation method counted and source lotus is coordinated |
| CN111224418A (en) * | 2018-11-27 | 2020-06-02 | 平高集团有限公司 | Micro-grid scheduling method and system based on electric vehicle energy storage |
| WO2021098352A1 (en) * | 2019-11-22 | 2021-05-27 | 国网福建省电力有限公司 | Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations |
| WO2022001262A1 (en) * | 2020-07-03 | 2022-01-06 | 石家庄科林电气股份有限公司 | Photovoltaic inverter-based power quality optimization method for distribution transformer area |
| CN113381451A (en) * | 2021-07-22 | 2021-09-10 | 南方电网科学研究院有限责任公司 | Photovoltaic power station inverter cluster regulation and control method and device |
| CN114069730A (en) * | 2021-11-15 | 2022-02-18 | 广东电网有限责任公司 | Reactive power coordination method and device for regional power distribution network |
| CN114400657A (en) * | 2021-12-28 | 2022-04-26 | 广东电网有限责任公司广州供电局 | Power distribution network cooperative regulation and control method and system considering electric vehicle access |
| CN114565153A (en) * | 2022-02-24 | 2022-05-31 | 中冶赛迪工程技术股份有限公司 | Regional wind, light and vehicle combination optimization method based on dispatchable capacity of electric vehicle |
Non-Patent Citations (5)
| Title |
|---|
| 丁泽俊;朱永强: "电能质量的经济损失评估与选择性治理", 现代电力, vol. 27, no. 5, pages 92 - 94 * |
| 叶婷等: "电动汽车充换储一体化电站的优化调度策略及应用", 电力需求侧管理, vol. 23, no. 4, pages 68 - 69 * |
| 周辉;杨洪耕;吴传来;: "基于灰色聚类的电能质量综合评估方法", 电力系统保护与控制, no. 15 * |
| 石磊磊 等: "基于数据驱动的电能质量分区治理策略", 中国电机工程学报, vol. 39, no. 4, pages 995 - 998 * |
| 耿光飞;杨仁刚;: "配电网无功优化控制研究", 沈阳农业大学学报, vol. 36, no. 6, pages 697 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116722608A (en) * | 2023-04-28 | 2023-09-08 | 国网河北省电力有限公司电力科学研究院 | Reactive power compensation system based on photovoltaic inverter |
| CN116544938A (en) * | 2023-07-07 | 2023-08-04 | 国网安徽省电力有限公司经济技术研究院 | Power quality analysis method for AC/DC flexible power distribution system |
| CN116544938B (en) * | 2023-07-07 | 2023-09-19 | 国网安徽省电力有限公司经济技术研究院 | Power quality analysis method for AC/DC flexible power distribution system |
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