CN115241901A - Intelligent energy storage soft switch data driving voltage control method considering data quality - Google Patents
Intelligent energy storage soft switch data driving voltage control method considering data quality Download PDFInfo
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Abstract
The invention relates to an intelligent energy storage soft switch data driving voltage control method considering data quality, which comprehensively considers the agnostic property of power distribution network line parameters, the position of a distributed power supply and the uncertainty of output conditions, establishes a data driving model through measured data, and adopts a density-based local outlier factor method to identify bad data and introduce an attenuation factor to inhibit the measured disturbance in the data driving and adjusting process because the measured bad data and the measured data disturbance in uploaded data can influence the voltage adjusting effect of a data driving power distribution network. The invention provides a method for controlling the voltage of a power distribution network, which can overcome the adverse effects on data driving voltage control caused by measuring bad data and measuring disturbance in measured data under the condition of poor data quality, regulate the voltage of the power distribution network comprising a multi-terminal intelligent energy storage soft switch, ensure the effectiveness and stability of the data driving voltage control, effectively improve the operation controllability and flexibility of the power distribution network, and have important significance for ensuring the safe and economic operation of the power distribution network.
Description
Technical Field
The invention relates to a voltage regulation method for a power distribution network. In particular to an intelligent energy storage soft switch data driving voltage control method considering data quality.
Background
Due to the high permeability access of the distributed power supply, the operation control of the power distribution network faces huge challenges including voltage out-of-limit, tidal current reverse transmission and the like. The multi-end intelligent energy storage soft switch is a novel flexible power electronic device replacing a traditional feeder line connection switch, can quickly and accurately control tide distribution to achieve flexible exchange of power between feeder lines in a larger range, combines the quick response characteristic of an energy storage link, and provides a better solution for dealing with the problem of safe and economical operation of a power grid of an intermittent power supply under the situation that the permeability of a distributed power supply is improved day by day. The application of the multi-terminal intelligent energy storage soft switch in the power distribution network provides quick response capacity for the power distribution network, helps to stabilize voltage fluctuation and adjust power distribution, and effectively improves operation controllability and flexibility of the power distribution network. Therefore, the reasonable regulation and control of the multi-terminal intelligent energy storage soft switch has important significance for guaranteeing the safe and economic operation of the power distribution network.
However, the multi-terminal intelligent energy storage soft switching optimization strategy based on the physical model cannot adapt to frequent changes of the running state of the power distribution network. And under the actual complicated operating environment, the accurate parameter of distribution network is difficult to obtain. The rapid development of the intelligent measurement terminal and the communication network promotes the high informatization of the power distribution system, and creates favorable conditions for the implementation of the data-driven optimization method. The data driving does not depend on detailed mathematical model information of a controlled system, the input and output relation of a complex link is described statistically by using measured data, the simulation construction of unknown characteristics of the complex link is realized, and the aim of controlling the voltage of the power distribution network is further realized. However, due to the limitations of the accuracy of the measurement device itself, measurement disturbance exists in the measurement data, and due to the appearance of external physical factors and other data sources, bad data also exists in the measurement data, so that the measurement upload data in the actual power distribution network has a data quality problem, while the data driving method completely depends on the measurement data, and the quality of the measurement data inevitably affects the voltage regulation effect of the data-driven flexible power distribution network.
Therefore, the bad data identification link is introduced to carry out online identification on the measured data, and attenuation factors are introduced aiming at the measurement disturbance, so that a data driving voltage control method considering the quality of the measured data is designed, the influence of the bad data and the measurement disturbance on the control effect is weakened, and the voltage regulation effect of the power distribution network is effectively improved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a data quality-considered intelligent energy storage soft switch data driving voltage control method which can regulate the voltage of a power distribution network comprising a multi-terminal intelligent energy storage soft switch under the condition of poor data quality.
The technical scheme adopted by the invention is as follows: an intelligent energy storage soft switching data driving voltage control method considering data quality comprises the following steps:
1) Inputting node voltage, injection power historical data and parameter information of the active power distribution network at each moment of the system per day according to the selected active power distribution network;
2) Obtaining bad data and measurement disturbance w in a period from t-delta t' to t according to the active power distribution network selected in the step 1) t The voltage measurement data of each node is used for establishing a voltage measurement matrix at the time t
For is to
Identifying bad data from the voltage measurement data, and calculating bad numberAccording to the processed average measured data of the voltage at the time t
3) According to the average measured data of the voltage at the time t
Combined node voltage reference interval
Judging whether voltage out-of-limit occurs or not, if not, turning to the step 7), and if so, calculating the voltage deviation at the time t
And are aligned with
Carrying out measurement disturbance suppression processing to obtain voltage deviation disturbance suppression data at the time t
4) Suppressing data according to the voltage deviation disturbance at the time t
Establishing a pseudo Jacobian matrix of the r-th port of a multi-terminal intelligent energy storage soft switch SOP link at the moment t
Combining voltage deviation disturbance suppression data at time t
And calculating a pseudo Jacobian estimation matrix of a multi-terminal intelligent energy storage soft switch energy storage link at t moment by adopting a multi-layer hierarchical prediction algorithm with data of corresponding moments in active power distribution network node voltage and injection power historical data at m days
5) Pseudo Jacobian matrix of the r port of the multi-terminal intelligent energy storage soft switch SOP link according to the time t
And t moment multi-terminal intelligent energy storage soft switch energy storage link pseudo Jacobian estimation matrix
The data drive multi-terminal intelligent energy storage soft switch data drive voltage control model of considering measured data quality is established, includes: the method comprises the steps of taking the minimum voltage deviation of each node of an active power distribution network as a target function, restraining capacity and loss of converters at each port of a multi-terminal intelligent energy storage soft switch SOP link, restraining upper and lower limits of output of active power and reactive power, restraining charge and discharge efficiency, restraining upper and lower limits of charge and discharge power and restraining a prediction domain T of a multi-terminal intelligent energy storage soft switch energy storage link p Internal charge state constraint and charge-discharge frequency limit constraint;
6) Adopting a mathematical solver to solve a data-driven multi-terminal intelligent energy storage soft switch data driving voltage control model considering the quality of measured data, issuing and executing a solution result, wherein the solution result comprises: charging and discharging power of multi-terminal intelligent energy storage soft switch energy storage link at time t
Active and reactive power output of the r-th port of multi-terminal intelligent energy storage soft switch SOP link at time t
7) Update control time t = t + Δ t, and determine t-t 0 ≥βΔT c If yes, let T p ＝T p -ΔT c β = β +1, if no, step 8) is performed, where β is the control parameter, Δ t is the control time interval, t 0 Is a starting time, T p To predict the field, Δ T c Is a control domain;
8) According to the control time t in the step 7), judging t-t 0 And (4) whether the time is longer than the optimization time T or not, if not, turning to the step 2), and if so, ending.
According to the intelligent energy storage soft switch data driving voltage control method considering the data quality, the unknown parameters of the power distribution network line, the distributed power supply position and the uncertainty of the output condition are comprehensively considered, the data driving model is established through the measured data, and the measured bad data and the measured data disturbance existing in the uploaded data influence the voltage regulation effect of the data driving power distribution network, so that the bad data are identified by adopting a density-based local outlier factor method, and the measured disturbance is restrained by introducing an attenuation factor in the data driving regulation process. According to the method, under the conditions of actual complex operation environment, frequent change of operation state of the power distribution network and difficulty in obtaining accurate parameters of the power distribution network, adverse effects on data driving voltage control caused by bad data measurement and disturbance in measurement data can be overcome, voltage regulation is performed on the power distribution network with the multi-terminal intelligent energy storage soft switch, effectiveness and stability of data driving voltage control are guaranteed, operation controllability and flexibility of the power distribution network are effectively improved, and the method has important significance for guaranteeing safe and economical operation of the power distribution network.
Drawings
FIG. 1 is a flow chart of the intelligent energy storage soft switch data driving voltage control method considering data quality according to the present invention;
FIG. 2 is a schematic diagram of an example of a flexible interconnection power distribution network with four-terminal intelligent energy storage soft switches in an improved Tianjin demonstration area;
FIG. 3 is a 24 hour distributed power output curve;
FIG. 4 is a 24 hour load curve;
FIG. 5 is a comparison graph of the global voltage maximum results of each scene in 24 hours;
FIG. 6 is a comparison graph of the global voltage minimum results for each scene at 24 hours;
FIG. 7 is a graph of the maximum node voltage distribution for each region in 24 hours;
FIG. 8 is a 24-hour multi-terminal intelligent energy storage soft switch power output diagram of each port;
fig. 9 is a reactive force diagram of each port of the 24-hour multi-port intelligent energy storage soft switch;
fig. 10 is a state of charge change diagram of the 24-hour energy storage link.
Detailed Description
The following describes the intelligent energy storage soft switching data driving voltage control method considering data quality in detail with reference to the embodiments and the accompanying drawings.
As shown in fig. 1, the intelligent energy storage soft switching data driving voltage control method considering data quality of the present invention includes the following steps:
1) Inputting node voltage, injection power historical data and parameter information of the active power distribution network at each time of day of the system according to the selected active power distribution network; the parameter information comprises: capacity, access position, initial charge state of energy storage link, charge-discharge power limit value and node voltage reference interval of multi-terminal intelligent energy storage soft switch
Prediction domain T p Control field Δ T c Control time interval Δ T, optimization time T, measured data acquisition time window Δ T', initialization control parameter β =1, start time T 0 Initialization control time t = t 0 。
2) Obtaining bad data and measurement disturbance w in a period from t-delta t' to t according to the active power distribution network selected in the step 1) t The voltage measurement data of each node is used for establishing a voltage measurement matrix at the time t
To pair
Identifying bad data by the voltage measurement data in the process, and calculating the average measurement data of the voltage at the time t after the bad data is processed
The method specifically comprises the following steps:
(2.1) establishing a voltage measurement matrix at the time t
Obtaining bad data and measurement disturbance w of each node in the period from t-delta t' to t t The voltage measurement data of the time point t, and a voltage measurement matrix at the time point t are established
The following were used:
U Δt′,ξ ＝[U Δt′,ξ (1),…,U Δt′,ξ (i),…,U Δt′,ξ (I)] T (2)
wherein z represents the voltage measurement data group number uploaded by each node in the period from t-delta t 'to t, I represents the node number of the active power distribution network, and U represents the voltage measurement data group number uploaded by each node in the period from t-delta t' to t Δt′,1 、U Δt′,ξ 、U Δt′,z Respectively representing the voltage measurement data of the 1 st, xi and z groups, U, uploaded by each node in the period from t-delta t' to t Δt′,ξ (1)、U Δt′,ξ (i)、U Δt′,ξ (I) Respectively representing data uploaded by 1 st, I th and I nodes of the active power distribution network in a xi group in a period from t-delta t' to t;
defining a voltage measurement matrix u t The two-dimensional coordinates of the medium voltage data are as follows:
[U Δt′,ξ (i),U Δt′,ξ (i)/σ norm (U Δt′,ξ )] (3)
in the formula, σ norm (U Δt′,ξ ) Indicating voltage measuring matrix
Standard deviation of group ξ data.
(2.2) determining the kth distance neighborhood of each voltage measurement data
Voltage measurement data U uploaded by xth node of active power distribution network in xi group in time period from t-delta t' to t Δt′,ξ (x) A k-distance neighborhood centered at N k [U Δt′,ξ (x)]Expressed as follows:
in the formula, o is a node.
(2.3) calculating the voltage measurement data U uploaded by the xth node Δt′,ξ (x) Distance d between the voltage measurement data uploaded by other nodes k [U Δt′,ξ (x),U Δt′,ξ (y)]Expressed as follows:
in the formula, d [ U ] Δt′,ξ (x),U Δt′,ξ (y)]Voltage measurement data U uploaded by the xth node of the active power distribution network in the xi group in the period from t-delta t' to t Δt′,ξ (x) The voltage measurement data U uploaded to the y node Δt′,ξ Euclidean distance of (y), d k [U Δt′,ξ (x)]Is represented in N k [U Δt′,ξ (x)]Internal and voltage measurement data U Δt′,ξ (x) The euclidean distance from the farthest data point, t, is any node in the k distances from node x.
(2.4) calculating local outlier factor of each voltage measurement data
Voltage measurement data U Δt′,ξ (x) Local outlier factor F of k [U Δt′,ξ (x)]Expressed as follows:
in the formula, ρ k [U Δt′,ξ (x)]、ρ k [U Δt′,ξ (y)]Respectively representVoltage measurement data U Δt′,ξ (x) And U Δt′,ξ (y) local density, expressed as follows:
(2.5) calculating a local outlier factor threshold
If F k [U Δt′,ξ (i)]>F k,max Then voltage measurement data U Δt′,ξ (i) For bad data, let U Δt′,ξ (i)＝0
In the formula, F k,max Representing a local outlier factor threshold, Q, set using four-quadrant analysis 1 、Q 3 Representing the local outlier set F k Lower quartile and upper quartile, F k ＝[F k [U Δt′,ξ (1)],…,F k [U Δt′,ξ (i)],…,F k [U Δt′,ξ (I)]]，F k [U Δt′,ξ (1)]、[U Δt′,ξ (i)]、F k [U Δt′,ξ (I)]Respectively represent voltage measurement data U Δt′,ξ (1)、U Δt′,ξ (i)、U Δt′,ξ (I) The local outlier factor of (a) is,
representing a local outlier set F k Standard deviation of [, ] 1 、η 2 And represents the standard deviation discrimination coefficient.
(2.6) calculating average measured data of voltage at t moment after bad data processing
The new voltage measurement matrix after the identification processing of the measured bad data is
Respectively representing voltage measurement matrix
Group 1, xi and z data;
according to a new voltage measurement matrix of
Calculating average measurement data of voltage at t moment
The following:
3) According to the average measured data of the voltage at the time t
Combined node voltage reference interval
Judging whether voltage out-of-limit occurs or not, if not, turning to the step 7), and if so, calculating the voltage deviation at the time t
And to
Carrying out measurement disturbance suppression processing to obtain voltage deviation disturbance suppression data at the time t
Said pair
Carrying out measurement disturbance suppression processing to obtain voltage deviation disturbance suppression data at the time t
Is represented as follows:
in the formula, e w,t Indicating a set node voltage reference value U ref Voltage average measurement data with time t
The vector of the difference values of (a),
mean measurement data of voltage at t
Voltage average measurement data at time t-delta t
Zeta denotes the attenuation factor, N k ＝ΔT c At, denotes the control field Δ T c Regulation and control times of internal multi-terminal intelligent energy storage soft switch, e s E represents a unit column vector as a deviation threshold parameter; n is a radical of w Is expressed at DeltaT c Within, from | e w,t |≥e s E phase switch to | E w,t |<e s The number of times that the multi-terminal intelligent energy storage soft switch has completed regulation and control in the stage E; β represents the attenuation factor amplification factor.
4) Suppressing data according to the voltage deviation disturbance at the time t
Establishing a pseudo Jacobian matrix of an r-th port of a multi-terminal intelligent energy storage soft switch SOP link at the time t
Combining voltage deviation disturbance suppression data at time t
And calculating a pseudo Jacobian estimation matrix of a multi-terminal intelligent energy storage soft switch energy storage link at the t moment by adopting a multi-layer hierarchical prediction algorithm with data of corresponding moments in historical data of node voltage and injection power of the active power distribution network at each moment of m days
Wherein,
the suppression data according to the voltage deviation disturbance at the time t
Establishing a pseudo Jacobian matrix of the r-th port of a multi-terminal intelligent energy storage soft switch SOP link at the moment t
Is represented as follows:
if it is
Or
Or
Then the
In the formula,
and
respectively representing pseudo Jacobian matrixes of an r-th port of an intelligent energy storage soft switch SOP link at the time t and the time t-delta t,
is composed of
An initial value of (1);
the variable quantity vector of the active control quantity of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time of t-delta t is shown,
respectively represents the output strategies of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time t-delta t and the time t-2 delta t,
respectively representing active power difference vectors and reactive power difference vectors of the r-th port of the multi-port intelligent energy storage soft switch SOP link at the time of t-delta t,
η SOP 、μ SoP representing the weight coefficients.
The combined t-time voltage deviation disturbance suppression data
Data corresponding to the node voltage and injection power of the active power distribution network at each moment of m days in historical dataCalculating a pseudo Jacobian estimation matrix of a multi-terminal intelligent energy storage soft switch energy storage link at the time t by adopting a multi-layer hierarchical prediction algorithm
Is represented as follows:
in the formula,
and (3) representing a pseudo Jacobian matrix of the energy storage link at the time t, wherein the iterative computation solving process comprises the following steps:
if it is
Or
Or
Then the
In the formula,
representing the difference vector of the charging and discharging power of the multi-terminal intelligent energy storage soft switch energy storage link at the time of t-delta t,
respectively represents the energy storage link at the t-delta t moment and the t-2 delta t momentThe active power output strategy of (a) is,
is composed of
Of initial value of ES 、μ ES Represents a weight coefficient, and epsilon represents a threshold coefficient;
in the formula (12), the reaction mixture is,
represents T + n Δ T c The pseudo jacobian prediction matrix at a time, N ∈ {1, \8230;, N }, N = T p /ΔT c Representing the prediction domain T p And control field Δ T c The calculation formula is as follows:
in the formula, theta α,t Denotes the prediction coefficient at time T, where α =1, \8230;, l, T d Representing an estimated time duration in days;
defining a regression coefficient vector θ t ＝(θ 1,t ,…,θ α,t ,…,θ l,t ) T ，θ 1,t 、θ α,t 、θ l,t Respectively represent time t theta t 1, α, l element, θ t The iterative solution formula is as follows:
in the formula, a pseudo-elegant predictive matrix set of t-delta t
And
respectively, using T + n Δ T c -T d Time sum T + n Δ T c -lT d A pseudo Jacobian matrix is obtained by calculating historical measurement data of the active power distribution network at any moment, delta represents a weight coefficient, and theta t-Δt Is the regression coefficient vector at the time t-delta t.
5) Pseudo Jacobian matrix of the r port of the multi-terminal intelligent energy storage soft switch SOP link according to the time t
And t moment multi-terminal intelligent energy storage soft switch energy storage link pseudo Jacobian estimation matrix
The data drive multi-terminal intelligent energy storage soft switch data drive voltage control model of the quality of the measured data is considered to be established, and the data drive multi-terminal intelligent energy storage soft switch data drive voltage control model comprises the following steps: the method comprises the steps of taking the minimum voltage deviation of each node of an active power distribution network as a target function, restraining capacity and loss of converters at each port of a multi-terminal intelligent energy storage soft switch SOP link, restraining upper and lower limits of output of active power and reactive power, restraining charge and discharge efficiency, restraining upper and lower limits of charge and discharge power and restraining a prediction domain T of a multi-terminal intelligent energy storage soft switch energy storage link p Internal charge state constraint and charge-discharge frequency limit constraint; wherein,
the objective function is expressed as follows:
wherein J represents an objective function, J 1 、J′ 1 The adjusted objective function is represented as a function of the target,
representing the voltage reference at time t + deltat,
represents the average measured data of the voltage at the time t,
respectively an upper limit and a lower limit of a voltage reference interval,
r represents the port number of the multi-terminal intelligent energy storage soft switch,
the difference vector of the charging and discharging power of the multi-terminal intelligent energy storage soft switch energy storage link at the moment t is shown,
respectively represents the charging and discharging power of the energy storage link at the time t and the time t-delta t,
the variable quantity vector of the active and reactive control quantity of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time t is shown,
respectively representing active power difference value vector and reactive power difference value vector of the r-th port of the multi-terminal intelligent energy storage soft switch SOP link at the moment t, lambda ES 、λ SOP Represents a weight coefficient, ζ represents an attenuation factor;
the estimated value of the voltage of each node at the time t + Δ t is expressed as follows:
in the formula,
representing the vector of the voltage regulation and control variation quantity of the SOP link of the multi-terminal intelligent energy storage soft switch at the time t,
a pseudo Jacobian matrix of an r-th port of a multi-terminal intelligent energy storage soft switch SOP link at the time t is represented;
denotes from time T to T + N Δ T c The multi-terminal intelligent energy storage soft switch energy storage link voltage regulation variation vector at a moment,
respectively represents time T, T + (N-1) DeltaT c Constantly multi-terminal intelligent energy storage soft switch energy storage link voltage regulation and control variable vectors;
expressing a pseudo Jacobian estimation matrix of a multi-terminal intelligent energy storage soft switch energy storage link at the time t;
denotes the time from T to T + (N-1) Δ T c The energy storage link charges and discharges the power variable quantity vector at all times,
respectively represent time t and t +(N-1)ΔT c And (3) charging and discharging power difference vectors of the moment energy storage link.
The capacity of each port converter, the loss constraint of each port converter and the active and reactive power output upper and lower limit constraints of each port converter in the multi-terminal intelligent energy storage soft switch SOP link are expressed as follows:
in the formula,
the actual charging and discharging power of the energy storage link is shown,
respectively representing active and reactive power output of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time t,
the loss of the current converter at the r-th port of the multi-terminal intelligent energy storage soft switch SOP link at the time t is shown,
the loss coefficient of the current converter at the r-th port of the SOP link of the multi-terminal intelligent energy storage soft switch is shown,
respectively represents the upper limit and the lower limit of active power of a multi-terminal intelligent energy storage soft switch SOP link,
the actual injection active power of the r-th port of the multi-terminal intelligent energy storage soft switch SOP link at the moment t is shown,
respectively representing the upper and lower limits of reactive power of a multi-terminal intelligent energy storage soft switch SOP link at the time t,
and the capacity of the r-th port converter of the multi-terminal intelligent energy storage soft switch SOP link is shown.
The charge-discharge efficiency constraint, the charge-discharge power upper and lower limit constraint and the prediction domain T of the multi-terminal intelligent energy storage soft switch energy storage link are adopted p The internal state of charge constraint and the charge-discharge number limit constraint are expressed as follows:
if it is
And is
In the formula,
the actual charging and discharging power of the energy storage link is shown,
respectively represents the charge and discharge power of the energy storage link at the time t and the time t-delta t,
respectively representing energy-storage ringsThe charge-discharge efficiency of the node is improved,
respectively represents the upper limit and the lower limit of the calculated value of the charging and discharging power of the energy storage link,
respectively represent T + DeltaT c Time sum T + (N-1) Δ T c Charging and discharging power difference of moment energy storage link, S ES The capacity of the energy storage link is shown,
representing the upper and lower limits of the charge state of the energy storage link,
respectively representing the initial state and the prediction domain T of the energy storage system p The state of charge after the end of the cycle,
which is indicative of a threshold coefficient of the value,
respectively representing the charge states of the multi-terminal intelligent energy storage soft switch energy storage link at T and T-delta T, wherein N = T p /ΔT c Representing the prediction domain T p And control field Δ T c The ratio of (a) to (b),
and the charging and discharging power difference of the multi-terminal intelligent energy storage soft switch energy storage link at the moment t is represented.
6) Adopting a mathematical solver to solve a data-driven multi-terminal intelligent energy storage soft switch data driving voltage control model considering the quality of measured data, issuing and executing a solution result, wherein the solution result comprises: charging and discharging power of multi-terminal intelligent energy storage soft switch energy storage link at time t
T moment multi-terminal intelligent energy storage soft switch SOP linkActive and reactive power output of the r-th port
7) Updating control time t = t + Δ t, and judging t-t 0 ≥βΔT c If true, let T p ＝T p -ΔT c β = β +1, if no, step 8) is performed, where β is the control parameter, Δ t is the control time interval, t is 0 Is a starting time, T p To predict the field, Δ T c Is a control domain;
8) According to the control time t in the step 7), judging t-t 0 And (3) whether the time length is greater than the optimization time length T, if not, turning to the step 2), and if so, ending.
Examples are given below:
the embodiment of the invention is an improved Tianjin demonstration area containing four-terminal intelligent energy storage soft switch flexible interconnection power distribution network example, and the topology is shown in figure 2. The detailed parameters are shown in tables 1-2.
TABLE 1 Tianjin demonstration area distribution network example load access position and power
TABLE 2 example line parameters for distribution network in Tianjin demonstration area
In the demonstration area, two 110kV transformer substations serve as centers to form a double-loop network structure comprising four feeders, four distribution network areas are flexibly interconnected through four-end intelligent energy storage soft switches, the voltage grades are set to be 10.5kV, and the total active power demand and the total reactive power demand of the load are respectively 9.9880MW and 7.3350Mvar.
In the embodiment, in order to fully consider the influence of the access of a high-permeability distributed power supply on the operation loss and the voltage fluctuation of a flexible power distribution network, 3 groups of photovoltaic systems and 3 wind turbine generators are respectively accessed in an example of a flexible interconnection power distribution network with four-terminal intelligent energy storage soft switches in a demonstration area, and the permeability of the distributed power supply reaches 90.11%. The access positions of the photovoltaic system are nodes 8, 37 and 41, and the capacity is 1MVA; the access positions of the wind turbine generator are nodes 12, 21 and 44, the capacity is 2MVA, and the output reduction of the distributed power supply is not allowed. The daily active power output curve of the distributed power supply is shown in fig. 3. In addition, in the calculation example, the capacity of a current converter of each port of the four-terminal intelligent energy storage soft switch is 3MVA, the capacity of an energy storage link is 2MWh, the limit of charge and discharge power is 0.5MW, and the charge state range is 20% -80%.
Setting each weight factor as lambda according to the convergence and control requirements of the model-free adaptive control method ES ＝20、μ Es ＝0.5、λ SOP ＝0.1、μ Es ＝10，η Es 、η SOP 、ρ SOP Delta is set to be 1, and the charge and discharge efficiency of the energy storage link is improved
Are all set to 99 percent, the loss coefficient of the converter
Threshold coefficient e =0.001, optimization time T set to 24 hours, prediction domain T p Set to 24 hours, control field Δ T c Set to 1 hour, the control time interval Δ t is set to 5 minutes, and the metrology data acquisition time window Δ t' is set to 0.5 minutes.
In order to fully verify the advancement of the intelligent energy storage soft switching data driving voltage control method considering the quality of measured data, in the embodiment, the following four scenes are adopted for comparative analysis:
scene 1: the multi-terminal intelligent energy storage soft switch is not controlled, and the initial running state of the power distribution network is obtained;
scene 2: the multi-terminal intelligent energy storage soft switch is subjected to data drive control, and links of bad data processing and measurement disturbance suppression are not included;
scene 3: the method is adopted to carry out data drive control on the multi-terminal intelligent energy storage soft switch, and comprises links of bad data processing and measurement disturbance suppression;
scene 4: and controlling the multi-terminal intelligent energy storage soft switch by adopting a centralized optimization method.
The computer hardware environment for executing the optimized calculation is Intel (R) Core (TM) i7-10700, the dominant frequency is 2.90GHz, and the internal memory is 24GB; the software environment is a Windows10 operating system.
The 24 hour global voltage maximum result pairs for scenarios 1, 2, 3, 4 are shown in fig. 5. The 24 hour global voltage minimum result pairs for scenarios 1, 2, 3, 4 are shown in fig. 6. Fig. 7 shows the maximum node voltage distribution in each region for 24 hours in scenarios 1, 2, 3, and 4. The active output of each port of the 24-hour multi-port intelligent energy storage soft switch in the scene 3 is shown in fig. 8. Reactive output of each port of the 24-hour multi-port intelligent energy storage soft switch in the scene 3 is shown in fig. 9. The state of charge change of the 24-hour energy storage link in the scenario 3 is shown in fig. 10. Table 3 shows voltage index ratio pairs in scenarios 1, 2, 3, and 4.
TABLE 3 comparison of Voltage indicators for various scenes
As can be seen from fig. 4 to 6, the scenario 3 can effectively adjust the voltage level of the power distribution network of the present embodiment. In combination with table 3, it can be seen by comparing the global voltage distributions of scenes 2 and 3 in fig. 4 and 5 that, after the bad data processing link and the measurement disturbance suppression link are added in the scene 3, the voltage level of the power distribution network is significantly increased compared with the scene 2, and it can be seen by comparing the global voltage distributions of the scenes 3 and 4 in fig. 4 and 5 that the voltage control effect of the data-driven voltage control scene considering the measured data quality is close to global optimum. It can be seen from fig. 4 to 9 that the intelligent energy storage soft switching data driving voltage control method considering the measured data quality can effectively solve the problem of optimal adjustment of the power distribution network voltage under the condition of poor data quality.
Claims (9)
1. An intelligent energy storage soft switching data driving voltage control method considering data quality is characterized by comprising the following steps:
1) Inputting node voltage, injection power historical data and parameter information of the active power distribution network at each time of day of the system according to the selected active power distribution network;
2) Obtaining bad data and measurement disturbance w in a period from t-delta t' to t according to the active power distribution network selected in the step 1) t The voltage measurement data of each node is used for establishing a voltage measurement matrix at the time t
To pair
Identifying bad data by the voltage measurement data in the process, and calculating the average measurement data of the voltage at the time t after the bad data is processed
3) According to the average measured data of the voltage at the time t
Combined node voltage reference interval
Judging whether voltage out-of-limit occurs or not, if not, turning to the step 7), and if so, calculating the voltage deviation at the time t
And to
Carrying out measurement disturbance suppression processing to obtain voltage deviation disturbance suppression data at the time t
4) Suppressing data according to the voltage deviation disturbance at the time t
Establishing a pseudo Jacobian matrix of the r-th port of a multi-terminal intelligent energy storage soft switch SOP link at the moment t
Combined with t time voltage deviation disturbance suppression data
And calculating a pseudo Jacobian estimation matrix of a multi-terminal intelligent energy storage soft switch energy storage link at the t moment by adopting a multi-layer hierarchical prediction algorithm with data of corresponding moments in historical data of node voltage and injection power of the active power distribution network at each moment of m days
5) Pseudo Jacobian matrix of the r port of the multi-terminal intelligent energy storage soft switch SOP link according to the time t
And t moment multi-terminal intelligent energy storage soft switch energy storage link pseudo Jacobian estimation matrix
The data drive multi-terminal intelligent energy storage soft switch data drive voltage control model of the quality of the measured data is considered to be established, and the data drive multi-terminal intelligent energy storage soft switch data drive voltage control model comprises the following steps: the method comprises the steps of taking the minimum voltage deviation of each node of an active power distribution network as a target function, restraining capacity and loss of converters at each port of a multi-terminal intelligent energy storage soft switch SOP link, restraining upper and lower limits of output of active power and reactive power, restraining charge and discharge efficiency, restraining upper and lower limits of charge and discharge power and restraining a prediction domain T of a multi-terminal intelligent energy storage soft switch energy storage link p Internal charge state constraint and charge-discharge frequency limit constraint;
6) Adopting a mathematical solver to solve a data-driven multi-terminal intelligent energy storage soft switch data driving voltage control model considering the quality of measured data, issuing and executing a solution result, wherein the solution result comprises: charging and discharging power of multi-terminal intelligent energy storage soft switch energy storage link at time t
Active and reactive power output of the r-th port of multi-terminal intelligent energy storage soft switch SOP link at time t
7) Update control time t = t + Δ t, and determine t-t 0 ≥βΔT c If true, let T p ＝T p -ΔT c β = β +1, if no, step 8) is performed, where β is the control parameter, Δ t is the control time interval, t is 0 Is a starting time, T p To predict the field, Δ T c Is a control domain;
8) According to the control time t in the step 7), judging t-t 0 And (4) whether the time is longer than the optimization time T or not, if not, turning to the step 2), and if so, ending.
2. The method for controlling the data driving voltage of the intelligent energy storage soft switch considering the data quality as claimed in claim 1, wherein the parameter information in step 1) comprises: capacity, access position, initial charge state of energy storage link, charge-discharge power limit value and node voltage reference interval of multi-terminal intelligent energy storage soft switch
Prediction domain T p Control field Δ T c Control time interval Δ T, optimization time T, measured data acquisition time window Δ T', initialization control parameter β =1, start time T 0 Initialization control time t = t 0 。
3. The intelligent energy storage soft switching data driving voltage control method considering data quality according to claim 1, wherein the step 2) specifically comprises:
(2.1) establishing a voltage measurement matrix at the time t
Obtaining bad data and measurement disturbance w of each node in the period from t-delta t' to t t The voltage measurement data of the time point t, and a voltage measurement matrix at the time point t are established
The following were used:
U Δt′,ξ ＝[U Δt′,ξ (1),…,U Δt′,ξ (i),…,U Δt′,ξ (I)] T (2)
wherein z represents the voltage measurement data group number uploaded by each node in the period from t-delta t 'to t, I represents the node number of the active power distribution network, and U represents the voltage measurement data group number uploaded by each node in the period from t-delta t' to t Δt′,1 、U Δt′,ξ 、U Δt′,z Respectively representing the voltage measurement data of the 1 st, xi and z groups, U, uploaded by each node in the period from t-delta t' to t Δt′,ξ (1)、U Δt′,ξ (i)、U Δt′,ξ (I) Respectively representing data uploaded by 1 st, I th and I nodes of the active power distribution network in the xi group in a period from t-delta t' to t;
defined voltage measurement matrix
The two-dimensional coordinates of the medium voltage data are as follows:
[U Δt′,ξ (i),U Δt′,ξ (i)/σ norm (U Δt′ , ξ )] (3)
in the formula, σ norm (U Δt′,ξ ) Indicating voltage measurement matrix
Standard deviation of group xi data;
(2.2) determining the k-th distance neighborhood of each voltage measurement data
Voltage measurement data U uploaded by xth node of active power distribution network in the xi group in the time period from t-delta t' to t Δt′,ξ (x) A k-distance neighborhood centered at N k [U Δt′,ξ (x)]Expressed as follows:
in the formula, o is a node;
(2.3) calculating the voltage measurement data U uploaded by the xth node Δt′,ξ (x) Distance d between the voltage measurement data uploaded by other nodes k [U Δt′,ξ (x),U Δt′,ξ (y)]Expressed as follows:
in the formula, d [ U ] Δt′,ξ (x),U Δt′,ξ (y)]Voltage measurement data U uploaded by the xth node of the active power distribution network in the xi group in the period from t-delta t' to t Δt′,ξ (x) The voltage measurement data U uploaded to the y node Δt′,ξ Euclidean distance of (y), d k [U Δt′,ξ (x)]Is represented in N k [U Δt′,ξ (x)]Internal and voltage measurement data U Δt′,ξ (x) The Euclidean distance from the farthest data point, y is any node in the k distances from node x;
(2.4) calculating local outlier factors of each voltage measurement data
Voltage measurement data U Δt′,ξ (x) Local outlier factor F of k [U Δt′,ξ (x)]Expressed as follows:
in the formula, ρ k [U Δt′,ξ (x)]、ρ k [U Δt′,ξ (y)]Respectively represent voltage measurement data U Δt′,ξ (x) And U Δt′,ξ (y) local density, expressed as follows:
(2.5) calculating local outlier threshold
If F k [U Δt′,ξ (i)]>F k,max Then voltage measurement data U Δt′,ξ (i) For bad data, order U Δt′,ξ (i)＝0
In the formula, F k,max Representing a local outlier factor threshold, Q, set using four-quadrant analysis 1 、Q 3 Representing a local outlier set F k Lower quartile and upper quartile, F k ＝[F k [U Δt′,ξ (1)],…,F k [U Δt′,ξ (i)],…,F k [U Δt′,ξ (I)]]，F k [U Δt′,ξ (1)]、[U Δt′,ξ (i)]、F k [U Δt′,ξ (I)]Respectively represent voltage measurement data U Δt′,ξ (1)、U Δt′,ξ (i)、U Δt′,ξ (I) The local outlier factor of (a) is,
representing the local outlier set F k Standard deviation of [, ] 1 、η 2 Representing a standard deviation discrimination coefficient;
(2.6) calculating average measured data of voltage at t moment after bad data processing
The new voltage measurement matrix after the identification processing of the measured bad data is
Respectively represent voltage measurement matrix
Group 1, xi and z data;
according to a new voltage measurement matrix of
Calculating average measurement data of voltage at t moment
The following were used:
4. the intelligent energy storage soft-switching data driving voltage control method considering data quality as claimed in claim 1, wherein the pair in step 3)
Carrying out measurement disturbance suppression processing to obtain voltage deviation disturbance suppression data at the time t
Is represented as follows:
in the formula, e w,t Indicating a set node voltage reference value U ref Voltage average measurement data with time t
The vector of the difference values of (a),
mean measurement data representing voltage at time t
Voltage average measurement data at time t-delta t
Zeta denotes the attenuation factor, N k ＝ΔT c At, denotes the control field Δ T c Regulation and control times of internal multi-terminal intelligent energy storage soft switch, e s E represents a unit column vector as a deviation threshold parameter; n is a radical of w Is expressed at DeltaT c Within, from | e w,t |≥e s E phase switch to | E w,t |<e s The number of times that the multi-terminal intelligent energy storage soft switch has completed regulation and control in the stage E; β represents an attenuation factor amplification factor.
5. The method for controlling the data driving voltage of the intelligent energy storage soft switch considering the data quality as claimed in claim 1, wherein the step 4) of suppressing the data according to the voltage deviation disturbance at the time t
Establishing a pseudo Jacobian matrix of the r-th port of a multi-terminal intelligent energy storage soft switch SOP link at the moment t
Is represented as follows:
if it is
Or
Or
Then
In the formula,
and
respectively representing pseudo Jacobian matrixes of an r-th port of an intelligent energy storage soft switch SOP link at the time t and the time t-delta t,
is composed of
An initial value of (1);
the variable quantity vector of the active control quantity of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time of t-delta t is represented,
respectively represents the output strategies of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time t-delta t and the time t-2 delta t,
respectively representing active power difference vectors and reactive power difference vectors of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time of t-delta t,
η SOP 、μ SOP representing the weight coefficients.
6. The method for controlling voltage driving according to claim 1, wherein the step 4) is combined with the suppression data of voltage deviation disturbance at time t
And calculating a pseudo Jacobian estimation matrix of a multi-terminal intelligent energy storage soft switch energy storage link at the t moment by adopting a multi-layer hierarchical prediction algorithm with data of corresponding moments in historical data of node voltage and injection power of the active power distribution network at each moment of m days
Is represented as follows:
in the formula,
and (3) representing a pseudo Jacobian matrix of the energy storage link at the time t, wherein the iterative computation solving process comprises the following steps:
if it is
Or
Or
Then
In the formula,
representing the difference vector of the charging and discharging power of the multi-terminal intelligent energy storage soft switch energy storage link at the time of t-delta t,
respectively representing the active power output strategies of the energy storage link at the t-delta t moment and the t-2 delta t moment,
is composed of
Of initial value of ES 、μ ES Represents a weight coefficient, and epsilon represents a threshold coefficient;
in the formula (12), the reaction mixture is,
denotes T + n.DELTA.T c The pseudo jacobian prediction matrix at a time, N ∈ {1, \8230;, N }, N = T p /ΔT c Representing the prediction domain T p And control field Δ T c The calculation formula is as follows:
in the formula, theta α,t Denotes the prediction coefficient at time T, where α =1, \8230;, l, T d Representing an estimated time duration in days;
defining a regression coefficient vector θ t ＝(θ 1,t ,…,θ α,t ,…,θ l,t ) T ，θ 1,t 、θ α,t 、θ l,t Respectively representing time t theta t Middle 1, alpha, l element, theta t The iterative solution formula is as follows:
in the formula, a pseudo-elegant predictive matrix set of t-delta t
And
respectively, using T + n Δ T c -T d Time sum T + n Δ T c -lT d A pseudo Jacobian matrix is obtained by calculating historical measurement data of the active power distribution network at any moment, delta represents a weight coefficient, and theta t-Δt Is the regression coefficient vector at the time t-delta t.
7. The intelligent energy storage soft switching data driving voltage control method considering data quality according to claim 1, wherein the objective function in step 5) is expressed as follows:
wherein J represents an objective function, J 1 、J′ 1 The adjusted objective function is represented as a function of the target,
representing the voltage reference at time t + deltat,
the average measured voltage data at time t is shown,
respectively are the upper limit and the lower limit of a voltage reference interval,
r represents the port number of the multi-terminal intelligent energy storage soft switch,
representing the difference vector of the charging and discharging power of the multi-terminal intelligent energy storage soft switch energy storage link at the time t,
respectively represents the charging and discharging power of the energy storage link at the time t and the time t-delta t,
the variable quantity vector of the active and reactive control quantity of the r-th port of the multi-terminal intelligent energy storage soft switch SOP link at the time t is shown,
respectively representing active power difference value vector and reactive power difference value vector of the r-th port of the multi-terminal intelligent energy storage soft switch SOP link at the moment t, lambda ES 、λ SOP Represents a weight coefficient, ζ represents an attenuation factor;
the estimated voltage value of each node at the time t + Δ t is expressed as follows:
in the formula,
representing the vector of the voltage regulation and control variation quantity of the SOP link of the multi-terminal intelligent energy storage soft switch at the time t,
a pseudo Jacobian matrix of an r-th port of a multi-terminal intelligent energy storage soft switch SOP link at the time t is represented;
denotes from time T to T + N Δ T c The voltage regulation and control variable vector of the multi-terminal intelligent energy storage soft switch energy storage link at a moment,
respectively, time T, T + (N-1) Δ T c Constantly multi-terminal intelligent energy storage soft switch energy storage link voltage regulation and control variable vectors;
expressing a pseudo Jacobian estimation matrix of a multi-terminal intelligent energy storage soft switch energy storage link at the time t;
represents the time from T to T + (N-1) Δ T c The charge and discharge power variable quantity vector of the energy storage link at any moment,
respectively, time T, T + (N-1) Δ T c And (3) charging and discharging power difference vectors of the moment energy storage link.
8. The method for controlling driving voltage of intelligent energy storage soft switch data considering data quality as claimed in claim 1, wherein the capacity of each port converter, the loss constraint of each port converter and the active and reactive power output upper and lower limits constraint of each port converter in the multi-terminal intelligent energy storage soft switch SOP link in step 5) are expressed as follows:
in the formula,
the actual charging and discharging power of the energy storage link is shown,
respectively showing the active power of the r port of a multi-terminal intelligent energy storage soft switch SOP link at the moment tAnd the reactive power output,
the loss of the current converter at the r-th port of the multi-terminal intelligent energy storage soft switch SOP link at the time t is shown,
the loss coefficient of the current converter at the r-th port of the SOP link of the multi-terminal intelligent energy storage soft switch is shown,
respectively represents the upper limit and the lower limit of active power of a multi-terminal intelligent energy storage soft switch SOP link,
the actual injection active power of the nth port of the multi-terminal intelligent energy storage soft switch SOP link at the time t is shown,
respectively representing the upper and lower limits of reactive power of a multi-terminal intelligent energy storage soft switch SOP link at the time t,
and the capacity of the r-th port converter of the multi-terminal intelligent energy storage soft switch SOP link is shown.
9. The method for controlling the data driving voltage of the intelligent energy storage soft switch considering the data quality as claimed in claim 1, wherein the charging and discharging efficiency constraint, the charging and discharging power upper and lower limit constraint and the prediction domain T of the multi-terminal intelligent energy storage soft switch energy storage link in the step 5) are implemented by the following steps p The internal state of charge constraint and the charge-discharge number limit constraint are expressed as follows:
if it is
And is
In the formula,
the actual charging and discharging power of the energy storage link is shown,
respectively represents the charge and discharge power of the energy storage link at the time t and the time t-delta t,
respectively shows the charge and discharge efficiency of the energy storage link,
respectively represents the upper limit and the lower limit of the calculated value of the charging and discharging power of the energy storage link,
respectively represent T + DeltaT c Time and T + (N-1) Δ T c Charging and discharging power difference S of the moment energy storage link ES The capacity of the energy storage link is shown,
the upper and lower limits of the charge state of the energy storage link are shown,
respectively representing the initial state and the prediction domain T of the energy storage system p The state of charge after the end of the cycle,
the value of the threshold coefficient is represented by,
respectively representing the charge states of the multi-terminal intelligent energy storage soft switch energy storage link at T and T-delta T, wherein N = T p /ΔT c Representing the prediction domain T p And control Domain Δ T c The ratio of (a) to (b),
and the charging and discharging power difference of the multi-terminal intelligent energy storage soft switch energy storage link at the time t is represented.
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