CN116646959A - Voltage real-time optimization method based on electric automobile V2G technology - Google Patents
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Abstract
The invention discloses a voltage real-time optimization method based on an electric automobile V2G technology, which comprises the following steps: s1, establishing a voltage sensitivity matrix; s2, establishing a power grid voltage and line loss prediction model; s3, optimizing voltage of nodes at two ends of the V2G type electric vehicle charging station by adopting an MPC rolling optimization model; and S4, updating a voltage sensitivity matrix according to an optimization result before a new round of voltage optimization control, and correcting the power grid voltage and line loss prediction model. Aiming at the problem of real-time voltage optimization of an active power distribution network, the invention takes the transmission power of the V2G electric vehicle charging station as a control object, can actively change the control strategy when voltage fluctuation occurs, and ensures the optimization effect.
Description
Technical Field
The invention relates to a voltage real-time optimization method based on an electric vehicle V2G technology, which is used in the field of electric vehicle networking and power grid optimization.
Background
In recent years, the electric vehicle charging station V2G technology is widely applied to an active power distribution network by virtue of the characteristics of continuous and controllable power and flexible control mode. As a fully-controlled power electronic device, the V2G type electric automobile charging station can replace part of the traditional tie switches in the power distribution network, and can accurately, rapidly and flexibly control the active and reactive power of the connected feeder line, so that the function of optimizing the voltage of the active power distribution network is realized. However, the voltage optimization process based on the V2G type electric vehicle charging station has the problems of uncertain external conditions, nonlinear control results and the like, and the existing method cannot be well adapted to the uncertainty of new energy output.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a voltage real-time optimization method based on an electric vehicle V2G technology, aims at the real-time voltage optimization problem of an active power distribution network, takes the transmission power of a V2G electric vehicle charging station as a control object, and can actively change a control strategy when voltage fluctuation occurs, so as to ensure the optimization effect.
The technical scheme for achieving the purpose is as follows: a voltage real-time optimization method based on an electric automobile V2G technology comprises the following steps:
s1, establishing a voltage sensitivity matrix;
s2, establishing a power grid voltage and line loss prediction model;
s3, optimizing voltage of nodes at two ends of the V2G type electric vehicle charging station by adopting an MPC rolling optimization model;
and S4, updating a voltage sensitivity matrix according to an optimization result before a new round of voltage optimization control, and correcting the power grid voltage and line loss prediction model.
Further, the specific method of S1 is as follows:
in order to establish a prediction model between the optimization problem control variable deltau and the power distribution network voltage V, a sensitivity matrix needs to be introduced to change the voltage caused by the change of the node injection power, a linear prediction model is established, and the node voltage is assumed to be the rated voltage U 0 The relation between the node injection power change amount and the voltage change amount of the feeder line node is shown in the formula (1) according to the line voltage drop formula,
wherein X is j Is the reactance of the line;
deltau is the amount of change in the voltage at the nodes of the distribution network,the sensitivity of the node voltage to the variation of the injection power can be obtained by using the relevant quantities of the feeder voltage and the distribution network impedance as shown in the formulas (2) and (3);
the generation of the sensitivity matrix depends on the running state of the power distribution network, the change is not obvious under the local disturbance, the frequent update is not needed, the sensitivity matrix is obtained by calculating the distribution network electrical quantity of a certain time section, and a voltage and line loss prediction model is established and is used as the basis of the real-time model prediction control.
Further, the specific method of S2 is as follows:
for a determined power distribution network topology and running state, based on a long-time scale optimization result and local measurement data, a power distribution network voltage and line loss prediction model is established for a power distribution network voltage at a future time k+i after the transmission power of the V2G type electric vehicle charging station is changed at a time k, the power distribution network voltage and line loss value at the future time k+i after the transmission power of the V2G type electric vehicle charging station is changed at the time k is predicted, and control variables of the voltage and loss prediction model at the time k are recorded as delta u (k) = [ delta P (k), delta Q n (k),ΔQ m (k)]Wherein ΔP (k), ΔQ n (k) And DeltaQ m (k) Respectively representing the active transmission power and the reactive transmission power at two ends of the V2G electric vehicle charging station; the prediction model of the voltage and the line loss is shown as (4) to (6);
a sensitivity matrix for the node voltage to the control variable;
in the formula, i=1, …, N p -1，N p Representing a prediction step size; v (k)) is the voltage amplitude at the moment k, and is obtained through real-time measurement; v (k+i|k) represents the predicted future voltage magnitude at k+i at k; Δu (k+i|k) represents the control variable increment at k time to predict future k+i time, and is the control variable of the predictive control model.
Further, the specific method of S3 is as follows:
in the rolling optimization process, on the premise that the voltage of nodes at two ends meets the operation requirement, the operation cost and the power distribution network loss of the V2G electric vehicle charging station are minimized, and a real-time optimization objective function based on model prediction control is established on the basis of a long-time scale optimization result as follows;
in DeltaP ope Is the operation cost;
the voltage optimization model based on MPC can simultaneously consider the influence of node injection active power delta P and reactive power delta Q on the running state of the distribution network, and the reactive transmission power of the V2G electric vehicle charging station is used as a control variable delta u as follows;
Δu i ＝[ΔP i ΔQ m,i ΔQ n,i ] (8)
optimizing operating cost ΔP in objective function ope
Wherein: p (P) p 、P q 、P l By adjusting the active and reactive control costs and the loss costs of the line losses, respectively, of the electric vehicle charging station, it is assumed that there is P due to the high efficiency of the electric vehicle charging station l >P p ＝P q Is a relationship of (2);
constraint V by increasing the upper and lower voltage limits min And V is equal to max The voltage optimization of nodes at two ends of the V2G type electric vehicle charging station is realized, as shown in a formula (10);
V min (k+i)≤V(k+i|k)≤V max (k+i) (10)
wherein the rolling voltage predicted value is shown in formula (11);
when the voltage fluctuation is large, the voltage is difficult to adjust to a target range in one control period, and in order to ensure that the optimization model has a solution, voltage gradual constraint shown as a formula (12) is introduced;
wherein ρ is a voltage progression coefficient, V min (k+i) and V max (k+i) gradually approaches V as the solution turns increase min And V is equal to max ；
Considering the operating state limit and the maximum capacity S of a V2G electric vehicle charging station max With upper reactive output limit P max 、Q max The transmission power constraints on a V2G type electric vehicle charging station are as follows:
the invention has the following advantages:
1. in the optimization process, only the electric information of nodes at two ends of the V2G electric vehicle charging station is required to be collected, and the voltage of the key node is optimized, so that the rapid and real-time optimization control of the distribution network voltage is realized, and the overall voltage quality is improved;
2. when voltage fluctuation occurs, the control strategy can be actively changed, the voltage of the distribution network is optimized in advance, and the optimization effect is ensured;
3. the accuracy of the rolling optimization model is guaranteed through the feedback correction optimization model, and the amplitude of voltage fluctuation is effectively reduced.
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Fig. 1 is a schematic diagram of model predictive control of a voltage real-time optimization method based on an electric vehicle V2G technology.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is given by way of specific examples:
please refer to fig. 1. The invention discloses a voltage real-time optimization method based on an electric automobile V2G technology, which comprises the following steps:
s1, establishing a voltage sensitivity matrix.
In order to establish a prediction model between the optimization problem control variable deltau and the power distribution network voltage V, a sensitivity matrix needs to be introduced to change the voltage caused by the change of the node injection power, a linear prediction model is established, and the node voltage is assumed to be the rated voltage U 0 The relation between the node injection power change amount and the voltage change amount of the feeder line node is shown in the formula (1) according to the line voltage drop formula,
wherein X is j Is the reactance of the line;
deltau is the amount of change in the voltage at the nodes of the distribution network,the sensitivity of the node voltage to the variation of the injection power can be obtained by using the relevant quantities of the feeder voltage and the distribution network impedance as shown in the formulas (2) and (3);
the generation of the sensitivity matrix depends on the running state of the power distribution network, the change is not obvious under the local disturbance, the frequent update is not needed, the sensitivity matrix is obtained by calculating the distribution network electrical quantity of a certain time section, and a voltage and line loss prediction model is established and is used as the basis of the real-time model prediction control.
S2, establishing a power grid voltage and line loss prediction model.
And (3) at the time k, for a determined power distribution network topology and running state, the power distribution network voltage at the time k+i in the future after the transmission power of the V2G electric vehicle charging station is changed, based on a long-time scale optimization result and local measurement data, establishing a power network voltage and line loss prediction model, and predicting the power distribution network voltage and line loss value at the time k+i in the future after the transmission power of the V2G electric vehicle charging station is changed. The control variable of the voltage and loss prediction model at the time k is denoted as deltau (k) = [ deltap (k), deltaq n (k),ΔQ m (k)]Wherein ΔP (k), ΔQ n (k) And DeltaQ m (k) Respectively representing the active transmission power and the reactive transmission power at two ends of the V2G electric vehicle charging station; the prediction model of the voltage and the line loss is shown as (4) to (6);
a sensitivity matrix for the node voltage to the control variable;
in the formula, i=1, …, N p -1，N p Representing a prediction step size; v (k)) is the voltage amplitude at the moment k, and is obtained through real-time measurement; v (k+i|k) represents the predicted future voltage magnitude at k+i at k; Δu (k+i|k) represents the control variable increment at k time to predict future k+i time, and is the control variable of the predictive control model.
And S3, optimizing the voltage of nodes at two ends of the V2G electric vehicle charging station by adopting an MPC rolling optimization model.
In the rolling optimization process, on the premise that the voltage of nodes at two ends meets the operation requirement, the operation cost and the power distribution network loss of the V2G electric vehicle charging station are minimized, and a real-time optimization objective function based on model prediction control is established on the basis of a long-time scale optimization result as follows;
in DeltaP ope Is the operation cost;
the voltage optimization model based on MPC can simultaneously consider the influence of node injection active power delta P and reactive power delta Q on the running state of the distribution network, and the reactive transmission power of the V2G electric vehicle charging station is used as a control variable delta u as follows;
Δu i ＝[ΔP i ΔQ m,i ΔQ n,i ] (8)
optimizing operating cost ΔP in objective function ope
In the middle of：P p 、P q 、P l By adjusting the active and reactive control costs and the loss costs of the line losses, respectively, of the electric vehicle charging station, it is assumed that there is P due to the high efficiency of the electric vehicle charging station l >P p ＝P q Is a relationship of (2);
constraint V by increasing the upper and lower voltage limits min And V is equal to max The voltage optimization of nodes at two ends of the V2G type electric vehicle charging station is realized, as shown in a formula (10);
V min (k+i)≤V(k+i|k)≤V max (k+i) (10)
wherein the rolling voltage predicted value is shown in formula (11);
when the voltage fluctuation is large, the voltage is difficult to adjust to a target range in one control period, and in order to ensure that the optimization model has a solution, voltage gradual constraint shown as a formula (12) is introduced;
wherein ρ is a voltage progression coefficient, V min (k+i) and V max (k+i) gradually approaches V as the solution turns increase min And V is equal to max ；
Considering the operating state limit and the maximum capacity S of a V2G electric vehicle charging station max With upper reactive output limit P max 、Q max The transmission power constraints on a V2G type electric vehicle charging station are as follows:
and S4, because the sensitivity matrix is generated based on the measurement result of a long time scale, when a new round of predictive control is performed, the injection power items of the nodes at the two ends of the V2G type electric vehicle charging station in the sensitivity matrix need to be changed and updated according to the working state of the V2G type electric vehicle charging station, so that a new voltage sensitivity matrix is obtained. And the running condition of the distribution network is continuously changed, and a new round of prediction model is required to be corrected according to the previous round of prediction control deviation when real-time control is carried out, so that the prediction precision of the power grid voltage prediction model is improved.
In the actual control process, a feedback correction link is introduced in a voltage prediction control link, and meanwhile, the actual voltage of the distribution network is used as the initial value of a new round of rolling optimization scheduling to form voltage closed-loop control. In order to make the prediction control result as close as possible to the actual control result, the prediction model is corrected according to the previous control deviation, and the prediction accuracy of the voltage prediction model is improved, as follows.
ΔV err (k+1)＝V(k+1)-V(k+1|k) (15)
V err Is an error correction term ρ v Is an error correction coefficient.
The voltage optimization flow is as follows:
1) Establishing a voltage prediction model, and taking the reactive output increment of the V2G type electric vehicle charging station as a control variable;
2) With minimum operation cost of a V2G type electric vehicle charging station as an objective function and node voltage meeting operation requirements as constraint, a rolling optimization model is established and control variable sequences { Deltau (k+ 1|k), deltau (k+ 2|k), …, deltau (k+N) of N time periods in the future are solved p |k)}
3) Only issuing a first instruction of a control variable sequence, namely that the V2G electric vehicle charging station at the moment k+1 has reactive power transmission delta u (k+ 1|k);
4) The actual measurement voltage value of the system at the time of k+1 is taken as the initial value V (k+1) at the time of k+1, and the error quantity DeltaV err (k+1) as a prediction model correction amount, and performing a new round of optimization.
After the completion of the voltage optimization,introducing network voltage deviation index V dev The evaluation of the voltage deviation level of the power distribution network is shown in a formula (17).
And (3) withU thr Is the upper and lower limit of the allowable voltage, and n is the number of distribution network nodes.
It will be appreciated by persons skilled in the art that the above embodiments are provided for illustration only and not for limitation of the invention, and that variations and modifications of the above described embodiments are intended to fall within the scope of the claims of the invention as long as they fall within the true spirit of the invention.
Claims (4)
1. The voltage real-time optimization method based on the electric automobile V2G technology is characterized by comprising the following steps of:
s1, establishing a voltage sensitivity matrix;
s2, establishing a power grid voltage and line loss prediction model;
s3, optimizing voltage of nodes at two ends of the V2G type electric vehicle charging station by adopting an MPC rolling optimization model;
and S4, updating a voltage sensitivity matrix according to an optimization result before a new round of voltage optimization control, and correcting the power grid voltage and line loss prediction model.
2. The method for optimizing voltage in real time based on the electric automobile V2G technology according to claim 1, wherein the specific method of S1 is as follows:
in order to establish a prediction model between the optimization problem control variable deltau and the power distribution network voltage V, a sensitivity matrix needs to be introduced to change the voltage caused by the change of the node injection power, a linear prediction model is established, and the node voltage is assumed to be the rated voltage U 0 The node injection power change can be obtained by a line voltage drop formulaThe relation between the voltage variation of the feeder node and the voltage variation of the feeder node is shown in the formula (1),
wherein X is j Is the reactance of the line;
deltau is the amount of change in the voltage at the nodes of the distribution network,the sensitivity of the node voltage to the variation of the injection power can be obtained by using the relevant quantities of the feeder voltage and the distribution network impedance as shown in the formulas (2) and (3);
the generation of the sensitivity matrix depends on the running state of the power distribution network, the change is not obvious under the local disturbance, the frequent update is not needed, the sensitivity matrix is obtained by calculating the distribution network electrical quantity of a certain time section, and a voltage and line loss prediction model is established and is used as the basis of the real-time model prediction control.
3. The method for optimizing voltage in real time based on the electric automobile V2G technology according to claim 1, wherein the specific method of S2 is as follows:
for a determined power distribution network topology and running state, at the time k, power distribution network voltage at the time k+i in the future after the transmission power of the V2G type electric vehicle charging station is changed, a power network voltage and line loss prediction model is built based on a long-time scale optimization result and local measurement data, the power distribution network voltage and line loss value at the time k in the future after the transmission power of the V2G type electric vehicle charging station is changed are predicted, and the voltage and the line loss value are calculatedThe control variable of the loss prediction model at time k is denoted as Δu (k) = [ Δp (k), Δq n (k),ΔQ m (k)]Wherein ΔP (k), ΔQ n (k) And DeltaQ m (k) Respectively representing the active transmission power and the reactive transmission power at two ends of the V2G electric vehicle charging station; the prediction model of the voltage and the line loss is shown as (4) to (6);
a sensitivity matrix for the node voltage to the control variable;
in the formula, i=1, …, N p -1，N p Representing a prediction step size; v (k)) is the voltage amplitude at the moment k, and is obtained through real-time measurement; v (k+i|k) represents the predicted future voltage magnitude at k+i at k; Δu (k+i|k) represents the control variable increment at k time to predict future k+i time, and is the control variable of the predictive control model.
4. The method for optimizing voltage in real time based on the electric automobile V2G technology according to claim 1, wherein the specific method of S3 is as follows:
in the rolling optimization process, on the premise that the voltage of nodes at two ends meets the operation requirement, the operation cost and the power distribution network loss of the V2G electric vehicle charging station are minimized, and a real-time optimization objective function based on model prediction control is established on the basis of a long-time scale optimization result as follows;
in DeltaP ope Is the operation cost;
the voltage optimization model based on MPC can simultaneously consider the influence of node injection active power delta P and reactive power delta Q on the running state of the distribution network, and the reactive transmission power of the V2G electric vehicle charging station is used as a control variable delta u as follows;
Δu i ＝[ΔP i ΔQ m,i ΔQ n,i ] (8)
optimizing operating cost ΔP in objective function ope
Wherein: p (P) p 、P q 、P l By adjusting the active and reactive control costs and the loss costs of the line losses, respectively, of the electric vehicle charging station, it is assumed that there is P due to the high efficiency of the electric vehicle charging station l >P p ＝P q Is a relationship of (2);
constraint V by increasing the upper and lower voltage limits min And V is equal to max The voltage optimization of nodes at two ends of the V2G type electric vehicle charging station is realized, as shown in a formula (10);
V min (k+i)≤V(k+i|k)≤V max (k+i) (10)
wherein the rolling voltage predicted value is shown in formula (11);
when the voltage fluctuation is large, the voltage is difficult to adjust to a target range in one control period, and in order to ensure that the optimization model has a solution, voltage gradual constraint shown as a formula (12) is introduced;
wherein ρ is a voltage progression coefficient, V min (k+i) and V max (k+i) gradually approaches V as the solution turns increase min And V is equal to max ；
Considering the operating state limit and the maximum capacity S of a V2G electric vehicle charging station max With upper reactive output limit P max 、Q max The transmission power constraints on a V2G type electric vehicle charging station are as follows:
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| CN102856919A (en) * | 2012-09-21 | 2013-01-02 | 河海大学 | Reactive optimal online control method for analyzing mixed economic pressure difference and sensitivity |
| US20180048154A1 (en) * | 2016-08-15 | 2018-02-15 | Nec Laboratories America, Inc. | Two-level predictive based reactive power coordination and voltage restoration for microgrids |
| CN109245113A (en) * | 2018-10-19 | 2019-01-18 | 东北大学 | The electric distribution network reactive-voltage optimization method that distributed generation resource and electric car access on a large scale |
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