Three-phase imbalance real-time treatment method based on flexible soft switch

Abstract

The invention discloses a three-phase imbalance real-time treatment method based on a flexible soft switch, and the method comprises the steps: 1, building a voltage sensitivity matrix and a voltage prediction model, and taking the output increment of the intelligent soft switch as a control variable; step 2, establishing a rolling optimization model and solving a control variable sequence of N time periods in the future by taking the minimum operation cost of the intelligent soft switch as an objective function and taking the condition that node voltage meets the operation requirement as a constraint; 3, only issuing a first instruction of a control variable sequence, namely active and reactive power transmission power of the intelligent soft switch at the k + 1 moment; 4, performing a new round of optimization by taking the actual measured voltage value of the system at the k + 1 moment as an initial value at the k + 1 moment and taking the error amount as the prediction model correction amount; and 5, after voltage optimization is completed, introducing a network voltage deviation index to evaluate the voltage deviation level of the power distribution network. The transmission power of the intelligent soft switch is taken as a control object, the control strategy can be actively changed when the three-phase imbalance occurs in the power distribution network, and the optimization effect is ensured.

Description

Technical Field

[0001] The invention relates to a three-phase unbalance real-time control method based on a flexible soft switch used in the field of three-phase unbalance control. Background Art

[0002] Active distribution networks contain a large number of unbalanced loads. The single-phase access of electric vehicles and photovoltaics has aggravated the three-phase imbalance problem. In recent years, intelligent soft switches (soft open point, SOP) have been widely used in active distribution networks due to their continuous controllable power and flexible control methods. As a fully controlled power electronic device, SOP can replace some traditional tie switches in the distribution network, accurately, quickly and flexibly control the active and reactive power of the connected feeders, and realize the function of optimizing the voltage of the active distribution network. The flexible soft switch can not only adjust the power flow of the feeder by changing its power size and flow direction in real time, but also adjust the power between different phases of the same feeder. For the resulting three-phase imbalance problem, the flexible soft switch can improve the system imbalance by adjusting its own power, so that the power grid can operate more safely and reliably. Scholars have studied the problem of improving three-phase imbalance based on flexible soft switches, and a small number of research results have been achieved. However, the voltage optimization process based on SOP has problems such as uncertain external conditions and nonlinear control results. The existing methods cannot adapt well to the uncertainty of new energy output.

[0003] At present, the real-time governance method for three-phase imbalance in SOP participation is mainly based on modeling and calculation based on global system information, and then all terminal devices are uniformly scheduled, and the optimization result is close to the theoretical global optimum. However, this type of method has the following two problems:

[0004] 1. At present, the active distribution network controls primary equipment such as on-load voltage regulators and switchable capacitor banks. These primary equipment have slow response speeds and cannot continuously adjust output. It is difficult to respond in real time to the three-phase voltage imbalance of the distribution network caused by the rapid output changes of distributed power sources.

[0005] 2. The existing real-time optimization methods use voltage-reactive power droop curves to establish SOP based on short-term forecast data. The voltage-reactive power droop control capability is limited and they are all passive optimization methods. It is difficult to adjust the optimization strategy when three-phase voltage imbalance occurs, resulting in poor optimization effect. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a real-time three-phase imbalance management method based on flexible soft switches, which takes SOP transmission power as the control object and can actively change the control strategy when three-phase imbalance occurs in the distribution network to ensure the optimization effect.

[0007] A technical solution to achieve the above purpose is: a three-phase unbalance real-time management method based on flexible soft switch, comprising the following steps:

[0008] Step 1: Establish a voltage sensitivity matrix and a voltage prediction model, taking the active and reactive output increments of the intelligent soft switch as control variables;

[0009] Step 2: Taking the minimization of the intelligent soft switch operation cost as the objective function and the node voltage meeting the operation requirements as the constraint, a rolling optimization model is established and the control variable sequence for the next N time periods is solved;

[0010] Step 3: only issue the first instruction of the control variable sequence, i.e., the active and reactive transmission power of the intelligent soft switch at time k+1;

[0011] Step 4: Use the actual measured voltage value of the system at time k+1 as the initial value at time k+1, and the error amount as the correction amount of the prediction model to perform a new round of optimization;

[0012] Step 5: After completing the voltage optimization, the network voltage deviation index is introduced to evaluate the voltage deviation level of the distribution network.

[0013] Furthermore, in step 1, the voltage sensitivity matrix is ​​specifically:

[0014] Assuming that the voltage at node b is the rated voltage U0, the relationship between the change in the injected power at node b and the change in the voltage at node b can be obtained from the line voltage drop formula as shown in formula (1):

[0015]

[0016] In the formula represents a three-phase line, j is the node number, R j , X j are the resistance and reactance of the line respectively, is the change in active power and reactive power injected into the node in a single phase, T is the set of upstream nodes in the distribution network, is the single-phase voltage variation of the distribution network node, It is the sensitivity of the node voltage to the changes of injected active and reactive power, which can be obtained only by using the quantities related to the feeder voltage and the distribution network impedance as shown in equations (2) and (3), where U0 is the rated voltage of the distribution network,

[0017]

[0018] Furthermore, in step 1, for a certain distribution network topology and operating state, a grid voltage and line loss prediction model is established based on the long-time optimization results and local measurement data. At time k, the distribution network voltage value at the future k+i time after the intelligent soft switch transmission power changes is predicted. The control variable of the voltage prediction model at time k is recorded as Δu(k)=[ΔP(k),ΔQ n (k),ΔQ m (k)], where ΔP(k), ΔQ n (k) and ΔQ m (k) represents the active transmission power of the intelligent soft switch and the reactive transmission power at both ends, respectively, and m and n are the node numbers at both ends of the intelligent soft switch.

[0019] Furthermore, the voltage prediction model is specifically as follows:

[0020]

[0021] is the sensitivity matrix of node single-phase voltage to control variables;

[0022]

[0023] Where i = 1,…,N p -1,N p Represents the prediction step; V(k) is the voltage amplitude at time k, which is obtained through real-time measurement; V(k+i|k) represents the voltage amplitude predicted at time k+i in the future; Δu(k+i|k) represents the control variable increment predicted at time k+i in the future, which is the control variable of the predictive control model.

[0024] Furthermore, in step 2, the objective function is to minimize the intelligent soft switch operation cost:

[0025]

[0026] Where i is the control round, N p is the prediction step length, ΔP i，ope is the operating cost, f i,v It is the cost of three-phase voltage unbalance.

[0027] Furthermore, the rolling optimization model is specifically:

[0028] At the same time, considering the impact of node injected active power ΔP and reactive power ΔQ on the distribution network operation state, the reactive transmission power of SOP is used as the control variable Δu i As shown below,

[0029] Δu i =[ΔP i ΔQm,i ΔQ n,i ] (8)

[0030] Optimize the operation cost ΔP in the objective function ope

[0031]

[0032] Where: P p , P q They are respectively controlled by adjusting the active and reactive power of the intelligent soft switch.

[0033] Furthermore, due to the high efficiency of intelligent soft switching, assuming that P p =P q relationship.

[0034] Furthermore, in step 2, the constraints that the node voltage meets the operation requirements are specifically:

[0035] Voltage upper and lower limit constraints: V min With V max are the lower and upper limits of the nodes at both ends of the intelligent soft switch, as shown in formula (10),

[0036]

[0037] The rolling voltage prediction value is shown in formula (11):

[0038]

[0039] When the voltage fluctuation is large, it is difficult to adjust the voltage to the target range within one control cycle. In order to ensure that the optimization model has a solution, the voltage asymptotic constraint as shown in equation (12) is introduced:

[0040]

[0041] Where ρ is the voltage progressive coefficient, V min (k+i) and V max (k+i) gradually approaches V as the number of solving rounds increases min With V max ;

[0042] Considering the operating state limitation and maximum capacity S of the intelligent soft switch max And the upper limit of active and reactive output P max , Q max , the transmission power constraint of the intelligent soft switch is as follows,

[0043]

[0044] Furthermore, in step 4, the correction model is input and a new round of optimization is performed as follows:

[0045] When a new round of predictive control is carried out, it is necessary to update the injected power items of the nodes at both ends of the intelligent soft switch in the sensitivity matrix according to the change of the working state of the intelligent soft switch to obtain a new voltage sensitivity matrix. During the control process, a feedback correction link is introduced in the voltage predictive control link, and the actual voltage of the distribution network is used as the initial value of the new round of rolling optimization scheduling to form a voltage closed-loop control; in order to make the predictive control result as close as possible to the actual control result, the prediction model is corrected according to the control deviation of the previous round to improve the prediction accuracy of the voltage prediction model, as shown below,

[0046] ΔV err (k+1)=V(k+1)-V(k+1|k) (15)

[0047]

[0048] V err is the error correction term, ρ v is the error correction factor.

[0049] Furthermore, in step 5, the voltage deviation level of the distribution network is evaluated as follows:

[0050] Introducing network voltage deviation index V dev The voltage deviation level of the distribution network is evaluated as shown in formula (17):

[0051]

[0052] and U thr are the upper and lower limits of the allowable voltage, n is the number of distribution network nodes, and t is the optimization round.

[0053] The three-phase unbalance real-time control method based on flexible soft switch of the present invention has the following advantages:

[0054] 1. During the optimization process, only the electrical information of the nodes at both ends of the SOP needs to be collected. By optimizing the three-phase voltage of key nodes, the three-phase voltage of the distribution network can be quickly and real-time optimized and controlled to improve the overall three-phase voltage quality;

[0055] 2. When three-phase imbalance occurs, the control strategy can be actively changed to optimize the three-phase voltage of the distribution network to ensure the optimization effect;

[0056] 3. By feedback correction optimization model, the accuracy of rolling optimization model is ensured, and the amplitude of three-phase imbalance is more effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1The present invention is a flowchart of a method for real-time control of three-phase imbalance based on a flexible soft switch. DETAILED DESCRIPTION

[0058] In order to better understand the technical solution of the present invention, the following is a detailed description through specific embodiments:

[0059] See also Figure 1 The present invention provides a three-phase unbalance real-time management method based on a flexible soft switch, characterized in that it comprises the following steps:

[0060] Step 1: Establish a voltage sensitivity matrix and a voltage prediction model, taking the active and reactive output increments of the intelligent soft switch as control variables.

[0061] In order to establish a prediction model between the optimization problem control variable Δu and the distribution network voltage, it is necessary to introduce a sensitivity matrix to the voltage change caused by the change in node injection power and establish a linear prediction model. Assuming that the voltage of node b is the rated voltage U0, the relationship between the change in node b injection power and the change in node b voltage can be obtained from the line voltage drop formula as shown in formula (1).

[0062]

[0063] In the formula represents a three-phase line, j is the node number, R j , X j are the resistance and reactance of the line respectively, is the change in active power and reactive power injected into the node in a single phase, U j is the line voltage, which is approximately the voltage of the upstream node of the line, and T is the set of upstream nodes of the distribution network.

[0064] is the single-phase voltage variation of the distribution network node, It is the sensitivity of the node voltage to the change of the injected power, which can be obtained only by using the quantities related to the feeder voltage and the distribution network impedance as shown in equations (2) and (3), where U0 is the rated voltage of the distribution network.

[0065]

[0066] The generation of the sensitivity matrix depends on the operating state of the distribution network, and does not change significantly under local disturbances, so it does not need to be updated frequently. The sensitivity matrix is ​​calculated based on the electrical quantity of the distribution network at a certain time section, and a voltage and line loss prediction model is established as the basis for real-time model predictive control.

[0067] For a certain distribution network topology and operating status, a grid voltage and line loss prediction model can be established based on long-term optimization results and local measurement data to predict the distribution network voltage and line loss values ​​at the future k+i moment after the SOP transmission power changes at the k moment.

[0068] The control variable of the voltage prediction model at time k is recorded as Δu(k)=[ΔP(k),ΔQ n (k),ΔQ m (k)], where ΔP(k), ΔQ n (k) and ΔQ m (k) represents the active transmission power of SOP and the reactive transmission power at both ends, respectively, and m and n are the node numbers at both ends of SOP. is the sensitivity matrix of node single-phase voltage to control variables

[0069]

[0070] Where i = 1,…,N p -1,N p Represents the prediction step; V(k) is the voltage amplitude at time k, which is obtained through real-time measurement; V(k+i|k) represents the voltage amplitude predicted at time k+i in the future; Δu(k+i|k) represents the control variable increment predicted at time k+i in the future, which is the control variable of the predictive control model.

[0071] Step 2: Taking the minimization of the intelligent soft switch operation cost as the objective function and the node voltage meeting the operation demand as the constraint, a rolling optimization model is established and the control variable sequence {Δu(k+1|k),Δu(k+2|k),…,Δu(k+N p |k)}.

[0072] During the rolling optimization process, the SOP operation cost and distribution network loss are minimized while ensuring that the voltages of the two end nodes meet the operating requirements. Based on the long-term optimization results, the real-time optimization objective function based on model predictive control is established as shown below.

[0073]

[0074] Where i is the control round, N p is the prediction step length, ΔP i，ope is the operating cost, f i,v is the three-phase voltage unbalance cost. Different from the existing real-time optimization model based on droop control, this paper proposes a voltage optimization model based on MPC, which can simultaneously consider the impact of node injected active power ΔP and reactive power ΔQ on the distribution network operation state, and take the reactive transmission power of SOP as the control variable Δu i As shown below.

[0075] Δu i =[ΔP i ΔQ m,i ΔQ n,i ] (8)

[0076] Optimize the operation cost ΔP in the objective function ope

[0077]

[0078] Where: P p , P q They are respectively controlled by adjusting the active and reactive power of the intelligent soft switch.

[0079] Since the efficiency of intelligent soft switch is high, assuming P p =P q relationship.

[0080] By adding upper and lower voltage limits to constrain V min With V max , the voltage of the nodes at both ends of the SOP is optimized, as shown in formula (10).

[0081]

[0082] The rolling voltage prediction value is shown in formula (11).

[0083]

[0084] When the voltage fluctuates greatly, it is difficult to adjust the voltage to the target range within one control cycle. In order to ensure that the optimization model has a solution, the voltage asymptotic constraint shown in equation (12) is introduced.

[0085]

[0086] Where ρ is the voltage progressive coefficient, V min (k+i) and V max (k+i) gradually approaches V as the number of solving rounds increases min With V max .

[0087] Consider the operating state limit and maximum capacity S of SOP max And there is a reactive power output upper limit P max , Q max , the transmission power constraints on SOP are as follows.

[0088] Step 3: only issue the first instruction of the control variable sequence, i.e., the active and reactive transmission power Δu(k+1|k) of the intelligent soft switch at time k+1;

[0089] Step 4: Use the actual measured voltage value of the system at time k+1 as the initial value at time k+1 and the error amount as the correction amount of the prediction model to perform a new round of optimization.

[0090] Since the sensitivity matrix is ​​generated based on the measurement results of a long time scale, when a new round of predictive control is carried out, it is necessary to update the injected power items of the nodes at both ends of the SOP in the sensitivity matrix according to the change of the SOP working state to obtain a new voltage sensitivity matrix. In addition, the operation of the distribution network is constantly changing. When performing real-time control, it is necessary to correct the new round of prediction model according to the deviation of the previous round of prediction control to improve the prediction accuracy of the power grid voltage prediction model.

[0091] In the actual control process, a feedback correction link is introduced into the voltage prediction control link, and the actual voltage of the distribution network is used as the initial value of the new round of rolling optimization scheduling to form a 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 control deviation of the previous round to improve the prediction accuracy of the voltage prediction model, as shown below.

[0092] ΔV err (k+1)=V(k+1)-V(k+1|k) (15)

[0093]

[0094] V err is the error correction term, ρ v is the error correction factor.

[0095] Step 5: After completing voltage optimization, enter the network voltage deviation index V dev The voltage deviation level of the distribution network is evaluated as shown in formula (17).

[0096]

[0097] and U thr are the upper and lower limits of the allowable voltage, n is the number of distribution network nodes, and t is the optimization round.

[0098] The present invention uses an intelligent soft switch to optimize the voltage of the active distribution network in real time. By real-time regulating the transmission power of the intelligent soft switch, the three unbalanced voltages of the distribution network are quickly and in real time optimized. The present invention proposes a model predictive control method based on the voltage sensitivity of the distribution network. By constructing a prediction model, the impact of the power change of the intelligent soft switch on the operating state of the power grid is determined, so that the control strategy can be actively changed when the three voltage imbalances occur, and the advanced optimization control of the intelligent soft switch can be achieved. The present invention simultaneously regulates the active and reactive power transmitted by the intelligent soft switch in the real-time control stage. Compared with the existing real-time optimization method that uses the voltage-reactive curve for droop control, the regulation ability of the intelligent soft switch is more fully utilized to ensure the optimization effect of the intelligent soft switch. During the optimization process, the present invention only needs to collect the electrical information of the nodes at both ends of the intelligent soft switch. By optimizing the voltage of the key nodes, the rapid and real-time optimization control of the distribution network voltage is achieved, and the global voltage quality is improved.

[0099] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A three-phase unbalance real-time management method based on flexible soft switch, characterized in that: The steps include: Step 1: Establish a voltage sensitivity matrix and a voltage prediction model, taking the active and reactive output increments of the intelligent soft switch as control variables; Step 2: Taking the minimization of the intelligent soft switch operation cost as the objective function and the node voltage meeting the operation requirements as the constraint, a rolling optimization model is established and the control variable sequence for the next N time periods is solved; Step 3: only issue the first instruction of the control variable sequence, i.e., the active and reactive transmission power of the intelligent soft switch at time k+1; Step 4, taking the actual measured voltage value of the system at time k+1 as the initial value at time k+1, and the error amount as the correction amount of the prediction model, a new round of optimization is performed; Step 5: After completing voltage optimization, introduce the network voltage deviation index to evaluate the voltage deviation level of the distribution network.

2. A three-phase unbalance real-time control method based on flexible soft switch according to claim 1, characterized in that: In step 1, the voltage sensitivity matrix is ​​specifically: Assuming that the voltage of node b is the rated voltage U0, the relationship between the change in the injected power at node b and the change in the voltage at node b can be obtained from the line voltage drop formula as shown in formula (1): In the formula represents a three-phase line, j is the node number, R j , X j are the resistance and reactance of the line respectively, is the change in active power and reactive power injected into the node in a single phase, T is the set of upstream nodes in the distribution network, is the single-phase voltage variation of the distribution network node, It is the sensitivity of the node voltage to the changes of injected active and reactive power, which can be obtained only by using the quantities related to the feeder voltage and the distribution network impedance as shown in equations (2) and (3), where U0 is the rated voltage of the distribution network, 3. The method for real-time three-phase unbalance control based on flexible soft switch according to claim 1 is characterized in that: In step 1, for a certain distribution network topology and operating state, a grid voltage and line loss prediction model is established based on the long-time optimization results and local measurement data. At time k, the distribution network voltage value at the future k+i time after the intelligent soft switch transmission power changes is predicted. The control variable of the voltage prediction model at time k is recorded as Δu(k)=[ΔP(k),ΔQ n (k),ΔQ m (k)], where ΔP(k), ΔQ n (k) and ΔQ m (k) represents the active transmission power of the intelligent soft switch and the reactive transmission power at both ends, respectively, and m and n are the node numbers at both ends of the intelligent soft switch.

4. According to the method for real-time three-phase unbalance management based on flexible soft switch according to claim 3, it is characterized in that: The voltage prediction model is as follows: is the sensitivity matrix of node single-phase voltage to control variables; Where i = 1,…,N p -1, N p Represents the prediction step; V(k) is the voltage amplitude at time k, which is obtained through real-time measurement; V(k+i|k) represents the voltage amplitude predicted at time k+i in the future; Δu(k+i|k) represents the control variable increment predicted at time k+i in the future, which is the control variable of the predictive control model.

5. According to the method for real-time three-phase unbalance management based on flexible soft switch according to claim 1, it is characterized in that: In step 2, the objective function is to minimize the intelligent soft switch operation cost: Where i is the control round, N p is the prediction step length, ΔP i，ope is the operating cost, f i,v It is the cost of three-phase voltage unbalance.

6. According to the method for real-time three-phase unbalance management based on flexible soft switch according to claim 1, it is characterized in that: The rolling optimization model is as follows: At the same time, considering the impact of node injected active power ΔP and reactive power ΔQ on the distribution network operation state, the reactive transmission power of SOP is used as the control variable Δu i As shown below, Δu i =[ΔP i ΔQ m,i ΔQ n,i ] (8) Optimize the operation cost ΔP in the objective function ope Where: P p , P q They are respectively controlled by adjusting the active and reactive power of the intelligent soft switch.

7. According to the method for real-time three-phase unbalance management based on flexible soft switch according to claim 1, it is characterized in that: Since the efficiency of intelligent soft switch is high, assuming P p =P q relationship.

8. The method for predicting and optimizing line loss in three-phase unbalanced low-voltage areas according to claim 1, characterized in that: In step 2, the constraints that the node voltage meets the operation requirements are as follows: Voltage upper and lower limit constraints: V min With V max are the lower and upper limits of the nodes at both ends of the intelligent soft switch, as shown in formula (10), The rolling voltage prediction value is shown in formula (11): When the voltage fluctuation is large, it is difficult to adjust the voltage to the target range within one control cycle. In order to ensure that the optimization model has a solution, the voltage asymptotic constraint as shown in equation (12) is introduced: Where ρ is the voltage progressive coefficient, V min (k+i) and V max (k+i) gradually approaches V as the number of solving rounds increases min With V max ; Considering the operating state limitation and maximum capacity S of the intelligent soft switch max And the upper limit of active and reactive output P max , Q max , the transmission power constraint of the intelligent soft switch is as follows, 9. The method for predicting and optimizing line loss in three-phase unbalanced low-voltage areas according to claim 1, characterized in that: Step 4: Input the correction model and conduct a new round of optimization. When a new round of predictive control is carried out, it is necessary to update the injected power items of the nodes at both ends of the intelligent soft switch in the sensitivity matrix according to the change of the working state of the intelligent soft switch to obtain a new voltage sensitivity matrix. During the control process, a feedback correction link is introduced in the voltage predictive control link, and the actual voltage of the distribution network is used as the initial value of the new round of rolling optimization scheduling to form a voltage closed-loop control; in order to make the predictive control result as close as possible to the actual control result, the prediction model is corrected according to the control deviation of the previous round to improve the prediction accuracy of the voltage prediction model, as shown below, ΔV err (k+1)=V(k+1)-V(k+1|k) (15) V err is the error correction term, ρ v is the error correction factor.

10. The method for predicting and optimizing line loss in three-phase unbalanced low-voltage areas according to claim 1, characterized in that: In step 5, the voltage deviation level of the distribution network is evaluated as follows: Introducing network voltage deviation index V dev The voltage deviation level of the distribution network is evaluated as shown in formula (17): with U thr are the upper and lower limits of the allowable voltage, n is the number of distribution network nodes, and t is the optimization round.

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