Online predictive optimization method for active power distribution network considering voltage prediction

Abstract

The application discloses an online prediction optimization method of an active power distribution network considering voltage prediction, and proposes a time-varying optimal power flow online prediction optimization method for a power distribution network containing a large number of distributed photovoltaic power sources and energy storage power sources. The method can track and solve the time-varying optimal power flow under the premise that the voltages of all nodes at each moment are within a reasonable range, and can predict and solve the voltage state of the power distribution network nodes in the future period based on the voltage sensitivity thought, and then use the prediction value to make a decision on the distributed power source, so as to avoid the process of performing power flow calculation again after the load fluctuation and then making a decision on the calculation result, and the method has the advantages of scientific rationality, strong applicability, good effect and the like.

Description

Technical Field

[0001] This invention belongs to the field of optimal power flow in distribution networks, and particularly relates to an active distribution network online prediction optimization method that takes voltage prediction into account. Background Technology

[0002] In existing technologies, as the number of distributed power sources connected to the distribution network gradually increases and the penetration rate of distributed power sources in the distribution network gradually increases, the distribution network is gradually changing from passive to active. At the same time, the distribution network also gains a certain degree of initiative, thus giving rise to the concept of active distribution network. Active distribution network has the function of actively managing distributed power sources and energy storage devices. It can optimize the economic operation of the distribution network by actively adjusting the output setpoints of distributed power sources and energy storage devices. At present, how to determine the output setpoints of distributed power sources to achieve economical operation of the distribution network has become a key research issue in this field. Summary of the Invention

[0003] To address the problems existing in the prior art, the purpose of this invention is to provide a scientific, reasonable, highly applicable, and effective active distribution network online prediction and optimization method that takes voltage prediction into account, so as to predict the future operating state of the distribution network after the integration of distributed power sources and obtain the optimal setting value of the distributed power sources.

[0004] The technical solution adopted to achieve the purpose of this invention is: an active distribution network online prediction optimization method considering voltage prediction, characterized in that it includes the following steps:

[0005] Step 1: Construct a time-varying optimal power flow model for a distributed photovoltaic-storage distribution network.

[0006] 1) Objective function:

[0007]

[0008] In the formula: the superscript t represents time; n represents the number of decision points at time t; C t,τ (Δu t,τ Let Δu be the function representing the change in the operational index of the photovoltaic storage system at the τ-th decision point at time t; t,τ Δu represents the change in the setpoint of the distributed power source output at the τ-th decision point at time t. t,τ =u t,τ -u t,τ-1 =[ΔP t,τ ,ΔQ t,τ ] T ,ΔP t,τ =[…,ΔP i t,τ ,…] T , ΔP it,τ , These represent the changes in the active and reactive power outputs of the optical storage system at the τ-th decision point of node i at time t, respectively.

[0009] 2) Constraints

[0010] ① Current constraints

[0011]

[0012] In the formula: These are the active power and reactive power at the beginning of line ij at the τ-th decision point at time t, respectively. Let be the magnitude of the voltage at the τ-th decision point node j at time t; H represents the amplitude of the line current at the τ-th decision point at time t; j Let r be the set of nodes downstream of node j that are connected to it; ij x ij Let be the resistance and reactance of line ij, respectively. These are the active power and reactive power of the load at the τ-th decision node j at time t, respectively. Let be the active power and reactive power of the τ-th decision node j at time t, respectively;

[0013] ② Node voltage constraints

[0014]

[0015] In the formula: v represents the upper and lower limits of the allowable node voltage, respectively;

[0016] ③ Operational constraints of photovoltaic and energy storage

[0017]

[0018] In the formula: Let be the output power matrix of the distributed generation at the τ-th decision point at time t; These represent the active power and reactive power output by the distributed generation at the τ-th decision point at time t, respectively. The permissible operating range of the photovoltaic-storage power source at the τth decision point at time t is determined by its own physical characteristics.

[0019] Step 2: Solve the time-varying optimal power flow problem online based on the predicted solution.

[0020] 1) Construct the augmented objective function:

[0021]

[0022] In the formula: Let be the voltage upper limit penalty coefficient of the τ-th decision point node i at time t; Let be the voltage lower limit penalty coefficient of node i at the τ-th decision point at time t; This represents the voltage measurement matrix at the τ-th decision point at time t; Let t be the voltage measurement value of node i at the τth decision point at time t, that is, the voltage of the distribution network node after the distributed generation decision setting value is changed;

[0023] The distribution network time-varying optimal power flow model based on equation (5) is further described as follows:

[0024]

[0025] and λ t,τ The algorithm for solving the τth decision point at the initial time t is not exactly the same as that at other times t+i. Therefore, specific solution algorithms for two different time periods are given.

[0026] ①Time t and λ t,τ Solution algorithm:

[0027]

[0028]

[0029] In the formula: κ is the designed parameter; α is the step size; ε is the parameter used to accelerate convergence; * indicates in the set Projection on This represents the voltage amplitude at time t after the load fluctuation at the τ-th decision point node i.

[0030] ②Time t+i and λ t+i,τ Solution algorithm

[0031]

[0032]

[0033] 2) Node voltage prediction method

[0034] To predict the node voltage of the system, a prediction model should first be constructed, and then the required parameters should be solved based on the constructed prediction model to predict the distribution network node voltage after the load and distributed generation decision setpoints change.

[0035] ① Construction of the prediction model

[0036]

[0037] In the formula: This represents the initial node voltage measurement value at time t; This represents the voltage measurement matrix at time t. This represents the power fluctuation at time t of the load. This represents the power fluctuation of the load at the τ-th decision point, node i, at time t. Δu t =[…,Δu t,τ ,…], This represents the change in the setpoint of the distributed power supply connected to the τ-th decision point node i at time t;

[0038] ②Predicting the voltage at distribution network nodes after load fluctuations

[0039]

[0040] In the formula: Let be the voltage sensitivity calculated based on the voltage measurement at time t. The sensitivity of the voltage measurement at the i-th decision point at time t to the output power of the distributed source at the τ-th decision point at time t is given.

[0041] ③ Predicting the voltage at distribution network nodes after changes in distributed generation decision values

[0042]

[0043] In the formula: H t The voltage sensitivity at time t is calculated based on the predicted node voltage amplitude after load fluctuation. Let be the sensitivity of the node voltage value after the load change at the i-th decision point at time t to the output power of the distributed power source at the τ-th decision point at time t;

[0044] The parameters in the sensitivity matrix are temporally coupled. To facilitate calculation, this coupling relationship is decoupled.

[0045]

[0046] And because It can be described as:

[0047]

[0048] In the formula: This represents the sensitivity of the node voltage value after load fluctuation at the i-th decision point at time t to the node voltage value after load fluctuation at the τ-th decision point at time t.

[0049] The augmented objective function can also be described as:

[0050]

[0051] Therefore, its time-varying optimal power flow model for the distribution network can be described as follows:

[0052]

[0053] Equation (17) shows that the time-varying optimal power flow model of the distribution network is only related to the changes in load and distributed generation output setpoints.

[0054] This invention presents an active distribution network online prediction and optimization method that incorporates voltage prediction. Specifically, it proposes a time-varying optimal power flow online prediction and optimization method for distribution networks containing a large number of distributed photovoltaic (PV) and energy storage power sources. This method can track and solve for time-varying optimal power flow while ensuring that the voltage of each node is within a reasonable range at all times. It predicts the voltage state of distribution network nodes in future time periods based on the concept of voltage sensitivity, and then uses the predicted values ​​to make decisions about distributed energy sources. This avoids the process of calculating power flow based on load fluctuations and then using the calculation results for decision-making. The method has the advantages of being scientifically sound, highly applicable, and effective. Attached Figure Description

[0055] Figure 1 Framework diagram for online prediction optimization method;

[0056] Figure 2 A flowchart for predicting distribution network node voltage after load fluctuations;

[0057] Figure 3 Flowchart for implementing the proposed online prediction optimization method;

[0058] Figure 4 Inject the convergence process diagram of the objective function at each node under a given condition;

[0059] Figure 5 This is a statistical chart showing the maximum and minimum voltage values ​​at each node of the distribution network.

[0060] Figure 6 The graph shows the voltage amplitude variation at node 423 without using the online prediction optimization method.

[0061] Figure 7 This is a graph showing the voltage amplitude variation at node 423 during the period from 13:25 to 13:35 after adopting the online prediction optimization method.

[0062] Figure 8This is a graph showing the voltage amplitude changes at node 423 during the period from 19:55 to 20:05 after adopting the online prediction optimization method.

[0063] Figure 9 This is a graph showing the energy changes of the energy storage power source before and after adopting the online predictive optimization method. Detailed Implementation

[0064] The present invention will be further described in detail below with reference to specific embodiments. The following examples are used to illustrate the present invention, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0065] Reference Figure 1 and Figure 2 This invention discloses an active distribution network online prediction optimization method considering voltage prediction, comprising: first, establishing a time-varying optimal power flow model for a distribution network including distributed photovoltaic and energy storage, and determining the objective function and constraints; then, based on the prediction solution, solving the time-varying optimal power flow problem online, and constructing an augmented objective function; finally, predicting the node voltages of the system and constructing a prediction model, and then, based on the constructed prediction model, solving for the required parameters, thereby predicting the distribution network node voltages after changes in load and distributed generation decision setpoints. The specific steps are as follows:

[0066] Step 1: Construct a time-varying optimal power flow model for a distributed photovoltaic-storage distribution network.

[0067] 1) Objective function:

[0068]

[0069] In the formula: the superscript t represents time; n represents the number of decision points at time t; C t,τ (Δu t,τ Let Δu be the function representing the change in the operational index of the photovoltaic storage system at the τ-th decision point at time t; t,τ Δu represents the change in the setpoint of the distributed power source output at the τ-th decision point at time t. t,τ =u t,τ -u t,τ-1 =[ΔP t,τ ,ΔQ t,τ ] T ,ΔP t,τ =[…,ΔP i t,τ ,…] T , ΔP i t,τ , These represent the changes in the active and reactive power outputs of the optical storage system at the τ-th decision point of node i at time t, respectively.

[0070] 2) Constraints

[0071] ① Current constraints

[0072]

[0073] In the formula: These are the active power and reactive power at the beginning of line ij at the τ-th decision point at time t, respectively. Let be the magnitude of the voltage at the τ-th decision point node j at time t; H represents the amplitude of the line current at the τ-th decision point at time t; j Let r be the set of nodes downstream of node j that are connected to it; ij x ij Let be the resistance and reactance of line ij, respectively. These are the active power and reactive power of the load at the τ-th decision node j at time t, respectively. Let be the active power and reactive power of the τ-th decision node j at time t, respectively;

[0074] ② Node voltage constraints

[0075]

[0076] In the formula: v These are the upper and lower limits of the allowable node voltage, respectively;

[0077] ③ Operational constraints of photovoltaic and energy storage

[0078]

[0079] In the formula: Let P be the output power matrix of the distributed generation at the τ-th decision point at time t; i t,τ , These represent the active power and reactive power output by the distributed generation at the τ-th decision point at time t, respectively. The permissible operating range of the photovoltaic-storage power source at the τth decision point at time t is determined by its own physical characteristics.

[0080] Step 2: Solve the time-varying optimal power flow problem online based on the predicted solution.

[0081] 1) Construct the augmented objective function:

[0082]

[0083] In the formula: Let be the voltage upper limit penalty coefficient of the τ-th decision point node i at time t; Let be the voltage lower limit penalty coefficient of node i at the τ-th decision point at time t; This represents the voltage measurement matrix at the τ-th decision point at time t; Let t be the voltage measurement value of node i at the τth decision point at time t, that is, the voltage of the distribution network node after the distributed generation decision setting value is changed;

[0084] The distribution network time-varying optimal power flow model based on equation (5) is further described as follows:

[0085]

[0086] and λ t,τ The algorithm for solving the τth decision point at the initial time t is not exactly the same as that at other times t+i. Therefore, specific solution algorithms for two different time periods are given.

[0087] ①Time t and λ t,τ Solution algorithm:

[0088]

[0089]

[0090] In the formula: κ is the designed parameter; α is the step size; ε is the parameter used to accelerate convergence; * indicates in the set Projection on This represents the voltage amplitude at time t after the load fluctuation at the τ-th decision point node i.

[0091] ②Time t+i and λ t+i,τ Solution algorithm

[0092]

[0093]

[0094] 2) Node voltage prediction method

[0095] To predict the node voltage of the system, a prediction model should first be constructed, and then the required parameters should be solved based on the constructed prediction model to predict the distribution network node voltage after the load and distributed generation decision setpoints change.

[0096] ① Construction of the prediction model

[0097]

[0098] In the formula: This represents the initial node voltage measurement value at time t; This represents the voltage measurement matrix at time t. This represents the power fluctuation at time t of the load. This represents the power fluctuation of the load at the τ-th decision point, node i, at time t. Δu t =[…,Δu t,τ ,…], This represents the change in the setpoint of the distributed power supply connected to the τ-th decision point node i at time t;

[0099] ②Predicting the voltage at distribution network nodes after load fluctuations

[0100]

[0101] In the formula: Let be the voltage sensitivity calculated based on the voltage measurement at time t. Let be the sensitivity of the voltage measurement at the i-th decision point at time t to the output power of the distributed source at the τ-th decision point at time t.

[0102] ③ Predicting the voltage at distribution network nodes after changes in distributed generation decision values

[0103]

[0104] In the formula: H t The voltage sensitivity at time t is calculated based on the predicted node voltage amplitude after load fluctuation. Let be the sensitivity of the node voltage value after the load change at the i-th decision point at time t to the output power of the distributed power source at the τ-th decision point at time t;

[0105] The parameters in the sensitivity matrix are temporally coupled. To facilitate calculation, this coupling relationship is decoupled.

[0106]

[0107] And because It can be described as:

[0108]

[0109] In the formula: This represents the sensitivity of the node voltage value after load fluctuation at the i-th decision point at time t to the node voltage value after load fluctuation at the τ-th decision point at time t.

[0110] The augmented objective function can also be described as:

[0111]

[0112] Therefore, its time-varying optimal power flow model for the distribution network can be described as follows:

[0113]

[0114] Equation (17) shows that the time-varying optimal power flow model of the distribution network is only related to the changes in load and distributed generation output setpoints.

[0115] The process of obtaining the change in the decision setpoint of distributed power sources using the method proposed in this invention is as follows: Figure 3 As shown;

[0116] Step 3: Simulation analysis of specific examples.

[0117] In the example system, node 1 is set as the saturation node. All other nodes are connected to both photovoltaic and energy storage power sources. The iteration step size is set to α = 0.01; the objective function coefficient is a. pv =1, b pv =0.01, a b =0.25, b b =0.01; its allowable voltage upper and lower limits are 1.05 and 0.95 pu respectively, the voltage reference value is 12.66 kV, and the power reference value is 1 MVA.

[0118] To verify the convergence of the online prediction optimization method proposed in this invention, the convergence process of the objective function L is analyzed at time 13:25, when the injected power of each node is given. The analysis results are as follows: Figure 4 As shown; by Figure 4 It can be seen that the optimization method proposed in this invention can make the objective function L gradually approach the optimal value during the iteration process, thus verifying that the method proposed in this invention can make the objective function converge.

[0119] The raw data for one day without using online prediction optimization methods were analyzed, and the maximum and minimum values ​​of each node were statistically analyzed. The statistical results are as follows: Figure 5 As shown, by Figure 5 It can be seen that approximately 3 / 4 of the node voltages will exceed the limit; Figure 6 The voltage amplitude variation of the node with the most severe voltage overshoot (node ​​423) is further presented within a day;

[0120] The periods of most severe voltage exceedance at node 423 (13:25-13:35) and most severe exceedance at the lower limit (19:55-20:05) were selected, and optimization calculations were performed using the proposed online prediction optimization method. The voltage situation of node 423 during the period of 13:25-13:35 after applying the online prediction optimization method is as follows: Figure 7 As shown, Figure 7 Initially, the voltage exceeded the limit because voltage regulation takes time. Multiple iterations can bring the voltage within the allowable range. The voltage situation during the time period 19:55-20:05 is as follows: Figure 8 As shown, Figure 8 The voltage initially exceeds the limit because voltage regulation takes time. Through multiple iterations, the voltage exceeding the limit can be kept within the allowable range.

[0121] In summary, the online prediction and optimization method proposed in this invention can control the node voltage within an effective range. Since the method considers the optimal power flow problem over multiple future time periods, certain constraints need to be placed on the energy storage. Analysis of the 423-node energy storage power supply during the period from 13:25 to 13:35 shows the energy changes as follows: Figure 9 As shown, by Figure 9 It is known that before adopting the online prediction optimization method proposed in this invention, the change in energy storage did not take into account the demand for energy storage in the future time period. Therefore, after the energy reaches a certain value, it will no longer be charged. However, after adopting the optimization method proposed in this invention, since the energy storage power supply takes into account the demand for energy storage discharge in the future time period, the energy stored in the energy storage during that time period is more.

[0122] In the future, with the large-scale integration of distributed photovoltaic power sources and energy storage power sources into the distribution network, the fluctuation of the network net load will become more severe. Therefore, by predicting the voltage of distribution network nodes in the future, the output setpoints of each power source can be adjusted, which can effectively curb the problem of node voltage exceeding the limit and make the distribution network operate more stably. Therefore, this research has good engineering application value.

[0123] The embodiments of the present invention are not exhaustive and do not constitute a limitation on the scope of protection of the claims. Those skilled in the art, upon learning from the embodiments of the present invention, can conceive of other substantially equivalent alternatives without inventive effort, all of which are within the scope of protection of the present invention.

Claims

1. An active distribution network online prediction optimization method considering voltage prediction, characterized in that, It includes the following steps: Step 1: Construct a time-varying optimal power flow model for a distributed photovoltaic-storage distribution network. 1) Objective function: In the formula: the superscript t represents time; n represents the number of decision points at time t; C t,τ (Δu t,τ Let Δu be the function representing the change in the operational index of the photovoltaic storage system at the τ-th decision point at time t; t,τ Δu represents the change in the setpoint of the distributed power source output at the τ-th decision point at time t. t,τ =u t,τ -u t,τ-1 =[ΔP t,τ ,ΔQ t,τ ] T , These represent the changes in the active and reactive power outputs of the optical storage system at the τ-th decision point of node i at time t, respectively. 2) Constraints ① Current constraints In the formula: These are the active power and reactive power at the beginning of line ij at the τ-th decision point at time t, respectively. Let be the magnitude of the voltage at the τ-th decision point node j at time t; H represents the amplitude of the line current at the τ-th decision point at time t; j Let r be the set of nodes downstream of node j that are connected to it; ij x ij Let be the resistance and reactance of line ij, respectively. These are the active power and reactive power of the load at the τ-th decision node j at time t, respectively. Let be the active power and reactive power of the τ-th decision node j at time t, respectively; ② Node voltage constraints In the formula: v These are the upper and lower limits of the allowable node voltage, respectively; ③ Operational constraints of photovoltaic and energy storage In the formula: Let be the output power matrix of the distributed generation at the τ-th decision point at time t; These represent the active power and reactive power output by the distributed generation at the τ-th decision point at time t, respectively. The permissible operating range of the photovoltaic-storage power source at the τth decision point at time t is determined by its own physical characteristics. Step 2: Solve the time-varying optimal power flow problem online based on the predicted solution. 1) Construct the augmented objective function: In the formula: Let be the voltage upper limit penalty coefficient of the τ-th decision point node i at time t; Let be the voltage lower limit penalty coefficient of node i at the τ-th decision point at time t; This represents the voltage measurement matrix at the τ-th decision point at time t; Let t be the voltage measurement value of node i at the τth decision point at time t, that is, the voltage of the distribution network node after the distributed generation decision setting value is changed; The distribution network time-varying optimal power flow model based on equation (5) is further described as follows: and λ t,τ The algorithm for solving the τth decision point at the initial time t is not exactly the same as that at other times t+i. Therefore, specific solution algorithms for two different time periods are given. ①Time t and λ t,τ Solution algorithm: In the formula: κ is the designed parameter; α is the step size; ε is the parameter used to accelerate convergence; * indicates in the set Projection on This represents the voltage amplitude at time t after the load fluctuation at the τ-th decision point node i. ②Time t+i and λ t+i,τ Solution algorithm 2) Node voltage prediction method To predict the node voltage of the system, a prediction model should first be constructed, and then the required parameters should be solved based on the constructed prediction model to predict the distribution network node voltage after the load and distributed generation decision setpoints change. ① Construction of the prediction model In the formula: This represents the initial node voltage measurement value at time t; This represents the voltage measurement matrix at time t. This represents the power fluctuation at time t of the load. This represents the power fluctuation of the load at the τ-th decision point, node i, at time t. Δu t =[…,Δu t,τ ,…], This represents the change in the setpoint of the distributed power supply connected to the τ-th decision point node i at time t; ②Predicting the voltage at distribution network nodes after load fluctuations In the formula: Let be the voltage sensitivity calculated based on the voltage measurement at time t. The sensitivity of the voltage measurement at the i-th decision point at time t to the output power of the distributed source at the τ-th decision point at time t is given. ③ Predicting the voltage at distribution network nodes after changes in distributed generation decision values In the formula: H t The voltage sensitivity at time t is calculated based on the predicted node voltage amplitude after load fluctuation. Let be the sensitivity of the node voltage value after the load change at the i-th decision point at time t to the output power of the distributed power source at the τ-th decision point at time t; The parameters in the sensitivity matrix are temporally coupled. To facilitate calculation, this coupling relationship is decoupled. And because It can be described as: In the formula: This represents the sensitivity of the node voltage value after load fluctuation at the i-th decision point at time t to the node voltage value after load fluctuation at the τ-th decision point at time t. The augmented objective function can also be described as: Therefore, its time-varying optimal power flow model for the distribution network can be described as follows: Equation (17) shows that the time-varying optimal power flow model of the distribution network is only related to the changes in load and distributed generation output setpoints.

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