Active distribution network distributed online predictive optimization method based on model predictive control

Abstract

The application discloses a model prediction control-based active power distribution network distributed online prediction optimization method, aims at the multi-time period coupling relationship problem of distributed photovoltaic and energy storage power supply decision values, utilizes voltage sensitivity to predict the node voltage values of the power distribution network in future time periods, and then simultaneously decides the output powers of the distributed photovoltaic and energy storage power supply at multiple time points by using the predicted node voltage values of the power distribution network in the future time periods. The constructed collaborative optimization control strategy of the distributed photovoltaic and energy storage power supply can guarantee that the overall optimal of the power distribution network in multiple time periods and the node voltage in each time period is within a reasonable range, and the output of the distributed photovoltaic and energy storage power supply is optimal in economy, thereby providing theoretical support for the development of the collaborative control technology of the power distribution network containing high proportion of photovoltaic and energy storage power supply. The application has the advantages of scientific rationality, strong applicability and good effect.

Description

Technical Field

[0001] This invention belongs to the field of distributed photovoltaic and energy storage control, and particularly relates to an active distribution network distributed online predictive optimization method based on model predictive control. Background Technology

[0002] In the existing technology, as the number of distributed photovoltaic and energy storage power sources connected to the distribution network gradually increases, if a reasonable energy storage power source regulation strategy is not formulated, it may lead to problems such as low photovoltaic absorption rate and waste of resources. Therefore, how to reasonably regulate the output of distributed photovoltaic and energy storage power sources so that they can achieve the predetermined control target 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 model predictive control-based active distribution network distributed online predictive optimization method. This method can quantitatively describe the collaborative optimization control strategy of distributed photovoltaic (PV) and energy storage power sources. Within the constraints of network power flow and node voltage, it aims to further determine the output of distributed PV and energy storage power sources to achieve the optimal economic operation of distributed PV and energy storage power sources, providing theoretical support for the development of collaborative control technology with high proportions of PV and energy storage power sources.

[0004] The technical solution adopted to achieve the purpose of this invention is an active distribution network distributed online predictive optimization method based on model predictive control, characterized by comprising the following steps:

[0005] Step 1: Establish distribution network operation constraints

[0006] The constraints on power distribution network operation include power flow constraints, node voltage constraints, photovoltaic power source operation constraints, and energy storage power source operation constraints.

[0007] Step 2, establish the control objective function

[0008] Establish a control objective function to solve the problem of coordinated optimization of multiple distributed energy decision points in different regions within a time period;

[0009] Step 3: Construction of a distributed online predictive optimization method for active distribution networks

[0010] 1) Construction of the augmented objective function;

[0011] 2) Determining the coefficients related to the upper and lower voltage limits;

[0012] 3) Construction of a distributed prediction method for node voltage measurements in the future time period of the distribution network;

[0013] ① Construction of a prediction model for node voltage measurements,

[0014] ② Distributed solution of voltage sensitivity.

[0015] Furthermore, the establishment of distribution network operation constraints in step 1 includes:

[0016] 1) Current flow constraint is:

[0017]

[0018] In the formula, P represents the set of different regions of the distribution network, z.* represents the * variable in region z, ij represents the line number ij, and H j Let t.τ represent the set of nodes that are downstream of node j and connected to it, and let t.τ represent the τ-th time point in time interval t. This represents the active and reactive power of line ij at the τ-th time point in time period t. This represents the current in line ij at the τ-th time point within the time interval t. These represent the active and reactive power values ​​of the load at the τth time point in time period t, respectively. Let represent the active and reactive power values ​​generated by the distributed power source at the τ-th time point in time period t, respectively. Let r represent the voltage amplitude at node i at time point τ within time period t. z.ij x z.ij These represent the resistance and reactance of line ij, respectively.

[0019] 2) Node voltage constraints

[0020]

[0021] In the formula, Let represent the voltage at the i-th node in region z at the τ-th time point within time period t. Let represent the upper and lower voltage limits of the i-th node in region z at the τ-th time point within time period t, respectively;

[0022] 3) Operating constraints of photovoltaic power sources

[0023]

[0024] In the formula, This represents the actual active power output value of the photovoltaic power source connected to the i-th node in region z at the τ-th time point within time period t. This represents the maximum active power output value of the photovoltaic power source connected to the i-th node in region z at the τ-th time point in time period t. S represents the actual reactive power output value of the photovoltaic power source connected to the i-th node in region z at the τ-th time point in time period t. z.pv.iThis represents the rated capacity of the photovoltaic inverter connected to the i-th node in region z;

[0025] 4) Energy storage power supply operation constraints

[0026]

[0027] In the formula, This represents the actual active power output value of the energy storage power source connected to the i-th node in region z at the τ-th time point within time period t. S represents the actual reactive power output value of the energy storage power source connected to the i-th node in region z at the τ-th time point in time period t. z.b,i This represents the rated capacity of the energy storage inverter connected to the i-th node in region z. E represents the actual energy of the energy storage power source connected to the i-th node in region z at the τ-th time point in time period t. z.b.i This represents the rated energy of the energy storage connected to the i-th node in region z.

[0028] Furthermore, the control objective function established in step 2 is:

[0029]

[0030] In the formula, f t This represents the network operation goal for time period t. This represents the output power matrix of the distributed power source connected to the i-th node in region z at time point τ within time period t. K represents the number of time points included in time period t; N represents the set of distribution network nodes; It represents the operating cost of the distributed power source connected to the i-th node in the z region at the τ-th time point in the time period t. The expression form is different when the type of distributed power source is different. When it is a photovoltaic power source, it is as shown in formula (6), and when it is an energy storage power source, it is as shown in formula (7).

[0031]

[0032]

[0033] In the formula, These represent the active and reactive power regulation cost coefficients of the photovoltaic power source connected to the i-th node in region z, respectively. Let represent the active and reactive power regulation cost coefficients of the energy storage power source connected to the i-th node in region z, respectively.

[0034] because Δf represents the objective function value before a decision is made. tThis represents the change in the objective function value for the decision; the load values ​​of each node are assumed to be known before the decision is made; the active power of the photovoltaic system is based on the maximum active power output; the reactive power of the photovoltaic system and the active and reactive power outputs of the energy storage system are both 0. Therefore, the following settings are made: Therefore, the magnitude of formula (5) depends only on Δf t Therefore, formula (5) can also be described as:

[0035]

[0036] In the formula This represents the distributed power supply decision value connected to node i in region z at the τth time point in time period t.

[0037] Furthermore, step 3, 1), the construction of the augmented objective function, specifically involves:

[0038] Since solving formula (8) requires constraints from formulas (1)-(4), a penalty function is used to transfer the inequality constraints in formula (2) to the objective function, thereby constructing an augmented objective function:

[0039]

[0040] In the formula, Let represent the coefficients related to the upper and lower voltage limits in region z at the τ-th time point within time period t, respectively. ε is a parameter designed to accelerate convergence performance. This represents the matrix of node voltage measurements in region z at the τ-th time point within time period t. This represents the voltage measurement value of node i in region z at the τth time point within time period t. This represents the upper limit matrix of node voltages in region z at the τ-th time point within time period t. This represents the lower limit matrix of node voltages in region z at the τ-th time point within time period t.

[0041] Therefore, based on the expression of formula (9), formula (8) can also be described as:

[0042]

[0043] Furthermore, the calculation of the coefficients related to the upper and lower voltage limits in step 3, 2), is specifically as follows:

[0044] Solving formula (10) requires given coefficients. Therefore, coefficients are given. The solution method is as follows:

[0045]

[0046]

[0047] In the formula, * indicates in the set Projection on Let denot be the set of non-negative real numbers in region z, κ be the designed parameters, α be the step size, and ε be the parameters used to accelerate convergence.

[0048] Furthermore, the construction of the node voltage measurement prediction model in step 3) ① is as follows:

[0049]

[0050] In the formula, This represents the matrix showing the change in load in region z at the τ-th time point within time period t. Let represent the load change matrix of node i in region z at time point τ within time period t. This represents the matrix showing the change in output power of distributed energy sources in region z at the τ-th time point within time period t. g V (*) indicates the coupling relationship between voltage measurements, distributed generation decision values, and load changes.

[0051] g V The coupling relationship between the voltage measurement value, distributed generation decision value, and load change represented by (*) is expressed in matrix form, and its specific form is as follows:

[0052]

[0053] In the formula, These represent the sensitivity of voltage measurements to the power injected into the load node, and the sensitivity of the node voltage after load fluctuations to the power injected into the distributed power source access node, respectively.

[0054] Furthermore, the distributed solution of voltage sensitivity in step 3) ② is as follows:

[0055] The impact on the upstream region is mainly manifested in affecting its boundary injection power, and therefore can be decoupled as follows:

[0056]

[0057]

[0058] In the formula, Denotes the set of regions flowing through region z. This represents the matrix of line power measurements transmitted from region s to region z at the τ-th time point in time period t. This represents the matrix of predicted line power transmitted from region s to region z at the τ-th time point in time period t. These represent the sensitivity of voltage measurements to line power measurements, and the sensitivity of node voltage to predicted line power after load fluctuations, respectively. These represent the sensitivity of the measured line power value to the power injected into the load node and the sensitivity of the predicted line power value to the power injected into the distributed power source access node, respectively.

[0059] The impact on the downstream region is mainly manifested in its boundary voltage, and therefore it can be decoupled as follows:

[0060]

[0061]

[0062] In the formula, This represents the set of downstream regions of region z. Let represent the measured node voltage and the predicted node voltage at the boundary node between region s and region z at the τ-th time point in time period t, respectively. Let represent the sensitivity of the voltage measurement value of node s in region s at time point τ within time period t to the voltage measurement value of the boundary node between region s and region z, and the sensitivity of the voltage prediction value of node s in region s to the voltage prediction value of the boundary node between region s and region z, respectively. These represent the sensitivity of the voltage measurement values ​​at the boundary nodes of regions s and z at the τth time point in time period t to the power injected into the load nodes, and the sensitivity of the voltage prediction values ​​at the boundary nodes of regions s and z to the power injected into the distributed power supply access nodes, respectively.

[0063] The impact on the downstream region is mainly manifested in affecting its boundary injection power, which in turn affects the boundary voltage of the region. Therefore, it can be decoupled as follows:

[0064]

[0065]

[0066] In the formula, This represents the set of parallel regions of region z. Let represent the measured voltage value and the predicted voltage value of the root node of region s at the τ-th time point in time interval t, respectively. Let represent the sensitivity of the voltage measurement value of region s at the τth time point in time period t to the voltage measurement value of the root node of region s, and the sensitivity of the predicted voltage value of region s to the predicted voltage value of the root node of region s, respectively. These represent the sensitivity of the voltage measurement value of the root node of region s at the τth time point in time period t to the power injected into the load node, and the sensitivity of the voltage prediction value of the root node of region s to the power injected into the distributed power source access node, respectively.

[0067] The distributed solution of voltage sensitivity of the distribution network can be completed using formulas (15)-(20). From formulas (14)-(20), it can be seen that the solution of voltage sensitivity, the predicted node voltage after load fluctuation, and the voltage measurement value are only related to the load fluctuation value and the distributed generation decision value. Therefore, the coefficients are relatively simple. The solution is only related to the load fluctuation value and the distributed power source decision value. Therefore, formula (10) is only related to the load fluctuation value and the distributed power source decision value. Since the load fluctuation value is known, formula (10) is only related to the distributed power source decision value. Therefore, formula (10) can also describe formula (21).

[0068]

[0069] This invention presents an active distributed online predictive optimization method for distribution networks based on model predictive control. Addressing the multi-time-period coupling relationship of decision values ​​for distributed photovoltaic (PV) and energy storage power sources, it utilizes voltage sensitivity to predict the node voltage values ​​of the distribution network in future time periods. Then, using these predicted future node voltage values, it simultaneously determines the output power of distributed PV and energy storage power sources at multiple time points. The constructed collaborative optimization control strategy for distributed PV and energy storage power sources ensures overall optimality of the distribution network across multiple time periods and that node voltages in each time period remain within reasonable ranges. This optimizes the economic operation and output of distributed PV and energy storage power sources, providing theoretical support for the development of collaborative control technologies involving high proportions of PV and energy storage power sources. It boasts advantages such as scientific rationality, strong applicability, and excellent results. Attached Figure Description

[0070] Figure 1 This is a schematic diagram showing the distribution network areas and their divisions.

[0071] Figure 2 This is a convergence diagram of the constructed active distribution network distributed online predictive optimization method;

[0072] Figure 3 To verify the effectiveness of the constructed distributed online prediction optimization method for voltage regulation at node 57;

[0073] Figure 4 This study verifies the effectiveness of the constructed distributed online prediction optimization method for regulating the energy storage output of node 57. Detailed Implementation

[0074] 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.

[0075] This invention discloses an active distributed online predictive optimization method for distribution networks based on model predictive control, comprising: firstly, constructing distribution network operation constraints; secondly, constructing a control objective function; and finally, constructing a collaborative optimization control strategy for distributed photovoltaic (PV) and energy storage power sources. The constructed control strategy utilizes distributed voltage sensitivity prediction to solve for the node voltage values ​​of the distribution network in future time periods, and then uses the predicted node voltage values ​​of the distribution network in future time periods to determine the output power of distributed PV and energy storage power sources. The specific steps are as follows:

[0076] Step 1: Establish distribution network operation constraints

[0077] Distribution network operation constraints include power flow constraints, node voltage constraints, photovoltaic power source operation constraints, and energy storage power source operation constraints.

[0078] 1) Current constraints

[0079]

[0080] In the formula, P represents the set of different regions of the distribution network, z.* represents the * variable in region z, ij represents the line number ij, and H j Let t.τ represent the set of nodes that are downstream of node j and connected to it, and let t.τ represent the τ-th time point in time interval t. This represents the active and reactive power of line ij at the τ-th time point in time period t. This represents the current in line ij at the τ-th time point within the time interval t. These represent the active and reactive power values ​​of the load at the τth time point in time period t, respectively. Let represent the active and reactive power values ​​generated by the distributed power source at the τ-th time point in time period t, respectively. Let r represent the voltage amplitude at node i at time point τ within time period t. z.ij x z.ij These represent the resistance and reactance of line ij, respectively.

[0081] 2) Node voltage constraints

[0082]

[0083] In the formula, Let represent the voltage at the i-th node in region z at the τ-th time point within time period t. Let represent the upper and lower voltage limits of the i-th node in region z at the τ-th time point within time period t, respectively;

[0084] 3) Operating constraints of photovoltaic power sources

[0085]

[0086] In the formula, This represents the actual active power output value of the photovoltaic power source connected to the i-th node in region z at the τ-th time point within time period t. This represents the maximum active power output value of the photovoltaic power source connected to the i-th node in region z at the τ-th time point in time period t. S represents the actual reactive power output value of the photovoltaic power source connected to the i-th node in region z at the τ-th time point in time period t. z.pv.i This represents the rated capacity of the photovoltaic inverter connected to the i-th node in region z;

[0087] 4) Energy storage power supply operation constraints

[0088]

[0089] In the formula, This represents the actual active power output value of the energy storage power source connected to the i-th node in region z at the τ-th time point within time period t. S represents the actual reactive power output value of the energy storage power source connected to the i-th node in region z at the τ-th time point in time period t. z.b,i This represents the rated capacity of the energy storage inverter connected to the i-th node in region z. E represents the actual energy of the energy storage power source connected to the i-th node in region z at the τ-th time point in time period t. z.b.i This represents the rated energy of the energy storage connected to the i-th node in region z.

[0090] Step 2, establish the control objective function

[0091] To minimize the operating cost of distributed power sources and network losses, the control objective function is:

[0092]

[0093] In the formula, f t This represents the network operation goal for time period t. This represents the output power matrix of the distributed power source connected to the i-th node in region z at time point τ within time period t. K represents the number of time points included in time period t; N represents the set of distribution network nodes; It represents the operating cost of the distributed power source connected to the i-th node in the z region at the τ-th time point in the time period t. The expression form is different when the type of distributed power source is different. When it is a photovoltaic power source, it is as shown in formula (6), and when it is an energy storage power source, it is as shown in formula (7).

[0094]

[0095]

[0096] In the formula, These represent the active and reactive power regulation cost coefficients of the photovoltaic power source connected to the i-th node in region z, respectively. Let represent the active and reactive power regulation cost coefficients of the energy storage power source connected to the i-th node in region z, respectively.

[0097] because Δf represents the objective function value before a decision is made. t This represents the change in the objective function value for the decision; the load values ​​of each node are assumed to be known before the decision is made; the active power of the photovoltaic system is based on the maximum active power output; the reactive power of the photovoltaic system and the active and reactive power outputs of the energy storage system are both 0. Therefore, the following settings are made: Therefore, the magnitude of formula (5) depends only on Δf t Therefore, formula (5) can also be described as:

[0098]

[0099] In the formula This represents the distributed power supply decision value connected to node i in region z at the τth time point in time period t.

[0100] Step 3: Construction of a distributed online predictive optimization method for active distribution networks

[0101] 1) Construction of the augmented objective function

[0102] Since solving formula (8) requires constraints from formulas (1)-(4), a penalty function is used to transfer the inequality constraints in formula (2) to the objective function, thereby constructing an augmented objective function:

[0103]

[0104] In the formula, Let represent the coefficients related to the upper and lower voltage limits in region z at the τ-th time point within time period t, respectively. ε is a parameter designed to accelerate convergence performance. This represents the matrix of node voltage measurements in region z at the τ-th time point within time period t. This represents the voltage measurement value of node i in region z at the τth time point within time period t. This represents the upper limit matrix of node voltages in region z at the τ-th time point within time period t. This represents the lower limit matrix of node voltages in region z at the τ-th time point within time period t.

[0105] Therefore, based on the expression of formula (9), formula (8) can also be described as:

[0106]

[0107] 2) Determination of the coefficients related to the upper and lower voltage limits

[0108] Solving formula (10) requires providing coefficients. Therefore, coefficients are given. The solution method is as follows:

[0109]

[0110]

[0111] In the formula, * indicates in the set Projection on Let κ represent the set of non-negative real numbers in region z, α be the designed parameters, α be the step size, and ε be the parameters used to accelerate convergence.

[0112] 3) Construction of a distributed prediction method for node voltage measurements in the future time period of the distribution network

[0113] ① Construction of the prediction model for node voltage measurement

[0114]

[0115] In the formula, This represents the matrix showing the change in load in region z at the τ-th time point within time period t. Let represent the load change matrix of node i in region z at time point τ within time period t. This represents the matrix showing the change in output power of distributed energy sources in region z at the τ-th time point within time period t. g V (*) indicates the coupling relationship between voltage measurement values, distributed generation decision values, and load changes;

[0116] g V The coupling relationship between the voltage measurement values, distributed generation decision values, and load changes represented by (*) is expressed in matrix form, specifically as follows:

[0117]

[0118] In the formula, These represent the sensitivity of voltage measurements to the power injected into the load node, and the sensitivity of the node voltage after predicting load fluctuations to the power injected into the distributed power source access node, respectively.

[0119] ② Distributed solution of voltage sensitivity

[0120] The impact on the upstream region is mainly manifested in affecting its boundary injection power, and therefore can be decoupled as follows:

[0121]

[0122]

[0123] In the formula, Denotes the set of regions flowing through region z. This represents the matrix of line power measurements transmitted from region s to region z at the τ-th time point in time period t. This represents the matrix of predicted line power transmitted from region s to region z at the τ-th time point in time period t. These represent the sensitivity of voltage measurements to line power measurements, and the sensitivity of node voltage to predicted line power after load fluctuations, respectively. These represent the sensitivity of the measured line power value to the power injected into the load node and the sensitivity of the predicted line power value to the power injected into the distributed power access node, respectively.

[0124] The impact on the downstream region is mainly manifested in its boundary voltage, and therefore it can be decoupled as follows:

[0125]

[0126]

[0127] In the formula, This represents the set of downstream regions of region z. Let represent the measured node voltage and the predicted node voltage at the boundary node between region s and region z at the τ-th time point in time period t, respectively. Let represent the sensitivity of the voltage measurement value of node s in region s at time point τ within time period t to the voltage measurement value of the boundary node between region s and region z, and the sensitivity of the voltage prediction value of node s in region s to the voltage prediction value of the boundary node between region s and region z, respectively. These represent the sensitivity of the voltage measurement values ​​at the boundary nodes of regions s and z at the τth time point in time period t to the power injected into the load node, and the sensitivity of the voltage prediction values ​​at the boundary nodes of regions s and z to the power injected into the distributed power access node, respectively.

[0128] The impact on the downstream region is mainly manifested in affecting its boundary injection power, which in turn affects the boundary voltage of the region. Therefore, it can be decoupled as follows:

[0129]

[0130]

[0131] In the formula, This represents the set of parallel regions of region z. Let represent the measured voltage value and the predicted voltage value of the root node of region s at the τ-th time point in time interval t, respectively. Let represent the sensitivity of the voltage measurement value of region s at the τth time point in time period t to the voltage measurement value of the root node of region s, and the sensitivity of the predicted voltage value of region s to the predicted voltage value of the root node of region s, respectively. These represent the sensitivity of the voltage measurement value of the root node of region s at the τth time point in time period t to the power injected into the load node, and the sensitivity of the voltage prediction value of the root node of region s to the power injected into the distributed power access node, respectively.

[0132] Formulas (15)-(20) can be used to solve the distributed voltage sensitivity of the distribution network. From formulas (14)-(20), it is known that the solution of voltage sensitivity, the predicted node voltage after load fluctuation, and the voltage measurement value are only related to the load fluctuation value and the distributed generation decision value. Therefore, the coefficients are relatively simple. The solution depends only on the load fluctuation value and the distributed generation decision value. Therefore, formula (10) also depends only on the load fluctuation value and the distributed generation decision value. Since the load fluctuation value is known, formula (10) depends only on the distributed generation decision value. Therefore, formula (10) can also be described as:

[0133]

[0134] Step 4: Perform corresponding simulation analysis on specific examples using the analysis method of this invention.

[0135] The convergence, voltage regulation effectiveness, and energy storage power source regulation effectiveness of the active distribution network distributed online predictive optimization method of this invention are verified through simulation using the IEEE 118-node system.

[0136] The convergence diagram of the constructed method is shown below. Figure 2 As shown, from Figure 2 As can be seen, this method can achieve convergence under the condition that the injected power at each node is determined.

[0137] A schematic diagram of the voltage regulation method constructed is shown below. Figure 3 As shown, from Figure 3As can be seen, this method can keep the voltage within a reasonable range at different times.

[0138] A schematic diagram of the energy storage power supply regulation method is shown below. Figure 4 As shown, since this invention predicts the network operating status one minute in advance, therefore... Figure 4 As can be seen from the data, if the voltage of the distribution network nodes is predicted to exceed the limit in the future, then a decision needs to be made to charge the energy storage in the future (when the energy storage output power is negative). Therefore, a decision should be made to discharge the energy storage in the current period (when the energy storage output power is positive) in order to better meet the charging needs of the energy storage in the future.

[0139] 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. A distributed online predictive optimization method for active distribution networks based on model predictive control, characterized in that, It includes the following steps: Step 1: Establish distribution network operation constraints; The constraints on power distribution network operation include power flow constraints, node voltage constraints, photovoltaic power source operation constraints, and energy storage power source operation constraints. Step 2: Establish the control objective function; Establish a control objective function to solve the problem of coordinated optimization of multiple distributed energy decision points in different regions within a time period; Step 3: Construction of a distributed online predictive optimization method for active distribution networks; 1) Construction of the augmented objective function; 2) Determining the coefficients related to the upper and lower voltage limits; 3) Construction of a distributed prediction method for node voltage measurements in the future time period of the distribution network; ① Construction of a prediction model for node voltage measurements, ② Distributed solution for voltage sensitivity; The distributed solution of voltage sensitivity in step 3) 3) is as follows: The impact on the upstream region manifests as an influence on its boundary injection power, and therefore can be decoupled as follows: (15) (16) In the formula, Denotes the set of regions flowing through region z. Indicates the tth time interval A matrix of line power measurements transmitted from region s to region z at each time point. , Indicates the tth time interval A matrix of predicted line power values ​​from region s to region z at each time point. , , These represent the sensitivity of voltage measurements to line power measurements, and the sensitivity of node voltage to predicted line power after load fluctuations, respectively. , These represent the sensitivity of the measured line power value to the power injected into the load node and the sensitivity of the predicted line power value to the power injected into the distributed power source access node, respectively. The effect on the downstream region manifests as an influence on its boundary voltage, and therefore can be decoupled as follows: (17) (18) In the formula, This represents the set of downstream regions of region z. , They represent the tth time interval, respectively. Measured and predicted node voltage values ​​at the boundary nodes of regions s and z at time points. , They represent the tth time interval, respectively. The sensitivity of the voltage measurement value of node s in region s at a given time point to the voltage measurement value of the boundary node between region s and region z, and the sensitivity of the voltage prediction value of node s in region s to the voltage prediction value of the boundary node between region s and region z. , They represent the tth time interval, respectively. The sensitivity of voltage measurements at the boundary nodes of regions s and z at a given time point to the power injected into the load nodes, and the sensitivity of predicted voltage values ​​at the boundary nodes of regions s and z to the power injected into the distributed power supply access nodes. The impact on the downstream region manifests as an influence on its boundary injection power, which in turn affects the boundary voltage of the region. Therefore, it can be decoupled as follows: (19) (20) In the formula, This represents the set of parallel regions of region z. , They represent the tth time interval, respectively. Voltage measurement and predicted value at the root node of region s at each time point. , They represent the tth time interval, respectively. The sensitivity of the voltage measurement value of region s at a given time point to the voltage measurement value of the root node of region s, and the sensitivity of the voltage prediction value of region s to the voltage prediction value of the root node of region s. , They represent the tth time interval, respectively. The sensitivity of the voltage measurement value of the root node of region s at a given time point to the power injected into the load node, and the sensitivity of the voltage prediction value of the root node of region s to the power injected into the distributed power source access node, can be solved by formulas (15)-(20) in a distributed manner to obtain the voltage sensitivity of the distribution network.

2. The active distribution network distributed online predictive optimization method based on model predictive control as described in claim 1, characterized in that, The establishment of distribution network operation constraints in step 1 includes: 1) Current flow constraint is: (1) In the formula, P represents the set of different areas of the distribution network. Let * represent the variable in region z, and ij represent the line number ij. Let j be the set of nodes that are downstream of node j and connected to it. Indicates the tth time interval At a certain point in time, , Indicates the tth time interval The active and reactive power of line ij at each time point Indicates the tth time interval The current of line ij at a given time point , They represent the tth time interval, respectively. The active and reactive power values ​​of the load at node j at each time point. , They represent the tth time interval, respectively. The active and reactive power values ​​emitted by the distributed power source at node j at a given time point. Indicates the tth time interval The voltage amplitude at node i at time point i , These represent the resistance and reactance of line ij, respectively. 2) Node voltage constraints: (2) In the formula, Indicates the tth time interval The voltage of the i-th node in the time region z. , They represent the tth time interval, respectively. The upper and lower voltage limits of the i-th node in the time region z; 3) Operating constraints of photovoltaic power sources: (3) In the formula, Indicates the tth time interval The actual active power output value of the photovoltaic power source connected to the i-th node in the z-th time region. Indicates the tth time interval The maximum active power output value of the photovoltaic power source connected to the i-th node in the z-th time region. Indicates the tth time interval The actual reactive power output value of the photovoltaic power source connected to the i-th node in the z-th time region. This represents the rated capacity of the photovoltaic inverter connected to the i-th node in region z; 4) Energy storage power supply operation constraints: (4) In the formula, Indicates the tth time interval The actual active power output value of the energy storage power source connected to the i-th node in the z-th time region. Indicates the tth time interval The actual reactive power output value of the energy storage power source connected to the i-th node in the time region z. This represents the rated capacity of the energy storage inverter connected to the i-th node in region z. Indicates the tth time interval The actual energy of the energy storage power source connected to the i-th node in the z-th time region. This represents the rated energy of the energy storage connected to the i-th node in region z.

3. The active distribution network distributed online predictive optimization method based on model predictive control as described in claim 2, characterized in that, The control objective function established in step 2 is as follows: (5) In the formula, This represents the network operation goal for time period t. Indicates the tth time interval The output power matrix of the distributed power source connected to the i-th node in the z-th time region. K represents the number of time points included in time period t; N represents the set of distribution network nodes; Indicates the tth time interval The operating cost of the distributed power source connected to the i-th node in the z-th time region is expressed differently depending on the type of distributed power source. For photovoltaic power sources, it is expressed as formula (6), and for energy storage power sources, it is expressed as formula (7). (6) (7) In the formula, , These represent the active and reactive power regulation cost coefficients of the photovoltaic power source connected to the i-th node in region z, respectively. , Let represent the active and reactive power regulation cost coefficients of the energy storage power source connected to the i-th node in region z, respectively. because , This represents the objective function value before a decision is made; This represents the change in the value of the objective function of the decision. Before making a decision, it is assumed that the load values ​​of each node are known; the active power of photovoltaics is based on the maximum active power output; the reactive power of photovoltaics and the active and reactive power outputs of energy storage are both 0. Therefore, it is set as follows: Therefore, the magnitude of formula (5) is only related to... Therefore, formula (5) can also be described as: (8) In the formula Indicates the tth time interval The distributed power supply decision value for node i in region z at a given time point. .

4. The active distribution network distributed online predictive optimization method based on model predictive control as described in claim 3, characterized in that, Step 3, 1) Constructing the augmented objective function, specifically involves: Since solving formula (8) requires constraints from formulas (1) to (4), a penalty function is used to transfer the inequality constraints in formula (2) to the objective function, thereby constructing an augmented objective function: (9) In the formula, , They represent the tth time interval, respectively. The coefficients related to the upper and lower voltage limits in a time region z. It is a parameter designed to accelerate convergence performance. Indicates the tth time interval A matrix of node voltage measurement values ​​in region z at a given time point. , Indicates the tth time interval Voltage measurement value of node i in region z at time point. Indicates the tth time interval The upper limit matrix of node voltages in region z at time points. , Indicates the tth time interval The lower limit matrix of node voltages in region z at time points. ; Therefore, based on the expression of formula (9), formula (8) can also be described as: (10)。 5. The active distribution network distributed online predictive optimization method based on model predictive control as described in claim 4, characterized in that, The calculation of the coefficients related to the upper and lower voltage limits in step 3, section 2), is as follows: Solving formula (10) requires providing coefficients. , Therefore, the coefficients are given. , The solution method is as follows: (11) (12) In the formula, * indicates in the set The projection on Let z represent the set of non-negative real numbers in the region z. For the designed parameters, Step size, These are parameters used to accelerate convergence.

6. The active distribution network distributed online predictive optimization method based on model predictive control as described in claim 5, characterized in that, The construction of the node voltage measurement prediction model in step 3) ① is as follows: (13) In the formula, Indicates the tth time interval The load change matrix in region z at time points. , Indicates the tth time interval The load change matrix of node i in a time region z. , , Indicates the tth time interval The matrix of changes in the output power of distributed energy sources in region z at a given time point. , This indicates the coupling relationship between voltage measurements, distributed generation decision values, and load changes. Will The coupling relationship between the voltage measurement values, distributed generation decision values, and load changes is expressed in matrix form, specifically as follows: (14) In the formula, , These represent the sensitivity of voltage measurements to the power injected into the load node, and the sensitivity of the node voltage after load fluctuations to the power injected into the distributed power source access node, respectively.

7. The active distribution network distributed online predictive optimization method based on model predictive control as described in claim 6, characterized in that, From the formulas (14)-(20), it can be seen that solving for voltage sensitivity, predicting node voltage after load fluctuation, and voltage measurement values ​​are only related to load fluctuation values ​​and distributed generation decision values. Therefore, the coefficients are... , The solution is only related to the load fluctuation value and the distributed power source decision value. Therefore, formula (10) is only related to the load fluctuation value and the distributed power source decision value. Since the load fluctuation value is known, formula (10) is only related to the distributed power source decision value. Therefore, formula (10) can also describe formula (21). (21)。

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