Two-stage Voltage Control Method and Device for Distribution Network with Distributed Photovoltaic and Energy Storage Collaboration

Abstract

An embodiment of the present invention provides a two-stage voltage control method and device for a distribution network with distributed photovoltaic and energy storage collaboration. First, distribution network data is acquired, and then the power flow model of the distribution network is linearized to obtain a linear power flow model of the distribution network. In the first stage of distribution network voltage control, with the minimum voltage deviation of the photovoltaic and energy storage nodes as the optimization objective, the objective function and constraint conditions for distribution network voltage optimization are determined, and a distribution network voltage optimization control model is established. Based on the accelerated dual ascent algorithm, the distribution network voltage optimization control model is solved to obtain the reactive power output of the photovoltaic inverter. Then, when it is judged that the voltage of the photovoltaic and energy storage nodes after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold, the second stage of distribution network voltage control is entered. The distribution network voltage optimization control model is adjusted, and Lagrange multiplier information is communicated and exchanged with adjacent photovoltaic and energy storage nodes to obtain the active power of the distributed energy storage, realizing the collaborative control of distributed photovoltaic and energy storage on the distribution network voltage.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation control, and particularly to a two-stage voltage control method and device for a distribution network with distributed optical storage cooperation. Background Art

[0002] With the continuous development and application of renewable energy technologies, more and more photovoltaic systems are connected to the distribution network. However, since these distribution networks are usually located in remote areas, the coverage of the power supply network is limited. The power output of the photovoltaic system is affected by natural factors such as sunlight, resulting in large fluctuations and uncertainties in its power output. In remote areas, the load of the distribution network may be relatively small, and the power fluctuations of the photovoltaic system may account for a large proportion of the power supply of the power grid, leading to overvoltage problems.

[0003] Currently, high-penetration photovoltaic distribution networks usually adopt traditional voltage regulation methods, mainly including static var compensators, on-load tap changers, and capacitor banks. These traditional voltage regulation methods can solve the voltage stability problem to a certain extent. However, their response speed is usually slow, so they may perform poorly when dealing with rapidly changing photovoltaic power generation. Photovoltaic inverters can not only improve the flexibility and accuracy of voltage regulation, but also reduce equipment costs and operation and maintenance costs. Therefore, coordinating photovoltaic reactive power compensation is an effective means to solve voltage problems. However, the reactive power voltage control effect is limited by the apparent power and active power output. When the photovoltaic output reaches the peak, the inverter lacks reactive power regulation ability and cannot effectively regulate the voltage of the distribution network. This problem is particularly prominent in low-voltage distribution networks with large impedances. Summary of the Invention

[0004] In view of this, it is necessary to provide a two-stage voltage control method for a distribution network with distributed optical storage cooperation to solve the above defects of the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a two-stage voltage control method for a distribution network with distributed optical storage cooperation, including:

[0006] Step S1, obtaining distribution network data including distributed photovoltaic and distributed energy storage;

[0007] Step S2, linearly processing the power flow model of the distribution network according to the distribution network data to obtain a linear power flow model of the distribution network; considering the voltage control of the optical storage node equipped with a photovoltaic inverter, determining the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter;

[0008] Step S3: Based on the relationship between the voltage of the photovoltaic and energy storage node and the reactive power of the photovoltaic inverter, with the minimum deviation of the voltage of the photovoltaic and energy storage node as the optimization objective, determine the objective function and constraints for the distribution network voltage optimization, and establish a distribution network voltage optimization control model; the constraints at least include the distribution network power flow constraint, the reactive power output constraint of distributed photovoltaics, and the voltage constraint of the photovoltaic and energy storage node;

[0009] Step S4: Based on the accelerated dual ascent algorithm, solve the distribution network voltage optimization control model to obtain the reactive power output of the photovoltaic inverter, and send the reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the photovoltaic and energy storage node through the reactive power generated by the photovoltaic inverter;

[0010] Step S5: Determine whether the voltage of the photovoltaic and energy storage node after the adjustment of the photovoltaic inverter exceeds the preset voltage threshold. If so, add the active power constraint of the distributed energy storage to the distribution network voltage optimization control model, and construct the Lagrangian function of the adjusted distribution network voltage optimization control model;

[0011] Step S6: Update the Lagrange multiplier using the voltages of the photovoltaic and energy storage nodes and the photovoltaic reactive power at the previous two moments, communicate and exchange the Lagrange multiplier information with adjacent photovoltaic and energy storage nodes to obtain the active power of the distributed energy storage at the current moment, and send the active power scheduling instruction to the distributed energy storage controller.

[0012] Preferably, the distribution network data includes the voltage measurement data and reactive power data of the photovoltaic and energy storage node, the upper and lower limits of the reactive power of the photovoltaic inverter, the upper and lower limits of the active power of the distributed energy storage, and the upper and lower voltage constraints of the photovoltaic and energy storage node; among them, the photovoltaic and energy storage node refers to the distribution network node containing photovoltaics and energy storage.

[0013] Preferably, in step S2, the expression of the distribution network power flow model is:

[0014]

[0015] In the formula, P ij and Q ij represent the active and reactive powers flowing from node i to node j, p j and q j are the active and reactive powers output by node j, V i is the voltage value of node i, r ij and x ij are the line resistance and line reactance between node i and node j.

[0016] In step S2, the linearization process of the distribution network power flow model to obtain the linear power flow model of the distribution network includes:

[0017] Ignoring the active and reactive power losses and assuming that the voltages of each photovoltaic-storage node are close to the reference voltage, the power flow model of the distribution network is linearized as follows:

[0018]

[0019] V i -V j =r ij P ij +x ij Q ij (6)

[0020] The linear power flow model of the distribution network can be expressed in the following form:

[0021] v=1+Rp+Xq (7)

[0022] where v represents the column vector composed of node voltages, p represents the column vector composed of node active power injections, q represents the column vector composed of node reactive power injections, R is the resistance matrix, and X

[0023] is the reactance matrix;

[0024] In the above formula, 1 is the unit column vector, and R and X are expressed in the following form:

[0025]

[0026] In the above formula, L i(j) represents the path from node 0 to node i(j); each element of R and X is only related to the network topology and line impedance.

[0027] In step S2, considering the voltage control of the photovoltaic-storage nodes equipped with photovoltaic inverters, the relationship between the voltage of the photovoltaic-storage nodes and the reactive power of the photovoltaic inverters is determined, including:

[0028] Assuming that only the voltage of the photovoltaic-storage nodes is measured through the photovoltaic inverters and the reactive power injected into the distribution network is adjusted, the voltage control of the photovoltaic-storage nodes equipped with photovoltaic inverters is considered; to distinguish the voltages of the photovoltaic-storage nodes and the load nodes, the node voltage amplitude vector is decomposed as follows:

[0029]

[0030] In the above formula, v G is the voltage amplitude of the photovoltaic-storage nodes, and v L is the voltage amplitude of the load nodes;

[0031] The resistance matrix R and the reactance matrix X are decomposed as follows:

[0032]

[0033] Substitute equations (10), (11), and (12) into equation (7) to obtain the voltage amplitude of the photovoltaic-storage node:

[0034] v G = 1 + R GG (p PV + p BESS ) + R GL p L + X GG q PV + X GL q L (13)

[0035] In the above formula, p PV , q PV represent the active power and reactive power injected by the photovoltaic inverter into the distribution network, p BESS represents the active power injected by the distributed energy storage into the distribution network, p L , q L represent the active power and reactive power injected by the load; R GG is the sensitivity matrix of the voltage of the photovoltaic-storage node to the active power fluctuation of the photovoltaic, R GL is the sensitivity matrix of the voltage of the photovoltaic-storage node to the active power fluctuation of the load,

[0036] X GG is the sensitivity matrix of the voltage of the photovoltaic-storage node to the reactive power fluctuation of the photovoltaic, X GL is the sensitivity matrix of the voltage of the photovoltaic-storage node to the reactive power fluctuation of the load;

[0037] Assume that p G , p L , q L are constants, and the voltage of the photovoltaic-storage node is adjusted only by the reactive power generated by the photovoltaic inverter. Then, equation (13) is simplified to obtain the relationship between the voltage of the photovoltaic-storage node and the reactive power of the photovoltaic inverter as:

[0038]

[0039] In the formula, is the voltage curve of the photovoltaic-storage node without recovery power compensation.

[0040] Preferably, in step S3, based on the relationship between the voltage of the photovoltaic-storage node and the reactive power of the photovoltaic inverter, with the minimum voltage deviation of the photovoltaic-storage node as the optimization goal, determine the objective function and constraint conditions for the distribution network voltage optimization, and establish a distribution network voltage optimization control model. Specifically

[0041] include:

[0042] Based on equation (14), establish the objective function of the distribution network voltage optimization model as:

[0043]

[0044] In the formula, minF represents the optimization objective of minimizing the deviation of the voltage of the photovoltaic and energy storage node from the rated voltage value;

[0045] The voltage constraint of the photovoltaic and energy storage node is:

[0046] v min ≤v G ≤v max (17)

[0047] In the above formula, v min and v max are the upper and lower limits of the voltage of the node where the distributed photovoltaic is located, respectively;

[0048] The reactive power constraint of the distributed photovoltaic is:

[0049] q min ≤q PV ≤q max (18)

[0050] In the formula, and are the upper and lower limits of the reactive power output of the photovoltaic inverter, respectively; the photovoltaic inverter provides a limited apparent power, and the reactive power limit of the photovoltaic inverter depends on the active power of the photovoltaic inverter that changes with time:

[0051]

[0052] In the formula, S i 2 represents the apparent power of the photovoltaic inverter at node i, represents the active power of the photovoltaic inverter at node i at time t;

[0053] Equations (16), (17) and (18) constitute the distribution network voltage optimization control model.

[0054] Preferably, in step S4, solving the distribution network voltage optimization control model based on the accelerated dual ascent algorithm to obtain the reactive power output of the photovoltaic inverter includes:

[0055] Constructing the Lagrangian function based on the distribution network voltage optimization control model as follows:

[0056]

[0057] In the above formula, L is the Lagrangian function of the distribution network voltage optimization control model, and the vector λ 、 μ 、 is related to the inequality constraint q min≤q PV 、q PV ≤q max 、v min ≤v G 、v G ≤v max The relevant Lagrange multipliers. Define the vector a as containing all the Lagrange multipliers, The superscript T represents the transpose of the vector.

[0058] Solve for the optimal control variables of the Lagrangian function:

[0059]

[0060] Substitute q in Equation (22) PV into Equation (16) to obtain the Lagrangian dual function:

[0061]

[0062] where, represents the Lagrangian dual function;

[0063] In the above equation, B is the coefficient matrix and b is the coefficient vector, and the expressions are:

[0064]

[0065]

[0066] where, represents the transpose of the n - order identity matrix;

[0067] Use the accelerated projected gradient method to update the dual variables:

[0068]

[0069] where, a t+1 is the dual variable at time t + 1, is the accelerated variable obtained by the linear combination of the dual variables at the two most recent times, α is the step size of the accelerated projected gradient method, is the derivative of the function g(c t+1 ) with respect to the vector c t+1 , [] + represents projection onto the feasible region with non - negative constraints;

[0070] c t+1 = a t + β t+1 (a t - a t-1 ) (27)

[0071] In the above equation, β t+1The expression is:

[0072]

[0073] In the above formula, θ t+1 The iterative expression is:

[0074]

[0075] In the formula, β t and β t+1 are the auxiliary variables β at times t and t + 1 respectively, and θ t and θ t+1 are the auxiliary variables θ at times t and t + 1 respectively;

[0076] The local update form of the Lagrangian operator is expressed as follows:

[0077]

[0078]

[0079] In the formula, λ i,t+1 、 λ i,t and λ i,t-1 are the Lagrangian operators of node i at times t + 1, t, and t - 1 respectively, λ , q i,t and q i,t-1 are the PV reactive powers at times t and t - 1 respectively, and are the Lagrangian operators of node i at times t + 1, t, and t - 1 respectively, μ i,t+1 、 μ i,t and μ i,t-1 are the Lagrangian operators of node i at times t + 1, t, and t - 1 respectively, μ , v i,t and v i,t-1 are the voltages of the PV - storage nodes at times t and t - 1 respectively, and are the Lagrangian operators of node i at times t + 1, t, and t - 1 respectively, q i,min is the maximum reactive power absorption of PV inverter i, q i,max is the maximum reactive power injection of PV inverter i, v i,min is the upper limit of the node voltage of PV inverter i, v i,min is the lower limit of the node voltage of PV inverter i;

[0080] Matrix is a sparse matrix, and the value of each element is only related to the reactance between nodes i and j; calculate the reactive power output injected by the photovoltaic inverter into the distribution network:

[0081]

[0082] In the formula, q i，t+1 represents the reactive power injected by the photovoltaic inverter into the optical storage node i at the moment t + 1, is a matrix only related to nodes i and j element, N(i) is the set of nodes where the adjacent inverters of the photovoltaic inverter i are located, is the voltage value of node j before control, λ k，t+1 is the Lagrange operator of node k at the moment t + 1 λ , is the Lagrange operator of node k at the moment t + 1 μ j,t+1 is the Lagrange operator of node j at the moment t + 1 μ , is the Lagrange operator of node j at the moment t + 1.

[0083] Preferably, in step S5, add the active power constraint of the distributed energy storage to the distribution network voltage optimization control model, and construct the Lagrangian function of the adjusted distribution network voltage optimization control model, including:

[0084] The active power constraint of the distributed energy storage is as follows:

[0085] p min ≤ p BESS ≤ p max (35)

[0086] In the above formula, and are respectively the upper limit and the lower limit of the active power output of the energy storage, considering the energy storage capacity and the active power limit, the upper limit and the lower limit of the active power output of the distributed energy storage are as follows: p BESS represents the active power injected by the distributed energy storage into the distribution network;

[0087]

[0088] In the formula, represents the lower limit of the active power output of the energy storage at node i at the moment t, represents the upper limit of the active power output of the energy storage at node i at the moment t, and are the maximum charging power and maximum discharging power of the energy storage at node i, respectively. and represent the minimum and maximum charging level constraints of the energy storage at node i, respectively. and represent the charge and discharge coefficients of the energy storage at node i; the storage capacity of the energy storage at node i at time t is expressed as:

[0089]

[0090] The objective function in Equation (16) and the constraint conditions in Equations (17), (18), and (35) constitute the adjusted distribution network voltage optimization control model.

[0091] Based on the adjusted distribution network voltage optimization control model, the Lagrangian function is constructed as follows:

[0092]

[0093] where L’ is the Lagrangian function of the adjusted distribution network voltage optimization control model, and the vectors and are the Lagrange multipliers related to the inequality constraints p min ≤ p BESS , p BESS ≤ p max respectively.

[0094] From it can be obtained that:

[0095]

[0096] where represents the reciprocal of L’ with respect to the active power p BESS of the distributed energy storage.

[0097] Preferably, in step S6, the Lagrange multiplier is updated by using the voltages of the photovoltaic-storage nodes and the photovoltaic reactive power at the previous two moments, and the Lagrange multiplier information is communicated and exchanged with adjacent photovoltaic-storage nodes to obtain the active power of the distributed energy storage at the current moment, including:

[0098] The photovoltaic-storage node i can update the active power of the distributed energy storage by communicating with the adjacent photovoltaic-storage node j:

[0099]

[0100] where p i，t+1 represents the active power of the distributed energy storage of the photovoltaic-storage node i at time t + 1, is the element in the i-th row and j-th column of the matrix and is a matrix the element in the j-th row and k-th column, q i,t+1 is the reactive power injected by the photovoltaic inverter into the optical storage node i at the moment of t + 1, and are respectively the lower limit and upper limit of the active power of the distributed energy storage at node i at the moment of t + 1.

[0101] In a second aspect, an embodiment of the present invention provides a two-stage voltage control device for a distribution network with distributed optical storage collaboration, and the device includes:

[0102] An acquisition module, configured to acquire distribution network data including distributed photovoltaic and distributed energy storage;

[0103] A processing module, configured to linearly process the power flow model of the distribution network according to the distribution network data to obtain a linear power flow model of the distribution network; considering the voltage control of the optical storage node equipped with a photovoltaic inverter, determining the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter;

[0104] A model establishment module, configured to determine the objective function and constraint conditions for optimizing the distribution network voltage based on the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter, with the minimum voltage deviation of the optical storage node as the optimization target, and establish a distribution network voltage optimization control model; the constraint conditions at least include distribution network power flow constraints, distributed photovoltaic reactive power output constraints, and optical energy storage node voltage constraints;

[0105] A reactive voltage control module, configured to solve the distribution network voltage optimization control model based on the accelerated dual ascent algorithm to obtain the reactive power output of the photovoltaic inverter, and send a reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the optical storage node through the reactive power generated by the photovoltaic inverter;

[0106] An optical storage collaboration module, configured to determine whether the voltage of the optical storage node after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold. If so, add a distributed energy storage active power constraint to the distribution network voltage optimization control model, and construct a Lagrangian function of the adjusted distribution network voltage optimization control model;

[0107] An active voltage control module, configured to update the Lagrangian multiplier using the voltages of the optical storage nodes and the photovoltaic reactive power in the previous two moments, communicate and exchange Lagrangian multiplier information with adjacent optical storage nodes to obtain the active power of the distributed energy storage at the current moment, and send an active power scheduling instruction to the distributed energy storage controller.

[0108] In a third aspect, the present invention further provides an electronic device, including a memory and a processor, wherein,

[0109] The memory is used to store programs;

[0110] The processor, which is coupled to the memory, is configured to execute the program stored in the memory to implement the steps in the two-stage voltage control method for a distribution network with distributed optical storage collaboration as described in the embodiments of the first aspect of the present invention.

[0111] In a fourth aspect, the present invention further provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the two-stage voltage control method for a distribution network with distributed optical storage collaboration as described in the embodiments of the first aspect of the present invention.

[0112] The beneficial effects of adopting the above embodiments are as follows:

[0113] The two-stage voltage control method and device for a distribution network with distributed optical storage collaboration provided by the present invention acquire the data of a distribution network with distributed photovoltaic and distributed energy storage, linearly process the power flow model of the distribution network according to the distribution network data to obtain a linear power flow model of the distribution network. In the first stage of distribution network voltage control, with the minimum voltage deviation of the optical storage node as the optimization objective, the objective function and constraint conditions for distribution network voltage optimization are determined, and a distribution network voltage optimization control model is established; based on the accelerated dual ascent algorithm, the distribution network voltage optimization control model is solved to obtain the reactive power output of the photovoltaic inverter. Then, when it is judged that the voltage of the optical storage node after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold, the second stage of distribution network voltage control is entered, the distribution network voltage optimization control model is adjusted, after adding the upper and lower limits of the active power of the distributed energy storage to the model, the Lagrangian function of the adjusted distribution network voltage optimization control model is constructed, and the Lagrange multiplier information is communicated and exchanged with adjacent optical storage nodes to obtain the active power of the distributed energy storage at the current moment. The present invention coordinates energy storage and photovoltaic in a distributed manner to ensure that the voltage of the distribution network is maintained within a safe range, and at the same time effectively reduces the total energy storage capacity, making the operation of the distribution network more economical and efficient. The present invention starts the voltage control based on the active power of the distributed energy storage when the reactive power of the photovoltaic device is insufficient, which can timely make up for the insufficient reactive power regulation ability in the distribution network and maintain the voltage stability of the distribution network. Description of the Drawings

[0114] Figure 1 It is a flowchart of the two-stage voltage control method for a distribution network with distributed optical storage collaboration provided by the embodiments of the present invention;

[0115] Figure 2 It is a distribution network model with distributed photovoltaic and distributed energy storage provided by the embodiments of the present invention;

[0116] Figure 3 It is a topology diagram of a 123-node IEEE distribution network with distributed photovoltaic and distributed energy storage provided by the embodiments of the present invention;

[0117] Figure 4 Schematic diagram of the accelerated dual ascent algorithm provided by an embodiment of the present invention;

[0118] Figure 5 Photovoltaic active power curve diagram provided by an embodiment of the present invention;

[0119] Figure 6 Load active power curve diagram provided by an embodiment of the present invention;

[0120] Figure 7 Curve diagram of the voltage change of the energy storage node without control provided by an embodiment of the present invention;

[0121] Figure 8 Curve diagram of the voltage change of the energy storage node considering only photovoltaic reactive power control provided by an embodiment of the present invention;

[0122] Figure 9 Curve diagram of the photovoltaic reactive power change considering only photovoltaic reactive power control provided by an embodiment of the present invention;

[0123] Figure 10 Curve diagram of the voltage change of the energy storage node under the coordinated control of photovoltaic and energy storage provided by an embodiment of the present invention;

[0124] Figure 11 Curve diagram of the active power change of the energy storage under the coordinated control of photovoltaic and energy storage provided by an embodiment of the present invention;

[0125] Figure 12 Curve diagram of the energy change of the energy storage under the coordinated control of photovoltaic and energy storage provided by an embodiment of the present invention;

[0126] Figure 13 Structure block diagram of the two-stage voltage control device for a distribution network with coordinated distributed photovoltaic and energy storage provided by an embodiment of the present invention;

[0127] Figure 14 Structure schematic diagram of the electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0128] The following will specifically describe the preferred embodiments of the present invention with reference to the accompanying drawings, where the accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principle of the present invention, rather than to limit the scope of the present invention.

[0129] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment of this application. The phrase appears at various positions in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0130] In recent years, photovoltaic power generation has been one of the most promising renewable energy sources. Due to the intermittency and uncertainty of photovoltaics, the voltage fluctuations caused by photovoltaic generators are often greater. At the same time, a major obstacle to the large-scale integration of photovoltaic power generation into the existing medium / low-voltage power grid is the induced voltage rise caused by the reverse power flow in the distribution feeder. This phenomenon is bound to intensify under the high penetration rate of photovoltaic power generation.

[0131] In view of this, the embodiments of the present invention provide a two-stage voltage control method for a distribution network with distributed photovoltaic and energy storage coordination. In the present invention, energy storage and photovoltaics are coordinated in a distributed manner to ensure that the voltage of the distribution network remains within a safe range, while effectively reducing the total energy storage capacity, making the operation of the distribution network more economical and efficient. The following will be described and introduced through multiple embodiments.

[0132] Figure 1 It is a flowchart of the two-stage voltage control method for a distribution network with distributed photovoltaic and energy storage coordination provided by the present invention. As Figure 1 shown, the two-stage voltage control method for a distribution network with distributed photovoltaic and energy storage coordination includes:

[0133] Step S1, obtaining the distribution network data including distributed photovoltaic and distributed energy storage.

[0134] Specifically, the distribution network data obtained in this embodiment at least includes: voltage measurement data and reactive power data of the energy storage and photovoltaic nodes, upper and lower limits of the reactive power of the photovoltaic inverter, upper and lower limits of the active power of the distributed energy storage, and upper and lower voltage constraints of the energy storage and photovoltaic nodes. Among them, the energy storage and photovoltaic nodes refer to the distribution network nodes containing photovoltaics and energy storage.

[0135] Step S2, linearly processing the power flow model of the distribution network according to the distribution network data to obtain a linear power flow model of the distribution network; considering the voltage control of the energy storage and photovoltaic nodes equipped with photovoltaic inverters, determining the relationship between the voltage of the energy storage and photovoltaic nodes and the reactive power of the photovoltaic inverter.

[0136] Figure 2 It is a distribution network model including distributed photovoltaic and distributed energy storage provided by the present invention, Figure 3 It is a topology diagram of the 123-node IEEE distribution network including distributed photovoltaic and distributed energy storage in the embodiments of the present invention. Referring to Figure 2 and Figure 3 , a radial distribution network can be simulated by a tree diagram T = {N, E}. N = {0, 1,..., n} represents the node set. The index of the substation node is n = 0, which is a balanced node. We model the remaining nodes as PQ nodes, mainly including energy storage and photovoltaic nodes and load nodes. The PQ nodes output the given active power and reactive power and solve the voltage amplitude of the nodes. P = {1, 2,..., n} represents the PQ nodes. E = {e j{(i,j)|i = p(j), j ∈ N} represents the distribution line, where p(j) represents the parent node of node j j, that is, the node immediately preceding node j. For each node, let v i be its voltage magnitude, p i be its active power, and q i be its reactive power. For each distribution line segment (i,j) ∈ E, let r ij and x ij be its resistance and reactance, and P ij and Q ij be the power flow from sending node i. C(j) is the set of child nodes of node j, that is, the set of nodes directly following node j in the distribution network.

[0137] In step S2, the expression of the distribution network power flow model is:

[0138]

[0139] where P ij , Q ij represent the active and reactive power flowing from node i to node j, p j , q j are the active and reactive power output by node j, V i is the voltage value of node i, r ij , x ij are the line resistance and line reactance between node i and node j. N j represents the set of adjacent nodes of node j, k represents the adjacent node k of node j, and P jk and Q jk represent the active and reactive power flowing from node j to its adjacent node k.

[0140] In step S2, the power flow model of the distribution network is linearized to obtain the linear power flow model of the distribution network, including:

[0141] In this embodiment, for the convenience of analysis, the active and reactive line losses are ignored, and it is assumed that the voltages of each energy storage node are close to the reference voltage and the angle difference between adjacent energy storage nodes is very small. Then, the power flow model of the distribution network is linearized as follows:

[0142]

[0143] V i - V j = r ij P ij + x ij Q ij (6)

[0144] The linear power flow model of the distribution network can be expressed in the following form:

[0145] v = 1 + Rp + Xq (7)

[0146] Wherein, v represents the column vector composed of node voltages, p represents the column vector composed of node active power injections, q represents the column vector composed of node reactive power injections, R is the resistance matrix, and X is the reactance matrix;

[0147] In the above formula, 1 is the unit column vector, and R and X are expressed in the following forms:

[0148]

[0149] In the above formula, L i(j) represents the path from node 0 to node i(j); each element of R and X is only related to the network topology and line impedance.

[0150] In step S2, considering the voltage control of the energy storage node equipped with a photovoltaic inverter, determine the relationship between the voltage of the energy storage node and the reactive power of the photovoltaic inverter, including:

[0151] In this embodiment, it is assumed that only the energy storage node has the capabilities of measurement, calculation, and communication. The photovoltaic energy storage system obtains the operating state through internal real-time monitoring and data acquisition technology. Adjacent energy storage nodes can communicate with each other and share the iterative values of their respective current interaction variables to achieve the optimal scheduling of the entire system. We define the set of energy storage nodes as M (where |M| = m), and the set of load nodes as U (where |U| = n - m). Assuming that only the voltage of the energy storage node is measured by the photovoltaic inverter and the reactive power injected into the distribution network is adjusted, consider the voltage control of the energy storage node equipped with a photovoltaic inverter; in order to distinguish the voltage of the energy storage node from the voltage of the load node, the node voltage amplitude vector is decomposed as follows:

[0152]

[0153] In the above formula, v G is the voltage amplitude of the energy storage node, and v L is the voltage amplitude of the load node; the resistance matrix R and the reactance matrix X are decomposed as follows:

[0154]

[0155]

[0156] Substitute equations (10), (11), and (12) into equation (7) to obtain the voltage amplitude of the energy storage node:

[0157] v G = 1 + R GG (p PV + p BESS ) + RGL p L +X GG q PV +X GL q L (13)

[0158] In the above formula, p PV and q PV represent the active power and reactive power injected by the photovoltaic inverter into the distribution network, p BESS represents the active power injected by the energy storage into the distribution network, p L and q L represent the active power and reactive power injected by the load; R GG is the sensitivity matrix of the energy storage node voltage to the active power fluctuation of the photovoltaic, R GL is the sensitivity matrix of the energy storage node voltage to the active power fluctuation of the load, X GG is the sensitivity matrix of the energy storage node voltage to the reactive power fluctuation of the photovoltaic, X GL is the sensitivity matrix of the energy storage node voltage to the reactive power fluctuation of the load; V0 is the voltage amplitude of the root node (the first node of the distribution network), R LG and X LG are the resistance and reactance sub-matrices from the load node to the energy storage node, R LL

[0159] and X LL are the resistance and reactance sub-matrices between the load nodes.

[0160] Assume that p G , p L , q L are constants, and the voltage of the energy storage node is regulated only by the reactive power generated by the photovoltaic inverter. Then, Equation (13) is simplified to obtain the relationship between the voltage of the energy storage node and the reactive power of the photovoltaic inverter as:

[0161]

[0162] In the formula, is the voltage curve of the energy storage node without recovery power compensation.

[0163] Step S3, based on the relationship between the voltage of the energy storage node and the reactive power of the photovoltaic inverter, with the minimum voltage deviation of the energy storage node as the optimization goal, determine the objective function and constraint conditions for the voltage optimization of the distribution network, and establish a voltage optimization control model for the distribution network; the constraint conditions at least include the distribution network power flow constraint, the distributed photovoltaic reactive power output constraint, and the energy storage node voltage constraint.

[0164] Specifically, after determining the relationship between the voltage of the energy storage node and the reactive power of the photovoltaic inverter, based on Equation (14), the objective function for establishing the voltage optimization model of the distribution network is:

[0165]

[0166] In the formula, minF represents the optimization goal of minimizing the deviation of the voltage of the photovoltaic-storage node relative to the rated voltage value;

[0167] The voltage constraint of the photovoltaic-storage node is:

[0168] v min ≤v G ≤v max (17)

[0169] In the above formula, v min and v max are respectively the upper limit and the lower limit of the voltage of the node where the distributed photovoltaic is located;

[0170] The reactive power constraint of the distributed photovoltaic is:

[0171] q min ≤q PV ≤q max (18)

[0172] In the formula, and are respectively the upper limit and the lower limit of the reactive power output of the photovoltaic inverter; the photovoltaic inverter provides a limited apparent power, and the reactive power limit of the photovoltaic inverter depends on the active power of the photovoltaic inverter varying with time:

[0173]

[0174] In the formula, S i 2 represents the apparent power of the photovoltaic inverter at node i, represents the active power of the photovoltaic inverter at node i at time t;

[0175] Equations (16), (17) and (18) constitute the distribution network voltage optimization control model.

[0176] Step S4, based on the accelerated dual ascent algorithm, solve the distribution network voltage optimization control model to obtain the reactive power output of the photovoltaic inverter, and issue the reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the photovoltaic-storage node through the reactive power generated by the photovoltaic inverter.

[0177] Figure 4 This is the schematic diagram of the accelerated dual ascent algorithm provided by the embodiment of the present invention. Figure 4The photovoltaic energy storage system i and the adjacent energy storage system j in the present invention refer to the energy storage node i and its adjacent energy storage node j in the embodiments of the present invention. The accelerated dual ascent algorithm can be used to solve equations (16)-(18) and solve the quadratic programming problem with box constraints. This is a distributed online algorithm that provides a generalized solution to the constrained optimization problem. Specifically, we use the accelerated projected gradient algorithm to solve the dual problem and can obtain the optimal solution of the primal problem from the dual optimal solution. It has a faster convergence speed compared to the traditional dual ascent method and can be implemented distributively, thereby reducing the complexity of the communication network.

[0178] Based on the distribution network voltage optimization control model constituted by equations (16), (17) and (18), the Lagrangian function is constructed as follows:

[0179]

[0180] In the above formula, the vector λ 、 μ 、 are the Lagrange multipliers related to the inequality constraints q min ≤q PV 、q PV ≤q max 、v min ≤v G 、v G ≤v max Define the vector a as containing all the Lagrange multipliers, The superscript T represents the transpose of the vector.

[0181] Solve the optimal control variables of the Lagrangian function:

[0182]

[0183] Substitute q PV in equation (22) into equation (16) to obtain the Lagrangian dual function:

[0184]

[0185] In the formula, represents the Lagrangian dual function;

[0186] In the above formula, B is the coefficient matrix and b is the coefficient vector, and the expressions are:

[0187]

[0188] In the formula, represents the transpose of the n-order identity matrix;

[0189] Use the accelerated projected gradient method to update the dual variables:

[0190]

[0191] wherein, a t+1 is the dual variable at time t+1, is the acceleration variable obtained by the linear combination of the dual variables at the two closest times, α is the step size of the accelerated projection gradient method, is the derivative of the function g(c t+1 ) with respect to the vector c t+1 , and [] + represents the projection onto the feasible region with non-negative constraints;

[0192] c t+1 = a t + β t+1 (a t - a t-1 ) (27)

[0193] In the above formula, the expression of β t+1 is:

[0194]

[0195] In the above formula, the iterative expression of θ t+1 is:

[0196]

[0197] wherein, β t and β t+1 are the auxiliary variables β at times t and t+1 respectively, and θ t and θ t+1 are the auxiliary variables θ at times t and t+1 respectively;

[0198] The local update form of the Lagrangian operator is expressed as follows:

[0199]

[0200] wherein, λ i,t+1 , λ i,t and λ i,t-1 are the Lagrangian operators λ of node i at times t+1, t, and t-1 respectively, q i,t and q i,t-1 are the PV reactive powers of node i at times t and t-1 respectively, and are the Lagrangian operators μ i,t+1 of node i at times t+1, t, and t-1 respectively,μ i,t and μ i,t-1 are the Lagrange multipliers of node i at times t+1, t, and t-1 respectively μ , v i,t and v i,t-1 are the voltages of the PV and storage node at times t and t-1 respectively, and are the Lagrange multipliers of node i at times t+1, t, and t-1 respectively q i,min is the maximum reactive power absorption of PV inverter i, q i,max is the maximum reactive power injection of PV inverter i, v i,min is the upper limit of the node voltage of PV inverter i, v i,min is the lower limit of the node voltage of PV inverter i;

[0201] Since the matrix is a sparse matrix, the non-zero elements only appear in the neighbor nodes of node i. Moreover, the matrix non-zero elements only appear in the neighbor nodes of node i and the neighbor nodes of the neighbor nodes. Then the PV inverter updates its reactive power injection in a distributed manner; calculate the reactive power output injected by the PV inverter into the distribution network:

[0202]

[0203] where q i，t+1 is the PV reactive power of the PV and storage node i at time t+1. is a matrix related only to nodes i and j element, N(i) is the set of nodes where the adjacent inverters of PV inverter i are located, is the voltage value of node j before control, is the Lagrange multiplier of node k at time t+1 λ , is the Lagrange multiplier of node k at time t+1 μ j,t+1 is the Lagrange multiplier of node j at time t+1 μ , is the Lagrange multiplier of node j at time t+1.

[0204] In this embodiment, the Lagrange multipliers are updated using the voltages and PV reactive powers of the PV and storage node i at the previous two times, communicate with the adjacent PV and storage node j to exchange Lagrange multiplier information, and obtain the reactive power of the PV inverter at the current time. In the parameters of this article, t-1 and t represent the previous two times, and t+1 represents the current time.

[0205] In step S5, it is determined whether the voltage of the photovoltaic energy storage node after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold. If so, an active power constraint of the distributed energy storage is added to the distribution network voltage optimization control model, and the Lagrangian function of the adjusted distribution network voltage optimization control model is constructed.

[0206] It can be understood that the photovoltaic inverter can not only improve the flexibility and accuracy of voltage regulation, but also reduce the equipment cost and operation and maintenance cost. Therefore, coordinating photovoltaic reactive power compensation is an effective means to solve the voltage problem. However, the reactive power voltage control effect is limited by the apparent power and active output. When the photovoltaic output reaches the peak, the inverter lacks the reactive power regulation ability and cannot effectively regulate the distribution network voltage. This problem is particularly prominent in low-voltage distribution networks with large impedance. To ensure the stable operation of the power system, it is usually necessary to implement additional active power control. A common method is to use photovoltaic active power curtailment technology to provide regulation margin. In this case, the photovoltaic system may be restricted to operate at a power level lower than its maximum potential, resulting in potential energy losses. Since the energy storage can flexibly provide or absorb additional active power when needed, it can be used to smooth voltage fluctuations.

[0207] Specifically, in step S5, it is determined whether the voltage of the photovoltaic energy storage node after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold. If so, it means that the reactive power regulation ability of the photovoltaic inverter is insufficient and it cannot effectively regulate the distribution network voltage. Among them, the preset voltage threshold is ±5% of the rated voltage value.

[0208] In this embodiment, when the reactive power regulation ability of the photovoltaic inverter is insufficient, the voltage control based on the active power of the distributed energy storage is started to maintain the voltage stability of the distribution network.

[0209] The active power constraint of the distributed energy storage is as follows:

[0210] p min ≤p BESS ≤p max (35)

[0211] In the above formula, and are the upper and lower limits of the active power output of the energy storage respectively. Considering the energy storage capacity and active power limit, the upper and lower limits of the active power output of the distributed energy storage are as follows: p BESS represents the active power injected by the distributed energy storage into the distribution network;

[0212]

[0213] In the formula, represents the lower limit of the active power output of the energy storage at node i at time t, Denote the upper limit of the active power output of the energy storage at node \(i\) at time \(t\). and are respectively the maximum charging power and the maximum discharging power of the energy storage at node \(i\). and respectively represent the minimum and maximum charging level constraints of the energy storage at node \(i\). and respectively represent the charge-discharge coefficients of the energy storage at node \(i\).

[0214] The storage capacity of the energy storage at node \(i\) at time \(t\) is expressed as:

[0215]

[0216] The objective function in Equation (16) and the constraint conditions in Equations (17), (18), and (35) constitute the adjusted distribution network voltage optimization control model.

[0217] Based on the adjusted distribution network voltage optimization control model, the Lagrangian function is constructed as follows:

[0218]

[0219] In the formula, \(L'\) is the Lagrangian function of the adjusted distribution network voltage optimization control model, and the vectors and are the Lagrangian multipliers related to the inequality constraints \(p\) min ≤ \(p\) BESS , \(p\) BESS ≤ \(p\) max .

[0220] From it can be obtained that:

[0221]

[0222] In the formula, represents the reciprocal of \(L'\) with respect to the active power \(p\) BESS of the distributed energy storage.

[0223] Step S6: Update the Lagrangian multipliers using the voltages of the photovoltaic-storage nodes and the photovoltaic reactive power at the previous two moments, communicate and exchange the Lagrangian multiplier information with adjacent photovoltaic-storage nodes to obtain the active power of the distributed energy storage at the current moment, and issue the active power scheduling instruction to the distributed energy storage controller.

[0224] Specifically, after constructing the Lagrangian function formula (39) of the adjusted distribution network voltage optimization control model, refer to the accelerated dual ascent algorithm in formulas (22)-(33), use the photovoltaic storage node voltage and photovoltaic reactive power at the previous two moments to update the Lagrangian multiplier, communicate with adjacent photovoltaic storage nodes to exchange Lagrangian multiplier information, and obtain the active power of the distributed energy storage at the current moment.

[0225] Solar energy storage node i can update the active power of distributed energy storage by communicating with adjacent solar energy storage node j:

[0226]

[0227] In the formula, p i，t+1 represents the distributed energy storage active power of the photovoltaic storage node i at time t+1, For the matrix The element in row i and column j, For the matrix The element in row j and column k, q i,t+1 is the reactive power injected into the photovoltaic storage node i by the photovoltaic inverter at time t+1, and They are respectively the lower and upper limits of the active power of the distributed energy storage at node i at time t+1.

[0228] After obtaining the active power of the distributed energy storage at the current moment, the active power dispatching instruction is sent to the distributed energy storage controller to adjust the active power of the distributed energy storage. This embodiment starts the voltage control based on the active power of the distributed energy storage when the reactive power of the photovoltaic equipment is insufficient to compensate for the insufficient reactive power regulation capability in the distribution network and maintain the voltage stability of the distribution network.

[0229] It should be noted that, according to different communication control methods, the voltage regulation problem of photovoltaic energy storage systems can be divided into the following three types: centralized control, decentralized control and distributed control. Decentralized control adjusts the voltage according to local measurement results. Since each system operates independently, it can operate without network communication, thereby reducing implementation and operating costs. Centralized control can better optimize the operating state of the system, but it will increase the computational burden of the system. The embodiment of the present invention adopts a distributed control method, which has better flexibility, reliability and low cost. The distributed control method only needs to communicate with adjacent busbars in the distribution network, without the need for a complex communication network, and can provide effective technical support for the stable operation of the distribution network voltage.

[0230] The two-stage voltage control method for a distribution network with distributed photovoltaic and energy storage collaboration provided by the present invention obtains the data of the distribution network with distributed photovoltaic and distributed energy storage, linearizes the power flow model of the distribution network according to the distribution network data, and obtains the linear power flow model of the distribution network. In the first stage of the distribution network voltage control, with the minimum voltage deviation of the photovoltaic and energy storage nodes as the optimization goal, the objective function and constraints of the distribution network voltage optimization are determined, and a distribution network voltage optimization control model is established; based on the accelerated dual ascent algorithm, the distribution network voltage optimization control model is solved to obtain the reactive power output of the photovoltaic inverter. Then, when it is judged that the voltage of the photovoltaic and energy storage nodes after the adjustment of the photovoltaic inverter exceeds the preset voltage threshold, the second stage of the distribution network voltage control is entered, the distribution network voltage optimization control model is adjusted, the upper and lower limits of the active power of the distributed energy storage are added to the model, and the Lagrangian function of the adjusted distribution network voltage optimization control model is constructed. The Lagrangian multiplier information is communicated and exchanged with adjacent photovoltaic and energy storage nodes to obtain the active power of the distributed energy storage at the current moment. The present invention coordinates energy storage and photovoltaic in a distributed manner to ensure that the voltage of the distribution network is maintained within a safe range, and at the same time effectively reduces the total energy storage capacity, making the operation of the distribution network more economical and efficient. When the reactive power of the photovoltaic device is insufficient, the present invention starts the voltage control based on the active power of the distributed energy storage, which can timely make up for the insufficient reactive power regulation ability in the distribution network and maintain the voltage stability of the distribution network.

[0231] Figure 5 is the active power curve graph of the photovoltaic provided by the embodiment of the present invention; as Figure 5 shown, the IEEE 123 bus feeder with distributed photovoltaic and distributed energy storage is used to numerically evaluate the proposed two-stage voltage control method for a distribution network with distributed photovoltaic and energy storage collaboration. Among them, the rated voltage value is 4.16 kV, and the acceptable range is set to ±5% of the rated value; the upper and lower limits of the energy stored in the BESS (Battery Energy Storage System) are 0.8 MW and 0.06 MW respectively, and the maximum active power absorption or release capacity is 0.1 MW. In this embodiment, the BESS battery energy storage system refers to the distributed energy storage device of the distribution network photovoltaic and energy storage nodes. The rated capacity of the photovoltaic inverter is 7.35 kVA, and the rated output power is 7 kW.

[0232] Figure 6 is the active power curve graph of the load provided by the embodiment of the present invention; Figure 7 is the curve graph of the voltage change of the photovoltaic and energy storage nodes without control in the embodiment of the present invention. In this embodiment, the simulation of the voltage of the distribution network photovoltaic and energy storage nodes lasts for a whole day, and the time resolution is 15 minutes. Figure 8 is the curve graph of the voltage change of the photovoltaic and energy storage nodes when only considering the reactive power control of the photovoltaic in the embodiment of the present invention. Specifically, Figure 8The voltage curves of all buses without coordinated control of PV inverters and BESS are given. At noon, the PV power generation reaches its peak while the demand load is relatively low, resulting in overvoltage problems. Conversely, at night, when the PV power generation decreases and the demand load increases, undervoltage problems occur in the distribution network. Therefore, it is necessary to coordinate the PV reactive power and the BESS active power to alleviate the node voltage violation problems.

[0233] Figure 9 This is the PV reactive power change curve diagram considering only PV reactive power control provided by the embodiment of the present invention; as Figure 9 shown, only using the reactive power control of PV cannot meet the voltage standard. This is because of the reactive power of the PV inverter. The reactive power output of the PV inverter at different nodes.

[0234] Figure 10 This is the voltage change curve diagram of the PV-BESS coordinated control provided by the embodiment of the present invention. As Figure 11 shown, the strategy proposed by the present invention is that the coordinated use of the active power of distributed energy storage and the reactive power of PV inverters can effectively alleviate the voltage fluctuations in the distribution network.

[0235] Figure 11 This is the active power change curve diagram of energy storage during PV-BESS coordinated control provided by the embodiment of the present invention. Figure 12 This is the energy change curve diagram of energy storage during PV-BESS coordinated control provided by the embodiment of the present invention. The active power output and energy change of energy storage at different nodes are as Figure 11 and 12 shown. Since the strategy proposed by the present invention is to start the voltage control based on the active power of distributed energy storage only when the reactive power of PV devices is insufficient, it can timely make up for the insufficient reactive power regulation ability in the distribution network and maintain voltage stability.

[0236] Figure 13 This is the structural block diagram of the two-stage voltage control device for a distribution network with distributed PV-BESS coordination provided by the present invention. Referring to Figure 13 , the two-stage voltage control device 1300 for a distribution network with distributed PV-BESS coordination includes:

[0237] An acquisition module 1301, configured to acquire the data of a distribution network with distributed PV and distributed energy storage;

[0238] A processing module 1302, configured to linearize the power flow model of the distribution network according to the distribution network data to obtain a linear power flow model of the distribution network; considering the voltage control of the PV-BESS nodes equipped with PV inverters, determining the relationship between the voltage of the PV-BESS nodes and the reactive power of the PV inverters;

[0239] A model establishment module 1303 is configured to determine an objective function and constraint conditions for optimizing the distribution network voltage with the minimum voltage deviation of the optical storage node as the optimization objective based on the relationship between the optical storage node voltage and the reactive power of the photovoltaic inverter, and establish a distribution network voltage optimization control model; the constraint conditions at least include distribution network power flow constraints, distributed photovoltaic reactive power output constraints, and optical storage node voltage constraints;

[0240] A reactive voltage control module 1304 is configured to solve the distribution network voltage optimization control model based on the accelerated dual ascent algorithm to obtain the reactive power output of the photovoltaic inverter, and send a reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the optical storage node through the reactive power generated by the photovoltaic inverter;

[0241] An optical storage cooperation module 1305 is configured to determine whether the voltage of the optical storage node after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold. If so, an active power constraint of the distributed energy storage is added to the distribution network voltage optimization control model, and a Lagrangian function of the adjusted distribution network voltage optimization control model is constructed;

[0242] An active voltage control module 1306 is configured to update the Lagrange multiplier by using the voltages of the optical storage nodes and the photovoltaic reactive power at the previous two moments, communicate and exchange Lagrange multiplier information with adjacent optical storage nodes to obtain the active power of the distributed energy storage at the current moment, and send an active power scheduling instruction to the distributed energy storage controller.

[0243] The two-stage voltage control device for a distribution network with distributed optical storage cooperation provided by the present invention realizes the two-stage voltage control of the distribution network with optical storage cooperation by using the above-mentioned modules. Since the method for the two-stage voltage control of the distribution network with distributed optical storage cooperation has been described in detail in the above method embodiments, it will not be elaborated here in this embodiment.

[0244] Figure 14 It is a structural block diagram of an electronic device provided by the present invention. As Figure 14 shown, the present invention further provides an electronic device. The electronic device 1400 may be a computing device such as a mobile terminal, a desktop computer, a notebook, a handheld computer, and a server. The electronic device 1400 includes a processor 1401 and a memory 1402, wherein a two-stage voltage control program 1403 for a distribution network with distributed optical storage cooperation is stored on the memory 1402.

[0245] The memory 1402 may be an internal storage unit of a computer device in some embodiments, such as the hard disk or memory of the computer device. The memory 1402 may also be an external storage device of the computer device in other embodiments, such as a plug-in hard disk equipped on the computer device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 1402 may also include both the internal storage unit and the external storage device of the computer device. The memory 1402 is used to store application software installed on the computer device and various types of data, such as program codes installed on the computer device. The memory 1402 may also be used to temporarily store data that has been output or will be output. In one embodiment, when the distributed optical storage collaborative two-stage voltage control program 1403 of the distribution network is executed by the processor 1401, the following steps are implemented:

[0246] Step S1, obtaining distribution network data including distributed photovoltaic and distributed energy storage;

[0247] Step S2, linearizing the power flow model of the distribution network according to the distribution network data to obtain a linear power flow model of the distribution network; considering the voltage control of the optical storage node equipped with a photovoltaic inverter, determining the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter;

[0248] Step S3, based on the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter, taking the minimum voltage deviation of the optical storage node as the optimization goal, determining the objective function and constraint conditions for the voltage optimization of the distribution network, and establishing a voltage optimization control model for the distribution network; the constraint conditions at least include distribution network power flow constraints, distributed photovoltaic reactive power output constraints, and optical energy storage node voltage constraints;

[0249] Step S4, based on the accelerated dual ascent algorithm, solving the voltage optimization control model of the distribution network to obtain the reactive power output of the photovoltaic inverter, and sending a reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the optical storage node through the reactive power generated by the photovoltaic inverter;

[0250] Step S5, determining whether the voltage of the optical storage node after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold. If so, adding a distributed energy storage active power constraint to the voltage optimization control model of the distribution network, and constructing a Lagrangian function of the adjusted voltage optimization control model of the distribution network;

[0251] Step S6: Update the Lagrange multiplier using the optical storage node voltages and photovoltaic reactive powers at the previous two moments, communicate and exchange Lagrange multiplier information with adjacent optical storage nodes to obtain the active power of the distributed energy storage at the current moment, and issue the active power scheduling instruction to the distributed energy storage controller.

[0252] In some embodiments, the processor 1401 may be a central processing unit (CPU), a microprocessor, or other data processing chips, which are used to run the program code stored in the memory 1402 or process data, such as executing the two-stage voltage control program for the distribution network with distributed optical storage collaboration.

[0253] This embodiment also provides a computer-readable storage medium, on which a two-stage voltage control program for the distribution network with distributed optical storage collaboration is stored. When the two-stage voltage control program for the distribution network with distributed optical storage collaboration is executed by the processor, the following steps are implemented:

[0254] Step S1: Obtain the distribution network data including distributed photovoltaic and distributed energy storage.

[0255] Step S2: Linearize the power flow model of the distribution network according to the distribution network data to obtain the linear power flow model of the distribution network; consider the voltage control of the optical storage nodes equipped with photovoltaic inverters, and determine the relationship between the optical storage node voltage and the reactive power of the photovoltaic inverter.

[0256] Step S3: Based on the relationship between the optical storage node voltage and the reactive power of the photovoltaic inverter, with the minimum optical storage node voltage deviation as the optimization goal, determine the objective function and constraints of the distribution network voltage optimization, and establish a distribution network voltage optimization control model; the constraints at least include the distribution network power flow constraint, the distributed photovoltaic reactive power output constraint, and the optical energy storage node voltage constraint.

[0257] Step S4: Based on the accelerated dual ascent algorithm, solve the distribution network voltage optimization control model to obtain the reactive power output of the photovoltaic inverter, and issue the reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the optical storage node through the reactive power generated by the photovoltaic inverter.

[0258] Step S5: Determine whether the optical storage node voltage after the adjustment of the photovoltaic inverter exceeds the preset voltage threshold. If so, add the distributed energy storage active power constraint to the distribution network voltage optimization control model, and construct the Lagrange function of the adjusted distribution network voltage optimization control model.

[0259] Step S6: Update the Lagrange multiplier using the optical storage node voltages and photovoltaic reactive powers at the previous two moments, communicate and exchange the Lagrange multiplier information with adjacent optical storage nodes to obtain the active power of the distributed energy storage at the current moment, and issue the active power scheduling instruction to the distributed energy storage controller.

[0260] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

[0261] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A two-stage voltage control method for a distribution network with distributed optical storage collaboration, characterized in that Including: Step S1: Obtain the distribution network data containing distributed photovoltaic and distributed energy storage. Step S2: Linearly process the power flow model of the distribution network according to the distribution network data to obtain the linear power flow model of the distribution network; considering the voltage control of the optical storage node equipped with a photovoltaic inverter, determine the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter. Step S3: Based on the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter, with the minimum voltage deviation of the optical storage node as the optimization objective, determine the objective function and constraints for the voltage optimization of the distribution network, and establish a voltage optimization control model for the distribution network; the constraints at least include the distribution network power flow constraint, the distributed photovoltaic reactive power output constraint, and the optical energy storage node voltage constraint. Step S4: Based on the accelerated dual ascent algorithm, solve the voltage optimization control model of the distribution network to obtain the reactive power output of the photovoltaic inverter, and send the reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the optical storage node through the reactive power generated by the photovoltaic inverter. Step S5: Determine whether the voltage of the optical storage node after the adjustment of the photovoltaic inverter exceeds the preset voltage threshold. If so, add the distributed energy storage active power constraint to the voltage optimization control model of the distribution network, and construct the Lagrangian function of the adjusted voltage optimization control model of the distribution network. Step S6: Update the Lagrange multiplier using the voltages of the optical storage nodes and the photovoltaic reactive power at the previous two moments, communicate and exchange the Lagrange multiplier information with adjacent optical storage nodes to obtain the active power of the distributed energy storage at the current moment, and send the active power scheduling instruction to the distributed energy storage controller.

2. The two-stage voltage control method for a distribution network with distributed optical storage collaboration according to claim 1, characterized in that The distribution network data includes the voltage measurement data and reactive power data of the optical storage nodes, the upper and lower limits of the reactive power of the photovoltaic inverter, the upper and lower limits of the active power of the distributed energy storage, and the upper and lower voltage constraints of the optical storage nodes; among them, the optical storage node refers to the distribution network node containing photovoltaic and energy storage.

3. The two-stage voltage control method for a distribution network with distributed optical storage collaboration according to claim 1, characterized in that, In Step S2, the expression of the distribution network power flow model is: Where P ij and Q ij represent the active and reactive power flowing from node i to node j, p j and q j are the active and reactive power output by node j, V i is the voltage value of node i, r ij and x ij are the line resistance and line reactance between node i and node j; In Step S2, the linear processing of the power flow model of the distribution network to obtain the linear power flow model of the distribution network includes: Ignoring the active and reactive line losses and assuming that the voltages of each optical storage node are close to the reference voltage, the power flow model of the distribution network is linearized as follows: V i -V j = r ij P ij + x ij Q ij (6) The linear power flow model of the distribution network can be expressed in the following form: v = 1 + Rp + Xq (7) Where, v represents the column vector composed of node voltages, p represents the column vector composed of node active power injections, q represents the column vector composed of node reactive power injections, R is the resistance matrix, and X is the reactance matrix. In the above formula, 1 is the unit column vector, and R and X are expressed in the following form: In the above formula, Li represents the path from node 0 to node i, and Lj represents the path from node 0 to node j; each element of R and X is only related to the network topology and line impedance. In Step S2, considering the voltage control of the optical storage node equipped with a photovoltaic inverter, determining the relationship between the voltage of the optical storage node and the reactive power of the photovoltaic inverter includes: Assume that the voltage of the energy storage node with PV is measured only by the PV inverter and the reactive power injected into the distribution network is adjusted, and the voltage control of the energy storage node with PV is considered. To distinguish the voltage of the energy storage node from that of the load node, the node voltage amplitude vector is decomposed as follows: In the above formula, v G is the voltage amplitude of the optical storage node, and v L is the voltage amplitude of the load node; the resistance matrix R and the reactance matrix X are decomposed as follows: Substitute Eqs. (10), (11) and (12) into Eq. (7) to obtain the voltage amplitude of the energy storage node with PV: v G = 1 + R GG (p PV + p BESS ) + R GL p L + X GG q PV + X GL q L (13) In the above formula, p PV , q PV represent the active power and reactive power injected by the PV inverter into the distribution network, p BESS represents the active power injected by the distributed energy storage into the distribution network, p L , q L represent the active power and reactive power injected by the load; R GG is the sensitivity matrix of the voltage of the energy storage node to the active power fluctuation of the PV, R GL is the sensitivity matrix of the voltage of the energy storage node to the active power fluctuation of the load, X GG is the sensitivity matrix of the voltage of the energy storage node to the reactive power fluctuation of the PV, X GL is the sensitivity matrix of the voltage of the energy storage node to the reactive power fluctuation of the load; Assume p G , p L , q L are constants. If the voltage of the PV energy storage node is regulated only by the reactive power generated by the PV inverter, then Equation (13) is simplified, and the relationship between the voltage of the PV energy storage node and the reactive power of the PV inverter is obtained as follows: In the formula, is the voltage curve of the energy storage node without recovery power compensation.

4. The two-stage voltage control method for a distribution network with distributed optical storage collaboration according to claim 3, characterized in that In step S3, based on the relationship between the voltage of the energy storage node with PV and the reactive power of the PV inverter, with the minimum voltage deviation of the energy storage node with PV as the optimization objective, the objective function and constraint conditions for the distribution network voltage optimization are determined, and the distribution network voltage optimization control model is established, specifically including: Based on Eq. (14), the objective function of the distribution network voltage optimization model is established as: In the formula, minF represents the optimization objective of minimizing the deviation of the voltage of the energy storage node with PV from the rated voltage; The voltage constraint of the energy storage node with PV is: v min ≤ v G ≤ v max (17) In the above formula, v min and v max are respectively the upper and lower limits of the voltage at the node where the distributed photovoltaic is located; The reactive power constraint of the distributed PV is: q min ≤q PV ≤q max (18) In the formula, and are the upper and lower limits of the reactive power output of the PV inverter, respectively; the PV inverter provides a limited apparent power, and the reactive power limit of the PV inverter depends on the active power of the PV inverter varying with time: In the formula, S i represents the apparent power of the PV inverter at node i, and p i，t represents the active power of the PV inverter at node i at time t; Eqs. (16), (17) and (18) constitute the distribution network voltage optimization control model.

5. The two-stage voltage control method for a distribution network with distributed optical storage collaboration according to claim 4, characterized in that, In step S4, the reactive power output of the PV inverter is obtained by solving the distribution network voltage optimization control model based on the accelerated dual ascent algorithm, including: Based on the distribution network voltage optimization control model, the Lagrangian function is constructed as follows: In the above formula, L is the Lagrangian function of the distribution network voltage optimization control model, and the vectors λ and μ and are the Lagrange multipliers related to the inequality constraints q min ≤q PV , q PV ≤q max , v min ≤v G , v G ≤v max . Define the vector a as containing all the Lagrange multipliers, The superscript T represents the transpose of the vector; Solve the optimal control variables of the Lagrangian function: Substitute \(q\) in Equation (22) PV into Equation (16) to obtain the Lagrangian dual function: In the formula, represents the Lagrangian dual function; B is the coefficient matrix and b is the coefficient vector, and the expressions are: In the formula, represents the transpose of the n-order identity matrix; Use the accelerated projection gradient method to update the dual variables: where a t+1 is the dual variable at time t + 1, is the acceleration variable obtained by the linear combination of the dual variables at the two most recent times, and α is the step size of the accelerated projected gradient method, is the derivative of the function g(c t+1 ) with respect to the vector c t+1 , and [] + denotes projection onto the feasible region with non - negative constraints; c t+1 = a t + β t+1 (a t - a t-1 ) (27) In the above formula, β t+1 has the following expression: In the above formula, θ t+1 The iterative expression is: where β t and β t+1 are the auxiliary variables β at times t and t + 1 respectively, and θ t and θ t+1 are the auxiliary variables θ at times t and t + 1 respectively; The local update form of the Lagrangian operator is expressed as follows: Wherein, λ i,t+1 , λ i,t and λ i,t-1 are the Lagrangian operators of the optical storage node i at times t+1, t, and t-1 respectively λ , q i,t and q i,t-1 are the photovoltaic reactive powers of the optical storage node i at times t and t-1 respectively, and are the Lagrangian operators of the optical storage node i at times t+1, t, and t-1 respectively μ i,t+1 , μ i,t and μ i,t-1 are the Lagrangian operators of the optical storage node i at times t+1, t, and t-1 respectively μ , v i,t and v i,t-1 are the voltages of the optical storage node at times t and t-1 respectively, and are the Lagrangian operators of the optical storage node i at times t+1, t, and t-1 respectively q i,min is the maximum reactive power absorbed by the optical storage node i, q i,max is the maximum reactive power of the optical storage node i, v i,min is the upper voltage limit of the optical storage node i, v i,min is the lower voltage limit of the optical storage node i; Matrix is a sparse matrix, and the value of each element is only related to the reactance between nodes i and j; calculate the reactive power output injected by the photovoltaic inverter into the distribution network: where q i，t+1 represents the reactive power injected by the PV inverter into the energy storage node i at the moment t + 1, is a matrix related only to the energy storage nodes i and j element, N(i) is the set of nodes where the adjacent PV inverters of the energy storage node i are located, is the voltage value of the energy storage node j before control, λ k，t+1 is the Lagrangian operator of the energy storage node k at the moment t + 1 λ , is the Lagrangian operator of the energy storage node k at the moment t + 1 μ j,t+1 is the Lagrangian operator of the energy storage node j at the moment t + 1 μ , is the Lagrangian operator of the energy storage node j at the moment t + 1.

6. The two-stage voltage control method for a distribution network with distributed optical storage collaboration according to claim 5, characterized in that In step S5, the active power constraint of the distributed energy storage is added to the distribution network voltage optimization control model, and the Lagrangian function of the adjusted distribution network voltage optimization control model is constructed, including: The active power constraint of the distributed energy storage is as follows: p min ≤ p BESS ≤ p max (35) In the above formula, p BESS represents the active power injected by the distributed energy storage into the distribution network; and are respectively the upper and lower limits of the active power output of the energy storage. Considering the energy storage capacity and the active power limit, the upper and lower limits of the active power output of the distributed energy storage are as follows: Wherein, represents the lower limit of the active power output of the energy storage at node i at time t, represents the upper limit of the active power output of the energy storage at node i at time t, and are respectively the maximum charging power and the maximum discharging power of the energy storage at node i; and respectively represent the minimum and maximum charging level constraints of the energy storage at node i, and respectively represent the charge-discharge coefficients of the energy storage at node i; the storage capacity of the energy storage at node i at time t is expressed as: The objective function in Eq. (16) and the constraint conditions in Eqs. (17), (18) and (35) constitute the adjusted distribution network voltage optimization control model; Based on the adjusted distribution network voltage optimization control model, the Lagrangian function is constructed as follows: In the formula, L’ is the Lagrangian function of the optimized control model of the adjusted distribution network voltage, and the vectors and are the Lagrange multipliers related to the inequality constraints p min ≤p BESS , p BESS ≤p max ; From it can be obtained that: In the formula, represents the derivative of L’ with respect to the active power p of the distributed energy storage BESS .

7. The two-stage voltage control method for a distribution network with distributed optical storage collaboration according to claim 6, wherein In step S6, the Lagrange multipliers are updated using the voltages of the energy storage nodes with PV and the PV reactive powers at the previous two moments, and the Lagrange multiplier information is communicated and exchanged with adjacent energy storage nodes with PV to obtain the active power of the distributed energy storage at the current moment, including: The energy storage node i can update the active power of the distributed energy storage by communicating with the adjacent energy storage node j: Where, p i，t+1 represents the active power of the distributed energy storage of the optical storage node i at time t+1, is the element in the i-th row and j-th column of the matrix is the element in the j-th row and k-th column of the matrix i,t+1 q is the reactive power injected by the photovoltaic inverter into the optical storage node i at time t+1, and are respectively the lower limit and upper limit of the active power of the distributed energy storage at node i at time t+1.

8. A two-stage voltage control device for a distribution network with distributed optical storage collaboration, characterized in that Including: An acquisition module, configured to acquire the distribution network data including distributed PV and distributed energy storage; A processing module, configured to linearly process the power flow model of the distribution network according to the distribution network data to obtain the linear power flow model of the distribution network; considering the voltage control of the energy storage node with PV, determine the relationship between the voltage of the energy storage node with PV and the reactive power of the PV inverter; A model establishment module, which is used to determine the objective function and constraint conditions for the distribution network voltage optimization based on the relationship between the optical storage node voltage and the reactive power of the photovoltaic inverter, with the minimum deviation of the optical storage node voltage as the optimization goal, and establish a distribution network voltage optimization control model; the constraint conditions at least include the distribution network power flow constraint, the distributed photovoltaic reactive power output constraint, and the optical storage node voltage constraint; A reactive power voltage control module, which is used to solve the distribution network voltage optimization control model based on the accelerated dual ascent algorithm, obtain the reactive power output of the photovoltaic inverter, and send a reactive power scheduling instruction to the photovoltaic inverter controller to adjust the voltage of the optical storage node through the reactive power generated by the photovoltaic inverter; An optical storage coordination module, which is used to determine whether the voltage of the optical storage node after the adjustment of the photovoltaic inverter exceeds a preset voltage threshold. If so, add a distributed energy storage active power constraint to the distribution network voltage optimization control model and construct the Lagrangian function of the adjusted distribution network voltage optimization control model; An active power voltage control module, which is used to update the Lagrangian multiplier by using the voltages of the optical storage nodes and the photovoltaic reactive powers at the previous two moments, communicate and exchange the Lagrangian multiplier information with adjacent optical storage nodes to obtain the active power of the distributed energy storage at the current moment, and send an active power scheduling instruction to the distributed energy storage controller.

9. An electronic device, characterized in that, It includes a memory and a processor, where the memory is used to store programs; the processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the two-stage voltage control method for the distribution network with distributed optical storage coordination described in any one of claims 1 to 7 above.

10. A computer-readable storage medium, characterized in that, It is used to store computer-readable programs or instructions, and when the programs or instructions are executed by the processor, they can implement the steps in the two-stage voltage control method for the distribution network with distributed optical storage coordination described in any one of claims 1 to 7 above.

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