Photovoltaic and light storage cooperative reactive voltage control method and device and storage medium

Abstract

The invention provides a photovoltaic and optical storage cooperative reactive voltage control method and device and a storage medium, and the method comprises the steps: determining whether an electric control condition is satisfied or not according to an obtained parameter, if the electric control condition is not satisfied, enabling a photovoltaic to participate in voltage regulation control, constructing a reactive voltage droop coefficient optimization model of the photovoltaic to participate in the voltage regulation control, and carrying out the optimization of the reactive voltage droop coefficient; the target droop coefficient in the droop coefficient optimization model is solved, photovoltaic voltage regulation control is performed according to the target droop coefficient, and the target droop coefficient is the optimal droop coefficient, so that effective combination of photovoltaic real-time control and reactive power optimization scheduling is realized, and improvement of photovoltaic operation stability is facilitated. Whether parameters output by photovoltaic voltage regulation control according to the target droop coefficient meet electric control conditions or not is determined, if the parameters do not meet the electric control conditions, optical storage self-adaptive droop voltage regulation control is carried out, under energy storage capacity sharing, the problem that photovoltaic power with the small reactive power margin needs to bear many reactive power tasks and distribution is not uniform is solved, and the photovoltaic power generation efficiency is improved. And the reactive voltage level of the power grid is indirectly improved.

Description

Technical Field

[0001] The present invention belongs to the field of photovoltaic technology, and in particular relates to a photovoltaic and photovoltaic storage coordinated reactive voltage control method, device and storage medium. Background Art

[0002] At present, China continues to vigorously promote the transformation of clean and low-carbon energy, and the access of distributed power sources, mainly photovoltaics, will become the trend of future distribution network development. When distributed photovoltaics are connected on a large scale, the high output of photovoltaics will increase the voltage of the distribution network, and the volatility and randomness of photovoltaic output will lead to increased voltage fluctuations along the distribution network. In addition, photovoltaic output will also drop sharply on cloudy days and at night, and there is a time-space mismatch between output and load, which will cause low voltage problems in the distribution network. Therefore, there is a high-penetration photovoltaic access distribution network with high voltage coexistence.

[0003] However, although the existing literature on distributed photovoltaics, energy storage, and the coordinated voltage control of the two covers local reactive power control of distribution networks, global reactive power optimization control, active power sharing, etc., it does not mention the control framework that combines local control of photovoltaic inverters with global voltage control, and the active voltage control of energy storage is not effectively combined with the reactive voltage control of photovoltaic inverters, which leads to the uneven distribution problem that photovoltaic inverters with smaller reactive power margins have to bear more reactive power tasks, and the capacity of energy storage is not considered, so that the energy storage power and SOC (battery charge) cannot be coordinated and controlled, and the reactive voltage level of the power grid is low. Summary of the invention

[0004] In view of the above problems, the present invention proposes a photovoltaic and photovoltaic storage coordinated reactive voltage control method, the method comprising:

[0005] Determine whether the electrical control conditions are met according to the acquired parameters. If the electrical control conditions are not met, the photovoltaic participates in the voltage regulation control;

[0006] Constructing a reactive voltage droop coefficient optimization model for the photovoltaic voltage regulation control;

[0007] Solving the target droop coefficient in the droop coefficient optimization model to perform photovoltaic voltage regulation control according to the target droop coefficient, wherein the target droop coefficient is an optimal droop coefficient;

[0008] Confirm whether the parameters of the photovoltaic voltage regulation control output according to the target droop coefficient meet the electrical control conditions. If the electrical control conditions are not met, the photovoltaic storage adaptive droop voltage regulation control is performed.

[0009] Optionally, before confirming whether the electric control condition is met according to the acquired parameters, the following steps are included:

[0010] Obtain parameters from the operating distribution grid and calculate reactive-voltage and active-voltage sensitivity matrices of node voltage and injected power;

[0011] Calculate the reactive-voltage sensitivity matrix of grid node voltage and injected power:

[0012]

[0013] in, is the conjugate complex number of the voltage at the disturbance node a; Z ao Q is the impedance of the common tie line from the disturbance node a to the grid node and from the observation node o to the grid node, a is the reactive power of node a, V O is the approximate reactive voltage from node o to node a, i is an imaginary number, ΔS a is the disturbance amount;

[0014] Calculate the active-voltage sensitivity matrix of grid node voltage and injected power:

[0015]

[0016] Among them, P a is the active power of node a, is the conjugate complex number of the voltage at the disturbance node a; Z ao V is the impedance of the common connection line from the disturbance node a to the grid node and from the observation node o to the grid node, O is the approximate active voltage from node o to node a, ΔS a is the disturbance amount.

[0017] Optionally, the step of constructing a reactive voltage droop coefficient optimization model involving photovoltaics includes:

[0018] The lower limit of the voltage deviation of the distribution network node is used as the photovoltaic control voltage target;

[0019] The droop coefficient optimization model is constructed based on the distribution network node voltage limit, photovoltaic reactive power margin limit or power factor limit as constraints.

[0020] Optionally, solving the target droop coefficient in the droop coefficient optimization model includes:

[0021] The target droop coefficient in the reactive voltage droop coefficient optimization model involving photovoltaics is solved according to the whale group calculation, so as to control the reactive output of photovoltaics.

[0022] Optionally, the target droop coefficient in the reactive voltage droop coefficient optimization model in which the photovoltaic power generation participates is calculated based on the whale group to control the reactive output of the photovoltaic power generation, including:

[0023] Initialize the control variables in the droop coefficient optimization model to generate the initial population;

[0024] Randomly initialize the positions and velocities of the particles in the initial population according to the particle swarm algorithm;

[0025] Update the position and velocity coefficient vector of the whale surrounding the particle, and randomly select the target number;

[0026] Determine whether the target number is less than a first preset value, and if it is greater than the first preset value, use a spiral bubble attack to update the position of the whale surrounding the particle, and calculate the droop coefficient based on this result;

[0027] If it is less than the first preset value, determine whether the coefficient vector is less than the second preset value. If it is less than the second preset value, use the method of surrounding the prey to update the individual position of the whale, update the speed and position of the particle group, and calculate the droop coefficient.

[0028] If it is greater than the second preset value, the individual position of the whale is updated by searching for prey and the droop coefficient is calculated.

[0029] Optionally, the step of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model in which the photovoltaic power generation participates according to the whale group, so as to control the reactive output of the photovoltaic power generation, further includes:

[0030] According to the obtained droop coefficient, calculate whether the individual fitness of the whale meets the preset conditions;

[0031] If the preset conditions are met, the droop coefficient is output;

[0032] Otherwise, the step of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model involving photovoltaics by the whale group is re-executed until the preset condition is met.

[0033] Optionally, if the electrical control condition is not met, the photovoltaic storage adaptive droop voltage regulation control includes:

[0034] Each energy storage is provided with a separate droop coefficient, wherein each energy storage has a different regulation capability;

[0035] Calculate the droop coefficient of each energy storage according to the whale group;

[0036] The droop coefficient of each energy storage is adaptively adjusted and controlled according to the energy storage state.

[0037] Optionally, the electrical control condition is:

[0038] V min ≤V i (t)≤V max Among them, V min and Vmax are the minimum and maximum values ​​allowed for the node voltage respectively.

[0039] The present invention also provides a photovoltaic and photovoltaic storage coordinated reactive voltage control device, comprising:

[0040] The photovoltaic module determines whether the electrical control conditions are met according to the acquired parameters. If the electrical control conditions are not met, the photovoltaic module participates in the voltage regulation control;

[0041] A construction module is used to construct a reactive voltage droop coefficient optimization model for the photovoltaic voltage regulation control;

[0042] A solution module, which solves the target droop coefficient in the droop coefficient optimization model to perform photovoltaic voltage regulation control according to the target droop coefficient, wherein the target droop coefficient is an optimal droop coefficient;

[0043] The photovoltaic storage module confirms whether the parameters of the photovoltaic voltage regulation control output according to the target droop coefficient meet the electrical control conditions. If the electrical control conditions are not met, the photovoltaic storage performs adaptive droop voltage regulation control.

[0044] The present invention also provides a storage medium on which a computer program is stored, and when the program is processed and executed, the photovoltaic and photovoltaic storage coordinated reactive voltage control method is implemented.

[0045] The photovoltaic and photovoltaic storage coordinated reactive voltage control method of the present invention confirms whether the electrical control conditions are met based on the acquired parameters. If the electrical control conditions are not met, the photovoltaic participates in the voltage regulation control, and constructs a reactive voltage droop coefficient optimization model in which the photovoltaic participates in the voltage regulation control. The target droop coefficient in the droop coefficient optimization model is solved to perform photovoltaic voltage regulation control according to the target droop coefficient, wherein the target droop coefficient is the optimal droop coefficient, thereby realizing the effective combination of photovoltaic real-time control and reactive power optimization scheduling, and is conducive to enhancing the stability of photovoltaic operation. Confirm whether the parameters output by the photovoltaic voltage regulation control according to the target droop coefficient meet the electrical control conditions. If the electrical control conditions are not met, the photovoltaic storage adaptively performs droop voltage regulation control. Under the capacity sharing of energy storage, the problem of uneven distribution of photovoltaics with smaller reactive margins having to bear more reactive power tasks is avoided, and the reactive voltage level of the power grid is indirectly improved.

[0046] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A schematic diagram of the process of the photovoltaic and photovoltaic storage coordinated reactive voltage control method in an embodiment of the present invention is shown;

[0049] Figure 2 A schematic diagram of a specific execution flow of a photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to an embodiment of the present invention is shown;

[0050] Figure 3 A schematic diagram of the flow of calculating the droop optimization coefficient of the whale emission solution of the photovoltaic and photovoltaic storage coordinated reactive voltage control method in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] like Figure 1 As shown, the present invention provides a photovoltaic and photovoltaic storage coordinated reactive voltage control method, the method comprising:

[0053] Step S10, confirm whether the electric control condition is met according to the acquired parameters, if the electric control condition is not met, the photovoltaic participates in the voltage regulation control. It should be noted that according to the acquired parameters, the high or low voltage problem caused by the time and space mismatch between the distributed photovoltaic and the load is analyzed, and the reactive-voltage and active-voltage sensitivity matrices of the node voltage and the injected power are calculated.

[0054] In one embodiment, before confirming whether the electric control condition is met according to the acquired parameters, the following steps are included:

[0055] Obtain parameters from the operating distribution grid and calculate reactive-voltage and active-voltage sensitivity matrices of node voltage and injected power;

[0056] Calculate the reactive-voltage sensitivity matrix of grid node voltage and injected power:

[0057]

[0058] in, is the conjugate complex number of the voltage at the disturbance node a; Z ao Q is the impedance of the common tie line from the disturbance node a to the grid node and from the observation node o to the grid node, a is the reactive power of node a, V O is the approximate reactive voltage from node o to node a, i is an imaginary number, ΔS a is the disturbance amount;

[0059] Calculate the active-voltage sensitivity matrix of grid node voltage and injected power:

[0060]

[0061] Among them, P a is the active power of node a, is the conjugate complex number of the voltage at the disturbance node a; Z ao V is the impedance of the common connection line from the disturbance node a to the grid node and from the observation node o to the grid node, O is the approximate active voltage from node o to node a, V o is the voltage at node o; i is an imaginary number, and the disturbance is ΔS a .

[0062] Furthermore, in a typical distribution network, when each node is connected to photovoltaics and loads, the node voltage rise and fall problem is usually caused by the time and space mismatch between photovoltaic output and load. In order to further analyze the intrinsic relationship between node voltage and power flow, the mathematical expression of voltage deviation and power of adjacent nodes in the distribution network is established as follows:

[0063]

[0064] Among them, V i 、V i-1 are the voltages of nodes i and i-1 respectively; R k , X k are the line resistance and reactance between the kth node and the k-1th node respectively; P k , Q k are respectively the active and reactive output of the photovoltaic on the kth node; P L,k , Q L,k are the active and reactive loads on the kth node respectively; N is the number of nodes in the distribution network. Furthermore, the voltage V at the end node of the distribution network can be obtained: N The deviation from the transformer low-voltage side node voltage V0 is:

[0065]

[0066] According to the classic radial network topology, node o is used as the observation point and node a is used as the disturbance point to calculate the power and voltage sensitivity between the two nodes. Assume that there is a power disturbance at node a, and the disturbance amount is ΔS a , the voltage change value of node o caused by the power disturbance and the power disturbance value have the following relationship:

[0067]

[0068] Among them, V a * is the conjugate complex number of the voltage at the disturbance node a; Z ao It is the impedance of the common connecting line from the disturbance node a to the grid node and from the observation node o to the grid node.

[0069] Step S20, constructing a reactive voltage droop coefficient optimization model for the photovoltaic participating in voltage regulation control. It should be noted that in this embodiment, a mathematical model is proposed to perform real-time online optimization and adjustment of the droop control gain coefficient of distributed photovoltaics with the minimum voltage deviation of the distribution network node as the control target and the node voltage, photovoltaic reactive margin limit, power factor limit, etc. as constraints. According to the reactive voltage variable droop control curve, when the photovoltaic inverter works in the droop control area, the reactive expression of the i-th distributed photovoltaic is:

[0070]

[0071] Among them, R i is the droop control coefficient of the i-th distributed photovoltaic; V i is the voltage of the i-th node; Q i is the reactive power that the i-th distributed photovoltaic should generate; Q ref For reference reactive power, it is generally set to 0. In addition, if it is confirmed that the obtained parameters meet the electrical control conditions, the self-distribution network can operate normally, the voltage is normal, and there is no overload operation, that is, there is no need to use droop optimized photovoltaics to participate in reactive voltage regulation, that is, there is no need for photovoltaics to regulate voltage (boost or buck). Specifically, for the voltage at any time, the following electrical control conditions should be met:

[0072] V min ≤V i (t)≤V max

[0073] Among them, V min and V max They are the minimum and maximum values ​​allowed for the node voltage, and are set to 0.95 and 1.05 respectively in the present invention.

[0074] In one embodiment, the step of constructing a photovoltaic-involved reactive voltage droop coefficient optimization model includes:

[0075] The lower limit of the voltage deviation of the distribution network node is used as the photovoltaic control voltage target;

[0076] The droop coefficient optimization model is constructed based on the distribution network node voltage limit, photovoltaic reactive power margin limit or power factor limit as the constraint conditions. Since this embodiment proposes to minimize the distribution network node voltage deviation as the control target, and the node voltage, photovoltaic reactive power margin limit, power factor limit, etc. as the constraint conditions, a mathematical model for real-time online optimization and adjustment of the droop control gain coefficient of distributed photovoltaics is proposed. The objective function is:

[0077]

[0078] Among them, V ref is the reference voltage value, which is set to 1.0 in the present invention. Define V = [V1, V2, ..., V N ],P=[P1,P2,…,P N ],Q=[Q1,Q2,…,Q N ], respectively representing the node voltage, active power and reactive power generated by photovoltaics. Therefore, the full node voltage of the distribution network caused by the change of active and reactive power of distributed photovoltaics is:

[0079] ΔV=S VQ ΔQ+S VP ΔP

[0080] Among them, S VQ is the fixed reactive voltage sensitivity matrix of the distribution network node to the distributed photovoltaic node, S VP is the corresponding fixed active voltage sensitivity matrix. Assuming t0 is the current moment and t1 is the next moment, the voltage at moment t1 predicted based on the information at moment t0 is:

[0081] V(t1)=V(t0)+S VQ ΔQ(t0)+S VP ΔP(t0)

[0082] Among them, ΔQ can be calculated by the distributed photovoltaic reactive power expression; ΔP is the difference between the photovoltaic active output at the next moment and the current active output.

[0083] For the voltage at any time, the following constraints should be satisfied:

[0084] V min ≤V i (t)≤V max

[0085] V min and V maxThey are the minimum and maximum values ​​allowed for the node voltage, respectively, and are set to 0.95 and 1.05 in the present invention, respectively. Within this range, photovoltaic voltage regulation is not required.

[0086] Considering the reactive power margin of distributed photovoltaics, its reactive power output should meet the following requirements:

[0087]

[0088] Among them, S i is the apparent power of the ith photovoltaic; P i is the active power generated by the i-th photovoltaic at the current moment; Q i is the reactive power margin of the ith photovoltaic. The reactive power of distributed photovoltaic is also limited by the power factor. The present invention sets the power factor interval of distributed photovoltaic operation to [-1, -0.9] and [0.9, 1], so:

[0089]

[0090] Among them, P PF is the power factor. The second limiting condition of the distributed photovoltaic reactive power margin is further obtained as follows:

[0091]

[0092] The power grid can operate normally when the photovoltaic reactive power is within this range. If it exceeds this range, photovoltaic regulation intervention is required.

[0093] Step S30, solving the target droop coefficient in the droop coefficient optimization model, so as to perform photovoltaic voltage regulation control according to the target droop coefficient, wherein the target droop coefficient is the optimal droop coefficient. By solving the optimal droop coefficient, the photovoltaic inverter can be optimally regulated according to the optimal droop coefficient, thereby realizing the effective combination of photovoltaic real-time control and reactive power optimization scheduling, and is conducive to enhancing the stability of photovoltaic operation.

[0094] In one embodiment, solving the target droop coefficient in the droop coefficient optimization model includes:

[0095] According to the whale swarm algorithm, the target droop coefficient in the reactive voltage droop coefficient optimization model of the photovoltaic participation is solved, so as to control the reactive output of photovoltaic. It should be noted that the whale swarm algorithm is used to solve the model, obtain the optimal droop coefficient of the distributed photovoltaic inverter, and calculate the reactive output of the inverter. The basic idea of ​​the whale swarm algorithm is as follows: first, the particle swarm algorithm is used to set the basic parameters and the maximum number of iterations, and initialize the control variables in the optimization model; second, the fitness of each individual is calculated; finally, according to the encirclement search strategy of the whale algorithm, the speed and position of the particle are updated.

[0096] In one embodiment, the step of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model in which the photovoltaic power generation participates according to the whale group, so as to control the reactive output of the photovoltaic power generation, includes:

[0097] The control variables in the droop coefficient optimization model are initialized to generate the first generation population. It should be noted that when the voltage of the power grid fluctuates, the droop coefficient optimization system is initialized to generate the first generation population.

[0098] The positions and velocities of the particles in the initial population are randomly initialized according to the particle swarm algorithm.

[0099] The position and speed coefficient vector of the whale surrounding the particle are updated, and the target number is randomly selected. Specifically, the whale surrounding position and speed coefficient vector A, C are updated, and the random number P is selected, which is randomly selected according to the probability of hunting behavior.

[0100] It is determined whether the target number is less than a first preset value. If it is greater than the first preset value, the spiral bubble attack is used to update the position of the whale surrounding the particle, and the droop coefficient is calculated based on the result. The first preset value is 0.5.

[0101] If it is less than the first preset value, determine whether the coefficient vector is less than the second preset value. If it is less than the second preset value, use the method of surrounding the prey to update the individual position of the whale, update the speed and position of the particle group, and calculate the droop coefficient. The second preset value is 1, that is, the coefficient is |A|<1.

[0102] If it is greater than the second preset value, the individual position of the whale is updated by searching for prey and the droop coefficient is calculated.

[0103] In one embodiment, the step of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model in which the photovoltaic power generation participates according to the whale group, so as to control the reactive output of the photovoltaic power generation, further includes:

[0104] According to the obtained droop coefficient, calculate whether the individual fitness of the whale meets the preset conditions;

[0105] If the preset conditions are met, the droop coefficient is output;

[0106] Otherwise, the step of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model involving photovoltaics by the whale group is re-executed until the preset condition is met.

[0107] It should be noted that the mathematical model calculation method of the whale group algorithm's search for prey, encirclement of prey, and hunting behavior strategy is as follows:

[0108] 1) Searching for prey. When searching for prey, humpback whales constantly update their positions to achieve the purpose of hunting food, taking the position of the whale group that is currently in the best hunting position as the target prey or close to the best target prey. The updated position equation is

[0109]

[0110] Where: t is the current iteration number; X rand is the current randomly selected position space of the whale population; X is the current individual position space; A and C are coefficients. The calculation formulas of coefficients A and C are:

[0111]

[0112] Where a is a value that gradually decreases between [2, 0] during the iteration process; r is a random number distributed between [0, 1]; M is the upper limit of the number of iterations.

[0113] 2) Surround the prey. Assuming that the best individual in the current population is the optimal solution, the other individuals will move closer to the best individual.

[0114]

[0115] Where: X* is the best position found by the current group.

[0116] 3) Spiral bubble attack. According to the hunting behavior of humpback whales, they will shrink the encirclement around the spiral line. It is generally assumed that there is a 50% probability of shrinking the encirclement circle and a 50% probability of spiral renewal encirclement, which can be expressed as

[0117]

[0118] D′=|X * (-Xt)|

[0119] In the formula: b is a constant used to define the spiral shape; l is a random number between -1 and 1; P is the probability of selecting the shrinking encirclement mechanism and the spiral encirclement mechanism, which is generally taken as 0.5.

[0120] In order to introduce good global search capabilities into the whale group hunting strategy, the speed and position update formula in the particle swarm algorithm is:

[0121]

[0122] Where: ω is the inertia weight factor; is the velocity of particle i in the jth dimension at the t+1th iteration; is the position of particle i in the jth dimension at the t+1th iteration; is the historical optimal position of the individual particle; is the global optimal position of the particle swarm; c1 and c2 are learning factors; r1 and r2 are random values ​​in [0,1].

[0123] In order to facilitate understanding of the target droop coefficient in the reactive voltage droop coefficient optimization model of photovoltaic participation, please refer to Figure 3 .

[0124] Step S40, confirm whether the parameters of the photovoltaic voltage regulation control output according to the target droop coefficient meet the electrical control conditions. If the electrical control conditions are not met, the photovoltaic storage adaptive droop voltage regulation control is performed. It should be noted that when the photovoltaic power is fully generated, the distributed photovoltaic reactive power margin is insufficient due to capacity limitations; when the photovoltaic output is relatively small, although there is sufficient remaining capacity, the available reactive power margin is also very small due to power factor limitations. Both of these situations are difficult to meet the voltage regulation requirements of the distribution network, and it is necessary to call on the active power of energy storage to participate in the active voltage regulation of the distribution network.

[0125] In order to facilitate the understanding of this technical solution, Figure 2 According to the flowchart shown in the figure, the voltage sensitivity needs to be calculated according to the acquired parameters first, and then the calculated voltage is judged according to the preset electrical control conditions to see whether it meets the electrical control conditions. If it does, there is no need to use photovoltaic parameters to adjust the voltage control. If it does not meet the change conditions, the photovoltaic parameters need to adjust the voltage control. In this process, it is necessary to establish a droop control gain coefficient optimization model. This model can find the optimal droop coefficient of photovoltaics, making photovoltaic regulation more stable and making the voltage output by conditional control more stable. Of course, the optimal droop coefficient of photovoltaics is solved by the whale group algorithm, and the voltage output by photovoltaic regulation control needs to be judged again whether it meets the electrical control conditions. If the electrical control conditions are met, the voltage output by volt regulation control is output. If the electrical control conditions are not met, the photovoltaic regulation control cannot control the voltage to be output stably. Energy storage participation and photovoltaic regulation control are required to control the voltage. Through energy storage adaptive droop control, the output voltage is finally stabilized.

[0126] In one embodiment, if the electrical control condition is not met, the photovoltaic storage adaptive droop voltage regulation control includes:

[0127] Each energy storage is provided with a separate droop coefficient, wherein each energy storage has a different regulation capability;

[0128] Calculate the droop coefficient of each energy storage according to the whale group;

[0129] The droop coefficient of each energy storage is adaptively adjusted and controlled according to the energy storage state. It should be noted that the voltage dead zone setting range of the energy storage active voltage droop curve is [0.95, 1.05], which means that the node voltage is within this interval and the energy storage does not participate in voltage regulation. When the node voltage is less than 0.95 or greater than 1.05, it indicates that distributed photovoltaics are difficult to solve the problem of voltage exceeding the limit of the distribution network. Specifically, the active power of the energy storage is:

[0130]

[0131] Among them, M i is the droop control coefficient of the i-th energy storage; P BESSi is the active power that the i-th energy storage should emit, positive power indicates energy storage discharge, and negative power indicates energy storage charging; P rref For reference reactive power, it is also set to 0. The above formula shows that energy storage active voltage regulation is only a supplementary means for distributed photovoltaic reactive voltage regulation, and does not participate in the global voltage control of the power grid, but only controls the voltage of the node where the energy storage is located within the safe operating range. In addition, since the droop coefficient solution process of energy storage is similar to that of photovoltaic, it will not be described here.

[0132] The capacity and state of charge of each energy storage are different, and their active voltage regulation capabilities are inconsistent. At this time, it is hoped that the energy storage with a larger SOC will be charged less and discharged more, while the energy storage with a smaller SOC will be charged more and discharged less, so as to achieve a relative balance of the SOC of each energy storage. Each energy storage is set with a separate droop coefficient M i :

[0133]

[0134] Among them, S SOCi is the charge state of the i-th energy storage; k c , k d is 2 constants; E i is the capacity of the i-th energy storage; f ch (S SOCi ), f dis (S SOCi ) are the charging and discharging functions of the energy storage device, respectively, and their expressions are:

[0135]

[0136] Among them, m and n are constants. From the above formulas, it can be seen that when the node voltage is in [0.90, 0.95], the droop control coefficient of each energy storage is positive, the energy storage works in the discharge state, and the discharge power of each energy storage unit is positively correlated with its own SOC. When the node voltage is in [1.05, 1.1], the droop control coefficient of each energy storage is negative, and the energy storage works in the charging state, that is, the energy storage device discharges power through each energy storage unit and its own SOC. The droop coefficient is adaptively adjusted according to the energy storage SOC state to achieve relative balance of the SOC of multiple energy storage units.

[0137] In one embodiment, the electrical control condition is:

[0138] V min ≤V i (t)≤V max

[0139] Among them, V min and V max They are the minimum and maximum values ​​allowed for the node voltage, respectively. If the grid voltage in the present invention is within this voltage range, no photovoltaic inverter is required for regulation. If it exceeds this voltage range, a photovoltaic inverter is required for regulation. If the optimal droop coefficient is selected to regulate and output the voltage, if the voltage is within this voltage range, no energy storage is required for regulation. If it exceeds this voltage range, energy storage is required for regulation.

[0140] In summary, the present invention analyzes the high / low voltage problem caused by the time-space mismatch between distributed photovoltaics and loads, and then calculates the reactive-voltage and active-voltage sensitivity matrices of voltage and injected power. Then, with the minimum node voltage deviation as the goal, and the node voltage, photovoltaic reactive margin limit, power factor limit, etc. as constraints, a reactive-voltage variable droop coefficient optimization model involving distributed photovoltaics is constructed, and the whale swarm algorithm is used to solve the optimal droop coefficient; when the reactive capacity of the photovoltaic inverter is insufficient, an active-voltage adaptive droop control strategy with energy storage participating in voltage regulation is proposed. The global voltage management and the reasonable allocation of the reactive margin of the photovoltaic inverter are realized, avoiding the uneven distribution problem of photovoltaic inverters with smaller reactive margins having to bear more reactive power tasks; the capacity and SOC of energy storage are taken into consideration, and the relative balanced control of the SOC of multiple energy storage devices is realized.

[0141] The present invention also provides a photovoltaic and photovoltaic storage coordinated reactive voltage control device, comprising:

[0142] The photovoltaic module determines whether the electrical control conditions are met according to the acquired parameters. If the electrical control conditions are not met, the photovoltaic module participates in the voltage regulation control;

[0143] A construction module is used to construct a reactive voltage droop coefficient optimization model for the photovoltaic voltage regulation control;

[0144] A solution module, which solves the target droop coefficient in the droop coefficient optimization model to perform photovoltaic voltage regulation control according to the target droop coefficient, wherein the target droop coefficient is an optimal droop coefficient;

[0145] The photovoltaic storage module confirms whether the parameters of the photovoltaic voltage regulation control output according to the target droop coefficient meet the electrical control conditions. If the electrical control conditions are not met, the photovoltaic storage adaptively droops the voltage regulation control. Through various modules, the problem of uneven distribution of photovoltaics with smaller reactive margins having to bear more reactive power tasks is avoided, and the reactive voltage level of the power grid is indirectly improved.

[0146] The present invention also provides a storage medium on which a computer program is stored, and when the program is processed and executed, a photovoltaic and photovoltaic storage coordinated reactive voltage control method is implemented. The operation method of the present invention can be executed through the storage medium, and by running the method, the problem of uneven distribution of more reactive power tasks that photovoltaics with smaller reactive margins need to bear is avoided.

[0147] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic and photovoltaic energy storage coordinated reactive voltage control method, characterized in that: The method comprises: Determine whether the electrical control conditions are met according to the acquired parameters. If the electrical control conditions are not met, the photovoltaic participates in the voltage regulation control; Constructing a reactive voltage droop coefficient optimization model for the photovoltaic voltage regulation control; Solving the target droop coefficient in the droop coefficient optimization model to perform photovoltaic voltage regulation control according to the target droop coefficient, wherein the target droop coefficient is an optimal droop coefficient; Confirm whether the parameters of the photovoltaic voltage regulation control output according to the target droop coefficient meet the electrical control conditions. If the electrical control conditions are not met, the photovoltaic storage adaptive droop voltage regulation control is performed.

2. The photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to claim 1 is characterized in that: Before confirming whether the electric control condition is met according to the acquired parameters, the method includes: Obtain parameters from the operating distribution grid and calculate reactive-voltage and active-voltage sensitivity matrices of node voltage and injected power; Calculate the reactive-voltage sensitivity matrix of grid node voltage and injected power: in, is the conjugate complex number of the voltage at the disturbance node a; Z ao is the impedance of the common tie line from the disturbance node a to the grid node and from the observation node o to the grid node, Q a is the reactive power of node a, V O is the approximate reactive voltage from node o to node a, i is an imaginary number, ΔS a is the disturbance amount; Calculate the active-voltage sensitivity matrix of grid node voltage and injected power: Among them, P a is the active power of node a, is the conjugate complex number of the voltage at the disturbance node a; Z ao V is the impedance of the common tie line from the disturbance node a to the grid node and from the observation node o to the grid node, O is the approximate active voltage from node o to node a, ΔS a is the disturbance amount.

3. The photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to claim 1, characterized in that: The construction of a reactive voltage droop coefficient optimization model involving photovoltaics includes: The lower limit of the voltage deviation of the distribution network node is used as the photovoltaic control voltage target; The droop coefficient optimization model is constructed based on the distribution network node voltage limit, photovoltaic reactive power margin limit or power factor limit as constraints.

4. The photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to claim 1, characterized in that: The step of solving the target droop coefficient in the droop coefficient optimization model includes: The target droop coefficient in the reactive voltage droop coefficient optimization model involving photovoltaics is solved according to the whale group calculation, so as to control the reactive output of photovoltaics.

5. The photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to claim 4 is characterized in that: The method of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model in which photovoltaics participate according to the whale group, so as to control the reactive output of photovoltaics, includes: Initialize the control variables in the droop coefficient optimization model to generate the initial population; Randomly initialize the positions and velocities of the particles in the initial population according to the particle swarm algorithm; Update the position and velocity coefficient vector of the whale surrounding the particle, and randomly select the target number; Determine whether the target number is less than a first preset value, and if it is greater than the first preset value, use a spiral bubble attack to update the position of the whale surrounding the particle, and calculate the droop coefficient based on this result; If it is less than the first preset value, determine whether the coefficient vector is less than the second preset value. If it is less than the second preset value, use the method of surrounding the prey to update the individual position of the whale, update the speed and position of the particle group, and calculate the droop coefficient. If it is greater than the second preset value, the individual position of the whale is updated by searching for prey and the droop coefficient is calculated.

6. The photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to claim 5, characterized in that: The step of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model in which photovoltaics participate according to the whale group, so as to control the reactive output of photovoltaics, further includes: According to the obtained droop coefficient, calculate whether the individual fitness of the whale meets the preset conditions; If the preset conditions are met, the droop coefficient is output; Otherwise, the step of calculating the target droop coefficient in the reactive voltage droop coefficient optimization model involving photovoltaics by the whale group is re-executed until the preset condition is met.

7. The photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to claim 1, characterized in that: If the electrical control condition is not met, the photovoltaic storage adaptive droop voltage regulation control includes: Each energy storage is provided with a separate droop coefficient, wherein each energy storage has a different regulation capability; Calculate the droop coefficient of each energy storage according to the whale group; The droop coefficient of each energy storage is adaptively adjusted and controlled according to the energy storage state.

8. The photovoltaic and photovoltaic energy storage coordinated reactive voltage control method according to any one of claims 1 to 7, characterized in that: The electrical control conditions are: V min ≤V i (t)≤V max Among them, V min and V max are the minimum and maximum values ​​allowed for the node voltage respectively.

9. A photovoltaic and photovoltaic storage coordinated reactive voltage control device, characterized in that: include: The photovoltaic module determines whether the electrical control conditions are met according to the acquired parameters. If the electrical control conditions are not met, the photovoltaic module participates in the voltage regulation control; A construction module is used to construct a reactive voltage droop coefficient optimization model for the photovoltaic voltage regulation control; A solution module, which solves the target droop coefficient in the droop coefficient optimization model to perform photovoltaic voltage regulation control according to the target droop coefficient, wherein the target droop coefficient is an optimal droop coefficient; The photovoltaic storage module confirms whether the parameters of the photovoltaic voltage regulation control output according to the target droop coefficient meet the electrical control conditions. If the electrical control conditions are not met, the photovoltaic storage performs adaptive droop voltage regulation control.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is processed and executed, the photovoltaic and photovoltaic storage coordinated reactive voltage control method as described in any one of claims 1-8 is implemented.

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