Power distribution network voltage control method based on distributed photovoltaic active-reactive cooperation

Abstract

The invention discloses a power distribution network voltage control method based on distributed photovoltaic active-reactive cooperation, and the method comprises the steps: analyzing the influence of reactive / active injection on voltage stability through building a sensitivity matrix, deducing a critical control gain constraint condition, and guaranteeing the static stability of the voltage of a power distribution network; a photovoltaic inverter active / reactive cooperative control general model suitable for different scenes is constructed based on an intra-day scheduling and real-time control two-stage framework, and control curve parameters of each node photovoltaic inverter are optimized by taking minimization of network loss cost, active reduction cost, inverter voltage regulation cost and voltage deviation as targets in the intra-day scheduling stage; in the real-time control stage, active power reduction and reactive power regulation are dynamically coordinated based on an optimal control curve, reactive power margin is preferentially utilized to suppress voltage rise, and power flow distribution is directly regulated through active power reduction when reactive power capacity is saturated. According to the method, the safety and economy of the power grid are considered, and theoretical support and a practical solution are provided for voltage control of the high-permeability photovoltaic power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of distribution network voltage control, and particularly to a distribution network voltage control method based on active-reactive power coordination of distributed photovoltaic power generation. Background Art

[0002] As a clean and sustainable energy form, the proportion of photovoltaic power generation in the power system has been increasing year by year. However, the output characteristics of photovoltaic power generation have significant intermittency and volatility. Especially at noon on sunny days, the photovoltaic active power reaches its peak, which may lead to reverse power flow in the distribution network. The occurrence of reverse power flow will change the power flow distribution of the traditional distribution network, causing significant changes in the voltage distribution characteristics, and further leading to the problem of grid voltage over-limit. Voltage over-limit will not only affect the power quality, but also pose a threat to the safe operation of grid equipment. In severe cases, it may even cause equipment damage or grid collapse.

[0003] Traditional voltage regulation means mainly rely on reactive power control. For example, the reactive power output of the inverter is adjusted or capacitor banks are switched on and off to maintain voltage stability. However, at noon when photovoltaic power generation is high, the reactive power regulation ability of the inverter is often limited, and the reactive power margin is insufficient. It is difficult to effectively solve the voltage over-limit problem only by reactive power control. Especially in distribution networks with high penetration of photovoltaic power generation, the effect of reactive power control is more limited. Therefore, simply relying on reactive power control can no longer meet the requirements of grid safe operation, and more flexible and diversified control strategies must be considered.

[0004] Active power curtailment has gradually attracted attention as an effective voltage control means. By actively reducing the active power output of the photovoltaic power generation system, active power curtailment reduces the impact of reverse power flow on the distribution network, thereby alleviating the voltage over-limit problem. Compared with reactive power control, active power curtailment can directly change the power flow distribution of the grid and has a more significant voltage regulation effect. However, the implementation of active power curtailment also faces many challenges, such as how to determine the mapping relationship between the reactive power output of the inverter and the node voltage. Therefore, studying the optimization method of the active power curtailment strategy and balancing the grid security and economy has become an important direction in the current research on photovoltaic grid-connected technology. Summary of the Invention

[0005] Object of the Invention: The present invention aims to provide a distribution network voltage control method based on active-reactive power coordination of distributed photovoltaic power generation to solve the voltage control problem in high-penetration photovoltaic distribution networks.

[0006] Technical Solution: The distribution network voltage control method based on active-reactive power coordination of distributed photovoltaic power generation according to the present invention includes the following steps: (1) Based on the small-signal analysis method, the sensitivity matrix of the change in the node voltage amplitude with respect to the increment of reactive / active power injection is respectively derived according to the Jacobian matrix, and the control gain constraint conditions of each inverter are determined; (2) Based on the active - voltage PV and reactive - voltage QV coordination of photovoltaic inverters in the intraday scheduling stage and the local real - time control stage, a distribution network voltage control framework is constructed, and a general model for the coordination control of PV and QV of photovoltaic inverters is built. (3) According to the critical control gains of each inverter obtained in step (1) and the general model for the coordination control of PV and QV of photovoltaic inverters obtained in step (2), while considering the distribution network power flow constraints and the lifetime loss of photovoltaic inverters participating in reactive power compensation, with the objectives of minimizing the distribution network minimum network loss cost, minimum voltage deviation, minimum active power curtailment, and minimum reactive power regulation cost, a distribution network voltage control optimization model based on distributed photovoltaic active - reactive coordination is established to realize the voltage regulation of photovoltaic inverters.

[0007] Further, when considering the QV control stability constraint of the photovoltaic inverter and ignoring the active power increment, the sensitivity matrix of the reactive power injection increment to the node voltage amplitude change is obtained. is ; Among them, is the change amount of the node voltage amplitude, is the reactive power injection increment of the photovoltaic inverter, and N, M, K, and L respectively represent the elements in the Jacobian matrix ; ; The diagonal matrix is introduced, and the reactive power injection increment of the photovoltaic inverter is ; Among them, the diagonal elements of the diagonal matrix are , and the diagonal element represents the influence of reactive power change on the local node voltage amplitude; is the inverter reactive - voltage control gain, is the voltage change amount; According to the small - signal analysis method, the critical control gain during reactive power injection is determined; During reactive power injection, the constraint condition of the control gain i of the - th inverter is as follows: ; Among them, is or .

[0008] Furthermore, ignoring the reactive power impact and considering the active power of the PV inverter on the voltage stability constraint, calculate the sensitivity matrix of the active power injection increment to the change in the node voltage magnitude. For ; Wherein, is the change in the magnitude of the node voltage, is the active power injection increment of the PV inverter, and N, M, K, and L respectively represent the elements in the Jacobian matrix ; ; Introduce the diagonal matrix , and the active power injection increment of the PV inverter is ; Wherein, the diagonal elements of the diagonal matrix are , and the diagonal element represents the impact of the active power change on the local node voltage magnitude; is the deviation between the real-time measured voltage and the desired voltage of the node at time t; When injecting active power, according to the small-signal analysis method, determine the critical control gain ; When injecting active power, the constraint condition of the control gain i of the th inverter is as follows: ; Wherein, is or .

[0009] Furthermore, in step (2), during the intraday scheduling stage, according to the PV and load output data, network topology, and parameter information of each scheduling period, optimize to obtain the parameters of each local control curve of the PV inverter with the minimum network loss and average node voltage deviation as the objectives; During the local real-time control stage, within each scheduling interval, the inverter adjusts the active / reactive power output value of the inverter in real time according to the assigned PV and QV control curves, reduces the active power output while releasing the reactive power margin, and suppresses voltage over-limit.

[0010] Furthermore, in step (2), the general model of the PV and QV coordinated control of the PV inverter is specifically as follows: The active power output value of the PV inverter is ; Wherein, is the set value of PV active power output, i.e., the reduced active power output value; is the i control gain of the th inverter; the diagonal element represents the impact of active power change on the voltage magnitude of the local node; is the dead zone range; is the voltage magnitude of the i-th node at time t in scenario r.

[0011] Furthermore, the reactive power output value of the PV inverter is ; wherein, and are the upper and lower limits of the inverter reactive power output respectively; is the reactive power output value of the inverter when in the voltage dead zone range; and represent the control gains of the curve drooping region respectively; the diagonal element represents the impact of reactive power change on the voltage magnitude of the local node; is the dead zone range; is the voltage magnitude of the i-th node at time t in scenario r.

[0012] Furthermore, the set value of PV active power output , the upper limit of the inverter reactive power output , the lower limit of the inverter reactive power output and the reactive power output value of the inverter when in the voltage dead zone range satisfy the following constraint conditions: ; ; ; ; ; wherein, is the inverter capacity; is the predicted PV output; is the PV output active power quota; is the load active power.

[0013] Furthermore, in step (3), the objective functions of minimizing network loss cost, minimizing active power curtailment, minimizing inverter voltage regulation cost, and minimizing voltage deviation are as follows: ; ; ; ; The cost of life loss caused by the participation of a photovoltaic inverter in reactive power compensation is calculated as follows: ; ; ; ; Among them, is the network loss cost, is the active power curtailment cost, is the inverter voltage regulation cost, represents the average node voltage deviation of all scenarios, is the weight factor; is the total number of preset scenarios for each time period; is the electricity price at time t; represents the branch resistance; represents the rated voltage; is the total number of nodes; and respectively represent the network active power loss and the photovoltaic active power curtailment; , , are constants; and are respectively the active power and reactive power flowing through line ; is the voltage amplitude of node t at scenario r.

[0014] Furthermore, in step (3), the following constraint conditions are satisfied: Inverter capacity constraint: ; Among them, is the reactive power output value of the inverter, is the active power output value of the photovoltaic inverter, is the capacity of the photovoltaic inverter; The active power balance equation is ; The reactive power balance equation is ; Among them, and are respectively the active power and reactive power flowing through line ; is the active power flowing into node i from other branches, is the reactive power flowing into node i from other branches; and are the active load and reactive load respectively; is the set of branches where power flows into node i; The voltage relationship between buses is as follows: ; where is the voltage magnitude of node j, represents the branch resistance, is the branch reactance, represents the rated voltage; Node voltage magnitude constraint: ; where and are the upper and lower limits of the node voltage magnitude respectively; Constraint on the transmission capacity of line active power: ; where is the maximum transmission capacity of the line active power; PV control stability constraint: ; QV control stability constraint: .

[0015] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: 1. Aiming at the problem of voltage control in a high-penetration photovoltaic distribution network, by constructing a Jacobian matrix sensitivity model, the critical gain threshold of distributed photovoltaic active / reactive power control is derived, and a voltage stability constraint mechanism is embedded in the optimization process, solving the voltage instability problem caused by excessive control gain in traditional methods; 2. The present invention proposes a two-stage collaborative framework of "intraday scheduling - real-time control". At the minute-level optimization layer, pre-decision of the inverter control curve parameters for the optimal economy of the entire network is realized. At the second-level execution layer, the capacity limit of a single regulation means is broken through by the collaborative strategy of reactive power priority - active power curtailment. The measured results show that the voltage overlimit suppression efficiency is improved, and the photovoltaic accommodation rate increases year-on-year; 3. In the intraday scheduling stage, the control curve parameters of photovoltaic inverters at each node are optimized with the goal of minimizing network loss cost, active power curtailment cost, inverter voltage regulation cost, and voltage deviation. In the real-time control stage, based on the optimal control curve, active power curtailment and reactive power regulation are dynamically coordinated. Reactive power margin is preferentially used to suppress voltage rise. When the reactive power capacity is saturated, the active power is curtailed to directly regulate the power flow distribution; 4. The present invention constructs a PV-QV collaborative model suitable for different scenarios, uses piecewise linearization modeling to generate a general control curve that takes into account fairness and dynamic response ability, and still ensures control accuracy in the scenario of severe light fluctuations, reducing voltage overlimit compared with the traditional fixed slope mode. A four-dimensional objective function including network loss cost, active power curtailment cost, equipment life loss, and voltage deviation is constructed, and first, through the weighting coefficient realize the Pareto optimization of safe and economic operation; 5. The present invention forms a complete technical chain from mechanism innovation, architecture design, model construction to engineering application, providing a voltage control paradigm with stability, economy and adaptability for the grid connection of high-proportion new energy under the new power system. Brief description of the drawings

[0016] Figure 1 is a schematic structural diagram of the distribution network voltage control framework for PV-QV coordination of the photovoltaic inverter of the present invention; Figure 2 is a PV-QV collaborative voltage control curve diagram of the photovoltaic inverter of the present invention; Figure 3 is a schematic diagram of the network topology and photovoltaic positions of the test system in the embodiment; Figure 4 is a voltage comparison diagram of Case 1 to Case 3. Detailed implementation manners

[0017] The following further describes the present invention with reference to the drawings.

[0018] The method for controlling the voltage of a distribution network based on the active-reactive power coordination of distributed photovoltaic includes the following steps: (1)Based on the small-signal analysis method, the sensitivity matrix of the reactive / power injection increment to the node voltage magnitude change is derived according to the Jacobian matrix respectively, and the control gain constraint conditions of each inverter are determined.

[0019] The relationship between the power injection increment of the PV inverter and the node voltage change can be expressed as follows: ; When considering the QV control stability constraint of the PV inverter, the sensitivity matrix of the reactive injection increment to the node voltage magnitude change can be expressed as ; In the formula, The diagonal elements of represent the influence of the reactive power change on the local node voltage magnitude; ignoring the influence of the active increment on the voltage fluctuation, according to formula (1), we get . Introduce the diagonal matrix , the diagonal elements of this matrix are . The sensitivity matrix of the reactive injection increment of the PV node to the node voltage magnitude change is .

[0020] The reactive injection increment of the PV inverter is ; In the formula, is the reactive-voltage control gain of the inverter. Considering that the number of nodes with PV inverters is , so can be simplified to , is dimensional column vector: ; In the formula, is the identity matrix. If we want to ensure the system voltage stability, all eigenvalues of should be inside the unit circle. Define as the e-th eigenvalue of, and the real number is the e-th eigenvalue of. According to the characteristics of the eigenvalues, the following relationship is obtained: ; In the formula, can be expressed as ; To ensure , the reactive-voltage control gain must satisfy the following constraint: ; Define a critical control gain , Optimize the limit in the reactive power-voltage control function gain. If the control gain is greater than , it will cause the voltage instability of the distribution network. Calculate the critical control gain as ; To satisfy constraint (7), the control gain of each inverter should be less than the critical control gain . Therefore, the voltage stability constraint can be expressed as Inequality (9) is a constraint in the optimization model, where is a very small positive number, generally taking or . The critical control gain can be calculated in advance and is a constant in the constraint.

[0021] Similarly, only considering the active power of the PV inverter on the voltage stability constraint, the sensitivity matrix of the active power injection increment to the node voltage magnitude change can be expressed as ; In the formula, the diagonal element of represents the influence of the active power change on the local node voltage magnitude; ignoring the influence of the reactive power increment on the voltage fluctuation, according to formula (1), we get . The voltage stability constraint only considers the nodes where the PV system is installed, and the number of PV nodes is counted as . Therefore, introduce a diagonal matrix , and the diagonal elements of this matrix are . The sensitivity matrix of the active power injection increment of the PV node to the node voltage magnitude change is

[0022] At time t, the deviation between the real-time measured voltage and the desired voltage of the node can be expressed as In the formula, is the measured voltage, is the desired voltage.

[0023] The active power injection increment of the PV inverter is ; In the formula, is the active power-voltage control gain of the inverter. Considering that the number of nodes with PV inverters is , therefore, we can Simplified to , is a column vector of dimension

[0024] ; To maintain the stability of the network voltage, all eigenvalues of are defined as the e-th eigenvalue of and the real number is the e-th eigenvalue of ; In the formula, can be expressed as ; To ensure , the active-power - voltage control gain must satisfy the following constraint: ; Define a critical control gain , which is the limit in optimizing the active-power - voltage control function gain. If the control gain is greater than it will cause the voltage instability of the distribution network. Calculate the critical control gain before optimization as ; To ensure that Equation (16) is satisfied, the control gain of each inverter should be less than the critical control gain . Therefore, the voltage stability constraint can be expressed as ; In the formula is a very small positive number, generally taking or , and the active-power - voltage stability constraint can be incorporated into the optimization model.

[0025] (2) Based on the voltage control framework of the coordinated PV and QV of photovoltaic inverters in the intraday scheduling stage and the local real-time control stage, construct a general model for the coordinated control of PV and QV of photovoltaic inverters.

[0026] The control framework includes the intraday scheduling stage (minute level) and the local real-time control stage (second level). Based on the PV and load output data, network topology and parameter information of each scheduling period in the intraday scheduling stage, with the goal of minimizing network losses and average node voltage deviation, the parameters of the local control curve of each PV inverter are optimized. Within each scheduling interval, the inverter adjusts the active / reactive output value of the inverter in real time according to the assigned PV and QV control curves, reducing the active output while releasing the reactive margin to further suppress voltage over-limit.

[0027] (3) According to the critical control gains of each inverter obtained in step (1) and the general model of PV and QV coordinated control of the PV inverter obtained in step (2), while considering the power flow constraints of the distribution network and the life loss of the PV inverter participating in reactive power compensation, with the goals of minimizing the distribution network loss cost, minimizing voltage deviation, minimizing active power curtailment, and minimizing reactive power regulation cost, a voltage control optimization model for the distribution network based on distributed PV active-reactive coordination is established to realize the voltage regulation of the PV inverter.

[0028] The relationship between the node active power, line power and voltage phase angle is as follows: ; In the formula, and respectively represent the node injected active power and the line active power; is the voltage phase angle; and are the node susceptance matrix and the line-node susceptance matrix respectively, and are specifically expressed as follows: ; In the formula, N is the number of nodes in the distribution network, is the line reactance value between node i and node j.

[0029] From formula (19), the relationship between the node injected power and the line power flow can be obtained as follows: ; In the formula, P is the PTDF matrix, with the dimension of the number of branches the number of nodes. With the dimension of the number of branches the number of PV nodes, representing the influence factor coefficient of the PV nodes on the power flow of each branch.

[0030] When the distributed PV is high at noon, it causes reverse power flow in the distribution network. From the Jacobian matrix in polar coordinates, it can be seen that the node voltage is affected by the node injected power. Therefore, in this section, a voltage sensitivity matrix is introduced to reflect the influence of the reverse power flow caused by PV on the node voltage amplitude. The modified equation in polar coordinates is as follows: ; Among them, is the Jacobian matrix, and the photovoltaic node voltage - active power sensitivity matrix can be expressed as ; Among them, is the voltage amplitude of the m - th node, is the active power deviation of the i - th node; represents the phase - angle deviation between node i and node j .

[0031] Introduce the influence matrices and of the photovoltaic access points on the line power flow and node voltage: ; Use the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to calculate the ranking of the influence of each distributed photovoltaic entity on the line power flow and the ranking of the influence on the node voltage, and finally obtain the comprehensive influence ranking of each distributed photovoltaic entity on the distribution network by weighted averaging. The calculation process of this method is as follows: Matrix standardization. Take the matrix as an example: ; In the formula, K is the number of photovoltaics in the distribution network; L is the number of branches. Standardize the matrix to obtain matrix Z, and the standardization formula of the element and the standardized matrix are expressed as follows: ; Define the optimal and worst solutions and respectively: ; Calculate the distances and of the i - th node from the optimal and worst solutions: ; Calculate the overall influence score of each photovoltaic on the distribution network power flow: ; Similarly, the overall influence scores of the K photovoltaic entities on the node voltage can be obtained.

[0032] After obtaining the overall influence scores of the K photovoltaic entities on the distribution network power flow and node voltage, take the average of these two score matrices to obtain the overall comprehensive influence score matrix M of the photovoltaic entities on the distribution network: ; The above comprehensive score matrix is the ratio basis for allocating power reverse injection quotas to distributed PV entities, i.e., allocating according to the elements of matrix M. When the allocated quota makes the distribution network just not have overvoltage at that moment, the reverse injection power quota values Q of K distributed PV entities can be obtained: ; Allocate the reverse injection power quota of the node according to the degree of influence on the power flow and the degree of influence on the voltage of the remaining nodes. When the reverse injection power value of the node is greater than the quota, the system has a risk of overvoltage, that is, the node will bear the responsibility for overvoltage. In addition, this reverse injection power quota is the net reverse injection power, that is, the grid-connected power quota after self-generation and self-consumption. Therefore, the distribution network can control the PV curtailment power to make the reverse injection power not exceed the quota.

[0033] The general model for the coordinated control of PV inverters PV and QV is as follows: The main parameters of the PV curve include the dead zone range and the control gain , and the active power output value of the PV inverter is ; In the formula, is the set value of the PV active power output, that is, the reduced active power output value. When the node voltage exceeds the threshold , the inverter further reduces the active power output to suppress the voltage rise. Generally, the maximum allowable voltage is taken as 1.05 p.u.

[0034] Similarly, the QV control curve realizes voltage support by adjusting the reactive power output. Its parameters include the dead zone range , the control gain and as well as the voltage thresholds and .

[0035] ; In the formula, and are the upper and lower limits of the reactive power output of the inverter respectively; is the reactive power output value of the inverter within the voltage dead zone range; these three parameters are all variables to be optimized. Generally, the minimum allowable voltage is taken as 0.95 p.u.; is a parameter to be optimized. If the node voltage amplitude is less than , the inverter emits reactive power with the maximum remaining capacity; is a parameter to be optimized. If the node voltage amplitude is greater than , the inverter absorbs reactive power with the maximum remaining capacity; Generally, the maximum value of the allowable voltage is taken as 1.05 p.u.

[0036] In addition, the PV and QV control parameters need to satisfy the capacity constraint of the inverter: ; ; ; ; ; In the formula, is the capacity of the inverter; is the predicted PV output; is the active power quota of the PV output; is the active power of the load.

[0037] By optimizing the local control curve parameters of the PV inverter, while taking into account the minimization of the system network loss cost, the active power curtailment cost, the inverter voltage regulation cost, and the node voltage deviation minimization, including: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; The objective function (39) aims to minimize the network loss cost, the active power curtailment, the inverter voltage regulation cost, and the voltage deviation, where is the network loss cost, is the active power curtailment cost, is the inverter voltage regulation cost, represents the average node voltage deviation of all scenarios, is the weight factor. Equations (41)-(46) respectively give the calculation methods of various costs and voltage deviation in the objective function, where R is the total number of preset scenarios in each time period; is the electricity price at time t; represents the branch resistance; represents the rated voltage; N is the total number of nodes; and respectively represent the network active power loss and the PV active power curtailment; Equation (43) is used to calculate the cost of life loss caused by the PV inverter participating in reactive power compensation, where 、 and are constants; and are respectively the active power and reactive power flowing through line ij; is the voltage magnitude of node i at time t in scenario r. Equation (47) is the inverter capacity constraint; Equations (48) and (49) are respectively the active power balance equation and the reactive power balance equation. Where is the predicted active power output of the PV; is the reactive power output value of the inverter; and are respectively the active load and the reactive load; In the Distflow model, in the branch power loss, the quadratic term is much smaller than the linear term, and the influence of the quadratic term can be ignored. Therefore, the voltage relationship between buses can be expressed as a linear model, as shown in Equation (50). Equation (51) is the node voltage magnitude constraint, and are respectively the upper and lower limits of the node voltage magnitude. Equation (52) is the constraint of the line active power transmission capacity, is the maximum transmission capacity of the line active power. Equations (53) and (54) are the PV and QV control stability constraints.

[0038] To verify the effectiveness of the present invention, the present invention connects 8 PV units to the grid, and the access capacity and location information of the PV and the inverter are shown in Table 1. Figure 3 is the network topology and PV location of the test system. The capacity of the PV inverter is set to 1.1 times the rated active power of the PV, so that even when the inverter outputs at the rated active power, the inverter can provide reactive power support. The required voltage range is set to 0.95 p.u. to 1.05 p.u. Take and They are set to 0.95 p.u. and 1.05 p.u. respectively. The weight coefficient is set to 0.5. By comparing the key indicators such as the voltage distribution, network loss, and active power curtailment before and after optimization, the advantages of the proposed method in improving system performance are evaluated. The instruction scheduling interval of the local PV and QV control parameters within a day is set to 1 hour, and the expected number of scenarios of the internal source and load during the scheduling interval. Finally, the effectiveness of the proposed control strategy is verified through three different control cases.

[0039] Case 1: There is no local control strategy on the photovoltaic inverter, and the initial operating state is obtained only by "minute-level" scheduling.

[0040] Case 2: The optimization model is solved by calling the solver to obtain the local control parameters of PV and QV of the photovoltaic inverter to adjust the reactive power of the distributed photovoltaic.

[0041] Case 3: By solving the optimization model of the present invention, the local control parameters of PV and QV of the distributed photovoltaic are obtained to adjust the active power and reactive power of the inverter, achieving global optimization.

[0042] Table 1 ; The voltage of the test system is controlled by using the method of the present invention. As Figure 4 shown, the voltage distributions of the PVs at nodes 18 and 32 are presented. The high proportion of distributed photovoltaic access in the distribution network leads to frequent voltage fluctuations at the nodes where distributed photovoltaic is connected in Case 1, and the voltage deviation is serious or even exceeds the limit. Taking node 18 as an example. During 12:00 - 14:00, reverse power flow occurs on the entire feeder, which is caused by the relatively high active power output of the photovoltaic and the relatively low load level at this time, resulting in the voltage at node 18 exceeding 1.05 p.u. Through the corresponding control strategy proposed in Scheme 2, the photovoltaic inverter adjusts the reactive power in real time according to the local QV control curve, and the voltage distribution of the system is improved. However, at this time, the reactive power margin of the inverter is insufficient, the voltage support for the system is insufficient, and the voltage fluctuates greatly, and there is still a situation of exceeding the limit. In Scheme 3, the local control strategy of the distributed photovoltaic inverter adopts a comprehensive objective function, considering the minimum network loss cost, the minimum active power curtailment cost, and the minimum average node voltage deviation at the same time. Therefore, when a relatively serious voltage deviation has occurred at node 18, the active power curtailment strategy of the photovoltaic is adopted during 12:00 - 14:00 to achieve the comprehensive optimization of the objective function. At the same time, the minimum active power curtailment of the distributed photovoltaic is considered in the objective function. When the reactive power is sufficient, frequent active power curtailment is avoided to maximize the utilization of photovoltaic resources.

Claims

1. A distribution network voltage control method based on distributed photovoltaic active-reactive coordination, characterized in that: The following steps are involved: (1) Based on the small disturbance analysis method, the sensitivity matrices of reactive / active injection increments to node voltage amplitude changes are derived according to the Jacobian matrix, and the control gain constraints of each inverter are determined; (2) Based on the distribution network voltage control framework of the coordinated active power-voltage PV and reactive power-voltage QV of the photovoltaic inverter in the intraday scheduling stage and the local real-time control stage, a general model of the coordinated control of photovoltaic inverter PV and QV is constructed; (3) Based on the critical control gains of each inverter obtained in step (1) and the general model of PV and QV coordinated control of photovoltaic inverters obtained in step (2), the power flow constraints of the distribution network and the life loss of photovoltaic inverters participating in reactive power compensation are considered at the same time. With the goal of minimum network loss cost, minimum voltage deviation, minimum active power reduction and minimum reactive power regulation cost of the distribution network, a distribution network voltage control optimization model based on distributed photovoltaic active-reactive coordination is established to achieve voltage regulation of photovoltaic inverters.

2. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to claim 1 is characterized in that: When considering the stability constraint of the PV inverter QV control, ignoring the active power increment, the sensitivity matrix of the reactive power injection increment to the node voltage amplitude change is obtained: for ； in, is the amplitude change of the node voltage, is the reactive power injection increment of the photovoltaic inverter, N, M, K, and L represent the Jacobian matrix Elements in ； Introducing the diagonal matrix , the reactive power injection increment of the PV inverter for ； Among them, the diagonal matrix The diagonal elements of , the diagonal elements Indicates the impact of reactive power changes on the local node voltage amplitude; is the inverter reactive-voltage control gain, is the voltage change; Determine the critical control gain during reactive injection based on the small disturbance analysis method ; When reactive power is injected, i The control gain of each inverter The constraints are as follows: ； in, for or .

3. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to claim 1 is characterized in that: Ignoring the influence of reactive power and considering the active power of photovoltaic inverters on voltage stability, the sensitivity matrix of active power injection increment to node voltage amplitude change is calculated. for ； in, is the amplitude change of the node voltage, is the increment of active power injection of the photovoltaic inverter, N, M, K, L represent the Jacobian matrix Elements in ； Introducing the diagonal matrix , the active power injection increment of the PV inverter for ； Among them, the diagonal matrix The diagonal elements of , the diagonal elements Indicates the impact of active power changes on the local node voltage amplitude; is the deviation between the real-time measured voltage and the expected voltage of the node at time t; When active power is injected, the critical control gain is determined according to the small disturbance analysis method. ; When active power is injected, i The control gain of each inverter The constraints are as follows: ； in, for or .

4. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to any one of claims 2 or 3, characterized in that: In step (2), during the intraday scheduling phase, based on the photovoltaic and load output data, network topology and parameter information of each scheduling period, the local control curve parameters of each photovoltaic inverter are optimized with the goal of minimizing network loss and average node voltage deviation; In the local real-time control stage, within each scheduling interval, the inverter adjusts the active / reactive output value of the inverter in real time according to the assigned PV and QV control curves, reduces the active output while releasing the reactive margin and suppressing voltage over-limit.

5. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to claim 4 is characterized in that: In step (2), the general model of cooperative control of photovoltaic inverter PV and QV is as follows: Photovoltaic inverter active power output value for ； in, It is the photovoltaic active output setting value, that is, the active output value after reduction; For the i The control gains of the inverters; the diagonal elements Indicates the impact of active power changes on the local node voltage amplitude; is the dead zone range; is the threshold value; is the voltage amplitude of the i-th node at time t in scenario r.

6. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to claim 5 is characterized in that: Reactive power output value of photovoltaic inverter for ； in, and They are the upper and lower limits of the inverter reactive output respectively; The reactive output value of the inverter when it is within the voltage dead zone range; and Represent the control gains in the droop region of the curve; the diagonal elements Indicates the impact of reactive power changes on the local node voltage amplitude; is the dead zone range; is the voltage amplitude of the i-th node at time t in scenario r.

7. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to claim 6 is characterized in that: Photovoltaic active output setting value , the upper limit of the inverter reactive output , the lower limit of the inverter reactive output The reactive output value of the inverter when the voltage is in the dead zone range The following constraints are met: ； ； ； ； ； in, is the inverter capacity; To predict photovoltaic output; Output active power quota for photovoltaic power; is the load active power.

8. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to claim 7 is characterized in that: In step (3), the objective functions of minimizing network loss cost, minimizing active power reduction, minimizing inverter voltage regulation cost, and minimizing voltage deviation are as follows: ； ； ； ； The cost of the life loss caused by the PV inverter participating in reactive power compensation is calculated as follows: ； ； ； ； in, is the network loss cost, It is a meritorious cost reduction. is the inverter voltage regulation cost, represents the average node voltage deviation of all scenarios, is the weight factor; The total number of preset scenes for each time period; is the electricity price at time t; Indicates branch resistance; Indicates rated voltage; is the total number of nodes; and They represent the network active power loss and PV active power reduction respectively; , , is a constant; and Line The active power and reactive power flowing through it; for Voltage amplitude of node r at time t.

9. The distribution network voltage control method based on distributed photovoltaic active-reactive coordination according to claim 8 is characterized in that: Step (3) satisfies the following constraints: Inverter capacity constraints: ； in, is the inverter reactive output value, is the active power output value of the photovoltaic inverter, is the capacity of the PV inverter; The active power balance equation is ； The reactive power balance equation is ； in, and Line The active power and reactive power flowing through it; is the active power from other branches flowing into node i, is the reactive power from other branches flowing into node i; and They are active load and reactive load respectively; is the set of branches where power flows into node i; The voltage relationship between the buses is as follows: ； in, is the voltage amplitude of node j, represents the branch resistance, is the branch reactance, Indicates rated voltage; Node voltage amplitude constraint: ; in, and are the upper and lower limits of the node voltage amplitude respectively; Constraints on line active power transmission capacity: ； in, is the maximum transmission capacity of the line active power; PV control stability constraints: ； QV control stability constraints: 。

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