Layered voltage control method for power distribution network, medium and equipment

Abstract

The invention discloses a power distribution network layered voltage control method, a medium and equipment. According to the method, firstly, a reactive power-voltage model of distributed photovoltaic and electric vehicle energy storage is constructed, and in consideration of differences of EV battery capacities and states of charge (SoC), a voltage adjustment degree consistency principle is proposed and reactive power output of the EV is corrected. On the basis, reactive power changes of the PV and the EV and tap changes of an on-load tap changing transformer (OLTC) are comprehensively considered, multi-time-step-scale MPC optimization is used for achieving centralized control, and the correcting value of the PV steady-state VQ characteristic and the reactive power control adjusting quantity of the EV are calculated. And the PV and EV access points perform local control on a local unit after receiving the regulating variable of the upper layer, so as to ensure that the voltage of each node of the active power distribution network does not exceed the limit. According to the method, the PV and the EV can be cooperated to participate in active power distribution network voltage control, the adjustable margin of the battery capacity of each EV node is ensured to be reduced according to a consistency principle, and the adjustment frequency of the OLTC tap during voltage fluctuation is reduced.

Description

Technical Field

[0001] This invention relates to the field of voltage control technology, specifically to a method, medium, and equipment for hierarchical voltage control in power distribution networks. Background Technology

[0002] In recent years, with the deterioration of energy shortages and environmental problems, developing renewable energy power generation has become an important development strategy for my country. Distributed photovoltaic (PV) power generation is increasingly penetrating the distribution network. However, due to the strong correlation between PV power output and complex stochastic weather factors, integrating PV power into the grid may significantly increase the uncertainty of grid voltage fluctuations, leading to frequent voltage exceedances in the distribution network. Therefore, it is necessary to develop a voltage control method for high-penetration distributed PV connected to the distribution network to ensure the safe and stable operation of the grid. Battery energy storage has the advantages of rapid charging and discharging and strong ramping capability. Currently, the number of electric vehicles (EVs) in active distribution networks is greatly increasing, and EVs have good dispatch potential during their non-driving phases. Using EV battery energy storage for renewable energy consumption in the power system has become a feasible option. During peak PV power generation periods, EV batteries can be used to store energy that the system cannot consume. When PV power generation is low and its output cannot meet load demand, this energy can be released to control the voltage stability of the power system.

[0003] Compared to traditional control methods, model predictive control can handle multivariable and multi-objective problems. The combined system targeted by this method contains multiple thermal power and battery energy storage units, with high system order and complex time delay relationships, which better demonstrates the advantages of model predictive control. Summary of the Invention

[0004] The present invention proposes a distribution network hierarchical voltage control method, medium and equipment, and equipment and storage medium, which can at least solve one of the technical problems in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method, medium, and device for hierarchical voltage control in a power distribution network, wherein the method executes the following steps via computer equipment: S100. Construct a voltage-reactive dynamic response model for distributed photovoltaic (PV) and a consensus-based dynamic response model for EV battery energy storage, along with a consensus algorithm. S200, based on the PV voltage-reactive dynamic response model and the EV battery energy storage dynamic response model, comprehensively considers the number of tap changes of the on-load tap changer (OLTC), the speed and accuracy of voltage control, and the subsequent regulation capability of battery energy storage. It sets target weights and control parameters, uses MPC optimization with multiple time steps to achieve centralized control, and calculates the correction amount of PV steady-state VQ characteristics and the reactive power regulation amount of EV. S300: Construct an upper-level voltage control architecture. After receiving the local signal, the upper-level controller performs slow-time-scale MPC optimization based on the optimization model constructed in step S200. After receiving the PV steady-state VQ characteristic correction and EV reactive power regulation output from the upper-level controller's MPC optimization, the local controller adjusts the reactive power output according to the dynamic response models of PV and EV to achieve voltage correction. S400: Construct a lower-level voltage control architecture. The lower-level controller works in conjunction with the upper-level voltage control architecture to perform reactive power compensation on-site.

[0006] Furthermore, the method for constructing the distributed photovoltaic voltage-reactive power dynamic response model in step S100 of the present invention includes: For distributed photovoltaic (PV) systems, in steady state, the reactive power output of the PV under local control changes according to the piecewise linear VQ characteristic shown in the following formula;

[0007] The measured terminal voltage is in the dead zone. Within the dead zone, the reactive power generated by distributed photovoltaic (PV) remains zero. Outside the dead zone, PV responds to overvoltage or undervoltage by consuming or generating reactive power. When the voltage is lower or higher At that time, the reactive power output remained at or .

[0008] Furthermore, the method for constructing the EV battery energy storage dynamic response model in step S100 of the present invention includes: Based on the EV consensus algorithm, the SoC state of all EV batteries is controlled by the central controller to be as close as possible, thereby effectively utilizing the energy storage capacity of the EV and improving the regulation margin of the EV. For EVs connected to the power grid, their output should satisfy the following formula:

[0009]

[0010] In the formula, It is the initial state of charge of the EV user at node i when it joins the network. It is the final state of charge of the electric vehicle (EV) user at node i when the EV user leaves the grid. This state of charge should not be lower than the user's expected value. It refers to charge / discharge efficiency; It is the maximum battery capacity of the EV user at node i; and These represent the minimum and maximum state of charge, respectively. It is the active power output of EV at node i; Define voltage control regulation as :

[0011]

[0012] In the formula, and These represent the voltage regulation at the dominant node and node i, respectively; and These represent the actual charging and discharging power of the EV at the dominant node and node i, respectively. and These are the maximum charge and discharge power of the EV at the dominant node and node i, respectively; , The weight is the voltage regulation contribution, which is usually related to the EV battery capacity. Here, we assume that it is proportional to the EV battery capacity. The adjustment degree of the dominant node and all other nodes can be obtained by the following formula:

[0013]

[0014] In the formula, and The parameters are adjustable and are related to the convergence speed and accuracy. These are the elements in the connected matrix A; The average SoC of all EV batteries connected to the power grid is calculated using the following formula:

[0015] In the formula, This represents the SoC value of EV at node i; It is a 0-1 variable, indicating whether there is an EV connected to the system at node i; Based on the SoC value of EV at node i and the average SoC of the system, the correction amount for the voltage regulation contribution can be calculated:

[0016] In the formula, This is a correction factor during charging. This is a correction factor during discharge; and This is a positive adjustment parameter, related to the convergence rate; After correcting for the voltage regulation contribution, the EV charging and discharging power at the i-th node during the voltage regulation process can be obtained as follows:

[0017] Voltage fluctuations are mainly related to reactive power changes; therefore, the reactive power change during EV charging and discharging can be calculated using the following formula. :

[0018] In the formula, This refers to the power factor of the battery in an electric vehicle (EV).

[0019] Furthermore, in step S200 of the present invention, setting the target weights and control parameters, and using multi-time-step MPC optimization to implement the centralized control method includes: Define the objective function as:

[0020] In the formula, and These are matrices formed by the reactive power changes of PV and EV access nodes at time t+i, respectively. and It is a diagonal matrix, which allows different weights to be assigned to different PVs and EVs; Indicates the OLTC tap changer action value. It refers to the weight; the greater the weight, the fewer adjustments the OLTC tap changer needs to be made. It is a slack variable; Solving the optimization problem based on the objective function constraints, the final output is the correction amount for the steady-state VQ characteristics of PV in the next Nc steps. and the reactive power regulation of EV It can be obtained from the following formula:

[0021]

[0022] In the formula, U is the identity matrix; R is a diagonal matrix whose diagonal elements are droop coefficients; It is the system voltage-reactive power sensitivity matrix, S QQ It is the sensitivity of photovoltaic reactive power and system reactive power. Furthermore, the objective function optimization conditions of the present invention include: Voltage constraint:

[0023]

[0024]

[0025] Current constraint:

[0026]

[0027] In the formula, and For the sensitivity matrix, It can be obtained by inverting the reactive power Jacobian matrix.

[0028] Reactive power constraint:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Transformer tap change constraints:

[0035]

[0036] EV battery constraints:

[0037]

[0038]

[0039]

[0040] Power balance constraints:

[0041] .

[0042] Furthermore, the voltage correction method implemented in step S300 of the present invention includes: After the optimization problem is solved, the centralized controller will output a... The signal is then sent to each local controller, which, upon receiving the correction signal from the upper layer, controls the voltage using a dynamic response model. In this system, the PV nodes achieve voltage control by correcting the steady-state VQ curve of the distributed photovoltaic system. After receiving a signal from the centralized controller, the local controller accumulates this increment.

[0043]

[0044] In the formula, R This is the droop coefficient. V cor Even the correction signal from the local controller; Subsequently, the local controller uses the correction signal received from the centralized controller to shift the steady-state VQ characteristic of the PV parallel to the x-axis. The VQ characteristic curve after the shift can be expressed by the following formula:

[0045] In the formula, R is the droop coefficient; For EV nodes, the local controller receives the signal from the upper-level controller. Then, by adjusting the charging power of the charging pile and the inverter mode, the reactive power output can be changed.

[0046] In summary, this invention achieves global optimization across multiple time scales through an upper-level model predictive controller, enabling precise control of the system voltage. A voltage regulation consistency algorithm is proposed, establishing a correlation model between node voltage deviation and the charge / discharge margin of electric vehicles (EVs). A dynamic feedback correction mechanism based on state-of-charge (SOC) consistency is introduced, prioritizing discharge for EVs with higher SOCs and charging for EVs with lower SOCs. This ensures that the SOC of all EV batteries is consistent while achieving the voltage regulation target, effectively preventing some batteries from prematurely reaching their charge / discharge limits and exiting regulation. This significantly improves the overall adjustability margin and continuous control capability of the EV cluster. It provides a systematic solution for voltage security in distribution networks with high proportions of renewable energy integration. Attached Figure Description

[0047] Figure 1 This is a flowchart of the layered voltage control method of the present invention; Figure 2 A schematic diagram illustrating the adjustable margin of energy storage for electric vehicles; Figure 3 This is the dynamic response model for the control scheme. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0049] like Figure 1 As shown in this embodiment, a hierarchical voltage control method for a power distribution network is implemented by a computer, comprising the following steps: S100. Construct a voltage-reactive dynamic response model for distributed photovoltaic (PV) and a consensus-based dynamic response model for electric vehicle (EV) battery energy storage, along with a consensus algorithm. S200, based on the PV voltage-reactive dynamic response model and the EV battery energy storage dynamic response model, comprehensively considers the number of tap changes of the on-load tap changer (OLTC), the speed and accuracy of voltage control, and the subsequent regulation capability of battery energy storage. It sets target weights and control parameters, uses MPC optimization with multiple time steps to achieve centralized control, and calculates the correction amount of PV steady-state VQ characteristics and the reactive power regulation amount of EV. S300: Construct an upper-level voltage control architecture. After receiving the local signal, the upper-level controller performs slow-time-scale MPC optimization based on the optimization model constructed in step S200. After receiving the PV steady-state VQ characteristic correction and EV reactive power regulation output from the upper-level controller's MPC optimization, the local controller adjusts the reactive power output according to the dynamic response models of PV and EV to achieve voltage correction. S400: Construct a lower-level voltage control architecture. The lower-level controller works in conjunction with the upper-level voltage control architecture (the lower-level voltage control is less sensitive than the upper-level control; the local controller will only react immediately when there are severe voltage fluctuations) to perform reactive power compensation on-site and reduce voltage fluctuations.

[0050] The following provides a detailed explanation of each step: S100. Construct a voltage-reactive dynamic response model for distributed photovoltaic (PV) and a consensus-based dynamic response model for EV battery energy storage, along with a consensus algorithm. This invention proposes an active distribution network hierarchical voltage control scheme based on the consistency of distributed photovoltaic (PV) and electric vehicle (EV) energy storage, which is applied in the dispatch and control center of the power system. The overall process of this method is attached. Figure 1 As shown, it includes the following steps: Construct a voltage-reactive power dynamic response model for PV; For distributed photovoltaic (PV) systems, in steady state, the reactive power output of the PV under local control varies according to the piecewise linear VQ characteristic shown in the following formula.

[0051]

[0052] As long as the measured terminal voltage is within the dead zone Within the dead zone, the reactive power generated by distributed photovoltaic (PV) systems remains zero, typically to minimize internal losses within the PV system. Outside this dead zone, the PV system responds to overvoltage or undervoltage by consuming or generating reactive power. When the voltage is lower than... or higher At that time, the reactive power output remained at or .

[0053] Under an ideal static load model, voltage and reactive power may exhibit a linear relationship. The intersection of this relationship with the voltage-reactive power characteristic curve of the PV (Power Generation Vehicle) is the system operating point. During normal system operation, this operating point is generally within the dead zone, and the reactive power generated by the PV is zero. When a disturbance occurs, the system characteristics change, and the intersection with the PV's voltage-reactive power characteristic curve is no longer in the dead zone. This indicates that the PV responds in real-time to changes in grid voltage by generating or absorbing reactive power to reduce voltage deviation. However, relying solely on the local control of the PV to regulate voltage is insufficient. Therefore, centralized control of the PV and EV battery energy storage are needed for coordinated optimization.

[0054] Constructing a consistency-based dynamic response model for EV battery energy storage: The EV-based consensus algorithm controls the SoC state of all EV batteries to be as close as possible through the central controller, thereby effectively utilizing the energy storage capacity of EVs and improving the regulation margin of EVs.

[0055] For EVs connected to the power grid, their output should satisfy the following formula:

[0056]

[0057] In the formula, It is the initial state of charge of the EV user at node i when it joins the network. It is the final state of charge of the electric vehicle (EV) user at node i when the EV user leaves the grid. This state of charge should not be lower than the user's expected value. It refers to charge / discharge efficiency; It is the maximum battery capacity of the EV user at node i; and These represent the minimum and maximum state of charge, respectively. It is the active power output of EV at node i.

[0058] like Figure 2The figure illustrates the relationship between the adjustable capacity of each electric vehicle (EV) connected to the grid and time. The upper boundary represents the EV being charged immediately to its maximum battery capacity after grid connection and then kept in standby mode. The lower boundary represents the EV being discharged immediately to its maximum error charge (the maximum deviation between the actual charge and the expected charge during charging or discharging) after grid connection and kept in standby mode for a period of time until the control system forces it to charge to ensure that the required charge is reached when the user disconnects from the grid. This charging process is uncontrollable. Therefore, to avoid EVs reaching their maximum battery capacity or maximum error charge too early after grid connection, which would prevent them from participating in voltage control during subsequent charging, a EV charging and discharging consistency coordination is proposed to ensure that the battery capacity of each EV does not reach its upper or lower limit too early, thus reducing the adjustable margin.

[0059] Define voltage control regulation as :

[0060]

[0061] In the formula, and These represent the voltage regulation at the dominant node and node i, respectively; and These represent the actual charging and discharging power of the EV at the dominant node and node i, respectively. and These are the maximum charge and discharge power of the EV at the dominant node and node i, respectively; , The weight is the contribution of voltage regulation, which is usually related to the EV battery capacity. Here, we assume that it is proportional to the EV battery capacity.

[0062] The adjustment degree of the dominant node and all other nodes can be obtained by the following formula:

[0063]

[0064] In the formula, and The parameters are adjustable and are related to the convergence speed and accuracy. Let be the elements in the connected matrix A.

[0065] Since the initial state of charge (SOC) of each EV is different when it is connected to the grid, EVs with a higher initial SOC will be more likely to reach the maximum SOC when the charging power is greater than zero, while EVs with a lower initial SOC will be more likely to reach the minimum SOC when the charging power is less than zero. Both of these situations will lead to a decrease in the adjustable margin of EVs participating in voltage regulation. Therefore, it is necessary to correct the voltage regulation contribution of each EV based on SoC consistency so that the battery SOC of each EV connected to the grid is similar.

[0066] First, calculate the average SoC of all EV batteries connected to the power grid. The calculation formula is as follows:

[0067] In the formula, This represents the SoC value of EV at node i; It is a 0-1 variable, indicating whether there is an EV connected to the system at node i.

[0068] Based on the SoC value of EV at node i and the average SoC of the system, the correction amount for the voltage regulation contribution can be calculated:

[0069] In the formula, This is a correction factor during charging. This is a correction factor during discharge; and This is a positive adjustment parameter, which is related to the convergence rate.

[0070] After correcting for the voltage regulation contribution, the EV charging and discharging power at the i-th node during the voltage regulation process can be obtained as follows:

[0071] Voltage fluctuations are mainly related to reactive power changes; therefore, the reactive power change during EV charging and discharging can be calculated using the following formula. :

[0072] In the formula, This refers to the power factor of the battery in an electric vehicle (EV).

[0073] S200, based on the PV voltage-reactive dynamic response model and the EV battery energy storage dynamic response model, comprehensively considers the number of tap changes of the on-load tap changer (OLTC), the speed and accuracy of voltage control, and the subsequent regulation capability of battery energy storage. It sets target weights and control parameters, uses MPC optimization with multiple time steps to achieve centralized control, and calculates the correction amount of PV steady-state VQ characteristics and the reactive power regulation amount of EV. Taking into account the number of tap changes of the on-load tap changer (OLTC), the speed and accuracy of voltage control, and the subsequent regulation capability of battery energy storage, target weights and control parameters are set, and centralized control is achieved using multi-time-step MPC optimization. The objective function is:

[0074] In the formula, and These are matrices formed by the reactive power changes of PV and EV access nodes at time t+i, respectively. and It is a diagonal matrix, which allows different weights to be assigned to different PVs and EVs; Indicates the OLTC tap changer action value. It refers to the weight; the greater the weight, the fewer adjustments the OLTC tap changer needs to be made. It is a slack variable.

[0075] The objective function has the following constraints: Voltage constraint:

[0076]

[0077]

[0078] Current constraint:

[0079]

[0080] In the formula, and For the sensitivity matrix, It can be obtained by inverting the reactive power Jacobian matrix.

[0081] Reactive power constraint:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Transformer tap change constraints:

[0088]

[0089] EV battery constraints:

[0090]

[0091]

[0092]

[0093] Power balance constraints:

[0094]

[0095] Solving the optimization problem, the final output is the correction amount for the steady-state VQ characteristics of PV in the next Nc steps. and the reactive power regulation of EV It can be obtained from the following formula:

[0096]

[0097] In the formula, U is the identity matrix; R is a diagonal matrix whose diagonal elements are droop coefficients; It is the system voltage-reactive power sensitivity matrix, S QQ It is the sensitivity of photovoltaic reactive power and system reactive power. The correction values ​​for the steady-state VQ characteristics of the PV and the reactive power regulation of the EV are transmitted to the lower-level local controller to achieve voltage correction. Wait for the next time step and repeat the above optimization steps.

[0098] S300: Construct an upper-level voltage control architecture. After receiving the local signal, the upper-level controller performs slow-time-scale MPC optimization based on the optimization model constructed in step S200. After receiving the PV steady-state VQ characteristic correction and EV reactive power regulation output from the upper-level controller's MPC optimization, the local controller adjusts the reactive power output according to the dynamic response models of PV and EV to achieve voltage correction. After the optimization problem is solved, the centralized controller will output a... And it is then sent down to each local controller. After receiving the correction signal from the upper layer, the local controller processes it as follows: Figure 3 The control flow shown implements voltage control. Specifically, the PV node uses a method of correcting the steady-state VQ curve of the distributed photovoltaic system to achieve voltage control. After receiving a signal from the central controller, the local controller accumulates this increment.

[0099]

[0100] In the formula, R This is the droop coefficient. V corEven the correction signal from the local controller. The local controller then uses the correction signal, i.e., the correction signal received from the centralized controller, to shift the steady-state VQ characteristic of the PV parallel to the x-axis. The VQ characteristic curve after the shift can be expressed by the following formula:

[0101] In the formula, R is the droop coefficient.

[0102] For EV nodes, the local controller receives the signal from the upper-level controller. Then, by adjusting the charging power of the charging pile and the inverter mode, the reactive power output can be changed.

[0103] S400: Construct a lower-level voltage control architecture. The lower-level controller works in conjunction with the upper-level voltage control architecture (the lower-level voltage control is less sensitive than the upper-level control; the local controller will only react immediately when there are severe voltage fluctuations) to perform reactive power compensation on-site and reduce voltage fluctuations.

[0104] The local controller continuously monitors voltage fluctuations in shorter time steps and responds quickly to mitigate voltage fluctuations when the voltage exceeds local limits.

[0105] Centralized control and local control specify different voltage limits. Because centralized control uses multi-time-step MPC optimization, its control has a certain delay. Therefore, local control needs to mitigate voltage deviation in the first few seconds after a voltage fluctuation occurs. Generally, Less than The lower-level controller is set to monitor the voltage level every five minutes. If the voltage exceeds the local limit, the local controller will first reduce the voltage deviation, and then wait for the central controller to take the next time step before the central controller will further reduce the voltage to the normal level.

[0106] In summary, this invention achieves global optimization across multiple time scales through an upper-level model predictive controller, enabling precise control of the system voltage. A voltage regulation consistency algorithm is proposed, establishing a correlation model between node voltage deviation and the charge / discharge margin of electric vehicles (EVs). A dynamic feedback correction mechanism based on state-of-charge (SOC) consistency is introduced, prioritizing discharge for EVs with higher SOCs and charging for EVs with lower SOCs. This ensures that the SOC of all EV batteries is consistent while achieving the voltage regulation target, effectively preventing some batteries from prematurely reaching their charge / discharge limits and exiting regulation. This significantly improves the overall adjustability margin and continuous control capability of the EV cluster. It provides a systematic solution for voltage security in distribution networks with high proportions of renewable energy integration.

[0107] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0108] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the mobile source emission prediction methods based on time-series feature migration described in the above embodiments.

[0109] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0110] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0111] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0112] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power distribution network hierarchical voltage control method, medium and device, characterized in that, The following steps are performed by a computer device: S100, constructing a distributed photovoltaic (PV) voltage-reactive power dynamic response model and an EV battery energy storage dynamic response model based on consistency, and a consistency algorithm; S200, based on the PV voltage-reactive power dynamic response model and the EV battery energy storage dynamic response model, comprehensively considering the OLTC tap changer frequency, the rapidity and accuracy of voltage control, and the subsequent regulation capacity of battery energy storage, setting target weights and control parameters, using multi-time step MPC optimization to realize centralized control, calculating the correction amount of PV steady-state VQ characteristics and the reactive power regulation amount of EV; S300, constructing an upper-layer voltage control architecture, and the upper-layer controller receives local signals and performs MPC optimization at a slow time scale based on the optimization model constructed in step S200; after receiving the PV steady-state VQ characteristic correction amount and the EV reactive power regulation amount output by the MPC optimization of the upper-layer controller, the local controller adjusts the reactive power output according to the dynamic response model of PV and EV to realize voltage correction; S400, constructing a lower-layer voltage control architecture, and the lower-layer controller cooperates with the upper-layer voltage control architecture to perform local reactive power compensation.

2. The hierarchical voltage control method for power grid according to claim 1, characterized in that, The method for constructing the distributed photovoltaic voltage-reactive power dynamic response model in step S100 includes: For the distributed photovoltaic PV, under local control, the reactive power output of the PV changes according to the segmented linear VQ characteristic shown in the following formula at steady state; The measured terminal voltage is in a dead zone Within the dead zone, the reactive power produced by the distributed photovoltaic (PV) is kept to zero, outside the dead zone, the PV reacts to over- or under-voltage by consuming or producing reactive power, respectively; When the voltage is below or above , the reactive output is kept at or .

3. The layered voltage control method for power distribution network according to claim 1, characterized in that, The method for constructing the EV battery energy storage dynamic response model in step S100 includes: Based on the consistency algorithm of EV, the central controller controls the SoC states of all EV batteries to be as close as possible, thereby effectively utilizing the energy storage capacity of EV and improving the regulation margin of EV; For the EV connected to the power grid, the output should satisfy the following formula: wherein, is the initial state of charge when the EV user plugs in at node i, is the final state of charge when the EV user unplugs at node i, which should not be lower than the user's desired value; is the charging and discharging efficiency; is the maximum capacity of the EV user's battery at node i; and are the minimum and maximum state of charge, respectively; is the active power of the EV at node i; The voltage control adjustment degree is defined as : where, and are the voltage regulation degree at the dominant node and node i, respectively; and are the actual charging / discharging power of EVs at the dominant node and node i, respectively; and are the maximum charging / discharging power of EVs at the dominant node and node i, respectively; , is the weight, i.e., the voltage regulation contribution degree, which is usually related to the EV battery capacity and is assumed to be proportional to the EV battery capacity in this paper. The adjustment degree of the main node and the remaining nodes can be obtained by the following formula: wherein and are tuning parameters related to convergence speed and accuracy; are elements in the connectivity matrix A; The average value of the SoC of all EV batteries connected to the power grid is calculated according to the following formula: wherein represents the SoC value of the EV at node i; is a 0-1 variable representing whether there is an EV accessing the system at node i; According to the SoC value of the EV at node i and the average value of the system SoC, the correction amount of the voltage regulation contribution degree can be calculated: wherein is a correction factor for charging, is a correction factor for discharging; and is a positive adjustment parameter related to the convergence speed; After the voltage regulation contribution degree is corrected, the charging and discharging power of the EV at the i-th node in the voltage regulation process can be obtained as follows: The voltage variation is mainly related to the reactive power variation, and thus the reactive power variation amount at the time of EV charging and discharging is calculated using the following equation : In the formula, is the power factor of the electric vehicle (EV) battery.

4. The layered voltage control method of a power distribution network according to claim 1, characterized by, The method for setting target weights and control parameters and using multi-time step MPC optimization to realize centralized control in step S200 includes: The objective function is defined as follows: wherein, and are matrices formed by the reactive power variation of PV and EV access nodes at time t+i, respectively; and are diagonal matrices that allow different weights to be given to different PV and EVs; denotes the OLTC tap action value, is the weight, the greater the weight the less the OLTC tap adjustment; is the slack variable; The final output is the correction amount of the PV steady-state VQ characteristic of the future Nc steps according to the constraint condition of the objective function and the reactive power regulation amount of the EV which can be obtained by the following formula: where U is the identity matrix; R is a diagonal matrix whose diagonal elements are the droop coefficients; is the sensitivity matrix of system voltage-reactive power, S QQ is the sensitivity of photovoltaic reactive power to system reactive power.

5. The layered voltage control method of the power distribution network according to claim 4, characterized in that, The optimization conditions of the objective function include: Voltage constraint: Current constraint: wherein and is the sensitivity matrix, may be obtained from the inverse transpose of the reactive power Jacobian matrix; Reactive power constraint: Transformer tap changer constraint: EV battery constraint: Power balance constraint: 。 6. The layered voltage control method of a power distribution network according to claim 1, characterized by, The method for realizing voltage correction in step S300 includes: After the optimization problem is solved, the centralized controller outputs a and sends it to each local controller. After receiving the correction signal from the upper layer, the local controller controls the voltage through a dynamic response model. The PV node realizes voltage control by correcting the steady-state VQ curve of the distributed photovoltaic, and after receiving the signal from the centralized controller, the local controller accumulates the increment: wherein R is the droop coefficient, V cor the modified signal of the local controller Then, the local controller uses the correction signal, i.e., the correction signal received from the centralized controller, to shift the PV steady-state VQ characteristic parallel to the x-axis, and the shifted VQ characteristic curve is expressed as follows: In the formula, R is the droop coefficient; For EV node, the local controller adjusts the charging power of the charging pile and the mode of the inverter to change the reactive power after receiving the instruction from the upper controller. after, by adjusting the charging pile charging power and inverter mode, change the reactive power.

7. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by a processor, and the processor executes the method according to any one of claims 1 to 6. 8.A computer device, comprising a memory and a processor, and characterized in that, The memory stores a computer program which, when executed by the processor, causes the processor to perform the method of any one of claims 1 to 6.

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