Power distribution network voltage dynamic regulation method, device and equipment based on intelligent energy storage soft switch

Abstract

The application relates to the technical field of automatic control of power distribution networks, and provides a power distribution network voltage dynamic regulation method, device and equipment based on an intelligent energy storage soft switch, which comprises the following steps: acquiring the topological structure of a power distribution network; acquiring the node number, branch number, first electrical quantity parameter of each node and second electrical quantity parameter of each branch of the power distribution network according to the topological structure; determining a voltage out-of-limit penalty coefficient according to the first electrical quantity parameter of each node; performing model construction according to all the first electrical quantity parameters, all the second electrical quantity parameters and all the voltage out-of-limit penalty coefficients to obtain a power distribution network voltage dynamic regulation optimization model comprising a target function and constraint condition data; solving the power distribution network voltage dynamic regulation optimization model by using a solver according to the constraint condition data to obtain power distribution network voltage dynamic regulation data with the minimum numerical value of the target function; and controlling the operation of the power distribution network according to the power distribution network voltage dynamic regulation data to realize voltage dynamic regulation of the power distribution network.

Description

Technical Field

[0001] This application relates to the field of automation control technology for power distribution networks, and in particular to a method, device and equipment for dynamic voltage regulation of power distribution networks based on intelligent energy storage soft switches. Background Technology

[0002] As a crucial component of the power system, the distribution network plays a vital role in distributing and delivering electrical energy from the transmission network to end users. The operational status of the distribution network directly impacts users' electricity experience and the overall stability of the power system. In the power system, voltage is one of the core indicators for measuring power quality. With the large-scale integration of distributed power sources and the increasing complexity and diversity of loads, voltage fluctuations in the distribution network have become increasingly prominent. Dynamic voltage regulation can respond in real time to changes in power within the distribution network, ensuring that node voltages remain within a reasonable range. This effectively guarantees power quality and reduces equipment damage and energy efficiency reduction caused by excessive voltage deviations. Simultaneously, effective dynamic voltage regulation helps improve the stability of the power system, reduces the risk of cascading failures caused by voltage exceeding limits, and ensures reliable operation of the power system.

[0003] With continuous technological advancements, smart grids have become a crucial direction for the development of the power industry. Smart grids aim to achieve intelligent and efficient operation of power systems by integrating advanced communication, sensing, and control technologies. Against this backdrop, traditional voltage regulation methods for distribution networks are increasingly unable to meet the ever-complexing operational demands.

[0004] Current methods for dynamic voltage control in distribution networks are diverse. One common method involves switching on-load tap-changing transformers (OLTCs) and capacitor banks. OLTCs can adjust voltage by changing the transformer ratio under load, offering advantages such as mature technology and high reliability. However, their response speed is relatively slow, making them difficult to handle rapidly changing voltage fluctuations. Capacitor banks, by switching on and off to change reactive power output to support voltage, are relatively low-cost; however, their regulation method is step-wise and cannot achieve continuous and precise control.

[0005] Dynamic voltage regulation methods for distribution networks based on renewable energy sources are emerging as renewable energy sources are increasingly integrated into distribution networks. For example, using photovoltaic inverters for voltage regulation, the reactive power output of the inverters is adjusted to influence node voltage. This dynamic voltage regulation method based on renewable energy sources can fully utilize the potential of renewable energy equipment and achieve effective utilization of clean energy. However, the intermittent and fluctuating power output of renewable energy sources leads to unstable regulation capabilities and susceptibility to natural factors such as weather.

[0006] In existing dynamic voltage control methods for distribution networks, the distribution network can be approximated as a purely power-consuming load. In the power system, the voltage level decreases node by node along the feeder to the load. Under heavy load conditions, the voltage at the end nodes of the power system is prone to exceeding the lower limit. In new power systems, the large-scale grid connection of distributed renewable energy sources changes the original power consumption nature of the distribution network, transforming it from a traditional load into a new type of load with adjustable or bidirectional power flow. Because the output of distributed renewable energy sources is greatly affected by the environment or weather, its active power output exhibits strong fluctuations and intermittency during daily operation. When the output of distributed power sources is excessive, the node voltage at the power source connection node is prone to exceeding the upper limit. Furthermore, the grid connection of renewable energy sources such as photovoltaics and wind power involves converters that convert the DC power from distributed power sources into AC power that can be transmitted and used by the AC grid. In this conversion process, the power electronic devices introduce a large amount of harmonic voltage into the AC grid, which can, to some extent, cause an increase in the total voltage amplitude, leading to voltage exceeding the limit.

[0007] Therefore, existing methods for dynamic voltage control in power distribution networks have voltage over-limit issues. Summary of the Invention

[0008] This application provides a method, device, and equipment for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switching, which solves the technical problem that existing methods for regulating voltage fluctuations in distribution networks can lead to voltage exceeding limits and power system instability.

[0009] To achieve the above objectives, this application provides the following technical solution: On the one hand, a method for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switching is provided, including the following steps: Obtain the topology of the distribution network, and based on the topology, obtain the number of nodes, the number of branches, the first electrical quantity parameters of each node, and the second electrical quantity parameters of each branch; based on the first electrical quantity parameters of each node, determine the voltage over-limit penalty coefficient corresponding to the node; Based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients, a model is constructed to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and the operation constraints of intelligent energy storage soft switching; the distribution network voltage dynamic regulation optimization model includes objective function and constraint condition data. The distribution network voltage dynamic regulation optimization model is solved using a solver based on the constraint data to obtain the distribution network voltage dynamic regulation data that minimizes the objective function value; the distribution network voltage dynamic regulation data includes distribution network day-ahead power purchase strategy data, regulation strategy data, branch current data, and node voltage data; The operation of the distribution network is controlled based on the dynamic voltage regulation data of the distribution network, thereby realizing dynamic voltage regulation of the distribution network; The day-ahead power purchase strategy data of the distribution network includes the injected active power and injected reactive power from the upper-level distribution network to the distribution network nodes. The control strategy data includes the active power output of the nodes connected to distributed power sources, the reactive power output of the nodes connected to distributed power sources, the total reactive power compensation capacity of the nodes, the reactive power injected into the nodes by energy storage devices, the discharge active power injected into the nodes by energy storage devices, the active power injected into the nodes by the converters in the reactive power compensation devices, the reactive power injected into the nodes by the converters in the reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network. The branch current data is the upper limit of the branch current, and the node voltage data is the node voltage.

[0010] Optionally, a model is constructed based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and intelligent energy storage soft-switching operation constraints, including: Constraints are constructed based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients to obtain constraint data; the constraint data includes voltage over-limit penalty constraints, first voltage regulation constraints of distributed power sources, second voltage regulation constraints of reactive power compensation devices, third voltage regulation constraints of energy storage devices, fourth voltage regulation constraints of smart energy storage soft switches, and power flow constraints applicable to the Distflow model of distribution networks. Based on all the first electrical quantity parameters, the voltage over-limit penalty coefficient, and all the second electrical quantity parameters, a function is constructed to obtain an objective function that minimizes the cost of the distribution network purchasing electricity from the upstream distribution network, the network loss cost, the energy storage operation cost, and the voltage over-limit cost.

[0011] Optionally, the first electrical quantity parameter includes the injected active power from the upstream distribution network to the distribution network node, the discharge active power of the node, and the load active power of the node; the second electrical quantity parameter includes branch current and branch resistance; and the objective function is: ; ; ; ; ; In the formula, , , , They are respectively in the time periodt The costs of purchasing electricity from the distribution network to the superior distribution network, network loss costs, energy storage operation costs, and voltage limit violation penalty costs; , and These are the unit price of electricity purchased from the upstream distribution network, the unit operating cost of energy storage, and the unit cost of load loss caused by voltage exceeding limits; , and They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j The injected active power and the discharged active power of the energy storage device; For nodes i With nodes j Branch roads in time period t The branch current, For nodes i With nodes j Branch resistance of the intermediate branch, For the number of branch roads, The number of energy storage units in the distribution network. The number of converters in the distribution network. For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, F Let be the objective function. For the time period t No. i Power loss of each energy storage unit, For the time period t No. The power loss of the converter.

[0012] Optionally, the first electrical quantity parameters include node voltage, active power, reactive power, active output, reactive output, active load, reactive load, total reactive power compensation capacity, load active power, injected active power, injected reactive power, maximum injected active power limit, and maximum injected reactive power limit for each time period. The second electrical quantity parameters include branch transmitted active power, branch transmitted reactive power, branch current, branch voltage, branch reactance, maximum branch current limit, and branch resistance. The voltage over-limit penalty constraint condition is: ; ; ; The power flow constraint is as follows: ; ; ; ; ; ; ; ; ; ; In the formula, For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, and For time period t node j and nodes i node voltage, , These are the squares of the ideal upper limit voltage and the ideal lower limit voltage of the node, respectively. , These are the squares of the node's safe upper limit voltage and the squares of the node's safe lower limit voltage, respectively. and Time periods t node i With nodes j The active power and reactive power transmitted between branches. and They are nodes i With nodes j Branch roads in time period t The branch current and branch voltage, For nodes i With nodes j Branch reactance between branches; , Time periods t node j Active power and reactive power; , Time periods t node j The active and reactive power outputs of the distributed power source are connected; , Time periods t node j The active and reactive loads connected; , and They are respectively in the time period t From the upper-level distribution network to the distribution network nodes j The injected reactive power, the maximum upper limit of injected active power, and the maximum upper limit of injected reactive power; This is the upper limit of the branch current; and They are nodes j The lower and upper voltage limits, , , , They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j Injected active power, energy storage unit injection node j The active power and reactive power compensation device of the converter injection node j The active power; For the time period t Energy storage devices are injected into distribution network nodes. j reactive power, For the time period t Energy storage devices are injected into distribution network nodes. j The active power of discharge, For time period t node j Total reactive power compensation capacity, For nodes i With nodes j Branch resistance of the intermediate branch, Injection nodes for converters in reactive power compensation devices j The reactive power.

[0013] Optionally, the first electrical quantity parameter includes the node voltage for each time period. Based on the first electrical quantity parameter of each node, the voltage over-limit penalty coefficient corresponding to that node is determined, including: Obtain the upper limit of the ideal voltage at the node, the lower limit of the ideal voltage at the node, the upper limit of the safe voltage at the node, and the lower limit of the safe voltage at the node; Based on the node voltage for each time period, determine the voltage over-limit judgment data for the corresponding node during that time period; If the voltage over-limit judgment data for a time period is not less than the lower limit of the ideal voltage of the node and not greater than the upper limit of the ideal voltage of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is 0. If the voltage over-limit judgment data for a time period is not less than the ideal voltage upper limit of the node and not greater than the safe voltage upper limit of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is calculated based on the voltage over-limit judgment data, the ideal voltage upper limit of the node, and the safe voltage upper limit of the node. If the voltage over-limit judgment data for a time period is not less than the node's safe voltage lower limit and not greater than the node's ideal voltage lower limit, then the voltage over-limit penalty coefficient for the node in the corresponding time period is calculated based on the voltage over-limit judgment data, the node's safe voltage lower limit, and the node's ideal voltage lower limit. If the voltage over-limit judgment data for a given time period is less than the lower limit of the node's safe voltage or greater than the upper limit of the node's safe voltage, then the voltage over-limit penalty coefficient for the node in the corresponding time period is 1.

[0014] On the other hand, a distribution network voltage dynamic regulation device based on intelligent energy storage soft switch is provided, including: a data acquisition module, a model building module, a regulation data solving module and a regulation execution module; The data acquisition module is used to acquire the topology of the distribution network, acquire the number of nodes and branches of the distribution network, as well as the first electrical quantity parameters of each node and the second electrical quantity parameters of each branch, based on the topology; and determine the voltage over-limit penalty coefficient corresponding to each node based on the first electrical quantity parameters of each node. The model building module is used to build a model based on all the first electrical quantity parameters, all the second electrical quantity parameters and all the voltage over-limit penalty coefficients, to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and the operation constraints of smart energy storage soft switching; the distribution network voltage dynamic regulation optimization model includes objective function and constraint condition data. The control data solving module is used to solve the distribution network voltage dynamic control optimization model using a solver based on the constraint data, to obtain the distribution network voltage dynamic control data that minimizes the objective function value; the distribution network voltage dynamic control data includes distribution network day-ahead power purchase strategy data, control strategy data, branch current data, and node voltage data; The control execution module is used to control the operation of the distribution network according to the distribution network voltage dynamic control data, so as to realize the voltage dynamic control of the distribution network. The day-ahead power purchase strategy data of the distribution network includes the injected active power and injected reactive power from the upper-level distribution network to the distribution network nodes. The control strategy data includes the active power output of the nodes connected to distributed power sources, the reactive power output of the nodes connected to distributed power sources, the total reactive power compensation capacity of the nodes, the reactive power injected into the nodes by energy storage devices, the discharge active power injected into the nodes by energy storage devices, the active power injected into the nodes by the converters in the reactive power compensation devices, the reactive power injected into the nodes by the converters in the reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network. The branch current data is the upper limit of the branch current, and the node voltage data is the node voltage.

[0015] Optionally, the model building module includes: a constraint construction submodule and a model building submodule; The constraint construction submodule is used to construct constraints based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients to obtain constraint data. The constraint data includes voltage over-limit penalty constraints, first voltage regulation constraints of distributed power sources, second voltage regulation constraints of reactive power compensation devices, third voltage regulation constraints of energy storage devices, fourth voltage regulation constraints of smart energy storage soft switches, and power flow constraints applicable to the Distflow model of distribution networks. The model construction submodule is used to construct a function based on all the first electrical quantity parameters, the voltage over-limit penalty coefficient and all the second electrical quantity parameters, to obtain an objective function that minimizes the cost of purchasing electricity from the distribution network to the upper-level distribution network, network loss cost, energy storage operation cost and voltage over-limit cost. The objective function is: ; ; ; ; ; In the formula, , , , They are respectively in the time period t The costs of purchasing electricity from the distribution network to the superior distribution network, network loss costs, energy storage operation costs, and voltage limit violation penalty costs; , and These are the unit price of electricity purchased from the upstream distribution network, the unit operating cost of energy storage, and the unit cost of load loss caused by voltage exceeding limits; , and They are respectively in the time period tnode j The active power of the load, the power from the upstream distribution network to the distribution network nodes j The injected active power and the discharged active power of the energy storage device; For nodes i With nodes j Branch roads in time period t The branch current, For nodes i With nodes j Branch resistance of the intermediate branch, For the number of branch roads, The number of energy storage units in the distribution network. The number of converters in the distribution network. For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, F Let be the objective function. For the time period t No. i Power loss of each energy storage unit, For the time period t No. The power loss of the converter.

[0016] Optionally, the first electrical quantity parameters include node voltage, active power, reactive power, active output, reactive output, active load, reactive load, total reactive power compensation capacity, load active power, injected active power, injected reactive power, maximum injected active power limit, and maximum injected reactive power limit for each time period. The second electrical quantity parameters include branch transmitted active power, branch transmitted reactive power, branch current, branch voltage, branch reactance, maximum branch current limit, and branch resistance. The voltage over-limit penalty constraint condition is: ; ; ; The power flow constraint is as follows: ; ; ; ; ; ; ; ; ; ; In the formula, For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, and For time period t node j and nodes i node voltage, , These are the squares of the ideal upper limit voltage and the ideal lower limit voltage of the node, respectively. , These are the squares of the node's safe upper limit voltage and the squares of the node's safe lower limit voltage, respectively. and Time periods t node i With nodes j The active power and reactive power transmitted between branches. and They are nodes i With nodes j Branch roads in time period t The branch current and branch voltage, For nodes i With nodes j Branch reactance between branches; , Time periods t node j Active power and reactive power; , Time periods t node j The active and reactive power outputs of the distributed power source are connected; , Time periods t node j The active and reactive loads connected; , and They are respectively in the time period t From the upper-level distribution network to the distribution network nodes j The injected reactive power, the maximum upper limit of injected active power, and the maximum upper limit of injected reactive power; This is the upper limit of the branch current; and They are nodes j The lower and upper voltage limits, , , , They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j Injected active power, energy storage unit injection node j The active power and reactive power compensation device of the converter injection node j The active power; For the time period t Energy storage devices are injected into distribution network nodes. j reactive power, For the time period t Energy storage devices are injected into distribution network nodes. j The active power of discharge, For time period t node j Total reactive power compensation capacity, For nodes i With nodes j Branch resistance of the intermediate branch, Injection nodes for converters in reactive power compensation devices j The reactive power.

[0017] Optionally, the first electrical quantity parameter includes the node voltage for each time period. Based on the first electrical quantity parameter of each node, the voltage over-limit penalty coefficient corresponding to that node is determined, including: Obtain the upper limit of the ideal voltage at the node, the lower limit of the ideal voltage at the node, the upper limit of the safe voltage at the node, and the lower limit of the safe voltage at the node; Based on the node voltage for each time period, determine the voltage over-limit judgment data for the corresponding node during that time period; If the voltage over-limit judgment data for a time period is not less than the lower limit of the ideal voltage of the node and not greater than the upper limit of the ideal voltage of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is 0. If the voltage over-limit judgment data for a time period is not less than the ideal voltage upper limit of the node and not greater than the safe voltage upper limit of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is calculated based on the voltage over-limit judgment data, the ideal voltage upper limit of the node, and the safe voltage upper limit of the node. If the voltage over-limit judgment data for a time period is not less than the node's safe voltage lower limit and not greater than the node's ideal voltage lower limit, then the voltage over-limit penalty coefficient for the node in the corresponding time period is calculated based on the voltage over-limit judgment data, the node's safe voltage lower limit, and the node's ideal voltage lower limit. If the voltage over-limit judgment data for a given time period is less than the lower limit of the node's safe voltage or greater than the upper limit of the node's safe voltage, then the voltage over-limit penalty coefficient for the node in the corresponding time period is 1.

[0018] On the other hand, a terminal device is provided, including a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the above-described method for dynamic voltage control of distribution networks based on intelligent energy storage soft switches according to the instructions in the program code.

[0019] This invention discloses a method, device, and equipment for dynamic voltage control of distribution networks based on intelligent energy storage soft switching. The method includes: acquiring the topology of the distribution network; obtaining the number of nodes, branches, first electrical quantity parameters of each node, and second electrical quantity parameters of each branch based on the topology; determining the voltage exceedance penalty coefficient corresponding to each node based on the first electrical quantity parameters; constructing a model based on all first electrical quantity parameters, all second electrical quantity parameters, and all voltage exceedance penalty coefficients to obtain a dynamic voltage control optimization model for the distribution network that includes the Distflow model and the operating constraints of the intelligent energy storage soft switch; the dynamic voltage control optimization model includes an objective function and constraint data; and solving the dynamic voltage control optimization model using a solver based on the constraint data to obtain the solution with the minimum objective function value. Dynamic voltage control data for the distribution network includes day-ahead power purchase strategy data, control strategy data, branch current data, and node voltage data. The distribution network operation is controlled based on this dynamic voltage control data to achieve dynamic voltage regulation. Specifically, the day-ahead power purchase strategy data includes the injected active and reactive power from the upstream distribution network to the distribution network nodes; the control strategy data includes the active power output of nodes connected to distributed power sources, the reactive power output of nodes connected to distributed power sources, the total reactive power compensation capacity of nodes, the reactive power injected into nodes by energy storage devices, the active power discharged from energy storage devices into nodes, the active power injected into nodes by converters in reactive power compensation devices, the reactive power injected into nodes by converters in reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network; the branch current data represents the upper limit of the branch current; and the node voltage data represents the node voltage.

[0020] As can be seen from the above technical solutions, this application has the following advantages: The distribution network voltage dynamic control method based on intelligent energy storage soft switching first determines the voltage over-limit penalty coefficient of each node by obtaining the electrical quantity parameters of the distribution network, and then constructs a distribution network voltage dynamic control optimization model containing the Distflow model and the operation constraints of intelligent energy storage soft switching based on the electrical quantity parameters and the voltage over-limit penalty coefficient. The solver is called to solve the distribution network voltage dynamic control optimization model, and the distribution network voltage dynamic control data of the day-ahead distribution network is given to control the operation of the distribution network, thereby realizing the dynamic control of the distribution network voltage. This solves the technical problem that the existing control methods for distribution network voltage fluctuations have voltage over-limit issues, leading to power system instability.

[0021] This distribution network voltage dynamic control device based on intelligent energy storage soft switch acquires electrical parameters of the distribution network through a data acquisition module, a model building module, a control data solving module, and a control execution module. First, it determines the voltage over-limit penalty coefficient for each node. Then, based on the electrical parameters and the voltage over-limit penalty coefficient, it constructs a distribution network voltage dynamic control optimization model that includes the Distflow model and the operating constraints of the intelligent energy storage soft switch. The solver is then called to solve the distribution network voltage dynamic control optimization model, providing the current-day distribution network voltage dynamic control data to control the operation of the distribution network and achieve dynamic voltage control of the distribution network. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the steps of the power distribution network voltage dynamic control method based on intelligent energy storage soft switch described in the embodiments of this application; Figure 2 This is a flowchart illustrating the dynamic voltage control method for distribution networks based on intelligent energy storage soft switches as described in the embodiments of this application. Figure 3 This is a simplified circuit diagram of the grid connection of the energy storage device in the power distribution network voltage dynamic control method based on intelligent energy storage soft switch described in the embodiments of this application; Figure 4 This is a schematic diagram of the framework of a power distribution network voltage dynamic control device based on intelligent energy storage soft switch, as described in another embodiment of this application. Figure 5 This is a schematic diagram of the terminal device described in an embodiment of this application. Detailed Implementation

[0024] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] In the description of the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] In the embodiments of this application, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0027] Patent terminology used in this application: A Soft Open Point (SOP) is a flexible interconnection device based on a back-to-back converter structure. The "AC-DC-AC" operation mode of the SOP provides an ideal interface for the connection of DC Energy Storage Systems (ESS) to the distribution network. By combining energy storage units with the SOP, a Soft Open Point with Energy Storage (ESOP) is formed. The ESOP's structure fully utilizes the existing converter equipment, enabling not only charge and discharge control of the energy storage units but also significantly improving the overall economic efficiency of the DC energy storage system. Compared to the standalone construction of the SOP, the integrated construction of the ESOP achieves efficient utilization of the power electronic converter equipment, effectively reducing system construction investment and operation and maintenance costs. In terms of operational performance, the introduction of energy storage units significantly improves the dynamic response characteristics of the flexible interconnection device, giving it stronger transient stability maintenance capabilities and energy balance regulation functions. From the perspective of distribution network operation, the intelligent energy storage soft switch ESOP with energy storage function realizes the spatiotemporal extension of energy management. It supports real-time power regulation between different feeders and can also perform important functions such as power fluctuation smoothing and load peak-valley regulation over a longer period. This dual regulation capability makes ESOP a key technical means to improve the level of renewable energy consumption, improve power supply quality, and optimize distribution network operation efficiency.

[0028] A branch is a path through which the same current flows between any two nodes.

[0029] The DistFlow model is a mathematical model used to describe power flow in distribution networks. It considers line resistance and reactance, as well as power losses. By introducing variables such as branch power and node voltage, this model can accurately describe the power flow distribution in distribution networks. In distribution networks containing distributed generation (DG), the DistFlow model can better reflect the impact of DG on system power flow, providing a more accurate basis for optimization and reconfiguration.

[0030] A reactive power compensation device is a power equipment used to improve power quality, specifically for reactive power compensation and harmonic mitigation. Its main functions include reactive power compensation and three-phase balance regulation, covering voltage levels from 6 to 110 kV. This device offsets the reactive power generated by the load through parallel capacitors or reactors, reducing line losses and improving the power factor. Its core principle is the compensation mechanism of Qnet = QL - Qc. Reactive power compensation devices can also be passive power filters, which combine reactive power compensation and voltage regulation functions. These filters are typically custom-designed based on the parameters of the harmonic source, the electrical characteristics of the installation point, and user requirements. Static Var Compensators (SVCs) are also available, serving as important devices for comprehensively managing voltage fluctuations, flicker, harmonics, and voltage imbalances. Active Power Filters (APFs) are dynamic power electronic devices that suppress harmonics and compensate for reactive power. They can compensate for harmonics and reactive currents with varying frequencies and amplitudes, and are primarily used in low-voltage distribution systems.

[0031] Smart energy storage soft-switching technology, as an emerging power electronics technology, has brought new opportunities for voltage regulation in distribution networks. This technology integrates the flexible control capabilities of smart soft switches with the energy storage characteristics of energy storage systems, enabling rapid and precise regulation of active and reactive power in the distribution network, effectively addressing fluctuations in distributed power output and load. The demand for smart energy storage soft-switching technology is increasingly urgent, with the aim of enhancing the voltage regulation capabilities of distribution networks and improving the flexibility and reliability of power systems to meet the requirements of smart grid development.

[0032] Smart energy storage soft-switching technology has received widespread attention in the distribution network field in recent years. This study focuses on in-depth research into dynamic voltage regulation methods for distribution networks incorporating smart energy storage soft switches. Specifically, it analyzes the structure and working principle of smart energy storage soft switches, explores their key technologies in dynamic voltage regulation of distribution networks, and analyzes the characteristics and applicable scenarios of different control strategies. Simultaneously, by establishing models of the distribution network and smart energy storage soft switches, theoretical analysis and modeling are conducted to provide a theoretical basis for dynamic voltage regulation. Furthermore, simulation experiments and practical case studies are used to verify the effectiveness and feasibility of the proposed dynamic voltage regulation method for distribution networks incorporating smart energy storage soft switches. From a theoretical perspective, this method helps enrich and improve the theoretical system of dynamic voltage regulation in distribution networks and promotes technological innovation in related fields. From a practical perspective, this dynamic voltage regulation method for distribution networks incorporating smart energy storage soft switches can effectively improve the voltage regulation level of distribution networks, improve power supply quality, reduce network losses, enhance the system's adaptability to distributed power sources and complex loads, and provide strong technical support for the construction and operation of smart grids. Numerous research results have already demonstrated its unique advantages in voltage regulation. At the theoretical level, relevant research has constructed a mathematical model of intelligent energy storage soft switching, analyzed its working principle and control strategy, and laid the foundation for practical applications. In terms of practical applications, intelligent energy storage soft switching has been introduced into distribution networks in some regions. For example, in areas with high penetration of distributed power sources, intelligent energy storage soft switching has enabled flexible power transfer between different feeders and control of the charging and discharging of energy storage devices, effectively improving voltage distribution and reducing the risk of voltage exceeding limits. Regarding voltage regulation, intelligent energy storage soft switching can quickly respond to voltage changes, adjusting power flow in both time and space dimensions, significantly improving the voltage stability and regulation flexibility of the distribution network.

[0033] Current research on dynamic voltage regulation methods for distribution networks with intelligent energy storage soft switches has fully recognized the value of intelligent energy storage soft switches in distribution network voltage regulation, and has achieved certain results in theoretical modeling, control strategies, and practical applications. However, most existing dynamic voltage regulation methods for distribution networks with intelligent energy storage soft switches focus on a single technical level, and research on the coordinated regulation of intelligent energy storage soft switches with other voltage regulating devices is still insufficient. Furthermore, the optimization of the overall regulation strategy needs to be improved when dealing with complex and ever-changing distribution network operation scenarios.

[0034] Therefore, it is necessary to explore methods for dynamic voltage regulation of distribution networks with intelligent energy storage soft switches, focusing on the synergistic optimization strategies of intelligent energy storage soft switches, traditional voltage regulating equipment, and new energy regulation methods, with the aim of improving the voltage regulation performance of distribution networks under various complex scenarios.

[0035] As a power electronic device deeply coupled with the smart soft switch (SOP) and the energy storage system (ESS), the intelligent energy storage soft switch typically uses a back-to-back voltage source converter as its core architecture. This converter consists of multiple insulated-gate bipolar transistors (IGBTs) and other power electronic devices, achieving precise control of electrical energy through the rapid switching of these devices. Structurally, the converter also includes an energy storage unit for storing or releasing electrical energy, as well as a corresponding detection and control system to monitor the equipment's operating status in real time and issue control commands. The basic working principle of the power electronic device is as follows: when the distribution network voltage fluctuates, the intelligent energy storage soft switch adjusts the output active and reactive power in real time through the power electronic converter. For example, when a low voltage is detected in a certain area, the energy storage unit releases electrical energy, which is converted to a suitable voltage level by the converter and injected into the distribution network to boost the voltage in that area; conversely, when the voltage is high, the converter can convert excess electrical energy in the distribution network into a suitable form and store it in the energy storage unit, thereby achieving dynamic regulation of the distribution network voltage and ensuring that the voltage remains stable within a reasonable range.

[0036] As mentioned above, achieving dynamic voltage regulation in distribution networks involves several key technologies, such as power control technology and energy management technology. Specifically: Power control technology is a key component, enabling precise control of the active and reactive power output of intelligent energy storage soft switches. Through precise control of the converter's trigger pulses, it can quickly respond to changes in the distribution network voltage and adjust power output in a timely manner to maintain voltage stability. For example, converters controlled based on direct power control strategies can directly calculate the required active and reactive power output based on real-time voltage and current measurements, achieving rapid power regulation.

[0037] Energy management technology is equally crucial, responsible for the rational allocation and management of energy from energy storage units. Given the limited capacity of energy storage units, energy management technology must formulate optimal energy charging and discharging strategies based on factors such as the real-time load demand of the distribution network and the output of distributed power sources. For example, during off-peak periods, excess electrical energy is stored; during peak periods, the stored energy is released to balance the power of the distribution network, indirectly achieving voltage regulation. These key technologies work together: power control technology ensures rapid voltage response and stability in the short term, while energy management technology guarantees stable system operation and dynamic voltage regulation from a long-term and overall perspective.

[0038] Existing methods for dynamic voltage regulation in distribution networks with intelligent energy storage soft switching can employ various control strategies, including model predictive control and adaptive control strategies. Specifically: Model-based predictive control (MMCC) is based on a mathematical model of the distribution network. By predicting changes in voltage, power, and other states of the distribution network over a future period, it makes optimal control decisions in advance. This MCC strategy can fully consider system uncertainties and constraints, such as fluctuations in distributed generation output and load changes, to achieve precise voltage regulation. Its advantages include foresight and the ability to effectively handle complex and ever-changing distribution network operation scenarios. However, it involves relatively large computational loads and requires high hardware computing power, making it suitable for scenarios with high voltage regulation accuracy and complex systems.

[0039] The adaptive control strategy automatically adjusts control parameters and strategies based on the real-time operating status of the distribution network. It monitors voltage, current, and other information in the distribution network in real time, and rapidly adjusts the control mode of the intelligent energy storage soft switch to adapt to new operating conditions when the power system's operating status changes. For example, when a sudden increase in load causes a voltage drop, the adaptive control strategy can automatically increase the active power output of the intelligent energy storage soft switch to boost the voltage. This adaptive control strategy offers strong flexibility and adaptability, enabling rapid response to system changes. However, it is highly dependent on the accuracy of power system parameters and is suitable for distribution network scenarios where operating conditions change frequently and are difficult to predict accurately.

[0040] Distribution networks have complex structures and contain numerous components. To accurately study the dynamic voltage regulation of distribution networks with intelligent energy storage soft switches, a precise model based on the actual structure is necessary. During modeling, line parameters, such as resistance and reactance, are crucial factors that cannot be ignored, as they directly affect the power transmission characteristics within the distribution network. Simultaneously, load characteristics also significantly impact distribution network operation. Different types of loads, such as industrial and residential loads, exhibit varying power variation patterns and different requirements for voltage stability. By comprehensively considering these factors, constructing a distribution network model incorporating intelligent energy storage soft switches can realistically reflect the electrical characteristics of the distribution network in actual operation, laying the foundation for subsequent in-depth analysis of dynamic voltage regulation.

[0041] As a core device for dynamic voltage regulation in distribution networks, the establishment of a detailed model for intelligent energy storage soft switches is crucial. From an electrical characteristics perspective, intelligent energy storage soft switches typically consist of power electronic converters and energy storage units. Power electronic converters enable flexible energy conversion, and their switching frequency, modulation method, and other parameters affect the efficiency and quality of energy conversion. Energy storage units are responsible for storing and releasing energy, and their capacity, charging and discharging power, and other characteristics determine their ability to regulate power fluctuations in the distribution network. In terms of control logic, intelligent energy storage soft switches need to adjust their operating mode in real time based on the operating status of the distribution network, such as voltage and current information. For example, when a low voltage is detected in a certain area, the control logic causes the energy storage unit to release energy to raise the voltage in that area. Establishing a model of intelligent energy storage soft switches that encompasses both electrical characteristics and control logic provides strong support for subsequent analysis of its role in dynamic voltage regulation in distribution networks.

[0042] Based on the established distribution network model and smart energy storage soft-switching model, a mathematical model for dynamic voltage regulation can be further constructed. This mathematical model aims to describe the intrinsic relationship between voltage and various parameters of the smart energy storage soft-switching. From a mathematical perspective, a system of equations is established to represent the relationship between power flow, voltage distribution, and parameters such as the output power and energy storage state of the smart energy storage soft-switching in the distribution network. For example, using basic principles of electrical engineering such as Kirchhoff's laws, combined with the power control strategy of the smart energy storage soft-switching, expressions for voltage and the power injected or absorbed by the smart energy storage soft-switching are derived. Through these mathematical relationships, the dynamic regulation effect of the smart energy storage soft-switching on the distribution network voltage can be quantitatively analyzed, providing a theoretical basis for optimizing control strategies and improving voltage regulation performance.

[0043] Research on intelligent energy storage soft switches (ESOPs) with energy storage capabilities mainly focuses on the following technical aspects: In terms of planning, the research focuses on the site selection and layout of ESOPs, port converter design and capacity configuration, selection of internal energy storage DC / DC converters, and capacity optimization of energy storage units; in terms of operation and control, the research mainly studies the control strategies of ESOPs for variables such as converter transmission power and converter voltage in different operating scenarios of distribution networks, as well as the implementation methods for their participation in system optimization scheduling.

[0044] This application provides a method, device, and equipment for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switching, which solves the technical problem that existing methods for regulating voltage fluctuations in distribution networks can lead to voltage exceeding limits and power system instability.

[0045] Example 1: Figure 1 This is a flowchart illustrating the steps of the power distribution network voltage dynamic control method based on intelligent energy storage soft switching described in the embodiments of this application. Figure 2This is a simplified circuit diagram of the distribution network in the distribution network voltage dynamic control method based on intelligent energy storage soft switch described in the embodiments of this application.

[0046] like Figure 1 and Figure 2 As shown in the figure, this application provides a method for dynamic voltage regulation of a distribution network based on intelligent energy storage soft switching, including the following steps: S1. Obtain the topology of the distribution network, and based on the topology, obtain the number of nodes, the number of branches, the first electrical quantity parameters of each node, and the second electrical quantity parameters of each branch; based on the first electrical quantity parameters of each node, determine the voltage over-limit penalty coefficient corresponding to the node.

[0047] It should be noted that in the process of dynamic voltage control of distribution networks based on intelligent energy storage soft switching, such as Figure 2 As shown, multi-dimensional data of the distribution network is obtained through step S1 based on the topology. This multi-dimensional data includes the number of nodes, the number of branches, and the first electrical quantity parameters of each node and the second electrical quantity parameters of each branch, providing analytical data for subsequent steps to obtain dynamic voltage control of the distribution network. In step S1, the voltage exceedance penalty coefficient for each node is calculated using a preset formula or preset rule based on the first electrical quantity parameters of each node. In this embodiment, the first electrical quantity parameters include node voltage, active power, reactive power, active output, reactive output, active load, reactive load, total reactive power compensation capacity, load active power, injected active power, injected reactive power, maximum injected active power limit, and maximum injected reactive power limit for each time period. The second electrical quantity parameters include branch transmitted active power, branch transmitted reactive power, branch current, branch voltage, branch reactance, maximum branch current limit, and branch resistance.

[0048] In this embodiment of the application, in order to characterize the effect of voltage regulation, the distribution network voltage dynamic regulation method based on intelligent energy storage soft switch can determine the voltage over-limit penalty coefficient by using a piecewise linearization method based on the first electrical quantity parameter of each node, which is used to calculate the unit load loss cost caused by voltage over-limit, and quantify the voltage regulation result into voltage over-limit cost.

[0049] like Figure 2 As shown in the embodiments of this application, Figure 2 The simplified circuit diagram of the power distribution network clearly depicts the voltage and power flow relationships between each node. Figure 2 middle: , , These are the voltages of the starting node, the upstream node of node m, and node m, respectively. , These are the resistance and reactance at node m of the line, respectively; and These represent the injected power at node m; and These are the active and reactive loads at the nodes, respectively. and These represent the active and reactive power of the node distributed power source, respectively.

[0050] In the embodiments of this application, step S1 can be understood as obtaining historical or real-time multidimensional data (such as multidimensional data for 24 hours in a day) through power data acquisition systems, power system databases, etc.

[0051] In this embodiment of the application, the method for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switches further includes data cleaning and preprocessing of the acquired multidimensional data.

[0052] It should be noted that data cleaning and preprocessing methods include missing value handling, outlier detection and correction, etc., to ensure data quality and improve the accuracy of obtaining dynamic voltage control data for the distribution network. In this embodiment, missing value handling first identifies null values ​​for electrical quantity parameters. For records that are missing for no more than a few consecutive days, linear interpolation is used to fill in the missing values; for electrical quantity parameters that are missing for more than a few consecutive days, no filling is performed, and they are directly marked as missing. Data from this period will be excluded from subsequent analysis. Short-term missing values ​​can be reasonably inferred, but forced interpolation for long-term missing values ​​will create false trends. Outlier handling involves setting reasonable ranges for both fields. For obvious input errors (such as negative numbers or obviously unreasonable values), they are directly corrected or removed.

[0053] S2. Based on all first electrical quantity parameters, all second electrical quantity parameters, and all voltage over-limit penalty coefficients, a model is constructed to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and the operation constraints of intelligent energy storage soft switching; the distribution network voltage dynamic regulation optimization model includes objective function and constraint condition data.

[0054] It should be noted that, in order to analyze the voltage exceedance problem in new distribution networks with a high proportion of renewable energy access, step S2 involves constructing a model based on the multi-dimensional data obtained in step S1 using preset rules. This results in a distribution network voltage dynamic regulation optimization model that includes the Distflow model and the operational constraints of the smart energy storage soft switch. This model enables coordinated voltage regulation by multiple devices in the distribution network, including distributed power sources, reactive power compensation devices, energy storage devices, and smart energy storage soft switches. This model can solve for the distribution network voltage dynamic regulation data based on the smart energy storage soft switch, providing a computational model for subsequent calculations of the distribution network voltage dynamic regulation data. In this embodiment, the distribution network voltage dynamic regulation method based on the smart energy storage soft switch constructs a distribution network voltage dynamic regulation optimization model containing constraint data and an objective function for distribution network voltage regulation using the electrical quantity parameters and voltage exceedance penalty coefficient obtained in step S1. This model serves as the foundation for calculating the distribution network voltage dynamic regulation data based on the smart energy storage soft switch.

[0055] In this embodiment, the distribution network voltage dynamic regulation method based on intelligent energy storage soft switching adjusts the control variables of the distribution network by solving the distribution network voltage dynamic regulation optimization model, thereby achieving optimal voltage distribution security and optimal operational economy for power purchase loss reduction. The objective function for constructing the distribution network voltage dynamic regulation optimization model, in addition to considering the voltage over-limit security target based on the constraint data proposed in step S2, also optimizes day-ahead scheduling based on economic targets such as the amount of electricity purchased from the upstream power source, network losses, and energy storage losses.

[0056] It should be noted that, using the constraints of multi-device coordinated voltage regulation (such as the first voltage regulation constraint of distributed power sources, the second voltage regulation constraint of reactive power compensation devices, the third voltage regulation constraint of energy storage devices, and the fourth voltage regulation constraint of intelligent energy storage soft switches) and the grid Distflow model as constraints, and taking the minimum voltage over-limit cost and grid power purchase and loss cost as objective functions, a distribution network voltage dynamic regulation optimization model is constructed so that the distribution network voltage dynamic regulation optimization model can meet the Distflow model of the distribution network and the operation constraints of intelligent energy storage soft switches.

[0057] In other embodiments, the distribution network voltage dynamic control method based on intelligent energy storage soft switch can construct the distribution network voltage dynamic control optimization model in the process of building the distribution network voltage dynamic control optimization model by using traditional statistical models, machine learning models, deep learning models, ensemble learning models, Bayesian models and rule-based models, etc., based on all electrical quantity parameters and voltage over-limit penalty coefficients obtained in step S1.

[0058] S3. Based on the constraint data, the solver is used to solve the distribution network voltage dynamic regulation optimization model to obtain the distribution network voltage dynamic regulation data with the minimum objective function value; the distribution network voltage dynamic regulation data includes the day-ahead power purchase strategy data, regulation strategy data, branch current data and node voltage data of the distribution network.

[0059] It should be noted that the day-ahead power purchase strategy data for the distribution network includes the injected active and reactive power from the upstream distribution network to the distribution network nodes. The control strategy data includes the active power output of the nodes connected to distributed power sources, the reactive power output of the nodes connected to distributed power sources, the total reactive power compensation capacity of the nodes, the reactive power injected into the nodes by energy storage devices, the discharge active power injected into the nodes by energy storage devices, the active power injected into the nodes by the converters in the reactive power compensation devices, the reactive power injected into the nodes by the converters in the reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network. The branch current data is the upper limit of the branch current, and the node voltage data is the node voltage. Step S3 is to construct a dynamic control optimization model for the distribution network voltage based on step S2, which includes the Distflow model of the distribution network and the operating constraints of the intelligent energy storage soft switch (or other energy storage flexible interconnection device). The distribution network voltage dynamic regulation optimization model contains square terms and their multiplications, making it a nonlinear model. To expedite the solution process, a second-order cone relaxation technique can be used to linearize and simplify the model by setting the square terms as variables. The transformed model can then be solved using commercial solvers such as CPLEX or GUROBI to obtain distribution network voltage dynamic regulation data, providing control data for subsequent steps of dynamic voltage regulation of the distribution network.

[0060] In this embodiment, the distribution network voltage dynamic regulation method based on intelligent energy storage soft switching uses a commercial solver in step S3 to provide day-ahead voltage regulation strategy and operation optimization cost results (such as distribution network voltage dynamic regulation data). This distribution network voltage dynamic regulation method based on intelligent energy storage soft switching realizes the integrated control of intelligent energy storage soft switching and multi-device collaborative voltage regulation in the distribution network, significantly improving the voltage stability and economy of high-proportion renewable energy distribution networks, and providing quantifiable and optimizable technical support for renewable energy consumption.

[0061] S4. Control the operation of the distribution network based on the dynamic voltage regulation data of the distribution network to achieve dynamic voltage regulation of the distribution network.

[0062] It should be noted that step S4 controls the operation of the distribution network based on the dynamic voltage regulation data obtained in step S3, thereby achieving dynamic voltage regulation of the distribution network based on smart energy storage soft switches. In this embodiment, the dynamic voltage regulation method for distribution networks based on smart energy storage soft switches uses the minimum cost of electricity purchase from the upstream distribution network, network loss cost, energy storage operation cost, and voltage over-limit cost as the objective function. It constructs a multi-period optimization problem for distribution networks (such as flexible interconnected distribution networks) based on smart energy storage soft switches, and uses a commercial solver to solve the dynamic voltage regulation optimization model of the distribution network that includes the Distflow model and the operating constraints of smart energy storage soft switches. Finally, it obtains the operating characteristic parameters such as the electricity purchase from the upstream distribution network, node voltage, branch power flow, energy storage charging and discharging power, and port power of flexible interconnected devices (such as smart energy storage soft switches) under typical working days, providing data support for distribution network security assessment, equipment selection optimization, and regulation strategy improvement in power systems.

[0063] This application provides a method for dynamic voltage regulation of a distribution network based on intelligent energy storage soft switching. The method includes: acquiring the topology of the distribution network; obtaining the number of nodes, branches, and first electrical quantity parameters of each node and second electrical quantity parameters of each branch based on the topology; determining the voltage exceedance penalty coefficient corresponding to each node based on the first electrical quantity parameters; constructing a model based on all first electrical quantity parameters, all second electrical quantity parameters, and all voltage exceedance penalty coefficients to obtain a dynamic voltage regulation optimization model for the distribution network that includes the Distflow model and the operational constraints of the intelligent energy storage soft switching; the dynamic voltage regulation optimization model includes an objective function and constraint data; and solving the dynamic voltage regulation optimization model using a solver based on the constraint data to obtain the dynamic voltage regulation data of the distribution network that minimizes the objective function. The dynamic voltage control data for the distribution network includes day-ahead power purchase strategy data, control strategy data, branch current data, and node voltage data. The distribution network operation is controlled based on this dynamic voltage control data to achieve dynamic voltage regulation. Specifically, the day-ahead power purchase strategy data includes the injected active and reactive power from the upstream distribution network to the distribution network nodes; the control strategy data includes the active power output of nodes connected to distributed power sources, the reactive power output of nodes connected to distributed power sources, the total reactive power compensation capacity of nodes, the reactive power injected into nodes by energy storage devices, the active power discharged from energy storage devices into nodes, the active power injected into nodes by converters in reactive power compensation devices, the reactive power injected into nodes by converters in reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network; the branch current data represents the upper limit of the branch current; and the node voltage data represents the node voltage.

[0064] This distribution network voltage dynamic control method based on intelligent energy storage soft switching first determines the voltage over-limit penalty coefficient of each node by acquiring the electrical quantity parameters of the distribution network. Then, based on the electrical quantity parameters and the voltage over-limit penalty coefficient, it constructs a distribution network voltage dynamic control optimization model that includes the Distflow model and the operating constraints of intelligent energy storage soft switching. The solver is called to solve the distribution network voltage dynamic control optimization model, and the dynamic control data of the distribution network voltage before daytime is given to control the operation of the distribution network. This achieves dynamic voltage control of the distribution network and solves the technical problem of voltage over-limit leading to power system instability in existing methods for controlling voltage fluctuations in the distribution network.

[0065] In the operation of a power system, it is crucial to maintain the voltage level as close as possible to the rated voltage: at this level, electrical equipment can achieve optimal operating efficiency, the aging rate of transmission equipment insulation can be reduced to the design reference value, and a high dynamic voltage margin can be provided to effectively resist load changes and fault disturbances. When the voltage deviates from the rated value by a certain range, the power system should immediately issue an early warning and initiate voltage regulation measures to prevent the voltage situation from deteriorating further. In particular, when the voltage deviation exceeds the critical value of 10% of the rated value, it will seriously endanger the stability of the power system, and emergency control measures must be taken.

[0066] In one embodiment of this application, the first electrical quantity parameter includes the node voltage for each time period. Determining the voltage over-limit penalty coefficient corresponding to each node based on the first electrical quantity parameter includes: Obtain the upper limit of the ideal voltage at the node, the lower limit of the ideal voltage at the node, the upper limit of the safe voltage at the node, and the lower limit of the safe voltage at the node; Based on the node voltage for each time period, determine the voltage over-limit judgment data for the corresponding node during that time period; If the voltage over-limit judgment data for a time period is not less than the lower limit of the ideal voltage of the node and not greater than the upper limit of the ideal voltage of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is 0. If the voltage over-limit judgment data for a time period is not less than the node's ideal voltage upper limit and not greater than the node's safe voltage upper limit, then the voltage over-limit penalty coefficient for the corresponding time period node is calculated based on the voltage over-limit judgment data, the node's ideal voltage upper limit, and the node's safe voltage upper limit. If the voltage over-limit judgment data for a time period is not less than the node's safe voltage lower limit and not greater than the node's ideal voltage lower limit, then the voltage over-limit penalty coefficient for the corresponding time period node is calculated based on the voltage over-limit judgment data, the node's safe voltage lower limit, and the node's ideal voltage lower limit. If the voltage over-limit judgment data for a time period is less than the lower limit of the node's safe voltage or greater than the upper limit of the node's safe voltage, then the voltage over-limit penalty coefficient for the corresponding node in that time period is 1.

[0067] It should be noted that the square of the ideal upper limit voltage of a node is used as the ideal upper limit voltage of the node. The square of the ideal lower limit voltage at the node is used as the ideal lower limit value of the node voltage. The square of the node's safe upper limit voltage is used as the node's safe upper limit voltage value. The square of the node safety lower limit voltage is used as the node safety lower limit value. In this embodiment, a piecewise linear function is used to characterize the voltage over-limit situation based on the first electrical quantity parameter of each node, and this is reduced to a voltage over-limit penalty coefficient. This is used to measure the degree of voltage deviation at distribution network nodes. When the node voltage is within the ideal voltage range, the penalty factor is 0; outside the actual safe range, the penalty factor is 1; in other ranges, the penalty factor is linearly represented by the actual node voltage.

[0068] In this embodiment, the voltage over-limit penalty coefficient corresponding to each node is calculated and determined using the voltage over-limit penalty formula based on the first electrical quantity parameter of each node. The voltage over-limit penalty formula is as follows: ; In the formula, For the time period t node j Voltage over-limit penalty coefficient, For time period t node j node voltage, , These are the squares of the ideal upper limit voltage and the ideal lower limit voltage of the node, respectively. , These are the squares of the node's safe upper limit voltage and the squares of the node's safe lower limit voltage, respectively.

[0069] In one embodiment of this application, a model is constructed based on all first electrical quantity parameters, all second electrical quantity parameters, and all voltage over-limit penalty coefficients to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and intelligent energy storage soft-switching operation constraints, comprising: Constraints are constructed based on all first electrical quantity parameters, all second electrical quantity parameters, and all voltage over-limit penalty coefficients to obtain constraint data. The constraint data includes voltage over-limit penalty constraints, first voltage regulation constraints of distributed power sources, second voltage regulation constraints of reactive power compensation devices, third voltage regulation constraints of energy storage devices, fourth voltage regulation constraints of smart energy storage soft switches, and power flow constraints applicable to the Distflow model of distribution networks. Based on all the first electrical quantity parameters, the voltage over-limit penalty coefficient, and all the second electrical quantity parameters, a function is constructed to obtain the objective function that minimizes the cost of the distribution network purchasing electricity from the upstream distribution network, the network loss cost, the energy storage operation cost, and the voltage over-limit cost.

[0070] In the embodiments of this application, during the process of obtaining constraint data, distributed power sources, reactive power compensation devices, energy storage devices, intelligent energy storage soft switches, and voltage over-limit penalty coefficients are coordinated to achieve coordinated voltage regulation of multiple power devices (such as distributed power sources, reactive power compensation devices, energy storage devices, and intelligent energy storage soft switches), voltage over-limit control, and power flow constraints of the Distflow model, thereby avoiding voltage fluctuations in the distribution network and improving the operational stability of the power system.

[0071] In one embodiment of this application, the voltage over-limit penalty constraint is as follows: ; ; ; In the formula, This represents the number of nodes.

[0072] In one embodiment of this application, the first voltage regulation constraint of the distributed power source is: ; ; In the formula, , , and These are the upper limit of active power output, the lower limit of active power output, the upper limit of reactive power output, and the lower limit of reactive power output for distributed power sources. P G and Q G These refer to the active power output and reactive power output of the distributed power source, respectively.

[0073] It should be noted that, as Figure 2 As shown, the first voltage regulation constraint of the distributed generation can be obtained based on the Distflow power flow equation, which is: ; In the formula, and The lines are respectively k Nodes inject active and reactive power; and The lines are respectively kNode resistance and node reactance. It can be seen that the voltage level at node m is affected by the power flow along the power transmission path from the starting node to node m. When the output of the distributed generation is lower than the current load demand of the system, As the output of distributed power sources increases, the voltage gradually returns to the rated voltage level; when the output of distributed power sources exceeds the load demand of the power system... As the output of distributed power sources increases, situations may arise where distributed power nodes feed power back to the starting node, causing the voltage of the power source and its nearby nodes to deviate from the rated voltage level. To address the challenge of voltage exceeding limits in the distribution network caused by high-proportion distributed photovoltaic (PV) grid integration, the first voltage regulation constraint for distributed power sources can be mitigated by rationally controlling the active power output of distributed PV in the distribution network to reduce its reverse transmission to the grid; alternatively, the flexible adjustment capability of PV inverters can be fully utilized to increase the inductive reactive power generated by distributed power sources.

[0074] In one embodiment of this application, it can be seen from the Distflow power flow equation that adjusting the injected power at nodes on a line can effectively improve the voltage distribution at those nodes. Therefore, reactive power compensation devices, such as parallel reactors or parallel capacitors, can be connected in parallel at the nodes to consume or absorb the inductive reactive power of the power system, thereby adjusting the injected reactive power at the nodes and improving the node voltage level. It is worth noting that the reactive power compensation device consists of several reactors or capacitors; therefore, the capacity of the device being switched on and off is a discrete value, not a continuous value. This differs from the first voltage regulation constraint condition of the distributed power source described above. The second voltage regulation constraint condition for the reactive power compensation device is: ; In the formula, The total reactive power compensation capacity of the nodes; This represents the minimum reactive power compensation unit capacity. The number of electronic components (such as IGBTs) in the reactive power compensation device.

[0075] Figure 3 This is a simplified circuit diagram of the grid connection of the energy storage device in the power distribution network voltage dynamic control method based on intelligent energy storage soft switching described in the embodiments of this application. Figure 3 In Chinese: ESS stands for Energy Storage System; Apparent power of energy storage; This refers to the bus voltage of the distribution network; This refers to the grid connection node voltage for the energy storage device. R and X These are the grid connection line resistance and grid connection line reactance for the energy storage device.

[0076] Because energy storage devices can store electrical energy for short periods and have the ability to transfer electrical energy over time, excess electrical energy can be stored during periods of light load or low electricity prices, and released during periods of high load or high electricity prices to achieve peak shaving and valley filling and improve economic efficiency. Figure 3 As shown, the expression for the distribution network bus voltage is: ; In the formula, This refers to the power factor angle of the energy storage device's output power. By adjusting the apparent power of the energy storage and its power factor angle, the bus voltage of the distribution network can be regulated. When the apparent power of the energy storage... When the apparent power of the energy storage device is greater than 0, it discharges into the distribution network, acting as a power source and boosting the voltage; when the apparent power of the energy storage device is greater than 0, it discharges into the distribution network, acting as a power source and boosting the voltage. When the voltage is less than 0, the energy storage device absorbs electrical energy from the distribution network, acting as a load and thus suppressing voltage. Therefore, in one embodiment of this application, the third voltage regulation constraint condition for the energy storage device is: ; ; In the formula, , These represent the active and reactive power injected into the distribution network bus nodes by the energy storage device.

[0077] In distribution networks, flexible interconnection devices (such as smart energy storage soft switches) consist of converters at the ports and DC lines in the middle, exhibiting the characteristic of "AC-DC-AC" power transfer. Typically, flexible interconnection devices (such as smart energy storage soft switches) flexibly interconnect the nodes of medium-voltage distribution networks, transforming the "closed-loop design, open-loop operation" mode of the power grid into a "closed-loop operation" mode. Because the flexible interconnection device isolates the AC grid at both ends through a DC link, closed-loop operation based on flexible interconnection devices (such as smart energy storage soft switches) does not suffer from the problems associated with closed-loop operation based on tie switches, such as increased short-circuit current, circulating current in the AC ring network, and increased complexity in AC system relay protection coordination. Regulating the port power of flexible interconnection devices (such as smart energy storage soft switches) can alter the power flow distribution of the power system, thereby affecting the voltage distribution of the entire distribution network. Therefore, in one embodiment of this application, the fourth voltage regulation constraint of the smart energy storage soft switch includes: ; ; ; ; In the formula, For time period tarea r Power loss of the side converter; For the region r The power loss factor of the side converter; For time period t Power loss of energy storage units in converters within flexible interconnect devices; For time period t Shared energy storage charging and discharging power within the distribution network; a positive value indicates energy storage discharge. For the region r Capacity of the side converter.

[0078] It should be noted that flexible interconnected devices (such as smart energy storage soft switches) utilize the energy storage and release within their internal storage to transfer electrical energy over time. Shared energy storage, during operation, needs to meet energy state constraints and charging / discharging power constraints to avoid overcharging and discharging, ensuring the operational safety of flexible interconnected devices (such as smart energy storage soft switches). Simultaneously, it must also meet energy storage cycle constraints to ensure that the energy storage's state of charge is the same as its initial state of charge at the end of a trading day, guaranteeing fairness in the shared energy storage market. The fourth voltage regulation constraint for smart energy storage soft switches also includes: ; ; ; In the formula, and These are the lower limit and upper limit of shared energy storage power, respectively. , These are the initial electrical energy and the final electrical energy of the day, respectively. This represents the upper limit of the energy storage discharge power.

[0079] In one embodiment of this application, the power flow constraints of the distribution network Distflow model are applied. The power flow constraints include active power balance constraints, reactive power balance constraints, grid loss constraints, distribution network voltage and current constraints, and main grid injected power constraints.

[0080] It should be noted that the power flow constraint is as follows: ; ; ; ; ; ; ; ; ; ; In the formula, and Time periods t node i With nodes j The active power and reactive power transmitted between branches. and They are nodes i With nodes j Branch roads in time period t The branch current and branch voltage, For nodes i With nodes j Branch reactance between branches; , Time periods t node j Active power and reactive power; , Time periods t node j The active and reactive power outputs of the distributed power source are connected; , Time periods t node j The active and reactive loads connected; , and They are respectively in the time period t From the upper-level distribution network to the distribution network nodes j The injected reactive power, the maximum upper limit of injected active power, and the maximum upper limit of injected reactive power; This is the upper limit of the branch current; and They are nodes j The lower and upper voltage limits, , , , They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j Injected active power, energy storage unit injection node j The active power and reactive power compensation device of the converter injection node j The active power; For the time period t Energy storage devices are injected into distribution network nodes. j reactive power, For the time period t Energy storage devices are injected into distribution network nodes. j The active power of discharge, For time period t node j Total reactive power compensation capacity, For nodes i With nodes j Branch resistance of the intermediate branch, Injection nodes for converters in reactive power compensation devices j The reactive power.

[0081] In one embodiment of this application, the objective function is: ; ; ; ; ; In the formula, , , , They are respectively in the time period t The costs of purchasing electricity from the distribution network to the superior distribution network, network loss costs, energy storage operation costs, and voltage limit violation penalty costs; , and These are the unit price of electricity purchased from the upstream distribution network, the unit operating cost of energy storage, and the unit cost of load loss caused by voltage exceeding limits; , and They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j The injected active power and the discharged active power of the energy storage device; For nodes i With nodes j Branch roads in time period t The branch current, For nodes i With nodes j Branch resistance of the intermediate branch, For the number of branch roads, The number of energy storage units in the distribution network. The number of converters in the distribution network. For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes,F Let be the objective function. For the time period t No. i Power loss of each energy storage unit, For the time period t No. The power loss of the converter.

[0082] Example 2: Figure 4 This is a schematic diagram of the framework of the power distribution network voltage dynamic control device based on intelligent energy storage soft switch described in the embodiments of this application.

[0083] like Figure 4 As shown, this application embodiment provides a power distribution network voltage dynamic control device based on intelligent energy storage soft switch, including: a data acquisition module 100, a model construction module 200, a control data solving module 300, and a control execution module 400; The data acquisition module 100 is used to acquire the topology of the distribution network, and based on the topology, acquire the number of nodes, the number of branches, the first electrical quantity parameters of each node, and the second electrical quantity parameters of each branch; based on the first electrical quantity parameters of each node, determine the voltage over-limit penalty coefficient corresponding to the node. The model building module 200 is used to build a model based on all first electrical quantity parameters, all second electrical quantity parameters and all voltage over-limit penalty coefficients, to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and the operation constraints of smart energy storage soft switching; the distribution network voltage dynamic regulation optimization model includes objective function and constraint condition data; The regulation data solving module 300 is used to solve the dynamic regulation optimization model of the distribution network voltage using a solver based on the constraint data, and obtain the dynamic regulation data of the distribution network voltage with the minimum objective function value; the dynamic regulation data of the distribution network voltage includes the day-ahead power purchase strategy data, regulation strategy data, branch current data and node voltage data of the distribution network; The control execution module 400 is used to control the operation of the distribution network based on the dynamic control data of the distribution network voltage, so as to realize the dynamic control of the distribution network voltage. Among them, the day-ahead power purchase strategy data of the distribution network includes the injected active power and injected reactive power from the upper-level distribution network to the distribution network nodes, the control strategy data includes the active power output of the nodes connected to distributed power sources, the reactive power output of the nodes connected to distributed power sources, the total reactive power compensation capacity of the nodes, the reactive power injected into the nodes by energy storage devices, the active power of the energy storage devices injected into the nodes by the discharge of energy storage devices, the active power injected into the nodes by the converters in the reactive power compensation devices, the reactive power injected into the nodes by the converters in the reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network. The branch current data is the upper limit of the branch current, and the node voltage data is the node voltage.

[0084] It should be noted that the content of each module in the device of Embodiment 2 corresponds to the content of each step in the method of Embodiment 1. Therefore, the content of each module of the distribution network voltage dynamic control device based on intelligent energy storage soft switch will not be repeated in this embodiment. This distribution network voltage dynamic control device based on intelligent energy storage soft switch obtains the electrical quantity parameters of the distribution network through a data acquisition module, a model construction module, a control data solving module, and a control execution module. First, it determines the voltage over-limit penalty coefficient for each node. Then, based on the electrical quantity parameters and the voltage over-limit penalty coefficient, it constructs a distribution network voltage dynamic control optimization model that includes the Distflow model and the operating constraints of the intelligent energy storage soft switch. The solver is then called to solve the distribution network voltage dynamic control optimization model, providing the current-day distribution network voltage dynamic control data to control the operation of the distribution network and achieve dynamic voltage control of the distribution network.

[0085] In this embodiment of the application, the model building module includes: a constraint construction submodule and a model building submodule; The constraint construction submodule is used to construct constraints based on all first electrical quantity parameters, all second electrical quantity parameters, and all voltage limit penalty coefficients to obtain constraint data. The constraint data includes voltage limit penalty constraints, first voltage regulation constraints of distributed power sources, second voltage regulation constraints of reactive power compensation devices, third voltage regulation constraints of energy storage devices, fourth voltage regulation constraints of smart energy storage soft switches, and power flow constraints applicable to the Distflow model of distribution networks. The model building submodule is used to construct functions based on all first electrical quantity parameters, voltage over-limit penalty coefficient and all second electrical quantity parameters, to obtain the objective function that minimizes the cost of purchasing electricity from the distribution network to the upper-level distribution network, network loss cost, energy storage operation cost and voltage over-limit cost. The objective function is: ; ; ; ; ; In the formula, , , , They are respectively in the time period t The costs of purchasing electricity from the distribution network to the superior distribution network, network loss costs, energy storage operation costs, and voltage limit violation penalty costs; , and These are the unit price of electricity purchased from the upstream distribution network, the unit operating cost of energy storage, and the unit cost of load loss caused by voltage exceeding limits; , and They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j The injected active power and the discharged active power of the energy storage device; For nodes i With nodes j Branch roads in time period t The branch current, For nodes i With nodes j Branch resistance of the intermediate branch, For the number of branch roads, The number of energy storage units in the distribution network. The number of converters in the distribution network. For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, F Let be the objective function. For the time period t No. i Power loss of each energy storage unit, For the time period t No. The power loss of the converter.

[0086] In one embodiment of this application, the first electrical quantity parameters include the node voltage, active power, reactive power, active output, reactive output, active load, reactive load, total reactive power compensation capacity, load active power, injected active power, injected reactive power, maximum injected active power limit, and maximum injected reactive power limit for each time period. The second electrical quantity parameters include branch transmitted active power, branch transmitted reactive power, branch current, branch voltage, branch reactance, maximum branch current limit, and branch resistance. The voltage over-limit penalty constraint condition is: ; ; ; The power flow constraint is: ; ; ; ; ; ; ; ; ; ; In the formula, For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, and For time period t node j and nodes i node voltage, , These are the squares of the ideal upper limit voltage and the ideal lower limit voltage of the node, respectively. , These are the squares of the node's safe upper limit voltage and the squares of the node's safe lower limit voltage, respectively. and Time periods t node i With nodes j The active power and reactive power transmitted between branches. and They are nodes i With nodes j Branch roads in time period t The branch current and branch voltage, For nodes i With nodes j Branch reactance between branches; , Time periods t node j Active power and reactive power; , Time periods t node j The active and reactive power outputs of the distributed power source are connected; , Time periods t node j The active and reactive loads connected; , and They are respectively in the time period t From the upper-level distribution network to the distribution network nodes jThe injected reactive power, the maximum upper limit of injected active power, and the maximum upper limit of injected reactive power; This is the upper limit of the branch current; and They are nodes j The lower and upper voltage limits, , , , They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j Injected active power, energy storage unit injection node j The active power and reactive power compensation device of the converter injection node j The active power; For the time period t Energy storage devices are injected into distribution network nodes. j reactive power, For the time period t Energy storage devices are injected into distribution network nodes. j The active power of discharge, For time period t node j Total reactive power compensation capacity, For nodes i With nodes j Branch resistance of the intermediate branch, Injection nodes for converters in reactive power compensation devices j The reactive power.

[0087] In one embodiment of this application, the first electrical quantity parameter includes the node voltage for each time period. Determining the voltage over-limit penalty coefficient corresponding to each node based on the first electrical quantity parameter includes: Obtain the upper limit of the ideal voltage at the node, the lower limit of the ideal voltage at the node, the upper limit of the safe voltage at the node, and the lower limit of the safe voltage at the node; Based on the node voltage for each time period, determine the voltage over-limit judgment data for the corresponding node during that time period; If the voltage over-limit judgment data for a time period is not less than the lower limit of the ideal voltage of the node and not greater than the upper limit of the ideal voltage of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is 0. If the voltage over-limit judgment data for a time period is not less than the node's ideal voltage upper limit and not greater than the node's safe voltage upper limit, then the voltage over-limit penalty coefficient for the corresponding time period node is calculated based on the voltage over-limit judgment data, the node's ideal voltage upper limit, and the node's safe voltage upper limit. If the voltage over-limit judgment data for a time period is not less than the node's safe voltage lower limit and not greater than the node's ideal voltage lower limit, then the voltage over-limit penalty coefficient for the corresponding time period node is calculated based on the voltage over-limit judgment data, the node's safe voltage lower limit, and the node's ideal voltage lower limit. If the voltage over-limit judgment data for a time period is less than the lower limit of the node's safe voltage or greater than the upper limit of the node's safe voltage, then the voltage over-limit penalty coefficient for the corresponding node in that time period is 1.

[0088] Example 3: Figure 5 This is a schematic diagram of the terminal device described in an embodiment of this application.

[0089] like Figure 5 As shown, this application provides a terminal device, including a processor and a memory; Memory is used to store program code and transfer the program code to the processor; The processor is used to execute the above-mentioned method for dynamic voltage control of distribution networks based on intelligent energy storage soft switches according to the instructions in the program code.

[0090] It should be noted that the processor is used to execute the steps in the above-described embodiment of a method for dynamic voltage regulation of a distribution network based on intelligent energy storage soft switching, according to the instructions in the program code. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described system / device embodiments.

[0091] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory and executed by a processor to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0092] Terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that this does not constitute a limitation on the terminal device, which may include more or fewer components than illustrated, or combinations of certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.

[0093] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0094] Memory can be an internal storage unit of a terminal device, such as a hard drive or RAM. Memory can also be an external storage device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal and external storage units. Memory is used to store computer programs and other programs and data required by the terminal device. Memory can also be used to temporarily store data that has been output or will be output.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 this application.

Claims

1. A method for dynamic voltage control of distribution networks based on intelligent energy storage soft switching, characterized in that, Includes the following steps: Obtain the topology of the distribution network, and based on the topology, obtain the number of nodes, the number of branches, the first electrical quantity parameters of each node, and the second electrical quantity parameters of each branch; based on the first electrical quantity parameters of each node, determine the voltage over-limit penalty coefficient corresponding to the node; Based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients, a model is constructed to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and the operation constraints of intelligent energy storage soft switching; the distribution network voltage dynamic regulation optimization model includes objective function and constraint condition data. The distribution network voltage dynamic regulation optimization model is solved using a solver based on the constraint data to obtain the distribution network voltage dynamic regulation data that minimizes the objective function value; the distribution network voltage dynamic regulation data includes distribution network day-ahead power purchase strategy data, regulation strategy data, branch current data, and node voltage data; The operation of the distribution network is controlled based on the dynamic voltage regulation data of the distribution network, thereby realizing dynamic voltage regulation of the distribution network; The day-ahead power purchase strategy data of the distribution network includes the injected active power and injected reactive power from the upper-level distribution network to the distribution network nodes. The control strategy data includes the active power output of the nodes connected to distributed power sources, the reactive power output of the nodes connected to distributed power sources, the total reactive power compensation capacity of the nodes, the reactive power injected into the nodes by energy storage devices, the discharge active power injected into the nodes by energy storage devices, the active power injected into the nodes by the converters in the reactive power compensation devices, the reactive power injected into the nodes by the converters in the reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network. The branch current data is the upper limit of the branch current, and the node voltage data is the node voltage.

2. The method for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switching according to claim 1, characterized in that, Based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients, a model is constructed to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and intelligent energy storage soft-switching operation constraints, including: Constraints are constructed based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients to obtain constraint data; the constraint data includes voltage over-limit penalty constraints, first voltage regulation constraints of distributed power sources, second voltage regulation constraints of reactive power compensation devices, third voltage regulation constraints of energy storage devices, fourth voltage regulation constraints of smart energy storage soft switches, and power flow constraints applicable to the Distflow model of distribution networks. Based on all the first electrical quantity parameters, the voltage over-limit penalty coefficient, and all the second electrical quantity parameters, a function is constructed to obtain an objective function that minimizes the cost of the distribution network purchasing electricity from the upstream distribution network, the network loss cost, the energy storage operation cost, and the voltage over-limit cost.

3. The method for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switching according to claim 2, characterized in that, The first electrical quantity parameter includes the injected active power from the upstream distribution network to the distribution network node, the discharge active power of the node, and the load active power of the node. The second electrical quantity parameter includes branch current and branch resistance. The objective function is: ； ； ； ； ； In the formula, , , , They are respectively in the time period t The costs of purchasing electricity from the distribution network to the superior distribution network, network loss costs, energy storage operation costs, and voltage limit violation penalty costs; , and These are the unit price of electricity purchased from the upstream distribution network, the unit operating cost of energy storage, and the unit cost of load loss caused by voltage exceeding limits; , and They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j The injected active power and the discharged active power of the energy storage device; For nodes i With nodes j Branch roads in time period t The branch current, For nodes i With nodes j Branch resistance of the intermediate branch, For the number of branch roads, The number of energy storage units in the distribution network. The number of converters in the distribution network. For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, F Let be the objective function. For the time period t No. i Power loss of each energy storage unit, For the time period t No. The power loss of the converter.

4. The method for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switching according to claim 2, characterized in that, The first electrical quantity parameter includes the node voltage, active power, reactive power, active output, reactive output, active load, reactive load, total reactive power compensation capacity, load active power, injected active power, injected reactive power, maximum injected active power limit, and maximum injected reactive power limit for each time period. The second electrical quantity parameter includes branch transmitted active power, branch transmitted reactive power, branch current, branch voltage, branch reactance, maximum branch current limit, and branch resistance. The voltage over-limit penalty constraint condition is: ； ； ； The power flow constraint is as follows: ； ； ； ； ； ； ； ； ； ； In the formula, For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, and For time period t node j and nodes i node voltage, , These are the squares of the ideal upper limit voltage and the ideal lower limit voltage of the node, respectively. , These are the squares of the node's safe upper limit voltage and the squares of the node's safe lower limit voltage, respectively. and Time periods t node i With nodes j The active power and reactive power transmitted between branches. and They are nodes i With nodes j Branch roads in time period t The branch current and branch voltage, For nodes i With nodes j Branch reactance between branches; , Time periods t node j Active power and reactive power; , Time periods t node j The active and reactive power outputs of the distributed power source are connected; , Time periods t node j The active and reactive loads connected; , and They are respectively in the time period t From the upper-level distribution network to the distribution network nodes j The injected reactive power, the maximum upper limit of injected active power, and the maximum upper limit of injected reactive power; This is the upper limit of the branch current; and They are nodes j The lower and upper voltage limits, , , , They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j Injected active power, energy storage unit injection node j The active power and reactive power compensation device of the converter injection node j The active power; For the time period t Energy storage devices are injected into distribution network nodes. j reactive power, For the time period t Energy storage devices are injected into distribution network nodes. j The active power of discharge, For time period t node j Total reactive power compensation capacity, For nodes i With nodes j Branch resistance of the intermediate branch, Injection nodes for converters in reactive power compensation devices j The reactive power.

5. The method for dynamic voltage regulation of distribution networks based on intelligent energy storage soft switching according to any one of claims 1-4, characterized in that, The first electrical quantity parameter includes the node voltage for each time period. Based on the first electrical quantity parameter of each node, the voltage over-limit penalty coefficient corresponding to the node is determined, including: Obtain the upper limit of the ideal voltage at the node, the lower limit of the ideal voltage at the node, the upper limit of the safe voltage at the node, and the lower limit of the safe voltage at the node; Based on the node voltage for each time period, determine the voltage over-limit judgment data for the corresponding node during that time period; If the voltage over-limit judgment data for a time period is not less than the lower limit of the ideal voltage of the node and not greater than the upper limit of the ideal voltage of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is 0. If the voltage over-limit judgment data for a time period is not less than the ideal voltage upper limit of the node and not greater than the safe voltage upper limit of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is calculated based on the voltage over-limit judgment data, the ideal voltage upper limit of the node, and the safe voltage upper limit of the node. If the voltage over-limit judgment data for a time period is not less than the node's safe voltage lower limit and not greater than the node's ideal voltage lower limit, then the voltage over-limit penalty coefficient for the node in the corresponding time period is calculated based on the voltage over-limit judgment data, the node's safe voltage lower limit, and the node's ideal voltage lower limit. If the voltage over-limit judgment data for a given time period is less than the lower limit of the node's safe voltage or greater than the upper limit of the node's safe voltage, then the voltage over-limit penalty coefficient for the node in the corresponding time period is 1.

6. A power distribution network voltage dynamic control device based on intelligent energy storage soft switching, characterized in that, include: The system comprises a data acquisition module, a model building module, a data solution module for regulation, and a regulation execution module. The data acquisition module is used to acquire the topology of the distribution network, acquire the number of nodes and branches of the distribution network, as well as the first electrical quantity parameters of each node and the second electrical quantity parameters of each branch, based on the topology; and determine the voltage over-limit penalty coefficient corresponding to each node based on the first electrical quantity parameters of each node. The model building module is used to build a model based on all the first electrical quantity parameters, all the second electrical quantity parameters and all the voltage over-limit penalty coefficients, to obtain a distribution network voltage dynamic regulation optimization model that includes the Distflow model and the operation constraints of smart energy storage soft switching; the distribution network voltage dynamic regulation optimization model includes objective function and constraint condition data. The control data solving module is used to solve the distribution network voltage dynamic control optimization model using a solver based on the constraint data, to obtain the distribution network voltage dynamic control data that minimizes the objective function value; the distribution network voltage dynamic control data includes distribution network day-ahead power purchase strategy data, control strategy data, branch current data, and node voltage data; The control execution module is used to control the operation of the distribution network according to the distribution network voltage dynamic control data, so as to realize the voltage dynamic control of the distribution network. The day-ahead power purchase strategy data of the distribution network includes the injected active power and injected reactive power from the upper-level distribution network to the distribution network nodes. The control strategy data includes the active power output of the nodes connected to distributed power sources, the reactive power output of the nodes connected to distributed power sources, the total reactive power compensation capacity of the nodes, the reactive power injected into the nodes by energy storage devices, the discharge active power injected into the nodes by energy storage devices, the active power injected into the nodes by the converters in the reactive power compensation devices, the reactive power injected into the nodes by the converters in the reactive power compensation devices, and the shared energy storage charging and discharging power of the distribution network. The branch current data is the upper limit of the branch current, and the node voltage data is the node voltage.

7. The power distribution network voltage dynamic control device based on intelligent energy storage soft switch according to claim 6, characterized in that, The model building module includes: a constraint construction submodule and a model building submodule; The constraint construction submodule is used to construct constraints based on all the first electrical quantity parameters, all the second electrical quantity parameters, and all the voltage over-limit penalty coefficients to obtain constraint data. The constraint data includes voltage over-limit penalty constraints, first voltage regulation constraints of distributed power sources, second voltage regulation constraints of reactive power compensation devices, third voltage regulation constraints of energy storage devices, fourth voltage regulation constraints of smart energy storage soft switches, and power flow constraints applicable to the Distflow model of distribution networks. The model construction submodule is used to construct a function based on all the first electrical quantity parameters, the voltage over-limit penalty coefficient and all the second electrical quantity parameters, to obtain an objective function that minimizes the cost of purchasing electricity from the distribution network to the upper-level distribution network, network loss cost, energy storage operation cost and voltage over-limit cost. The objective function is: ； ； ； ； ； In the formula, , , , They are respectively in the time period t The costs of purchasing electricity from the distribution network to the superior distribution network, network loss costs, energy storage operation costs, and voltage limit violation penalty costs; , and These are the unit price of electricity purchased from the upstream distribution network, the unit operating cost of energy storage, and the unit cost of load loss caused by voltage exceeding limits; , and They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j The injected active power and the discharged active power of the energy storage device; For nodes i With nodes j Branch roads in time period t The branch current, For nodes i With nodes j Branch resistance of the intermediate branch, For the number of branch roads, The number of energy storage units in the distribution network. The number of converters in the distribution network. For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, F Let be the objective function. For the time period t No. i Power loss of each energy storage unit, For the time period t No. The power loss of the converter.

8. The power distribution network voltage dynamic control device based on intelligent energy storage soft switch according to claim 7, characterized in that, The first electrical quantity parameter includes the node voltage, active power, reactive power, active output, reactive output, active load, reactive load, total reactive power compensation capacity, load active power, injected active power, injected reactive power, maximum injected active power limit, and maximum injected reactive power limit for each time period. The second electrical quantity parameter includes branch transmitted active power, branch transmitted reactive power, branch current, branch voltage, branch reactance, maximum branch current limit, and branch resistance. The voltage over-limit penalty constraint condition is: ； ； ； The power flow constraint is as follows: ； ； ； ； ； ； ； ； ； ； In the formula, For the time period t node j Voltage over-limit penalty coefficient, For the number of nodes, and For time period t node j and nodes i node voltage, , These are the squares of the ideal upper limit voltage and the ideal lower limit voltage of the node, respectively. , These are the squares of the node's safe upper limit voltage and the squares of the node's safe lower limit voltage, respectively. and Time periods t node i With nodes j The active power and reactive power transmitted between branches. and They are nodes i With nodes j Branch roads in time period t The branch current and branch voltage, For nodes i With nodes j Branch reactance between branches; , Time periods t node j Active power and reactive power; , Time periods t node j The active and reactive power outputs of the distributed power source are connected; , Time periods t node j The active and reactive loads connected; , and They are respectively in the time period t From the upper-level distribution network to the distribution network nodes j The injected reactive power, the maximum upper limit of injected active power, and the maximum upper limit of injected reactive power; This is the upper limit of the branch current; and They are nodes j The lower and upper voltage limits, , , , They are respectively in the time period t node j The active power of the load, the power from the upstream distribution network to the distribution network nodes j Injected active power, energy storage unit injection node j The active power and reactive power compensation device of the converter injection node j The active power; For the time period t Energy storage devices are injected into distribution network nodes. j reactive power, For the time period t Energy storage devices are injected into distribution network nodes. j The active power of discharge, For time period t node j Total reactive power compensation capacity, For nodes i With nodes j Branch resistance of the intermediate branch, Injection nodes for converters in reactive power compensation devices j The reactive power.

9. The distribution network voltage dynamic control device based on intelligent energy storage soft switch according to any one of claims 6-8, characterized in that, The first electrical quantity parameter includes the node voltage for each time period. Based on the first electrical quantity parameter of each node, the voltage over-limit penalty coefficient corresponding to the node is determined, including: Obtain the upper limit of the ideal voltage at the node, the lower limit of the ideal voltage at the node, the upper limit of the safe voltage at the node, and the lower limit of the safe voltage at the node; Based on the node voltage for each time period, determine the voltage over-limit judgment data for the corresponding node during that time period; If the voltage over-limit judgment data for a time period is not less than the lower limit of the ideal voltage of the node and not greater than the upper limit of the ideal voltage of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is 0. If the voltage over-limit judgment data for a time period is not less than the ideal voltage upper limit of the node and not greater than the safe voltage upper limit of the node, then the voltage over-limit penalty coefficient of the node for the corresponding time period is calculated based on the voltage over-limit judgment data, the ideal voltage upper limit of the node, and the safe voltage upper limit of the node. If the voltage over-limit judgment data for a time period is not less than the node's safe voltage lower limit and not greater than the node's ideal voltage lower limit, then the voltage over-limit penalty coefficient for the node in the corresponding time period is calculated based on the voltage over-limit judgment data, the node's safe voltage lower limit, and the node's ideal voltage lower limit. If the voltage over-limit judgment data for a given time period is less than the lower limit of the node's safe voltage or greater than the upper limit of the node's safe voltage, then the voltage over-limit penalty coefficient for the node in the corresponding time period is 1.

10. A terminal device, characterized in that, Including the processor and memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the power distribution network voltage dynamic control method based on intelligent energy storage soft switch as described in any one of claims 1-5, according to the instructions in the program code.

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