Power distribution network voltage regulation and control method, system, equipment and medium

Abstract

The invention discloses a power distribution network voltage regulation and control method, system and device and a medium, and the method comprises the steps: obtaining power grid physical parameters and operation and environment data of a power distribution network, and forming a power distribution network basic data set; a single temperature control load operation model is obtained by combining the operation characteristics of the temperature control load and the thermodynamic principle; carrying out aggregation modeling on the homogeneous temperature control load in the region to obtain a temperature control load cluster aggregation model; obtaining a distributed energy storage aggregation model by integrating energy, power and charge state constraints of each distributed energy storage device in the same node; establishing an optimization problem including alternating current power flow, voltage, current and two types of resource constraints by fusing physical parameters of a power grid to obtain a voltage regulation and control optimization model; and solving by calling an optimization algorithm to obtain an optimal collaborative regulation strategy. According to the invention, the precision, the physical feasibility and the calculation efficiency of voltage regulation and control of the power distribution network are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of voltage regulation technology for power distribution networks, and in particular to a method, system, device, and medium for voltage regulation of power distribution networks. Background Technology

[0002] With the rapid development of distributed renewable energy, the large-scale integration of distributed photovoltaic, wind power, and other new energy sources into the distribution network has brought severe challenges to the safe and stable operation of the power grid. Due to the strong volatility and randomness of new energy output, and the continuous increase in load-side electricity consumption, some nodes in the distribution network frequently experience large differences in power between source and load, resulting in frequent voltage fluctuations and even serious voltage exceeding limits. Traditional voltage regulation measures, such as on-load tap-changing transformers and reactive power compensation devices, while able to alleviate voltage problems to some extent, suffer from slow response speed, low regulation accuracy, and high cost, making it difficult to meet the voltage regulation requirements under the background of large-scale new energy integration into the distribution network.

[0003] To effectively address voltage exceedance issues, current research focuses on regulating the power grid by controlling adjustable resources on the load side. Temperature-controlled loads (such as air conditioners, refrigerators, and electric water heaters) offer rapid response and high flexibility, enabling them to proactively and flexibly adjust load characteristics or provide bidirectional power exchange, effectively improving node voltage quality. Especially during peak summer electricity consumption periods, air conditioning loads account for over 30% of peak loads, indicating significant potential for regulation.

[0004] In existing technologies, some solutions segment the feeder based on its voltage and the configuration of the control equipment, implementing targeted voltage regulation for specific adjustable segments. Others utilize hybrid energy storage systems composed of supercapacitors and batteries to absorb electricity generated by distributed power sources when photovoltaic and other new energy sources have high output and low load, and release the electricity when new energy output is low and load is high, thereby solving the voltage over-limit problem caused by new energy absorption. Still others input relevant data from the power grid and mobile energy storage into a multi-node power grid model to obtain mobile energy storage movement and charging / discharging control strategies. However, none of the aforementioned methods have ignored the huge control potential of temperature-controlled loads on the load side; and they mainly focus on the flexible utilization of a single type of resource, without considering the collaborative control research of distributed energy storage and temperature-controlled loads. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method, system, device and medium for voltage regulation of power distribution networks to solve the problems of slow response and low accuracy of existing voltage regulation methods for power distribution networks.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for voltage regulation in a power distribution network, comprising: Acquire the power grid physical parameters and operation and environmental data of the distribution network to form a basic dataset for the distribution network; Based on the basic dataset of the distribution network, and combined with the operating characteristics and thermodynamic principles of temperature-controlled loads, an operating model for a single temperature-controlled load is obtained. Based on the single temperature-controlled load operation model, the homogeneous temperature-controlled loads in the region are aggregated and modeled to obtain a temperature-controlled load cluster aggregation model. Based on the physical parameters of the power grid, a distributed energy storage aggregation model is obtained by integrating the energy, power and state of charge constraints of each distributed energy storage device within the same node. Based on the temperature-controlled load cluster aggregation model and the distributed energy storage aggregation model, and by integrating the power grid physical parameters, an optimization problem including AC power flow, voltage, current and two types of resource constraints is established, resulting in a voltage regulation optimization model. Based on the voltage regulation optimization model and the basic dataset of the distribution network, the optimal coordinated regulation strategy is obtained by calling the optimization algorithm.

[0008] As a preferred embodiment of the power distribution network voltage regulation method described in this invention, the step of obtaining a single temperature-controlled load operation model includes: Based on the start-up and shutdown time series of temperature-controlled loads identified from operational and environmental data, the process of ambient temperature change, and user temperature setpoints, dynamic input variables and control boundary conditions are determined. Based on the identified start-stop time sequence, ambient temperature change process, and user temperature setpoint, an equivalent circuit model structure is constructed. Based on the constructed equivalent circuit model structure, a dynamic response equation is established; Based on the identified user temperature setpoint and allowable fluctuation range, determine the upper and lower temperature limits corresponding to the equipment start-stop control; Based on the determined upper and lower temperature limits, the equipment start-up and shutdown rules are set to form a two-position control logic; Based on the established dynamic response equation and the formed dual-position control logic, a single temperature-controlled load operation model is obtained.

[0009] The beneficial effect of this preferred technical solution is that by integrating operational and environmental data and user temperature setting behavior, a single temperature-controlled load operation model that combines thermodynamic mechanisms and actual control logic is constructed, thereby improving the accuracy of characterizing the dynamic characteristics of the load.

[0010] As a preferred embodiment of the power distribution network voltage regulation method described in this invention, the step of obtaining the temperature-controlled load cluster aggregation model includes: Temperature-controlled loads with similar thermodynamic properties and power levels are classified into several categories. Based on the classification, the equivalent thermal resistance, equivalent heat capacity, and rated cooling power of each type of load are weighted and averaged to obtain representative aggregate parameters for each type. Based on the obtained representative aggregation parameters, a dynamic response equation describing the overall temperature change law of the cluster is established; Based on the established dynamic response equation, and combined with the user's acceptable upper and lower temperature limits and the total equipment capacity constraints, the power adjustable range of the temperature-controlled load cluster is formed. Based on the established dynamic response equation and the formed power adjustable range, a temperature-controlled load cluster aggregation model is obtained.

[0011] As a preferred embodiment of the distribution network voltage regulation method described in this invention, the step of obtaining the distributed energy storage aggregation model includes: The rated energy capacity of all distributed energy storage devices within the same power grid node is calculated and summed to form the aggregated total capacity. The maximum charging power and maximum discharging power of each device are summed to determine the aggregation power capability. Based on the aggregated capacity and aggregated power capability, an energy evolution relationship characterizing the energy accumulation and consumption process of aggregated energy storage units in a time series is constructed. Based on the mutual exclusion characteristics of charging and discharging of distributed energy storage devices and the allowable operating range of state of charge, operational constraints for aggregated energy storage are established. Based on the aggregated capacity, aggregated power capability, energy evolution relationship, and operational constraints, a distributed energy storage aggregation model is obtained.

[0012] As a preferred embodiment of the distribution network voltage regulation method of the present invention, the step of obtaining the voltage regulation optimization model includes: Based on the deviation of the voltage of each node in the system from its rated value, an optimization objective function is constructed with the goal of minimizing the sum of squared deviations; Based on the topology and operating characteristics of AC power grids, the active and reactive power flow balance equations of AC power grids are introduced as basic physical constraints. Based on the safety operation requirements of power equipment, upper and lower limits of node voltage amplitude and upper limit of branch current amplitude are set. Based on the constructed objective function, the introduced power flow balance equation, and the set voltage and current constraints, a voltage regulation optimization model is obtained.

[0013] The beneficial effect of this preferred technical solution is that by constructing an optimization model that aims to minimize voltage deviation and takes into account AC power flow and equipment safety constraints, the accuracy and physical feasibility of distribution network voltage regulation are achieved.

[0014] As a preferred embodiment of the distribution network voltage regulation method of the present invention, the step of obtaining the optimal coordinated regulation strategy includes: The optimization model containing nonlinear AC power flow equations is subjected to convex relaxation treatment. A second-order cone relaxation algorithm is used to transform the AC power flow equations into a second-order cone constraint form, resulting in a relaxed optimization model. Based on the transformed relaxed optimization model, the interior point algorithm is called to perform iterative solution calculations to obtain the iterative solution calculation results; Based on the iterative solution calculation results, the charging and discharging power plan of each node energy storage unit and the power adjustment command sequence of the temperature-controlled load cluster are generated for multiple future time periods; Based on the generated charging and discharging power plan and power regulation command sequence, the optimal coordinated control strategy is obtained.

[0015] As a preferred embodiment of the power distribution network voltage regulation method described in this invention, the two-position control logic includes: When the indoor temperature rises to the upper limit of the temperature range, the device will start cooling. When the indoor temperature drops to the lower limit of the specified temperature, the device stops cooling; When the indoor temperature is within the dead zone between the upper and lower temperature limits, the equipment maintains its current operating state.

[0016] In a second aspect, the present invention provides a power distribution network voltage regulation system, comprising: The distribution network basic dataset construction module is used to acquire the power grid physical parameters and operation and environmental data of the distribution network to form the distribution network basic dataset; The single temperature-controlled load modeling module is used to obtain the operating model of a single temperature-controlled load based on the basic dataset of the distribution network and the operating characteristics and thermodynamic principles of the temperature-controlled load. The temperature control load cluster aggregation module is used to aggregate and model homogeneous temperature control loads in a region based on the operation model of a single temperature control load, and obtain a temperature control load cluster aggregation model. The distributed energy storage aggregation modeling module is used to obtain a distributed energy storage aggregation model based on the physical parameters of the power grid by integrating the energy, power and state of charge constraints of each distributed energy storage device within the same node. The voltage regulation optimization modeling module is used to establish an optimization problem that includes AC power flow, voltage, current and two types of resource constraints based on the temperature-controlled load cluster aggregation model and the distributed energy storage aggregation model, and by integrating the physical parameters of the power grid, to obtain the voltage regulation optimization model. The optimal coordinated control solution module is used to obtain the optimal coordinated control strategy by calling optimization algorithms based on the voltage regulation optimization model and the basic dataset of the distribution network.

[0017] Thirdly, the present invention provides an electronic device, comprising: Memory, used to store programs; A processor is configured to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the power distribution network voltage regulation method.

[0018] Fourthly, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the power distribution network voltage regulation method.

[0019] The beneficial effects of this invention are as follows: By integrating operational and environmental data and user temperature setting behavior, this invention constructs a single temperature-controlled load operation model that includes thermodynamic dynamic response and dual-position control logic, achieving a refined and physical mechanism-based characterization of the individual electricity consumption behavior of temperature-controlled loads; by classifying and aggregating the equivalent thermal resistance, equivalent heat capacity, and rated cooling power of homogeneous temperature-controlled loads, and combining the upper and lower temperature limits with equipment capacity constraints to form an adjustable power range, it achieves equivalent modeling and large-scale characterization of the dispatchable capability of temperature-controlled load clusters; by accumulating the rated energy capacity and maximum charge / discharge power of energy storage devices within the same node, and integrating... By establishing energy evolution relationships and operational constraints such as state of charge and charge-discharge mutual exclusion, the system accurately aggregates the charging and discharging flexibility of distributed energy storage groups. Through the establishment of a collaborative optimization model with the minimum sum of squared node voltage deviations as the optimization objective, and incorporating AC power flow equations, voltage and current safety limits, and constraints on temperature-controlled loads and energy storage aggregation resources, the system achieves global optimality and physical feasibility of distribution network voltage regulation with the participation of multiple types of flexible resources. Furthermore, by employing a second-order cone relaxation method to convexify the nonlinear AC power flow equations and calling the interior-point method for efficient solution, the system achieves a balance between solvability and computational efficiency in complex non-convex optimization problems. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a basic flowchart of a power distribution network voltage regulation method provided in one embodiment of the present invention; Figure 2 An improved IEEE 33-node distribution network structure diagram of a distribution network voltage regulation method provided in one embodiment of the present invention; Figure 3 A node voltage change diagram without any voltage regulation measures provided in an embodiment of the present invention for a distribution network voltage regulation method; Figure 4 The diagram shows the voltage changes of nodes 17 and 32 after voltage regulation in a distribution network voltage regulation method provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for regulating voltage in a power distribution network is provided, comprising: S100: Acquire the power grid physical parameters and operation and environmental data of the distribution network to form a basic dataset of the distribution network; S200: Based on the basic dataset of the distribution network, and combined with the operating characteristics and thermodynamic principles of temperature-controlled loads, an operating model for a single temperature-controlled load is obtained; S300: Based on the single temperature-controlled load operation model, aggregate modeling is performed on homogeneous temperature-controlled loads within the region to obtain a temperature-controlled load cluster aggregation model; S400: Based on the physical parameters of the power grid, a distributed energy storage aggregation model is obtained by integrating the energy, power and state of charge constraints of each distributed energy storage device within the same node; S500: Based on the temperature-controlled load cluster aggregation model and the distributed energy storage aggregation model, and by integrating the power grid physical parameters, an optimization problem including AC power flow, voltage, current and two types of resource constraints is established to obtain the voltage regulation optimization model; S600: Based on the voltage regulation optimization model and the basic dataset of the distribution network, the optimal coordinated regulation strategy is obtained by calling the optimization algorithm.

[0023] It should be noted that existing distribution network voltage regulation methods face multiple challenges during operation: With the large-scale integration of intermittent power sources such as distributed photovoltaics, the power flow direction of the distribution network has changed from unidirectional to bidirectional, leading to frequent voltage over-limit problems at local nodes; traditional voltage regulation methods, such as on-load tap changer of transformers and capacitor bank switching, have slow response speeds and discrete regulation, making it difficult to adapt to rapidly fluctuating voltage changes; at the same time, although a large number of flexible resources such as distributed energy storage and temperature-controlled loads have considerable regulation potential, their dispersion, heterogeneity, and user behavior uncertainty make aggregation modeling difficult and difficult to incorporate into a unified optimization and regulation framework; in addition, most regulation models use DC power flow approximation or ignore the impact of reactive power, causing the optimization results to deviate from the actual physical characteristics of the power grid, affecting the feasibility of regulation; in addition, the lack of detailed characterization of user comfort constraints can easily lead to a decline in the willingness to participate in demand response, restricting the accuracy, real-time performance, and feasibility of active distribution network regulation.

[0024] Therefore, to address the issues of slow response and low accuracy in existing distribution network voltage regulation methods, the S100-S600 steps establish a refined temperature-controlled load model that integrates thermodynamic mechanisms and user behavior, a distributed energy storage aggregation model, and a collaborative optimization regulation framework that takes into account AC power flow. This achieves high-precision modeling, physical feasibility assurance, and global optimal solution for distribution network voltage regulation with the participation of multiple types of flexible resources, effectively improving system voltage stability and renewable energy absorption capacity.

[0025] Example 2, this is an embodiment of the present invention, which provides a distribution network voltage regulation method based on the previous embodiment, including: In this embodiment, the distribution network basic dataset in step S100 is a unified data platform integrating static attributes and dynamic information of the power grid. Node access information, equipment parameters (such as line impedance and rated capacity), power flow parameters, and voltage and current safety boundaries constitute the power grid physical parameters, which are static or quasi-static data used to define the system topology and operational constraints. Equipment operating status, ambient temperature, light intensity, and user behavior characteristics are dynamic operational and environmental data, reflecting the real-time operating conditions of the system. This dataset provides comprehensive support for modeling: on the one hand, the power grid physical parameters are used to determine the access locations and electrical boundary conditions of energy storage and temperature-controlled loads; on the other hand, the start-up and shutdown patterns, temperature response characteristics, and user settings extracted from the dynamic data, combined with thermodynamic principles, are used to construct individual temperature-controlled load models and further aggregate them into cluster models. Therefore, the power grid physical parameters provide the "framework" and "constraints," while the operational and environmental data embody "behavior" and "flexibility." These two are integrated into the basic dataset, jointly supporting the construction and solution of the subsequent collaborative voltage regulation model.

[0026] In this embodiment of the application, step S200, which obtains a single temperature-controlled load operation model, includes: Based on the start-up and shutdown time series of temperature-controlled loads identified from operational and environmental data, the process of ambient temperature change, and user temperature setpoints, dynamic input variables and control boundary conditions are determined. Based on the identified start-stop time sequence, ambient temperature change process, and user temperature setpoint, an equivalent circuit model structure is constructed. Based on the constructed equivalent circuit model structure, a dynamic response equation is established; Based on the identified user temperature setpoint and allowable fluctuation range, determine the upper and lower temperature limits corresponding to the equipment start-stop control; Based on the determined upper and lower temperature limits, the equipment start-up and shutdown rules are set to form a two-position control logic; Based on the established dynamic response equation and the formed dual-position control logic, a single temperature-controlled load operation model is obtained.

[0027] In this embodiment of the application, the modeling method for a single temperature-controlled load in step S200 includes using a differential equation model based on thermodynamic equivalent circuits (first-order RC model), combined with dual-position control logic (Bang-Bang control), and constructing a dynamic response model that includes physical mechanisms and user behavior by identifying the user temperature setpoint, ambient temperature and start-stop sequence.

[0028] In an optional implementation, the modeling method for a single temperature-controlled load in step S200 can also be achieved by collecting historical operating and environmental data (such as room temperature, outdoor temperature, start-stop status, power, etc.), using LSTM or neural networks to train a load behavior prediction model, and directly establishing a nonlinear mapping relationship between input variables and equipment power or temperature response, thereby realizing data-driven modeling of the dynamic characteristics of a single temperature-controlled load.

[0029] In an optional implementation, the modeling method for a single temperature-controlled load in step S200 can also construct a state transition probability matrix by statistically analyzing the transition frequency of the temperature-controlled load between different states (such as "cooling" and "off") in historical operating data, and dynamically adjust the transition probability in combination with the ambient temperature and user settings to realize the stochastic process modeling of the start-up and shutdown behavior of a single temperature-controlled load.

[0030] In this application embodiment, the temperature control load mainly refers to equipment such as air conditioners, refrigerators, and water heaters. Taking the air conditioner load as an example, its thermodynamic equivalent model based on the user's cooling demand is expressed as follows: in, For users The equivalent heat capacity; For users The equivalent thermal resistance; and Representing users respectively Indoor temperature and outdoor temperature; Rated power of the equipment; The energy efficiency ratio of the equipment; This represents the switch state, where 0 indicates off and 1 indicates on.

[0031] In this embodiment of the application, the two-bit control logic includes: When the indoor temperature rises to the upper limit of the temperature range, the device will start cooling. When the indoor temperature drops to the lower limit of the specified temperature, the device stops cooling; When the indoor temperature is within the dead zone between the upper and lower temperature limits, the equipment maintains its current operating state.

[0032] In this embodiment of the application, step S300, which obtains the temperature-controlled load cluster aggregation model, includes: Temperature-controlled loads with similar thermodynamic properties and power levels are classified into several categories. Based on the classification, the equivalent thermal resistance, equivalent heat capacity, and rated cooling power of each type of load are weighted and averaged to obtain representative aggregate parameters for each type. Based on the obtained representative aggregation parameters, a dynamic response equation describing the overall temperature change law of the cluster is established; Based on the established dynamic response equation, and combined with the user's acceptable upper and lower temperature limits and the total equipment capacity constraints, the power adjustable range of the temperature-controlled load cluster is formed. Based on the established dynamic response equation and the formed power adjustable range, a temperature-controlled load cluster aggregation model is obtained.

[0033] In this embodiment of the application, the temperature control load cluster aggregation modeling method in step S300 includes using a classification weighted average algorithm to divide temperature control loads with similar thermodynamic parameters (R, C, P) into several categories, performing equivalent parameter aggregation on each category, and establishing a unified temperature dynamic equation and power adjustable range.

[0034] In an optional implementation, the temperature-controlled load cluster aggregation modeling method in step S300 can also be used to treat the distribution characteristics of the equivalent thermal resistance, thermal capacity and power parameters of the temperature-controlled loads in the statistical area as random variables that follow a specific probability distribution, and use the mean and variance to construct a statistical dynamic model of the cluster as a whole, so as to realize the aggregation characterization of the heterogeneity characteristics of the load group.

[0035] In an optional implementation, the temperature-controlled load cluster aggregation modeling method in step S300 can also be used to define the capacity, maximum power and energy evolution relationship of the adjustable cooling energy of the temperature-controlled load cluster as the charging and discharging capacity of "virtual energy storage", establish a dynamic model similar to energy storage, and realize the unified aggregation representation of the cluster's flexible resources.

[0036] In this embodiment of the application, the temperature-controlled load cluster aggregation model has a discrete time step. The dynamic change of indoor temperature is approximately as follows: in, For the current moment Temperature-controlled load cluster aggregated power; For the current moment Outdoor temperature; For the current moment Indoor temperature; and These represent the equivalent heat capacity and equivalent thermal resistance after polymerization, respectively. The indoor temperature at the previous moment; This is the equivalent comprehensive energy efficiency ratio; This is the temperature inertia decay factor.

[0037] In this embodiment of the application, to best meet user needs, the adjustable range of the temperature-controlled load cluster aggregation model is constrained by the following constraints: in, and These represent the upper and lower limits of the acceptable indoor temperature for users, respectively. This refers to the total capacity of the adjustable temperature control load equipment.

[0038] In this embodiment of the application, the distributed energy storage aggregation model obtained in step S400 includes: The rated energy capacity of all distributed energy storage devices within the same power grid node is calculated and summed to form... Total Aggregate Capacity ; The maximum charging power and maximum discharging power of each device are summed separately to determine the aggregate charging power. limit and upper limit of discharge power ; Based on the aggregated capacity and aggregated power capability, an energy evolution relationship characterizing the energy accumulation and consumption process of aggregated energy storage units in a time series is constructed. Based on the mutual exclusion characteristics of charging and discharging of distributed energy storage devices and the allowable operating range of state of charge, operational constraints for aggregated energy storage are established. Based on the aggregated capacity, aggregated power capability, energy evolution relationship, and operational constraints, a distributed energy storage aggregation model is obtained.

[0039] In this embodiment of the application, the constraint construction method of the energy storage aggregation model in step S400 includes using the capacity and power accumulation method to sum the rated capacity and maximum charge and discharge power of the energy storage devices in the same node, construct the aggregation energy evolution equation, and integrate constraints such as SOC, charge and discharge mutual exclusion, and energy conservation.

[0040] In an optional implementation, the constraint construction method of the energy storage aggregation model in step S400 can also be achieved by equating each energy storage device in the same node with a circuit element having a uniform internal resistance, capacitance and voltage source, establishing an equivalent circuit model that includes charging and discharging losses and dynamic response characteristics, and deriving its voltage-current-power relationship based on circuit laws, thereby realizing a refined aggregation characterization of the physical behavior of the energy storage cluster.

[0041] In an optional implementation, the constraint construction method of the energy storage aggregation model in step S400 can also be achieved by statistically analyzing the operational reliability, failure rate, and user behavior uncertainty of each energy storage device, modeling its aggregated capacity and power capability as random variables that follow a specific probability distribution, and introducing chance constraints in the optimization to ensure the confidence level of the system's safe operation, thereby realizing the probabilistic aggregation representation of the uncertainty of the energy storage cluster.

[0042] In this embodiment, based on the formed aggregated capacity and aggregated power capability, an energy evolution relationship characterizing the energy accumulation and consumption process of the aggregated energy storage unit over time is constructed, and its energy balance constraint is: in, Represents a node During the period Energy storage and aggregation; and Representing nodes respectively During the period The energy storage aggregates charging and discharging power.

[0043] In this embodiment of the application, based on the mutual exclusion characteristics of charging and discharging of distributed energy storage devices and the allowable operating range of state of charge, operating constraints for aggregated energy storage are established, including state of charge constraints, charging and discharging power constraints, and energy balance constraints before and after scheduling. In this embodiment of the application, the state of charge constraint is expressed as: in, and Representing nodes respectively Lower and upper limits of energy storage aggregation state of charge; Represents a node The total capacity of all energy storage devices participating in the aggregation.

[0044] In this embodiment, the charge / discharge power constraint is expressed as: in, Represents a node Maximum power limit for energy storage aggregation charging; Represents a node Energy storage Maximum discharge power.

[0045] In this embodiment of the application, the energy balance constraint before and after scheduling is expressed as follows: in, and These represent the aggregated energy stored at the start and end times of the scheduling, respectively.

[0046] In this embodiment of the application, the voltage regulation optimization model obtained in step S500 includes: Based on the deviation of the voltage of each node in the system from its rated value, an optimization objective function is constructed with the goal of minimizing the sum of squared deviations; Based on the topology and operating characteristics of AC power grids, the active and reactive power flow balance equations of AC power grids are introduced as basic physical constraints. Based on the safety operation requirements of power equipment, upper and lower limits of node voltage amplitude and upper limit of branch current amplitude are set. Based on the constructed objective function, the introduced power flow balance equation, and the set voltage and current constraints, a voltage regulation optimization model is obtained.

[0047] In this embodiment of the application, based on the deviation of the voltage of each node in the system from its rated value, an optimization objective function is constructed with the goal of minimizing the sum of squared deviations, as expressed in the following form: in, For nodes The voltage; is the reference voltage; N is the set of all nodes in the distribution network.

[0048] In this embodiment of the application, based on the topology and operating characteristics of the AC power grid, the active and reactive power flow balance equations are introduced as basic physical constraints, expressed as follows: in, and They are nodes The active and reactive power of the power supply; and They are nodes The active and reactive power of the load; and They are nodes and nodes The elements of the admittance matrix; For nodes and nodes The phase difference between them.

[0049] In this embodiment of the application, based on the safety operation requirements of power equipment, operating limits for node voltage amplitude and branch current amplitude are set, specifically including grid-side safety constraints and flexible resource-side safety constraints; grid-side safety constraints include node voltage constraints and branch current constraints; flexible resource-side safety constraints include temperature-controlled load constraints and energy storage constraints; In this embodiment, the node voltage constraint is expressed as: in, The node's rated voltage; This is the voltage deviation rate limit.

[0050] In this embodiment, the branch current constraint is expressed as: in, Indicates a branch Current on; Indicates a branch Maximum current carrying capacity.

[0051] In this embodiment of the application, the temperature control load constraint is expressed as: In this embodiment of the application, the energy storage constraint is expressed as: in, Represents a node The capacity of the energy storage device on it; Indicates the upper limit of the system's energy storage capacity; Represents a node Should energy storage state variables be configured? This indicates the maximum amount of energy storage that the system can be configured with.

[0052] In this embodiment of the application, to ensure that the system meets basic physical laws during the optimization and control process and to guarantee the real-time balance of power input and output at each node, a node power balance constraint is further introduced, expressed as: in, Represents a node The active power of power generation; Represents a node The active power of the load; Represents a node The energy storage discharge power; Represents a node Energy storage charging power; Represents a node and nodes The power flow of the inter-line.

[0053] In this embodiment of the application, obtaining the optimal coordinated control strategy in step S600 includes: The optimization model containing nonlinear AC power flow equations is subjected to convex relaxation treatment. A second-order cone relaxation algorithm is used to transform the AC power flow equations into a second-order cone constraint form, resulting in a relaxed optimization model. Based on the transformed relaxed optimization model, the interior point algorithm is called to perform iterative solution calculations to obtain the iterative solution calculation results; Based on the iterative solution calculation results, the charging and discharging power plan of each node energy storage unit and the power adjustment command sequence of the temperature-controlled load cluster are generated for multiple future time periods; Based on the generated charging and discharging power plan and power regulation command sequence, an optimal coordinated control strategy is obtained. This strategy is automatically triggered when the voltage exceeds the specified deviation range, and achieves rapid recovery of node voltage and minimization of deviation by coordinating energy storage charging and discharging with temperature-controlled load power regulation.

[0054] In this embodiment of the application, the solution method of the voltage regulation optimization model in step S600 includes using second-order cone relaxation (SOC Relaxation) and interior point algorithm to transform the non-convex AC power flow equation into second-order cone constraints, and efficiently solve the global approximate optimal solution through convex optimization method.

[0055] In an optional implementation, the solution method for the voltage regulation optimization model in step S600 can also be achieved by linearizing the AC power flow equation using models such as LinDistFlow, and then linearizing the nonlinear terms piecewise to construct a mixed integer linear optimization problem. Solvers such as CPLEX or Gurobi can be called for efficient solution to realize rapid optimization calculation of the voltage regulation strategy.

[0056] In an optional implementation, the solution method for the voltage regulation optimization model in step S600 can also be achieved by decomposing the global optimization problem into multiple sub-problems according to the power grid region or node, with each sub-region solving independently based on local data and iteratively exchanging boundary variable information, and using the Alternating Direction Multiplier Method (ADMM) to coordinate convergence, thereby realizing the distributed collaborative solution of the voltage regulation model.

[0057] Example 3, referring to Figures 2-4 This is one embodiment of the present invention, which provides a method for regulating voltage in a power distribution network. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through specific implementation methods and implementation effects.

[0058] The specific details of this embodiment are as follows: To verify the feasibility of this invention, an analysis was conducted using an improved IEEE-33 node distribution network, the topology of which is as follows: Figure 2 As shown in the figure, the distribution network is connected to temperature-controlled load clusters at nodes 8, 15, 20, 23, and 28, respectively. The number of controllable air conditioners in temperature-controlled load clusters T1 to T5 are 40, 60, 50, 50, and 55, respectively. The temperature setting for air conditioner users is 25℃, and the temperature dead zone is set to ℃. The equivalent thermal resistance of the air conditioner load is 2.0℃ / kW, the equivalent heat capacity is 2.1kWh / ℃, and the energy efficiency ratio is 2.5. The energy storage devices are installed at nodes 4, 10, and 30, with a charge / discharge efficiency of 0.95 and a SOC range of 0.2~0.8. The simulation equipment uses an AMD Ryzen 9 7945HX CPU, 32GB of memory, and Matlab 2023a as the programming software. Because voltage over-limiting is prone to occur at the end nodes of the distribution network after the integration of new energy sources, [the following is a continuation of the previous sentence] Figure 2 The analysis focuses on nodes 17 and 32, which have a higher probability of voltage exceeding limits. The voltage conditions of nodes without any voltage regulation measures are as follows: Figure 3 As shown, the node effect of voltage regulation using the method proposed in this invention is as follows: Figure 4 As shown. Combined with Figure 3 and Figure 4It is known that the safe voltage range is 0.93~1.07 pu. Without voltage regulation of the distribution network system, the voltages of nodes 17 and 32 both exceeded the limit to varying degrees during the periods of 10:00-12:00 and 18:00-20:00. However, after aggregating distributed energy storage and temperature-controlled load clusters to participate in node voltage regulation, the voltage exceeding the limit at nodes 17 and 32 was significantly improved, fluctuating within the safe voltage range and meeting the voltage deviation requirements. This indicates that the method of the present invention can effectively restore the node voltage to stability.

[0059] Example 4 is an embodiment of the present invention. This embodiment differs from the first embodiment in that it provides a power distribution network voltage regulation system.

[0060] It should be noted that the technical solution of the distribution network voltage regulation system is based on the same concept as the technical solution of the distribution network voltage regulation method described above. For details not described in detail in the technical solution of the distribution network voltage regulation system in this embodiment, please refer to the description of the technical solution of the distribution network voltage regulation method described above.

[0061] This embodiment provides a power distribution network voltage regulation system, comprising: The distribution network basic dataset construction module is used to acquire the power grid physical parameters and operation and environmental data of the distribution network to form the distribution network basic dataset; The single temperature-controlled load modeling module is used to obtain the operating model of a single temperature-controlled load based on the basic dataset of the distribution network and the operating characteristics and thermodynamic principles of the temperature-controlled load. The temperature control load cluster aggregation module is used to aggregate and model homogeneous temperature control loads in a region based on the operation model of a single temperature control load, and obtain a temperature control load cluster aggregation model. The distributed energy storage aggregation modeling module is used to obtain a distributed energy storage aggregation model based on the physical parameters of the power grid by integrating the energy, power and state of charge constraints of each distributed energy storage device within the same node. The voltage regulation optimization modeling module is used to establish an optimization problem that includes AC power flow, voltage, current and two types of resource constraints based on the temperature-controlled load cluster aggregation model and the distributed energy storage aggregation model, and by integrating the physical parameters of the power grid, to obtain the voltage regulation optimization model. The optimal coordinated control solution module is used to obtain the optimal coordinated control strategy by calling optimization algorithms based on the voltage regulation optimization model and the basic dataset of the distribution network.

[0062] This embodiment also provides an electronic device applicable to a power distribution network voltage regulation method, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a power distribution network voltage regulation method as described in the above embodiments.

[0063] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a power distribution network voltage regulation method as proposed in the above embodiments.

[0064] The storage medium proposed in this embodiment and the method for implementing a power distribution network voltage regulation proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0065] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for voltage regulation of a power distribution network, characterized by, The application relates to a voltage regulation optimization method and system. The application comprises the following steps: obtaining power grid physical parameters and operation and environment data of a power distribution network to form a power distribution network basic data set; based on the power distribution network basic data set, combining the operation characteristics of temperature control loads and thermodynamic principles, obtaining a single temperature control load operation model; based on the single temperature control load operation model, performing aggregate modeling on homogeneous temperature control loads in a region to obtain a temperature control load cluster aggregate model; based on the power grid physical parameters, integrating the energy, power and state of charge constraints of all distributed energy storage devices in the same node to obtain a distributed energy storage aggregate model; based on the temperature control load cluster aggregate model and the distributed energy storage aggregate model, and by fusing the power grid physical parameters, an optimization problem containing alternating current power flow, voltage, current and two types of resource constraints is established to obtain a voltage regulation optimization model; 2. The power distribution grid voltage regulation method of claim 1, wherein: based on the voltage regulation optimization model and the power distribution network basic data set, an optimal collaborative regulation strategy is obtained by calling an optimization algorithm for solving. The single temperature control load operation model is obtained by the following steps: determining dynamic input variables and control boundary conditions according to the temperature control load start-stop time sequence, the environment temperature change process and the user temperature set value identified from the operation and environment data; constructing an equivalent circuit model structure based on the identified start-stop time sequence, the environment temperature change process and the user temperature set value; establishing a dynamic response equation based on the constructed equivalent circuit model structure; determining the temperature upper limit and the temperature lower limit corresponding to the device start-stop control based on the identified user temperature set value and the allowable fluctuation range; setting the device start-stop action rule based on the determined temperature upper limit and the temperature lower limit to form a two-position control logic; 3. The power distribution grid voltage regulation method of claim 1 or 2, wherein: obtaining the single temperature control load operation model based on the established dynamic response equation and the formed two-position control logic. The temperature control load cluster aggregate model is obtained by the following steps: dividing temperature control loads with similar thermodynamic characteristics and power levels into several categories; based on the divided categories, performing weighted average processing on the equivalent thermal resistance, equivalent heat capacity and rated refrigeration power of each type of load to obtain representative aggregate parameters of each type; based on the obtained representative aggregate parameters, establishing a dynamic response equation describing the overall temperature variation law of the cluster; based on the established dynamic response equation, combining the user-acceptable temperature upper and lower limits and the total capacity constraint of the device to form a power adjustable interval of the temperature control load cluster; 4. The power distribution grid voltage regulation method of claim 3, wherein: based on the established dynamic response equation and the formed power adjustable interval, obtaining the temperature control load cluster aggregate model. The distributed energy storage aggregate model is obtained by the following steps: accumulating the rated energy capacity of all distributed energy storage devices in the same power grid node to form an aggregate total capacity; summing the maximum charging power and the maximum discharging power of each device to determine the aggregate power capacity; based on the formed aggregate capacity and the aggregate power capacity, constructing an energy evolution relationship representing the energy accumulation and consumption process of the aggregate energy storage unit under a time sequence; based on the charge-discharge mutual exclusion characteristics of the distributed energy storage device and the allowable operation range of the state of charge, establishing an operation constraint condition of the aggregate energy storage; based on the formed aggregate capacity, the aggregate power capacity, the energy evolution relationship and the operation constraint condition, obtaining the distributed energy storage aggregate model.

5. The power distribution grid voltage regulation method of claim 4, wherein: The voltage regulation optimization model is obtained, including: Based on the deviation of each node voltage in the system from its rated value, an optimization objective function is constructed to minimize the sum of the squares of the deviations; Based on the topology and operating characteristics of the AC power grid, the active and reactive power flow balance equations of the AC power grid are introduced as basic physical constraints; Based on the safe operation requirements of power equipment, the upper and lower limits of the node voltage amplitude and the upper limit of the branch current amplitude are set; Based on the constructed optimization objective function, the introduced power flow balance equation and the set voltage and current constraint conditions, the voltage regulation optimization model is obtained.

6. The power distribution grid voltage regulation method of claim 5, wherein: The optimal coordinated regulation strategy is obtained, including: The optimization model containing the nonlinear AC power flow equation is subjected to convex relaxation processing, and the AC power flow equation is converted into a second-order cone constraint form by using a second-order cone relaxation algorithm to obtain a relaxed optimization model; Based on the converted relaxed optimization model, an interior point algorithm is called to perform iterative solving calculation to obtain an iterative solving calculation result; Based on the iterative solving calculation result, a charging and discharging power plan of each node energy storage unit and a power regulation instruction sequence of the temperature control load cluster in multiple future time periods are generated; Based on the generated charging and discharging power plan and power regulation instruction sequence, the optimal coordinated regulation strategy is obtained.

7. The power distribution grid voltage regulation method of claim 6, wherein: The dual-position control logic includes: When the indoor temperature rises to the temperature upper limit, the device starts refrigeration; When the indoor temperature drops to the temperature lower limit, the device stops refrigeration; When the indoor temperature is in the dead zone interval between the temperature upper limit and the temperature lower limit, the device maintains the current operating state unchanged.

8. A power distribution network voltage regulation system applying the method of any one of claims 1-7, characterized by, It includes: A power distribution network basic data set construction module is configured to obtain power grid physical parameters and operation and environmental data of a power distribution network to form a power distribution network basic data set; A single temperature control load modeling module is configured to obtain a single temperature control load operation model based on the power distribution network basic data set and in combination with the operating characteristics and thermodynamic principles of the temperature control load; A temperature control load cluster aggregation module is configured to aggregate and model homogenized temperature control loads in a region based on the single temperature control load operation model to obtain a temperature control load cluster aggregation model; A distributed energy storage aggregation modeling module is configured to obtain a distributed energy storage aggregation model by integrating energy, power and state of charge constraints of various distributed energy storage devices in the same node based on the power grid physical parameters; A voltage regulation optimization modeling module is configured to establish an optimization problem containing AC power flow, voltage, current and two types of resource constraints based on the temperature control load cluster aggregation model and the distributed energy storage aggregation model and by fusing the power grid physical parameters to obtain a voltage regulation optimization model; An optimal coordinated regulation solving module is configured to obtain an optimal coordinated regulation strategy by calling an optimization algorithm for solving based on the voltage regulation optimization model and the power distribution network basic data set.

9. An electronic device, comprising: It includes: A memory is configured to store a program; A processor is configured to load the program to perform the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, The program is executed by the processor to implement the steps of the method according to any one of claims 1-7.

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