New power system multi-time scale voltage control method and system considering sandstorm

Abstract

The application discloses a novel power system multi-time scale voltage control method and system considering sandstorm, and the method comprises the following steps: in the second time scale, based on the dynamic space state model and the adaptive target function, the active / reactive power of the fan is adjusted in real time, and the voltage fluctuation caused by the sandstorm is quickly responded; in the minute time scale, the switch state is adjusted through multi-objective optimization and two-stage topology reconstruction, and the distribution network resilience is improved. The time periods of the second time scale and the minute time scale are sequentially increased, the second time scale focuses on the quick response to the wind speed fluctuation, and the minute time scale focuses on the medium and long term network optimization. The application aims to improve the system robustness under extreme weather through the voltage collaborative control of the second time scale and the minute time scale, and meet the voltage stability, power supply reliability and economic and efficient operation demand under the background of new energy access.

Description

Technical Field

[0001] This invention relates to the field of operation control technology for novel power systems, specifically to a novel multi-timescale voltage control method and system for power systems that takes sandstorms into account. Background Technology

[0002] Currently, the proportion of new energy sources, represented by wind power, in the power system continues to increase, and the power system is evolving towards a new type of power system with a high proportion of new energy integration. However, the inherent intermittency, volatility, and uncertainty of new energy sources such as wind power pose severe challenges to the safe and stable operation of the power grid, especially under extreme weather conditions. Some areas in my country are located in areas prone to sandstorms. Sandstorms can cause drastic changes in wind speed, leading to sharp fluctuations in the output of wind farms, which in turn cause drastic voltage fluctuations or even exceedances at the grid connection point and even the entire distribution network, seriously threatening the voltage stability and power supply reliability of the power grid. Existing power system control methods are usually designed for normal operating conditions or only consider a single type of fault. When dealing with extreme weather events such as sandstorms, which are long-lasting and have a wide impact, there are obvious shortcomings: 1. Single control objective and lack of adaptability: Traditional control strategies often take economy as the main objective (such as minimizing network losses) or restore power supply as the sole objective after a fault. These strategies fail to dynamically adjust the priority of control objectives according to different stages of sandstorm weather (such as warning, passage, and dissipation). In extreme situations such as sandstorms, ensuring power supply to critical loads and maintaining voltage stability should be the highest priority, while economic objectives must be temporarily set aside. Existing methods lack this adaptive capability. 2. Insufficient time-scale coordination: Voltage control involves different time scales. Existing technologies often separate fast control (such as second-level voltage regulation) from slow optimization (such as network reconfiguration at the minute level or above). Second-level control lacks accurate predictive models of wind turbine status, and its response speed and accuracy need improvement; while minute-level optimization, due to its high computational complexity, is difficult to make real-time decisions in rapidly changing scenarios caused by sandstorms, and cannot achieve rapid topology reconfiguration to improve grid resilience. The lack of effective coordination between the two results in the system being unable to quickly suppress voltage fluctuations at the second level and fundamentally enhance the grid's ability to withstand continuous disturbances at the minute level through network reconfiguration. 3. Insufficient utilization of the control capabilities of renewable energy power plants: Under sandstorm conditions, relying solely on grid-side measures (such as switching capacitors and adjusting transformer taps) often results in insufficient response speed and limited regulation capabilities. Existing methods fail to fully leverage the active / reactive power regulation potential of wind farms as distributed resources, and do not establish a refined state-space model for wind turbines in scenarios with drastic wind speed changes, thus failing to enable wind farms to quickly and autonomously mitigate their own voltage fluctuations. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a novel multi-timescale voltage control method and system for power systems that takes sandstorms into account, addressing the aforementioned problems in the prior art. This invention aims to adapt to the dynamic characteristics of sandstorm weather through coordinated voltage control at the second and minute timescales, improve system robustness under extreme weather conditions such as sandstorms, fully tap the regulation potential on both sides of the power source and grid, and meet the requirements of voltage stability, reliable power supply, and economical and efficient operation under the background of new energy access.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A novel multi-timescale voltage control method for power systems considering sandstorms includes second-level and minute-level coordinated voltage control for wind farms and distribution networks in the novel power system. The second-level and minute-level coordinated voltage control includes: for wind farms in the novel power system, adjusting the active or reactive power of wind turbines in real time and responding quickly to voltage fluctuations in wind farms caused by sandstorms through second-level wind farm voltage control; and for distribution networks in the novel power system, adjusting the switching state of the distribution network through minute-level distribution network topology reconfiguration to improve the resilience of the distribution network.

[0005] Optionally, the second-level wind farm voltage control includes: S101 collects the speed, active power measurement, reactive power measurement, terminal voltage and wind speed of all wind turbines; S102, Establish an adaptive weighted objective function that weights voltage safety and power generation economy to respond to wind speed fluctuations and dynamically adjust the weights of voltage safety and power generation economy according to sandstorm intensity; S103. For each wind turbine, a discrete state-space equation is constructed for wind speed, power, and voltage. Based on the discrete state-space equation, a voltage change prediction model is constructed to predict the increments of active power, reactive power, and terminal voltage. S104: Set operating constraints, construct a constrained optimization problem based on the adaptive weight objective function and voltage change prediction model, dynamically solve the constrained optimization problem based on the real-time sandstorm intensity to obtain the active and reactive power control commands of the wind farm, and issue the active and reactive power control commands of the wind farm to the wind farm for execution to adjust the active or reactive power of the wind turbine in real time and quickly respond to the voltage fluctuations of the wind farm caused by the sandstorm.

[0006] Optionally, in step S102, the functional expression of the adaptive weight objective function is: ; in, For the adaptive weight objective function, To predict the total number of time steps within the time range, For the number of wind turbines, and These are the weighting coefficients for the two objectives of voltage security and power generation economy, respectively. and These are the actual terminal voltage and reference voltage values ​​of the i-th wind turbine at the k-th time step, respectively. For the goal of voltage safety, For the goal of economical power generation, and These are the actual active power and reference active power values ​​of the i-th wind turbine at the k-th time step, respectively. The sandstorm intensity is determined based on wind speed. The sandstorm is divided into four phases: normal wind speed phase, sandstorm warning phase, sandstorm passage phase, and sandstorm dissipation phase. During the normal wind speed phase: ; During the sandstorm warning phase: ; ; During the sandstorm's passage phase: ; ; During the sandstorm's remission phase: ; ; in, Rated wind speed, and These are the preset voltage and power weights for the normal wind speed phase, respectively. and These are the voltage weighting growth factor and the power weighting attenuation factor, respectively, for the sandstorm warning phase. and These are the preset voltage and power weights for the sandstorm passage phase. and These are the voltage weight attenuation coefficient and power weight growth coefficient during the sandstorm dissipation phase, respectively.

[0007] Optionally, in step S102, the functional expression of the constructed discrete state-space equation is: ; ; in, and Let be the state variables of the i-th wind turbine at time steps k+1 and k, respectively. Let be the input variable for the i-th wind turbine at the k-th time step. Let i be the output variable of the i-th wind turbine at the k-th time step. The state matrix, For the input matrix, Let be the output matrix, and we have: ; ; ; in, These represent the increments of the rotational speed, active power, and reactive power of the i-th wind turbine at the k-th time step. These represent the active power reference value, reactive power reference value, and wind speed increment at the k-th time step for the i-th wind turbine. These represent the increments of active power, reactive power, and terminal voltage for the i-th wind turbine at the (k+1)-th time step, respectively.

[0008] Optionally, in step S104, setting the operating constraints includes setting upper and lower limits for the terminal voltage, active power, reactive power, and speed of each wind turbine at the k-th time step, as well as upper and lower limits for the increment of active power and the increment of reactive power between the k-th and (k-1)-th time steps of each wind turbine.

[0009] Optionally, the minute-level distribution network topology reconfiguration includes: S201 collects voltage, power, and branch status information of distribution network nodes; S202: Construct a multi-objective optimization function consisting of three weighted objectives: minimizing load loss, reducing network loss, and controlling voltage deviation. The weights of the three objectives are adaptively adjusted according to the sandstorm stage. Voltage safety boundary constraints and topology radial constraints are set for the multi-objective optimization function to ensure that the reconfiguration scheme meets the requirements of operational safety and network connectivity. S203, Initial topology reconfiguration of distribution network based on sequential switch opening strategy to obtain radial distribution network topology; S204. Perform distribution network topology optimization based on branch switching strategy on the obtained radial distribution network topology to adjust the switching state of the distribution network to improve the resilience of the distribution network.

[0010] Optionally, the functional expression of the multi-objective optimization function in step S202 is: ; ; ; ; in, For multi-objective optimization functions, , and The weights are respectively for the three objectives of minimizing load loss, reducing network loss, and controlling voltage deviation. The objective function is to minimize load loss. The objective function is to reduce network loss. The objective function for voltage deviation control; The number of distribution network nodes, Let i be the load importance weight of node i in the distribution network. For the active power load loss of distribution network node i, and The voltage magnitudes at nodes i and j after topology reconstruction. This represents the real part operator. Let the admittance be the line between nodes i and j after topology reconstruction. and The voltage phase angles of nodes i and j after topology reconstruction are given, and the sandstorm intensity is determined by wind speed. The process is divided into four phases: normal wind speed phase, sandstorm warning phase, sandstorm passage phase, and sandstorm dissipation phase. During the normal wind speed phase, the weights of the three objectives satisfy the following: During the sandstorm warning phase, the weights of the three objectives satisfy: The weights of the three objectives satisfy the following during the sandstorm's passage and dissipation phases: In step S202, when setting voltage safety boundary constraints and topology radial constraints for the multi-objective optimization function, the voltage safety boundary constraints refer to setting maximum and minimum values ​​for the voltage amplitude of nodes after topology reconstruction. The functional expression of the topology radial constraints is: ; in, To represent the set of all branches in a distribution network, branch road A binary variable representing a branch The switch state variables, Indicates a branch closure, Indicates a branch disconnect.

[0011] Optionally, step S203 includes: turning on all switches in the distribution network to form a network structure containing loops; calculating the current value of each switch through linear power flow analysis to generate a list of switches arranged in ascending order of current value; and then iteratively turning off the switch with the smallest current value for the switch list until the network structure of the distribution network forms a radial distribution network topology.

[0012] Optionally, step S204, which involves performing distribution network topology optimization based on a branch switching strategy on the obtained radial distribution network topology, includes: S301, For the obtained radial distribution network topology, the tie switches are traversed, and for each tie switch that is in the open state, a closing operation is performed to form a network loop; S302, Identify and disconnect a sectionalizing switch in the new network loop to re-establish the new radial distribution network; S303, Perform linear power flow analysis on the new radial distribution network, calculate the current of each branch and the voltage of each node, and compare the current of each branch with the corresponding current limit and the voltage of each node with the allowable voltage range; if all branch currents and node voltages meet the constraints and the topology maintains the radial structure, then the switch state exchange scheme of the tie switch and the sectionalizing switch is deemed feasible, and proceed to step S304; otherwise, the switch state exchange scheme of the tie switch and the sectionalizing switch is deemed infeasible, the original topology is restored, and return to step S301 to select a new combination of tie switch and sectionalizing switch for re-optimization; S304, calculate the function value of the multi-objective optimization function of the radial distribution network before and after the switching operation of the tie switch and the sectionalizing switch. If the function value of the multi-objective optimization function of the radial distribution network after the switching operation of the tie switch and the sectionalizing switch is smaller than the function value of the multi-objective optimization function of the radial distribution network before the switching operation of the tie switch and the sectionalizing switch, then update the topology of the current distribution network according to the found feasible switch state exchange scheme, and jump to step S301 to continue iterating until all tie switches and sectionalizing switch combinations have been traversed and the iteration stops, the optimization ends and exits.

[0013] Furthermore, the present invention also provides a novel multi-timescale voltage control system for power systems that takes sandstorms into account, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the novel multi-timescale voltage control method for power systems that takes sandstorms into account.

[0014] Compared with existing technologies, this invention mainly achieves the following beneficial effects: The method of this invention includes, on a second-level timescale, adjusting the active / reactive power of wind turbines in real time based on a dynamic state-space model and an adaptive objective function to quickly respond to voltage fluctuations caused by sandstorms; on a minute-level timescale, adjusting switching states through multi-objective optimization (minimizing load loss, network loss, and voltage deviation) and two-stage topology reconfiguration to improve the resilience of the distribution network. The time periods of the second-level and minute-level timescales increase sequentially, with the second-level scale focusing on rapid response to wind speed fluctuations and the minute-level scale focusing on medium- to long-term network optimization. This invention aims to improve system robustness under extreme weather conditions through coordinated voltage control on second- and minute-level timescales, meeting the requirements for voltage stability, reliable power supply, and cost-effective operation in the context of new energy integration. Attached Figure Description

[0015] Figure 1This is a schematic diagram of the basic process of the method in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of the voltage coordinated control process at the second and minute time scales in an embodiment of the present invention.

[0017] Figure 3 This is an example of the wind speed change of a wind turbine in a wind farm over 5 hours in an embodiment of the present invention.

[0018] Figure 4 For the embodiments of the present invention Figure 2 The voltage changes within 5 hours after the fan is controlled in real time. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0020] like Figure 1 As shown, this embodiment of the novel power system voltage control method considering sandstorms includes second-level and minute-level time-scale coordinated voltage control for wind farms and distribution networks within the novel power system. This second-level and minute-level coordinated voltage control includes: for wind farms, second-level wind farm voltage control to adjust the active or reactive power of wind turbines in real time and quickly respond to voltage fluctuations caused by sandstorms; and for distribution networks, minute-level distribution network topology reconfiguration to adjust the switching status of the distribution network and improve its resilience. The root causes of wind farm voltage fluctuations lie mainly in two factors: first, the random changes in wind resources, leading to intermittent and fluctuating wind power output; and second, external voltage disturbance events, such as grid faults or rapid load changes. These factors work together to make the voltage fluctuation problem within wind farms exceptionally complex. To address this challenge, this embodiment proposes a voltage control method based on a dynamic state-space model and an adaptive objective function for second-level timescales. This method enables rapid response to voltage fluctuations in wind farms caused by sandstorms and real-time adjustment of active / reactive power of wind turbines. At the second-level timescale, by implementing adaptive model predictive control based on a dynamic state-space model in the wind farm, the method significantly improves the power generation economy under normal operating conditions and the autonomous voltage stabilization capability and operational resilience under severe sandstorm disturbances.

[0021] On a second-level timescale, voltage control is implemented in wind farms to suppress the randomness and fluctuations in wind farm output, ensuring that the voltage at all nodes remains within a safe range. For example... Figure 2 As shown, the second-level wind farm voltage control in this embodiment includes: S101 collects the speed, active power measurement, reactive power measurement, terminal voltage and wind speed of all wind turbines; S102, Establish an adaptive weighted objective function that weights voltage safety and power generation economy to respond to wind speed fluctuations and dynamically adjust the weights of voltage safety and power generation economy according to sandstorm intensity; S103. For each wind turbine, a discrete state-space equation is constructed for wind speed, power, and voltage. Based on the discrete state-space equation, a voltage change prediction model is constructed to predict the increments of active power, reactive power, and terminal voltage. S104: Set operating constraints, construct a constrained optimization problem based on the adaptive weight objective function and voltage change prediction model, dynamically solve the constrained optimization problem based on the real-time sandstorm intensity to obtain the active and reactive power control commands of the wind farm, and issue the active and reactive power control commands of the wind farm to the wind farm for execution to adjust the active or reactive power of the wind turbine in real time and quickly respond to the voltage fluctuations of the wind farm caused by the sandstorm.

[0022] In step S102 of this embodiment, the functional expression of the adaptive weight objective function is: ; in, For the adaptive weight objective function, To predict the total number of time steps within the time range, For the number of wind turbines, and These are the weighting coefficients for the two objectives of voltage security and power generation economy, respectively. and These are the actual terminal voltage and reference voltage values ​​of the i-th wind turbine at the k-th time step, respectively. For the goal of voltage safety, For the goal of economical power generation, and These represent the actual active power and reference active power values ​​of the i-th wind turbine at the k-th time step, respectively. To ensure excellent operational performance, the weighting coefficients for voltage safety and power generation economy are adaptively adjusted according to the sandstorm weather process. In this embodiment, the sandstorm intensity is based on wind speed. The sandstorm is divided into four phases: normal wind speed phase, sandstorm warning phase, sandstorm passage phase, and sandstorm dissipation phase. During the normal wind speed phase: ; During the sandstorm warning phase: ; ; During the sandstorm's passage phase: ; ; During the sandstorm's remission phase: ; ; in, Rated wind speed, and These are the preset voltage and power weights for the normal wind speed phase, respectively. and These are the voltage weighting growth factor and the power weighting attenuation factor, respectively, for the sandstorm warning phase. and These are the preset voltage and power weights for the sandstorm passage phase. and These are the voltage weight attenuation coefficient and power weight growth coefficient during the sandstorm dissipation phase, respectively.

[0023] In step S102 of this embodiment, the functional expression of the constructed discrete state-space equation is as follows: ; ; in, and Let be the state variables of the i-th wind turbine at time steps k+1 and k, respectively. Let be the input variable for the i-th wind turbine at the k-th time step. Let i be the output variable of the i-th wind turbine at the k-th time step. The discrete-time state matrix, For discrete-time input matrices, Let be the output matrix, and we have: ; ; ; in, These represent the increments of the rotational speed, active power, and reactive power of the i-th wind turbine at the k-th time step. These represent the active power reference value, reactive power reference value, and wind speed increment at the k-th time step for the i-th wind turbine. These represent the increments of active power, reactive power, and terminal voltage for the i-th wind turbine at the (k+1)-th time step, respectively. In this embodiment, the functional expressions for the state matrix, input matrix, and output matrix are: ; ; ; ; ; ; in, It is a natural constant. Let i be the continuous-time state matrix of the i-th wind turbine. Sampling time, For integration variables, Let i be the continuous-time input matrix for the i-th wind turbine. , and Let be the terminal voltage, active power, and reactive power of the i-th wind turbine, respectively. Let be the initial active power, mechanical power, inertial time constant, and initial speed of the i-th wind turbine, respectively. are the time constants of the active and reactive power control loops for the i-th wind turbine, respectively. Let be the sensitivity coefficient of the mechanical power of the i-th fan to wind speed. Sampling time. These represent the sensitivity of the terminal voltage of the i-th wind turbine to active power and reactive power, respectively. To take the real part of the complex number. Let be the voltage vector (including magnitude and phase angle) of the i-th wind turbine. for . conjugate.

[0024] In step S104 of this embodiment, setting operational constraints includes setting upper and lower limits for the terminal voltage, active power, reactive power, and rotational speed of each wind turbine at the k-th time step, as well as upper and lower limits for the increments of active power and reactive power between the k-th and (k-1)-th time steps for each wind turbine. This can be expressed as: ; in, These are the minimum and maximum values ​​of the terminal voltage for safe operation of the wind turbine. , Let be the minimum and maximum reference values ​​of the active power of the i-th wind turbine. Let be the minimum and maximum reference values ​​for the reactive power of the i-th wind turbine. Let be the minimum and maximum reference values ​​for the active power ramping constraint of the i-th wind turbine. Let be the minimum and maximum reference values ​​for the reactive power ramping constraint of the i-th wind turbine. These are the minimum and maximum speeds required for the wind turbine to operate safely.

[0025] At the minute-level timescale, this method achieves optimized operation of the distribution network under all operating conditions by implementing adaptive dynamic topology reconfiguration that considers multiple operating scenarios. The reconfiguration model has multiple objectives: minimizing load loss, network loss, and voltage deviation. It adapts to different operating scenarios through an intelligent weight adjustment mechanism: during normal operating conditions and sandstorm warning phases, it progressively optimizes network loss to improve system operating economy; during sandstorms, it prioritizes ensuring power supply to critical loads and voltage stability, significantly reducing user outage time caused by extreme weather. This method employs a two-stage topology reconfiguration to meet the real-time requirements of minute-level optimization. By setting dynamic voltage safety boundaries and radial operating constraints, it ensures that the reconfigured network structure always maintains safe and stable operation. Figure 2 As shown, the minute-level distribution network topology reconfiguration in this embodiment includes: S201 collects voltage, power, and branch status information of distribution network nodes; S202: Construct a multi-objective optimization function consisting of three weighted objectives: minimizing load loss, reducing network loss, and controlling voltage deviation. The weights of the three objectives are adaptively adjusted according to the sandstorm stage. Voltage safety boundary constraints and topology radial constraints are set for the multi-objective optimization function to ensure that the reconfiguration scheme meets the requirements of operational safety and network connectivity. S203, Initial topology reconfiguration of distribution network based on sequential switch opening strategy to obtain radial distribution network topology; S204. Perform distribution network topology optimization based on branch switching strategy on the obtained radial distribution network topology to adjust the switching state of the distribution network to improve the resilience of the distribution network.

[0026] The function expression of the multi-objective optimization function in step S202 of this embodiment is as follows: ; ; ; ; in, For multi-objective optimization functions, , and The weights are respectively for the three objectives of minimizing load loss, reducing network loss, and controlling voltage deviation. The objective function is to minimize load loss. The objective function is to reduce network loss. The objective function for voltage deviation control is the first term. To maximize power supply reliability, especially to ensure the supply of critical loads. The second item... For economic purposes, reduce losses during power transmission. (Third item) To achieve power quality goals, all node voltages must be maintained within permissible ranges to prevent voltage exceedances. The number of distribution network nodes, Let i be the load importance weight of node i in the distribution network. For the active power load loss of distribution network node i, and The voltage magnitudes at nodes i and j after topology reconstruction. This represents the real part operator. Let the admittance be the line between nodes i and j after topology reconstruction. and The voltage phase angles of nodes i and j after topology reconstruction are given, and the sandstorm intensity is determined by wind speed. The process is divided into four phases: normal wind speed phase, sandstorm warning phase, sandstorm passage phase, and sandstorm dissipation phase. During the normal wind speed phase, the weights of the three objectives satisfy the following: Under normal circumstances, economic efficiency is prioritized, with the primary objective being to reduce network losses; during sandstorm warning phases, the weights of the three objectives must satisfy: During sandstorm warnings, the primary objectives are power supply reliability and voltage quality; during the sandstorm's passage and dissipation phases, the weights of the three objectives satisfy the following: In the event of a sandstorm, survivability is prioritized, with the primary objective being to ensure power supply and voltage stability for critical loads regardless of cost. In step S202, when setting voltage safety boundary constraints and topology radial constraints for the multi-objective optimization function, the voltage safety boundary constraints refer to setting maximum and minimum values ​​for the voltage amplitude of nodes after topology reconstruction. The functional expression for the topology radial constraints is: ; in, To represent the set of all branches in a distribution network, branch road A binary variable representing a branch The switch state variables, Indicates a branch closure, Indicates a branch disconnect.

[0027] In this embodiment, step S203 includes: turning on all switches in the distribution network to form a network structure containing loops; calculating the current value of each switch through linear power flow analysis to generate a switch list arranged in ascending order of current value; and then iteratively turning off the switch with the smallest current value for the switch list until the network structure of the distribution network forms a radial distribution network topology.

[0028] In this embodiment, step S204 involves performing distribution network topology optimization based on a branch switching strategy on the obtained radial distribution network topology, including: S301, For the obtained radial distribution network topology, the tie switches are traversed, and for each tie switch that is in the open state, a closing operation is performed to form a network loop; S302, Identify and disconnect a sectionalizing switch in the new network loop to re-establish the new radial distribution network; S303, Perform linear power flow analysis on the new radial distribution network, calculate the current of each branch and the voltage of each node, and compare the current of each branch with the corresponding current limit and the voltage of each node with the allowable voltage range; if all branch currents and node voltages meet the constraints and the topology maintains the radial structure, then the switch state exchange scheme of the tie switch and the sectionalizing switch is deemed feasible, and proceed to step S304; otherwise, the switch state exchange scheme of the tie switch and the sectionalizing switch is deemed infeasible, the original topology is restored, and return to step S301 to select a new combination of tie switch and sectionalizing switch for re-optimization; S304, calculate the function value of the multi-objective optimization function of the radial distribution network before and after the switching operation of the tie switch and the sectionalizing switch. If the function value of the multi-objective optimization function of the radial distribution network after the switching operation of the tie switch and the sectionalizing switch is smaller than the function value of the multi-objective optimization function of the radial distribution network before the switching operation of the tie switch and the sectionalizing switch, then update the topology of the current distribution network according to the found feasible switch state exchange scheme, and jump to step S301 to continue iterating until all tie switches and sectionalizing switch combinations have been traversed and the iteration stops, the optimization ends and exits.

[0029] To verify the effectiveness of the novel multi-timescale voltage control method for power systems considering sandstorms in this embodiment, a 33-node distribution network including a wind farm was employed. Over a 5-hour period, the control strategy planning and implementation of the novel power system were comprehensively considered, taking into account weather conditions including sandstorms, dynamic changes in network load and power injection. Specifically, the wind farm performs voltage control once per second to ensure voltage stability of the wind turbines. A topology reconfiguration of the distribution network is performed every 30 minutes to optimize network operation. Notably, the wind farm is connected at node 26, and the initial switch configuration is set to sectionalizing switches 1 to 32 and tie switches 33 to 37. Figure 3 The data shows wind speed variations over a 5-hour period, exhibiting dramatic fluctuations and highlighting the necessity of second-level control. Further analysis is needed... Figure 4The voltage control effect shows that the wind farm voltage control is performed at the second level, and the objective function is adaptively adjusted according to different weather conditions, successfully stabilizing the voltage within a safe range. This verifies the ability of the novel multi-timescale voltage control method for power systems considering sandstorms in this embodiment to suppress rapid voltage changes due to drastic wind speed fluctuations. At the minute-level timescale, topology reconfiguration, as a core means to improve the resilience of the distribution network, effectively copes with the continuous disturbances of sandstorms by dynamically adjusting the switching state through adaptive multi-objective optimization and two-stage topology reconfiguration. The reconfiguration process is based on the adaptive adjustment of the objective function according to the operating state and the sandstorm intensity: economy is prioritized in the normal stage, while survivability is the primary objective in the sandstorm-passing stage to generate the optimal switching scheme. The experimental results are shown in Table 1.

[0030]

[0031] Table 1 shows that the reconfiguration strategy of the novel multi-timescale voltage control method for power systems considering sandstorms in this embodiment dynamically changes with the sandstorm stage. This adaptive reconfiguration mechanism of the novel multi-timescale voltage control method for power systems considering sandstorms in this embodiment not only improves power supply reliability but also ensures power quality through voltage deviation control, verifying the effectiveness of minute-level optimization in enhancing grid resilience under extreme weather conditions. It is evident that... Figure 3 , Figure 4 As can be seen from the results in Table 1, the effectiveness and practicality of the method in this embodiment have been verified by the power distribution network.

[0032] In summary, the method in this embodiment includes adjusting the active / reactive power of wind turbines in real time based on a dynamic state-space model and an adaptive objective function at the second-level time scale to quickly respond to voltage fluctuations caused by sandstorms; and adjusting the switching state at the minute-level time scale through multi-objective optimization and two-stage topology reconfiguration to improve the resilience of the distribution network. The time periods of the second-level and minute-level time scales increase sequentially, with the second-level scale focusing on rapid response to wind speed fluctuations and the minute-level scale focusing on medium- and long-term network optimization. This embodiment's method, through coordinated voltage control at the second-level and minute-level time scales, can improve system robustness under extreme weather conditions and meet the requirements of voltage stability, reliable power supply, and economical and efficient operation in the context of new energy integration.

[0033] Those skilled in the art will understand that the technical solutions provided by embodiments of the present invention may take the form of methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. For example, the present invention may provide a novel multi-timescale voltage control system for power systems considering sandstorms, including an interconnected microprocessor and a memory, the microprocessor being programmed or configured to execute the novel multi-timescale voltage control method for power systems considering sandstorms. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0034] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A novel multi-timescale voltage control method for power systems considering sandstorms, characterized in that, This includes second- and minute-scale coordinated voltage control for wind farms and distribution networks in new power systems. The second- and minute-scale coordinated voltage control includes: for wind farms in new power systems, adjusting the active or reactive power of wind turbines in real time and responding quickly to voltage fluctuations caused by sandstorms through second-scale wind farm voltage control; and for distribution networks in new power systems, adjusting the switching status of distribution networks through minute-scale distribution network topology reconfiguration to improve the resilience of distribution networks.

2. The novel multi-time-scale voltage control method for power systems considering sandstorms according to claim 1, characterized in that, The second-level wind farm voltage control includes: S101 collects the speed, active power measurement, reactive power measurement, terminal voltage and wind speed of all wind turbines; S102, Establish an adaptive weighted objective function that weights voltage safety and power generation economy to respond to wind speed fluctuations and dynamically adjust the weights of voltage safety and power generation economy according to sandstorm intensity; S103. For each wind turbine, a discrete state-space equation is constructed for wind speed, power, and voltage. Based on the discrete state-space equation, a voltage change prediction model is constructed to predict the increments of active power, reactive power, and terminal voltage. S104: Set operating constraints, construct a constrained optimization problem based on the adaptive weight objective function and voltage change prediction model, dynamically solve the constrained optimization problem based on the real-time sandstorm intensity to obtain the active and reactive power control commands of the wind farm, and issue the active and reactive power control commands of the wind farm to the wind farm for execution to adjust the active or reactive power of the wind turbine in real time and quickly respond to the voltage fluctuations of the wind farm caused by the sandstorm.

3. The novel multi-time-scale voltage control method for power systems considering sandstorms according to claim 2, characterized in that, In step S102, the functional expression of the adaptive weight objective function is: ； in, For the adaptive weight objective function, To predict the total number of time steps within the time range, For the number of wind turbines, and These are the weighting coefficients for the two objectives of voltage security and power generation economy, respectively. and These are the actual terminal voltage and reference voltage values ​​of the i-th wind turbine at the k-th time step, respectively. For the goal of voltage safety, For the goal of economical power generation, and These are the actual active power and reference active power values ​​of the i-th wind turbine at the k-th time step, respectively. The sandstorm intensity is determined based on wind speed. The sandstorm is divided into four phases: normal wind speed phase, sandstorm warning phase, sandstorm passage phase, and sandstorm dissipation phase. During the normal wind speed phase: ； During the sandstorm warning phase: ； ； During the sandstorm's passage phase: ； ； During the sandstorm's remission phase: ； ； in, Rated wind speed, and These are the preset voltage and power weights for the normal wind speed phase, respectively. and These are the voltage weighting growth factor and the power weighting attenuation factor, respectively, for the sandstorm warning phase. and These are the preset voltage and power weights for the sandstorm passage phase. and These are the voltage weight attenuation coefficient and power weight growth coefficient during the sandstorm dissipation phase, respectively.

4. The novel multi-timescale voltage control method for power systems considering sandstorms according to claim 2, characterized in that, In step S102, the functional expression of the constructed discrete state-space equation is: ； ； in, and Let be the state variables of the i-th wind turbine at time steps k+1 and k, respectively. Let be the input variable for the i-th wind turbine at the k-th time step. Let i be the output variable of the i-th wind turbine at the k-th time step. The state matrix, For the input matrix, Let be the output matrix, and we have: ； ； ； in, These represent the increments of the rotational speed, active power, and reactive power of the i-th wind turbine at the k-th time step. These represent the active power reference value, reactive power reference value, and wind speed increment at the k-th time step for the i-th wind turbine. These represent the increments of active power, reactive power, and terminal voltage for the i-th wind turbine at the (k+1)-th time step, respectively.

5. The novel multi-timescale voltage control method for power systems considering sandstorms according to claim 2, characterized in that, In step S104, setting the operating constraints includes setting upper and lower limits for the terminal voltage, active power, reactive power, and speed of each wind turbine at the k-th time step, as well as upper and lower limits for the increments of active power and reactive power between the k-th and (k-1)-th time steps of each wind turbine.

6. The novel multi-timescale voltage control method for power systems considering sandstorms according to claim 1, characterized in that, The minute-level distribution network topology reconfiguration includes: S201 collects voltage, power, and branch status information of distribution network nodes; S202: Construct a multi-objective optimization function consisting of three weighted objectives: minimizing load loss, reducing network loss, and controlling voltage deviation. The weights of the three objectives are adaptively adjusted according to the sandstorm stage. Voltage safety boundary constraints and topology radial constraints are set for the multi-objective optimization function to ensure that the reconfiguration scheme meets the requirements of operational safety and network connectivity. S203, Initial topology reconfiguration of distribution network based on sequential switch opening strategy to obtain radial distribution network topology; S204. Perform distribution network topology optimization based on branch switching strategy on the obtained radial distribution network topology to adjust the switching state of the distribution network to improve the resilience of the distribution network.

7. The novel multi-timescale voltage control method for power systems considering sandstorms according to claim 6, characterized in that, The function expression of the multi-objective optimization function in step S202 is as follows: ； ； ； ； in, For multi-objective optimization functions, , and The weights are respectively for the three objectives of minimizing load loss, reducing network loss, and controlling voltage deviation. The objective function is to minimize load loss. The objective function is to reduce network loss. The objective function for voltage deviation control; The number of distribution network nodes, Let i be the load importance weight of node i in the distribution network. For the active power load loss of distribution network node i, and The voltage magnitudes at nodes i and j after topology reconstruction. This represents the real part operator. Let the admittance be the line between nodes i and j after topology reconstruction. and The voltage phase angles of nodes i and j after topology reconstruction are given, and the sandstorm intensity is determined by wind speed. The process is divided into four phases: normal wind speed phase, sandstorm warning phase, sandstorm passage phase, and sandstorm dissipation phase. During the normal wind speed phase, the weights of the three objectives satisfy the following: During the sandstorm warning phase, the weights of the three objectives satisfy: The weights of the three objectives satisfy the following during the sandstorm's passage and dissipation phases: In step S202, when setting voltage safety boundary constraints and topology radial constraints for the multi-objective optimization function, the voltage safety boundary constraints refer to setting maximum and minimum values ​​for the voltage amplitude of nodes after topology reconstruction. The functional expression of the topology radial constraints is: ； in, To represent the set of all branches in a distribution network, branch road A binary variable representing a branch The switch state variables, Indicates a branch closure, Indicates a branch disconnect.

8. The novel multi-time-scale voltage control method for power systems considering sandstorms according to claim 6, characterized in that, Step S203 includes: turning on all switches in the distribution network to form a network structure containing loops; calculating the current value of each switch through linear power flow analysis to generate a list of switches arranged in ascending order of current value; and then iteratively turning off the switch with the smallest current value for the switch list until the network structure of the distribution network forms a radial distribution network topology.

9. The novel multi-time-scale voltage control method for power systems considering sandstorms according to claim 7, characterized in that, Step S204 involves performing distribution network topology optimization based on branch switching strategy on the obtained radial distribution network topology, including: S301, For the obtained radial distribution network topology, the tie switches are traversed, and for each tie switch that is in the open state, a closing operation is performed to form a network loop; S302, Identify and disconnect a sectionalizing switch in the new network loop to re-establish the new radial distribution network; S303, Perform linear power flow analysis on the new radial distribution network, calculate the current of each branch and the voltage of each node, and compare the current of each branch with the corresponding current limit and the voltage of each node with the allowable voltage range; if all branch currents and node voltages meet the constraints and the topology maintains the radial structure, then the switch state exchange scheme of the tie switch and the sectionalizing switch is deemed feasible, and proceed to step S304; otherwise, the switch state exchange scheme of the tie switch and the sectionalizing switch is deemed infeasible, the original topology is restored, and return to step S301 to select a new combination of tie switch and sectionalizing switch for re-optimization; S304, calculate the function value of the multi-objective optimization function of the radial distribution network before and after the switching operation of the tie switch and the sectionalizing switch. If the function value of the multi-objective optimization function of the radial distribution network after the switching operation of the tie switch and the sectionalizing switch is smaller than the function value of the multi-objective optimization function of the radial distribution network before the switching operation of the tie switch and the sectionalizing switch, then update the topology of the current distribution network according to the found feasible switch state exchange scheme, and jump to step S301 to continue iterating until all tie switches and sectionalizing switch combinations have been traversed and the iteration stops, the optimization ends and exits.

10. A novel multi-timescale voltage control system for power systems considering sandstorms, comprising interconnected microprocessors and memory, characterized in that, The microprocessor is programmed or configured to execute the novel multi-timescale voltage control method for power systems that takes sandstorms into account, as described in any one of claims 1 to 9.

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