A wind storage power distribution network active and reactive power coordination optimization method and system considering transient voltage stability and a medium

Abstract

The application discloses a kind of active and reactive power coordination optimization method, system and medium of wind storage power distribution network considering transient voltage stability, the method includes the following steps: grid frequency deviation is as input, adjusts the active power of wind turbine, provides virtual inertia response, inhibits the initial drop of grid frequency;Quantify the virtual inertia support capability of energy storage;Adopt constant frequency control strategy, track system frequency signal by PI controller, support grid frequency until steady state;Build the multi-objective optimization function with the minimum wind curtailment rate, minimize network loss and minimize voltage deviation as target;Introduce adaptive feedback factor and arithmetic crossover operator optimization slime mold algorithm, the multi-objective optimization function is optimized and handled, and the optimal system configuration parameter is output, and active and reactive power coordination optimization is carried out.The application solves the problems of insufficient system inertia, transient voltage instability, frequency fluctuation, high wind curtailment rate, large network loss and poor economy caused by high proportion of wind power access distribution network.

Description

Technical Field

[0001] This invention relates to the field of power system and renewable energy integration technology, specifically to a method, system and medium for coordinated optimization of active and reactive power in a wind power-storage distribution network that considers transient voltage stability. Background Technology

[0002] Against the backdrop of the new energy revolution, actively developing and utilizing renewable energy has become an important way to address energy and environmental issues, and is gradually evolving into the mainstream direction of global energy development and transformation. The large-scale replacement of traditional synchronous generators has led to a significant decrease in the overall equivalent rotational inertia of the power system. This results in the receiving-end grid facing the risk of low-inertia operation, a marked weakening of supply and demand regulation capabilities and voltage support capabilities, and serious challenges to the transient stability of voltage and frequency. Simultaneously, the output of distributed power sources is easily affected by external factors such as weather and geography, resulting in significant uncertainty in power generation. For example, wind power output fluctuates with wind speed and is also affected by climate change and human intervention, easily causing local voltage fluctuations and flicker in the distribution network. If the time-varying characteristics of the load are added, these unstable factors may further exacerbate voltage fluctuations and flicker, posing greater challenges to the operation of the distribution network. In the future, as the scale of wind power integration expands, its uncertainty will cause the power grid to exhibit "two-sided randomness," making the structure, response, and interaction relationships between power sources, grids, and loads more complex. On the one hand, the capacity and method of wind power integration will be coupled with the main grid structure, the layout and operation of conventional power sources, affecting the system's economics and wind power absorption capacity. On the other hand, the overall power fluctuation characteristics of wind power clusters along the Yellow River differ significantly from those of individual wind farms, greatly impacting the power flow distribution of the main grid. Therefore, rationally designing wind power cluster integration schemes and coordinating regional power grid planning in the Yellow River Basin are of great significance for ensuring the operational safety of the Henan power grid and optimizing investment and construction. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, and medium for coordinating and optimizing the active and reactive power of a wind power-storage distribution network, taking into account transient voltage stability, in order to solve the problems of insufficient system inertia, transient voltage instability, frequency fluctuation, high wind curtailment rate, large network loss, and poor economic efficiency caused by a high proportion of wind power being connected to the distribution network.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for coordinated optimization of active and reactive power in a wind power-storage distribution network considering transient voltage stability, characterized by the following steps: S1: In the power control stage of the doubly fed wind turbine, the grid frequency deviation is used as the control input, and the active power of the wind turbine is adjusted according to the linkage between the grid frequency deviation and the wind turbine rotor speed to provide virtual inertial response and suppress the initial drop of grid frequency. S2: Based on the response time window brought about by the virtual inertial response, combined with the system frequency security requirements and energy storage configuration capacity, the virtual inertial support capability of energy storage is quantified; a constant frequency control strategy is adopted, and the system frequency signal is tracked in real time through the PI controller to dynamically adjust the energy storage output to support the grid frequency until steady state; S3: When the grid frequency recovers to a steady state, the frequency regulation state transfer control of the first-order inertial element is introduced to achieve stable and phased withdrawal of energy storage power, and smoothly transfer the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within the safe limit. S4: After the transient voltage stabilizes, when the system encounters a small disturbance and the energy storage output does not reach the upper limit, the remaining capacity of the energy storage is used in conjunction with the reactive power compensation device for optimization, and a multi-objective optimization function is constructed with the goals of minimizing wind curtailment rate, minimizing grid loss and minimizing voltage deviation. S5: Introduce adaptive feedback factors and arithmetic crossover operators to optimize the slime mold algorithm, and use the optimized slime mold algorithm to perform optimization processing on the multi-objective optimization function, output the configuration parameters of the optimal system, and perform active and reactive power coordination optimization of the distribution network.

[0005] To optimize the above technical solution, the specific limitations also include: In step S1, adjusting the active power of the wind turbine based on the linkage between the grid frequency deviation and the wind turbine rotor speed to provide a virtual inertial response and suppress the initial drop in grid frequency specifically involves calculating the virtual inertial time constant of the wind turbine. The calculation formula is as follows:

[0006]

[0007] in, This represents the virtual inertia of the wind turbine. The electric angular velocity of the synchronous generator. For the number of pole pairs of the wind turbine, The rated capacity of the wind turbine unit. This represents the change in the electric angular velocity of the fan rotor. The electric angular velocity of the fan rotor. This refers to the change in the electrical angular velocity of the synchronous generator. The initial value of the electric angular velocity of the fan rotor. This refers to the inherent inertia of the wind turbine. This represents the virtual inertial time constant of the wind turbine.

[0008] Further, in step S2, the virtual inertia support capability of the quantified energy storage specifically involves calculating the virtual inertia time constant of the energy storage, and the calculation formula is as follows:

[0009] in, The virtual inertial time constant. The inertia supports the power for energy storage. For frequency conversion extreme time, The initial value of the synchronous electric angular velocity, This represents the extreme value of the synchronous electric angular velocity.

[0010] Furthermore, in step S2, the constant frequency control strategy is adopted, which uses a PI controller to track the system frequency signal in real time and dynamically adjust the energy storage output to support the grid frequency. Specifically, this is achieved by monitoring the deviation between the system frequency reference value and the frequency measurement value in real time, and then using the PI controller to dynamically generate and adjust the power reference value and current reference value of the energy storage to quickly respond to frequency changes.

[0011] Furthermore, in step S3, the frequency regulation state transfer control that introduces a first-order inertial link to achieve stable and phased withdrawal of energy storage power and smoothly transfer the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within the safe limit, specifically includes: when the grid frequency recovers to a stable state, the system gradually reduces the power output of the energy storage device through the first-order inertial link, while gradually returning the grid frequency regulation task to the synchronous generator set; By controlling the energy storage exit rate and suppressing system voltage fluctuations, a minimum inertia assessment is used to quantify safety constraints, specifically by calculating the minimum inertia requirement:

[0012] in, To minimize inertia requirements, For disturbance power deviation, The maximum permissible rate of change of system frequency; The energy storage phase-out process incorporates virtual inertia calculations and optimizes the configuration by adjusting the energy storage and wind power integration ratio. The calculation formula is as follows:

[0013] in, The ratio of energy storage to wind power installations. For wind power penetration rate, For frequency conversion extreme time, For synchronous generator inertia, The initial value of the synchronous electric angular velocity, This represents the extreme value of the synchronous electric angular velocity.

[0014] Preferably, in step S4, constructing a multi-objective optimization function aimed at minimizing wind curtailment rate, minimizing grid loss, and minimizing voltage offset specifically includes:

[0015] in, To minimize network loss, Minimum voltage offset, To minimize wind curtailment rate, To optimize variables, For state variables, The total number of load nodes. Let t be the total active power loss of the system at time t. Let be the voltage of load node i at time t. for, The maximum allowable voltage offset for node i. For the number of wind farms, This represents the total theoretical power generation of wind power. For the first The actual dispatch of each wind farm has active power output; The calculation formula is based on branch conductance and voltage phase difference:

[0016] in, branch road The conductivity on For nodes ,node The voltage phase difference between them for, for.

[0017] Furthermore, in step S4, constraints are set for the multi-objective optimization function, considering constraints on the reactive power output range of SVG, the reactive power output range of wind turbines, the number of capacitors switched on and off, node voltage constraints, branch apparent power constraints, node index constraints, and the turns ratio constraint of the voltage regulating transformer; simultaneously, the equality constraints satisfy the system power flow equality constraints:

[0018] in, , They are nodes Injected active and reactive power; , They are nodes The active and reactive power consumed by the load.

[0019] Specifically, in step S5, the formula for calculating the slime mold algorithm is:

[0020] in, This represents the current iteration number. The current optimal individual position, and To randomly select the positions of two individuals, W is an adaptive weight. and For control parameters, where ∈[- , ], It decreases linearly from 1 to 0. It is a random number between [0, 1], and p is a control variable; The slime mold algorithm introduces an adaptive feedback factor and an arithmetic crossover operator to accelerate convergence: The mathematical model for the adaptive feedback factor is:

[0021] in, As an adaptive feedback factor, This represents the current iteration number. The maximum number of iterations, As a regulating factor; The mathematical model for the arithmetic crossover operator is:

[0022] in, This represents the current iteration number. and These represent the positions of the two offspring individuals generated by the crossover. For the current individual position, The optimal position for the current population. The parameter is a random parameter that takes the value (0, 1).

[0023] This invention also proposes a coordinated optimization system for active and reactive power in a wind power-storage distribution network that considers transient voltage stability, comprising: The virtual inertia simulation module is used in the power control stage of a doubly-fed wind turbine to take the grid frequency deviation as the control input and adjust the active power of the wind turbine according to the linkage between the grid frequency deviation and the wind turbine rotor speed, providing a virtual inertial response and suppressing the initial drop in grid frequency. The inertia support capability quantification and control module is used to quantify the virtual inertia support capability of energy storage based on the response time window brought about by the virtual inertia response, combined with the system frequency security requirements and energy storage configuration capacity; adopting a constant frequency control strategy, the PI controller tracks the system frequency signal in real time and dynamically adjusts the energy storage output to support the grid frequency until steady state. The energy storage power withdrawal module is used to introduce a first-order inertial link frequency regulation state transfer control to achieve smooth and graded withdrawal of energy storage power after the grid frequency recovers to a steady state, and smoothly transfer the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within safe limits. The multi-objective function construction module is used to optimize the system after transient voltage stabilization, when the system encounters a small disturbance and the energy storage output has not reached its upper limit. It utilizes the remaining capacity of the energy storage and the reactive power compensation device to construct a multi-objective optimization function with the objectives of minimizing wind curtailment rate, minimizing grid loss and minimizing voltage deviation. The optimization execution module is used to introduce adaptive feedback factors and arithmetic crossover operators to optimize the slime mold algorithm, and to use the optimized slime mold algorithm to perform optimization processing on the multi-objective optimization function, output the configuration parameters of the optimal system, and perform active and reactive power coordination optimization of the distribution network.

[0024] The present invention also proposes a computer-readable storage medium storing a computer program that enables a computer to execute the active and reactive power coordination optimization method for wind power-storage distribution networks, as described above, which considers transient voltage stability.

[0025] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a method for coordinating and optimizing the active and reactive power of a wind power-storage distribution network that considers transient voltage stability. By using the virtual inertial control of doubly-fed induction generators and the constant-frequency stable exit coordination control mechanism of energy storage, it provides the system with fast, continuous and exitable active power support, directly compensating for the synchronous inertia lost due to the high proportion of wind power access, effectively suppressing the initial frequency drop and subsequent fluctuations after disturbances, and alleviating the problems of insufficient system inertia and frequency fluctuations.

[0026] This invention establishes a coordinated framework that uses frequency stability control as a prerequisite for voltage stability and leverages the synergistic optimization of remaining energy storage capacity and reactive power compensation devices such as SVG to support frequency stability and regulate voltage stability. Based on rapid frequency recovery, refined reactive power scheduling is implemented with the goal of minimizing voltage deviation, enhancing the system's ability to withstand voltage dips and crashes, and specifically addressing the transient voltage instability problem that is prone to occur in high-proportion renewable energy scenarios.

[0027] This invention constructs and solves a multi-objective optimization model during the steady-state or small-disturbance phases of the system, focusing on minimizing wind curtailment rate, network losses, and voltage deviation. This model coordinates the scheduling of various resources, including energy storage, wind turbines, SVG (Enhanced Power Storage), and transformers. An optimized slime mold algorithm is then employed to find the global optimum while meeting strict safety constraints. This approach ensures system transient stability while reducing wind curtailment rate and network losses, thus comprehensively improving the economic efficiency of distribution network operation. Attached Figure Description

[0028] Figure 1 : A flowchart illustrating a method for coordinating and optimizing active and reactive power in a wind power-storage distribution network that considers transient voltage stability, according to the present invention.

[0029] Figure 2 The control framework diagram of the active and reactive power coordination optimization method for wind power-storage distribution networks considering transient voltage stability is shown in the present invention.

[0030] Figure 3 The present invention provides a framework diagram for the virtual inertia control of energy storage in a wind power-storage distribution network that considers the coordinated optimization of active and reactive power in a wind power-storage distribution network under transient voltage stability.

[0031] Figure 4 The present invention provides a three-dimensional relationship diagram for evaluating the minimum inertia of a wind power-storage distribution network that considers the coordination and optimization of active and reactive power in a transient voltage-stable distribution network. Detailed Implementation

[0032] The present invention will be further described in detail below through specific embodiments, but it should not be construed as limiting the scope of the present invention to the following embodiments. All technologies implemented based on the above content of the present invention fall within the scope of the present invention.

[0033] The technical solution of the present invention will be further described in detail below with reference to specific embodiments: In one embodiment, this invention proposes a method for coordinated optimization of active and reactive power in a wind power-storage distribution network, considering transient voltage stability. The flowchart is shown below. Figure 1 As shown, the entire method includes the following steps: S1: In the power control stage of the doubly fed wind turbine, the grid frequency deviation is used as the control input, and the active power of the wind turbine is adjusted according to the linkage between the grid frequency deviation and the wind turbine rotor speed to provide virtual inertial response and suppress the initial drop of grid frequency. S2: Based on the response time window brought about by the virtual inertial response, combined with the system frequency security requirements and energy storage configuration capacity, the virtual inertial support capability of energy storage is quantified; a constant frequency control strategy is adopted, and the system frequency signal is tracked in real time through the PI controller to dynamically adjust the energy storage output to support the grid frequency until steady state. S3: When the grid frequency recovers to a steady state, the frequency regulation state transfer control of the first-order inertial element is introduced to achieve stable and phased withdrawal of energy storage power, and smoothly transfer the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within the safe limit. S4: After the transient voltage stabilizes, when the system encounters a small disturbance and the energy storage output does not reach the upper limit, the remaining capacity of the energy storage is used in conjunction with the reactive power compensation device for optimization, and a multi-objective optimization function is constructed with the goals of minimizing wind curtailment rate, minimizing grid loss and minimizing voltage deviation. S5: Introduce adaptive feedback factors and arithmetic crossover operators to optimize the slime mold algorithm, and use the optimized slime mold algorithm to perform optimization processing on the multi-objective optimization function, output the configuration parameters of the optimal system, and perform active and reactive power coordination optimization of the distribution network.

[0034] like Figure 2 As shown, the overall system control is divided into energy storage configuration, transient voltage stabilization, and active and reactive power co-optimization.

[0035] The core objective of energy storage configuration is to quantitatively determine the configuration capacity of the energy storage system to ensure that the system can cope with large disturbances. First, a minimum inertia estimate is performed, such as... Figure 4 As shown, according to the synchronous generator rotor motion equation, the system frequency change rate is related to the power disturbance and the total system inertia. To ensure system safety, the system voltage fluctuation is suppressed by controlling the energy storage withdrawal rate. The minimum inertia assessment is used to quantify the safety constraints. The maximum frequency change rate occurs in the early stage of the disturbance. At this time, the formula for calculating the minimum inertia requirement of the system is:

[0036] in, To minimize inertia requirements, For disturbance power deviation, The maximum permissible rate of change of system frequency; Subsequently, the inertia support provided by existing synchronous generator units was assessed, and the virtual inertia required by energy storage was calculated in conjunction with wind power penetration. The quantitative formula for the virtual inertia time constant of energy storage is as follows:

[0037] in, The inertia supports the power for energy storage. The initial value of the synchronous electric angular velocity, The extreme value of synchronous electric angular velocity The frequency extreme value time is defined as the time from the occurrence of the disturbance to the lowest point of frequency drop. The formula for calculating the extreme time of frequency conversion is:

[0038] in The equivalent inertial time constant of the turbine. D The system damping coefficient is... This is the system load adjustment coefficient. This is the droop coefficient of the synchronizing machine. For wind power penetration rate, For the system damping ratio, The system's damped oscillation angular frequency, The natural angular frequency of the system (undamped). Let be the total inertial time constant of the system. These are the characteristic coefficients of the turbine.

[0039] Ultimately, while ensuring the system meets minimum inertia requirements, the required energy storage capacity for different wind turbine penetration rates is determined. This ensures that the synchronous machine and energy storage together provide sufficient inertia support for the system, guaranteeing transient voltage stability. The installation ratio of energy storage to wind power must meet the following conditions:

[0040] in, The ratio of energy storage to wind power installations. For wind power penetration rate, For frequency conversion extreme time, For synchronous generator inertia, The initial value of the synchronous electric angular velocity, This represents the extreme value of the synchronous electric angular velocity.

[0041] After the energy storage configuration is completed, the initial frequency drop and voltage stability are suppressed through the coordinated response of the wind turbine and energy storage. First, the virtual inertia of the wind turbine is controlled. In the power control stage of the doubly-fed induction generator (DFIG), the grid frequency deviation is used as the control input, and the active power of the wind turbine is adjusted according to the linkage between the grid frequency deviation and the turbine rotor speed to provide a virtual inertial response. The formula for calculating the virtual inertial time constant is as follows:

[0042]

[0043] in, This represents the virtual inertia of the wind turbine. The electric angular velocity of the synchronous generator. For the number of pole pairs of the wind turbine, The rated capacity of the wind turbine unit. This represents the change in the electric angular velocity of the fan rotor. The electric angular velocity of the fan rotor. This refers to the change in the electrical angular velocity of the synchronous generator. The initial value of the electric angular velocity of the fan rotor. This refers to the inherent inertia of the wind turbine. This represents the virtual inertial time constant of the wind turbine.

[0044] Secondly, the virtual inertia support capability of energy storage is quantified. Based on system frequency security requirements, the virtual inertia of energy storage differs from that of wind power; energy storage possesses continuous power support capability. Therefore, the virtual inertia response of energy storage can track frequency signals to maintain a constant frequency state, sustain power support during frequency recovery periods, and exhibit a smooth withdrawal capability, thereby fully unleashing the frequency regulation potential of energy storage. By quantitatively calculating the virtual inertia support performance of energy storage during extreme frequency fluctuation periods, a reliable virtual inertia can be estimated for safe grid operation. The response can track frequency signals to maintain a constant frequency state, sustain power support during frequency recovery periods, and exhibit a smooth withdrawal capability, thereby fully unleashing the frequency regulation potential of energy storage.

[0045] In constant frequency control strategies, such as Figure 3 As shown, the system uses a PI controller, which monitors the system frequency reference value in real time. With frequency measurement value The deviation between them dynamically adjusts the power reference value of energy storage. With power measurement value and the corresponding current reference value With current measurement value This allows for rapid adjustment of the energy storage system's output. This control method enables a quick response to changes in system frequency, effectively providing inertia support and executing subsequent frequency regulation tasks.

[0046] The virtual inertia of energy storage is related to energy, and the calculation formula is:

[0047]

[0048] in, For energy storage, virtual inertia The inertia of a synchronous machine of equal capacity. The energy stored in the energy storage system. The rotational kinetic energy stored in the rotor of a synchronous machine with the same capacity as the energy storage device. The virtual inertial time constant for energy storage. The inertia supports the power for energy storage. For frequency conversion extreme time, The initial value of the synchronous electric angular velocity, This represents the extreme value of the synchronous electric angular velocity.

[0049] The frequency regulation state transfer control by introducing a first-order inertial element achieves stable and phased power output reduction of energy storage devices and smoothly transfers the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within safe limits. Specifically, when the grid frequency recovers to a stable state, the system gradually reduces the power output of the energy storage device through the first-order inertial element, while gradually returning the grid frequency regulation task to the synchronous generator set. This process ensures that the frequency change rate is always below the safe limit, guaranteeing transient voltage stability.

[0050] Under small disturbance conditions, the system enters the optimization phase. Utilizing the remaining energy storage capacity in conjunction with the reactive power compensation device, and aiming to minimize wind curtailment rate, grid loss, and voltage deviation, a multi-objective optimization function is first constructed, specifically including:

[0051] in, To minimize network loss, Minimum voltage offset, To minimize wind curtailment rate, To optimize variables, For state variables, The total number of load nodes. Let t be the total active power loss of the system at time t. Let be the voltage of load node i at time t. This is the per-unit voltage reference value. The maximum allowable voltage offset for node i. For the number of wind farms, This represents the theoretical maximum power generation of wind power. For the first The actual dispatch of each wind farm has active power output; The calculation formula is based on branch conductance and voltage phase difference:

[0052] in, branch road The conductivity on For nodes ,node The voltage phase difference between them Let be the voltage amplitude at node i. Let be the voltage amplitude at node j.

[0053] Further constraints are set for the multi-objective optimization function, considering constraints on the reactive power output range of static var generators (SVG), reactive power output range of wind turbines, number of capacitors switched on / off, node voltage constraints, branch apparent power constraints, node index constraints, and transformer turns ratio constraints, etc. Reactive power constraints:

[0054] Wherein, is the reactive power output of the wind turbine, is the maximum reactive power allowed to be output by the wind turbine, and is the minimum reactive power allowed to be output by the wind turbine.

[0055] Wherein, is the actual reactive power output of the Static Var Generator (SVG), is the maximum reactive power allowed to be output by the Static Var Generator, and is the minimum reactive power allowed to be output by the Static Var Generator; Equipment operation constraints:

[0056] Wherein, is the actual number of capacitor banks put into operation, is the maximum number of capacitor banks allowed to be put into operation, and is the minimum number of capacitor banks allowed to be put into operation; Voltage safety constraints:

[0057] Where, is the actual voltage value of node i, is the maximum allowed voltage value of node i, and is the minimum allowed voltage value of node i; Active power output constraints:

[0058] Wherein, is the actual active power output of the wind turbine, is the maximum active power allowed to be output by the wind turbine, and is the minimum active power allowed to be output by the wind turbine. System stability constraints:

[0059] Where, is the system node voltage stability index, is the voltage stability index value of node i, and is the set of all nodes in the system; Equipment capacity constraints:

[0060] Where, is the apparent power flowing through branch i, and is the maximum apparent power allowed to pass through branch i;

[0061]

[0062] in, The charging power of the energy storage device, For the discharge power of energy storage devices, For maximum allowable charging power, Maximum permissible discharge power; Constraints of energy storage systems:

[0063] Wherein, is the current state of charge of the energy storage system, is the maximum allowable state of charge of the energy storage system, and is the minimum allowable state of charge of the energy storage system; Transformer constraints

[0064] Where, is the actual turns ratio of transformer a, is the maximum allowable turns ratio, and is the minimum allowable turns ratio;

[0065] in, The reactive power output of the energy storage system, For the rated apparent power capacity of the energy storage converter, This represents the current active power output of the energy storage system.

[0066] Simultaneously, the equality constraints satisfy the system power flow equality constraints:

[0067] in, , They are nodes Injected active and reactive power; , They are nodes The active and reactive power consumed by the load; branch road The conductivity on For nodes ,node Voltage phase difference between them; The susceptance element of the admittance matrix quantifies the reactance characteristics of the branch, affecting reactive power flow and voltage distribution. Combined with constraints, it ensures that the strategy generated by the algorithm conforms to the actual operation of the power grid.

[0068] To solve the multi-objective problem, an improved slime mold algorithm (ISMA) is used for optimization. The global search capability is enhanced through an adaptive feedback factor and an arithmetic crossover operator. Specific steps include: Polarity Individual Position Update:

[0069] in, This represents the current iteration number. The current optimal individual position, and To randomly select the positions of two individuals, W is an adaptive weight. and For control parameters, where ∈[- , ], It decreases linearly from 1 to 0. It is a random number between [0, 1], and p is a control variable; The formula for calculating the control variable p is:

[0070] Where p is the pattern selection probability threshold, which determines whether an individual performs global exploration or local development; The fitness value of the current i-th individual reflects the quality of the solution; The current global best fitness value is the fitness value corresponding to the currently found optimal solution; random number. At times, algorithms tend to be developed locally; when At that time, conduct a global exploration; The formula for calculating parameter a is:

[0071] Where 'a' is the search range control parameter, and 't' is the current iteration number. This represents the maximum number of iterations. The mathematical model for the adaptive weight W is as follows:

[0072]

[0073] in, The adaptive weights reflect the relative merits of different control strategies. Individuals with higher weights correspond to coordinated solutions that can effectively reduce network losses, improve voltage deviation, and reduce wind curtailment rates. The random number is used to simulate the uncertainty factors in actual power grid optimization; The optimal fitness for the current iteration corresponds to the comprehensive performance index of the best coordination strategy found so far. The fitness of the current individual reflects the quality of the current solution; The worst fitness in the current iteration corresponds to a poorly performing coordination strategy; It is a collection of the top half of the population, representing the best individuals. The collection of ordinary individuals in the latter half of the population; The odor index is used to rank odors by fitness. The resulting indicators are used to distinguish the quality levels of individuals; Introducing adaptive feedback factors and arithmetic crossover operators to accelerate convergence: The mathematical model for the adaptive feedback factor is:

[0074] in, As an adaptive feedback factor, This represents the current iteration number. The maximum number of iterations, As a regulating factor; The mathematical model for the arithmetic crossover operator is:

[0075] in, This represents the current iteration number. and These represent the positions of the two offspring individuals generated by the crossover. For the current individual position, The optimal position for the current population. The parameter is a random parameter that takes the value (0, 1).

[0076] Ultimately, the algorithm achieves a coordinated balance between active and reactive power by iteratively outputting the optimal configuration parameters.

[0077] This invention also proposes a coordinated optimization system for active and reactive power in a wind power-storage distribution network that considers transient voltage stability, comprising: The virtual inertia simulation module is used in the power control stage of a doubly-fed wind turbine to take the grid frequency deviation as the control input and adjust the active power of the wind turbine according to the linkage between the grid frequency deviation and the rotor speed of the wind turbine, providing a virtual inertial response and suppressing the initial drop in grid frequency. The inertia support capability quantification and control module is used to quantify the virtual inertia support capability of energy storage based on the response time window brought about by the virtual inertia response, combined with the system frequency security requirements and energy storage configuration capacity; it adopts a constant frequency control strategy, and dynamically adjusts the energy storage output by tracking the system frequency signal in real time through a PI controller to support the grid frequency until steady state. The energy storage power withdrawal module is used to introduce a first-order inertial link frequency regulation state transfer control to achieve smooth and graded withdrawal of energy storage power after the grid frequency recovers to a steady state, and smoothly transfer the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within safe limits. The multi-objective function construction module is used to optimize the system after transient voltage stabilization, when the system encounters a small disturbance and the energy storage output has not reached its upper limit. It utilizes the remaining capacity of the energy storage and the reactive power compensation device to construct a multi-objective optimization function with the objectives of minimizing wind curtailment rate, minimizing grid loss and minimizing voltage deviation. The optimization execution module is used to introduce adaptive feedback factors and arithmetic crossover operators to optimize the slime mold algorithm. The optimized slime mold algorithm is then used to perform optimization processing on the multi-objective optimization function, output the configuration parameters of the optimal system, and perform active and reactive power coordination optimization of the distribution network.

[0078] The present invention also proposes a computer-readable storage medium storing a computer program that enables a computer to execute the above-mentioned method for coordinating and optimizing the active and reactive power of a wind power-storage distribution network, taking into account transient voltage stability.

[0079] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent substitutions, and improvements made by those skilled in the art to the above embodiments without departing from the scope of the technical solution of the present invention, based on the technical essence of the present invention, shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for coordinated optimization of active and reactive power in a wind power-storage distribution network considering transient voltage stability, characterized in that, Includes the following steps: S1: In the power control stage of the doubly fed wind turbine, the grid frequency deviation is used as the control input, and the active power of the wind turbine is adjusted according to the linkage between the grid frequency deviation and the wind turbine rotor speed to provide virtual inertial response and suppress the initial drop of grid frequency. S2: Based on the response time window brought about by the virtual inertial response, combined with the system frequency security requirements and energy storage configuration capacity, the virtual inertial support capability of energy storage is quantified; a constant frequency control strategy is adopted, and the system frequency signal is tracked in real time through the PI controller to dynamically adjust the energy storage output to support the grid frequency until steady state; S3: When the grid frequency recovers to a steady state, the frequency regulation state transfer control of the first-order inertial element is introduced to achieve stable and phased withdrawal of energy storage power, and smoothly transfer the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within the safe limit. S4: After the transient voltage stabilizes, when the system encounters a small disturbance and the energy storage output does not reach the upper limit, the remaining capacity of the energy storage is used in conjunction with the reactive power compensation device for optimization, and a multi-objective optimization function is constructed with the goals of minimizing wind curtailment rate, minimizing grid loss and minimizing voltage deviation. S5: Introduce adaptive feedback factors and arithmetic crossover operators to optimize the slime mold algorithm, and use the optimized slime mold algorithm to perform optimization processing on the multi-objective optimization function, output the configuration parameters of the optimal system, and perform active and reactive power coordination optimization of the distribution network.

2. The method for coordinated optimization of active and reactive power in a wind power-storage distribution network considering transient voltage stability as described in claim 1, characterized in that: In step S1, adjusting the active power of the wind turbine based on the linkage between the grid frequency deviation and the wind turbine rotor speed to provide a virtual inertial response and suppress the initial drop in grid frequency specifically involves calculating the virtual inertial time constant of the wind turbine. The calculation formula is as follows: in, This represents the virtual inertia of the wind turbine. The electric angular velocity of the synchronous generator. For the number of pole pairs of the wind turbine, The rated capacity of the wind turbine unit. This represents the change in the electric angular velocity of the fan rotor. The electric angular velocity of the fan rotor. This refers to the change in the electrical angular velocity of the synchronous generator. The initial value of the electric angular velocity of the wind turbine rotor. This refers to the inherent inertia of the wind turbine. This represents the virtual inertial time constant of the wind turbine.

3. The method for coordinated optimization of active and reactive power in a wind power-storage distribution network considering transient voltage stability as described in claim 1, characterized in that: In step S2, the virtual inertia support capability of the quantized energy storage is specifically calculated by determining the virtual inertia time constant of the energy storage, using the following formula: in, The virtual inertial time constant. The inertia supports the power for energy storage. For frequency conversion extreme time, The initial value of the synchronous electric angular velocity, This represents the extreme value of the synchronous electric angular velocity.

4. The active and reactive power coordination optimization method for a wind power-storage distribution network considering transient voltage stability as described in claim 1, characterized in that: In step S2, the constant frequency control strategy is adopted, which uses a PI controller to track the system frequency signal in real time and dynamically adjust the energy storage output to support the grid frequency. Specifically, this is achieved by monitoring the deviation between the system frequency reference value and the frequency measurement value in real time, and then using the PI controller to dynamically generate and adjust the power reference value and current reference value of the energy storage to quickly respond to frequency changes.

5. The method for coordinated optimization of active and reactive power in a wind power-storage distribution network considering transient voltage stability as described in claim 1, characterized in that: In step S3, the frequency regulation state transfer control that introduces a first-order inertial link achieves stable and phased withdrawal of energy storage power and smoothly transfers the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within safe limits. Specifically, this includes: when the grid frequency recovers to a stable state, the system gradually reduces the power output of the energy storage device through a first-order inertial link, while gradually returning the grid frequency regulation task to the synchronous generator set. By controlling the energy storage exit rate and suppressing system voltage fluctuations, a minimum inertia assessment is used to quantify safety constraints, specifically by calculating the minimum inertia requirement: in, To minimize inertia requirements, For disturbance power deviation, The maximum permissible rate of change of system frequency; The energy storage phase-out process incorporates virtual inertia calculations and optimizes the configuration by adjusting the energy storage and wind power integration ratio. The calculation formula is as follows: in, The ratio of energy storage to wind power installations. For wind power penetration rate, For frequency conversion extreme time, For synchronous generator inertia, The initial value of the synchronous electric angular velocity, This represents the extreme value of the synchronous electric angular velocity.

6. The method for coordinated optimization of active and reactive power in a wind power-storage distribution network considering transient voltage stability as described in claim 1, characterized in that: In step S4, the construction of a multi-objective optimization function aimed at minimizing wind curtailment rate, minimizing grid loss, and minimizing voltage offset specifically includes: in, To minimize network loss, Minimum voltage offset, To minimize wind curtailment rate, To optimize variables, For state variables, The total number of load nodes. Let t be the total active power loss of the system at time t. Let be the voltage of load node i at time t. for, The maximum allowable voltage offset for node i. For the number of wind farms, This represents the total theoretical power generation of wind power. For the first The actual dispatch of each wind farm has active power output; The calculation formula is based on branch conductance and voltage phase difference: in, branch road The conductivity on For nodes ,node The voltage phase difference between them for, for.

7. The active and reactive power coordination optimization method for a wind power-storage distribution network considering transient voltage stability according to claim 1, characterized in that: In step S4, constraints are set for the multi-objective optimization function, considering constraints on the reactive power output range of SVG, the reactive power output range of wind turbines, the number of capacitors switched on and off, node voltage constraints, branch apparent power constraints, node index constraints, and the transformer turns ratio constraints; simultaneously, the equality constraints satisfy the system power flow equality constraints: in, , They are nodes Injected active and reactive power; , They are nodes The active and reactive power consumed by the load.

8. The method for coordinated optimization of active and reactive power in a wind power-storage distribution network considering transient voltage stability as described in claim 1, characterized in that: In step S5, the formula for calculating the slime mold algorithm is: in, This represents the current iteration number. The current optimal individual position, and To randomly select the positions of two individuals, W is an adaptive weight. and For control parameters, where ∈[- , ], It decreases linearly from 1 to 0. It is a random number between [0, 1], and p is a control variable; The slime mold algorithm introduces an adaptive feedback factor and an arithmetic crossover operator to accelerate convergence: The mathematical model for the adaptive feedback factor is: in, As an adaptive feedback factor, This represents the current iteration number. The maximum number of iterations, As a regulating factor; The mathematical model for the arithmetic crossover operator is: in, This represents the current iteration number. and These represent the positions of the two offspring individuals generated by the crossover. For the current individual position, This represents the optimal position for an individual in the current population. The parameter is a random parameter that takes the value (0, 1).

9. A wind power-storage distribution network active and reactive power coordination optimization system considering transient voltage stability, characterized in that, include: The virtual inertia simulation module is used in the power control stage of a doubly-fed wind turbine to take the grid frequency deviation as the control input and adjust the active power of the wind turbine according to the linkage between the grid frequency deviation and the wind turbine rotor speed, providing a virtual inertial response and suppressing the initial drop in grid frequency. The inertia support capability quantification and control module is used to quantify the virtual inertia support capability of energy storage based on the response time window brought about by the virtual inertia response, combined with the system frequency security requirements and energy storage configuration capacity. A constant frequency control strategy is adopted, which uses a PI controller to track the system frequency signal in real time and dynamically adjust the energy storage output to support the grid frequency until steady state. The energy storage power withdrawal module is used to introduce a first-order inertial link frequency regulation state transfer control to achieve smooth and graded withdrawal of energy storage power after the grid frequency recovers to a steady state, and smoothly transfer the grid frequency regulation task to the traditional synchronous generator set, ensuring transient voltage stability and keeping the system frequency change rate within safe limits. The multi-objective function construction module is used to optimize the system after transient voltage stabilization, when the system encounters a small disturbance and the energy storage output has not reached its upper limit. It utilizes the remaining capacity of the energy storage and the reactive power compensation device to construct a multi-objective optimization function with the objectives of minimizing wind curtailment rate, minimizing grid loss and minimizing voltage deviation. The optimization execution module is used to introduce adaptive feedback factors and arithmetic crossover operators to optimize the slime mold algorithm, and to use the optimized slime mold algorithm to perform optimization processing on the multi-objective optimization function, output the configuration parameters of the optimal system, and perform active and reactive power coordination optimization of the distribution network.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program causes the computer to execute the active and reactive power coordination optimization method for wind power-storage distribution networks that considers transient voltage stability as described in any one of claims 1-8.

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