Power oscillation damping method of new energy power station, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to the field of new energy power stations, and discloses a power oscillation damping method of a new energy power station, electronic equipment and a storage medium. The method comprises the steps that when power oscillation of a power grid is recognized, the total damping power requirement for suppressing the power oscillation is determined; acquiring physical state parameters of a plurality of power generation devices in the power station; the multiple pieces of power generation equipment comprise at least one of power generation equipment in a wind turbine generator, power generation equipment in an energy storage system and power generation equipment in a photovoltaic system; calculating the loss weight of each power generation device in the plurality of power generation devices based on the physical state parameters of the plurality of power generation devices; calculating target damping power of each power generation device based on the total damping power demand and the loss weight of each power generation device by taking minimization of the total loss of the system as a target; and a control instruction is issued to each power generation device according to the target damping power. The problems of non-uniform equipment aging, short service life and high system operation and maintenance cost in the prior art are solved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of new energy power plants, and particularly to a power oscillation damping method, electronic equipment and storage medium for new energy power plants. Background Technology

[0002] In the field of grid-connected power generation from new energy sources, as the penetration rate of renewable energy such as wind and solar power continues to increase, the inertia and damping level of the power system are gradually decreasing, and the problem of power oscillation is becoming increasingly prominent. In order to maintain the stability of the power grid, it is usually necessary to utilize the ability of new energy equipment to quickly adjust active or reactive power in new energy power plants (such as wind farms, photovoltaic power plants, and energy storage power plants) to provide damping support.

[0003] In related technologies, the strategies for damping control in new energy power plants are often quite simplistic. For example, some solutions primarily determine whether to activate energy storage or wind turbines based on a comparison between the power demand and a preset threshold; or they simply switch damping control on or off based on the current operating conditions of the equipment (such as whether the wind turbine is in a constant speed range). However, this control approach based on fixed rules or single conditions often ignores the significant differences in "dynamic response costs" between different types of equipment (wind, solar, and storage) and between different individuals of the same type. During long-term operation, this control strategy may lead to some fast-responding but lifespan-sensitive equipment (such as energy storage units with poor battery health or wind turbines with high mechanical fatigue) being over-activated, while other healthy equipment fails to adequately share the load. This not only results in severely uneven aging of equipment within the plant, shortening the lifespan of critical equipment, but also significantly increases the overall maintenance costs and potential downtime risks of the power plant. Summary of the Invention

[0004] The purpose of this invention is to provide a power oscillation damping method, electronic device and storage medium for a new energy power plant, so as to solve the problems of uneven equipment aging, shortened service life and high system operation and maintenance costs caused by the lack of comprehensive consideration of the physical state and loss cost of the equipment in the control strategy in related technologies.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a power oscillation damping method for a new energy power plant, comprising: upon identifying power oscillations in the power grid, determining the total damping power requirement for suppressing the power oscillations; acquiring physical state parameters of multiple power generation devices in the power plant; the multiple power generation devices include at least one of power generation devices in a wind turbine, power generation devices in an energy storage system, and power generation devices in a photovoltaic system; calculating the loss weight of each power generation device based on the physical state parameters of the multiple power generation devices; calculating the target damping power of each power generation device based on the total damping power requirement and the loss weight of each power generation device, with the goal of minimizing the total system loss; and issuing control commands to each power generation device according to the target damping power.

[0006] Embodiments of the present invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the power oscillation damping method for a new energy power plant as described above.

[0007] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the power oscillation damping method for a new energy power plant as described above.

[0008] In this embodiment of the invention, by acquiring the physical state parameters of power generation equipment such as wind, solar, and energy storage in real time and quantifying them into "loss weights" that reflect the cost of regulation, the traditional rigid power allocation model is changed. The system no longer only focuses on "whether the power demand can be met," but further optimizes "who should meet the demand in the most economical / safest way." By constructing an optimization model with the goal of minimizing the total system loss, the algorithm automatically tends to call on equipment with low loss weights (i.e., good health and low regulation costs) to undertake the main damping tasks, while "protecting" equipment in a state of high fatigue or low health. This mechanism ensures that the grid damping demand is met while realizing the global collaborative management of the lifespan of resources within the power plant, effectively extending the service life of key equipment and reducing the long-term operation and maintenance burden.

[0009] Furthermore, the multiple power generation devices include wind turbines, the energy storage system, and the power generation devices within the photovoltaic system. The physical state parameters include the mechanical fatigue damage degree of the wind turbines, the battery health status of the energy storage system, and the available power margin of the photovoltaic system. Acquiring the physical state parameters of the multiple power generation devices in the power station includes: acquiring the mechanical fatigue damage degree at a first update frequency; acquiring the health status at a second update frequency; and acquiring the available power margin at a third update frequency. The first and second update frequencies are both lower than the third update frequency. Therefore, by using differentiated acquisition frequencies based on the physical characteristics of parameter changes (rapid fluctuations in photovoltaic power, slow changes in battery state of health (SOH), and slow accumulation of mechanical fatigue), the real-time response capability of the control system to rapidly changing resources such as photovoltaics is ensured, while redundant calculations of slowly changing state parameters (such as fatigue degree) are avoided. This significantly reduces the computational load of the control system and improves the efficiency of real-time control.

[0010] Furthermore, the plurality of power generation devices include at least the power generation devices within the energy storage system, and the physical state parameters include the battery health status of the energy storage system. The battery health status includes at least the battery cycle count and battery health level. If the battery health level is greater than a preset threshold, the loss weight is linearly positively correlated with the battery cycle count; if the battery health level is less than or equal to the preset threshold, the loss weight increases exponentially as the battery health level decreases. This achieves a non-linear "soft protection" mechanism. For healthy batteries, only conventional equalization based on the cycle count is performed; while for batteries with low health, their loss weight is increased exponentially, causing a sharp decrease in the probability of them being selected or the power allocated to them, thereby effectively preventing "sick" batteries from being accelerated to failure due to overcharging and over-discharging.

[0011] Furthermore, the plurality of power generation devices include power generation devices within the photovoltaic system, and the plurality of power generation devices also include the energy storage system, and / or at least one power generation device within the wind turbine generator set. The physical state parameters at least include the available power margin of the photovoltaic system. Before constructing the linear programming model, the method includes: if the available power margin of the photovoltaic system meets the total damping power demand, then the total damping power demand is directly allocated to the photovoltaic system; if the available power margin of the photovoltaic system does not meet the total damping power demand, then the power of the available power margin is used as the target damping power of the photovoltaic system and allocated to the photovoltaic system; the remaining power demand after deducting the available power margin from the total damping power demand is used as the new total damping power demand, and the linear programming model is constructed and solved for the remaining power generation devices to obtain the target damping power of the remaining power generation devices. Thus, the highest priority of photovoltaic resources is established. Since photovoltaic power generation is a static component, its regulation process does not involve mechanical wear and has extremely low marginal costs. Prioritizing the use of photovoltaic margin for damping support can minimize the number of operations required for wind turbines (mechanical wear) and energy storage (chemical life loss), thereby achieving ultimate optimization of operating costs.

[0012] Furthermore, the multiple power generation devices include multiple power generation devices of the same type; the step of calculating the target damping power of each power generation device with the objective of minimizing total system losses, based on the total damping power demand and the loss weight of each power generation device, includes: calculating the required total damping power of each type of power generation device based on the total damping power demand and the loss weight of each power generation device, with the objective of minimizing total system losses; and allocating damping power among the power generation devices of each type of power generation device based on the total damping power, to obtain the target damping power of each power generation device. Thus, a hierarchical allocation architecture is adopted, first performing macroscopic allocation among different energy types, and then performing microscopic balancing within the same type of equipment. This architecture is particularly suitable for large-scale new energy power plants, effectively reducing the dimensionality of the optimization problem, improving the solution speed, and simultaneously taking into account the differences between macroscopic strategies and microscopic individuals.

[0013] Furthermore, the process of allocating damping power to each power generation device within each type of power generation equipment based on the total damping power of the classification, to obtain the target damping power for each power generation device, includes: calculating the allocation ratio coefficient for each power generation device based on its loss weight; and allocating the total damping power of the classification to each power generation device within that type according to the allocation ratio coefficient. Thus, within the same type of equipment (e.g., among multiple wind turbines), task allocation is strictly performed according to the proportion of loss weights, ensuring balanced lifespan losses within the same group of equipment and avoiding the "weakest link" effect within the group. Attached Figure Description

[0014] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0015] Figure 1 This is a flowchart of a power oscillation damping method for a new energy power plant provided in this application; Figure 2 This is a flowchart of a power oscillation damping method for a new energy power plant, which includes photovoltaic priority logic, according to the present application. Figure 3 This is a flowchart of power allocation for multiple similar power generation devices in a power oscillation damping method for a new energy power station provided in this application; Figure 4 This is a structural diagram of an electronic device provided in this application. Detailed Implementation

[0016] In the field of grid-connected power generation from new energy sources, as the penetration rate of renewable energy such as wind and solar power continues to increase, the inertia and damping level of the power system are gradually decreasing, and the problem of power oscillation is becoming increasingly prominent. In order to maintain the stability of the power grid, it is usually necessary to utilize the ability of new energy equipment to quickly adjust active or reactive power in new energy power plants (such as wind farms, photovoltaic power plants, and energy storage power plants) to provide damping support.

[0017] In related technologies, the strategies for damping control in new energy power plants are usually quite simplistic. For example, some solutions mainly determine whether to activate energy storage or wind turbines based on a comparison between the power demand and a preset threshold; or they simply switch damping control on or off based on the current operating conditions of the equipment (such as whether the wind turbine is in a constant speed range). However, the control strategies in these technologies often lead to severely uneven aging of equipment within the plant, shortening the lifespan of critical equipment.

[0018] The inventors discovered that the problem arises because existing damping control logic typically makes decisions based solely on the single dimension of "whether power demand is met," lacking a quantitative assessment mechanism for the "dynamic response cost" when different types of power generation equipment participate in regulation. Specifically, existing technologies fail to map the physical state parameters of power generation equipment, such as the mechanical fatigue level of wind turbines and the electrochemical lifespan degradation of energy storage systems, into "costs" or "loss weights" for participating in power regulation. Under this "cost-blind" control mode, the control system cannot identify which equipment is currently in a "high-loss" or "sub-healthy" state, leading the system to continuously assign regulation tasks to equipment with fast response speeds but potentially already in a state of high fatigue (such as batteries with low state of harmonics (SOH) or wind turbines with high mechanical stress), ultimately accelerating the local aging or even damage of these devices.

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the various embodiments of the present invention to facilitate a better understanding of this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the various embodiments below is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0020] One embodiment of the present invention relates to a power oscillation damping method for a new energy power plant, which is typically applied to the station-level control layer of a new energy power plant. The specific implementing entity can be an energy management system, a power oscillation damping controller, or a cloud server or edge computing node integrated with coordinated control functions deployed on the power plant side. The embodiment of the present invention includes: when power oscillations in the power grid are detected, determining the total damping power requirement for suppressing the power oscillations; acquiring the physical state parameters of multiple power generation devices in the power plant; the multiple power generation devices include at least one of power generation devices in wind turbines, power generation devices in energy storage systems, and power generation devices in photovoltaic systems; calculating the loss weight of each power generation device based on the physical state parameters of the multiple power generation devices; calculating the target damping power of each power generation device based on the total damping power requirement and the loss weight of each power generation device, with the goal of minimizing the total system loss; and issuing control commands to each power generation device according to the target damping power. In this embodiment of the present invention, by acquiring the physical state parameters of wind, solar, and energy storage power generation devices in real time and quantifying them into "loss weights" reflecting the adjustment costs, the traditional rigid power allocation model is changed. The system no longer only focuses on "whether the power demand can be met," but further optimizes "who should meet the demand in the most economical / safest way." By constructing an optimization model aimed at minimizing total system losses, the algorithm automatically prioritizes devices with low loss weights (i.e., good health and low adjustment costs) to undertake the main damping tasks, while "protecting" devices in high fatigue or low health states. This mechanism ensures that the grid damping requirements are met while achieving global collaborative management of the lifespan of internal power plant resources, effectively extending the service life of critical equipment and reducing long-term operation and maintenance burdens.

[0021] The following is a detailed description of the implementation details of the power oscillation damping method for new energy power plants according to an embodiment of the present invention. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0022] The method disclosed in this application is applied to a control system for a new energy power plant. This system is logically connected to the power grid and multiple power generation devices within the plant. These devices include at least one of the following: power generation equipment within a wind turbine (such as a doubly-fed induction generator or a direct-drive wind turbine), power generation equipment within an energy storage system (such as a lithium-ion battery energy storage unit or a supercapacitor), and power generation equipment within a photovoltaic system (such as a photovoltaic inverter). The control system communicates with the local controllers of each power generation device via an internal communication network (such as a fiber optic ring network or industrial Ethernet) to acquire data and issue control commands.

[0023] like Figure 1 As shown, the method includes steps 110 to 150.

[0024] In step 110, when a power oscillation in the power grid is detected, the total damping power requirement for suppressing the power oscillation is determined.

[0025] Specifically, the control system collects key electrical quantities (such as voltage, current, and frequency) at the grid connection point in real time, and extracts low-frequency oscillation components using signal processing techniques such as Fast Fourier Transform (FFT), sliding window wavelet analysis, or the Prony algorithm. Based on the extracted oscillation amplitude and frequency, and in conjunction with grid dispatching procedures or local damping control strategies, the total damping power demand P required to suppress the oscillation at the current moment is calculated. damp .

[0026] In step 120, physical state parameters of multiple power generation devices in the power station are obtained; the multiple power generation devices include at least one of the power generation devices in the wind turbine, the power generation devices in the energy storage system, and the power generation devices in the photovoltaic system.

[0027] In a specific example, physical state parameters are defined as physical quantities that directly reflect the current "health status" or "adjustment cost" of equipment, and can serve as reference indicators for equipment wear and tear / maintenance costs. Specifically, for wind turbines, physical state parameters include, but are not limited to, mechanical fatigue damage degree (D). wind For energy storage systems, physical state parameters include, but are not limited to, state of health (SOH) and battery cycle count; for photovoltaic systems, physical state parameters include, but are not limited to, available power margin (Pg) under the current environment. pv,ava ).

[0028] In an optional embodiment, considering the differences in the time-varying characteristics of different physical parameters, the system employs a multi-rate sampling mechanism. Mechanical fatigue damage is obtained at a first update frequency; health status is obtained at a second update frequency; and available power margin is obtained at a third update frequency. Specifically, the available power margin of photovoltaic systems changes rapidly with meteorological conditions such as cloud cover, therefore it is obtained at a higher third update frequency (e.g., on the order of seconds). In contrast, the mechanical fatigue damage of wind turbines and the state of harmonics (SOH) of batteries are slowly varying parameters, therefore they are obtained at lower first and second update frequencies (e.g., on the order of minutes or hours), or through intermittent calculation.

[0029] In step 130, the loss weight of each of the multiple power generation devices is calculated based on the physical state parameters of the multiple power generation devices.

[0030] This step aims to map the physical state of heterogeneous devices to a normalized "loss weight" (or adjustment cost coefficient). The larger the loss weight, the higher the "physical cost" of the device participating in the adjustment.

[0031] In a specific example, for a wind turbine, the loss weight is 1 / ɑ wind , where α wind The physical meaning of is the health status of the wind turbine equipment, calculated using the following formula: , of which F wind F is a state index determined based on mechanical fatigue damage degree. The higher the fatigue degree, the higher the F value. wind The larger the value, the greater the loss weight 1 / ɑ wind The larger; k wind This is the normalized coefficient for the wind turbine. For energy storage systems, the loss weight is 1 / ɑ. bess , where α bess The physical meaning of is the health status of the energy storage system's equipment, calculated using the following formula: , of which F bess This is a state index determined based on the battery's SOH.

[0032] Specifically, to achieve "soft protection" for devices in sub-optimal health, in one optional embodiment, a piecewise nonlinear mapping relationship is adopted between the energy storage system's loss weight and battery health. Specifically, if the battery health (SOH) is greater than a preset threshold (e.g., 80%), the loss weight is linearly positively correlated with the battery cycle count to achieve normal lifespan balancing; if the battery health is less than or equal to the preset threshold, the loss weight increases exponentially as the battery health decreases. Through this nonlinear penalty mechanism, when the battery approaches the end of its lifespan, its loss weight will tend towards infinity, thus being naturally "shielded" in the optimization algorithm, preventing overcharging and over-discharging.

[0033] In one optional example, the aforementioned mechanical fatigue damage degree is positively correlated with the loss weight, and the mechanical fatigue damage degree is calculated cumulatively using the rainflow counting method. For the fatigue load of the wind turbine, online or offline calculations can be performed based on the rainflow counting method. A single calculation result remains valid for a period of time, thus significantly reducing the computational burden on the real-time controller.

[0034] In step 140, with the goal of minimizing the total system loss, the target damping power of each power generation device is calculated based on the total damping power demand and the loss weight of each power generation device.

[0035] In a specific example, a linear programming model is constructed, with the objective function set as minimizing the sum of the products of loss weights and target damping power of multiple power generation devices. Constraints include the algebraic sum of the target damping power of multiple power generation devices equaling the total damping power demand, and the target damping power of each power generation device being between its current minimum and maximum output. The linear programming model is then solved to obtain the target damping power of each power generation device. Specifically, this step transforms the power allocation problem into a mathematical optimization problem. The system constructs a linear programming model whose objective function is to minimize the sum of the products of "loss weights" and "allocated power" of all participating devices, i.e.: , where 1 / ɑ i P represents the loss weight of the i-th power generation device. i The power allocation for i power generation devices is defined, where i ranges from 1 to the maximum number of power generation devices. The constraints include: (1) Power balance constraint: the power allocation P for all devices... i The algebraic sum must equal the total damping power demand P. damp (2) Equipment capacity constraints: the allocated power P of each device i It must be between its current minimum output P min With maximum output P max Between. By solving this linear programming model, the system can output the optimal power allocation scheme that minimizes the total loss of the entire station (i.e., the comprehensive physical adjustment cost) under the current physical state.

[0036] Considering that photovoltaic (PV) power generation equipment is a static component with no mechanical wear and extremely low marginal loss, the strategy incorporates a "PV priority" logic. In an optional embodiment, multiple power generation devices include those within a PV system, and also include an energy storage system, and / or at least one power generation device within a wind turbine. The physical state parameters at least include the available power margin of the PV system. Before constructing the linear programming model, the method includes: if the available power margin of the PV system meets the total damping power demand, then the total damping power demand is directly allocated to the PV system; if the available power margin of the PV system does not meet the total damping power demand, then the power of the available power margin is used as the target damping power of the PV system and allocated to the PV system; the remaining power demand after deducting the available power margin from the total damping power demand is used as the new total damping power demand, and a linear programming model is constructed and solved for the remaining power generation devices to obtain the target damping power of the remaining power generation devices. The specific process is: before constructing the linear programming model, the available power margin P of the PV system is first determined. pv,ava Does the total damping power requirement meet the demand? If so, the demand is directly allocated to the photovoltaic system, and the remaining equipment remains inactive; if not, the photovoltaic system operates at full capacity (i.e., outputting all available margin), and the remaining power demand (P) is allocated to the photovoltaic system.remain =P damp -P pv,ava Using this as a new input, the aforementioned linear programming allocation based on loss weights is performed between wind turbines and energy storage. This hierarchical processing strategy effectively addresses scenarios with fluctuating photovoltaic output while ensuring economic efficiency.

[0037] To more intuitively illustrate the allocation logic of prioritizing photovoltaic power and coordinating wind and energy storage, please refer to [link to relevant documentation]. Figure 2 .like Figure 2 As shown, the system first performs the "identify frequency oscillation" step. Then, it enters the judgment phase, comparing the total damping power required to suppress the oscillation (A) with the required total damping power. damp ) and the available power margin of the current photovoltaic system (A pv,ava If the judgment result is "No" (i.e., A). damp pv,ava This indicates that photovoltaic power alone can cover the damping requirement, and the system directly skips the complex wind and energy storage modeling process, entering the "control command generation" stage and only scheduling the output of the photovoltaic system. If the judgment result is "yes" (i.e., A), then... damp ≥A pv,ava This indicates insufficient photovoltaic output, requiring the participation of wind turbines or energy storage. At this point, the system activates the "Wind-Storage Power Allocation (Mechanical Life) Modeling" module to acquire the "fatigue load" data of the wind turbine and the "cycle count" data of the energy storage system. Subsequently, the data flows into the "Power Allocation Calculation" module, which, combined with the "Mechanical Life Loss Model" and "Actual Operating Constraints" (such as power limiting), calculates the optimal power deficit that each wind turbine and energy storage should bear. Finally, based on the calculation results, "Control Command Generation" is executed.

[0038] In step 150, control commands are sent to each power generation device according to the target damping power.

[0039] The system converts the calculated target damping power of each device into specific control commands (such as active power setpoints) and sends them to each wind turbine converter, energy storage converter and photovoltaic inverter for execution via the station's communication network.

[0040] In an optional embodiment, the system introduces a closed-loop feedback regulation mechanism. If, during execution, grid power oscillations are detected as not being effectively suppressed (e.g., the oscillation amplitude decay rate does not meet the standard), it indicates that the current power support is insufficient or that the actual response of some devices is lagging. In this case, the system will dynamically adjust the loss weight parameters of the relevant devices in the next control cycle (e.g., temporarily reduce the weight penalty of high-response-speed devices) to forcibly improve the overall damping response capability of the system and ensure grid safety.

[0041] ​In another optional embodiment of step 140, for large-scale new energy power plants containing a large number of similar power generation devices, a hierarchical allocation strategy is adopted to reduce the dimensionality of optimization calculations and improve the solution speed. Multiple power generation devices refer to a system containing multiple similar power generation devices. Step 140, as... Figure 3 As shown, the process includes: Step 141, with the objective of minimizing total system losses, calculating the required total damping power for each type of power generation equipment based on the total damping power demand and the loss weight of each power generation equipment; Step 142, based on the total damping power, allocating damping power to each power generation equipment within each type of power generation equipment to obtain the target damping power for each power generation equipment. Specifically, this step includes the following two sub-steps: 1. Calculate the total damping power for each category. Instead of directly optimizing hundreds or thousands of individual units, the system optimizes at the "energy type" level. The system first treats wind turbines and energy storage systems as two aggregates, obtaining the comprehensive loss weights for the wind power cluster (e.g., a weighted average of the weights of all wind turbines within the cluster) and the comprehensive loss weights for the energy storage cluster. With the goal of minimizing the total system loss, based on the total damping power requirement and the comprehensive loss weights for each type, the system calculates the total power P that the wind power cluster should bear. wind_total The total power P that the energy storage cluster should bear bess_total .

[0042] 2. Perform refined allocation of power to similar equipment within the station. After determining the total damping power for a certain type (e.g., wind power cluster), the system further allocates this power to each generating unit within that type. Specifically, based on the loss weight of each generating unit, the allocation ratio coefficient for each unit is calculated; according to the allocation ratio coefficient, the total damping power is allocated to each generating unit within that type. The allocation logic employs a "weighted allocation method." The system obtains the loss weight of each generating unit within that type (e.g., the weight of the j-th wind turbine is 1 / α). wind,j ), calculate the allocation ratio coefficient λ for each power generation device. j Since loss weight represents "cost," the larger the weight of a device, the smaller its allocation ratio should be. The calculation formula can be expressed as: Finally, the allocated power of the j-th device is P. j =P wind_total ×λ j In this way, the system strictly distributes tasks according to the proportion of wear and tear weights, ensuring a balanced lifespan wear within the same group of equipment and avoiding the "weakest link" effect within the group.

[0043] In this embodiment of the invention, by acquiring the physical state parameters of power generation equipment such as wind, solar, and energy storage in real time and quantifying them into "loss weights" that reflect the cost of regulation, the traditional rigid power allocation model is changed. The system no longer only focuses on "whether the power demand can be met," but further optimizes "who should meet the demand in the most economical / safest way." By constructing an optimization model with the goal of minimizing the total system loss, the algorithm automatically tends to call on equipment with low loss weights (i.e., good health and low regulation costs) to undertake the main damping tasks, while "protecting" equipment in a state of high fatigue or low health. This mechanism ensures that the grid damping demand is met while realizing the global collaborative management of the lifespan of resources within the power plant, effectively extending the service life of key equipment and reducing the long-term operation and maintenance burden.

[0044] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists, A and B exist simultaneously, and B exists. In addition, the character " / " in this document generally indicates that the related objects before and after it have an "or" relationship.

[0045] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0046] The steps described above are for clarity only. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the protection scope of this invention.

[0047] Furthermore, the examples mentioned in the above embodiments can be freely combined, and any combination can be understood as an embodiment. The terms "embodiment" or "example" appearing in various locations in the specification do not necessarily refer to the same embodiment, nor are they independent or alternative embodiments mutually exclusive with other embodiments. Those skilled in the art will understand that the embodiments described herein can be combined with other embodiments.

[0048] Another embodiment of the present invention relates to an electronic device, such as Figure 4 As shown, it includes at least one processor 201; and a memory 202 communicatively connected to at least one processor 201; wherein the memory 202 stores instructions that can be executed by at least one processor 201, the instructions being executed by at least one processor 201 to enable at least one processor 201 to execute the power oscillation damping method of the new energy power plant as described above.

[0049] The memory 202 and processor 201 are connected via a bus, which may include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 201 and memory 202 together. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 201 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 201.

[0050] Processor 201 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 202 can be used to store data used by processor 201 during operation.

[0051] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the method embodiments described above.

[0052] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0053] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A power oscillation damping method for a new energy power plant, characterized in that, The method includes: When power oscillations in the power grid are detected, the total damping power requirement for suppressing the power oscillations is determined; Obtain physical state parameters of multiple power generation devices in the power station; the multiple power generation devices include at least one of the power generation devices in a wind turbine, the power generation devices in an energy storage system, and the power generation devices in a photovoltaic system. Based on the physical state parameters of the plurality of power generation devices, calculate the loss weight of each of the plurality of power generation devices; With the goal of minimizing the total system loss, the target damping power of each power generation device is calculated based on the total damping power requirement and the loss weight of each power generation device. Control commands are issued to each power generation device according to the target damping power.

2. The power oscillation damping method for new energy power plants according to claim 1, characterized in that, The plurality of power generation devices include wind turbines, the energy storage system, and the power generation devices within the photovoltaic system. The physical state parameters include the mechanical fatigue damage degree of the wind turbines, the battery health status of the energy storage system, and the available power margin of the photovoltaic system. The acquisition of physical state parameters of multiple power generation devices in the power station includes: The mechanical fatigue damage degree is obtained at a first update frequency; The health status is obtained at a second update frequency; The available power margin is obtained at a third update frequency; The first update frequency and the second update frequency are both lower than the third update frequency.

3. The power oscillation damping method for new energy power plants according to claim 1, characterized in that, The plurality of power generation devices include at least the power generation devices within the wind turbine generator set, and the physical state parameters include at least the mechanical fatigue damage degree of the wind turbine generator set. The mechanical fatigue damage degree is positively correlated with the loss weight, and the mechanical fatigue damage degree is calculated cumulatively by the rainflow counting method.

4. The power oscillation damping method for a new energy power plant according to claim 1, characterized in that, The plurality of power generation devices include at least the power generation devices within the energy storage system, and the physical state parameters include the battery health status of the energy storage system, wherein the battery health status includes at least the battery cycle count and battery health. Wherein, if the battery health is greater than a preset threshold, the loss weight is linearly positively correlated with the number of battery cycles; if the battery health is less than or equal to the preset threshold, the loss weight increases exponentially as the battery health decreases.

5. The power oscillation damping method for a new energy power plant according to claim 1, characterized in that, The objective of minimizing total system losses involves calculating the target damping power for each power generation device based on the total damping power demand and the loss weight of each device, including: A linear programming model is constructed, and the objective function is set as minimizing the sum of the products of the loss weights of the multiple power generation devices and the target damping power; The constraints are that the algebraic sum of the target damping power of the plurality of power generation devices equals the total damping power requirement, and that the target damping power of each power generation device is between its current minimum output and maximum output. Solve the linear programming model to obtain the target damping power of each power generation device.

6. The power oscillation damping method for a new energy power plant according to claim 5, characterized in that, The plurality of power generation devices include power generation devices within the photovoltaic system, and the plurality of power generation devices also include the energy storage system, and / or at least one power generation device within the wind turbine, wherein the physical state parameters include at least the available power margin of the photovoltaic system; Before constructing the linear programming model, the method includes: If the available power margin of the photovoltaic system meets the total damping power requirement, then the total damping power requirement is directly allocated to the photovoltaic system; If the available power margin of the photovoltaic system does not meet the total damping power requirement, then the power of the available power margin will be allocated to the photovoltaic system as the target damping power. The remaining power demand after deducting the available power margin from the total damping power demand is taken as the new total damping power demand. The linear programming model is then constructed and solved on the remaining power generation equipment to obtain the target damping power of the remaining power generation equipment.

7. The power oscillation damping method for a new energy power plant according to claim 1, characterized in that, The plurality of power generation devices includes a plurality of power generation devices of the same type; The objective of minimizing total system losses involves calculating the target damping power for each power generation device based on the total damping power demand and the loss weight of each device, including: With the goal of minimizing the total system loss, the total damping power required for each type of power generation equipment is calculated based on the total damping power demand and the loss weight of each power generation equipment. Based on the total damping power of the classification, the damping power is allocated to each power generation device in each type of power generation equipment to obtain the target damping power of each power generation device.

8. The power oscillation damping method for a new energy power plant according to claim 7, characterized in that, The process of allocating damping power to each power generation device within each type of power generation device based on the total damping power of the classification, to obtain the target damping power for each power generation device, includes: Based on the loss weight of each power generation device, calculate the allocation ratio coefficient of each power generation device; Based on the allocation ratio coefficient, the total damping power of the classification is allocated to each power generation device in that type.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the power oscillation damping method for a new energy power plant as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the power oscillation damping method for a new energy power plant as described in any one of claims 1 to 8.

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