A wind farm unit reconstruction and energy storage configuration collaborative planning method and system

Abstract

The application discloses a wind farm unit reconstruction and energy storage configuration collaborative planning method and system, and relates to the technical field of new energy planning. The method comprises the following steps: obtaining equipment parameters and cost parameters of wind farm unit reconstruction and energy storage configuration, and respectively constructing a technical model and a cost model of the unit reconstruction and the energy storage configuration based on an operation performance optimization target; a wind farm grid connection examination and auxiliary service income calculation model embedded with time sequence operation simulation is constructed, and a quantitative mapping of technical features to income of the wind farm grid connection examination and auxiliary service income calculation model is utilized; based on the quantitative mapping relationship of the technical features to the income, a double-layer optimization framework of flexibility improvement considering investment cost and income is constructed according to the technical model and the cost model of the unit reconstruction and the energy storage configuration. The application can systematically improve the flexibility level and market competitiveness of the wind farm while ensuring safe and stable operation of the wind farm.

Description

Technical Field

[0001] This invention relates to the field of new energy planning technology, and in particular to a method and system for coordinated planning of wind farm unit retrofitting and energy storage configuration. Background Technology

[0002] The descriptions in this embodiment are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Wind power generation, due to its favorable conditions for large-scale development and commercial prospects, has become one of the most widely accepted, fastest-growing, and most technologically mature renewable energy generation technologies. As the proportion of new energy sources such as wind and solar power continues to increase, the volatility of wind and solar power sources is rapidly increasing the demand for power system regulation capabilities. Relying solely on traditional thermal power units to provide ancillary services for the entire system faces significant challenges both technically and economically. To address this challenge of fluctuating output from new energy units, the power system requires wind farms to enhance their flexibility, possessing the ability to participate in power system frequency regulation, peak shaving, and reserve functions. This ensures that wind power can control its own power fluctuations and adjust its output according to changes in power system supply and demand, thus participating in grid interaction.

[0004] There are several ways to improve the flexibility of wind farms, with typical solutions including turbine retrofitting and energy storage configuration. Turbine retrofitting can improve the response speed and regulation capabilities of wind turbines. For example, it can improve turbine power curve control methods, optimize start-up and shutdown strategies, and enhance control levels such as pitch and frequency conversion speed regulation. This allows wind turbines to quickly track load and system frequency changes, increasing operational flexibility. On the other hand, configuring energy storage for wind farms can decouple the original output of the wind farm from the grid-connected power through the synergistic effect of energy storage and wind power generation systems, enabling flexible adjustment of the wind-storage integrated system in terms of time and power.

[0005] Determining the appropriate technological approaches and methods for scaling up flexibility improvements is crucial for wind farm retrofitting planning. Currently, the main approach is the power generation variation method, considering either individual turbine retrofitting or energy storage configuration. However, both approaches have their own characteristics and limitations: retrofitting turbines requires no new equipment and is relatively simple, but it necessitates load shedding control to provide long-term regulation capabilities and participate in primary and secondary frequency regulation, potentially leading to wind curtailment and underutilization of the wind farm's power generation capacity. Configuring energy storage significantly improves wind farm flexibility, but energy storage equipment is expensive, and solely focusing on energy storage faces high investment costs and uncertain cost recovery. Furthermore, with the advancement of the electricity market, the benefits of increased flexibility are closely related to the electricity market mechanism and wind farm operation strategies. Wind farm revenue calculations must not only consider electricity sales revenue but also its contributions and penalties in ancillary services such as frequency regulation and grid connection performance evaluations.

[0006] However, current turbine retrofitting and energy storage configuration schemes cannot fully balance their investment costs, operational risks, and economic benefits. Especially against the backdrop of high wind power integration and increasing demand from the power system for peak shaving, frequency regulation, and backup services, existing turbine retrofitting and energy storage configuration schemes cannot establish an effective mapping relationship between flexibility enhancement measures and wind farm operating benefits, and lack scientific evaluation of the techno-economic characteristics of different flexibility enhancement methods. Summary of the Invention

[0007] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for coordinated planning of wind farm unit retrofitting and energy storage configuration, which can systematically improve the flexibility and market competitiveness of wind farms while ensuring their safe and stable operation.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: The first aspect of this invention provides a method for coordinated planning of wind farm turbine retrofitting and energy storage configuration, comprising the following steps: Obtain the equipment and cost parameters for wind farm turbine retrofitting and energy storage configuration, and construct technical and cost models for turbine retrofitting and energy storage configuration based on the operational performance optimization objectives. Construct a wind farm grid connection assessment and ancillary service revenue calculation model with embedded time-series operation simulation, and use the technical characteristics of the wind farm grid connection assessment and ancillary service revenue calculation model to quantitatively map revenue; Based on the quantitative mapping relationship between technical characteristics and benefits, a two-layer optimization framework is constructed according to the technical and cost models of unit retrofitting and energy storage configuration to improve flexibility while taking into account investment costs and benefits. The upper layer optimizes the combination strategy of unit retrofitting and energy storage configuration, and the lower layer optimizes the combination strategy of unit retrofitting and energy storage configuration determined by the upper layer by conducting operation simulation and benefit evaluation, so as to obtain the optimal combination strategy of unit retrofitting and energy storage configuration.

[0009] Furthermore, the equipment parameters for unit retrofitting include the normalized MPPT curve of the wind turbine, the cost parameters for unit retrofitting include the fixed investment and unit capacity cost of unit retrofitting, the equipment parameters for energy storage configuration include the maximum charge / discharge rate and charge / discharge efficiency, and the cost parameters for energy storage configuration include the unit power investment and unit power investment of the energy storage system.

[0010] Furthermore, based on time-series operation simulation methods, wind farm power generation output time-series data is generated, and the grid connection assessment cost of wind farm is calculated by considering volatility assessment, primary frequency regulation assessment, and secondary frequency regulation assessment. The revenue of ancillary services is calculated by further considering primary frequency regulation ancillary services and secondary frequency regulation ancillary services, and a wind farm grid connection assessment and ancillary service revenue calculation model is constructed.

[0011] Furthermore, based on time-series operation simulation methods, wind farm power generation output time-series data is generated. Considering volatility assessment, primary frequency regulation assessment, and secondary frequency regulation assessment, the grid connection assessment cost of the wind farm is calculated. Further considering primary and secondary frequency regulation ancillary services, the ancillary service revenue is calculated. The specific steps for constructing a wind farm grid connection assessment and ancillary service revenue calculation model are as follows: The baseline and actual power generation curves of the wind farm were obtained by using time-series operation simulation. Calculate electricity sales revenue based on the actual power generation curve and electricity price curve of the wind farm; Calculate volatility assessment fees, secondary frequency regulation revenue and allocation fees, and primary frequency regulation assessment fees and return fees; The total benefit is obtained by summing up all types of benefits and performance evaluations.

[0012] Furthermore, in the two-layer optimization framework, the upper layer is the planning layer and the lower layer is the operation layer. The upper planning layer considers site constraints to form unit modification and energy storage configuration schemes, and transmits them to the lower operation layer. The lower operation layer conducts time-series operation simulations under the planning scheme given by the upper planning layer, quantifies the power generation revenue and interaction revenue under the action of annual wind resource time-series curves and grid control instructions, and feeds the comprehensive revenue back to the upper layer. The upper layer adjusts the investment strategy based on the feedback data, and transmits the updated scheme again for operation verification, forming a closed-loop optimization.

[0013] Furthermore, based on the quantitative mapping relationship between technical characteristics and returns, the specific steps for constructing a two-tiered optimization framework that balances investment costs and returns while enhancing flexibility, according to the technical and cost models of unit retrofitting and energy storage configuration, are as follows: The optimization objective is to maximize the total return on investment and operation of the wind-storage integrated system, and all constraints are combined to form an upper-level planning optimization model. With the objective function of maximizing the overall benefits of the wind-storage integrated system, all constraints are combined to form an optimization model for the lower-level operation layer. A genetic algorithm is used to solve the two-level optimization framework.

[0014] A second aspect of the present invention provides a collaborative planning system for wind farm turbine retrofitting and energy storage configuration, comprising: The data acquisition module is configured to acquire equipment and cost parameters for wind farm turbine retrofitting and energy storage configuration, and to construct technical and cost models for turbine retrofitting and energy storage configuration based on operational performance optimization objectives. The revenue calculation module is configured to build a wind farm grid connection assessment and ancillary service revenue calculation model with embedded time-series operation simulation, and to use the technical characteristics of the wind farm grid connection assessment and ancillary service revenue calculation model to the quantitative mapping of revenue. The collaborative planning module is configured to quantify the mapping relationship from technical characteristics to benefits. Based on the technical and cost models of unit retrofitting and energy storage configuration, it constructs a two-layer optimization framework that balances investment costs and benefits to improve flexibility. The upper layer optimizes the combination strategy of unit retrofitting and energy storage configuration, while the lower layer optimizes the combination strategy of unit retrofitting and energy storage configuration determined by the upper layer by conducting operation simulation and benefit evaluation, thereby obtaining the optimal combination strategy of unit retrofitting and energy storage configuration.

[0015] A third aspect of the present invention provides a computer-readable storage medium storing a computer program adapted for loading by a processor and executing the steps in the wind farm turbine retrofitting and energy storage configuration collaborative planning method as described in the first aspect of the present invention.

[0016] A fourth aspect of the present invention provides a computer device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the wind farm turbine retrofitting and energy storage configuration collaborative planning method as described in the first aspect of the present invention.

[0017] A fifth aspect of the present invention provides a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the wind farm turbine retrofitting and energy storage configuration collaborative planning method as described in the first aspect of the present invention.

[0018] The above one or more technical solutions have the following beneficial effects: This invention discloses a collaborative planning method and system for wind farm turbine retrofitting and energy storage configuration. First, it analyzes the technical and economic characteristics of typical wind farm flexibility enhancement methods and constructs a corresponding technical cost model. Then, it builds a flexibility enhancement benefit calculation model with embedded time-series operation simulation, achieving a quantitative mapping from technical characteristics to economic benefits based on the operation simulation. Finally, it proposes a two-layer optimization framework that considers both investment costs and operational benefits of flexibility enhancement, and designs an efficient solution method based on a genetic algorithm to obtain the optimal retrofitting and configuration scheme, thereby optimizing the level and method of wind farm flexibility enhancement and enhancing its ability to participate in ancillary services such as peak shaving, frequency regulation, and reserve. This invention can comprehensively consider the investment and benefits of flexibility enhancement schemes, forming a flexibility enhancement scheme oriented towards maximizing overall benefits. Simultaneously, based on the time-series operation simulation module, it can also finely evaluate the composition and influencing factors of flexibility enhancement benefits, achieving comprehensive consideration of the randomness of wind power output, wind turbine and energy storage operation constraints, and wind farm management regulations, and further analyze the sensitivity of flexibility enhancement benefits to various influencing factors.

[0019] This invention aims to address the challenges of selecting the best wind farm flexibility enhancement schemes, the lack of consideration for grid ancillary services, and inadequate investment return assessments in existing methods. The method constructs a techno-economic model of typical wind turbine retrofitting and energy storage configuration, calculates the benefits of flexibility enhancement based on operational simulations, and further establishes a two-tiered optimization framework that balances investment costs and returns. This achieves synergistic optimization of wind farm retrofitting and energy storage configuration, thereby enhancing the overall regulation capacity and economic value of wind farms and strengthening their participation in ancillary services such as power system peak shaving, frequency regulation, and reserve.

[0020] This invention is applicable to the coordinated planning of wind farm turbine retrofitting and energy storage configuration under the background of high wind power integration. It mainly includes three parts: the construction of a technical cost model for typical wind farm flexibility improvement methods; a grid connection assessment and revenue calculation method based on operational simulation; and the optimization of flexibility improvement schemes that balance investment costs and returns. The first part first investigates the relevant equipment and cost parameters for turbine retrofitting and energy storage configuration. Then, it establishes technical and cost models for both types of methods, comprehensively characterizing their operational performance improvement effects, investment costs, and operational characteristics. The second part uses time-series operational simulation to generate time-series data of wind farm power output. It considers volatility assessment, primary frequency regulation assessment, and secondary frequency regulation assessment to calculate the wind farm grid connection assessment cost. Furthermore, it considers primary and secondary frequency regulation ancillary services to calculate ancillary service revenue, constructing a wind farm grid connection assessment and ancillary service revenue calculation model. The third part constructs a two-layer optimization framework for a flexibility enhancement scheme that takes into account both investment costs and returns. The upper layer optimizes the combination strategy of unit modification and energy storage configuration, while the lower layer optimizes the operation simulation and return assessment. A genetic algorithm is used to achieve efficient solution, thereby obtaining the optimal wind farm flexibility enhancement scheme.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

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

[0023] Figure 1 This is a flowchart of the collaborative planning method for wind farm turbine retrofitting and energy storage configuration in Embodiment 1 of the present invention; Figure 2 This is a flowchart illustrating the construction of a technical cost model for improving the flexibility of a typical wind farm in Embodiment 1 of the present invention. Figure 3 The flowchart of the flexibility improvement benefit calculation model for constructing an embedded time-series operation simulation in Embodiment 1 of the present invention is shown below; Figure 4 This is an optimized flowchart of the flexibility enhancement scheme that balances investment costs and returns in Embodiment 1 of the present invention; Figure 5 This is a scanned image of the wind-storage joint optimization scheme in Embodiment 1 of the present invention. Detailed Implementation

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0026] With the transformation of the global energy structure and the urgent need to address climate change, wind energy, as a clean and renewable energy source, has achieved rapid and large-scale development globally. However, the randomness and volatility of wind power pose a severe challenge to the stable operation and regulation capabilities of traditional power systems. Currently, wind farms typically operate in maximum power point tracking (MPPT) mode, meaning they operate at full capacity without participating in grid regulation such as peak shaving and frequency regulation. This is partly because wind farms may lack the rapid adjustment capabilities to track grid regulation commands, and partly because frequent adjustments by wind farms may damage equipment safety and result in the loss of some generating capacity, thus affecting the economic viability of wind farm operation. Therefore, large-scale wind power grid connection reduces grid inertia and reserve capacity, affects system frequency and voltage stability, and increases the demand for ancillary services such as peak shaving and frequency regulation. To adapt to the new power system with a high proportion of wind power penetration, it is urgent to improve the flexibility of wind farms themselves, transforming them from simple power generation units into controllable resources capable of proactively responding to grid demands and providing ancillary services.

[0027] The main technical means to improve the flexibility of wind farms can be divided into two categories: turbine retrofitting and energy storage configuration. Turbine retrofitting optimizes the wind turbine control system, using generator speed or pitch angle control to quickly adjust active power generation to meet grid regulation needs. The advantage of this approach is relatively low investment, as it mainly involves software or control strategy upgrades rather than large-scale hardware replacements. However, to provide continuous regulation capabilities, a portion of turbine capacity usually needs to be reserved. This means that at certain times, the turbines cannot operate at full power, sacrificing some power generation, which directly impacts the economic benefits of the wind farm. The second approach involves configuring energy storage systems to build a wind power-energy storage integrated system. In this model, flexibility is mainly provided through the rapid charging and discharging of energy storage devices. The energy storage system charges when wind power output is excessive and discharges when the grid needs it, thus achieving rapid and precise regulation of the total power of the wind-storage system. The advantage of this approach lies in its strong regulation capabilities and response speed, enabling the provision of high-quality ancillary services. However, the energy storage devices themselves require significant additional investment, and their economic value is highly dependent on the development of grid management models and market mechanisms. Therefore, how to organically combine the retrofitting of wind turbine units with the configuration of energy storage systems, and maximize the investment benefits of improving the flexibility of wind farms through systematic optimization methods, has become a key issue that urgently needs to be addressed.

[0028] Example 1: To address the aforementioned issues, Embodiment 1 of this invention provides a collaborative planning method for wind farm turbine retrofitting and energy storage configuration. This method constructs a model of wind farm flexibility enhancement technologies, designs grid connection assessment and revenue calculation methods, and further builds a two-layer optimization framework that balances investment costs and returns. A genetic algorithm is used for efficient solution, thereby obtaining the optimal wind farm flexibility enhancement scheme. First, technical and cost models are established for both turbine retrofitting and energy storage configuration, comprehensively characterizing their operational performance improvement effects, investment costs, and operational characteristics. Then, operational simulation is used to evaluate the cost performance of different flexibility enhancement schemes in power system grid connection assessments and their revenue levels in ancillary services such as frequency regulation. Finally, a two-layer optimization model balancing investment costs and operational returns is constructed. The upper planning layer considers site constraints to formulate turbine retrofitting and energy storage configuration schemes, which are then passed to the lower operation layer. The lower operation layer performs time-series operational simulations under a given planning scheme, quantifying the power generation revenue and interactive revenue under the annual wind resource time-series curve and grid control commands, and feeding back the comprehensive revenue to the upper layer. The upper level adjusts investment strategies based on feedback data (such as optimizing the reserved capacity ratio or energy storage configuration), and then transmits the updated plan for operational verification, forming a closed-loop optimization, and finally outputting a wind and storage collaborative decision-making scheme that balances improved flexibility with minimized life-cycle costs.

[0029] like Figure 1 As shown, the specific steps include: Step 1: Obtain the equipment and cost parameters for wind farm turbine retrofitting and energy storage configuration, and construct technical and cost models for turbine retrofitting and energy storage configuration based on the operational performance optimization objectives.

[0030] In one specific implementation, this embodiment constructs a technical cost model for typical wind farm flexibility enhancement methods, providing a foundation for subsequent comprehensive benefit calculations and equipment operation constraints. First, it investigates the relevant equipment and cost parameters for turbine retrofitting and energy storage configuration. Then, it establishes separate technical models for turbine retrofitting, cost models for turbine retrofitting, technical models for energy storage configuration, and cost models for energy storage configuration, comprehensively characterizing their performance improvement effects, investment costs, and operational characteristics. Specifically, the equipment parameters for turbine retrofitting include the normalized MPPT curve of the wind turbine. The cost parameters for turbine retrofitting include the fixed investment in turbine retrofitting and the unit capacity cost of turbine retrofitting. The equipment parameters for energy storage configuration include the maximum charge / discharge rate and charge / discharge efficiency. The cost parameters for energy storage configuration include the unit energy investment and unit power investment of the energy storage system.

[0031] like Figure 2 As shown, the specific steps are as follows: Step 1.1: Construct a technical cost model for unit retrofitting.

[0032] Specifically, the unit retrofit technology cost model includes a unit retrofit technology model and a unit retrofit cost model. The unit retrofit technology model reflects the range of regulation capabilities that the wind farm possesses after retrofitting through active load shedding. The unit retrofit cost model reflects the investment in unit retrofitting.

[0033] Step 1.1.1: Unit Retrofit Technology Model.

[0034] The survey focused on key operating parameters of the equipment, primarily the normalized MPPT curve of the wind turbine. .

[0035] Establish power variation range and unit retrofit capacity Relationship between them: (1).

[0036] In the formula, For wind turbines The change in power at any given time is considered an upward adjustment if it is greater than zero, and a downward adjustment if it is less than zero. The active load shedding rate reflects the level of deviation of the wind farm's power generation baseline from the MPPT curve, and can be set through the control system.

[0037] Step 1.1.2: Unit modification cost model.

[0038] The survey focused on the cost parameters for unit retrofitting, primarily the fixed investment in unit retrofitting. (Corresponding to the cost of upgrading the energy management system software at the power station), and the unit capacity cost of unit modification. (Cost of upgrading the control software and supporting control hardware for a single wind turbine); Establish unit renovation investment and unit renovation capacity Relationship between them: (2).

[0039] In the formula, Investment costs for unit modification.

[0040] Step 1.2: Construct a cost model for energy storage configuration technologies.

[0041] Specifically, the energy storage configuration technology cost model includes an energy storage configuration technology model and an energy storage configuration cost model. The energy storage technology model reflects the range of energy storage power. The energy storage configuration cost model reflects the investment corresponding to configuring energy storage, and is generally the sum of the portion proportional to capacity and the portion proportional to power.

[0042] Step 1.2.1: Energy storage configuration technology model.

[0043] The key operating parameters of energy storage were investigated, primarily the maximum charge / discharge rate. and charge / discharge efficiency .

[0044] Establish the energy storage power range and energy state in relation to the configured energy storage capacity. and rated power Relationship between them: (3).

[0045] In the formula, , for The energy storage power (positive value indicates charging, negative value indicates discharging) and stored energy at any given time. For the initial energy of energy storage, This refers to the scheduling control step size. When energy storage devices participate in peak shaving or frequency regulation in response to grid regulation commands during operation, their energy storage power must meet the above constraints.

[0046] Step 1.2.2: Energy storage configuration cost model.

[0047] The survey focused on energy storage cost parameters, primarily the investment per unit of electricity in energy storage systems. and investment per unit power ; Establish energy storage investment and configured energy storage capacity and rated power Relationship between them: (4).

[0048] In the formula, Investment costs for energy storage configuration.

[0049] Step 2: Construct a wind farm grid connection assessment and ancillary service revenue calculation model with embedded time-series operation simulation, and use the technical characteristics of the wind farm grid connection assessment and ancillary service revenue calculation model to quantitatively map revenue.

[0050] In one specific implementation, under a power market environment, wind farm revenue includes three aspects: electricity sales revenue, grid assessment and rebates, and grid ancillary service revenue and allocation. Considering the complexity and time-varying nature of the assessment process, this embodiment uses time-series simulation to generate time-series data of wind farm power output. It then considers volatility assessment, primary frequency regulation assessment, and secondary frequency regulation assessment to calculate the wind farm grid connection assessment cost. Furthermore, it considers primary and secondary frequency regulation ancillary services to calculate ancillary service revenue, constructing a wind farm grid connection assessment and ancillary service revenue calculation model. The specific steps are as follows: Figure 3 As shown.

[0051] Step 2.1: Use time-series simulation to obtain the baseline and actual power generation curve of the wind farm.

[0052] Step 2.1.1: Establish a baseline for the power generation capacity of wind farms that maximize electricity sales revenue.

[0053] Specifically, this embodiment simulates the long-term operation of a wind farm, aiming to maximize electricity sales revenue while reserving a certain adjustment margin. It optimizes the baseline power output of the wind turbine generators, the baseline power output of the energy storage system, and the baseline power output of the entire wind farm (i.e., the sum of the wind turbine generator power output and the energy storage power output). Based on this, it responds to ancillary service adjustment commands such as primary and secondary frequency regulation of the power grid. The power output of the command response, superimposed on the power baseline, is the actual power output of the wind farm. The long-term operation simulation includes two steps: constructing an operation optimization model and solving the model based on a mature solver.

[0054] Step 2.1.1.1: Constructing an operation optimization model that maximizes electricity sales revenue.

[0055] Determine the simulation step size for optimization. It can adopt the same time scale as the power system dispatch, such as 1 hour or 1 / 4 hour; Considering that the power generation of the wind turbine after modification does not exceed the upper limit of active load shedding, construct the output constraint of the wind turbine: (5).

[0056] In the formula, This serves as the baseline for wind turbine power generation.

[0057] Considering the operating characteristics of energy storage, the energy storage power baseline constraint is constructed using equation (3): (6).

[0058] In the formula, As a baseline for energy storage power; Considering grid connection constraints for wind farms, the total grid-connected power of the wind farm shall not exceed the grid connection line capacity and the grid absorption capacity limit: (7).

[0059] In the formula, This serves as the baseline for wind farm power generation. For grid connection capacity, This represents the maximum power that the power grid is allowed to connect to.

[0060] It should be noted that the positive and negative signs of wind farm power generation and energy storage power are related. Charging is a positive value and discharging is a negative value. During charging, the energy storage equipment (load) can be considered, while power is only supplied to the grid during discharging. Therefore, when calculating the power generation of a wind farm, the energy storage power needs to be negative.

[0061] Based on historical electricity price generation, assess annual electricity price curve If there are no significant changes in the electricity pricing policy, the previous year's annual electricity market day-ahead price curve can be used.

[0062] Using the maximization of annual electricity sales revenue as the objective function, an optimization model is constructed by simultaneously solving all constraints: (8).

[0063] In the formula, For the annual electricity sales revenue of the wind farm, For step-size based The total number of time periods within the year, This indicates a constraint.

[0064] Step 2.1.1.2: Solving the optimized model based on a mature solver.

[0065] The optimization model of the above formula (8) is programmed into a function model and solved using a mature commercial solver (such as CPLEX or Gurobi) to obtain the baseline power generation of the wind farm. Wind turbine output baseline and energy storage power baseline .

[0066] Step 2.1.2: Generate the actual power generation curve of the wind farm that maximizes interactive benefits.

[0067] Specifically, this embodiment simulates the operation of a wind farm on a short timescale. Based on the wind farm's power baseline, it simulates the wind farm's participation in AGC (Automatic Generation Control) ancillary services. The goal is to maximize the benefits of AGC interaction, and the interaction process is optimized using power limits adjusted based on the power baseline as constraints. This ultimately yields the actual power generation curve of the wind farm. The short-timescale operation simulation includes four steps: power baseline interpolation for grid interaction, formation of external conditions for AGC interaction, construction of the interaction optimization model, and model solving based on a mature solver.

[0068] Step 2.1.2.1: Power baseline interpolation for grid interaction.

[0069] Optimize AGC frequency modulation interaction to determine the simulation step size for interaction optimization. The simulation step size is usually selected according to the AGC instruction cycle, such as 1 minute or less.

[0070] Baseline of wind turbine output and energy storage power baseline Interpolation is performed to obtain the simulation step size with a short time scale. Lower power curve.

[0071] Step 2.1.2.2: Formation of external conditions for AGC interaction.

[0072] The external conditions for AGC interaction include three parts: the AGC regulation instructions given by the power grid, the AGC ancillary service price, and the AGC ancillary service regulation performance indicators. The specific rules for their formation are as follows: Based on the historical AGC (Automatic Gauge Control) regulation instructions from the wind farm, the AGC regulation instructions issued by the power grid to the wind farm in the assessment year are generated. If the power system structure has not changed significantly, historical data of AGC regulation commands received by wind farms in the previous year can be used. Based on the historical AGC (Automatic Gauge Control) frequency regulation ancillary service clearing price formation assessment annual AGC frequency regulation ancillary service price curve of the power system. If the power system structure has not changed significantly, the clearing price of AGC frequency regulation ancillary services published by the power trading institution in the previous year can be used.

[0073] Assuming the modified wind turbine and energy storage power regulation speed and accuracy are good enough, then the performance indicators... The optimal value can be used; if performance degradation needs to be considered, the adjustment performance index can be appropriately reduced.

[0074] Step 2.1.2.3: Constructing an AGC adjustment and optimization model that maximizes interactive benefits.

[0075] Considering the limitations of wind turbine output range, construct wind turbine power constraints: (9).

[0076] In the formula, The change in wind turbine output in response to AGC commands; Considering the operating characteristics of energy storage, the energy storage power constraint is constructed using equation (3): (10).

[0077] In the formula, The change in energy storage power in response to AGC commands.

[0078] Considering the wind farm's response to AGC regulation commands, the constraints governing the relationship between the actual power generation of the wind farm, the change in wind farm power generation, the baseline of wind farm power generation, and the power changes of wind power and energy storage are as follows: (11).

[0079] In the formula, This represents the actual power generation of the wind farm. This represents the change in power generation at the wind farm.

[0080] Using the maximization of interactive benefits as the objective function, we construct an optimization model by simultaneously applying all constraints: (12).

[0081] In the formula, For annual AGC ancillary service revenue of wind farms; For step-size based The total number of time periods within the year.

[0082] Step 2.1.2.4: Model solving based on mature solvers. Based on the duration of AGC adjustment commands, the entire year is divided into multiple AGC interaction periods. In this embodiment, one step size of a long-term simulation is selected as one AGC interaction period, and the entire year includes... T One interactive session; By taking the AGC adjustment commands, related power, energy curves, and external conditions during the first AGC interaction period, and programming them into a function model corresponding to the above model, and solving it using mature commercial solvers (such as CPLEX and Gurobi), the actual power generation of the wind farm during this AGC interaction period can be obtained. Changes in wind farm power generation Changes in wind turbine output and changes in energy storage power ; Check if all AGC interaction periods have been traversed; if so, then check the actual power generation of the wind farms within all time periods. Changes in wind farm power generation Changes in wind turbine output and changes in energy storage power The data is then spliced ​​together to form the corresponding annual curves. Conversely, the AGC adjustment commands, related power, energy curves, and external conditions within a given AGC interaction period are taken and programmed into a function model based on the above model. This model is then solved using a mature commercial solver (such as CPLEX or Gurobi) until all AGC interaction periods have been traversed.

[0083] In this embodiment, the quantization mapping process refers to the process of calculating the assessment and benefits from the actual power of the wind farm, namely the assessment and benefit calculation process in steps 2.2-2.3, the specific steps of which are as follows: Step 2.2: Calculate the electricity sales revenue based on the actual power generation curve and electricity price curve of the wind farm.

[0084] Step 2.2.1: Calculate the revenue from electricity sales and the volatility assessment fee.

[0085] Step 2.2.1.1: Forming external conditions for revenue calculation.

[0086] Specifically, the monthly average electricity price over the past year is formed based on historical electricity prices. If there are no significant changes in the electricity pricing policy, the average monthly electricity price corresponding to the electricity market in the previous year may be used.

[0087] The monthly unit volatility assessment electricity cost is estimated based on the historical volatility assessment costs of the power system. If the power system structure has not changed significantly, the unit volatility assessment electricity fee for the corresponding month published by the power trading institution in the previous year can be used.

[0088] Step 2.2.1.2: Calculate the revenue from electricity sales.

[0089] Specifically, based on the actual power generation of the wind farm For the first The monthly power generation of the wind farm is calculated as follows: (13).

[0090] In the formula, For the first Monthly power generation for one month; For step-size based The total number of time periods within a month.

[0091] For the For each month, substitute the monthly power generation and average monthly electricity price into the following formula to calculate the monthly electricity sales revenue: (14).

[0092] In the formula, For the first Monthly revenue from electricity sales.

[0093] By iterating through the 12 months of the year, the monthly electricity sales revenue is calculated according to formula (14), and the sum of these yields the annual electricity sales revenue: (15).

[0094] In the formula, This refers to the annual revenue from electricity sales.

[0095] Step 2.2.1.3: Calculate volatility assessment fees.

[0096] Based on the installed capacity of the wind farm Calculate the maximum limits for volatility in 1-minute and 10-minute timeframes: (16).

[0097] In the formula, and The power variation limits are set for 1 minute and 10 minutes, respectively.

[0098] Based on the actual power generation of wind farms in a given month Calculate the monthly wind farm volatility at each time point using the following formula: (17).

[0099] In the formula, and These represent the wind farm volatility over 1 minute and 10 minutes, respectively.

[0100] Considering that the monthly cumulative volatility assessment limit shall not exceed 1% of the monthly power generation, for the first Each month, the volatility assessment electricity volume and assessment fee are calculated based on the wind farm volatility and volatility ceiling. (18).

[0101] In the formula, and The electricity consumption is assessed based on volatility over 1 minute and 10 minutes, respectively. For the first Monthly volatility assessment of electricity consumption; For the first Volatility assessment fee for one month.

[0102] By iterating through the 12 months of the year, the volatility assessment fee for each month is calculated according to formula (18), and the sum of these amounts yields the annual volatility assessment fee: (19).

[0103] In the formula, Costs for annual volatility assessment.

[0104] Step 2.3: Calculate volatility assessment fees, secondary frequency adjustment revenue and allocation fees, and primary frequency adjustment assessment fees and return fees.

[0105] Step 2.3.1: Calculate the secondary frequency regulation revenue and the allocated costs.

[0106] In this embodiment, the actual power generation of the wind farm is subtracted from the power baseline to form the power change, which is then compared with the grid regulation command to form the basic data for assessment and revenue. This data is then substituted into the assessment and revenue calculation formula to form the corresponding response cost.

[0107] Step 2.3.1.1: Forming external conditions for revenue calculation.

[0108] Specifically, the AGC frequency regulation ancillary service price curve for the assessment year is formed based on the historical clearing price of power system AGC frequency regulation ancillary services. If the power system structure has not changed significantly, the clearing price of AGC frequency regulation ancillary services published by the power trading institution in the previous year can be used. Assuming the modified wind turbine and energy storage power regulation speed and accuracy are good enough, then the performance indicators... Take the optimal value; if performance degradation needs to be considered, the adjustment performance index can be appropriately reduced.

[0109] Survey on the total number of grid-connected entities participating in AGC frequency regulation auxiliary services This includes pumped storage, wind farms and photovoltaic power stations, independent new energy storage power stations and local public power plants.

[0110] The survey measured the monthly response power of various grid-connected entities participating in AGC frequency regulation auxiliary services. If the power system structure has not changed significantly, the response electricity volume for the corresponding month published by the power trading institution in the previous year can be used.

[0111] The survey investigated the average monthly clearing price of frequency regulation ancillary services for various grid-connected entities participating in AGC frequency regulation ancillary services. If the power system structure has not changed significantly, the average clearing price of frequency regulation ancillary services for the corresponding month published by the power trading institution in the previous year can be used.

[0112] The survey investigated the total power generation of various grid-connected entities, excluding this wind farm, that participated in AGC frequency regulation ancillary services. If the power system structure has not changed significantly, the power generation for the corresponding month published by the power trading agency in the previous year can be used.

[0113] Step 2.3.1.2: Calculate the secondary frequency modulation revenue.

[0114] Specifically, performance indicators will be adjusted. Clearing price of FM ancillary services Changes in wind farm power generation Substitute into the following formula to calculate the annual secondary frequency regulation revenue: (20).

[0115] In the formula, This is the annual revenue from secondary frequency regulation.

[0116] Step 2.3.1.3: Calculate the allocated costs.

[0117] Monthly response power of various grid-connected entities and monthly average clearing price Substituting into the following formula, the total monthly revenue is calculated as follows: (twenty one).

[0118] In the formula, Total monthly revenue from AGC frequency regulation ancillary services for various grid-connected entities.

[0119] The total monthly revenue of grid-connected entities will be allocated according to their power generation ratio. (twenty two).

[0120] In the formula, This is the monthly allocated cost.

[0121] By iterating through the 12 months of the year, the monthly refund is calculated according to formula (18), and the sum is obtained to obtain the annual refund: (twenty three).

[0122] In the formula, This refers to the annually allocated expenses.

[0123] Step 2.3.2: Calculate the cost of one frequency regulation assessment and the return cost.

[0124] Step 2.3.2.1: Actual power interpolation for primary frequency modulation. Specifically, the interaction simulation step size is determined for a single frequency modulation interaction. The analog step size is usually selected based on the frequency modulation command cycle, such as 1 second. Actual power generation of wind farms Interpolation is performed to obtain a shorter timescale simulation step size. Lower power curve; Step 2.3.2.2: Forming the external conditions for primary frequency modulation interaction.

[0125] The primary frequency regulation command issued by the power grid to the wind farm in the assessment year is formed based on the historical primary regulation commands of the wind farm. If the power system structure has not changed significantly, historical data of primary frequency regulation commands received by the wind farm in the previous year can be used.

[0126] according to Record the total number of frequency modulation events that need to be responded to each month during the simulation. .

[0127] The estimated monthly unit primary frequency regulation assessment cost is based on the historical primary frequency regulation assessment cost of the power system. If the power system structure has not changed significantly, the unit primary frequency regulation assessment electricity cost published by the power trading agency for the corresponding month in the previous year can be used.

[0128] The survey investigated the primary frequency regulation assessment coefficients stipulated by power trading institutions, including the monthly operational rate assessment coefficient. and output response assessment coefficient .

[0129] The survey covered the total number of grid-connected entities participating in a frequency regulation assessment. This includes pumped storage, wind farms and photovoltaic power stations, independent new energy storage power stations and local public power plants.

[0130] The survey included the monthly electricity consumption of various grid-connected entities participating in a frequency regulation assessment. If the composition of the power system has not changed significantly, the assessment electricity volume for the corresponding month published by the power trading agency in the previous year can be used.

[0131] The survey investigated the assessment electricity prices of various grid-connected entities participating in the primary frequency regulation assessment. If the composition of the power system has not changed significantly, the assessment electricity price for the corresponding month published by the power trading agency in the previous year can be used.

[0132] The survey investigated the total power generation of various grid-connected entities participating in the primary frequency regulation assessment, excluding this wind farm. If the power system structure has not changed significantly, the power generation for the corresponding month published by the power trading agency in the previous year can be used.

[0133] Step 2.3.2.3: Calculate the cost of a single frequency regulation assessment.

[0134] The upper and lower limits of power variation based on actual power generation can be calculated using equations (9) and (10): (twenty four).

[0135] In the formula, and These are the upper and lower limits of power variation based on actual power generation.

[0136] By comparing the magnitude of power change restrictions and regulation commands over time periods, the actual change in power generation of the wind farm in response to the primary frequency regulation command is calculated using the following formula: (25).

[0137] In the formula, The actual change in power generation of a wind farm in response to a primary frequency regulation command.

[0138] Record monthly response times Number of times Substituting into the following formula, the monthly commissioning rate and output response index of primary frequency regulation are calculated as follows: (26).

[0139] In the formula, and These are the monthly commissioning rate and output response index for primary frequency regulation, respectively.

[0140] For the Monthly, the unit's one-time frequency regulation assessment electricity cost will be... Primary frequency regulation assessment coefficient and Substituting into the following formula, the monthly assessment electricity consumption and assessment fee are calculated as follows: (27).

[0141] In the formula, , , The first The assessment electricity volume for the first frequency regulation monthly commissioning rate that does not meet the requirements, the assessment electricity volume for the first frequency regulation output response that is not up to standard, and the total assessment electricity volume for the first frequency regulation; For the first The cost of a frequency adjustment assessment once a month; For step-size based The total number of time periods within a month.

[0142] By iterating through the 12 months of the year, the monthly frequency regulation assessment fee is calculated according to formula (18), and the sum of these amounts yields the annual frequency regulation assessment fee: (28).

[0143] In the formula, This is the cost of the annual frequency adjustment assessment.

[0144] Step 2.3.2.4: Calculate the refund amount.

[0145] Monthly assessment electricity consumption of various grid-connected entities And assessment of electricity price Substituting into the following formula, the total monthly assessment fee is calculated as follows: (29).

[0146] In the formula, The total monthly assessment fee for all types of grid-connected entities.

[0147] The total monthly assessment fee for grid-connected entities will be refunded based on a proportion of their power generation. (30).

[0148] By iterating through the 12 months of the year, the monthly refund is calculated according to formula (18), and the sum is obtained to obtain the annual refund: (31).

[0149] In the formula, This is the annual refund fee.

[0150] Step 2.4: Sum all the benefits and the performance evaluation to obtain the total benefit.

[0151] Specifically, substituting the electricity sales revenue and volatility assessment fees, secondary frequency regulation revenue and allocated fees, and primary frequency regulation assessment fees and refund fees into the following formula, the annual comprehensive revenue based on system production simulation is calculated as follows: (32).

[0152] In the formula, The annual comprehensive revenue of the wind and energy storage integrated system.

[0153] Step 3: Based on the quantitative mapping relationship between technical characteristics and benefits, construct a two-layer optimization framework that balances investment costs and benefits by considering the technical and cost models of unit retrofitting and energy storage configuration.

[0154] In one specific implementation, this embodiment maximizes overall benefits by constructing a two-layer optimization model. The upper layer optimizes the combination strategy of unit modification and energy storage configuration, while the lower layer optimizes the combination strategy determined by the upper layer by conducting operational simulations and benefit assessments, thereby obtaining the optimal combination strategy of unit modification and energy storage configuration. In the two-layer optimization framework, the upper layer is the planning layer and the lower layer is the operation layer. The upper planning layer aims to maximize the total investment and operational benefits by optimizing the unit modification and energy storage configuration scheme, while the lower operation layer aims to maximize overall benefits by simulating the real-time scheduling of the wind-storage integrated system. The upper planning layer considers site constraints to form a unit modification and energy storage configuration scheme and transmits it to the lower operation layer. The lower operation layer then conducts time-series operation simulations under the planning scheme given by the upper planning layer, quantifies the power generation benefits and interactive benefits under the annual wind resource time-series curve and grid control commands, and feeds the overall benefits back to the upper layer. The upper layer adjusts the investment strategy based on the feedback data and transmits the updated scheme again for operational verification, forming a closed-loop optimization. To address the mixed-integer nonlinearity in the model, a genetic algorithm is used for global optimization, effectively coordinating the modification of reserved capacity of generating units with energy storage configuration. This helps to smooth out wind power output fluctuations and improve frequency regulation capabilities, ensuring optimal economy and flexibility of the solution while meeting the needs of ancillary services in the power market.

[0155] like Figure 4 As shown, the specific steps are as follows: Step 3.1: Taking the maximization of the total return on investment and operation of the wind-storage integrated system as the optimization objective, we establish an upper-level planning layer optimization model by combining all constraints.

[0156] Step 3.1.1 Constraint Construction Survey of the total installed capacity of the wind power plant This serves as the upper limit for the capacity of wind turbine retrofitting.

[0157] The survey investigated the total land area of ​​the wind farm where energy storage can be configured. and the floor space occupied by a single energy storage unit. Rated capacity Calculate the upper limit of the rated power of configurable energy storage. and rated capacity limit .

[0158] Considering the limitations of retrofit capacity and energy storage configuration, the construction constraints are as follows: (33).

[0159] Step 3.1.2 Construct the objective function.

[0160] The unit renovation investment cost is constructed from equations (2) and (4). and energy storage configuration investment costs Function, discount rate during the research and planning period and expected lifespan of equipment Substituting into the following formula, the annual investment cost function can be constructed as follows: (34).

[0161] In the formula, Annual investment cost; coefficient The investment recovery factor represents the proportion of the present value of a single investment converted into the net present value of an annuity over the investment period using the interest rate of that year.

[0162] A comprehensive revenue function can be constructed from the production simulation process in step 2; substituting the above costs and benefits into the following formula, the total revenue function of the wind-storage integrated system is constructed as follows: (35).

[0163] In the formula, The total revenue of the wind-storage integrated system.

[0164] With the goal of maximizing the total return on investment and operation of the wind-storage integrated system, a planning-level optimization model is formed by simultaneously applying all constraints: (36).

[0165] Step 3.2: Using the goal of maximizing the overall benefits of the wind-storage integrated system, we establish a combined set of all constraints to form an optimization model for the lower-level operation layer.

[0166] Step 3.2 Construct the runtime layer model.

[0167] Based on the wind farm grid connection assessment and ancillary service revenue calculation model of step 2, with the objective function of maximizing the comprehensive revenue of the wind-storage integrated system, an operation-level optimization model is formed by simultaneously solving all constraints: (37).

[0168] Step 3.3: Solve the two-layer optimization framework using a genetic algorithm.

[0169] Step 3.3.1: Under the condition of satisfying equation (33), randomly generate n The initial population consists of several different configuration schemes, and the decision variables to be optimized in each population are: , and Each decision variable is encoded using fixed-length binary encoding, with a specified bit length for each variable. This linearly maps the range of values ​​of a variable to the range of integers that the binary bit string can represent. .

[0170] Step 3.3.2: For each configuration scheme, first substitute the values ​​into equations (2), (4), and (34) to calculate the investment cost. Then, the comprehensive benefits under this scheme are obtained through simulation calculations using the wind farm grid connection assessment and ancillary service revenue calculation model from step 2. The comprehensive revenue is returned to the planning layer and the total revenue is calculated using equation (35). .

[0171] Step 3.3.3: Generate mating parent pairs using the tournament selection algorithm based on the total cost; randomly select two crossover points to exchange the middle segment of the decision variable encoding for the selected parent with the given crossover probability, and generate offspring; then for each newly generated offspring, iterate through each bit of the encoding and perform bit reversal with the given mutation probability; if a variable violates the constraint of equation (33) after decoding, then project the variable back to the nearest feasible value, i.e. the constraint boundary.

[0172] Step 3.3.4: Merge the parent and child generations, sort them by total revenue from largest to smallest, and retain the top [generation]. Elite individuals directly enter the next generation; the remaining positions are filled by new individuals generated through selection, crossover, and mutation, until a new population size is achieved. .

[0173] Step 3.3.5: Calculate the total revenue for the new population according to Step 3.3.2. Compare the payoff difference between the current generation and the previous generation's best individual. : .

[0174] in, and These represent the returns (i.e., maximum returns) of the best individuals in the previous and current generations, respectively. Indicates the number of iterations. If And continue When the maximum number of iterations is reached, the algorithm terminates and outputs the configuration scheme corresponding to the current optimal individual. , and and its corresponding , and Otherwise, continue iterating.

[0175] To verify the effectiveness of the wind farm turbine retrofitting and energy storage configuration collaborative planning method and system described in this embodiment, a wind farm is used as an example. This wind farm has a total of 50 MY2.0-118 doubly-fed wind turbines installed. Based on the method of this embodiment, the wind farm turbine retrofitting and energy storage configuration are collaboratively planned. The specific process is as follows: The survey investigated the resource capacity limitations of the wind farm, mainly including the total installed capacity of 100MW and the total land area of ​​the energy storage area. The floor area of ​​a single energy storage unit Rated capacity and rated power limit ; The basic economic parameters required for optimizing the survey plan are as follows: the on-grid electricity price is 3,949 yuan / 10,000 kWh, the fixed cost of unit modification is 100,000 yuan, the unit capacity modification cost of the unit is 10,000 yuan / MW, the unit power investment cost of energy storage equipment is 300,000 yuan / MW, the unit capacity investment cost of energy storage equipment is 2 million yuan / MWh, and the assessment unit electricity price is 3,393.9 yuan / 10,000 kWh.

[0176] Under the condition of satisfying equation (33), 25 different configuration schemes are randomly generated to form the initial population. Each variable is assigned a bit length of 8, and the value range of the variable is linearly mapped to the integer range that can be represented by the binary bit string. For each configuration scheme, first substitute the values ​​into equations (2), (4), and (34) to calculate the investment cost. Then, step 2 involves running a simulation to calculate the overall benefits under this scheme. The comprehensive revenue is returned to the planning layer to calculate the total revenue using equation (35). .

[0177] Set the basic parameters of the optimization algorithm as follows: particle size is 30, maximum number of iterations is 15, particle crossover probability is 0.9, particle mutation probability is 0.3, particle crossover parameter is 3, and particle mutation parameter is 3.

[0178] Based on the total cost, a tournament selection algorithm is used to generate mating parent pairs. For the selected parents, two crossover points are randomly chosen according to the given crossover probability to exchange the middle segment of the decision variable encoding, generating offspring. Then, for each newly generated offspring, each bit of the encoding is traversed, and bit reversal is performed with the given mutation probability. If a variable violates the constraint of equation (33) after decoding, the variable is projected back to the nearest feasible value, i.e., the constraint boundary. Parents and offspring are merged and sorted according to the total return from largest to smallest. The top 10 elite individuals are retained and directly enter the next generation. The remaining positions are filled by new individuals generated through selection, crossover, and mutation until the new population size reaches 25. The total return of the new population is calculated. Compare the payoff difference between the current generation and the previous generation's best individual. ,like The algorithm continues for 5 generations, or until the maximum number of iterations (30) is reached, at which point it terminates and outputs the configuration scheme corresponding to the current optimal individual and its corresponding benefit.

[0179] The iterative process of the wind-storage joint optimization scheme is as follows: Figure 5 As shown in Table 1, the investment and operating costs of the last-generation optimization scheme at iterative convergence are presented. The results show that the optimal configuration scheme is one that includes only 3.5MW of energy storage, with its overall benefit falling between that of the pure energy storage optimization scheme and the unit-only modification scheme. Although introducing energy storage brings higher investment costs (approximately RMB 646,000), significantly higher than the RMB 241,500 for the unit-only modification scheme, it significantly improves the wind farm's regulation flexibility. This improvement results in electricity sales revenue of RMB 90.45 million and ancillary revenue of RMB 2.07 million, thereby increasing the overall revenue to RMB 92.35 million, a significant increase compared to the RMB 89.42 million for the unit-only modification optimization scheme. Therefore, in the scenario of joint optimization of wind power and energy storage, although the investment cost is higher, energy storage configuration remains a reasonable choice due to its economic benefits in improving system flexibility.

[0180] Table 1. Investment and operating costs under different wind-storage joint optimization schemes

[0181] Example 2: Embodiment 2 of the present invention provides a collaborative planning system for wind farm turbine retrofitting and energy storage configuration, comprising: The data acquisition module is configured to acquire equipment and cost parameters for wind farm turbine retrofitting and energy storage configuration, and to construct technical and cost models for turbine retrofitting and energy storage configuration based on operational performance optimization objectives. The revenue calculation module is configured to build a wind farm grid connection assessment and ancillary service revenue calculation model with embedded time-series operation simulation, and to use the technical characteristics of the wind farm grid connection assessment and ancillary service revenue calculation model to the quantitative mapping of revenue. The collaborative planning module is configured to quantify the mapping relationship from technical characteristics to benefits. Based on the technical and cost models of unit retrofitting and energy storage configuration, it constructs a two-layer optimization framework that balances investment costs and benefits to improve flexibility. The upper layer optimizes the combination strategy of unit retrofitting and energy storage configuration, while the lower layer optimizes the combination strategy of unit retrofitting and energy storage configuration determined by the upper layer by conducting operation simulation and benefit evaluation, thereby obtaining the optimal combination strategy of unit retrofitting and energy storage configuration.

[0182] Example 3: Embodiment 3 of the present invention provides a computer-readable storage medium storing a computer program adapted for loading by a processor and executing the steps in the wind farm unit retrofitting and energy storage configuration collaborative planning method as described in Embodiment 1 of the present invention.

[0183] Example 4: Embodiment 4 of the present invention provides a computer device, the device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the steps in the wind farm unit retrofitting and energy storage configuration collaborative planning method as described in Embodiment 1 of the present invention.

[0184] Example 5: Embodiment 5 of the present invention provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the wind farm turbine retrofit and energy storage configuration collaborative planning method as described in Embodiment 1 of the present invention.

[0185] The steps and methods involved in Examples 2, 3, 4 and 5 above correspond to those in Example 1. For specific implementation methods, please refer to the relevant description section of Example 1.

[0186] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0187] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0188] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for coordinated planning of wind farm turbine retrofitting and energy storage configuration, characterized in that, Includes the following steps: Obtain the equipment and cost parameters for wind farm turbine retrofitting and energy storage configuration, and construct technical and cost models for turbine retrofitting and energy storage configuration based on the operational performance optimization objectives. Construct a wind farm grid connection assessment and ancillary service revenue calculation model with embedded time-series operation simulation, and use the technical characteristics of the wind farm grid connection assessment and ancillary service revenue calculation model to quantitatively map revenue; Based on the quantitative mapping relationship between technical characteristics and benefits, a two-layer optimization framework is constructed according to the technical and cost models of unit retrofitting and energy storage configuration to improve flexibility while taking into account investment costs and benefits. The upper layer optimizes the combination strategy of unit retrofitting and energy storage configuration, and the lower layer optimizes the combination strategy of unit retrofitting and energy storage configuration determined by the upper layer by conducting operation simulation and benefit evaluation, so as to obtain the optimal combination strategy of unit retrofitting and energy storage configuration.

2. The method for coordinated planning of wind farm turbine retrofitting and energy storage configuration as described in claim 1, characterized in that, The equipment parameters for turbine retrofitting include the normalized MPPT curve of the wind turbine. The cost parameters for turbine retrofitting include the fixed investment and unit capacity cost of turbine retrofitting. The equipment parameters for energy storage configuration include the maximum charge / discharge rate and charge / discharge efficiency. The cost parameters for energy storage configuration include the investment per unit of energy storage and the investment per unit of power.

3. The method for coordinated planning of wind farm turbine retrofitting and energy storage configuration as described in claim 1, characterized in that, Based on time-series operation simulation, wind farm power generation output time-series data is generated. The grid connection assessment cost of wind farm is calculated by considering volatility assessment, primary frequency regulation assessment, and secondary frequency regulation assessment. Furthermore, the revenue of ancillary services is calculated by considering primary frequency regulation ancillary services and secondary frequency regulation ancillary services. A calculation model for wind farm grid connection assessment and ancillary service revenue is constructed.

4. The method for coordinated planning of wind farm turbine retrofitting and energy storage configuration as described in claim 3, characterized in that, The specific steps for constructing a wind farm grid connection assessment and ancillary service revenue calculation model are as follows: This model is based on time-series operation simulation to generate wind farm power output time-series data. It also considers volatility assessment, primary frequency regulation assessment, and secondary frequency regulation assessment to calculate wind farm grid connection assessment costs. Furthermore, it considers primary and secondary frequency regulation ancillary services to calculate ancillary service revenue. The baseline and actual power generation curves of the wind farm were obtained by using time-series operation simulation. Calculate electricity sales revenue based on the actual power generation curve and electricity price curve of the wind farm; Calculate volatility assessment fees, secondary frequency regulation revenue and allocation fees, and primary frequency regulation assessment fees and return fees; The total benefit is obtained by summing up all types of benefits and performance evaluations.

5. The method for coordinated planning of wind farm turbine retrofitting and energy storage configuration as described in claim 1, characterized in that, In the two-layer optimization framework, the upper layer is the planning layer and the lower layer is the operation layer. The upper planning layer considers site constraints to form unit modification and energy storage configuration schemes, and then transmits them to the lower operation layer. The lower operation layer conducts time-series operation simulations under the planning scheme given by the upper planning layer, quantifies the power generation revenue and interaction revenue under the action of annual wind resource time-series curves and grid control instructions, and feeds the comprehensive revenue back to the upper layer. The upper layer adjusts its investment strategy based on the feedback data and transmits the updated scheme again for operation verification, forming a closed-loop optimization.

6. The method for coordinated planning of wind farm turbine retrofitting and energy storage configuration as described in claim 1, characterized in that, Based on the quantitative mapping relationship between technical characteristics and returns, the specific steps for constructing a two-tiered optimization framework that balances investment costs and returns to enhance flexibility, according to the technical and cost models of unit retrofitting and energy storage configuration, are as follows: The optimization objective is to maximize the total return on investment and operation of the wind-storage integrated system, and all constraints are combined to form an upper-level planning optimization model. With the objective function of maximizing the overall benefits of the wind-storage integrated system, all constraints are combined to form an optimization model for the lower-level operation layer. A genetic algorithm is used to solve the two-level optimization framework.

7. A collaborative planning system for wind farm turbine retrofitting and energy storage configuration, characterized in that, include: The data acquisition module is configured to acquire equipment and cost parameters for wind farm turbine retrofitting and energy storage configuration, and to construct technical and cost models for turbine retrofitting and energy storage configuration based on operational performance optimization objectives. The revenue calculation module is configured to build a wind farm grid connection assessment and ancillary service revenue calculation model with embedded time-series operation simulation, and to use the technical characteristics of the wind farm grid connection assessment and ancillary service revenue calculation model to the quantitative mapping of revenue. The collaborative planning module is configured to quantify the mapping relationship from technical characteristics to benefits. Based on the technical and cost models of unit retrofitting and energy storage configuration, it constructs a two-layer optimization framework that balances investment costs and benefits to improve flexibility. The upper layer optimizes the combination strategy of unit retrofitting and energy storage configuration, while the lower layer optimizes the combination strategy of unit retrofitting and energy storage configuration determined by the upper layer by conducting operation simulation and benefit evaluation, thereby obtaining the optimal combination strategy of unit retrofitting and energy storage configuration.

8. A computer program product, characterized in that, The computer program product includes a computer program, which, when executed by a processor, implements the collaborative planning method for wind farm unit retrofitting and energy storage configuration as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1-6, which is a collaborative planning method for wind farm turbine retrofitting and energy storage configuration.

10. A computer device, characterized in that, include: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the wind farm unit retrofitting and energy storage configuration collaborative planning method as described in any one of claims 1-6.

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