Optimization method, system and device for capacity configuration of wind storage system considering energy storage life

Abstract

This application provides a method, system, and equipment for optimizing the capacity configuration of wind power and energy storage systems considering energy storage lifetime. The method includes: constructing a wind farm capacity configuration model considering energy storage allocation; obtaining the wind farm capacity configuration model through a model objective function constructed based on the investment cost, variable operation and maintenance cost, energy market revenue, frequency regulation market revenue, and reduced deviation assessment penalty revenue of the wind power and energy storage system; adding energy storage lifetime loss constraints to the wind farm capacity configuration model to propose a wind farm capacity configuration model considering energy storage lifetime; and performing linearization optimization based on the wind farm capacity configuration model considering energy storage lifetime. This application addresses how to reduce model complexity, improve model solution efficiency, and provide accurate optimal capacity and output scheduling plans for wind power and energy storage, while comprehensively considering factors such as energy storage investment cost, lifetime loss cost, and revenue model.

Description

Technical Field

[0001] This application belongs to the field of wind farm grid connection technology, and relates to a method for optimizing the capacity configuration of wind storage system considering energy storage life, and particularly to a method, system and equipment for optimizing the capacity configuration of wind storage system considering energy storage life. Background Technology

[0002] The intermittent, random, and volatile nature of wind power means that large-scale grid connection can negatively impact grid stability and power quality. Energy storage, with its ability to rapidly absorb and release energy, is a high-quality, flexible regulatory resource. Deploying energy storage in wind farms can effectively alleviate the operational pressure on the system caused by large-scale wind power grid connection, improve grid stability, and enhance power quality. Therefore, under conditions of high-proportion renewable energy grid connection, optimizing wind farm capacity configuration while fully considering the impact of energy storage facilities is of significant practical importance to new energy companies.

[0003] The challenge in optimizing wind farm capacity configuration with added energy storage lies in the need to fully consider the impact of energy storage facilities, including investment costs, lifetime attrition costs, and revenue models, in order to make the configuration results as close as possible to the real environment. However, due to the highly nonlinear expression of energy storage charging and discharging losses, coupled with the need to consider energy flow and management between energy storage facilities and wind turbines, the wind farm capacity configuration optimization model considering energy storage lifetime is quite complex and difficult to solve.

[0004] Existing methods for configuring energy storage devices in wind farms often only consider how energy storage can help wind turbines balance output fluctuations, neglecting the benefits of frequency regulation ancillary services provided by energy storage facilities, and failing to fully account for the lifespan degradation caused by the charging and discharging behavior of energy storage devices. Current methods for configuring energy storage devices in wind farms typically fix the capacity of the allocated energy storage, and their optimization results only include the configured capacity of the wind turbines and the energy storage output scheduling plan, failing to determine the optimal energy storage configuration capacity. Furthermore, due to the high degree of nonlinearity in the models, existing methods often employ intelligent algorithms for solving the problem, which are prone to getting trapped in local optima. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, and equipment for optimizing the capacity configuration of wind power and energy storage systems considering energy storage lifespan. This is to address the problem of how to reduce the complexity of the model, improve the model solving efficiency, and provide an accurate optimal capacity and output scheduling plan for wind power and energy storage when comprehensively considering factors such as energy storage investment costs, lifespan loss costs, and revenue models.

[0006] In a first aspect, this application provides a method for optimizing the capacity configuration of a wind farm storage system considering energy storage lifetime. The method includes: constructing a wind farm capacity configuration model considering energy storage allocation; obtaining the wind farm capacity configuration model through a model objective function constructed based on the investment cost, variable operation and maintenance cost, energy market revenue, frequency regulation market revenue, and reduced deviation assessment penalty revenue of the wind farm storage system; adding energy storage lifetime loss constraints to the wind farm capacity configuration model to propose a wind farm capacity configuration model considering energy storage lifetime; and performing linearization optimization based on the wind farm capacity configuration model considering energy storage lifetime.

[0007] In this application, considering factors such as energy storage investment costs, lifetime loss costs, and revenue models, a wind farm capacity configuration model considering energy storage is constructed. Energy storage lifetime loss constraints are added to this model, resulting in a wind farm capacity configuration model considering energy storage lifetime. Furthermore, nonlinear simplification optimization is performed based on this model, reducing its complexity and improving its solution efficiency. This approach effectively models the revenue model of wind power and energy storage, providing accurate solutions for optimal wind power and energy storage capacity and output scheduling.

[0008] In one implementation of the first aspect, the objective function of the model is:

[0009] min F = Cost sys +[VC-(Income E +Income Reg +Income Dev )] / S

[0010] Where F is the optimal value of the model's objective function; Cost sys VC represents the investment cost of the wind and energy storage system; VC represents the variable operation and maintenance cost of the wind and energy storage system; Income E For system energy market revenue; Income Reg For revenue from the system frequency modulation market; Dev To reduce the revenue from deviation assessment penalties in the system; S represents the number of scenarios; based on the objective function of the model, the model constraints of the wind-storage system are constructed; the constraints include one or more of the following: energy storage configuration power capacity constraints, investment amount constraints, site constraints, upper and lower limits of system combined output constraints, wind and solar output constraints, and curtailment rate constraints.

[0011] In one implementation of the first aspect, adding energy storage lifetime loss constraints to the wind farm capacity configuration model and proposing a wind farm capacity configuration model considering energy storage lifetime includes: adding the lifetime loss cost of energy storage devices to the wind farm capacity configuration model to obtain a variable operation and maintenance cost considering energy storage lifetime loss; adding constraints corresponding to the lifetime loss of energy storage devices to the wind farm capacity configuration model; and obtaining a wind farm capacity configuration model considering energy storage lifetime based on the variable operation and maintenance cost considering energy storage lifetime loss and the constraints corresponding to the lifetime loss of energy storage devices.

[0012] In one implementation of the first aspect, the step of adding the life-cycle loss cost of the energy storage device to obtain a variable operation and maintenance cost considering the life-cycle loss of the energy storage device based on the wind farm capacity configuration model includes: adding the life-cycle loss cost of the energy storage device to the variable operation and maintenance cost of the wind-storage system based on the wind farm capacity configuration model to generate a variable operation and maintenance cost of the wind-storage system considering the life-cycle loss of the energy storage device; the life-cycle loss cost of the energy storage device is the life-cycle loss cost of the energy storage device during operation; the variable operation and maintenance cost considering the life-cycle loss of the energy storage device is expressed as: VC = VC es +C es ; wherein, the VC es This represents the variable operation and maintenance cost of energy storage; the C es This indicates the cost of energy storage equipment over its lifetime.

[0013] In one implementation of the first aspect, the constraint condition corresponding to the lifetime loss of the energy storage device based on the wind farm capacity configuration model includes: the lifetime loss of the energy storage device is mainly related to the depth of discharge of the energy storage device, and the constraint corresponding to the lifetime loss of the energy storage device is the depth of discharge of the energy storage device. for:

[0014]

[0015] in, This represents the discharge volume in the energy storage spot market at time t in scenario s; This represents the deviation in the amount of electricity released by the energy storage system at time t in scenario s; E represents the number of market applications for energy storage frequency regulation at time t in scenario s; ess The energy storage device's capacity is represented by Δt; Δt represents the unit of time; β s，t This represents the proportion of power loss due to frequency regulation requests for energy storage within time period t in scenario s.

[0016] In one implementation of the first aspect, the linearization optimization based on the wind farm capacity configuration model considering energy storage lifetime includes: simplifying and optimizing the expression of the high-order nonlinear term of energy storage lifetime loss using a piecewise linearization method based on the wind farm capacity configuration model considering energy storage lifetime; and simplifying and optimizing the expression of the nonlinear term of the product of two continuous variables using a binary expansion method based on the wind farm capacity configuration model considering energy storage lifetime.

[0017] In one implementation of the first aspect, the step of simplifying and optimizing the expression of the high-order nonlinear term of energy storage lifetime loss using a piecewise linearization method includes: obtaining the expression of the high-order nonlinear term in the wind farm capacity configuration model considering energy storage lifetime; and simplifying and optimizing the expression of the high-order nonlinear term using a piecewise linearization method to obtain a linearized expression of the expression of the high-order nonlinear term.

[0018] In one implementation of the first aspect, simplifying and optimizing the nonlinear term expression of the product of two continuous variables using the binary expansion method includes: obtaining the nonlinear term expression of the product of continuous variables in the wind farm capacity configuration model considering energy storage lifetime; and simplifying and optimizing the nonlinear term expression of the product of continuous variables using the binary expansion method to obtain the linearized expression of the product of continuous variables.

[0019] Secondly, this application provides a wind farm capacity configuration optimization system considering energy storage lifetime. The system includes: a model building module for constructing a wind farm capacity configuration model considering energy storage; the wind farm capacity configuration model is obtained through a model objective function constructed based on the investment cost, variable operation and maintenance cost, energy market revenue, frequency regulation market revenue, and reduced deviation assessment penalty revenue of the wind farm capacity configuration model; a model function adding module for adding energy storage lifetime loss constraints to the wind farm capacity configuration model to propose a wind farm capacity configuration model considering energy storage lifetime; and a model optimization module for performing linearization optimization based on the wind farm capacity configuration model considering energy storage lifetime.

[0020] Thirdly, this application provides an electronic device, the electronic device comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, so that the electronic device performs the above-described wind-storage system capacity configuration optimization method considering energy storage lifetime.

[0021] As described above, the wind-storage system capacity configuration optimization method, system, and equipment considering energy storage lifespan described in this application have the following beneficial effects:

[0022] In this application, considering factors such as energy storage investment costs, lifetime loss costs, and revenue models, a wind farm capacity configuration model considering energy storage is constructed. Energy storage lifetime loss constraints are added to the wind farm capacity configuration model, proposing a wind farm capacity configuration model considering energy storage lifetime. At the same time, linearization and simplification optimization are performed based on the wind farm capacity configuration model considering energy storage lifetime, which reduces the complexity of the model, improves the model solution efficiency, and can fully model the revenue model of wind power and energy storage, providing accurate optimal capacity and output scheduling plans for wind power and energy storage. Attached Figure Description

[0023] Figure 1 The diagram shown is a structural schematic of the wind storage system described in an embodiment of this application.

[0024] Figure 2 The diagram shown is a flowchart illustrating the capacity configuration optimization method considering energy storage lifetime as described in an embodiment of this application.

[0025] Figure 3 The diagram shows a flowchart illustrating the capacity configuration model construction method considering energy storage lifetime as described in an embodiment of this application.

[0026] Figure 4 The diagram shown is a schematic representation of the linearization optimization of the capacity configuration model considering energy storage lifetime as described in the embodiments of this application.

[0027] Figure 5 The diagram shown is a schematic representation of the process optimized using the piecewise linearization method as described in an embodiment of this application.

[0028] Figure 6 The diagram shown is a schematic representation of the optimized process using the binary expansion method described in the embodiments of this application.

[0029] Figure 7 The diagram shown is a structural schematic of the capacity configuration optimization system considering energy storage lifetime as described in an embodiment of this application.

[0030] Figure 8 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application.

[0031] Component designation explanation

[0032] 7. Wind-storage systems considering energy storage lifespan

[0033] Capacity Configuration Optimization System

[0034] 71 Model Building Module

[0035] 72. Add module for model functionality

[0036] 73 Model Optimization Module

[0037] 8 Electronic devices

[0038] 81 Memory

[0039] 82 processor

[0040] Steps S1 to Sn Detailed Implementation

[0041] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0042] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0043] The following embodiments of this application provide a method, system, and equipment for optimizing the capacity configuration of a wind power and energy storage system considering energy storage lifetime. By comprehensively considering factors such as energy storage investment cost, lifetime loss cost, and revenue model, a wind farm capacity configuration model considering energy storage is constructed. Energy storage lifetime loss constraints are added to the wind farm capacity configuration model, resulting in a wind farm capacity configuration model considering energy storage lifetime. Furthermore, nonlinear simplification optimization is performed based on this model, reducing model complexity and improving model solution efficiency. This approach can fully model the revenue model of wind power and energy storage, providing accurate optimal capacity and output scheduling plans for wind power and energy storage.

[0044] like Figure 1 As shown in the figure, this embodiment provides a structural diagram of a wind turbine and energy storage combined power generation system (hereinafter referred to as wind storage system), which includes a wind farm, an energy storage system and a power grid. The energy storage system is connected in parallel at the wind farm outlet, and the power generated by the wind farm and the battery pack will be fed into the AC power grid.

[0045] Those skilled in the art will understand that Figure 1 The structures shown do not constitute a limitation on the wind storage system and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. The wind storage system capacity configuration optimization method considering energy storage lifetime provided in this application embodiment can be based on... Figure 1 The wind storage system shown is implemented.

[0046] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0047] like Figure 2 As shown in the figure, this embodiment provides a method for optimizing the capacity configuration of a wind-storage system considering energy storage lifespan. The method includes the following steps:

[0048] Step S1: Construct a wind farm capacity configuration model that considers energy storage and distribution. The wind farm capacity configuration model is obtained by constructing a model objective function based on the investment cost, variable operation and maintenance cost, energy market revenue, frequency regulation market revenue, and reduced deviation assessment penalty revenue of the wind-storage system. The wind-storage system is a combined wind turbine and energy storage power generation system.

[0049] Step S2: Add energy storage lifetime loss constraints to the wind farm capacity configuration model and propose a wind farm capacity configuration model that considers energy storage lifetime.

[0050] Step S3: Perform linear optimization based on the wind farm capacity configuration model that takes into account energy storage lifetime.

[0051] In this application, considering factors such as energy storage investment costs, lifetime loss costs, and revenue models, a wind farm capacity configuration model considering energy storage is constructed. Energy storage lifetime loss constraints are added to this model, resulting in a wind farm capacity configuration model considering energy storage lifetime. Furthermore, nonlinear simplification optimization is performed based on this model, reducing its complexity and improving its solution efficiency. This approach effectively models the revenue model of wind power and energy storage, providing accurate solutions for optimal wind power and energy storage capacity and output scheduling.

[0052] In one embodiment of this application, the objective function of the model is shown in the following formula (1):

[0053] min F = Cost sys +[VC-(Income E +Income Reg +Income Dev Formula (1)

[0054] Where F is the optimal value of the model's objective function; Cost sys VC represents the investment cost of the wind and energy storage system; VC represents the variable operation and maintenance cost of the wind and energy storage system; Income E For system energy market revenue; Income Reg For revenue from the system frequency modulation market; Dev To reduce the revenue from deviation assessments and penalties in the system; S represents the number of scenarios.

[0055] Specifically, the objective function of the model is constructed considering the total profit of the wind turbine and energy storage combined generation system (hereinafter referred to as wind-storage system).

[0056] (1) Investment cost of wind power and energy storage system: The investment cost of wind power and energy storage system consists of the investment cost of wind power equipment and the investment cost of energy storage equipment. The calculation formula is shown in formula (2):

[0057]

[0058] Among them, P ess and E ess These represent the configured power and capacity of the energy storage device, respectively; P wd This refers to the configured power of the wind turbine; the parameter is Capex. wd , FOM wd FOM es CRF and Days, of which Capex wd and FOM wd These are the unit power cost and unit fixed operation and maintenance cost of wind power equipment, respectively. and FOM es These represent the unit power cost, unit capacity cost, and unit fixed operation and maintenance cost of the energy storage equipment, respectively; Days represents the number of days in a year; and CRF represents the capital recovery factor, the expression of which is shown in formula (3):

[0059]

[0060] Where: γ is the discount rate; τ is the planning period.

[0061] (2) Variable operation and maintenance costs of wind storage systems

[0062] The variable operation and maintenance cost of the wind storage system consists of the variable operation and maintenance cost of energy storage, and its calculation formula is shown in formula (4):

[0063] VC = VC es Formula (4)

[0064] Among them, VC es This represents the variable operation and maintenance cost (VC) of energy storage. es It is directly proportional to the change in electricity, and its calculation formula is shown in formula (5):

[0065]

[0066] In the formula: This represents the discharge volume in the energy storage spot market at time t in scenario s; This represents the amount of electricity absorbed by the energy storage system from wind power at time t in scenario s; This represents the number of market applications for energy storage frequency regulation at time t in scenario s; This represents the deviation in electricity absorbed by the energy storage system at time t in scenario s; VOM represents the deviation in electricity released by the energy storage system at time t in scenario s; es β represents the unit variable operation and maintenance cost of energy storage equipment. s，t Δt represents the proportion of energy loss due to frequency regulation application for energy storage within time period t in scenario s; Δt represents the unit time; S represents the number of scenarios; T represents the number of time periods; s∈[1,S], t∈[1,T].

[0067] (3) Energy market revenue of wind storage systems

[0068] Income from participating in the energy market with wind-storage combined power generation systems E As shown in formula (6):

[0069]

[0070] in: Let be a parameter representing the energy market clearing price at time t in scenario s; Let be a variable, representing the portion of wind power output at time t in scenario s that is sold in the energy market; Let be a variable, representing the deviation power balance output of the wind-storage system at time t in scenario s, and its expression is shown in formula (7).

[0071]

[0072] (4) Revenue from the frequency regulation market for wind storage systems

[0073] The "Interim Rules for the Operation of the Gansu Province Electricity Auxiliary Services Market," issued by the Gansu Energy Regulatory Office in 2022, established detailed implementation rules and compensation mechanisms for the provision of ancillary services by energy storage facilities. According to the rules, the frequency regulation market revenue obtained by energy storage facilities built within the metering outlets of new energy power plants after providing frequency regulation services to the power grid is [amount missing]. Reg The mileage measured by frequency modulation is expressed as shown in formula (8):

[0074]

[0075] In the formula: Let be the variable, representing the market declaration volume of the system frequency regulation at time t in scenario s; the parameter is . and K, of which This represents the price of frequency modulation capacity at time t in scenario s; denoted by t, representing the frequency regulation mileage at time t in scenario s; K represents the comprehensive performance index of the system frequency regulation, with the comprehensive performance index coefficient of the wind-storage system tentatively set at 1.5.

[0076] (5) Reduce deviation assessment and penalty income for wind storage systems

[0077] The discrepancy between wind power output forecasts and actual output, along with load fluctuations and turbine output, creates frequency regulation demand in the frequency regulation market. Energy storage facilities can be used to balance this discrepancy between day-ahead wind power reporting and ultra-short-term forecast output, reducing the penalty costs associated with deviation assessments during settlement, which is equivalent to generating revenue for wind farms.

[0078] Therefore, the wind-storage combined power generation system can obtain reduced performance penalty income, calculated as follows:

[0079]

[0080] In the formula: the parameter is C pun and k dev ,in C represents the deviation in power generation at time t in scenario s; pun Indicates the penalty price for deviation assessment; k dev This represents the performance deviation assessment coefficient.

[0081] In one embodiment of this application, the construction of a wind farm capacity configuration model considering energy storage and power storage includes: constructing model constraints for the wind-storage system based on the model objective function; the constraints include one or more of the following: energy storage configuration power capacity constraints, investment amount constraints, site constraints, upper and lower limits of system combined output constraints, wind and solar output constraints, and curtailment rate constraints.

[0082] Specifically, the constraints of the energy storage configuration include power capacity constraints, investment amount constraints, site constraints, upper and lower limits of system combined output constraints, wind and solar power output constraints, and curtailment rate constraints, thus constructing the system's constraints.

[0083] 1) The power capacity constraints of energy storage configuration are shown in formulas (10) and (11).

[0084] δ es ·P wd ≤P ess Formula (10)

[0085] E ess =k es ·P ess Formula (11)

[0086] Where: δ es The minimum percentage of wind turbine rated capacity allocated to energy storage; k es This refers to the capacity-to-power ratio of energy storage devices.

[0087] 2) The investment constraints for the renovation are shown in formula (12).

[0088]

[0089] Where: C max The maximum allowed total investment amount for renovations.

[0090] 3) Site constraints are shown in formula (13).

[0091] 0≤S wd ·P wd +S es ·P ess ≤S max Formula (13)

[0092] Wherein: S max The maximum permitted construction land area; S wd and S es These refer to the unit floor area of ​​wind turbine units and energy storage equipment, respectively.

[0093] 4) The wind power output balance constraint is shown in formula (14).

[0094]

[0095] in: This represents the total output of the wind farm at time t in scenario s; This represents the electricity sold by the wind farm at time t in the energy market at scenario s. This represents the amount of electricity stored in the energy storage at the wind farm station at time t in scenario s. This represents the amount of abandoned electricity at the wind farm at time t in scenario s.

[0096] (5) The upper and lower limits of the combined output of the system are constrained as shown in formula (15).

[0097]

[0098] in: This indicates the upper limit of the transmission capacity of the power transmission lines from the wind farm to the outside world.

[0099] (6) The system curtailment rate constraint is shown in formula (16).

[0100]

[0101] Where: ζ represents the power curtailment rate.

[0102] (7) The upper and lower limits of energy storage charging and discharging are shown in formula (17).

[0103]

[0104] (8) The energy storage charging and discharging shall not exceed the deviation energy constraint as shown in formula (18).

[0105]

[0106] in, This represents the deviation in electricity absorbed by the energy storage system at time t in scenario s; This represents the deviation in the amount of electricity released by the energy storage system at time t in scenario s; This represents the deviation in power generation at time t of the new energy power station in scenario s.

[0107] (9) The system frequency regulation application quantity constraint is shown in formula (19).

[0108] For a single unit of declaration submitted in the frequency regulation market, the energy storage device should have an upward and downward adjustment capacity of σ units.

[0109]

[0110] Where σ can be taken as 1.

[0111] (10) The upper and lower limits of energy storage capacity are constrained as shown in formula (20).

[0112] Considering energy loss, energy storage devices need to reserve sufficient energy to participate in the energy market and frequency regulation ancillary services market.

[0113]

[0114] Among them: E s,t Let be the variable representing the state of charge of the stored energy at time t in scenario s; the parameter is η. es , Δt and Δt reg , where η es Δt represents the charging efficiency of the energy storage device; Δt represents the continuous discharge time of the energy storage device in the energy market (typically 1 hour); Δt reg The continuous discharge time of energy storage devices in the frequency regulation market (typically 15 minutes).

[0115] (11) Energy storage SOC constraints are as shown in formulas (21)-(24).

[0116] For energy storage to operate safely, it must meet the state of charge constraints, as shown in the following formulas (39)-(41):

[0117] α·E ess ≤E s，t ≤E ess Formula (21)

[0118] E s，t =(1-δ)E s，t-1 +ΔE s，t Formula (22)

[0119]

[0120] Where: the parameters are α, δ, η and βs，t Where α is the minimum state of charge ratio of the energy storage device; δ is the self-discharge rate of the energy storage device; η is the charging efficiency of the energy storage device; β s，t The proportion of power loss due to frequency regulation application for energy storage within time period t in scenario s; E s，t-1 ΔE represents the state of charge of the stored energy at time t-1 in scenario s; s，t Δt represents the change in the amount of energy stored at time t in scenario s; Δt represents the unit of time, 1 hour.

[0121] During the scheduling period, the initial and final states of the energy storage must satisfy the constraints shown in formula (24):

[0122] E s，tmax =E s，t0 Formula (24)

[0123] Among them: E s,t0 and E s,t max , where are parameters representing the amount of electricity generated by the energy storage device at the beginning and end of the scheduling cycle, respectively.

[0124] like Figure 3 As shown, in one embodiment of this application, adding energy storage lifetime loss constraints to the wind farm capacity configuration model and proposing a wind farm capacity configuration model that considers energy storage lifetime includes the following steps:

[0125] Step S21: Based on the wind farm capacity configuration model, add the life loss cost of energy storage equipment to obtain the variable operation and maintenance cost considering the life loss of energy storage.

[0126] Step S22: Based on the wind farm capacity configuration model, add constraints corresponding to the lifespan loss of energy storage devices;

[0127] Step S23: Based on the variable operation and maintenance cost considering energy storage lifespan loss and the constraints corresponding to the lifespan loss of the energy storage equipment, a wind farm capacity configuration model considering energy storage lifespan is obtained.

[0128] In one embodiment of this application, the step of adding the life-cycle loss cost of energy storage devices based on the wind farm capacity configuration model to obtain a variable operation and maintenance cost that considers the life-cycle loss of energy storage devices includes:

[0129] Based on the wind farm capacity configuration model, the lifespan loss cost of the energy storage device is added to the variable operation and maintenance cost of the wind-storage system, generating the variable operation and maintenance cost of the wind-storage system considering the lifespan loss of the energy storage device; the lifespan loss cost of the energy storage device is the lifespan loss cost of the energy storage device during operation; the variable operation and maintenance cost considering the lifespan loss of the energy storage device is expressed as: VC = VC es +C es ;

[0130] Wherein, the VC es This represents the variable operation and maintenance cost of energy storage; the C es This indicates the cost of energy storage equipment over its lifetime.

[0131] Specifically, by adding an expression for energy storage lifespan loss, the discharge loss of each energy storage device is factored into the cost.

[0132] Battery lifespan is a key parameter for evaluating its operational economy, but nominal lifespan cannot accurately assess the impact of complex operating conditions on the battery in real-world scenarios. Since battery lifespan is closely related to factors such as depth of discharge, charge / discharge rate, and cycle count, considering irregular charge / discharge processes under non-rated conditions can accurately calculate battery life loss.

[0133] The total effective discharge capacity of the battery over its entire lifespan under rated conditions. R This can be represented as shown in formula (25):

[0134] Γ R =C R L R D R Formula (25)

[0135] Wherein: Γ R As a parameter, C R Rated capacity of the battery; L R D represents the battery's rated cycle life. R This is the rated depth of discharge.

[0136] To quantify the lifespan loss of a battery under non-rated irregular discharge conditions, it is necessary to perform an equivalent calculation of the irregular discharge process. This application mainly considers the effects of discharge depth and discharge rate, ignoring the impact of the charging process on battery lifespan. The equivalent method is shown in formula (26):

[0137]

[0138] Where: the variable is and in The actual discharge ampere-hours of the irregular discharge process at time t in scenario s; The effective discharge ampere-hours at time t in scenario s are equivalent to the rated conditions. The current ratio coefficient at time t during the discharge process in scenario s is used to reflect the influence of the discharge rate. This is the cycle lifetime ratio coefficient for the discharge process at time t in scenario s, used to reflect the influence of discharge depth.

[0139] in, It can be determined by the actual discharge power This is represented as shown in formula (27):

[0140]

[0141] Where: the variable is P R , and Where P R Rated power for configuring energy storage; and These represent the actual discharge current and actual discharge power during the discharge process at time t in scenario s; I R is a parameter representing the rated discharge current.

[0142] Can be determined by actual cycle life This is represented by formula (28):

[0143]

[0144] in: Let be a variable, representing the actual cycle life corresponding to the discharge process at time t in scenario s. Using The actual depth of discharge is calculated, and the calculation method is obtained by fitting multiple experiments. Taking lithium-ion batteries as an example, the expression of the two curves after fitting is shown in formula (29):

[0145]

[0146] Where u, v, and w are all fitting coefficients greater than 0. This data fitting method is also applicable to other types of battery energy storage.

[0147] Through the above process, the irregular discharge process of the battery under non-rated conditions can be equivalently represented by formula (30). After the battery has undergone multiple discharge processes, if the condition of formula (16) is met, the battery is considered to need to enter the scrapping process.

[0148]

[0149] The above process yields the battery life loss cost for each irregular discharge cycle. This can be expressed as shown in formula (31):

[0150]

[0151] Where: C cape is a parameter representing the initial investment cost of the battery.

[0152] If the battery undergoes n discharge cycles within time period Y, then the actual battery lifespan Y is... ES This can be represented as shown in formula (32):

[0153]

[0154] in, Y represents the i-th discharge process in n discharge processes; Y represents the time period.

[0155] In summary, the lifespan cost C of energy storage during operation... es This can be represented as shown in formula (33):

[0156]

[0157] Cost of life loss C es Adding the variable operation and maintenance cost formula (4) to the wind and energy storage system, the variable operation and maintenance cost of the wind and energy storage system becomes composed of the deep-tuning operation cost of the wind turbine, the start-up and shutdown cost of the wind turbine, the variable operation and maintenance cost of the energy storage, and the life loss cost. Its calculation formula is updated as follows:

[0158] VC = VC es +C es Formula (4)

[0159] In one embodiment of this application, the constraint conditions corresponding to the lifespan loss of energy storage devices based on the wind farm capacity configuration model include:

[0160] The lifespan loss of the energy storage device is mainly related to its depth of discharge. The constraint corresponding to the lifespan loss of the energy storage device is its depth of discharge. for:

[0161]

[0162] in, This represents the discharge volume in the energy storage spot market at time t in scenario s; This represents the deviation in the amount of electricity released by the energy storage system at time t in scenario s; E represents the number of market applications for energy storage frequency regulation at time t in scenario s; ess The energy storage device's capacity is represented by Δt; Δt represents the unit of time; β s，t This represents the proportion of power loss due to frequency regulation requests for energy storage within time period t in scenario s.

[0163] Specifically, the cycle life loss constraint of energy storage is given by the above formula (34) and equations (25)-(30).

[0164] like Figure 4 As shown, in one embodiment of this application, the linearization optimization based on the wind farm capacity configuration model considering energy storage lifetime includes the following steps:

[0165] Step S31: Based on the wind farm capacity configuration model that considers energy storage lifetime, simplify and optimize the expression of the high-order nonlinear term of energy storage lifetime loss using the piecewise linearization method.

[0166] Step S32: Based on the wind farm capacity configuration model that considers energy storage lifetime, the expression of the nonlinear term of the product of two continuous variables is simplified and optimized using the binary expansion method.

[0167] like Figure 5 As shown, in one embodiment of this application, the simplification and optimization of the high-order nonlinear term expression for energy storage lifetime loss using a piecewise linearization method includes:

[0168] Step S311: Obtain the expression for the higher-order nonlinear term in the wind farm capacity configuration model considering energy storage lifetime;

[0169] Step S312: Simplify and optimize the expression of the higher-order nonlinear term using the piecewise linearization method to obtain the linearized expression of the higher-order nonlinear term.

[0170] Specifically, piecewise linearization is used to simplify the expression for high-order nonlinear terms of energy storage lifetime loss;

[0171] From the expression (29), it can be seen that the energy storage lifetime loss If it is a high-order nonlinear function, then the lifespan loss cost... There is also nonlinearity, which is addressed by piecewise linearization in this application.

[0172] It should be noted that, as can be seen from formulas (25)-(34), the lifespan loss cost is... It concerns the actual depth of discharge of energy storage devices. The function, therefore, through It indicates the relationship between the two.

[0173] The discharge of energy storage devices meets certain physical constraints, variables The value of must be within [0, 1]. Therefore, by setting a reasonable number of segments within the interval [0, 1], such as N-1, there will be N segmentation points. (1) dod (2) , ..., dod (N) Find the corresponding value for each segment point. f[dod (1) ],...,f[dod (N) Introducing a new variable η s，t，i And it satisfies formula (35):

[0174]

[0175] Where: ηs，t，i It is a continuous variable that takes values ​​in the range of 0 to 1, reflecting the distribution on each segmented interval. The physical meaning of formula (35) is to regard the segmentation points as some extreme points of the feasible region and use the representation theorem to obtain the variables. The relationship with the poles.

[0176] The original nonlinear term can then be transformed into a linear form as shown in the following formulas (36) and (37) through piecewise linearization:

[0177]

[0178] like Figure 6 As shown, in one embodiment of this application, the simplification and optimization of the nonlinear term expression of the product of two continuous variables using the binary expansion method includes:

[0179] Step S321: Obtain the nonlinear term expression for the product of continuous variables in the wind farm capacity configuration model considering energy storage lifetime;

[0180] Step S322: Simplify and optimize the nonlinear expression of the product of continuous variables using the binary expansion method to obtain the linearized expression of the product of continuous variables.

[0181] Specifically, the expression for the product of two continuous variables is simplified using the binary expansion method.

[0182] The product of continuous variables can be linearized using a bivariate expansion method. Taking formula (34) as an example... For example, variables The value of must be in Therefore, nonlinear terms can be expressed using 0 and 1 variables. Expanded into formula (38):

[0183]

[0184] in, This indicates the ratio of the actual minimum discharge capacity of the energy storage device to its energy storage capacity; The ratio of the actual maximum discharge of an energy storage device to its energy storage capacity; ΔD es Indicates the proportion of unit discharge quantity; represents the 01 variables used for linearization; Q represents the positive integer that determines the precision of linearization.

[0185] Then multiply both sides of equation (38) by E. ess Formula (38) can be transformed into formula (40):

[0186]

[0187] Define variables Formula (40) can be transformed into formula (41):

[0188]

[0189] Equation The formulas (42) and (43) can be restated using the Big M method:

[0190]

[0191] Based on the above method, the original nonlinear terms are converted into linear terms through equations (41)-(43), where the positive integer Q determines the accuracy of the linearization. Similarly, other nonlinear terms in the model can also be linearized in the same way.

[0192] In summary, the wind farm capacity configuration optimization method, system, and equipment considering energy storage lifetime provided in this application constructs a wind farm capacity configuration model considering energy storage allocation, taking into account factors such as energy storage investment cost, lifetime loss cost, and revenue model. Energy storage lifetime loss constraints are added to the wind farm capacity configuration model, proposing a wind farm capacity configuration model considering energy storage lifetime. Furthermore, mathematical methods are used to linearize and simplify the wind farm capacity configuration model considering energy storage lifetime, reducing model complexity and improving model solution efficiency. This fully models the revenue model of wind and energy storage, providing accurate optimal capacity and output scheduling plans for wind power and energy storage.

[0193] Existing methods for configuring energy storage devices in existing wind farms often only consider how energy storage can help wind turbines balance output fluctuations, neglecting the benefits of frequency regulation ancillary services provided by energy storage facilities, and failing to fully consider the lifespan degradation caused by the charging and discharging behavior of energy storage devices. This invention fully considers the revenue model of wind-storage systems under the electricity market, studies the optimal capacity configuration of wind-storage systems, making the research in this field more closely aligned with real-world scenarios and considering more comprehensive factors. This invention utilizes mature linearization techniques to optimize the nonlinear terms in the model, simplifying it into a mixed-integer linear programming model, which can be solved analytically, yielding more accurate solutions than many existing methods that use intelligent algorithms.

[0194] The scope of protection of the wind-storage system capacity configuration optimization method considering energy storage life described in this application embodiment is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0195] This application also provides a wind-storage system capacity configuration optimization system that considers energy storage lifetime. This system can implement the wind-storage system capacity configuration optimization method that considers energy storage lifetime described in this application. However, the implementation device of the wind-storage system capacity configuration optimization method that considers energy storage lifetime described in this application includes, but is not limited to, the structure of the wind-storage system capacity configuration optimization system that considers energy storage lifetime listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0196] like Figure 7 As shown, this embodiment provides a wind farm capacity configuration optimization system considering energy storage lifetime. The system 7 includes a model building module 71, a model function addition module 72, and a model optimization module 73. The model building module 71 constructs a wind farm capacity configuration model considering energy storage. The wind farm capacity configuration model is obtained by constructing a model objective function based on the investment cost, variable operation and maintenance cost, energy market revenue, frequency regulation market revenue, and reduced deviation assessment penalty revenue of the wind farm capacity configuration model. The model function addition module 72 adds energy storage lifetime loss constraints to the wind farm capacity configuration model, proposing a wind farm capacity configuration model considering energy storage lifetime. The model optimization module 73 performs linearization optimization based on the wind farm capacity configuration model considering energy storage lifetime.

[0197] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0198] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0199] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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.

[0200] like Figure 8 As shown, this embodiment provides an electronic device 8, which includes a memory 81 and a processor 82. The memory 81 is used to store computer programs; the processor 82 is used to execute the computer programs stored in the memory 81, so that the electronic device 8 performs the wind-storage system capacity configuration optimization method considering energy storage lifetime described above.

[0201] This application also provides a computer-readable storage medium. 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 a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0202] This application embodiment may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application embodiment are generated. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0203] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.

[0204] The descriptions of the processes or structures corresponding to the above-mentioned figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0205] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for optimizing the capacity configuration of a wind-storage system considering energy storage lifespan, characterized in that, The method includes: A wind farm capacity configuration model considering energy storage and distribution is constructed. This model is obtained through a model objective function constructed based on the investment cost, variable operation and maintenance cost, energy market revenue, frequency regulation market revenue, and reduced deviation assessment penalty revenue of the wind-energy storage system. The model objective function is: minF = Cost sys + [VC - (Income E + Income Reg + Income Dev )] / S wherein F is the optimal value of the model objective function; Cost sys is the investment cost of the wind storage system; VC is the variable operation and maintenance cost of the wind storage system; Income E is the system energy market income; Income Reg is the system frequency modulation market income; Income Dev is the system reduced deviation assessment penalty income; S is the number of scenarios. Based on the objective function of the model, the model constraints of the wind-storage system are constructed; the constraints include one or more of the following: energy storage configuration power capacity constraints, investment amount constraints, site constraints, upper and lower limits of system combined output constraints, wind and solar output constraints, and curtailment rate constraints. A wind farm capacity configuration model considering energy storage lifetime loss is proposed by adding energy storage lifetime loss constraints to the wind farm capacity configuration model. Based on the wind farm capacity configuration model, the lifetime loss cost of energy storage devices is added to obtain the variable operation and maintenance cost considering energy storage lifetime loss. Based on the wind farm capacity configuration model, constraints corresponding to the lifetime loss of energy storage devices are added. Based on the variable operation and maintenance cost considering energy storage lifetime loss and the constraints corresponding to the lifetime loss of energy storage devices, a wind farm capacity configuration model considering energy storage lifetime is obtained. Linearization optimization is performed based on the wind farm capacity configuration model considering energy storage lifetime. The constraints corresponding to the lifespan loss of energy storage devices, based on the wind farm capacity configuration model, include: The lifespan loss of the energy storage device is mainly related to its depth of discharge. The constraint corresponding to the lifespan loss of the energy storage device is its depth of discharge. for: in, This represents the discharge volume in the energy storage spot market at time t in scenario s; This represents the deviation in the amount of electricity released by the energy storage system at time t in scenario s; E represents the number of market applications for energy storage frequency regulation at time t in scenario s; ess The energy storage device's capacity is represented by Δt; Δt represents the unit of time; β s，t This represents the proportion of power loss due to frequency regulation requests for energy storage within time period t in scenario s.

2. The wind-storage system capacity configuration optimization method considering energy storage lifespan according to claim 1, characterized in that, The variable operation and maintenance cost, which takes into account the lifespan loss of energy storage devices, is obtained by adding the lifespan loss cost of energy storage devices to the wind farm capacity configuration model. Based on the wind farm capacity configuration model, the lifespan loss cost of the energy storage device is added to the variable operation and maintenance cost of the wind-storage system, generating the variable operation and maintenance cost of the wind-storage system considering the lifespan loss of the energy storage device; the lifespan loss cost of the energy storage device is the lifespan loss cost of the energy storage device during operation; the variable operation and maintenance cost considering the lifespan loss of the energy storage device is expressed as: VC = VC es +C es ; Among them, VC es Indicates the variable operation and maintenance cost of energy storage; C es This indicates the lifespan loss cost of energy storage equipment.

3. The wind-storage system capacity configuration optimization method considering energy storage lifespan according to claim 1, characterized in that, The linearization optimization based on the wind farm capacity configuration model that considers energy storage lifetime includes: Based on the wind farm capacity configuration model that considers energy storage lifetime, the expression of the high-order nonlinear term of energy storage lifetime loss is simplified and optimized using the piecewise linearization method. Based on a wind farm capacity configuration model that considers energy storage lifetime, a binary expansion method is used to simplify and optimize the expression of the nonlinear term of the product of two continuous variables.

4. The wind-storage system capacity configuration optimization method considering energy storage lifespan according to claim 3, characterized in that, The method of simplifying and optimizing the expression of high-order nonlinear terms of energy storage lifetime loss using piecewise linearization includes: Obtain the expression for the higher-order nonlinear term in the wind farm capacity configuration model that considers energy storage lifetime; The expression for the higher-order nonlinear term is simplified and optimized using a piecewise linearization method to obtain a linearized expression for the higher-order nonlinear term.

5. The wind-storage system capacity configuration optimization method considering energy storage lifespan according to claim 3, characterized in that, The method of simplifying and optimizing the nonlinear expression of the product of two continuous variables using the binary expansion method includes: Obtain the nonlinear term expression for the product of continuous variables in the wind farm capacity configuration model that considers energy storage lifetime; The nonlinear expression of the product of continuous variables is simplified and optimized using the binary expansion method to obtain the linearized expression of the product of continuous variables.

6. A wind-storage system capacity configuration optimization system considering energy storage lifespan, characterized in that, The system includes: The model building module constructs a wind farm capacity configuration model considering wind farm storage and energy storage. This model is obtained through a model objective function constructed based on the investment cost, variable operation and maintenance cost, energy market revenue, frequency regulation market revenue, and the benefit of reducing deviation assessment penalties for the wind farm storage system. The model objective function is: minF=Cost sys +[VC-(Income E +Income Reg +Income Dev )] / S Where F is the optimal value of the model's objective function; Cost sys VC represents the investment cost of the wind and energy storage system; VC represents the variable operation and maintenance cost of the wind and energy storage system; Income E For system energy market revenue; Income Reg For revenue from the system frequency modulation market; Dev To reduce the revenue from performance evaluations and penalties for deviations in the system; S represents the number of scenarios; Based on the objective function of the model, the model constraints of the wind-storage system are constructed; the constraints include one or more of the following: energy storage configuration power capacity constraints, investment amount constraints, site constraints, upper and lower limits of system combined output constraints, wind and solar output constraints, and curtailment rate constraints. The model function addition module adds energy storage lifetime loss constraints to the wind farm capacity configuration model, proposing a wind farm capacity configuration model that considers energy storage lifetime; based on the wind farm capacity configuration model, it adds the lifetime loss cost of energy storage equipment to obtain the variable operation and maintenance cost considering energy storage lifetime loss; based on the wind farm capacity configuration model, it adds constraints corresponding to the lifetime loss of energy storage equipment; based on the variable operation and maintenance cost considering energy storage lifetime loss and the constraints corresponding to the lifetime loss of energy storage equipment, it obtains the wind farm capacity configuration model considering energy storage lifetime. The model optimization module performs linear optimization based on a wind farm capacity configuration model that takes into account energy storage lifespan; The constraints corresponding to the lifespan loss of energy storage devices, based on the wind farm capacity configuration model, include: The lifespan loss of the energy storage device is mainly related to its depth of discharge. The constraint corresponding to the lifespan loss of the energy storage device is its depth of discharge. for: in, This represents the discharge volume in the energy storage spot market at time t in scenario s; This represents the deviation in the amount of electricity released by the energy storage system at time t in scenario s; E represents the number of market applications for energy storage frequency regulation at time t in scenario s; ess The energy storage device's capacity is represented by Δt; Δt represents the unit of time; β s，t This represents the proportion of power loss due to frequency regulation requests for energy storage within time period t in scenario s.

7. An electronic device, characterized in that, The electronic device includes: Memory, used to store computer programs; A processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the wind-storage system capacity configuration optimization method considering energy storage lifetime as described in any one of claims 1 to 5.

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