Wind farm hybrid energy storage scheduling method, system and device containing retired batteries

Abstract

The application provides a wind farm mixed energy storage scheduling method, system and equipment containing retired batteries, and the method comprises the following steps: a target model of energy storage aggregators scheduling distributed energy storage participating in wind farm power fluctuation damping is established, relevant data such as retired battery continuous charging and discharging time scale and wind farm power fluctuation data is input into the target model, a mixed integer linear programming algorithm is combined to obtain an energy storage scheduling scheme, different energy storage scheduling schemes can be obtained by setting different retired battery continuous charging and discharging time scales, and according to the energy storage scheduling scheme, the energy storage aggregator rents new batteries and retired batteries from multiple distributed energy storages to participate in wind farm power fluctuation damping. According to the wind farm power fluctuation demand, the time scale of the retired battery continuous charging and discharging is set, the retired batteries and the new batteries are called to participate in the wind farm power fluctuation damping in the optimal economy of the energy storage aggregator, the application value of the retired batteries in the wind farm scheduling is embodied, and the battery utilization is improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a hybrid energy storage dispatching method, system and equipment for wind farms containing decommissioned batteries. Background Technology

[0002] Every year, a large number of power batteries enter the retirement stage. Recycling and reusing retired batteries can reduce the pressure of large-scale battery disposal, while increasing the recycling value of batteries and reducing corresponding resource consumption. Due to the randomness and fluctuation of wind farm output power, batteries with high safety and reliability are required for dispatching. Therefore, current research rarely focuses on retired batteries participating in ancillary services on the power supply side.

[0003] Compared to new batteries, retired batteries exhibit reduced storage capacity and lower charge / discharge rates, making them suitable for low-rate dispatching scenarios. However, due to frequent fluctuations in wind farm output power over various time periods, relying solely on retired batteries in such scenarios would accelerate their lifespan deterioration, significantly impacting the safe and stable operation of the system. While new batteries offer excellent energy storage and discharge rate characteristics and high reliability, relying entirely on them for dispatching would incur high initial investment costs. Therefore, to demonstrate the application value of retired batteries in wind farm dispatching, it is essential to propose a hybrid energy storage dispatching method that incorporates retired batteries into wind farms. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a hybrid energy storage scheduling method, system and equipment for wind farms containing decommissioned batteries, so as to overcome the above problems or at least partially solve the above problems.

[0005] A first aspect of this invention discloses a hybrid energy storage dispatch method for wind farms containing decommissioned batteries, the method comprising:

[0006] Establish a target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power fluctuation mitigation;

[0007] The continuous charge and discharge timescale of retired batteries, wind farm power fluctuation data, wind farm regional electricity price, transaction price between energy storage aggregators and distributed energy storage, and relevant parameters of distributed energy storage are input into the target model.

[0008] The target model is solved by combining mixed integer linear programming algorithm to obtain energy storage scheduling scheme. Different energy storage scheduling schemes can be obtained by setting different continuous charge and discharge time scales of retired batteries. The energy storage scheduling scheme includes: the total rated power of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, the rated capacity of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, environmental benefits and penalty costs.

[0009] According to the energy storage dispatch scheme, energy storage aggregators lease new and retired batteries from multiple distributed energy storage systems to participate in wind farm power smoothing.

[0010] Optionally, the establishment of the target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power fluctuation mitigation includes:

[0011] Determine the objective function for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing fluctuations, with the objective function aiming at the economic optimization of energy storage aggregators;

[0012] A first constraint is determined, which limits the charging power and discharging power of the new battery and the retired battery, and the total rated power of the new battery and the retired battery that need to be leased is calculated.

[0013] A second constraint is determined, which is used to calculate the rated capacity of the new battery that needs to be leased and the rated capacity of the retired battery;

[0014] A third constraint condition is determined, which is used to constrain the continuous charge-discharge timescale of the retired battery;

[0015] Solving the target model using a mixed-integer linear programming algorithm yields an energy storage scheduling scheme, including:

[0016] Using the first constraint, the second constraint, and the third constraint as constraints, the target model is solved by combining a mixed-integer linear programming algorithm to obtain the energy storage scheduling scheme.

[0017] Optionally, the objective function for the energy storage aggregator to schedule distributed energy storage to participate in wind farm power smoothing fluctuations can be expressed as:

[0018]

[0019] Wherein, U1 is the performance indicator function model with a time granularity of 1 minute for intraday power fluctuations, U2 is the performance indicator function model with a time granularity of 10 minutes for intraday power fluctuations, and U3 is the performance indicator function model for intraday power system accidents or special operating conditions. φ(t) represents the transaction price between energy storage aggregators and distributed energy storage, and φ(t) represents the regional grid electricity price. To leverage the environmental benefits of utilizing distributed energy storage to mitigate power fluctuations in wind farms, C PEN The penalty cost for failing to meet actual needs when calling upon distributed energy storage to participate in wind farm dispatch.

[0020] Optionally, the method of obtaining different energy storage scheduling schemes by setting different continuous charge-discharge timescales for retired batteries includes:

[0021] Analyzing the power fluctuation data of the wind farm, the continuous charge and discharge time scale of the retired battery was set to T. s ;

[0022] The continuous charge-discharge timescale is greater than or equal to T. s During a specific time period, energy storage aggregators utilize the retired batteries to participate in smoothing power fluctuations in wind farms; in continuous charge-discharge timescales less than T... s During the specified time period, energy storage aggregators utilize the new batteries to help smooth out power fluctuations in wind farms.

[0023] Different continuous charge and discharge timescales of retired batteries result in different time periods for utilizing the retired batteries, leading to different energy storage scheduling schemes.

[0024] Optionally, in the energy storage dispatch scheme, the process of determining the total rated power of new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, and the rated capacity of new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, includes:

[0025] Based on the wind farm power fluctuation data and the continuous charge and discharge time scale of the retired battery, determine the charging power and discharging power of the new battery, and the charging power and discharging power of the retired battery;

[0026] Based on the first constraint, the charging and discharging power of the new battery, and the charging and discharging power of the retired battery, the total rated power of the new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage is calculated.

[0027] Based on the second constraint, the charging and discharging power of the new battery, and the charging and discharging power of the retired battery, the rated capacity of the new battery and the rated capacity of the retired battery that the energy storage aggregator needs to lease from distributed energy storage are calculated.

[0028] Optionally, after obtaining different energy storage dispatch schemes, it is also necessary to analyze the economic benefits of different energy storage dispatch schemes, including:

[0029] Establish an economic benefit model for energy storage aggregators and analyze the economic benefits of different energy storage dispatch schemes.

[0030] Optionally, the target model for establishing the participation of energy storage aggregators in wind farm power fluctuation mitigation also includes:

[0031] A fourth constraint is determined, which is used to calculate the rated capacity of new batteries and the rated capacity of retired batteries that the energy storage aggregator needs to lease from distributed energy storage under different scheduling requirements.

[0032] Optionally, the transaction price between the energy storage aggregator and the distributed energy storage is obtained through negotiation between the energy storage aggregator and the distributed energy storage, and the negotiation process includes:

[0033] A negotiation model is established based on the cost model of new batteries and retired batteries in distributed energy storage and the intraday equivalent cycle count model.

[0034] Negotiations are conducted based on the aforementioned negotiation model to obtain the transaction price between the energy storage aggregator and the distributed energy storage.

[0035] A second aspect of this invention discloses a hybrid energy storage dispatch system for wind farms containing decommissioned batteries, the system comprising:

[0036] The model building module is used to build a target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing and fluctuations.

[0037] The parameter input module is used to input the continuous charge and discharge time scale of retired batteries, wind farm power fluctuation data, wind farm regional electricity price, transaction price between energy storage aggregators and distributed energy storage, and relevant parameters of distributed energy storage into the target model.

[0038] The model solving module is used to solve the target model by combining a mixed integer linear programming algorithm to obtain an energy storage scheduling scheme. Different energy storage scheduling schemes can be obtained by setting different continuous charge and discharge time scales of retired batteries. The energy storage scheduling scheme includes: the total rated power of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, the rated capacity of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, environmental benefits, and penalty costs.

[0039] The energy storage scheduling module is used to enable energy storage aggregators to lease new and retired batteries from multiple distributed energy storage facilities to participate in the smoothing of wind farm power fluctuations, according to the energy storage scheduling scheme.

[0040] A third aspect of the present invention discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes a hybrid energy storage scheduling method for wind farms containing decommissioned batteries as described in the first aspect of the present invention.

[0041] The embodiments of the present invention have the following advantages:

[0042] In this embodiment of the invention, multiple flexible distributed energy storage systems are centrally dispatched by energy storage aggregators to participate in wind farm power fluctuation mitigation, overcoming the previous limitation that single distributed energy storage systems could not participate in wind farm power fluctuation mitigation due to their low power and capacity. By constructing a target model for energy storage aggregators to dispatch distributed energy storage to participate in wind farm power fluctuation mitigation, an energy storage dispatch scheme is obtained to accurately estimate the power and capacity that energy storage aggregators need to lease from distributed energy storage systems for new and retired batteries within a day. At the same time, based on the wind farm power fluctuation requirements, a continuous charging and discharging time scale for retired batteries is set so that energy storage aggregators can economically lease retired and new batteries from multiple distributed energy storage systems to participate in wind farm power fluctuation mitigation. This demonstrates the application value of retired batteries in wind farm dispatch and improves battery utilization. Compared with the previous method of only using new batteries to participate in wind farm power fluctuation mitigation, this method also reduces the investment cost of energy storage aggregators. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of a hybrid energy storage scheduling method for wind farms containing decommissioned batteries, provided by an embodiment of the present invention.

[0045] Figure 2 This is a flowchart of the steps of a hybrid energy storage scheduling method for wind farms containing decommissioned batteries, provided by an embodiment of the present invention.

[0046] Figure 3 This is a schematic diagram of the structure of a hybrid energy storage scheduling method system for wind farms containing decommissioned batteries, provided in an embodiment of the present invention. Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] To demonstrate the application value of retired batteries in wind farms and the advantages of combining distributed energy storage with centralized trading, the applicant proposes the following technical concept: Based on the power fluctuation requirements of wind farms, highly flexible distributed energy storage will participate in the power smoothing of wind farms. Energy storage aggregators will use the most economically efficient energy storage dispatching scheme to lease new and retired batteries from multiple distributed energy storage systems to participate in the power smoothing of wind farms.

[0049] Specifically, the overall conceptualization process is as follows: Figure 1 As shown, firstly, the energy storage aggregator obtains relevant data on wind farm power fluctuations, and then acquires the capacity leasing capability of i distributed energy storage (DES). The energy storage aggregator (ESA) pays the rental fee to the distributed owner. To improve the utilization efficiency of the i DES and the rationality of the energy storage rental price, j ESAs need to intervene in the market transaction. After acquiring the right to use the DES, they control the changes in wind farm power fluctuations, limiting the power of the wind farm within an allowable range. The specific power change limits are shown in Table 1. An objective function is established with the optimal economic performance of the energy storage aggregator as the objective function, and relevant constraints are established, including charge / discharge constraints, state of charge (SOC) constraints, charge / discharge rate constraints, and time scale constraints for retired batteries, to solve for the energy storage dispatch configuration scheme. The solution method is to transform the relevant constraints into a mixed-integer linear programming problem. To demonstrate the application value of retired batteries on the power supply side, when the active power output of wind farms fluctuates frequently, energy storage aggregators participate in dispatching by leasing new batteries from distributed systems. If long-term charge and discharge conditions are involved, energy storage aggregators participate in dispatching by leasing retired batteries. Finally, the economic benefits of energy storage aggregators leasing distributed energy storage under different time scales of continuous charge and discharge of different retired batteries are analyzed to obtain the optimal energy storage dispatching scheme.

[0050] Table 1 Power Limits for Wind Farms

[0051]

[0052] Based on the above technical concept, embodiments of the present invention provide a hybrid energy storage dispatch method for wind farms containing decommissioned batteries, such as... Figure 2 As shown, Figure 2 A flowchart illustrating the steps of a hybrid energy storage scheduling method for wind farms containing decommissioned batteries, provided in this embodiment of the invention, includes:

[0053] Step S201: Establish a target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing fluctuations.

[0054] In this embodiment, distributed energy storage refers to a battery energy storage system within a region. This distributed energy storage is a hybrid system containing both new and retired batteries. Since the power and capacity of a single distributed energy storage unit are relatively small, it cannot independently participate in wind farm power fluctuation mitigation. Therefore, in this embodiment, multiple distributed energy storage units are centrally dispatched by an energy storage aggregator to participate in wind farm power fluctuation mitigation. That is, the energy storage aggregator simultaneously leases new and retired batteries from multiple distributed energy storage units for wind farm dispatch. To accurately estimate the power and capacity that the energy storage aggregator needs to lease from distributed energy storage units within a day, a target model for the energy storage aggregator to dispatch distributed energy storage units to participate in wind farm power fluctuation mitigation is established. Based on this target model, a specific energy storage dispatch scheme is solved.

[0055] In one feasible implementation, the establishment of a target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power fluctuation mitigation includes the following steps:

[0056] A1: Determine the objective function for energy storage aggregators to schedule distributed energy storage to participate in wind farm power fluctuation mitigation, wherein the objective function aims at the economic optimization of the energy storage aggregator. For example, the objective function for energy storage aggregators to participate in wind farm power fluctuation mitigation is expressed as:

[0057]

[0058] Wherein, U1 is the performance indicator function model with a time granularity of 1 minute for intraday power fluctuations, U2 is the performance indicator function model with a time granularity of 10 minutes for intraday power fluctuations, and U3 is the performance indicator function model for intraday power system accidents or special operating conditions. φ(t) represents the transaction price between energy storage aggregators and distributed energy storage, and φ(t) represents the regional grid electricity price. To leverage the environmental benefits of utilizing distributed energy storage to mitigate power fluctuations in wind farms, C PEN The penalty cost for failing to meet actual needs when calling upon distributed energy storage to participate in wind farm dispatch.

[0059] Specifically, the process of establishing the objective function for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing fluctuations is as follows:

[0060] First, an assessment model is established for different time granularities. Wind farm power fluctuation control is implemented according to the requirements of the national standard GB / T19963.1—2021. The assessment index function model for a power output time granularity of 1 minute is as follows:

[0061]

[0062]

[0063] Wherein, U1 is the performance indicator function model with a time granularity of 1 minute for intraday power fluctuations, U 1,sum D is a performance indicator model for power fluctuations within one year with a time granularity of 1 minute. s,day N represents the number of days of use in a typical scenario s. Y P(t) represents the total number of years that the energy storage aggregator participates in dispatching, and P(t) represents the real-time power output of the wind farm on a single day. lim P is the limit for the change in active power of a wind farm. n,s,d (t) represents the power output of the wind farm at time t on day d under typical scenario s in year n. Y T represents the total number of typical scenarios within a year, and T represents the total scheduling time within a day.

[0064] Among them, the real-time power output P(t) of the wind farm on a single day and the limit value of the change in the active power of the wind farm P lim The relationship between them can be represented as:

[0065]

[0066] Among them, P C (t) and P D (t) represents the charging power and discharging power of the battery, respectively. The batteries mentioned here include both new and retired batteries. The real-time daily power P(t) of the wind farm is compared with the active power variation limit P of the wind farm. lim The relationship between these factors determines the direction of charging and discharging in distributed energy storage.

[0067] The performance evaluation function model with a power output time granularity of 10 minutes is as follows:

[0068]

[0069]

[0070] Wherein, U2 is the performance indicator function model with a time granularity of 10 minutes for intraday power fluctuations, U 2,sum The evaluation index function model is a time granularity of 10 minutes for power fluctuations within one year.

[0071] When a power system accident or special operating conditions occur, and the power grid requires a reduction in the power output of wind farms to avoid equipment overload, the performance evaluation criteria for energy storage aggregators are as follows:

[0072]

[0073]

[0074] Among them, U3 is the performance indicator function model for intraday power system accidents or special operating conditions, U3,sum For the performance evaluation function model of the power system under faults or special operating conditions within one year, P BP Δt represents the active power limit for wind farm grid connection required by the power system in emergency situations, and Δt represents the time of a power system accident or special operating condition.

[0075] Secondly, consider the environmental benefits of reducing carbon emissions through energy storage. If the carbon emission reductions achieved by energy storage aggregators when using hybrid energy storage systems in dispatch are converted into reductions in coal-fired power generation, the daily environmental benefits from reduced carbon emissions are:

[0076]

[0077] in: For carbon emission prices, Δt' represents the carbon emission coefficient, and Δt' represents the wind farm dispatch time interval.

[0078] Furthermore, if energy storage aggregators fail to meet actual demand on the demand side when participating in wind farm dispatch, they should be subject to certain penalties. The penalty cost model is expressed as follows:

[0079]

[0080] in, The unit price for energy storage aggregators to participate in the power fluctuation mismatch penalty during wind farm migration, ΔP DEV (t) represents the deviation between the required reserve power and the actual consumed / supplied power.

[0081] Finally, by integrating the above-mentioned related functions, we obtain the objective function for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing fluctuations. The objective function includes the assessment benefits at different time granularities, the environmental benefits brought by participating in wind farm scheduling, and the penalty cost for not meeting the scheduling requirements.

[0082] A2: Determine the first constraint, which limits the charging and discharging power of the new battery and the retired battery, and calculate the total rated power of the new battery and the retired battery that needs to be leased. For example, the first constraint can be expressed as:

[0083]

[0084] in, and These are the charging power and discharging power of the new battery, respectively. and These are the charging power and discharging power of the retired battery, respectively, P max P is the maximum power output of the battery. rate The rated power of the hybrid energy storage system; ηC and η D These are the charging conversion efficiency and discharging conversion efficiency of the energy storage system, respectively.

[0085] A3: Determine the second constraint, which is used to calculate the rated capacity of the new battery to be leased and the rated capacity of the retired battery. For example, the second constraint can be expressed as:

[0086]

[0087]

[0088]

[0089] Among them, SOC new (t) and SOC re (t) represents the SOC value of the new battery and the SOC value of the retired battery at time t, respectively. The rated capacity of new batteries that intraday energy storage aggregators need to lease. ΔT represents the rated capacity of retired batteries that the intraday energy storage aggregator needs to lease from distributed energy storage, and ΔT is the granularity of the operating time for each point.

[0090] A4: Determine the third constraint condition, which is used to constrain the continuous charge and discharge time scale of the retired battery to obtain different configuration schemes.

[0091] To address the frequent changes in charge / discharge direction of retired batteries in distributed energy storage, a time scale for the charge / discharge direction of retired batteries is set. This scheme can reduce the impact of frequent charge / discharge on the lifespan of retired batteries, specifically as follows:

[0092]

[0093] in, and These represent the charging and discharging states of retired batteries, respectively; T s The timescale for continuous charge and discharge of retired batteries. Adjusting T s The size of the battery has a significant impact on the lifespan of retired batteries. Choosing a size that is too large will increase the configuration of new batteries, while choosing a size that is too small will affect the wear and tear of retired batteries.

[0094] In this embodiment, in order to accurately estimate the power and capacity that the energy storage aggregator needs to lease from distributed energy storage within the day, a target model is established for the energy storage aggregator to call on distributed energy storage to participate in the power smoothing of wind farms. In subsequent steps, the energy storage scheduling scheme corresponding to the continuous charging and discharging time scale of different retired batteries can be solved through this target model.

[0095] Step S202: Input the continuous charge and discharge timescale of retired batteries, wind farm power fluctuation data, wind farm regional electricity price, transaction price between energy storage aggregators and distributed energy storage, and relevant parameters of distributed energy storage into the target model.

[0096] In this embodiment, the continuous charge-discharge timescale of retired batteries refers to the number of consecutive charges or discharges equivalent to the retired batteries; wind farm power fluctuation data refers to the historical data of actual daily power changes of wind farms under a typical scenario; wind farm regional electricity price refers to the service unit price paid by the wind farm to the energy storage aggregator when the energy storage aggregator participates in the wind farm power fluctuation mitigation, which is generally designed as the grid-connected electricity price of the regional wind farm; the transaction price between the energy storage aggregator and distributed energy storage refers to the unit price at which the energy storage aggregator leases new and retired batteries from a distributed energy storage facility; the relevant parameters of distributed energy storage refer to battery-related parameters such as the maximum power, charging efficiency, and discharge efficiency of the new and retired batteries in the distributed energy storage facility.

[0097] Step S203: Solve the target model using a mixed-integer linear programming algorithm to obtain an energy storage scheduling scheme. Different energy storage scheduling schemes can be obtained by setting different continuous charge and discharge time scales for retired batteries. The energy storage scheduling scheme includes: the total rated power of new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, the rated capacity of new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, environmental benefits, and penalty costs.

[0098] In this embodiment, by analyzing the power fluctuation demand of the wind farm and setting the time scale of the continuous charging point of the retired battery, the time period for scheduling new batteries and retired batteries to participate in the power smoothing of the wind farm can be determined based on the set time scale of the continuous charging point of the retired battery. Based on the scheduling time periods of the new batteries and retired batteries and the parameters input in step S202, the specific energy storage scheduling scheme can be solved.

[0099] In one feasible implementation, the target model is solved using a mixed-integer linear programming algorithm to obtain an energy storage scheduling scheme, including:

[0100] Using the first constraint, the second constraint, and the third constraint as constraints, the target model is solved by combining a mixed-integer linear programming algorithm to obtain the energy storage scheduling scheme.

[0101] In this embodiment, the first, second, and third constraints are used as constraints, and the Big M method is used to transform the nonlinear constraints of the target model into a mixed integer linear programming problem, which is then solved to obtain the energy storage scheduling scheme.

[0102] In one feasible implementation, the different energy storage scheduling schemes obtained by setting different continuous charge-discharge timescales for retired batteries include:

[0103] Analyzing the power fluctuation data of the wind farm, the continuous charge and discharge time scale of the retired battery was set to T. s ;

[0104] The continuous charge-discharge timescale is greater than or equal to T. s During a specific time period, energy storage aggregators utilize the retired batteries to participate in smoothing power fluctuations in wind farms; in continuous charge-discharge timescales less than T... s During the specified time period, energy storage aggregators utilize the new batteries to help smooth out power fluctuations in wind farms.

[0105] The different continuous charge and discharge timescales of retired batteries, the different time periods for calling up the new batteries and the retired batteries, and the different energy storage scheduling schemes obtained by solving the problem.

[0106] In this embodiment, setting the continuous charge / discharge timescale of the retired battery is equivalent to setting the number of consecutive charges or discharges of the retired battery. For example, in a certain scenario, the operating time granularity ΔT at each power fluctuation point of the wind farm is 1 minute. If the continuous charge / discharge timescale of the retired battery is set to 10 minutes (T... s If the timescale is 10 minutes, it means the retired battery will be continuously charged or discharged 10 times. This means that retired batteries will only be used to participate in wind farm power stabilization when there are more than 10 continuous charging points or more than 10 continuous discharging points in the wind farm; otherwise, new batteries will be used. Different continuous charging and discharging timescales for retired batteries will result in different time periods during the day when retired and new batteries are used in wind farm scheduling. This, in turn, will cause changes in the real-time charging and discharging power and required rated capacity of retired and new batteries. Therefore, by setting different continuous charging and discharging timescales for retired batteries, different energy storage scheduling schemes can be obtained.

[0107] In this embodiment, in order to explore the application value of retired batteries in wind farm dispatch, by analyzing the historical power fluctuation data of wind farms, and based on the set continuous charge and discharge time scale of retired batteries, when the active power output of wind farms fluctuates frequently (i.e., the continuous charge and discharge time scale is less than the set continuous charge and discharge time scale of retired batteries), the energy storage aggregator leases new batteries from distributed energy storage to participate in dispatch. If the operation is under long-term charge and discharge conditions (i.e., the continuous charge and discharge time scale is greater than or equal to the set continuous charge and discharge time scale of retired batteries), the energy storage aggregator leases retired batteries to participate in dispatch.

[0108] In one feasible implementation, the process of determining the total rated power of new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, the rated capacity of new batteries that the energy storage aggregator needs to lease from distributed energy storage, and the rated capacity of retired batteries in the energy storage dispatch scheme includes:

[0109] Based on the wind farm power fluctuation data and the continuous charge and discharge time scale of the retired battery, determine the charging power and discharging power of the new battery, and the charging power and discharging power of the retired battery;

[0110] Based on the first constraint, the charging and discharging power of the new battery, and the charging and discharging power of the retired battery, the total rated power of the new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage is calculated.

[0111] Based on the second constraint, the charging and discharging power of the new battery, and the charging and discharging power of the retired battery, the rated capacity of the new battery and the rated capacity of the retired battery that the energy storage aggregator needs to lease from distributed energy storage are calculated.

[0112] In this embodiment, based on the power fluctuation data of the wind farm and the continuous charging and discharging timescale of the retired batteries, the time periods for the aggregator to schedule new batteries to participate in the power fluctuation mitigation of the wind farm and the time periods for scheduling retired batteries to participate in the power fluctuation mitigation of the wind farm can be determined respectively. Then, the charging power and discharging power of the new batteries and the charging power and discharging power of the retired batteries can be determined. Finally, based on the first constraint and the second constraint, the total rated power of the new batteries and retired batteries that the energy storage aggregator needs to lease from the distributed energy storage, as well as the rated capacity of the new batteries and the rated capacity of the retired batteries that the energy storage aggregator needs to lease from the distributed energy storage can be obtained respectively.

[0113] Step S204: According to the energy storage scheduling scheme, the energy storage aggregator leases new and retired batteries from multiple distributed energy storage facilities to participate in the power smoothing of wind farms.

[0114] In this embodiment, the energy storage aggregator schedules new or retired batteries to participate in wind farm power smoothing based on the set continuous charge and discharge time scale of retired batteries. The specific rated power and rated capacity required for scheduling are configured according to the energy storage scheduling scheme, which requires the energy storage aggregator to lease the total rated power of new and retired batteries from distributed energy storage, as well as the rated capacity of new and retired batteries that need to be leased from distributed energy storage.

[0115] In this embodiment, multiple flexible distributed energy storage systems are centrally dispatched by an energy storage aggregator to participate in wind farm power fluctuation mitigation. This overcomes the previous limitation that a single distributed energy storage system could not participate in wind farm power fluctuation mitigation due to its low power and capacity. Simultaneously, based on the wind farm power fluctuation requirements, a continuous charging and discharging timescale for retired batteries is set. This allows the energy storage aggregator to economically lease retired and new batteries from multiple distributed energy storage systems to participate in wind farm power fluctuation mitigation, demonstrating the application value of retired batteries in wind farm dispatch and improving battery utilization. Compared to the previous method of using only new batteries to participate in wind farm power fluctuation mitigation, this method also reduces the investment costs for energy storage aggregators.

[0116] In one feasible implementation, after obtaining different energy storage dispatch schemes, it is also necessary to analyze the economic benefits of the different energy storage dispatch schemes, including:

[0117] An economic benefit model for energy storage aggregators is established to analyze the economic benefits of different energy storage dispatch schemes.

[0118] In this implementation, the economic benefit model of the energy storage aggregator refers to the economic benefit of the energy storage aggregator's reference to the target model of distributed energy storage and wind farm power fluctuation mitigation within one year, which can be expressed as:

[0119]

[0120] Among them, F j For energy storage aggregators to participate in wind farm power fluctuation mitigation, g inf For the inflation rate, i ESA,j D represents the internal rate of return for energy storage aggregators. s,day N represents the number of days of use in a typical scenario s. Y S represents the total number of years that the energy storage aggregator participates in dispatching. Y T represents the total number of typical scenarios within a year, and T represents the total scheduling time within a day.

[0121] By analyzing the economics of energy storage dispatch schemes corresponding to different time scales of continuous charging and discharging of retired batteries, energy storage aggregators select the most economical method to dispatch retired and new batteries to distributed energy storage to participate in the power smoothing of wind farms.

[0122] Furthermore, in this embodiment, an economic benefit model for distributed energy storage is established to analyze its economic benefits. Distributed energy storage, through its interaction with energy storage aggregators, [is involved in this process]. max After rounds of negotiations, a more favorable transaction price was obtained. However The cost of distributed energy storage in mitigating power fluctuations in wind farms is directly related to its role in this process. Therefore, the full lifecycle economic benefit model for distributed energy storage is as follows:

[0123]

[0124] Among them, i DES,i For distributed energy storage, the internal rate of return (IRR) and These are the costs incurred by new and retired distributed energy storage batteries participating in dispatching. This refers to the transaction price between distributed energy storage and energy storage aggregators.

[0125] In one feasible implementation, the target model for establishing the participation of energy storage aggregators in wind farm power fluctuation mitigation also includes:

[0126] A fourth constraint is determined, which is used to calculate the rated capacity of new batteries and the rated capacity of retired batteries that the energy storage aggregator needs to lease from distributed energy storage under different scheduling requirements.

[0127] As national and provincial / municipal policies have been successively introduced regarding the allocation ratio and duration of energy storage participation in power generation-side dispatch, typically ranging from 2 to 4 hours, and to promote the integrated development of power generation, grid, and load, priority has recently been given to suppliers with durations exceeding 4 hours. Therefore, the requirements of these policies must be considered when configuring energy storage capacity and power. To calculate the rated capacity of new batteries and the rated capacity of retired batteries that energy storage aggregators need to lease from distributed energy storage under different dispatch requirements (i.e., different policy requirements), a rate constraint, namely the fourth constraint, is set. This fourth constraint can be expressed as:

[0128]

[0129] in, and These represent the rated capacity of new and retired batteries that intraday energy storage aggregators need to lease. For intraday energy storage aggregators, the rated capacity of retired batteries that need to be leased is γ re γ and γ represent the rate of return for retired batteries and distributed energy storage (hybrid energy storage system), respectively. re The values ​​for γ and γ can be set according to the policy requiring the energy storage to participate in the scheduling of charging and discharging.

[0130] When energy storage aggregators lease distributed energy storage to participate in wind farm dispatch, they can adjust the gamma. re We will use γ to analyze the optimal strategy for leasing distributed energy storage.

[0131] In one feasible implementation, the transaction price between the energy storage aggregator and the distributed energy storage is obtained through negotiation between the energy storage aggregator and the distributed energy storage, and the negotiation process includes:

[0132] A negotiation model is established based on the cost model of new batteries and retired batteries in distributed energy storage and the intraday equivalent cycle count model.

[0133] Negotiations are conducted based on the aforementioned negotiation model to obtain the transaction price between the energy storage aggregator and the distributed energy storage.

[0134] First, relevant cost parameters for distributed energy storage are obtained, and cost models for new and retired batteries are established. The energy storage aggregator obtains the active power fluctuation curve of the wind farm and determines the regional electricity price of the wind farm, i.e., the service unit price paid by the wind farm to the energy storage aggregator when the aggregator participates in smoothing power fluctuations. After the energy storage aggregator and the wind farm confirm the validity of the transaction, the aggregator negotiates with the distributed energy storage provider to determine the transaction price between them.

[0135] Secondly, a daily equivalent cycle count model for the battery is established. A power function curve is constructed showing the relationship between the battery's energy storage cycle count and its depth of discharge (DOD), as detailed below:

[0136] N cycle =f(DOD)

[0137] Establishing battery energy storage in DOD x The equivalent number of loops is as follows:

[0138]

[0139] Where, f(DOD) st (For fixed depth of discharge DOD) st The number of iterations under the given condition, f(DOD) x Depth of Discharge (DOD) x The number of loops below;

[0140] Then, the equivalent number of loops within the day is established, as shown in the following expression:

[0141]

[0142] Finally, negotiation models for distributed energy storage and energy storage aggregators are established respectively.

[0143] The Rubinstein model is widely used in economics for bilateral bargaining transactions. This model is used to simulate the price negotiation process between distributed energy storage and energy storage aggregators. First, let's assume the acceptable price range for distributed energy storage is... Minimum Acceptable Price This is related to the costs incurred when it participates in smoothing out power fluctuations in wind farms, as specifically expressed below:

[0144]

[0145]

[0146]

[0147] in, and These are the costs incurred by new and retired distributed energy storage batteries participating in dispatching. and These are the initial investment costs for new and retired batteries, respectively. and The maintenance costs for new and retired batteries are respectively. and These represent the maximum number of cycles for new and retired batteries, respectively. and These represent the equivalent cycle counts for new and retired batteries, respectively. This represents the minimum expected rate of return for distributed energy storage. The maximum acceptable price can be expressed as:

[0148]

[0149] in, This represents the average historical transaction price of distributed energy storage. This represents the highest expected rate of return for distributed energy storage.

[0150] The first round of decisions for distributed energy storage often starts with the highest price. The bidding process begins, continuing through rounds 2 through m until distributed energy storage and energy storage aggregators reach an acceptable price. Assuming distributed energy storage employs a linearly decreasing bidding strategy, the bid in round m will be... for:

[0151]

[0152] Where, m max This represents the maximum number of negotiation rounds.

[0153] Energy storage aggregators also formulate pricing strategies to ensure their own acceptable profit margins. Specifically, it is expressed as follows:

[0154]

[0155] in, and The lowest and highest bids from energy storage aggregators, and φ(t) represents the minimum and maximum acceptable rates of return for energy storage aggregators, and φ(t) represents the regional grid electricity price.

[0156] In contrast to distributed energy storage, energy storage aggregators offered the lowest price in the first round. The initial application strategy. In reality, energy storage aggregators are influenced by market transactions, therefore a correction factor χ² needs to be considered. j The energy storage aggregator's offer in the m-th round of negotiations is as follows:

[0157]

[0158] Where, χ j This is a price adjustment factor for energy storage aggregators.

[0159] If in the m-th round of negotiations... If the negotiation is successful, the real-time transaction price between distributed energy storage and energy storage aggregators will be expressed as follows:

[0160]

[0161] In this embodiment, in order to reduce transaction risks and avoid negotiation failures, distributed energy storage determines its pricing strategy based on the daily cycle count of batteries and the operating costs of new and retired batteries. Similarly, energy storage aggregators formulate their own pricing strategies to meet their own acceptable profit range. The energy storage aggregators and distributed energy storage need to negotiate to reach a reasonable transaction price.

[0162] Figure 3 This is a schematic diagram of a hybrid energy storage dispatch system for a wind farm containing decommissioned batteries, according to an embodiment of the present invention. Figure 3 As shown, the system includes:

[0163] Model building module 31 is used to build a target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing fluctuations.

[0164] The parameter input module 32 is used to input the continuous charge and discharge time scale of retired batteries, wind farm power fluctuation data, wind farm regional electricity price, transaction price between energy storage aggregators and distributed energy storage, and relevant parameters of distributed energy storage into the target model.

[0165] The model solving module 33 is used to solve the target model by combining a mixed integer linear programming algorithm to obtain an energy storage scheduling scheme. Different energy storage scheduling schemes can be obtained by setting different continuous charge and discharge time scales of retired batteries. The energy storage scheduling scheme includes: the total rated power of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, the rated capacity of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, environmental benefits and penalty costs.

[0166] The energy storage scheduling module 34 is used to enable the energy storage aggregator to lease new and retired batteries from multiple distributed energy storage facilities to participate in the power smoothing of wind farms, according to the energy storage scheduling scheme.

[0167] Optionally, the model building module includes:

[0168] The objective function determination unit is used to determine the objective function for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing fluctuations. The objective function aims at the economic optimization of energy storage aggregators.

[0169] The first condition determination unit is used to determine the first constraint condition, which is used to limit the charging power and discharging power of the new battery and the retired battery, and to calculate the total rated power of the new battery and the retired battery that need to be leased.

[0170] The second condition determination unit is used to determine the second constraint condition, which is used to calculate the rated capacity of the new battery that needs to be leased and the rated capacity of the retired battery.

[0171] The third condition determination unit is used to determine the third constraint condition, which is used to constrain the continuous charge and discharge time scale of the retired battery.

[0172] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When executed by the processor, the program implements the hybrid energy storage scheduling method for wind farms containing decommissioned batteries as described in the first aspect of this invention.

[0173] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0174] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0175] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0176] The above provides a detailed description of a hybrid energy storage dispatching method and system for wind farms containing decommissioned batteries provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A hybrid energy storage dispatch method for wind farms containing decommissioned batteries, characterized in that, The method includes: Establish a target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power fluctuation mitigation; The continuous charge and discharge timescale of retired batteries, wind farm power fluctuation data, wind farm regional electricity price, transaction price between energy storage aggregators and distributed energy storage, and relevant parameters of distributed energy storage are input into the target model. The target model is solved by combining mixed integer linear programming algorithm to obtain energy storage scheduling scheme. Different energy storage scheduling schemes can be obtained by setting different continuous charge and discharge time scales of retired batteries. The energy storage scheduling scheme includes: the total rated power of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, the rated capacity of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, environmental benefits and penalty costs. According to the energy storage dispatch scheme, energy storage aggregators lease new and retired batteries from multiple distributed energy storage systems to participate in wind farm power smoothing and fluctuations. The target model for establishing a system for energy storage aggregators to schedule distributed energy storage to participate in wind farm power fluctuation mitigation includes: Determine the objective function for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing fluctuations, with the objective function aiming at the economic optimization of energy storage aggregators; A first constraint is determined, which limits the charging power and discharging power of the new battery and the retired battery, and the total rated power of the new battery and the retired battery that need to be leased is calculated. A second constraint is determined, which is used to calculate the rated capacity of the new battery that needs to be leased and the rated capacity of the retired battery; A third constraint condition is determined, which is used to constrain the continuous charge-discharge timescale of the retired battery; Solving the target model using a mixed-integer linear programming algorithm yields an energy storage scheduling scheme, including: Using the first constraint, the second constraint, and the third constraint as constraints, the target model is solved by combining a mixed integer linear programming algorithm to obtain the energy storage scheduling scheme; The different energy storage scheduling schemes that can be obtained by setting different continuous charge and discharge time scales for retired batteries include: Analyzing the power fluctuation data of the wind farm, the continuous charge and discharge time scale of the retired batteries was set as follows: ; Continuous charge-discharge timescale greater than or equal to During a specific time period, energy storage aggregators utilize the retired batteries to participate in smoothing power fluctuations in wind farms; within a continuous charge / discharge timescale of less than [a specific timescale]. During the specified time period, energy storage aggregators utilize the new batteries to help smooth out power fluctuations in wind farms. Different continuous charge and discharge timescales of retired batteries result in different time periods for utilizing the retired batteries, leading to different energy storage scheduling schemes. The objective function for the energy storage aggregator to schedule distributed energy storage to participate in wind farm power fluctuation mitigation can be expressed as: in, The evaluation index function model is a 1-minute time granularity model for intraday power fluctuations. The evaluation index function model is a 10-minute time granularity model for intraday power fluctuations. This is a performance indicator function model for intraday power system accidents or special operating conditions. The transaction price between energy storage aggregators and distributed energy storage. For regional power grid electricity prices, To leverage distributed energy storage to mitigate environmental benefits from wind farm power fluctuations. The penalty cost for failing to meet actual needs when calling upon distributed energy storage to participate in wind farm dispatch.

2. The method according to claim 1, characterized in that, In the energy storage dispatch scheme, the process of determining the total rated power of new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, and the rated capacity of new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage includes: Based on the wind farm power fluctuation data and the continuous charge and discharge time scale of the retired battery, determine the charging power and discharging power of the new battery, and the charging power and discharging power of the retired battery; Based on the first constraint, the charging and discharging power of the new battery, and the charging and discharging power of the retired battery, the total rated power of the new and retired batteries that the energy storage aggregator needs to lease from distributed energy storage is calculated. Based on the second constraint, the charging and discharging power of the new battery, and the charging and discharging power of the retired battery, the rated capacity of the new battery and the rated capacity of the retired battery that the energy storage aggregator needs to lease from distributed energy storage are calculated.

3. The method according to claim 1, characterized in that, After obtaining the different energy storage dispatch schemes, it is also necessary to analyze the economic benefits of the different energy storage dispatch schemes, including: Establish an economic benefit model for energy storage aggregators and analyze the economic benefits of different energy storage dispatch schemes.

4. The method according to claim 1, characterized in that, The target model for establishing energy storage aggregators to participate in wind farm power fluctuation mitigation also includes: A fourth constraint is determined, which is used to calculate the rated capacity of new batteries and the rated capacity of retired batteries that the energy storage aggregator needs to lease from distributed energy storage under different scheduling requirements.

5. The method according to claim 1, characterized in that, The transaction price between the energy storage aggregator and the distributed energy storage is obtained through negotiation between the energy storage aggregator and the distributed energy storage, and the negotiation process includes: A negotiation model is established based on the cost model of new batteries and retired batteries in distributed energy storage and the intraday equivalent cycle count model. Negotiations are conducted based on the aforementioned negotiation model to obtain the transaction price between the energy storage aggregator and the distributed energy storage.

6. A hybrid energy storage dispatch system for wind farms containing decommissioned batteries, characterized in that, The system is implemented by the method described in any one of claims 1-5, the system comprising: The model building module is used to build a target model for energy storage aggregators to schedule distributed energy storage to participate in wind farm power smoothing and fluctuations. The parameter input module is used to input the continuous charge and discharge time scale of retired batteries, wind farm power fluctuation data, wind farm regional electricity price, transaction price between energy storage aggregators and distributed energy storage, and relevant parameters of distributed energy storage into the target model. The model solving module is used to solve the target model by combining a mixed integer linear programming algorithm to obtain an energy storage scheduling scheme. Different energy storage scheduling schemes can be obtained by setting different continuous charge and discharge time scales of retired batteries. The energy storage scheduling scheme includes: the total rated power of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, the rated capacity of new batteries and retired batteries that the energy storage aggregator needs to lease from distributed energy storage, environmental benefits, and penalty costs. The energy storage scheduling module is used to enable energy storage aggregators to lease new and retired batteries from multiple distributed energy storage facilities to participate in the smoothing of wind farm power fluctuations, according to the energy storage scheduling scheme.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executed, implements the hybrid energy storage scheduling method for wind farms with decommissioned batteries as described in any one of claims 1-5.

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