Wind power plant energy storage capacity optimization method and electronic equipment

Abstract

The invention discloses a wind power plant energy storage capacity optimization method and electronic equipment. The wind power plant energy storage capacity optimization method comprises the following steps: acquiring the total installed capacity of wind turbine generators of a wind power plant; constructing an under-power-generation target function and a power-abandoning target function; iteratively updating the battery energy storage capacity selection coefficient until an iteration ending condition is met, in each iteration, calculating the battery energy storage rated capacity according to the total installed capacity of the wind turbine generator of the wind power plant and the battery energy storage capacity selection coefficient, updating the under-power-generation objective function and the abandoned power generation objective function, and calculating a capacity configuration reference index; and taking the battery energy storage capacity selection coefficient when the capacity configuration reference index is minimum as the optimal battery energy storage capacity selection coefficient, and determining the optimal battery energy storage rated capacity. The optimal battery energy storage capacity selection coefficient is selected according to the wind curtailment condition and the vacancy condition, so that the optimal battery energy storage rated capacity is accurately determined by combining the total installed capacity of the wind turbine generator of the wind power plant, and the output of the wind turbine generator is stabilized.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power, and particularly to a method for optimizing the energy storage capacity of a wind farm, an electronic device, a storage medium, and a computer program product. Background Art

[0002] Few existing wind farms are equipped with a certain capacity of battery energy storage system to cooperate with wind turbines to participate in power system peak shaving, frequency modulation, and voltage regulation. With the access of large-scale new energy, due to the intermittent and fluctuating characteristics of new energy, the power system is gradually turning to a weak power grid environment. Configuring a certain capacity of battery energy storage system helps the safe and stable operation of the power system and increases the income of peak shaving and frequency modulation mileage.

[0003] However, the rated capacity of the battery energy storage configured by the existing technology is generally determined according to experience, and it is not the optimal rated capacity of the battery energy storage. Summary of the Invention

[0004] Based on this, in view of the technical problem that the existing technology lacks a method for determining the optimal rated capacity of battery energy storage for a wind farm, it is necessary to provide a method for optimizing the energy storage capacity of a wind farm, an electronic device, a storage medium, and a computer program product.

[0005] The present invention provides a method for optimizing the energy storage capacity of a wind farm, including: Obtaining the total installed capacity of the wind turbines in the wind farm; Constructing an under-generation objective function and a curtailment objective function; Iteratively updating the battery energy storage capacity selection coefficient until the iteration end condition is satisfied. In each iteration, calculate the rated capacity of the battery energy storage according to the total installed capacity of the wind turbines in the wind farm and the battery energy storage capacity selection coefficient, update the under-generation objective function and the curtailment objective function according to the rated capacity of the battery energy storage, and calculate the capacity configuration reference index according to the under-generation objective function and the curtailment objective function; Taking the battery energy storage capacity selection coefficient when the capacity configuration reference index is the smallest as the optimal battery energy storage capacity selection coefficient, and determining the optimal rated capacity of the battery energy storage according to the optimal battery energy storage capacity selection coefficient and the total installed capacity of the wind turbines in the wind farm.

[0006] Further, the constructing of the under-generation objective function includes: Determining the required discharge amount of the battery energy storage in all under-generation time periods in a typical day, where the under-generation time period is the time section with under-generation amount when tracking the power generation plan in a typical day; Constructing the under-generation objective function as: , where N is the number of under-generation time periods in the typical day, is the optimal state of charge of the battery energy storage, is the required discharge capacity of the battery energy storage for the i-th under-generation time period, is the rated capacity of the battery energy storage.

[0007] Furthermore, determining the required discharge capacity of the battery energy storage for all under-generation time periods in the typical day includes: Calculating the required discharge capacity of the battery energy storage for all under-generation time periods in the typical day based on the calculation formula for the required discharge capacity of the battery energy storage in the under-generation time period. The calculation formula for the required discharge capacity of the battery energy storage in the i-th under-generation time period in the typical day is: , where is the real-time under-generation output of the wind farm in the i-th under-generation time period, , is the real-time required output of the power generation plan in the i-th under-generation time period, is the comprehensive real-time output of all wind turbines in the wind farm in the i-th under-generation time period.

[0008] Further, constructing the abandoned power generation objective function includes: Determining the abandoned power generation in all abandoned power generation time periods in the typical day. The abandoned power generation time period is the time section with surplus power generation while tracking the power generation plan in the typical day; Constructing the abandoned power generation objective function as: , where Q is the number of under-generation time periods in this typical day, is the optimal state of charge of the battery energy storage, is the abandoned power generation in the i-th abandoned power generation time period, is the rated capacity of the battery energy storage.

[0009] Furthermore, determining the abandoned power generation in all abandoned power generation time periods in the typical day includes: Calculating the abandoned power generation in all abandoned power generation time periods in the typical day based on the calculation formula for the abandoned power generation in the abandoned power generation time period. The calculation formula for the abandoned power generation in the i-th abandoned power generation time period in the typical day is: , where is the real-time abandoned output of the wind farm in the i-th abandoned power generation time period, , is the real-time required output of the power generation plan in the i-th abandoned power generation time period, is the comprehensive real-time output of all wind turbines in the wind farm in the i-th abandoned power generation time period.

[0010] Further, in each iteration, the rated capacity of the battery energy storage is calculated according to the total installed capacity of the wind turbines in the wind farm and the selection coefficient of the battery energy storage capacity. According to the rated capacity of the battery energy storage, the under-generation objective function and the curtailment objective function are updated, and the capacity configuration reference index is calculated according to the under-generation objective function and the curtailment objective function, including: In each iteration, the following operations are performed: Update the selection coefficient of the battery energy storage capacity in this iteration to the selection coefficient of the battery energy storage capacity in the previous iteration plus a preset increment; Calculate the rated capacity of the battery energy storage in this iteration as the total installed capacity of the wind turbines in the wind farm multiplied by the selection coefficient of the battery energy storage capacity in this iteration; According to the rated capacity of the battery energy storage, the under-generation objective function and the curtailment objective function are updated, and the capacity configuration reference index is calculated according to the under-generation objective function and the curtailment objective function.

[0011] Further, the calculation of the capacity configuration reference index according to the under-generation objective function and the curtailment objective function includes: Calculate the capacity configuration reference index as , where is the capacity configuration reference index, is the value of the under-generation objective function, is the value of the curtailment objective function.

[0012] The present invention provides an electronic device, including: At least one processor; and, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the wind farm energy storage capacity optimization method as described above.

[0013] The present invention provides a storage medium that stores computer instructions, and when a computer executes the computer instructions, it is used to execute all steps of the wind farm energy storage capacity optimization method as described above.

[0014] The present invention provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the wind farm energy storage capacity optimization method as described above is implemented.

[0015] The present invention constructs an under-generation objective function and an abandoned-generation objective function, iteratively updates the battery energy storage capacity selection coefficient, updates the under-generation objective function and the abandoned-generation objective function based on the battery energy storage capacity selection coefficient, calculates a capacity configuration reference index according to the under-generation objective function and the abandoned-generation objective function, and finally determines the optimal battery energy storage rated capacity according to the battery energy storage capacity selection coefficient when the capacity configuration reference index is minimized. Therefore, the present invention selects the best battery energy storage capacity selection coefficient according to two situations of wind abandonment and shortage, so as to accurately determine the optimal battery energy storage rated capacity in combination with the total installed capacity of the wind turbines in the wind farm, which helps to suppress the output of the wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a working flowchart of a method for optimizing the energy storage capacity of a wind farm according to an embodiment of the present invention; Figure 2 is a working flowchart of a method for optimizing the energy storage capacity of a wind farm according to another embodiment of the present invention; Figure 3 is a schematic diagram of the power curve of a single wind turbine of a certain model; Figure 4 is a schematic diagram of the comprehensive output curve of all wind turbines in a wind farm of a certain model of wind turbine on a typical day; Figure 5 is a working flowchart of iteratively determining the optimal battery energy storage capacity selection coefficient according to the best embodiment of the present invention; Figure 6 is a schematic diagram of the hardware structure of an electronic device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following further describes the specific embodiments of the present invention with reference to the drawings. The same components are denoted by the same reference numerals. It should be noted that the terms "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to the directions in the drawings, and the terms "inner" and "outer" refer to the directions towards or away from the geometric center of a specific component, respectively.

[0018] As Figure 1 shown is a working flowchart of a method for optimizing the energy storage capacity of a wind farm according to an embodiment of the present invention, including: Step S101, obtaining the total installed capacity of the wind turbines in the wind farm; Step S102, constructing an under-generation objective function and an abandoned-generation objective function; Step S103: Iteratively update the battery energy storage capacity selection coefficient until the iteration end condition is met. In each iteration, calculate the rated battery energy storage capacity according to the total installed capacity of the wind turbines in the wind farm and the battery energy storage capacity selection coefficient. Update the under-generation objective function and the curtailment objective function according to the rated battery energy storage capacity, and calculate the capacity configuration reference index according to the under-generation objective function and the curtailment objective function. Step S104: Use the battery energy storage capacity selection coefficient when the capacity configuration reference index is minimized as the optimal battery energy storage capacity selection coefficient, and determine the optimal rated battery energy storage capacity according to the optimal battery energy storage capacity selection coefficient and the total installed capacity of the wind turbines in the wind farm.

[0019] Specifically, the present invention can be applied to an electronic device with processing capabilities, such as a computer.

[0020] This method selects a wind farm that needs to configure energy storage as the research object, samples the annual active power (referred to as output) of all the units in the wind farm according to the power model of a single wind turbine in the wind farm, and comprehensively selects the best battery energy storage capacity selection coefficient according to two situations of wind curtailment and power shortage.

[0021] First, execute step S101 to obtain the total installed capacity of the wind turbines in the wind farm.

[0022] Specifically, select a wind farm that needs to configure energy storage as the research object. If the wind farm is in the early stage of wind farm design, determine the total installed capacity of the wind turbines in the wind farm according to the design. If the wind farm has been operating for some time, determine the total installed capacity of the wind turbines in the wind farm according to the actual measured values.

[0023] Then, execute step S102 to construct the under-generation objective function and the curtailment objective function.

[0024] Specifically, the under-generation objective function is used to represent the standard deviation of the under-generated electricity and the optimal value of the state of charge (SOC) of the energy storage device. The curtailment objective function is used to represent the standard deviation of the curtailed electricity and the optimal value of the SOC of the energy storage device.

[0025] Then, execute step S103 to iteratively update the battery energy storage capacity selection coefficient until the iteration end condition is met. In each iteration, calculate the rated battery energy storage capacity according to the total installed capacity of the wind turbines in the wind farm and the battery energy storage capacity selection coefficient. Update the under-generation objective function and the curtailment objective function according to the rated battery energy storage capacity, and calculate the capacity configuration reference index according to the under-generation objective function and the curtailment objective function.

[0026] Specifically, initialize the selection coefficient of the input battery energy storage capacity to the initial value, and then increase the selection coefficient of the battery energy storage capacity in each iteration. Through iterative calculation, obtain the capacity configuration reference index under the selection coefficient of the battery energy storage capacity for each iteration. .

[0027] When the iteration end condition is met, stop the iteration and execute step S104. The iteration end condition can be that the number of iterations reaches the number threshold, or the capacity configuration reference index is less than the index threshold, or the selection coefficient of the battery energy storage capacity reaches the preset value.

[0028] Finally, execute step S104, take the selection coefficient of the battery energy storage capacity when the capacity configuration reference index is the smallest as the optimal selection coefficient of the battery energy storage capacity, and determine the optimal rated capacity of the battery energy storage according to the optimal selection coefficient of the battery energy storage capacity and the total installed capacity of the wind turbines in the wind farm.

[0029] Specifically, compare and obtain the minimum value of the capacity configuration reference index . The corresponding selection coefficient of the battery energy storage capacity is the optimal selection coefficient of the battery energy storage capacity. Then, determine the optimal rated capacity of the battery energy storage according to the optimal selection coefficient of the battery energy storage capacity and the total installed capacity of the wind turbines in the wind farm, and output the optimal rated capacity of the battery energy storage.

[0030] The present invention constructs an under-generation objective function and an abandoned-generation objective function, iteratively updates the selection coefficient of the battery energy storage capacity, updates the under-generation objective function and the abandoned-generation objective function based on the selection coefficient of the battery energy storage capacity, calculates the capacity configuration reference index according to the under-generation objective function and the abandoned-generation objective function, and finally determines the optimal rated capacity of the battery energy storage according to the selection coefficient of the battery energy storage capacity when the capacity configuration reference index is the smallest. Therefore, the present invention selects the best selection coefficient of the battery energy storage capacity according to the two situations of wind abandonment and shortage, so as to accurately determine the optimal rated capacity of the battery energy storage in combination with the total installed capacity of the wind turbines in the wind farm, which helps to suppress the output of the wind turbines.

[0031] As Figure 2 shown is the working flowchart of a method for optimizing the energy storage capacity of a wind farm in another embodiment of the present invention, including: Step S201, obtain the total installed capacity of the wind turbines in the wind farm.

[0032] Step S202, determine the required discharge amount of the battery energy storage during all under-generation time periods in the typical day, where the under-generation time period is the time section with under-generation power during the typical day when tracking the power generation plan; Construct the under-generation objective function as: , where N is the number of under-generation time periods in the typical day, is the optimal state of charge of the battery energy storage, is the required discharge capacity of the battery energy storage for the i-th under-generation time period, is the rated capacity of the battery energy storage.

[0033] Step S203: Determine the curtailed power in all curtailed power time periods in the typical day. The curtailed power time period is the time period with excess power generation when tracking the power generation plan in the typical day; Construct the curtailed power objective function as: , where Q is the number of under-generation time periods in this typical day, is the optimal state of charge of the battery energy storage, is the curtailed power in the i-th curtailed power time period, is the rated capacity of the battery energy storage.

[0034] Step S204: Iteratively update the battery energy storage capacity selection coefficient until the iteration end condition is met. In each iteration, perform the following operations: Update the battery energy storage capacity selection coefficient for this iteration to be the battery energy storage capacity selection coefficient of the previous iteration plus a preset increment; Calculate the rated capacity of the battery energy storage for this iteration as the total installed capacity of the wind turbines in the wind farm multiplied by the battery energy storage capacity selection coefficient for this iteration; According to the rated capacity of the battery energy storage, update the under-generation objective function and the curtailed power objective function, and calculate the capacity configuration reference index as , where, is the capacity configuration reference index, is the value of the under-generation objective function, is the value of the curtailed power objective function.

[0035] Step S205: Take the battery energy storage capacity selection coefficient when the capacity configuration reference index is the smallest as the optimal battery energy storage capacity selection coefficient, and determine the optimal rated capacity of the battery energy storage according to the optimal battery energy storage capacity selection coefficient and the total installed capacity of the wind turbines in the wind farm.

[0036] In this embodiment, by constructing a wind-storage power model, a wind farm that needs to configure energy storage is selected as the research object. According to the power model of a single wind turbine in this wind farm, the annual output of all the turbines in this wind farm is sampled. The day with the largest standard deviation of the active power output under the non-power limit output condition throughout the year is selected as the typical day. According to the two situations of wind curtailment and deficit and the optimal SOC state in the typical day, the optimal battery energy storage capacity selection coefficient is selected. Configuring a certain capacity of energy storage system according to the total installed capacity of the wind turbines in the wind farm and the output curve of this wind farm helps to smooth the output of the wind turbines.

[0037] The construction of the wind-storage power model is as follows: 1. Wind turbine model The power output of a wind turbine is determined by multiple physical conditions, involving air density, wind speed, wind turbine swept area, wind energy density, etc. The power output of the wind turbine is: (1-1) Where is the local air density of the wind farm, is the swept area of the wind turbine impeller, is the real-time wind speed, is the wind energy utilization coefficient, is the power generation efficiency of the wind turbine, is the power output of the wind turbine.

[0038] 2. Energy Storage Model Since battery energy storage has fast adjustment and response characteristics, and the response time can reach the millisecond level, battery energy storage is used as the energy storage type to cooperate with the wind turbine to participate in regulation in this method. The SOC (state of charge) of the battery energy storage at the current moment is:

[0039] is the state of charge of the battery energy storage in the current state; is the state of charge of the battery energy storage at the previous moment; is the output of the current battery energy storage. When the energy storage is in the discharge state, this value is positive, and when the battery energy storage is in the charge state, this value is negative; is the rated capacity of the battery energy storage.

[0040] Power Model of the Whole Wind Farm In an actual wind farm, the wind energy absorbed by different wind turbines at the same moment is different. The power of a single wind turbine is mainly determined by the wind speed and the wind energy utilization coefficient. The power curve of a certain type of single wind turbine is as Figure 3 shown, including the power curve 31 of the wind turbine and the wind energy utilization coefficient curve 32.

[0041] In the early stage of wind farm design, 365 groups of data with a 24-hour cycle are intercepted for the annual active power output through wind power prediction and the power curve of a single wind turbine. The day with the largest standard deviation of the active power (output) under the non-power limit condition within a 24-hour cycle on a certain day is selected as the typical day. Taking this typical day as the research object, a battery energy storage with a certain capacity and power is selected to cooperate with the power transmission of the wind farm.

[0042] In a wind farm that has been operating for some time, the active power (output) of each wind turbine in the whole wind farm throughout the year is sampled through the power curve of a single wind turbine. A certain day with the largest standard deviation of output under non-power-limiting conditions within a 24-hour cycle is selected as the typical day. Taking the comprehensive output curve of all wind turbines in the wind farm on this typical day as the research object, a battery energy storage with a certain capacity and power is selected to cooperate with the power transmission of the wind farm. After screening, the comprehensive output curve of all wind turbines in the typical day of a wind farm with a certain number of installed wind turbines of a certain model is obtained as shown in Figure 4 shown

[0043] The wind farm is configured with battery energy storage to participate in regional control regulation When the wind farm participates in power grid dispatching, it needs to track the planned power output in real time. The external load fluctuates over time. After configuring energy storage, based on the SOC state of the energy storage, the output of the wind energy storage station is made equal to the regional control demand (ARR), that is:

[0044]

[0045] is the planned power output demand, is the comprehensive output of all wind turbines in the wind farm

[0046] Using the same sampling rate, the wind load difference is calculated for the comprehensive output curve of all wind turbines and the external load demand curve of the wind farm on the typical day. The wind load difference is:

[0047] When the wind load difference is negative, the battery energy storage can be charged according to the SOC state of the battery energy storage. If the SOC state of the battery energy storage does not meet the charging requirements, a wind curtailment measure is taken; the wind curtailment power in this state is:

[0048] If the SOC state of the battery energy storage meets the charging requirements, the battery energy storage is charged until the SOC state no longer meets the charging requirements

[0049]

[0050] where is the charging power for the battery energy storage

[0051] When the wind load difference is positive, the battery energy storage is discharged according to the SOC state of the battery energy storage. If the SOC state of the battery energy storage meets the discharge requirements, the battery energy storage is discharged. At this time, the discharge power of the battery energy storage is:

[0052] If the current SOC state of the battery energy storage does not meet the discharge requirement, the battery energy storage stops discharging. At this time, the wind farm operates with insufficient power, and the value of the insufficient power is:

[0053] Therefore, it is necessary to select an appropriate rated capacity of the battery energy storage to minimize the curtailment measures and the operation with insufficient power.

[0054] In an operating wind farm, considering the operation economy, usually based on the total installed capacity of the wind turbines in the wind farm, a certain coefficient is multiplied by the total installed capacity of the wind turbines in the wind farm as the installed capacity of the energy storage. Therefore, in this embodiment, the rated capacity of the battery energy storage device that cooperates with the wind turbines to participate in regulation is optimized. The optimal selection coefficient of the battery energy storage capacity is obtained, and then the optimal selection coefficient of the battery energy storage capacity is multiplied by the total installed capacity of the wind turbines in the wind farm, that is, the optimal rated capacity of the battery energy storage device is obtained.

[0055] Specifically, first perform step S201 to obtain the total installed capacity of the wind turbines in the wind farm.

[0056] Specifically, select a wind farm that needs to configure energy storage as the research object. If the wind farm is in the early stage of wind farm design, the total installed capacity of the wind turbines in the wind farm is determined according to the design. If the wind farm has been operating for some time, the total installed capacity of the wind turbines in the wind farm is determined according to the actual measured value.

[0057] Then, perform step S202 to determine the required discharge amount of the battery energy storage during all under-generation time periods in a typical day, where the under-generation time period is the time section with under-generation amount when tracking the power generation plan in a typical day; Construct the under-generation objective function as: , where N is the number of under-generation time periods in this typical day, is the optimal state of charge of the battery energy storage, is the required discharge amount of the battery energy storage in the i-th under-generation time period, is the rated capacity of the battery energy storage.

[0058] Specifically, in the model optimization, the goal is to reduce the range of battery energy storage power adjustment and extend the service life of the battery energy storage. To maintain the effective adjustment ability, the SOC of the battery energy storage is kept at the optimal state of charge in the model. The variance in the mathematical expectation is used to represent the deviation degree between each random variable and the expected value. In a typical day, there are several under-generation time periods with under-generation amount compared with the tracked power generation plan. Therefore, it is necessary to minimize the sum of the standard deviations of the under-generation amount and the optimal value of SOC in each under-generation time period, that is, the objective function is:

[0059] Where N is the number of time periods with insufficient generated power in the tracking power generation plan on the typical day. is the optimal state of charge of the battery energy storage. Usually, the optimal state of charge is selected as 0.5.

[0060] In one embodiment, the determining the required discharge amount of the battery energy storage for all under-generated power time periods in the typical day includes: Based on the calculation formula for the required discharge amount of the battery energy storage for the under-generated power time period, calculate the required discharge amount of the battery energy storage for all under-generated power time periods in the typical day. Among them, the calculation formula for the required discharge amount of the battery energy storage for the i-th under-generated power time period in the typical day is: , where is the real-time under-generation output of the wind farm in the i-th under-generated power time period, , is the real-time required output of the power generation plan in the i-th under-generated power time period, is the comprehensive real-time output of all wind turbines in the wind farm in the i-th under-generated power time period.

[0061] Specifically, the real-time output of the power generation plan is tracked to obtain the external load demand curve of the wind farm on the typical day. Then, the comprehensive output curve of all wind turbines in the wind farm is sampled at a certain sampling rate to obtain the comprehensive real-time output of all wind turbines in the wind farm. Using the same sampling rate, the external load demand curve of the wind farm on the typical day is sampled to obtain the real-time required output of the power generation plan.

[0062] Subtract the comprehensive real-time output of all wind turbines in the wind farm from the real-time required output of the power generation plan obtained at the same sampling moment to obtain the wind load difference at this sampling moment.

[0063] Connect the sampling moments corresponding to consecutive positive wind load differences into an under-generated power time period. For each under-generated power time period, calculate the required discharge amount of the battery energy storage for this under-generated power time period respectively.

[0064] Specifically, for the i-th under-generated power time period, calculate the real-time under-generation output of the wind farm in the i-th under-generated power time period for each sampling moment within this under-generated power time period as: , where is the real-time required output of the power generation plan in the i-th under-generated power time period at the same sampling moment, is the comprehensive real-time output of all wind turbines in the wind farm in the i-th under-generated power time period at the same sampling moment.

[0065] Then, integrate the real-time power shortage of the wind farm during all sampling moments in the $i$-th power shortage time period to obtain the required battery energy storage discharge amount in the $i$-th power shortage time period: .

[0066] Then, perform step S203 to determine the curtailed power in all curtailed power time periods in the typical day, where the curtailed power time period is the time period with excess power generation when tracking the power generation plan in the typical day; Construct the curtailed power objective function as: , where $Q$ is the number of power shortage time periods in this typical day, is the optimal state of charge of the battery energy storage, is the curtailed power in the $i$-th curtailed power time period, is the rated capacity of the battery energy storage.

[0067] Specifically, in the model optimization, the goal is to reduce the range of battery energy storage power regulation to extend the service life of the battery energy storage. To maintain effective regulation ability, the SOC of the battery energy storage is maintained at the optimal state of charge in the model. In the typical day, there are several curtailed power time periods during which the wind farm has excess power generation compared to the tracked power generation plan. Therefore, it is necessary to minimize the sum of the standard deviations of the curtailed power in each curtailed power time period with excess power generation and the optimal value of SOC, that is, the objective function is:

[0068] where $Q$ is the number of power shortage time periods in this typical day, is the optimal state of charge of the battery energy storage, is the curtailed power in the $i$-th curtailed power time period, is the rated capacity of the battery energy storage.

[0069] In one embodiment, the determination of the curtailed power in all curtailed power time periods in the typical day includes: Based on the calculation formula for the curtailed power in the curtailed power time period, calculate the curtailed power in all curtailed power time periods in the typical day. Among them, the calculation formula for the curtailed power in the $i$-th curtailed power time period in the typical day is: , where, is the real-time curtailed output of the wind farm in the $i$-th curtailed power time period, , is the real-time required output of the power generation plan in the $i$-th curtailed power time period, is the comprehensive real-time output of all wind turbines in the wind farm in the $i$-th curtailed power time period.

[0070] Specifically, the output of the power generation plan is tracked in real time to obtain the external load demand curve of the wind farm on a typical day. Then, the comprehensive output curve of all wind turbines in the whole field is sampled at a certain sampling rate to obtain the comprehensive real-time output of all wind turbines in the wind farm. Using the same sampling rate, the external load demand curve of the wind farm on a typical day is sampled to obtain the real-time demand output of the power generation plan.

[0071] Subtract the comprehensive real-time output of all wind turbines in the wind farm from the real-time demand output of the power generation plan obtained at the same sampling moment to obtain the wind load difference at this sampling moment.

[0072] Connect the sampling moments corresponding to consecutive negative wind load differences into a curtailment power generation time period. For each curtailment power generation time period, calculate the curtailment power generation in this curtailment power generation time period respectively.

[0073] Specifically, for the i-th curtailment power generation time period, the real-time curtailed output of the wind farm in the i-th curtailment power generation time period is calculated for each sampling moment in this curtailment power generation time period as: , where is the real-time demand output of the power generation plan in the i-th curtailment power generation time period at the same sampling moment, is the comprehensive real-time output of all wind turbines in the wind farm in the i-th curtailment power generation time period at the same sampling moment.

[0074] Then integrate the real-time curtailed output of the wind farm in the i-th curtailment power generation time period for all sampling moments in the i-th curtailment power generation time period to obtain the required battery energy storage discharge amount in the i-th curtailment power generation time period: .

[0075] Then execute step S204, and iteratively update the battery energy storage capacity selection coefficient until the iteration end condition is met. In each iteration, perform the following operations: Update the battery energy storage capacity selection coefficient of this iteration to the battery energy storage capacity selection coefficient of the previous iteration plus a preset increment; Calculate the rated battery energy storage capacity of this iteration as the total installed capacity of the wind turbines in the wind farm multiplied by the battery energy storage capacity selection coefficient of this iteration; According to the rated battery energy storage capacity, update the under-generation objective function and the curtailment power generation objective function, and calculate the capacity configuration reference index as , where is the capacity configuration reference index, is the value of the under-generation objective function, is the value of the curtailment power generation objective function.

[0076] Specifically, define the sum of the objective functions in two time periods in a typical day as the capacity configuration reference index , namely:

[0077] The battery energy storage capacity ratio is determined according to the total installed capacity of the wind turbines in the wind farm, and the battery energy storage capacity is a certain coefficient of the total installed capacity of the wind turbines in the wind farm. Considering economy, the selected coefficient n for the battery energy storage capacity is set between 0.1 and 0.3, and the selected coefficient of the battery energy storage capacity is updated through iterative calculation.

[0078] Such as Figure 5 shown in the working flow chart for iteratively determining the optimal selected coefficient of the battery energy storage capacity in the best embodiment of the present invention, including: Step S501, obtain the total installed capacity W total of the wind turbines in the wind farm; Step S502, initialize i = 0, n0 = 0.09; Step S503, let i = i + 1, ni = ni + 0.01; Step S504, let ; Step S505, update the values of the under-generation objective function and the curtailment objective function, and calculate the capacity configuration reference index according to the under-generation objective function and the curtailment objective function ; Step S506, if ni < 0.3, execute Step S503, otherwise execute Step S507; Step S507, output the minimum capacity configuration reference index and the corresponding selected coefficient of the battery energy storage capacity as the optimal selected coefficient of the battery energy storage capacity.

[0079] Specifically, initialize the input i = 0, n0 = 0.09, and n0 is the initial value of the selected coefficient of the battery energy storage capacity. Then, each time the selected coefficient of the battery energy storage capacity increases by 0.01 during iteration, the selected coefficient of the battery energy storage capacity for the i-th iteration is ni + 0.01, that is, the selected coefficient of the battery energy storage capacity n1 for the first iteration is 0.1, the selected coefficient of the battery energy storage capacity n2 for the second iteration is 0.11, the selected coefficient of the battery energy storage capacity n3 for the third iteration is 0.12,..., until the selected coefficient of the battery energy storage capacity ni for the i-th iteration is 0.3, then the iterative calculation ends. In each iteration, calculate the capacity configuration reference index . Compare and obtain the minimum value of the capacity configuration reference index , and use the corresponding selected coefficient of the battery energy storage capacity as the optimal selected coefficient of the battery energy storage capacity.

[0080] Finally, step S205 is executed. The battery energy storage capacity selection coefficient when the capacity configuration reference index is the smallest is used as the optimal battery energy storage capacity selection coefficient, and the optimal battery energy storage rated capacity is determined according to the optimal battery energy storage capacity selection coefficient and the total installed capacity of the wind turbines in the wind farm.

[0081] Specifically, the product of the optimal battery energy storage capacity selection coefficient and the total installed capacity of the wind turbines in the wind farm is used as the optimal battery energy storage rated capacity.

[0082] In this embodiment, a specific under-generation objective function is constructed, the under-generation time periods of a typical day are divided to calculate the corresponding under-generated electricity, a specific curtailment objective function is constructed, and the curtailment time periods of a typical day are divided to calculate the corresponding curtailed electricity. At the same time, in the under-generation objective function and the curtailment objective function, the battery energy storage SOC is maintained at the best state of charge to avoid under-power operation caused by the SOC state not meeting the discharge requirements, or curtailment of wind power caused by the SOC state not meeting the charging requirements. Finally, through iterative calculation, the optimal battery energy storage capacity selection coefficient is found and the optimal battery energy storage rated capacity is calculated. In this embodiment, the optimal battery energy storage capacity selection coefficient is selected according to the two situations of curtailment of wind power and shortage and the best SOC state in a typical day. Configuring a certain capacity of energy storage system according to the total installed capacity of the wind turbines in the wind farm and the output curve of the wind farm helps to smooth the output of the wind turbines.

[0083] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0084] As Figure 6 shown in the figure is a schematic hardware structure diagram of an electronic device according to the present invention, including: At least one processor 601; and, A memory 602 communicatively connected to at least one of the processors 601; wherein, The memory 602 stores instructions executable by at least one of the processors. The instructions are executed by at least one of the processors so that at least one of the processors can execute the wind farm energy storage capacity optimization method as described above.

[0085] Figure 6 Taking one processor 601 as an example in

[0086] The electronic device may further include: an input device 603 and a display device 604.

[0087] The processor 601, the memory 602, the input device 603 and the display device 604 may be connected by a bus or other means. In the figure, the connection by a bus is taken as an example.

[0088] The memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the wind farm energy storage capacity optimization method in the embodiments of the present application. For example, Figure 1 , Figure 2 the method flow shown. The processor 601 executes various functional applications and data processing by running the non-volatile software programs, instructions, and modules stored in the memory 602, that is, implements the wind farm energy storage capacity optimization method in the above embodiments.

[0089] The memory 602 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the wind farm energy storage capacity optimization method, etc. In addition, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 602 may optionally include a memory remotely set relative to the processor 601, and these remote memories can be connected to the device executing the wind farm energy storage capacity optimization method through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0090] The input device 603 can receive input user clicks and generate signal inputs related to user settings and function controls of the wind farm energy storage capacity optimization method. The display device 604 may include a display screen and other display devices.

[0091] When the one or more modules are stored in the memory 602 and run by the one or more processors 601, the wind farm energy storage capacity optimization method in any of the above method embodiments is executed.

[0092] The present invention constructs an under-generation objective function and an abandoned-generation objective function, iteratively updates the battery energy storage capacity selection coefficient, updates the under-generation objective function and the abandoned-generation objective function based on the battery energy storage capacity selection coefficient, calculates a capacity configuration reference index according to the under-generation objective function and the abandoned-generation objective function, and finally determines the optimal battery energy storage rated capacity according to the battery energy storage capacity selection coefficient when the capacity configuration reference index is the smallest. Therefore, the present invention selects the best battery energy storage capacity selection coefficient according to the two situations of wind curtailment and deficit, so as to accurately determine the optimal battery energy storage rated capacity in combination with the total installed capacity of the wind turbines in the wind farm, which helps to suppress the output of the wind turbines.

[0093] An embodiment of the present invention provides a storage medium that stores computer instructions which, when executed by a computer, are used to perform all steps of the wind farm energy storage capacity optimization method described above.

[0094] In the context of the present disclosure, the storage medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The storage medium can be a machine-readable signal medium or a machine-readable storage medium. Optionally, the storage medium can be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium can be ROM, random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices, etc.

[0095] An embodiment of the present invention provides a computer program product, including a computer program / instructions, which when executed by a processor, implement the wind farm energy storage capacity optimization method described above.

[0096] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but should not be construed as a limitation on the scope of the present invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A method for optimizing the energy storage capacity of a wind farm, characterized in that, including: Obtaining the total installed capacity of wind turbines in a wind farm; Constructing an under-generation objective function and a curtailment objective function; Iteratively updating the battery energy storage capacity selection coefficient until the iteration end condition is met. In each iteration, calculate the rated capacity of the battery energy storage according to the total installed capacity of the wind turbines in the wind farm and the battery energy storage capacity selection coefficient. Update the under-generation objective function and the curtailment objective function according to the rated capacity of the battery energy storage, and calculate a capacity configuration reference index according to the under-generation objective function and the curtailment objective function; Take the battery energy storage capacity selection coefficient when the capacity configuration reference index is the smallest as the optimal battery energy storage capacity selection coefficient, and determine the optimal rated capacity of the battery energy storage according to the optimal battery energy storage capacity selection coefficient and the total installed capacity of the wind turbines in the wind farm.

2. The method for optimizing the energy storage capacity of a wind farm according to claim 1, wherein The constructing of the under-generation objective function includes: Determining the required discharge amount of the battery energy storage in all under-generation time periods in a typical day, where the under-generation time period is the time section with under-generation amount when tracking the power generation plan in a typical day; The under-generation objective function is constructed as follows: , where N is the number of under-generation time periods in the typical day, is the optimal state of charge of the battery energy storage, is the required discharge amount of the battery energy storage in the i-th under-generation time period, is the rated capacity of the battery energy storage.

3. The wind farm energy storage capacity optimization method according to claim 2, wherein The determining of the required discharge amount of the battery energy storage in all under-generation time periods in a typical day includes: Based on the calculation formula for the required discharge amount of the battery energy storage in the under-generation time period, calculate the required discharge amount of the battery energy storage in all under-generation time periods in a typical day. Among them, the calculation formula for the required discharge amount of the battery energy storage in the i-th under-generation time period in a typical day is: , where is the real-time power shortage of the wind farm during the i-th power shortage period, , is the real-time demand power output of the power generation plan during the i-th power shortage period, is the comprehensive real-time power output of all wind turbines in the wind farm during the i-th power shortage period.

4. The method for optimizing the energy storage capacity of a wind farm according to claim 1, wherein The constructing of the curtailment objective function includes: Determining the curtailed power generation amount in all curtailment time periods in a typical day, where the curtailment time period is the time section with excess power generation amount when tracking the power generation plan in a typical day; The objective function for constructing curtailed power generation is as follows: , where Q is the number of time periods with curtailed power generation during the typical day, is the optimal state of charge of the battery energy storage, is the curtailed power generation during the i-th time period of curtailed power generation, is the rated capacity of the battery energy storage.

5. The method for optimizing the energy storage capacity of a wind farm according to claim 4, characterized in that, The determining of the curtailed power generation amount in all curtailment time periods in a typical day includes: Based on the calculation formula for the curtailed power generation amount in the curtailment time period, calculate the curtailed power generation amount in all curtailment time periods in a typical day. Among them, the calculation formula for the curtailed power generation amount in the i-th curtailment time period in a typical day is: , where is the real-time curtailed output of the wind farm during the i-th curtailment power generation period, , is the real-time required output of the power generation plan during the i-th curtailment power generation period, is the comprehensive real-time output of all wind turbines in the wind farm during the i-th curtailment power generation period.

6. The method for optimizing the energy storage capacity of a wind farm according to claim 1, wherein In each iteration, calculating the rated capacity of the battery energy storage according to the total installed capacity of the wind turbines in the wind farm and the battery energy storage capacity selection coefficient, updating the under-generation objective function and the curtailment objective function according to the rated capacity of the battery energy storage, and calculating a capacity configuration reference index according to the under-generation objective function and the curtailment objective function includes: In each iteration, perform the following operations: Update the battery energy storage capacity selection coefficient of this iteration to the battery energy storage capacity selection coefficient of the previous iteration plus a preset increment; Calculate the rated capacity of the battery energy storage of this iteration as the total installed capacity of the wind turbines in the wind farm multiplied by the battery energy storage capacity selection coefficient of this iteration; Update the under-generation objective function and the curtailment objective function according to the rated capacity of the battery energy storage, and calculate a capacity configuration reference index according to the under-generation objective function and the curtailment objective function.

7. The method for optimizing the energy storage capacity of a wind farm according to claim 1, wherein The calculating of the capacity configuration reference index according to the under-generation objective function and the curtailment objective function includes: The reference index for computing capacity allocation is , where is the reference index for capacity allocation, is the value of the under-generation objective function, is the value of the curtailment objective function.

8. An electronic device, characterized in that, including: At least one processor; and, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the wind farm energy storage capacity optimization method according to any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium stores computer instructions, which are used to execute all steps of the wind farm energy storage capacity optimization method according to any one of claims 1 to 7 when the computer executes the computer instructions.

10. A computer program product comprising computer programs / instructions, characterized in that, When the computer program / instructions are executed by a processor, the wind farm energy storage capacity optimization method according to any one of claims 1 to 7 is implemented.

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