CN117613978A - Energy storage effect evaluation method and system for wind power plant - Google Patents
Energy storage effect evaluation method and system for wind power plant Download PDFInfo
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Abstract
The disclosure provides an energy storage effect evaluation method and system for a wind power plant, and relates to the technical field of wind power energy storage, wherein the method comprises the following steps: obtaining predicted wind power supply quantity in a power supply valley period based on the minimum predicted load, the minimum generated power and the preset wind power supply duty ratio; performing wind power generation characteristic analysis, and determining average power generation amount, maximum power generation amount and maximum power generation amount; constructing a loading capacity threshold and a charging and discharging power threshold; constructing an energy storage scheme optimizing space, and optimizing the energy storage scheme to obtain an optimal energy storage scheme; and evaluating the energy storage effect of the target energy storage scheme, and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme. The technical problems that the wind farm energy storage effect is poor and energy storage resources are wasted due to low configuration accuracy of the wind farm energy storage device can be solved, and the configuration accuracy and rationality of the energy storage device can be improved.
Description
Technical Field
The disclosure relates to the technical field of wind power energy storage, and more particularly, to an energy storage effect evaluation method and system of a wind power plant.
Background
Wind energy is an environment-friendly and renewable energy source, but due to factors such as weather, the fluctuation of wind power generation is large, the wind power station can generate excessive power during strong wind, and the wind power station is difficult to meet the requirement during weak wind, and the wind power energy storage technology can solve the problem, store the excessive power of wind power generation and release the excessive power when required, so that the stable supply of wind power is realized.
Therefore, how to accurately and reasonably configure the energy storage equipment for the wind power plants with different capacities, which does not cause the resource waste of the energy storage equipment and does not reach the expected energy storage effect due to insufficient energy storage capacity, is a key problem to be solved by the configuration of the energy storage equipment for the wind power plants.
The existing wind farm energy storage configuration method has the following defects: because the wind power plant energy storage device is low in configuration accuracy, the wind power plant energy storage effect is poor, and energy storage resource waste exists.
Disclosure of Invention
Therefore, in order to solve the above technical problems, the technical solution adopted in the embodiments of the present disclosure is as follows:
an energy storage effect evaluation method of a wind power plant comprises the following steps: acquiring a plurality of historical minimum loads of a power supply valley time period based on a historical power supply record of a target area, and carrying out power utilization trend prediction based on the historical minimum loads to generate a minimum predicted load; acquiring the minimum power generation power of a power plant in a target area, and calculating and acquiring the predicted wind power supply quantity in the period of the power supply valley based on the minimum predicted load, the minimum power generation power and the preset wind power supply duty ratio; a plurality of historical power generation data of a target wind power plant in the power supply valley period are called, wherein the historical power generation data comprise historical power generation amount and unit historical power generation power; performing wind power generation characteristic analysis based on the plurality of historical power generation data, and determining average power generation amount, average power generation power, maximum power generation amount and maximum power generation power in the power supply valley period; constructing a loading capacity threshold and a charging and discharging power threshold based on the predicted wind power supply quantity, the average power generation power, the maximum power generation quantity and the maximum power generation power; an energy storage scheme optimizing space is built based on the energy storage device type threshold value, the installed capacity threshold value and the charge and discharge power threshold value, and the energy storage scheme is optimized in the energy storage scheme optimizing space to obtain an optimal energy storage scheme; and evaluating the energy storage effect of the target energy storage scheme, and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme.
An energy storage effect evaluation system of a wind farm, comprising: the minimum predicted load generation module is used for acquiring a plurality of historical minimum loads of a power supply valley time period based on a historical power supply record of a target area, and carrying out power consumption trend prediction based on the historical minimum loads to generate a minimum predicted load; the predicted wind power supply quantity obtaining module is used for obtaining the minimum power generation power of a power plant in a target area, and calculating and obtaining the predicted wind power supply quantity in the period of the power supply valley based on the minimum predicted load, the minimum power generation power and the preset wind power supply duty ratio; the historical power generation data calling module is used for calling a plurality of historical power generation data of a target wind power plant in the power supply valley period, wherein the historical power generation data comprise historical power generation amount and unit historical power generation power; the wind power generation characteristic analysis module is used for carrying out wind power generation characteristic analysis based on the plurality of historical power generation data and determining average power generation capacity, average power generation power, maximum power generation capacity and maximum power generation power in the power supply valley period; the threshold value construction module is used for constructing a loading capacity threshold value and a charging and discharging power threshold value based on the predicted wind power supply quantity, the average power generation power, the maximum power generation quantity and the maximum power generation power; the energy storage scheme optimizing module is used for constructing an energy storage scheme optimizing space based on an energy storage device type threshold value, the installed capacity threshold value and the charge and discharge power threshold value, and optimizing the energy storage scheme in the energy storage scheme optimizing space to obtain an optimal energy storage scheme; the target energy storage scheme optimizing module is used for evaluating the energy storage effect of the target energy storage scheme and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme.
By adopting the technical method, compared with the prior art, the technical progress of the present disclosure has the following points:
the method can solve the technical problems that the wind farm energy storage effect is poor and energy storage resources are wasted due to low configuration accuracy of an energy storage device in the existing wind farm energy storage configuration method, firstly, a plurality of historical minimum loads of a power supply valley period are acquired based on a historical power supply record of a target area, and power utilization trend prediction is performed based on the historical minimum loads to generate a minimum predicted load; acquiring the minimum power generation power of a power plant in a target area, and calculating and acquiring the predicted wind power supply quantity in the period of the power supply valley based on the minimum predicted load, the minimum power generation power and the preset wind power supply duty ratio; a plurality of historical power generation data of a target wind power plant in the power supply valley period are called, wherein the historical power generation data comprise historical power generation amount and unit historical power generation power; performing wind power generation characteristic analysis based on the plurality of historical power generation data, and determining average power generation amount, average power generation power, maximum power generation amount and maximum power generation power in the power supply valley period; constructing a loading capacity threshold and a charging and discharging power threshold based on the predicted wind power supply quantity, the average power generation power, the maximum power generation quantity and the maximum power generation power; an energy storage scheme optimizing space is built based on the energy storage device type threshold value, the installed capacity threshold value and the charge and discharge power threshold value, and the energy storage scheme is optimized in the energy storage scheme optimizing space to obtain an optimal energy storage scheme; and evaluating the energy storage effect of the target energy storage scheme, and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme. The method can solve the technical problems that the wind farm energy storage effect is poor and energy storage resources are wasted due to low configuration accuracy of the energy storage device in the existing wind farm energy storage configuration method.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are used in the description of the embodiments will be briefly described below.
Fig. 1 is a schematic flow chart of a method for evaluating energy storage effect of a wind farm;
fig. 2 is a schematic flow chart of generating a minimum predicted load in a method for evaluating energy storage effect of a wind farm;
fig. 3 is a schematic structural diagram of an energy storage effect evaluation system of a wind farm.
Reference numerals illustrate: the system comprises a minimum predicted load generation module 01, a predicted wind power supply amount acquisition module 02, a historical power generation data retrieval module 03, a wind power generation characteristic analysis module 04, a threshold value construction module 05, an energy storage scheme optimizing module 06 and a target energy storage scheme optimizing module 07.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
Based on the above description, as shown in fig. 1, the present disclosure provides a method for evaluating energy storage effect of a wind farm, including:
Wind energy is a random and intermittent energy source, the active output of a wind farm changes along with weather conditions, seasons and daily time periods, the control capability of active output is limited, and the difficulty of adjusting the generated energy according to a scheduling instruction is high like a traditional power plant. Therefore, the stability of power supply of the wind power plant can be improved through configuration of the energy storage device, and meanwhile, the stability and reliability of power grid operation during wind power grid connection can be improved.
The method provided by the application is used for accurately evaluating the energy storage effect of the energy storage scheme planned in the wind power plant, and optimizing and adjusting the energy storage scheme according to the evaluation result, so that the purpose of improving the accuracy and rationality of the configuration of the energy storage device is achieved, and meanwhile, the resource consumption required by energy storage can be reduced on the premise of meeting the energy storage requirement of the wind power plant.
Acquiring a plurality of historical minimum loads of a power supply valley time period based on a historical power supply record of a target area, and carrying out power utilization trend prediction based on the historical minimum loads to generate a minimum predicted load;
in the embodiment of the application, firstly, a historical power supply record of a target area is called, wherein the target area refers to a power supply coverage area of a wind power plant, and a plurality of historical minimum loads of a power supply valley time period are obtained according to the historical power supply record, and the power supply valley time period refers to a power supply valley time period of the target area and can be set according to actual power consumption conditions; the historical minimum load refers to a minimum electricity load in a preset time period, wherein the preset time period can be set by a person skilled in the art according to actual situations, for example: and setting the preset time period to be 1 year, wherein the plurality of historical minimum loads are a plurality of historical minimum power consumption loads in the last N years, N is an integer larger than 3, and the higher the value of N is, the higher the power consumption trend analysis accuracy is. And then carrying out electricity consumption trend prediction according to the plurality of historical minimum loads, and generating minimum predicted loads according to electricity consumption trend prediction results. And by obtaining the minimum predicted load, data support is provided for wind power energy storage analysis in a power supply valley time period.
As shown in fig. 2, in one embodiment, the method further comprises:
wherein the historical minimum load has a time distance identifier;
constructing an electricity consumption trend analysis two-dimensional space, and distributing the plurality of historical minimum loads in the electricity consumption trend analysis two-dimensional space;
in the electricity utilization trend analysis two-dimensional space, performing serial fitting on the plurality of historical minimum loads from far to near according to the time distance to generate an electricity utilization trend curve;
and carrying out electricity consumption trend prediction in the current time period according to the electricity consumption trend curve to obtain the minimum predicted load.
In this embodiment of the present application, the historical minimum load has a time distance identifier, where the time distance identifier refers to a length from the current time, and the longer the time distance is, the larger the time distance is, and the lower the corresponding data value is.
And constructing an electricity consumption trend analysis two-dimensional space, wherein the electricity consumption trend analysis two-dimensional space is a two-dimensional rectangular coordinate system constructed by taking the reciprocal of a time distance mark as an X axis and taking a historical minimum load as a Y axis, and then distributing the historical minimum loads in the electricity consumption trend analysis two-dimensional space. And in the two-dimensional space of electricity consumption trend analysis, the plurality of historical minimum loads are subjected to series fitting from far to near according to the time distance, a curve obtained by series fitting is used as an electricity consumption trend curve, and the electricity consumption trend curve is generated, so that support is provided for electricity consumption trend prediction, and meanwhile, the accuracy and the efficiency of electricity consumption trend prediction can be improved.
And finally, inputting the current time node into the electricity utilization trend analysis two-dimensional space, and predicting the minimum electricity utilization load in the current time period according to the electricity utilization trend curve to obtain the minimum predicted load. And by obtaining the minimum predicted load, data support is provided for the calculation of the wind power supply quantity predicted by the next step.
Acquiring the minimum power generation power of a power plant in a target area, and calculating and acquiring the predicted wind power supply quantity in the period of the power supply valley based on the minimum predicted load, the minimum power generation power and the preset wind power supply duty ratio;
in the embodiment of the application, first, the minimum power generation power of the power plant in the target area is obtained, wherein the power plant in the target area refers to the thermal power plant in the target area, and the minimum power generation power refers to the minimum output power for maintaining the normal operation of the thermal power plant. And then subtracting the minimum generated power from the minimum predicted load to obtain a generated power deviation, wherein the generated power deviation is electric energy provided by other power generation modes except the thermal power plant, for example: photovoltaic power generation, wind power generation, and the like. And obtaining a preset wind power supply duty ratio, wherein the preset wind power supply duty ratio can be set according to actual conditions by a person skilled in the art, multiplying the preset wind power supply duty ratio by the generated power deviation, and taking the product of the preset wind power supply duty ratio and the generated power deviation as the predicted wind power supply amount in the period of the power supply valley. By generating the predicted wind power supply quantity, support is provided for wind power energy storage capacity calculation in the period of the next step of power supply valley time.
A plurality of historical power generation data of a target wind power plant in the power supply valley period are called, wherein the historical power generation data comprise historical power generation amount and unit historical power generation power;
in this embodiment of the present application, first, a power supply operation system of a target power plant is connected, and a plurality of historical power generation data of the target wind farm in the power supply valley period is retrieved based on the power supply operation system, where the historical power generation data includes a historical power generation amount and a unit historical power generation power, where the unit historical power generation power refers to wind power generation power of a unit time period in the power supply valley period, where the unit time period may be set according to an actual requirement and a wind fluctuation frequency, for example: the unit time period is set to be 5 minutes, and because the fluctuation of wind power generation is large, the wind power generation power analysis is carried out according to the time period, and the accuracy of the wind power generation power analysis can be improved.
Performing wind power generation characteristic analysis based on the plurality of historical power generation data, and determining average power generation amount, average power generation power, maximum power generation amount and maximum power generation power in the power supply valley period;
in the embodiment of the application, wind power generation characteristic analysis is performed according to the plurality of historical power generation data, and average power generation amount, maximum power generation amount and maximum power generation amount in the power supply valley period are determined according to wind power generation characteristic analysis results.
In one embodiment, the method further comprises:
extracting a plurality of historical power generation amounts and a plurality of historical power generation powers per unit time based on the plurality of historical power generation data, and acquiring the maximum power generation amount and the maximum power generation power based on the plurality of historical power generation amounts and the plurality of historical power generation powers per unit time;
clustering the plurality of historical power generation amounts according to a preset clustering index to obtain a plurality of historical power generation amount sets;
marking a historical power generation amount set, of which the number of the historical power generation amounts meets a preset number threshold, in the plurality of historical power generation amount sets as a frequent historical power generation amount set, so as to obtain a plurality of frequent historical power generation amount sets;
performing weighted calculation on the historical power generation amount average value of the plurality of frequent historical power generation amount sets to obtain the average power generation amount;
and calculating and obtaining the average generated power based on the plurality of historical generated power per unit time.
In the embodiment of the application, first, a plurality of historical power generation amounts and a plurality of historical power generation powers per unit time are extracted from the plurality of historical power generation data, wherein the historical power generation powers per unit time can be obtained by calculating the average value of the plurality of historical power generation powers per unit time in the period of the power supply valley. And then extracting the maximum values of the plurality of historical power generation amounts and the plurality of historical power generation powers in unit time to obtain the maximum power generation amount and the maximum power generation power.
Obtaining a preset clustering index, wherein the preset clustering index can be set according to actual conditions, for example: setting a preset clustering index of 50 kilowatts, namely clustering historical power generation amounts within a 50 kilowatt deviation range together, for example: the maximum value of the plurality of historical power generation amounts is 500, and the minimum value is 300, and the clustering range is 300-350, 350-400, 400-450 and 450-500; and then clustering the plurality of historical power generation amounts according to the preset clustering index to obtain a plurality of historical power generation amount sets.
Acquiring a preset quantity threshold, wherein the preset quantity threshold can be set according to actual data quantity, judging the quantity of the historical power generation in the plurality of historical power generation sets according to the preset quantity threshold, marking a historical power generation set with the quantity of the historical power generation larger than the preset quantity threshold in the plurality of historical power generation sets as a frequent historical power generation set, obtaining a plurality of frequent historical power generation sets, screening the power generation by setting the preset quantity threshold, and removing abnormal data, so that the accuracy and the rationality of acquiring the historical data are improved.
Setting corresponding weight values according to the number of the historical power generation in the frequent historical power generation sets, wherein the larger the number of the historical power generation is, the larger the corresponding weight values are, then carrying out weighted calculation on the average value of the historical power generation in the frequent historical power generation sets according to the weight values, wherein the average value of the historical power generation is the average value of the historical power generation in the frequent historical power generation sets, and taking the weighted calculation result as the average power generation.
And then calculating the plurality of historical power generation powers in unit time by using the same method for obtaining the average power generation amount to obtain the average power generation power. The average generating capacity, the average generating power, the maximum generating capacity and the maximum generating power in the period of the power supply valley time are obtained, so that a basis is provided for determining the energy storage capacity configuration and the energy storage charge-discharge power configuration in the next step.
Constructing a loading capacity threshold and a charging and discharging power threshold based on the predicted wind power supply quantity, the average power generation power, the maximum power generation quantity and the maximum power generation power;
in the embodiment of the application, the installed capacity threshold is constructed according to the predicted wind power supply quantity, the average power generation quantity and the maximum power generation quantity, and the charging and discharging power threshold is constructed according to the average power generation power and the maximum power generation power.
In one embodiment, the method further comprises:
respectively calculating the difference value of the average power generation amount, the maximum power generation amount and the predicted wind power supply amount to obtain an average installed capacity and a maximum installed capacity;
constructing the installed capacity threshold according to the average installed capacity and the maximum installed capacity;
Calculating to obtain unit power generation based on the average installed capacity and the power supply valley time period;
respectively calculating the difference value of the average power generation power, the maximum power generation power and the unit power generation power, and determining average charge-discharge power and maximum charge-discharge power;
and constructing the charge-discharge power threshold according to the average charge-discharge power and the maximum charge-discharge power.
In the embodiment of the application, firstly, subtracting the predicted wind power supply amount from the average power generation amount, and taking the difference between the average power generation amount and the predicted wind power supply amount as an average installed capacity; and subtracting the predicted wind power supply amount from the maximum power generation amount, and taking the difference between the maximum power generation amount and the predicted wind power supply amount as the maximum installed capacity. And then taking the average installed capacity as a lower threshold limit, and taking the maximum installed capacity as an upper threshold limit to construct an installed capacity threshold.
Taking the ratio of the average installed capacity to the time period in the power supply valley period as unit power generation, then respectively calculating the difference value of the average power generation power, the maximum power generation power and the unit power generation power, and taking the difference value of the average power generation power and the unit power generation power as average charge and discharge power; and taking the difference value of the maximum power generation power and the unit power generation power as the maximum charge and discharge power, and constructing the charge and discharge power threshold according to the average charge and discharge power and the maximum charge and discharge power.
By obtaining the installed capacity threshold and the charge-discharge power threshold, support is provided for constructing an energy storage scheme optimizing space in the next step.
An energy storage scheme optimizing space is built based on the energy storage device type threshold value, the installed capacity threshold value and the charge and discharge power threshold value, and the energy storage scheme is optimized in the energy storage scheme optimizing space to obtain an optimal energy storage scheme;
in the embodiment of the application, an energy storage scheme optimizing space is constructed, wherein the energy storage scheme optimizing space comprises an energy storage device type threshold, a loading capacity threshold and a charging and discharging power threshold, and then the energy storage scheme is optimized in the energy storage scheme optimizing space to obtain an optimal energy storage scheme.
In one embodiment, the method further comprises:
the energy storage device comprises a chargeable and dischargeable frequency index, a chargeable and dischargeable depth index, a performance attenuation coefficient, a unit capacity cost and a unit power cost;
randomly generating a plurality of energy storage schemes in the energy storage scheme optimizing space, and randomly selecting an energy storage scheme which is not replaced as a first energy storage scheme;
predicting the utilization rate of the first energy storage scheme based on a device utilization prediction channel, and outputting a first energy storage device utilization rate and a first wind resource utilization rate;
In this embodiment of the present application, the energy storage device in the energy storage device type threshold includes at least storage battery energy storage, flywheel energy storage, super capacitor energy storage, air compression energy storage, and the like, and those skilled in the art may also add or delete according to actual situations. The energy storage device comprises a chargeable and dischargeable frequency index, a chargeable and dischargeable depth index, a performance attenuation coefficient, a unit capacity cost and a unit power cost, wherein the longer the chargeable and dischargeable frequency is, the longer the service life of the energy storage device is, and the lower the cost is; the larger the charge and discharge depth is, the lower the energy storage cost is; the smaller the performance attenuation, the better the energy storage performance, and the lower the cost; the unit capacity cost and the unit power cost can be set according to the actual energy storage type, wherein the larger the charge and discharge power is, the higher the unit power cost is.
And randomly selecting a plurality of parameters from the type threshold value, the installed capacity threshold value and the charge and discharge power threshold value of the stored energy device in the energy storage scheme optimizing space, randomly forming a plurality of energy storage schemes, and randomly selecting an energy storage scheme which is not replaced from the plurality of energy storage schemes as a first energy storage scheme.
The equipment using prediction channel is constructed based on the BP neural network, wherein the equipment using prediction channel is a neural network model which can be subjected to iterative optimization in machine learning, and is obtained through monitoring training by a training data set, wherein input data of the equipment using prediction channel is an energy storage scheme, wind power resources and wind power supply quantity in a valley period, and output data is an energy storage device utilization rate and a wind power resource utilization rate. Based on big data technology, information retrieval is carried out by taking wind power energy storage as a retrieval condition, and a plurality of sample training data are obtained. And then performing supervision training on the equipment use prediction channel according to the plurality of sample training data to obtain the equipment use prediction channel which tends to be in a convergence state, and improving the accuracy and efficiency of the energy storage device utilization rate and the wind resource utilization rate prediction by constructing the equipment use prediction channel.
And then inputting the first energy storage scheme, the current wind power resource and the wind power supply quantity in the current valley period into the equipment to use a prediction channel for prediction, so as to obtain a first energy storage device utilization rate and a first wind resource utilization rate.
Performing energy storage resource consumption calculation on the first energy storage scheme according to a resource consumption evaluation function to obtain first resource consumption;
in one embodiment, the method further comprises:
the resource consumption evaluation function is:
wherein N is i For the resource consumption of the ith energy storage scheme, A i For the attenuation coefficient of the performance of the energy storage device in the ith energy storage scheme, J i Is the index of the chargeable and dischargeable times of the energy storage device in the ith energy storage scheme, D i C is the charge and discharge depth index of the energy storage device in the ith energy storage scheme 1 For the unit capacity cost of the energy storage device in the ith energy storage scheme, C is the installed capacity of the ith energy storage scheme, P 1 The unit power cost of the energy storage device in the ith energy storage scheme is calculated, and P is the charge and discharge power of the ith energy storage scheme;
the energy storage effect evaluation function is as follows: f (F) i ＝v 1 •B i +v 2 ·E i +v 3 N i ；
Wherein F is i Energy storage fitness for the ith energy storage protocol, v 1 For the utilization rate of the energy storage deviceWeight coefficient of B i Energy storage device utilization, v, for the ith energy storage scheme 2 A weight coefficient for wind resource utilization, E i Wind resource utilization for the ith energy storage scheme, v 3 Weight coefficient for resource consumption, N i Is the resource consumption of the ith energy storage scheme.
In the embodiment of the application, firstly, a resource consumption evaluation function is constructed, wherein the expression of the resource consumption evaluation function is as followsIn the resource consumption evaluation function, N i Resource consumption for the ith energy storage scheme, where N i The larger the characterization resource consumption, the larger A i For the attenuation coefficient of the performance of the energy storage device in the ith energy storage scheme, J i Is the index of the chargeable and dischargeable times of the energy storage device in the ith energy storage scheme, D i C is the charge and discharge depth index of the energy storage device in the ith energy storage scheme 1 For the unit capacity cost of the energy storage device in the ith energy storage scheme, C is the installed capacity of the ith energy storage scheme, P 1 And P is the charge and discharge power of the ith energy storage scheme, wherein the unit power cost of the energy storage device in the ith energy storage scheme is equal to the unit power cost of the energy storage device in the ith energy storage scheme.
Constructing an energy storage effect evaluation function, wherein the expression of the energy storage effect evaluation function is F i ＝v 1 ·B i +v 2 ·E i +v 3 N i The method comprises the steps of carrying out a first treatment on the surface of the In the energy storage effect evaluation function, F i Energy storage fitness for the ith energy storage protocol, where F i The larger the characterization is, the better the comprehensive energy storage effect is, v 1 Weight coefficient for energy storage device utilization ratio, B i Energy storage device utilization, v, for the ith energy storage scheme 2 A weight coefficient for wind resource utilization, E i Wind resource utilization for the ith energy storage scheme, v 3 Weight coefficient for resource consumption, N i Resource consumption for the ith energy storage scheme, where v 1 、v 2 、v 3 The value of (2) can be set according to the influence degree of the index on the energy storage fitness, wherein the larger the influence degree is, the larger the corresponding weight is, and the energy storage fitness can be realizedThe weight setting is performed by the existing coefficient of variation method, wherein the coefficient of variation method is a weighting method commonly used by those skilled in the art, and will not be described herein.
And then, calculating the energy storage resource consumption of the first energy storage scheme according to the resource consumption evaluation function to obtain a first resource consumption. By constructing the resource consumption evaluation function and the energy storage effect evaluation function, the accuracy of the resource consumption evaluation and the energy storage effect evaluation can be improved, and the accuracy of energy storage scheme optimization can be improved.
Comprehensively evaluating the utilization rate of the first energy storage device, the utilization rate of the first wind resource and the first resource consumption according to an energy storage effect evaluation function to obtain a first energy storage fitness;
Randomly selecting a second energy storage scheme which is not replaced in the plurality of energy storage schemes, and calculating to obtain a second energy storage fitness;
comparing the first energy storage fitness with the second energy storage fitness, when the first energy storage fitness is smaller than or equal to the second energy storage fitness, taking the second energy storage scheme as a current optimal energy storage scheme, and when the first energy storage fitness is larger than the second energy storage fitness, taking the second energy storage scheme as the current optimal energy storage scheme according to probability, wherein the probability is reduced along with the increase of optimizing times;
and continuously performing iterative optimization, and outputting a current optimal energy storage scheme as the optimal energy storage scheme until the current optimization times meet a preset optimization times threshold.
In the embodiment of the application, the first energy storage device utilization rate, the first wind resource utilization rate and the first resource consumption are comprehensively evaluated according to the energy storage effect evaluation function, so that a first energy storage fitness is obtained; and then randomly selecting a second energy storage scheme which is not replaced in the plurality of energy storage schemes, and calculating according to the resource consumption evaluation function and the energy storage effect evaluation function to obtain a second energy storage fitness.
Comparing the first energy storage fitness with the second energy storage fitness, when the first energy storage fitness is smaller than or equal to the second energy storage fitness, taking the second energy storage scheme as a current optimal energy storage scheme, and when the first energy storage fitness is larger than the second energy storage fitness, taking the second energy storage scheme as the current optimal energy storage scheme according to probability, wherein the probability is reduced along with the increase of optimizing times, and by setting probability in the optimizing process, the optimizing speed can be improved in the optimizing early stage, the trapping of local optimization is avoided, and the optimizing accuracy is improved in the optimizing later stage.
Iterative optimization is continuously performed, and a preset optimizing frequency threshold value is obtained, wherein the preset optimizing frequency threshold value can be set according to actual requirements by a person skilled in the art, and the higher the optimizing requirement precision is, the larger the preset optimizing frequency threshold value is, for example: and setting a preset optimizing frequency threshold as optimizing iteration frequency 1000 times, and outputting a current optimal energy storage scheme as the optimal energy storage scheme until the current optimizing frequency is equal to the preset optimizing frequency threshold. By optimizing the energy storage scheme by utilizing the optimizing algorithm, the accuracy of obtaining the optimal energy storage scheme can be improved due to the strong global searching capability of the algorithm.
And evaluating the energy storage effect of the target energy storage scheme, and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme.
In one embodiment, the method further comprises:
performing energy storage effect evaluation on the target energy storage scheme according to the resource consumption evaluation function and the energy storage effect evaluation function to obtain target energy storage fitness;
and when the target energy storage fitness is smaller than the energy storage fitness of the optimal energy storage scheme, replacing and updating the target energy storage scheme by using the optimal energy storage scheme.
In this embodiment of the present application, first, according to the resource consumption evaluation function and the energy storage effect evaluation function, an energy storage effect evaluation is performed on the target energy storage scheme, so as to obtain a target energy storage fitness, then, according to the energy storage fitness of the optimal energy storage scheme, the target energy storage fitness is judged, when the target energy storage fitness is smaller than the energy storage fitness of the optimal energy storage scheme, the energy storage effect of the characterization target energy storage scheme is lower than the energy storage effect of the optimal energy storage scheme, and then, the optimal energy storage scheme is used to replace the target energy storage scheme. According to the method, the technical problems that the wind farm energy storage effect is poor due to the fact that the existing wind farm energy storage configuration method is low in energy storage device configuration accuracy and energy storage resource waste exists can be solved, the accuracy and the rationality of energy storage device configuration can be improved, and therefore the resource consumption required by energy storage is reduced on the premise that the wind farm energy storage requirement is met.
In one embodiment, as shown in fig. 3, there is provided an energy storage effect evaluation system of a wind farm, comprising: the system comprises a minimum predicted load generation module 01, a predicted wind power supply amount acquisition module 02, a historical power generation data retrieval module 03, a wind power generation characteristic analysis module 04, a threshold value construction module 05, an energy storage scheme optimizing module 06 and a target energy storage scheme optimizing module 07, wherein:
the minimum predicted load generation module 01 is used for acquiring a plurality of historical minimum loads of a power supply valley time period based on a historical power supply record of a target area, and carrying out power utilization trend prediction based on the historical minimum loads to generate a minimum predicted load;
the predicted wind power supply quantity obtaining module 02 is used for obtaining the minimum power generation power of a power plant in a target area, and calculating and obtaining the predicted wind power supply quantity in the period of the power supply valley based on the minimum predicted load, the minimum power generation power and the preset wind power supply duty ratio;
a historical power generation data calling module 03, wherein the historical power generation data calling module 03 is used for calling a plurality of historical power generation data of a target wind power plant in the power supply valley period, and the historical power generation data comprises a historical power generation amount and a unit historical power generation power;
The wind power generation characteristic analysis module 04 is used for carrying out wind power generation characteristic analysis based on the plurality of historical power generation data, and determining average power generation amount, average power generation power, maximum power generation amount and maximum power generation power in the power supply valley period;
the threshold value construction module 05 is used for constructing a loading capacity threshold value and a charging and discharging power threshold value based on the predicted wind power supply quantity, the average power generation power, the maximum power generation quantity and the maximum power generation power;
the energy storage scheme optimizing module 06, wherein the energy storage scheme optimizing module 06 is configured to construct an energy storage scheme optimizing space based on an energy storage device type threshold, the installed capacity threshold and the charge-discharge power threshold, and optimize an energy storage scheme in the energy storage scheme optimizing space to obtain an optimal energy storage scheme;
the target energy storage scheme optimizing module 07, wherein the target energy storage scheme optimizing module 07 is used for evaluating the energy storage effect of the target energy storage scheme and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme.
In one embodiment, the system further comprises:
The time distance identification module is used for identifying the time distance of the historical minimum load;
the electricity consumption trend analysis two-dimensional space construction module is used for constructing an electricity consumption trend analysis two-dimensional space and distributing the plurality of historical minimum loads in the electricity consumption trend analysis two-dimensional space;
the electricity consumption trend curve generation module is used for performing series fitting on the plurality of historical minimum loads from far to near according to the time distance in the electricity consumption trend analysis two-dimensional space to generate an electricity consumption trend curve;
and the minimum predicted load obtaining module is used for predicting the power consumption trend in the current time period according to the power consumption trend curve to obtain the minimum predicted load.
In one embodiment, the system further comprises:
the maximum power generation information acquisition module is used for extracting a plurality of historical power generation amounts and a plurality of historical power generation powers in unit time based on the plurality of historical power generation data and acquiring the maximum power generation amount and the maximum power generation power based on the plurality of historical power generation amounts and the plurality of historical power generation powers in unit time;
The historical power generation amount set obtaining module is used for clustering the plurality of historical power generation amounts according to preset clustering indexes to obtain a plurality of historical power generation amount sets;
the frequent historical power generation amount set obtaining module is used for marking a historical power generation amount set, of which the number of the historical power generation amounts meets a preset number threshold, in the plurality of historical power generation amount sets as a frequent historical power generation amount set to obtain a plurality of frequent historical power generation amount sets;
the average power generation amount obtaining module is used for carrying out weighted calculation on the historical power generation amount average value of the plurality of frequent historical power generation amount sets to obtain the average power generation amount;
and the average power generation power obtaining module is used for obtaining the average power generation power based on the power generation power calculation of the plurality of historical unit time.
In one embodiment, the system further comprises:
the installed capacity obtaining module is used for respectively calculating the difference value of the average power generation amount, the maximum power generation amount and the predicted wind power supply amount to obtain average installed capacity and maximum installed capacity;
The installed capacity threshold value construction module is used for constructing the installed capacity threshold value according to the average installed capacity and the maximum installed capacity;
the unit power generation calculation module is used for calculating and obtaining unit power generation based on the average installed capacity and the power supply valley time period;
the charge-discharge power determining module is used for respectively calculating the average power generation power, the maximum power generation power and the difference value of the unit power generation power and determining average charge-discharge power and maximum charge-discharge power;
and the charge-discharge power threshold construction module is used for constructing the charge-discharge power threshold according to the average charge-discharge power and the maximum charge-discharge power.
In one embodiment, the system further comprises:
the energy storage device type module is characterized in that the energy storage device type comprises a chargeable and dischargeable frequency index, a charge and discharge depth index, a performance attenuation coefficient, a unit capacity cost and a unit power cost;
the first energy storage scheme selection module is used for randomly generating a plurality of energy storage schemes in the energy storage scheme optimizing space, and randomly selecting an energy storage scheme which is not replaced as a first energy storage scheme;
The utilization rate prediction module is used for predicting the utilization rate of the first energy storage scheme based on the equipment utilization prediction channel and outputting a first energy storage device utilization rate and a first wind resource utilization rate;
the first resource consumption obtaining module is used for calculating the energy storage resource consumption of the first energy storage scheme according to a resource consumption evaluation function to obtain first resource consumption;
the first energy storage fitness obtaining module is used for comprehensively evaluating the utilization rate of the first energy storage device, the utilization rate of the first wind resource and the first resource consumption according to an energy storage effect evaluation function to obtain first energy storage fitness;
the second energy storage fitness calculation module is used for randomly selecting a second energy storage scheme which is not replaced in the plurality of energy storage schemes and calculating to obtain second energy storage fitness;
the fitness comparison module is used for comparing the first energy storage fitness with the second energy storage fitness, when the first energy storage fitness is smaller than or equal to the second energy storage fitness, the second energy storage scheme is used as a current optimal energy storage scheme, and when the first energy storage fitness is larger than the second energy storage fitness, the second energy storage scheme is used as the current optimal energy storage scheme according to probability, wherein the probability is reduced along with the increase of optimizing times;
The optimal energy storage scheme obtaining module is used for continuously carrying out iterative optimization, and outputting the current optimal energy storage scheme as the optimal energy storage scheme until the current optimization times meet the preset optimization times threshold.
In one embodiment, the system further comprises:
a resource consumption evaluation function module, wherein the resource consumption evaluation function module refers to that the resource consumption evaluation function is as follows:
a function parameter module, wherein the function parameter module refers to N i For the resource consumption of the ith energy storage scheme, A i For the attenuation coefficient of the performance of the energy storage device in the ith energy storage scheme, J i Is the index of the chargeable and dischargeable times of the energy storage device in the ith energy storage scheme, D i C is the charge and discharge depth index of the energy storage device in the ith energy storage scheme 1 For the unit capacity cost of the energy storage device in the ith energy storage scheme, C is the installed capacity of the ith energy storage scheme, P 1 The unit power cost of the energy storage device in the ith energy storage scheme is calculated, and P is the charge and discharge power of the ith energy storage scheme;
the energy storage effect evaluation function module is characterized in that the energy storage effect evaluation function module is that: f (F) i ＝v 1 ·B i +v 2 ·E i +v 3 N i ；
Function parameter moduleThe function parameter module refers to F i Energy storage fitness for the ith energy storage protocol, v 1 Weight coefficient for energy storage device utilization ratio, B i Energy storage device utilization, v, for the ith energy storage scheme 2 A weight coefficient for wind resource utilization, E i Wind resource utilization for the ith energy storage scheme, v 3 Weight coefficient for resource consumption, N i Is the resource consumption of the ith energy storage scheme.
In one embodiment, the system further comprises:
the target energy storage fitness obtaining module is used for carrying out energy storage effect evaluation on the target energy storage scheme according to the resource consumption evaluation function and the energy storage effect evaluation function to obtain target energy storage fitness;
and the scheme replacement updating module is used for replacing and updating the target energy storage scheme by the optimal energy storage scheme when the target energy storage fitness is smaller than the energy storage fitness of the optimal energy storage scheme.
In summary, compared with the prior art, the embodiments of the present disclosure have the following technical effects:
(1) The target energy storage scheme is optimally adjusted by generating the optimal energy storage scheme, so that the configuration accuracy and rationality of the energy storage device can be improved, and the resource consumption required by energy storage is reduced on the premise of meeting the energy storage requirement of the wind power plant.
(2) By constructing the resource consumption evaluation function and the energy storage effect evaluation function, the accuracy of the resource consumption evaluation and the energy storage effect evaluation can be improved, and the accuracy of energy storage scheme optimization can be improved.
(3) By optimizing the energy storage scheme by utilizing the optimizing algorithm, the accuracy of obtaining the optimal energy storage scheme can be improved due to the strong global searching capability of the algorithm.
The above examples merely represent a few embodiments of the present disclosure and are not to be construed as limiting the scope of the invention. Accordingly, various alterations, modifications and variations may be made by those having ordinary skill in the art without departing from the scope of the disclosed concept as defined by the following claims and all such alterations, modifications and variations are intended to be included within the scope of the present disclosure.
Claims (8)
1. A method for evaluating energy storage effects of a wind farm, the method comprising:
acquiring a plurality of historical minimum loads of a power supply valley time period based on a historical power supply record of a target area, and carrying out power utilization trend prediction based on the historical minimum loads to generate a minimum predicted load;
acquiring the minimum power generation power of a power plant in a target area, and calculating and acquiring the predicted wind power supply quantity in the period of the power supply valley based on the minimum predicted load, the minimum power generation power and the preset wind power supply duty ratio;
A plurality of historical power generation data of a target wind power plant in the power supply valley period are called, wherein the historical power generation data comprise historical power generation amount and unit historical power generation power;
performing wind power generation characteristic analysis based on the plurality of historical power generation data, and determining average power generation amount, average power generation power, maximum power generation amount and maximum power generation power in the power supply valley period;
constructing a loading capacity threshold and a charging and discharging power threshold based on the predicted wind power supply quantity, the average power generation power, the maximum power generation quantity and the maximum power generation power;
an energy storage scheme optimizing space is built based on the energy storage device type threshold value, the installed capacity threshold value and the charge and discharge power threshold value, and the energy storage scheme is optimized in the energy storage scheme optimizing space to obtain an optimal energy storage scheme;
and evaluating the energy storage effect of the target energy storage scheme, and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme.
2. The method of claim 1, wherein the predicting the power usage trend based on the plurality of historical minimum loads generates a minimum predicted load, further comprising:
Wherein the historical minimum load has a time distance identifier;
constructing an electricity consumption trend analysis two-dimensional space, and distributing the plurality of historical minimum loads in the electricity consumption trend analysis two-dimensional space;
in the electricity utilization trend analysis two-dimensional space, performing serial fitting on the plurality of historical minimum loads from far to near according to the time distance to generate an electricity utilization trend curve;
and carrying out electricity consumption trend prediction in the current time period according to the electricity consumption trend curve to obtain the minimum predicted load.
3. The method of claim 1, wherein the determining the average power generation, maximum power generation within the supply valley period based on the wind power generation signature analysis of the plurality of historical power generation data further comprises:
extracting a plurality of historical power generation amounts and a plurality of historical power generation powers per unit time based on the plurality of historical power generation data, and acquiring the maximum power generation amount and the maximum power generation power based on the plurality of historical power generation amounts and the plurality of historical power generation powers per unit time;
clustering the plurality of historical power generation amounts according to a preset clustering index to obtain a plurality of historical power generation amount sets;
Marking a historical power generation amount set, of which the number of the historical power generation amounts meets a preset number threshold, in the plurality of historical power generation amount sets as a frequent historical power generation amount set, so as to obtain a plurality of frequent historical power generation amount sets;
performing weighted calculation on the historical power generation amount average value of the plurality of frequent historical power generation amount sets to obtain the average power generation amount;
and calculating and obtaining the average generated power based on the plurality of historical generated power per unit time.
4. The method of claim 3, wherein the constructing a installed capacity threshold and a charge-discharge power threshold based on the predicted wind power supply amount, the average power generation amount, the maximum power generation amount, and the maximum power generation amount further comprises:
respectively calculating the difference value of the average power generation amount, the maximum power generation amount and the predicted wind power supply amount to obtain an average installed capacity and a maximum installed capacity;
constructing the installed capacity threshold according to the average installed capacity and the maximum installed capacity;
calculating to obtain unit power generation based on the average installed capacity and the power supply valley time period;
respectively calculating the difference value of the average power generation power, the maximum power generation power and the unit power generation power, and determining average charge-discharge power and maximum charge-discharge power;
And constructing the charge-discharge power threshold according to the average charge-discharge power and the maximum charge-discharge power.
5. The method of claim 1, wherein the optimizing the energy storage scheme in the energy storage scheme optimizing space to obtain an optimal energy storage scheme further comprises:
the energy storage device comprises a chargeable and dischargeable frequency index, a chargeable and dischargeable depth index, a performance attenuation coefficient, a unit capacity cost and a unit power cost;
randomly generating a plurality of energy storage schemes in the energy storage scheme optimizing space, and randomly selecting an energy storage scheme which is not replaced as a first energy storage scheme;
predicting the utilization rate of the first energy storage scheme based on a device utilization prediction channel, and outputting a first energy storage device utilization rate and a first wind resource utilization rate;
performing energy storage resource consumption calculation on the first energy storage scheme according to a resource consumption evaluation function to obtain first resource consumption;
comprehensively evaluating the utilization rate of the first energy storage device, the utilization rate of the first wind resource and the first resource consumption according to an energy storage effect evaluation function to obtain a first energy storage fitness;
randomly selecting a second energy storage scheme which is not replaced in the plurality of energy storage schemes, and calculating to obtain a second energy storage fitness;
Comparing the first energy storage fitness with the second energy storage fitness, when the first energy storage fitness is smaller than or equal to the second energy storage fitness, taking the second energy storage scheme as a current optimal energy storage scheme, and when the first energy storage fitness is larger than the second energy storage fitness, taking the second energy storage scheme as the current optimal energy storage scheme according to probability, wherein the probability is reduced along with the increase of optimizing times;
and continuously performing iterative optimization, and outputting a current optimal energy storage scheme as the optimal energy storage scheme until the current optimization times meet a preset optimization times threshold.
6. The method of claim 5, wherein the method further comprises:
the resource consumption evaluation function is:
wherein N is i For the resource consumption of the ith energy storage scheme, A i For the attenuation coefficient of the performance of the energy storage device in the ith energy storage scheme, J i Is the index of the chargeable and dischargeable times of the energy storage device in the ith energy storage scheme, D i C is the charge and discharge depth index of the energy storage device in the ith energy storage scheme 1 For the unit capacity cost of the energy storage device in the ith energy storage scheme, C is the installed capacity of the ith energy storage scheme, P 1 The unit power cost of the energy storage device in the ith energy storage scheme is calculated, and P is the charge and discharge power of the ith energy storage scheme;
The energy storage effect evaluation function is as follows: f (F) i ＝v 1 ·B i +v 2 •E i +v 3 N i ；
Wherein F is i Energy storage for the ith energy storage schemeFitness, v 1 Weight coefficient for energy storage device utilization ratio, B i Energy storage device utilization, v, for the ith energy storage scheme 2 A weight coefficient for wind resource utilization, E i Wind resource utilization for the ith energy storage scheme, v 3 Weight coefficient for resource consumption, N i Is the resource consumption of the ith energy storage scheme.
7. The method of claim 6, wherein the evaluating the energy storage effect of the target energy storage solution and the optimizing the target energy storage solution according to the evaluation result and the optimal energy storage solution further comprise:
performing energy storage effect evaluation on the target energy storage scheme according to the resource consumption evaluation function and the energy storage effect evaluation function to obtain target energy storage fitness;
and when the target energy storage fitness is smaller than the energy storage fitness of the optimal energy storage scheme, replacing and updating the target energy storage scheme by using the optimal energy storage scheme.
8. A wind farm energy storage effect assessment system, characterized by the steps for performing any of the methods of a wind farm energy storage effect assessment method according to claims 1-7, the system comprising:
The minimum predicted load generation module is used for acquiring a plurality of historical minimum loads of a power supply valley time period based on a historical power supply record of a target area, and carrying out power consumption trend prediction based on the historical minimum loads to generate a minimum predicted load;
the predicted wind power supply quantity obtaining module is used for obtaining the minimum power generation power of a power plant in a target area, and calculating and obtaining the predicted wind power supply quantity in the period of the power supply valley based on the minimum predicted load, the minimum power generation power and the preset wind power supply duty ratio;
the historical power generation data calling module is used for calling a plurality of historical power generation data of a target wind power plant in the power supply valley period, wherein the historical power generation data comprise historical power generation amount and unit historical power generation power;
the wind power generation characteristic analysis module is used for carrying out wind power generation characteristic analysis based on the plurality of historical power generation data and determining average power generation capacity, average power generation power, maximum power generation capacity and maximum power generation power in the power supply valley period;
The threshold value construction module is used for constructing a loading capacity threshold value and a charging and discharging power threshold value based on the predicted wind power supply quantity, the average power generation power, the maximum power generation quantity and the maximum power generation power;
the energy storage scheme optimizing module is used for constructing an energy storage scheme optimizing space based on an energy storage device type threshold value, the installed capacity threshold value and the charge and discharge power threshold value, and optimizing the energy storage scheme in the energy storage scheme optimizing space to obtain an optimal energy storage scheme;
the target energy storage scheme optimizing module is used for evaluating the energy storage effect of the target energy storage scheme and optimizing and adjusting the target energy storage scheme according to the evaluation result and the optimal energy storage scheme.
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