CN116454983B - Wind-solar-energy-storage combined optimal control management method, system and equipment - Google Patents

Wind-solar-energy-storage combined optimal control management method, system and equipment Download PDF

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CN116454983B
CN116454983B CN202310128807.2A CN202310128807A CN116454983B CN 116454983 B CN116454983 B CN 116454983B CN 202310128807 A CN202310128807 A CN 202310128807A CN 116454983 B CN116454983 B CN 116454983B
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energy storage
energy
power supply
power
storage unit
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CN116454983A (en
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丁闵
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Naiman Banner Guangxing Power Distribution Co ltd
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Naiman Banner Guangxing Power Distribution Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention provides a wind-solar-energy-storage combined optimal control management method, a system and equipment, wherein the method comprises the following steps: acquiring state parameters of the power supply energy storage equipment based on a communication network, and determining target power and real-time load requirements required by each energy storage unit in the power supply energy storage equipment based on the state parameters; setting a plurality of environment parameter sampling moments based on discretization processing results of time of day, collecting wind power data and light energy data at different moments based on the environment parameter sampling moments, and determining the relative proportion of wind power and photovoltaic based on the wind power data and the light energy data; and formulating a power distribution criterion based on the relative proportion, the target power required by each energy storage unit and the real-time load demand, and carrying out energy scheduling on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion to complete energy compensation processing. The power supply effect of the power supply energy storage equipment is guaranteed, and meanwhile, the utilization efficiency of energy sources is improved.

Description

Wind-solar-energy-storage combined optimal control management method, system and equipment
Technical Field
The invention relates to the technical field of equipment control, in particular to a wind-solar-energy-storage combined optimal control management method, a wind-solar-energy-storage combined optimal control management system and wind-solar-energy-storage combined optimal control management equipment.
Background
Along with the promotion of the environmental protection concept, the renewable energy sources are increasingly focused on the utilization of renewable energy sources, wherein the renewable energy sources comprise wind energy, light energy, tidal energy and the like, and the utilization of renewable energy sources can greatly reduce the excavation of non-renewable resources and also facilitate the reduction of the production cost, so that the combination of renewable energy sources and power energy storage equipment is one of them;
at present, in the working mode of wind-solar energy storage combination, the power supply mode is mostly only singly immobilized, namely, only one renewable energy source can be combined with the power supply energy storage equipment, and the power supply power of the power supply energy storage equipment cannot be adjusted in real time according to the change condition of the renewable energy source when the two energy sources work, so that a large amount of renewable energy sources are wasted, and meanwhile, the combination control effect of the renewable energy sources and the power supply energy storage equipment is poor because only single power supply is performed;
therefore, the invention provides a wind-solar-energy-storage combined optimal control management method, a wind-solar-energy-storage combined optimal control management system and wind-solar-energy-storage combined optimal control management equipment, which are used for effectively formulating corresponding power distribution criteria according to environmental parameters and time by determining target power and real-time load requirements required by each energy storage unit in power supply energy storage equipment and the relative proportion of wind power and photovoltaic at the current moment, so that the power supply effect of the power supply energy storage equipment is ensured, and the utilization efficiency of energy is improved.
Disclosure of Invention
The invention provides a wind-solar-energy-storage combined optimal control management method, a system and equipment, which are used for effectively formulating corresponding power distribution criteria according to environmental parameters and time by determining target power and real-time load requirements required by energy storage units in power supply energy storage equipment and the relative proportion of wind power and photovoltaic at the current moment, so that the power supply effect of the power supply energy storage equipment is ensured, and the utilization efficiency of energy is improved.
The invention provides a wind-solar-energy-storage combined optimal control management method, which comprises the following steps:
step 1: acquiring state parameters of the power supply energy storage equipment based on a communication network, and determining target power and real-time load requirements required by each energy storage unit in the power supply energy storage equipment based on the state parameters;
step 2: setting a plurality of environment parameter sampling moments based on discretization processing results of time of day, collecting wind power data and light energy data at different moments based on the environment parameter sampling moments, and determining the relative proportion of wind power and photovoltaic based on the wind power data and the light energy data;
step 3: and formulating a power distribution criterion based on the relative proportion, the target power required by each energy storage unit and the real-time load demand, and carrying out energy scheduling on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion to complete energy compensation processing.
Preferably, in step 1, a method for managing wind-solar energy-storage combined optimal control, based on a communication network, acquires state parameters of power energy storage equipment, including:
basic information of the power supply energy storage equipment is obtained, unit energy storage devices contained in the power supply energy storage equipment are determined based on the basic information, and statistics is carried out on the unit energy storage devices to obtain target quantity of the unit energy storage devices;
setting device identifiers for the unit energy storage devices based on the target quantity, setting preset sensors for the corresponding unit energy storage devices based on the device identifiers, and constructing a distributed communication network between the preset sensors and the data receiving terminal based on communication addresses of the preset sensors;
setting a daily multi-breakpoint acquisition time for each preset sensor based on the data receiving terminal, synchronously adapting a clock trigger at the data receiving terminal based on the daily multi-breakpoint acquisition time, and generating a corresponding data acquisition instruction at a target time based on the adapted clock trigger;
and sending a data acquisition instruction to a corresponding preset sensor based on the distributed communication network, and controlling the preset sensor to acquire the current operation parameters of each unit energy storage device to obtain the state parameters of the power supply energy storage device.
Preferably, a method for wind-solar-energy-storage combined optimal control management obtains state parameters of power energy storage equipment, including:
acquiring state parameters of the obtained power supply energy storage equipment, and clustering the obtained state parameters based on a preset classification label to obtain a sub-state parameter set;
extracting data characteristics of each sub-state parameter set, and matching corresponding target data cleaning rules from a preset data cleaning rule base based on the data characteristics;
and cleaning the corresponding sub-state parameter set based on the target data cleaning rule to obtain the standard state parameter of the power supply energy storage equipment.
Preferably, in step 1, determining target power and real-time load requirements required by each energy storage unit in the power energy storage device based on state parameters, where the method includes:
acquiring state parameters of the obtained power supply energy storage equipment, splitting the state parameters, and associating the target state parameter set obtained after splitting with a corresponding energy storage unit;
generating an electric quantity state change curve of each energy storage unit based on the association result and the target value of each target state parameter group, and determining a target charge quantity required by each energy storage unit based on the electric quantity state change curve;
Acquiring receivable power curves of all energy storage units, and performing first processing on a receivable power range corresponding to the receivable power curves based on a target charge amount to obtain an allowable power range;
extracting the charge state information of each energy storage unit contained in the target state parameter set, determining the real-time load demand of each energy storage unit based on the charge state information, determining the consumption power of each energy storage unit based on the real-time load demand and the power supply power of each energy storage unit, and determining the lowest power difference between receivable power and consumption power based on the energy supplementing requirement;
and carrying out second processing on the allowable power range based on the minimum power difference and limiting conditions of preset thermal management equipment in each energy storage unit on the temperature of the energy storage unit, so as to obtain target power required by each energy storage unit.
Preferably, in step 2, a plurality of environmental parameter sampling moments are set based on discretization processing results of time of day, wind power data and light energy data at different moments are collected based on the environmental parameter sampling moments, and relative proportions of wind power and photovoltaic are determined based on the wind power data and the light energy data, including:
Discretizing the time of day to obtain a target time sequence, simultaneously acquiring weather actual measurement data monitored by a weather station in a target area in a preset time period, and determining wind speeds and wind directions at different times of day and illumination starting time and illumination ending time in the target area based on the weather actual measurement data;
determining the wind-light climate characteristics of a target area based on wind speeds and wind directions at different times of the day in the target area and illumination starting time and illumination ending time in the day, and dividing a target time sequence into N parameter acquisition intervals based on the wind-light climate characteristics;
determining key time points of obvious transition of climate parameters in each parameter acquisition interval based on wind-solar climate characteristics, and particularly to acquisition hours and acquisition minutes;
determining an environmental parameter sampling time based on the acquisition hours and the acquisition minutes, and controlling a preset parameter acquisition device to acquire wind power data and light energy data at the current time through a data acquisition instruction based on the environmental parameter sampling time;
extracting key data in wind power data and light energy data acquired at the last acquisition time and the current acquisition time, and respectively determining first energy conversion efficiency and second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time based on a preset energy conversion rule;
Generating a sample sequence pair according to the first energy conversion efficiency and the second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time, and generating a data vector matrix based on the sample sequence pair;
preprocessing a data vector matrix, inputting the preprocessed data vector matrix into a preset energy measurement function for analysis, and obtaining a first theoretical power supply contribution capability and a second theoretical power supply contribution capability of wind power data and light energy data acquired at the last acquisition moment and the current acquisition moment to power energy storage equipment;
performing first comparison and second comparison on the first theoretical power supply contribution capability and the second theoretical power supply contribution capability and the corresponding actual power supply contribution capability respectively, performing parameter optimization on a preset energy measurement function when the difference values of the first comparison and the second comparison exceed a preset threshold value, and obtaining a standard energy measurement function based on an optimization result;
and re-analyzing the wind power data and the light energy data acquired at the current acquisition moment based on the standard energy measurement function to obtain a third theoretical power supply contribution capability and a fourth theoretical power supply contribution capability, and determining the relative proportion of wind power and photovoltaic based on the third theoretical power supply contribution capability and the fourth theoretical power supply contribution capability.
Preferably, the wind-solar-energy-storage combined optimal control management method re-analyzes wind power data and light energy data acquired at the current acquisition moment based on a standard energy measurement function, and comprises the following steps:
acquiring a plurality of groups of third theoretical power supply contribution capacity and fourth theoretical power supply contribution capacity which are obtained by re-analyzing wind power data and light energy data acquired at the current acquisition moment based on a standard energy metric function, and determining influence factors of the third theoretical power supply contribution capacity and the fourth theoretical power supply contribution capacity on the wind power data and the light energy data based on the acquisition result, wherein the influence factors are at least two;
determining the association relation between the influence factors and the power supply contribution capacity, and training a plurality of groups of wind power data and light energy data and corresponding third theoretical power supply contribution capacity and fourth theoretical power supply contribution capacity based on the association relation to obtain a sub-prediction model without influence factors;
summarizing all the sub-prediction models and performing iterative training until the loss value of the model meets a preset loss value threshold value to obtain a target prediction model;
and inputting the wind power data and the light energy data acquired at the current acquisition time and the influence factors at the next acquisition time into a target prediction model for analysis, and predicting the relative proportion of wind power and photovoltaic at the next acquisition time based on the analysis result.
Preferably, in the step 3, a power distribution criterion is formulated based on the relative proportion, the target power required by each energy storage unit and the real-time load requirement, and energy scheduling is performed on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion, so that energy compensation processing is completed.
Acquiring state parameters of power supply energy storage equipment based on a communication network, determining the current working state of each energy storage unit in the power supply energy storage equipment based on the state parameters, and determining the current residual electric quantity of each energy storage unit when each energy storage unit is in the working state;
acquiring a time stamp of a charging request sent by each energy storage unit, determining the charging priority of each energy storage unit based on the residual electric quantity and the time stamp, and determining the first power distribution priority of each energy storage unit based on the charging priority;
meanwhile, the relative proportion of wind power and photovoltaic at the current moment is obtained, a target power supply mode of the power supply energy storage equipment is determined based on the relative proportion, and available power supply power which can be provided by the wind power and the photovoltaic is determined in the target power supply mode;
determining a power distribution criterion for each energy storage unit based on available power supply, a first power distribution priority, target power required by each energy storage unit and real-time load requirements, determining first power supply for each energy storage unit based on the power distribution criterion, performing first power supply on each energy storage unit based on the first power supply, simultaneously monitoring a first power supply process in real time, determining real-time energy storage information of each energy storage unit, and establishing a dynamic scheduling optimization model for charging power of each energy storage unit based on preset charging switching conditions;
And docking the dynamic scheduling optimization model with the monitored real-time energy storage information, determining the second power supply of each energy storage unit at the next moment based on the docking result, and carrying out second power supply on each energy storage unit based on the first power supply to complete energy compensation processing of each energy storage unit in the power supply energy storage equipment.
Preferably, the wind-solar-energy-storage combined optimal control management method completes energy compensation processing of each energy storage unit in the power supply energy storage equipment, and comprises the following steps:
the method comprises the steps of obtaining a wind speed value at the current moment and the wavelength of natural light at the current moment, calculating an available electric energy value generated in unit time based on the wind speed value at the current moment and the wavelength of the natural light at the current moment, and calculating the utilization rate of the available electric energy value when the power supply energy storage equipment is charged based on the available electric energy value, wherein the specific steps comprise:
the available power value generated per unit time is calculated according to the following formula:
wherein W represents the available electrical energy value generated in a unit time; η represents conversion efficiency of wind energy into electric energy, and the value range is (0, 1); ρ represents the air density, the dimension is kg/m3; v represents wind speed, and dimension is m/s; s represents a cross-sectional area perpendicular to wind speed; The conversion efficiency of light energy to electric energy is represented, and the value range is (0, 1); h represents the Planck constant and has a value of 6.6260693 (11). Times.10 -34 J.s; ω represents the propagation speed of light; λ represents a wavelength value of light;
the utilization ratio of the available electric energy value is calculated according to the following formula:
wherein, gamma represents the utilization rate of the available electric energy value, and the value range is (0, 1); μ represents an error coefficient, and the value range is (0.02, 0.04); i represents the number of the current energy storage units contained in the power supply energy storage equipment, and the value range is [1, n ]]The method comprises the steps of carrying out a first treatment on the surface of the n represents the total number of energy storage units contained in the power supply energy storage equipment; p is p i Representing a charging power value at the time of charging the i-th energy storage unit; t is t i A charging time length value representing charging of the ith energy storage unit; τ represents the thermal energy value consumed when charging the ith energy storage unit;
comparing the calculated utilization rate with a preset utilization rate threshold value;
if the calculated utilization rate is greater than or equal to a preset utilization rate threshold value, judging that the formulated power distribution criterion is qualified, and supplying power to each energy storage unit in the power supply energy storage equipment based on the power distribution criterion;
otherwise, judging that the formulated power distribution criterion is unqualified, and optimizing the power distribution criterion until the calculated utilization rate is greater than or equal to a preset utilization rate threshold.
The invention provides a wind-solar-energy-storage combined optimal control management system, which comprises:
the parameter acquisition module is used for acquiring state parameters of the power supply energy storage equipment based on the communication network and determining target power and real-time load requirements required by each energy storage unit in the power supply energy storage equipment based on the state parameters;
the proportion determining module is used for setting a plurality of environment parameter sampling moments based on discretization processing results of time of day, collecting wind power data and light energy data at different moments based on the environment parameter sampling moments, and determining the relative proportion of wind power and photovoltaic based on the wind power data and the light energy data;
and the energy scheduling module is used for formulating a power distribution criterion based on the relative proportion, the target power required by each energy storage unit and the real-time load demand, and performing energy scheduling on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion to complete energy compensation processing.
The invention provides wind-solar-energy-storage combined optimal control management equipment, which comprises: the device comprises a data acquisition device, a processor and a memory;
the data acquisition device is used for acquiring data;
the memory is used for storing one or more program instructions;
the processor being configured to execute one or more program instructions for performing the method of any of claims 1-8.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flowchart of a wind-solar-energy-storage combined optimal control management method in an embodiment of the invention;
FIG. 2 is a flowchart of step 1 in a wind-solar-energy-storage combined optimal control management method according to an embodiment of the present invention;
fig. 3 is a structural diagram of a wind-solar-energy-storage combined optimal control management system in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the embodiment provides a wind-solar-energy-storage combined optimal control management method, as shown in fig. 1, comprising the following steps:
step 1: acquiring state parameters of the power supply energy storage equipment based on a communication network, and determining target power and real-time load requirements required by each energy storage unit in the power supply energy storage equipment based on the state parameters;
step 2: setting a plurality of environment parameter sampling moments based on discretization processing results of time of day, collecting wind power data and light energy data at different moments based on the environment parameter sampling moments, and determining the relative proportion of wind power and photovoltaic based on the wind power data and the light energy data;
step 3: and formulating a power distribution criterion based on the relative proportion, the target power required by each energy storage unit and the real-time load demand, and carrying out energy scheduling on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion to complete energy compensation processing.
In this embodiment, the communication network is a distributed communication network constructed according to communication requirements, so as to facilitate timely and effective acquisition of state parameters of each energy storage unit in the power energy storage device.
In this embodiment, the state parameter refers to operation data generated in the working process of the power energy storage device, so that the working state of each energy storage unit in the power energy storage device is effectively confirmed.
In this embodiment, the energy storage unit refers to the smallest unit of work, i.e. a single cell, in the power storage device that stores energy.
In this embodiment, the target power refers to the charging power that each energy storage unit needs to provide, so as to ensure that each energy storage unit can have a sufficient amount of electric energy.
In this embodiment, the real-time meeting requirement refers to the load of each energy storage unit, that is, the degree of electric energy that each energy storage unit needs to output in unit time.
In this embodiment, the discretization of the time of day is aimed at splitting the time of day into a plurality of time points, thereby facilitating the determination of the data sampling instant.
In this embodiment, the environmental parameter sampling instant is a specific time used to characterize the acquisition of current environmental data.
In this embodiment, the wind data refers to the current wind speed, wind direction, vertical contact area of wind with the power generation device, and the like.
In this embodiment, the optical energy data refers to the wavelength, transmission speed, illumination intensity, and the like of light.
In this embodiment, determining the relative proportions of wind power and photovoltaic based on wind power data and light energy data is used to characterize the relative amounts of wind power and photovoltaic generated electrical energy when powering the power storage device, thereby facilitating formulation of corresponding power distribution guidelines.
In the embodiment, the power distribution criterion is used for representing the charging power distributed by different energy storage units in the power supply energy storage equipment at the same time, so that the electric energy generated by wind power and photovoltaic is effectively utilized.
The beneficial effects of the technical scheme are as follows: the target power and the real-time load demand required by each energy storage unit in the power supply energy storage device and the relative proportion of wind power and photovoltaic at the current moment are determined, so that the corresponding power distribution criterion is formulated effectively according to the environmental parameters and time, the power supply effect of the power supply energy storage device is ensured, and the energy utilization efficiency is improved.
Example 2:
on the basis of embodiment 1, the present embodiment provides a wind-solar energy-storage combined optimal control management method, as shown in fig. 2, in step 1, state parameters of a power supply energy storage device are obtained based on a communication network, including:
step 101: basic information of the power supply energy storage equipment is obtained, unit energy storage devices contained in the power supply energy storage equipment are determined based on the basic information, and statistics is carried out on the unit energy storage devices to obtain target quantity of the unit energy storage devices;
step 102: setting device identifiers for the unit energy storage devices based on the target quantity, setting preset sensors for the corresponding unit energy storage devices based on the device identifiers, and constructing a distributed communication network between the preset sensors and the data receiving terminal based on communication addresses of the preset sensors;
Step 103: setting a daily multi-breakpoint acquisition time for each preset sensor based on the data receiving terminal, synchronously adapting a clock trigger at the data receiving terminal based on the daily multi-breakpoint acquisition time, and generating a corresponding data acquisition instruction at a target time based on the adapted clock trigger;
step 104: and sending a data acquisition instruction to a corresponding preset sensor based on the distributed communication network, and controlling the preset sensor to acquire the current operation parameters of each unit energy storage device to obtain the state parameters of the power supply energy storage device.
In this embodiment, the basic information refers to information such as specifications of the power storage device and constituent components.
In this embodiment, the unit energy storage device refers to the smallest energy storage device, i.e., a single cell, included in the power storage device.
In this embodiment, the device identification is a tag label for marking different units of energy storage devices.
In this embodiment, the preset sensor is set in advance, and is used to collect state parameters of each unit energy storage device, and may specifically be a voltage sensor, a power sensor, etc.
In this embodiment, the daily multi-breakpoint acquisition time is set in advance, and is used to characterize the frequency of the acquisition of each unit energy accumulator, and can be adjusted.
In this embodiment, the target time point refers to a specific point in time at which data acquisition is required.
The beneficial effects of the technical scheme are as follows: the basic information of the power energy storage equipment is analyzed, accurate and effective locking of unit energy storage devices contained in the power energy storage equipment is achieved, and secondly, the acquisition time interval is set, so that the state parameters of the power energy storage equipment are accurately and effectively acquired through the sensor at corresponding moments, and convenience and basis are provided for formulating power distribution criteria for the power energy storage equipment.
Example 3:
on the basis of embodiment 2, the embodiment provides a wind-solar-energy-storage combined optimal control management method, which obtains state parameters of power supply energy storage equipment, and comprises the following steps:
acquiring state parameters of the obtained power supply energy storage equipment, and clustering the obtained state parameters based on a preset classification label to obtain a sub-state parameter set;
extracting data characteristics of each sub-state parameter set, and matching corresponding target data cleaning rules from a preset data cleaning rule base based on the data characteristics;
and cleaning the corresponding sub-state parameter set based on the target data cleaning rule to obtain the standard state parameter of the power supply energy storage equipment.
In this embodiment, the preset classification label is set in advance, and is a reference basis for classifying the obtained state parameters.
In this embodiment, the sub-state parameter set refers to each type of parameter set obtained by classifying the obtained state parameters.
In this embodiment, the data features refer to the data types contained in the sub-state parameter set and the value ranges of the data of each type.
In this embodiment, the preset data cleansing rule base is set in advance, and is used for storing different data cleansing rules.
In this embodiment, the target data cleansing rule refers to a rule for data cleansing a current type of sub-state parameter set.
In this embodiment, the standard state parameter refers to a final state parameter obtained by cleaning the obtained state parameter.
The beneficial effects of the technical scheme are as follows: by classifying and cleaning the obtained state parameters, the accuracy and reliability of the obtained state parameters are ensured, and convenience and guarantee are provided for formulating a power distribution criterion, so that the energy utilization rate is improved conveniently.
Example 4:
on the basis of embodiment 1, the embodiment provides a wind-solar-energy-storage combined optimal control management method, in step 1, target power and real-time load requirements required by each energy storage unit in power energy storage equipment are determined based on state parameters, and the method comprises the following steps:
Acquiring state parameters of the obtained power supply energy storage equipment, splitting the state parameters, and associating the target state parameter set obtained after splitting with a corresponding energy storage unit;
generating an electric quantity state change curve of each energy storage unit based on the association result and the target value of each target state parameter group, and determining a target charge quantity required by each energy storage unit based on the electric quantity state change curve;
acquiring receivable power curves of all energy storage units, and performing first processing on a receivable power range corresponding to the receivable power curves based on a target charge amount to obtain an allowable power range;
extracting the charge state information of each energy storage unit contained in the target state parameter set, determining the real-time load demand of each energy storage unit based on the charge state information, determining the consumption power of each energy storage unit based on the real-time load demand and the power supply power of each energy storage unit, and determining the lowest power difference between receivable power and consumption power based on the energy supplementing requirement;
and carrying out second processing on the allowable power range based on the minimum power difference and limiting conditions of preset thermal management equipment in each energy storage unit on the temperature of the energy storage unit, so as to obtain target power required by each energy storage unit.
In this embodiment, the target state group refers to a data set obtained by splitting a state parameter of the power energy storage device.
In this embodiment, the electric quantity state change curve is a graph for representing the current electric quantity value and the change trend of each energy storage unit in the power supply energy storage device.
In this embodiment, the target charge amount refers to electric energy that each energy storage unit needs to be charged.
In this embodiment, the receivable power curves are used to characterize the different charge power values that each energy storage unit is capable of accepting.
In this embodiment, the first process refers to narrowing the receivable power range so as to facilitate determination of the target power corresponding to the target charge amount.
In this embodiment, the allowable power range refers to an available power range obtained by reducing the receivable power range according to the target charge amount.
In this embodiment, the state of charge information is used to characterize the current load level experienced by each energy storage unit.
In this embodiment, the power supply power is used to characterize the power level currently output by each energy storage unit.
In this embodiment, the energy supplementing requirement is set in advance, and specifically, the energy output is offset, and meanwhile, the charging requirement of the energy storage unit needs to be met.
In this embodiment, the lowest power difference is the minimum difference between the charging power and the consumed power, so as to achieve the purpose of charging the energy storage unit.
In this embodiment, the preset thermal management device is a temperature monitor in the power storage device, ensuring that the temperature during charging of the power storage device is in a normal range.
In this embodiment, the second process refers to re-narrowing the range of the obtained allowable power to determine the target power that is ultimately required.
The beneficial effects of the technical scheme are as follows: through analyzing the state of each energy storage unit in the power supply energy storage equipment, the target power and the real-time load demand of each energy storage unit are effectively obtained, a basis is provided for reasonably supplying power to the power supply energy storage equipment, the obtained energy is reasonably distributed according to the charging demand, and the energy utilization rate is improved.
Example 5:
on the basis of embodiment 1, the embodiment provides a wind-solar-energy-storage combined optimal control management method, in step 2, a plurality of environment parameter sampling moments are set based on discretization processing results of time of day, wind power data and light energy data at different moments are collected based on the environment parameter sampling moments, and relative proportions of wind power and photovoltaic are determined based on the wind power data and the light energy data, and the wind-solar-energy-storage combined optimal control management method comprises the following steps:
Discretizing the time of day to obtain a target time sequence, simultaneously acquiring weather actual measurement data monitored by a weather station in a target area in a preset time period, and determining wind speeds and wind directions at different times of day and illumination starting time and illumination ending time in the target area based on the weather actual measurement data;
determining the wind-light climate characteristics of a target area based on wind speeds and wind directions at different times of the day in the target area and illumination starting time and illumination ending time in the day, and dividing a target time sequence into N parameter acquisition intervals based on the wind-light climate characteristics;
determining key time points of obvious transition of climate parameters in each parameter acquisition interval based on wind-solar climate characteristics, and particularly to acquisition hours and acquisition minutes;
determining an environmental parameter sampling time based on the acquisition hours and the acquisition minutes, and controlling a preset parameter acquisition device to acquire wind power data and light energy data at the current time through a data acquisition instruction based on the environmental parameter sampling time;
extracting key data in wind power data and light energy data acquired at the last acquisition time and the current acquisition time, and respectively determining first energy conversion efficiency and second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time based on a preset energy conversion rule;
Generating a sample sequence pair according to the first energy conversion efficiency and the second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time, and generating a data vector matrix based on the sample sequence pair;
preprocessing a data vector matrix, inputting the preprocessed data vector matrix into a preset energy measurement function for analysis, and obtaining a first theoretical power supply contribution capability and a second theoretical power supply contribution capability of wind power data and light energy data acquired at the last acquisition moment and the current acquisition moment to power energy storage equipment;
performing first comparison and second comparison on the first theoretical power supply contribution capability and the second theoretical power supply contribution capability and the corresponding actual power supply contribution capability respectively, performing parameter optimization on a preset energy measurement function when the difference values of the first comparison and the second comparison exceed a preset threshold value, and obtaining a standard energy measurement function based on an optimization result;
and re-analyzing the wind power data and the light energy data acquired at the current acquisition moment based on the standard energy measurement function to obtain a third theoretical power supply contribution capability and a fourth theoretical power supply contribution capability, and determining the relative proportion of wind power and photovoltaic based on the third theoretical power supply contribution capability and the fourth theoretical power supply contribution capability.
In this embodiment, the target time series refers to different time points obtained by discretizing the time of day, and each time point is independent of the other.
In this embodiment, the preset time period is set in advance, specifically may be one day or one week, and may be adjusted.
In this embodiment, the target area refers to the area where the power storage device is located.
In this embodiment, the wind-solar climate characteristic is used to characterize the change in wind and light in the target area over the day, thereby facilitating the determination of the proportion of energy that both can produce at different points in time.
In this embodiment, the key time point for determining that the climate parameters in each parameter acquisition interval are obviously changed based on the wind-light climate characteristics refers to the corresponding time point when the wind speed is suddenly changed to 50m/s at 5m/s and the time point when the illumination intensity suddenly darkens or lightens.
In this embodiment, the number of collection hours and collection minutes is used to characterize a particular hour and minute, and may be, for example, 12 points 42, etc.
In this embodiment, the preset parameter collecting device is set in advance, and is used for collecting wind power data and light energy data at the current moment, and specifically may be a wind speed sensor, an illumination sensor, and the like.
In this embodiment, the key data refers to a data segment that can characterize the valued characteristics of the wind and light energy data at the current time.
In this embodiment, the conditions for conversion of wind energy into electrical energy and the capabilities of conversion and the conditions for conversion of light energy into electrical energy are characterized based on preset energy conversion rules, which are known in advance.
In this embodiment, the first energy conversion efficiency is used to characterize the conversion of wind energy into electrical energy.
In this embodiment, the second energy conversion efficiency is used to characterize the conversion of light energy into electrical energy.
In this embodiment, the sample sequence pairs refer to data pairs obtained by mapping wind data with corresponding first energy conversion efficiencies and mapping light energy data with corresponding second energy conversion efficiencies.
In this embodiment, the data vector matrix refers to a final matrix obtained by preprocessing a sample sequence pair in a matrix form, and is used for analyzing the power supply contribution capability of wind power and electric energy to the power supply energy storage device at the current moment.
In this embodiment, preprocessing refers to processing such as matrix conversion of data vectors.
In this embodiment, the preset energy metric function is set in advance for determining the condition of converting wind power and light energy into electric energy.
In this embodiment, the first theoretical power contribution capability is a capability size that characterizes the current moment in time when wind energy is converted into electrical energy and is able to power the power storage device.
In this embodiment, the second theoretical power contribution capability is a capability size that characterizes the current moment in time when the light energy is converted to electrical energy and is capable of powering the power storage device.
In this embodiment, the first comparison and the second comparison refer to comparing the resulting first theoretical power supply contribution capability and second theoretical power supply contribution capability with the corresponding actual power supply contribution capability, respectively.
In this embodiment, the preset threshold is set in advance to characterize the maximum allowable difference.
In this embodiment, the standard energy metric function refers to a final energy metric function obtained by performing parameter optimization on a preset energy metric function.
In this embodiment, the third theoretical power supply contribution capability and the fourth theoretical power supply contribution capability refer to the capability of supplying power to the power energy storage device obtained after re-analyzing the wind power data and the light energy data collected at the current collection time through the optimized energy measurement function.
The beneficial effects of the technical scheme are as follows: the time of day is discretized, and the time sequence obtained after discretization is combined with the wind-solar climate characteristics of the target area for analysis, so that the acquisition time of the environmental parameters is accurately and effectively locked, the reliability and representativeness of the acquired environmental data are conveniently ensured, the acquired wind power data and the acquired light energy data are analyzed through a preset energy measurement function, the relative proportions of wind power and photovoltaic at different moments are accurately and effectively acquired, convenience and guarantee are provided for realizing the establishment of a power distribution criterion, the acquired energy is conveniently and reasonably distributed, and the energy utilization rate is conveniently improved.
Example 6:
on the basis of embodiment 5, the embodiment provides a wind-solar-energy-storage combined optimal control management method, which is characterized in that wind-energy data and light-energy data acquired at the current acquisition time are re-analyzed based on a standard energy metric function, and the method comprises the following steps:
acquiring a plurality of groups of third theoretical power supply contribution capacity and fourth theoretical power supply contribution capacity which are obtained by re-analyzing wind power data and light energy data acquired at the current acquisition moment based on a standard energy metric function, and determining influence factors of the third theoretical power supply contribution capacity and the fourth theoretical power supply contribution capacity on the wind power data and the light energy data based on the acquisition result, wherein the influence factors are at least two;
determining the association relation between the influence factors and the power supply contribution capacity, and training a plurality of groups of wind power data and light energy data and corresponding third theoretical power supply contribution capacity and fourth theoretical power supply contribution capacity based on the association relation to obtain a sub-prediction model without influence factors;
summarizing all the sub-prediction models and performing iterative training until the loss value of the model meets a preset loss value threshold value to obtain a target prediction model;
and inputting the wind power data and the light energy data acquired at the current acquisition time and the influence factors at the next acquisition time into a target prediction model for analysis, and predicting the relative proportion of wind power and photovoltaic at the next acquisition time based on the analysis result.
In this embodiment, the influencing factors are used to characterize the influence of the natural conditions on the third theoretical power supply contribution capability and the fourth theoretical power supply contribution capability at different moments, including the kinds of influence and the corresponding influence degrees of the various kinds.
In this embodiment, the sub-prediction model refers to a prediction model capable of generating corresponding power supply capacity under different influence factors obtained after training a plurality of groups of wind power data and light energy data and corresponding third theoretical power supply contribution capacity and fourth theoretical power supply contribution capacity.
In this embodiment, the preset loss value threshold is set in advance to characterize the maximum allowable loss value.
In this embodiment, the target prediction model is obtained by summarizing all the sub-prediction models, and is used for predicting the relative proportion of wind power and photovoltaic at the next moment.
The beneficial effects of the technical scheme are as follows: by carrying out association analysis on the obtained wind power data and light energy data and the corresponding third theoretical power supply contribution capability and fourth theoretical power supply contribution capability, the accurate and effective construction of the target prediction model is realized, so that the accurate and effective determination of the relative proportion of wind power and photovoltaic at the next moment is realized, the basis is conveniently provided for the power distribution criterion at the next moment in time, and the energy utilization rate at different moments is ensured.
Example 7:
on the basis of embodiment 1, the embodiment provides a wind-solar-energy-storage combined optimal control management method, and in step 3, a power distribution criterion is formulated based on relative proportion, target power required by each energy storage unit and real-time load requirement, and energy scheduling is carried out on each energy storage unit in power supply energy storage equipment based on the power distribution criterion, so that energy compensation processing is completed.
Acquiring state parameters of power supply energy storage equipment based on a communication network, determining the current working state of each energy storage unit in the power supply energy storage equipment based on the state parameters, and determining the current residual electric quantity of each energy storage unit when each energy storage unit is in the working state;
acquiring a time stamp of a charging request sent by each energy storage unit, determining the charging priority of each energy storage unit based on the residual electric quantity and the time stamp, and determining the first power distribution priority of each energy storage unit based on the charging priority;
meanwhile, the relative proportion of wind power and photovoltaic at the current moment is obtained, a target power supply mode of the power supply energy storage equipment is determined based on the relative proportion, and available power supply power which can be provided by the wind power and the photovoltaic is determined in the target power supply mode;
determining a power distribution criterion for each energy storage unit based on available power supply, a first power distribution priority, target power required by each energy storage unit and real-time load requirements, determining first power supply for each energy storage unit based on the power distribution criterion, performing first power supply on each energy storage unit based on the first power supply, simultaneously monitoring a first power supply process in real time, determining real-time energy storage information of each energy storage unit, and establishing a dynamic scheduling optimization model for charging power of each energy storage unit based on preset charging switching conditions;
And docking the dynamic scheduling optimization model with the monitored real-time energy storage information, determining the second power supply of each energy storage unit at the next moment based on the docking result, and carrying out second power supply on each energy storage unit based on the first power supply to complete energy compensation processing of each energy storage unit in the power supply energy storage equipment.
In this embodiment, the time stamp is a time sequence for characterizing the sending of the charging request by each energy storage unit to the charging control terminal.
In this embodiment, the charging priority is used to characterize the sequence of charging the different energy storage units.
In this embodiment, the first power allocation priority is used to characterize the sequence of allocating charging power to different energy storage units.
In this embodiment, the target power supply mode is related to the relative proportion of wind power and photovoltaic, when the relative proportion of wind power and photovoltaic is consistent, the first power supply mode is adopted, when wind power exceeds photovoltaic, the second power supply mode is adopted, when wind power is smaller than photovoltaic, the third power supply mode is adopted, when wind power is only, the fourth power supply mode is adopted, and when photovoltaic is only, the fifth power supply mode is adopted, and the power distribution in the different modes is accurately different.
In this embodiment, the available power supply is a total value of power supply that is used to characterize the power that can be generated by the combined action between wind power and photovoltaic.
In this embodiment, the first power supply refers to the power supply supplied to the different energy storage units at the initial power supply time.
In this embodiment, the first power supply means that power supply operations are performed to different energy storage units according to the first power supply power, respectively.
In this embodiment, the preset charging switching conditions are set in advance, and are used for characterizing the conditions for switching the charging power of different energy storage units, that is, when the energy storage units are full, the power supply of the current energy storage unit is reduced, and meanwhile, the power supply of other energy storage units which are not fully charged is enhanced.
In this embodiment, the dynamic scheduling optimization model is used to perform optimal scheduling on real-time energy of different energy storage units, so as to facilitate ensuring energy balance between the energy storage units.
In this embodiment, the second power supply refers to the power supply of each energy storage unit at the current moment after the first power supply of each energy storage unit is adjusted when the energy storage unit is fully charged.
In this embodiment, the second power supply means that each energy storage unit is supplied with power according to the second power supply.
The beneficial effects of the technical scheme are as follows: the power distribution criterion is formulated according to the relative proportion, the target power required by each energy storage unit and the real-time load requirement, so that the corresponding power supply power is distributed to the corresponding energy storage units according to the current power supply mode, the power supply power of each energy storage unit in the power supply process is optimally scheduled, the power supply effect of the power supply energy storage equipment is ensured, and the utilization efficiency of energy is improved.
Example 8:
on the basis of embodiment 7, the embodiment provides a wind-solar-energy-storage combined optimal control management method, which completes energy compensation processing of each energy storage unit in power supply energy storage equipment, and comprises the following steps:
the method comprises the steps of obtaining a wind speed value at the current moment and the wavelength of natural light at the current moment, calculating an available electric energy value generated in unit time based on the wind speed value at the current moment and the wavelength of the natural light at the current moment, and calculating the utilization rate of the available electric energy value when the power supply energy storage equipment is charged based on the available electric energy value, wherein the specific steps comprise:
the available power value generated per unit time is calculated according to the following formula:
wherein W represents the available electrical energy value generated in a unit time; η represents conversion efficiency of wind energy into electric energy, and the value range is (0, 1); ρ represents the air density, the dimension is kg/m3; v represents wind speed, and dimension is m/s; s represents a cross-sectional area perpendicular to wind speed;the conversion efficiency of light energy to electric energy is represented, and the value range is (0, 1); h represents the Planck constant and has a value of 6.6260693 (11). Times.10 -34 J.s; ω represents the propagation speed of light; λ represents a wavelength value of light;
the utilization ratio of the available electric energy value is calculated according to the following formula:
Wherein, gamma represents the utilization rate of the available electric energy value, and the value range is (0, 1); μ represents an error coefficient, and the value range is (0.02, 0.04); i represents the number of the current energy storage units contained in the power supply energy storage equipment, and the value range is [1, n ]]The method comprises the steps of carrying out a first treatment on the surface of the n represents the total number of energy storage units contained in the power supply energy storage equipment; p is p i Representing a charging power value at the time of charging the i-th energy storage unit; t is t i A charging time length value representing charging of the ith energy storage unit; τ represents the thermal energy value consumed when charging the ith energy storage unit;
comparing the calculated utilization rate with a preset utilization rate threshold value;
if the calculated utilization rate is greater than or equal to a preset utilization rate threshold value, judging that the formulated power distribution criterion is qualified, and supplying power to each energy storage unit in the power supply energy storage equipment based on the power distribution criterion;
otherwise, judging that the formulated power distribution criterion is unqualified, and optimizing the power distribution criterion until the calculated utilization rate is greater than or equal to a preset utilization rate threshold.
In this embodiment, the available energy value refers to an energy value that can be utilized by the energy storage device of the power source after converting wind energy and light energy into electrical energy.
In this embodiment, the preset utilization threshold is set in advance, and is used to characterize the minimum energy utilization value that needs to be achieved, and can be adjusted.
The beneficial effects of the technical scheme are as follows: the utilization rate of the available electric energy value is calculated, so that the current utilization effect of the new energy source can be effectively mastered in real time, and when the utilization rate does not meet the expected requirement, the formulated power distribution criterion is optimized in time, the efficient utilization of the energy source is ensured, and meanwhile, the power supply effect of the power supply energy storage equipment is also ensured.
Example 9:
the embodiment provides a wind-solar-energy-storage combined optimal control management system, as shown in fig. 3, including:
the parameter acquisition module is used for acquiring state parameters of the power supply energy storage equipment based on the communication network and determining target power and real-time load requirements required by each energy storage unit in the power supply energy storage equipment based on the state parameters;
the proportion determining module is used for setting a plurality of environment parameter sampling moments based on discretization processing results of time of day, collecting wind power data and light energy data at different moments based on the environment parameter sampling moments, and determining the relative proportion of wind power and photovoltaic based on the wind power data and the light energy data;
And the energy scheduling module is used for formulating a power distribution criterion based on the relative proportion, the target power required by each energy storage unit and the real-time load demand, and performing energy scheduling on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion to complete energy compensation processing.
The beneficial effects of the technical scheme are as follows: the target power and the real-time load demand required by each energy storage unit in the power supply energy storage device and the relative proportion of wind power and photovoltaic at the current moment are determined, so that the corresponding power distribution criterion is formulated effectively according to the environmental parameters and time, the power supply effect of the power supply energy storage device is ensured, and the energy utilization efficiency is improved.
Example 10:
the embodiment provides wind-solar-energy-storage combined optimal control management equipment, which comprises: the device comprises a data acquisition device, a processor and a memory;
the data acquisition device is used for acquiring data;
the memory is used for storing one or more program instructions;
the processor is configured to execute one or more program instructions, and is configured to execute any one of the wind-solar-storage combined optimal control management methods.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (7)

1. The wind-solar-energy-storage combined optimal control management method is characterized by comprising the following steps of:
step 1: acquiring state parameters of the power supply energy storage equipment based on a communication network, and determining target power and real-time load requirements required by each energy storage unit in the power supply energy storage equipment based on the state parameters;
step 2: setting a plurality of environment parameter sampling moments based on discretization processing results of time of day, collecting wind power data and light energy data at different moments based on the environment parameter sampling moments, and determining the relative proportion of wind power and photovoltaic based on the wind power data and the light energy data;
step 3: formulating a power distribution criterion based on the relative proportion, target power required by each energy storage unit and real-time load demand, and performing energy scheduling on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion to complete energy compensation processing;
wherein, in step 1, it includes:
acquiring state parameters of the obtained power supply energy storage equipment, splitting the state parameters, and associating the target state parameter set obtained after splitting with a corresponding energy storage unit;
generating an electric quantity state change curve of each energy storage unit based on the association result and the target value of each target state parameter group, and determining a target charge quantity required by each energy storage unit based on the electric quantity state change curve;
Acquiring receivable power curves of all energy storage units, and performing first processing on a receivable power range corresponding to the receivable power curves based on a target charge amount to obtain an allowable power range;
extracting the charge state information of each energy storage unit contained in the target state parameter set, determining the real-time load demand of each energy storage unit based on the charge state information, determining the consumption power of each energy storage unit based on the real-time load demand and the power supply power of each energy storage unit, and determining the lowest power difference between the receivable power and the consumption power based on the energy supplementing requirement;
performing second processing on the allowable power range based on the minimum power difference and limiting conditions of preset thermal management equipment in each energy storage unit on the temperature of the energy storage unit to obtain target power required by each energy storage unit;
wherein, step 2 includes:
discretizing the time of day to obtain a target time sequence, simultaneously acquiring weather actual measurement data monitored by a weather station in a target area in a preset time period, and determining wind speeds and wind directions at different times of day and illumination starting time and illumination ending time in the target area based on the weather actual measurement data;
Determining the wind-light climate characteristics of a target area based on wind speeds and wind directions at different times of the day in the target area and illumination starting time and illumination ending time in the day, and dividing a target time sequence into N parameter acquisition intervals based on the wind-light climate characteristics;
determining key time points of obvious transition of climate parameters in each parameter acquisition interval based on wind-solar climate characteristics, and particularly to acquisition hours and acquisition minutes;
determining an environmental parameter sampling time based on the acquisition hours and the acquisition minutes, and controlling a preset parameter acquisition device to acquire wind power data and light energy data at the current time through a data acquisition instruction based on the environmental parameter sampling time;
extracting key data in wind power data and light energy data acquired at the last acquisition time and the current acquisition time, and respectively determining first energy conversion efficiency and second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time based on a preset energy conversion rule;
generating a sample sequence pair according to the first energy conversion efficiency and the second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time, and generating a data vector matrix based on the sample sequence pair;
Preprocessing a data vector matrix, inputting the preprocessed data vector matrix into a preset energy measurement function for analysis, and obtaining a first theoretical power supply contribution capability and a second theoretical power supply contribution capability of wind power data and light energy data acquired at the last acquisition moment and the current acquisition moment to power energy storage equipment;
performing first comparison and second comparison on the first theoretical power supply contribution capability and the second theoretical power supply contribution capability and the corresponding actual power supply contribution capability respectively, performing parameter optimization on a preset energy measurement function when the difference values of the first comparison and the second comparison exceed a preset threshold value, and obtaining a standard energy measurement function based on an optimization result;
re-analyzing wind power data and light energy data acquired at the current acquisition moment based on a standard energy measurement function to obtain third theoretical power supply contribution capability and fourth theoretical power supply contribution capability, and determining the relative proportion of wind power and photovoltaic based on the third theoretical power supply contribution capability and the fourth theoretical power supply contribution capability;
wherein, in step 3, it includes:
acquiring state parameters of power supply energy storage equipment based on a communication network, determining the current working state of each energy storage unit in the power supply energy storage equipment based on the state parameters, and determining the current residual electric quantity of each energy storage unit when each energy storage unit is in the working state;
Acquiring a time stamp of a charging request sent by each energy storage unit, determining the charging priority of each energy storage unit based on the residual electric quantity and the time stamp, and determining the first power distribution priority of each energy storage unit based on the charging priority;
meanwhile, the relative proportion of wind power and photovoltaic at the current moment is obtained, a target power supply mode of the power supply energy storage equipment is determined based on the relative proportion, and available power supply power which can be provided by the wind power and the photovoltaic is determined in the target power supply mode;
determining a power distribution criterion for each energy storage unit based on available power supply, a first power distribution priority and target power and real-time load requirements required by each energy storage unit, determining first power supply for each energy storage unit based on the power distribution criterion, performing first power supply on each energy storage unit based on the first power supply, simultaneously monitoring a first power supply process in real time, determining real-time energy storage information of each energy storage unit, and establishing a dynamic scheduling optimization model for charging power of each energy storage unit based on preset charging switching conditions;
and docking the dynamic scheduling optimization model with the monitored real-time energy storage information, determining the second power supply of each energy storage unit at the next moment based on the docking result, and carrying out second power supply on each energy storage unit based on the second power supply, thereby completing the energy compensation processing of each energy storage unit in the power supply energy storage device.
2. The method for combined optimal control and management of wind, solar and energy storage according to claim 1, wherein in step 1, obtaining state parameters of power energy storage equipment based on a communication network comprises:
basic information of the power supply energy storage equipment is obtained, unit energy storage devices contained in the power supply energy storage equipment are determined based on the basic information, and statistics is carried out on the unit energy storage devices to obtain target quantity of the unit energy storage devices;
setting device identifiers for the unit energy storage devices based on the target quantity, setting preset sensors for the corresponding unit energy storage devices based on the device identifiers, and constructing a distributed communication network between the preset sensors and the data receiving terminal based on communication addresses of the preset sensors;
setting a daily multi-breakpoint acquisition time for each preset sensor based on the data receiving terminal, synchronously adapting a clock trigger at the data receiving terminal based on the daily multi-breakpoint acquisition time, and generating a corresponding data acquisition instruction at a target time based on the adapted clock trigger;
and sending a data acquisition instruction to a corresponding preset sensor based on the distributed communication network, and controlling the preset sensor to acquire the current operation parameters of each unit energy storage device to obtain the state parameters of the power supply energy storage device.
3. The method for optimal control and management of wind, solar and energy storage according to claim 2, wherein obtaining the state parameters of the power energy storage device comprises:
acquiring state parameters of the obtained power supply energy storage equipment, and clustering the obtained state parameters based on a preset classification label to obtain a sub-state parameter set;
extracting data characteristics of each sub-state parameter set, and matching corresponding target data cleaning rules from a preset data cleaning rule base based on the data characteristics;
and cleaning the corresponding sub-state parameter set based on the target data cleaning rule to obtain the standard state parameter of the power supply energy storage equipment.
4. The wind-solar-energy-storage combined optimal control management method according to claim 1, wherein re-analyzing wind power data and light energy data collected at the current collection time based on a standard energy metric function comprises the following steps:
acquiring a plurality of groups of third theoretical power supply contribution capacity and fourth theoretical power supply contribution capacity which are obtained by re-analyzing wind power data and light energy data acquired at the current acquisition moment based on a standard energy metric function, and determining influence factors of the third theoretical power supply contribution capacity and the fourth theoretical power supply contribution capacity on the wind power data and the light energy data based on the acquisition result, wherein the influence factors are at least two;
Determining the association relation between the influence factors and the power supply contribution capacity, and training a plurality of groups of wind power data and light energy data and corresponding third theoretical power supply contribution capacity and fourth theoretical power supply contribution capacity based on the association relation to obtain a sub-prediction model without influence factors;
summarizing all the sub-prediction models and performing iterative training until the loss value of the model meets a preset loss value threshold value to obtain a target prediction model;
and inputting the wind power data and the light energy data acquired at the current acquisition time and the influence factors at the next acquisition time into a target prediction model for analysis, and predicting the relative proportion of wind power and photovoltaic at the next acquisition time based on the analysis result.
5. The method for optimally controlling and managing wind, light and energy storage according to claim 1, wherein the energy compensation process of each energy storage unit in the power supply energy storage device is completed, and the method comprises the following steps:
the method comprises the steps of obtaining a wind speed value at the current moment and the wavelength of natural light at the current moment, calculating an available electric energy value generated in unit time based on the wind speed value at the current moment and the wavelength of the natural light at the current moment, and calculating the utilization rate of the available electric energy value when the power supply energy storage equipment is charged based on the available electric energy value, wherein the specific steps comprise:
The available power value generated per unit time is calculated according to the following formula:
wherein W represents the available electrical energy value generated in a unit time; η represents conversion efficiency of wind energy into electric energy, and the value range is (0, 1); ρ represents the air density, the dimension is kg/m3; v represents wind speed, and dimension is m/s; s represents a cross-sectional area perpendicular to wind speed;the conversion efficiency of light energy to electric energy is represented, and the value range is (0, 1); h represents the Planck constant and has a value of 6.6260693 (11). Times.10 -34 J.s; ω represents the propagation speed of light; λ represents a wavelength value of light;
the utilization ratio of the available electric energy value is calculated according to the following formula:
wherein, gamma represents the utilization rate of the available electric energy value, and the value range is (0, 1); μ represents an error coefficient, and the value range is (0.02, 0.04); i represents the number of each current energy storage unit contained in the power supply energy storage equipment, and the value range is [1, n ]]The method comprises the steps of carrying out a first treatment on the surface of the n represents the total number of energy storage units contained in the power supply energy storage equipment; p is p i Representing a charging power value at the time of charging the i-th energy storage unit; t is t i A charging time length value representing charging of the ith energy storage unit; τ represents the thermal energy value consumed when charging the ith energy storage unit;
Comparing the calculated utilization rate with a preset utilization rate threshold value;
if the calculated utilization rate is greater than or equal to a preset utilization rate threshold value, judging that the formulated power distribution criterion is qualified, and supplying power to each energy storage unit in the power supply energy storage equipment based on the power distribution criterion;
otherwise, judging that the formulated power distribution criterion is unqualified, and optimizing the power distribution criterion until the calculated utilization rate is greater than or equal to a preset utilization rate threshold value.
6. The wind-solar-energy-storage combined optimal control management system is characterized by comprising:
the parameter acquisition module is used for acquiring state parameters of the power supply energy storage equipment based on the communication network and determining target power and real-time load requirements required by each energy storage unit in the power supply energy storage equipment based on the state parameters;
the proportion determining module is used for setting a plurality of environment parameter sampling moments based on discretization processing results of time of day, collecting wind power data and light energy data at different moments based on the environment parameter sampling moments, and determining the relative proportion of wind power and photovoltaic based on the wind power data and the light energy data;
the energy scheduling module is used for formulating a power distribution criterion based on the relative proportion, the target power required by each energy storage unit and the real-time load demand, and performing energy scheduling on each energy storage unit in the power supply energy storage equipment based on the power distribution criterion to complete energy compensation processing;
The parameter acquisition module is specifically configured to:
acquiring state parameters of the obtained power supply energy storage equipment, splitting the state parameters, and associating the target state parameter set obtained after splitting with a corresponding energy storage unit;
generating an electric quantity state change curve of each energy storage unit based on the association result and the target value of each target state parameter group, and determining a target charge quantity required by each energy storage unit based on the electric quantity state change curve;
acquiring receivable power curves of all energy storage units, and performing first processing on a receivable power range corresponding to the receivable power curves based on a target charge amount to obtain an allowable power range;
extracting the charge state information of each energy storage unit contained in the target state parameter set, determining the real-time load demand of each energy storage unit based on the charge state information, determining the consumption power of each energy storage unit based on the real-time load demand and the power supply power of each energy storage unit, and determining the lowest power difference between the receivable power and the consumption power based on the energy supplementing requirement;
performing second processing on the allowable power range based on the minimum power difference and limiting conditions of preset thermal management equipment in each energy storage unit on the temperature of the energy storage unit to obtain target power required by each energy storage unit;
The proportion determining module is specifically configured to:
discretizing the time of day to obtain a target time sequence, simultaneously acquiring weather actual measurement data monitored by a weather station in a target area in a preset time period, and determining wind speeds and wind directions at different times of day and illumination starting time and illumination ending time in the target area based on the weather actual measurement data;
determining the wind-light climate characteristics of a target area based on wind speeds and wind directions at different times of the day in the target area and illumination starting time and illumination ending time in the day, and dividing a target time sequence into N parameter acquisition intervals based on the wind-light climate characteristics;
determining key time points of obvious transition of climate parameters in each parameter acquisition interval based on wind-solar climate characteristics, and particularly to acquisition hours and acquisition minutes;
determining an environmental parameter sampling time based on the acquisition hours and the acquisition minutes, and controlling a preset parameter acquisition device to acquire wind power data and light energy data at the current time through a data acquisition instruction based on the environmental parameter sampling time;
extracting key data in wind power data and light energy data acquired at the last acquisition time and the current acquisition time, and respectively determining first energy conversion efficiency and second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time based on a preset energy conversion rule;
Generating a sample sequence pair according to the first energy conversion efficiency and the second energy conversion efficiency corresponding to the wind power data and the light energy data acquired at the last acquisition time and the current acquisition time, and generating a data vector matrix based on the sample sequence pair;
preprocessing a data vector matrix, inputting the preprocessed data vector matrix into a preset energy measurement function for analysis, and obtaining a first theoretical power supply contribution capability and a second theoretical power supply contribution capability of wind power data and light energy data acquired at the last acquisition moment and the current acquisition moment to power energy storage equipment;
performing first comparison and second comparison on the first theoretical power supply contribution capability and the second theoretical power supply contribution capability and the corresponding actual power supply contribution capability respectively, performing parameter optimization on a preset energy measurement function when the difference values of the first comparison and the second comparison exceed a preset threshold value, and obtaining a standard energy measurement function based on an optimization result;
re-analyzing wind power data and light energy data acquired at the current acquisition moment based on a standard energy measurement function to obtain third theoretical power supply contribution capability and fourth theoretical power supply contribution capability, and determining the relative proportion of wind power and photovoltaic based on the third theoretical power supply contribution capability and the fourth theoretical power supply contribution capability;
The energy scheduling module is specifically configured to:
acquiring state parameters of power supply energy storage equipment based on a communication network, determining the current working state of each energy storage unit in the power supply energy storage equipment based on the state parameters, and determining the current residual electric quantity of each energy storage unit when each energy storage unit is in the working state;
acquiring a time stamp of a charging request sent by each energy storage unit, determining the charging priority of each energy storage unit based on the residual electric quantity and the time stamp, and determining the first power distribution priority of each energy storage unit based on the charging priority;
meanwhile, the relative proportion of wind power and photovoltaic at the current moment is obtained, a target power supply mode of the power supply energy storage equipment is determined based on the relative proportion, and available power supply power which can be provided by the wind power and the photovoltaic is determined in the target power supply mode;
determining a power distribution criterion for each energy storage unit based on available power supply, a first power distribution priority and target power and real-time load requirements required by each energy storage unit, determining first power supply for each energy storage unit based on the power distribution criterion, performing first power supply on each energy storage unit based on the first power supply, simultaneously monitoring a first power supply process in real time, determining real-time energy storage information of each energy storage unit, and establishing a dynamic scheduling optimization model for charging power of each energy storage unit based on preset charging switching conditions;
And docking the dynamic scheduling optimization model with the monitored real-time energy storage information, determining the second power supply of each energy storage unit at the next moment based on the docking result, and carrying out second power supply on each energy storage unit based on the second power supply, thereby completing the energy compensation processing of each energy storage unit in the power supply energy storage device.
7. A wind-solar-energy-storage combined optimal control management device, comprising: the device comprises a data acquisition device, a processor and a memory;
the data acquisition device is used for acquiring data;
the memory is used for storing one or more program instructions;
the processor being configured to execute one or more program instructions for performing the method of any of claims 1-5.
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