CN114418249A - Operation control method and device for light storage flexible system - Google Patents

Operation control method and device for light storage flexible system Download PDF

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CN114418249A
CN114418249A CN202210335813.0A CN202210335813A CN114418249A CN 114418249 A CN114418249 A CN 114418249A CN 202210335813 A CN202210335813 A CN 202210335813A CN 114418249 A CN114418249 A CN 114418249A
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CN114418249B (en
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彭晋卿
罗正意
邹斌
李厚培
曹静宇
罗伊默
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Abstract

The application discloses a method and a device for controlling the operation of a light storage flexible system, wherein the method comprises the following steps: calculating the power generation power of the distributed photovoltaic system on the prediction day and the power consumption power of the inflexible load on the prediction day; constructing response models of various flexible loads according to the energy utilization attribute information of the various flexible loads, and constructing a storage battery model; according to the response models of the power generation power, the power consumption power and various flexible loads and the storage battery model on the prediction day, an optimization model is constructed according to the goals of minimum operation and maintenance cost of a user, minimum carbon dioxide emission and maximum electric power self-satisfaction rate, the optimization model is solved, the operation plans of various flexible loads and storage batteries on the prediction day are obtained, and the operation of various flexible loads and storage batteries on the prediction day is correspondingly controlled. The technical scheme disclosed in the application adjusts the power load curve of building through changing the flexible load power consumption mode, absorbs distributed photovoltaic power generation as far as possible to reduce the installation capacity of battery, reduce the cost of absorption.

Description

Operation control method and device for light storage flexible system
Technical Field
The application relates to the technical field of new energy, in particular to a method and a device for controlling operation of a light storage flexible system.
Background
Under the promotion of the double-carbon target, the renewable energy power generation technology can be widely applied in the future, wherein the distributed photovoltaic power generation technology has a wide development prospect. However, the distributed photovoltaic power generation is affected by solar radiation, has intermittence, volatility and uncontrollable property, and brings huge challenges to the stable operation of the power grid due to the fact that power is frequently taken or transmitted to the power grid and impacts on the power grid.
At present, distributed photovoltaic power generation is usually consumed by adopting a mode of matching with a storage battery, but a storage battery with a large capacity needs to be configured to realize complete consumption of the distributed photovoltaic power generation, so that the investment cost is high, and the large-scale application is difficult in engineering.
In summary, how to reduce the capacity of the storage battery and the consumption cost of the distributed photovoltaic power generation is a technical problem to be solved urgently by those skilled in the art at present.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method and an apparatus for controlling operation of a light storage flexible system, which are used to reduce capacity of a storage battery and consumption cost of distributed photovoltaic power generation.
In order to achieve the above purpose, the present application provides the following technical solutions:
a light-storing flexible system operation control method comprises the following steps:
calculating the power generation power of the distributed photovoltaic system on a prediction day according to weather data of the distributed photovoltaic system on the prediction day, and determining the power consumption power of the inflexible load on the prediction day according to historical power consumption data of the inflexible load in a building where the distributed photovoltaic system is located;
determining energy utilization attribute information of various flexible loads in a building where the distributed photovoltaic system is located, correspondingly constructing response models of the various flexible loads according to the energy utilization attribute information, and constructing a storage battery model according to information of a storage battery;
according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery model, constructing an optimization model with the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum power self-satisfaction rate, and solving the optimization model to obtain the operation plans of various flexible loads and storage batteries on the prediction day;
and correspondingly controlling the operation of each type of flexible load and the operation of the storage battery on the forecast day according to the flexible load and the operation plan of the storage battery on the forecast day.
Preferably, the constructing of the response model of the temperature-controlled load according to the energy use attribute information of the temperature-controlled load includes:
constructing a thermodynamic model of the temperature control load according to the energy consumption behavior information and the energy consumption mode information of the temperature control load:
Figure 676525DEST_PATH_IMAGE001
power model in cooling mode:
Figure 796928DEST_PATH_IMAGE002
binary variable in refrigeration mode
Figure 717611DEST_PATH_IMAGE003
Comprises the following steps:
Figure 820477DEST_PATH_IMAGE004
power model in heating mode:
Figure 572532DEST_PATH_IMAGE005
binary variable in heating mode
Figure 660574DEST_PATH_IMAGE003
Comprises the following steps:
Figure 334132DEST_PATH_IMAGE006
(ii) a Wherein the content of the first and second substances,
Figure 736294DEST_PATH_IMAGE007
，
Figure 670752DEST_PATH_IMAGE008
，
Figure 365914DEST_PATH_IMAGE009
，
Figure 261188DEST_PATH_IMAGE010
，
Figure 201463DEST_PATH_IMAGE011
the temperature inside the ith temperature controlled load at time t +1,
Figure 662531DEST_PATH_IMAGE012
is the temperature coefficient of the ith temperature controlled load,
Figure 357954DEST_PATH_IMAGE013
the temperature inside the ith temperature controlled load at time t,
Figure 301377DEST_PATH_IMAGE014
is the ambient temperature at which the ith load is located,
Figure 717446DEST_PATH_IMAGE015
for the mode of operation in which the temperature controlled load is located,
Figure 564180DEST_PATH_IMAGE016
for the output power of the ith temperature controlled load,
Figure 164925DEST_PATH_IMAGE017
the coefficient of refrigeration performance for the ith temperature controlled load,
Figure 97109DEST_PATH_IMAGE018
for the ith temperature-controlled load at time tThe power is input into the power-generating device,
Figure 80983DEST_PATH_IMAGE019
is a binary variable which represents the starting and stopping state of the temperature control load,
Figure 844540DEST_PATH_IMAGE020
as the heating performance coefficient of the ith temperature-controlled load,
Figure 553870DEST_PATH_IMAGE021
is the thermal resistance of the ith temperature controlled load,
Figure 707771DEST_PATH_IMAGE022
is the heat capacity of the ith temperature controlled load,
Figure 731222DEST_PATH_IMAGE023
in the form of a time interval,
Figure 779644DEST_PATH_IMAGE024
a temperature is set for the ith temperature controlled load,
Figure 659875DEST_PATH_IMAGE025
a temperature threshold is set for the ith temperature controlled load,
Figure 363389DEST_PATH_IMAGE026
and
Figure 190531DEST_PATH_IMAGE027
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
Figure 600783DEST_PATH_IMAGE028
and
Figure 150451DEST_PATH_IMAGE029
the operation starting time and the operation ending time of the ith temperature control load are respectively.
Preferably, the constructing a response model of the transferable load according to the energy utilization attribute information of the transferable load includes:
constructing an energy consumption model of the transferable load according to the energy consumption mode information of the transferable load:
Figure 13365DEST_PATH_IMAGE030
(ii) a Wherein the content of the first and second substances,
Figure 972094DEST_PATH_IMAGE031
，
Figure 971274DEST_PATH_IMAGE032
，
Figure 426263DEST_PATH_IMAGE033
the power at time t for the jth transferable load,
Figure 42053DEST_PATH_IMAGE034
for the jth transferable load's power in different phases of operation,
Figure 210997DEST_PATH_IMAGE035
for the time when the jth transferable load starts running,
Figure 330263DEST_PATH_IMAGE036
for the running time of the jth transferable load in the running phase w,
Figure 221733DEST_PATH_IMAGE037
the time frame for which the operation is allowed for the jth transferable load,
Figure 387135DEST_PATH_IMAGE038
the operation duration for the jth transferable load.
Preferably, the constructing of the load reducible response model based on the load reducible energy attribute information includes:
constructing an energy consumption model of the load with adjustable lighting power according to the energy consumption mode information of the load with adjustable lighting power, wherein the energy consumption model comprises the following steps:
Figure 156508DEST_PATH_IMAGE039
(ii) a Wherein the content of the first and second substances,
Figure 599122DEST_PATH_IMAGE040
，
Figure 162958DEST_PATH_IMAGE041
is as follows
Figure 992155DEST_PATH_IMAGE042
The power of the load capable of adjusting the lighting power at the moment t,
Figure 299639DEST_PATH_IMAGE043
is as follows
Figure 127918DEST_PATH_IMAGE044
The adjustment factor of the load of adjustable lighting power at time t,
Figure 597077DEST_PATH_IMAGE045
is as follows
Figure 642131DEST_PATH_IMAGE046
The rated power of the load for which the lighting power can be adjusted,
Figure 550044DEST_PATH_IMAGE047
and
Figure 701671DEST_PATH_IMAGE048
are respectively the first
Figure 607310DEST_PATH_IMAGE049
A load start operation time and an operation end time of the adjustable lighting power;
constructing an energy consumption model of the load of the adjustable working gear according to the energy consumption mode information of the load of the adjustable working gear in the reducible load:
Figure 405239DEST_PATH_IMAGE050
(ii) a Wherein the content of the first and second substances,
Figure 54527DEST_PATH_IMAGE051
，
Figure 185294DEST_PATH_IMAGE052
is as follows
Figure 261834DEST_PATH_IMAGE053
The load of the individual adjustable operating gears is at the power in the e gear at time t,
Figure 782945DEST_PATH_IMAGE054
is as follows
Figure 734458DEST_PATH_IMAGE055
The load of each adjustable working gear is at different gears
Figure 657415DEST_PATH_IMAGE056
The power of (a) is determined,
Figure 904857DEST_PATH_IMAGE057
and
Figure 116526DEST_PATH_IMAGE058
is as follows
Figure 889309DEST_PATH_IMAGE059
The load start running time and the running end time of each adjustable working gear are adjusted.
Preferably, constructing a response model of the battery load according to the energy use attribute information of the battery load includes:
constructing an energy consumption model of the battery load according to the energy consumption mode information of the battery load:
Figure 994668DEST_PATH_IMAGE060
(ii) a Wherein the content of the first and second substances,
Figure 413011DEST_PATH_IMAGE061
，
Figure 315239DEST_PATH_IMAGE062
，
Figure 437916DEST_PATH_IMAGE063
，
Figure 568421DEST_PATH_IMAGE064
，…，
Figure 157665DEST_PATH_IMAGE065
，
Figure 406244DEST_PATH_IMAGE066
，
Figure 739136DEST_PATH_IMAGE067
，
Figure 724147DEST_PATH_IMAGE068
，
Figure 749872DEST_PATH_IMAGE069
representing the amount of power stored by the nth battery load by time t,
Figure 157851DEST_PATH_IMAGE070
represents the state of charge of the nth battery load, which has a value of 1 when charged and a value of 0 when uncharged,
Figure 294434DEST_PATH_IMAGE071
the charging power for the nth battery load,
Figure 133952DEST_PATH_IMAGE072
in order to achieve a high charging efficiency,
Figure 330578DEST_PATH_IMAGE073
in the form of a time interval,
Figure 819328DEST_PATH_IMAGE074
representing the amount of power stored by the nth battery load by the time t-1,
Figure 494023DEST_PATH_IMAGE075
for the nth battery loadThe charging power at the different charging phases,
Figure 459486DEST_PATH_IMAGE076
the time to start charging for the nth battery load,
Figure 827013DEST_PATH_IMAGE077
the charging period of phase 1 for charging the nth battery load,
Figure 740743DEST_PATH_IMAGE078
indicating the state of charge of the nth battery load during charging phase 1,
Figure 953549DEST_PATH_IMAGE079
indicating a charging phase 1 according to
Figure 33239DEST_PATH_IMAGE080
And
Figure 978192DEST_PATH_IMAGE081
the number of divided sub-charging nodes,
Figure 644797DEST_PATH_IMAGE082
the charging period for the nth battery load charging phase r,
Figure 159829DEST_PATH_IMAGE083
the time frame for which charging is allowed for the nth battery load,
Figure 595490DEST_PATH_IMAGE084
and
Figure 367137DEST_PATH_IMAGE085
respectively the minimum amount of electricity and the maximum amount of energy that the nth battery load can store,
Figure 786617DEST_PATH_IMAGE086
the total charge time period for the nth battery load.
Preferably, the building of the battery model based on the information of the battery includes:
constructing the storage battery model:
Figure 606805DEST_PATH_IMAGE087
(ii) a Wherein the content of the first and second substances,
Figure 864349DEST_PATH_IMAGE088
，
Figure 72477DEST_PATH_IMAGE089
，
Figure 448094DEST_PATH_IMAGE090
，
Figure 71974DEST_PATH_IMAGE091
the internal electric quantity of the storage battery at the moment t,
Figure 482226DEST_PATH_IMAGE092
is a binary variable, with a charge of 1, a discharge of 0,
Figure 963718DEST_PATH_IMAGE093
and
Figure 826632DEST_PATH_IMAGE094
respectively representing the charging power and the discharging power of the storage battery,
Figure 988623DEST_PATH_IMAGE095
and
Figure 253382DEST_PATH_IMAGE096
respectively showing the charge efficiency and the discharge efficiency of the secondary battery,
Figure 239530DEST_PATH_IMAGE097
in the form of a time interval,
Figure 589740DEST_PATH_IMAGE098
and
Figure 289843DEST_PATH_IMAGE099
individual watchShowing the maximum charging power and the maximum discharging power of the storage battery,
Figure 409109DEST_PATH_IMAGE100
and
Figure 129940DEST_PATH_IMAGE101
respectively representing the minimum and maximum electric quantities that the accumulator can store.
Preferably, the building of the optimization model according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery model with the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum power self-satisfaction rate includes:
constructing the optimization model:
Figure 465981DEST_PATH_IMAGE102
；
Figure 969775DEST_PATH_IMAGE103
represents a set of decision variables that are to be made,
Figure 943547DEST_PATH_IMAGE104
，
Figure 507383DEST_PATH_IMAGE105
and
Figure 726793DEST_PATH_IMAGE106
respectively representing the charging power and the discharging power of the storage battery,
Figure 47660DEST_PATH_IMAGE107
a temperature is set for the ith temperature controlled load,
Figure 672676DEST_PATH_IMAGE108
for the time when the jth transferable load starts running,
Figure 923527DEST_PATH_IMAGE109
for the mth one that can reduce the power of the load,
Figure 673308DEST_PATH_IMAGE110
indicating the state of charge of the nth battery load,
Figure 50063DEST_PATH_IMAGE111
to purchase or sell electric power for the grid,
Figure 998428DEST_PATH_IMAGE112
an objective function representing the optimization model,
Figure 231963DEST_PATH_IMAGE113
the operation and maintenance cost of the user is shown,
Figure 29892DEST_PATH_IMAGE114
the amount of carbon dioxide emissions is expressed,
Figure 616863DEST_PATH_IMAGE115
，
Figure 950892DEST_PATH_IMAGE116
the self-satisfaction rate of the electric power is represented,
Figure 355328DEST_PATH_IMAGE117
representing the constraints of the inequality therein,
Figure 142019DEST_PATH_IMAGE118
expressing the equality constraint and the power balance, wherein the power balance is as follows:
Figure 827953DEST_PATH_IMAGE119
，
Figure 954172DEST_PATH_IMAGE120
a decision space is represented in the form of,
Figure 467193DEST_PATH_IMAGE121
generating power for the distributed photovoltaic system on a predicted day,
Figure 803496DEST_PATH_IMAGE122
is a binary variable, with a charge of 1, a discharge of 0,
Figure 60165DEST_PATH_IMAGE123
the total power usage for all non-compliant loads,
Figure 539426DEST_PATH_IMAGE124
the input power of the ith temperature control load at the moment t, I is the total amount of the temperature control loads,
Figure 223348DEST_PATH_IMAGE125
the power of the jth transferable load at time t, J the total amount of transferable loads, M the total amount of reducible loads,
Figure 781368DEST_PATH_IMAGE126
is the charging power of the nth battery load, and N is the total amount of the battery loads.
Preferably, solving the optimization model to obtain the operation plans of the various flexible loads and the storage battery on the prediction days comprises:
and solving the optimization model by using a non-dominated sorting genetic algorithm, a sorting method approaching an ideal value and an information entropy method to obtain various flexible loads and an operation plan of the storage battery on a prediction day.
Preferably, the method further comprises the following steps:
and receiving energy utilization attribute information of the target flexible load in the building where the distributed photovoltaic system is located, which is sent by a user.
A light storing flexible system operation control device comprising:
the calculation module is used for calculating the power generation power of the distributed photovoltaic system on the prediction day according to the weather data of the distributed photovoltaic system on the prediction day, and determining the power consumption power of the inflexible load on the prediction day according to the historical power consumption data of the inflexible load in the building where the distributed photovoltaic system is located;
the first construction module is used for determining energy utilization attribute information of various flexible loads in a building where the distributed photovoltaic system is located, correspondingly constructing response models of the various flexible loads according to the energy utilization attribute information, and constructing a storage battery model according to information of a storage battery;
the second construction module is used for constructing an optimization model according to the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum electric power self-satisfaction rate according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery model, and solving the optimization model to obtain the operation plans of various flexible loads and the storage battery on the prediction day;
and the control module is used for correspondingly controlling the operation of the flexible loads and the operation of the storage battery on the prediction day according to the flexible loads and the operation plan of the storage battery on the prediction day.
The application provides a method and a device for controlling the operation of a light storage flexible system, wherein the method comprises the following steps: calculating the power generation power of the distributed photovoltaic system on the prediction day according to the weather data of the distributed photovoltaic system on the prediction day, and determining the power consumption power of the inflexible load on the prediction day according to the historical power consumption data of the inflexible load in the building where the distributed photovoltaic system is located; determining energy utilization attribute information of various flexible loads in a building where the distributed photovoltaic system is located, correspondingly constructing response models of the various flexible loads according to the energy utilization attribute information, and constructing a storage battery model according to information of a storage battery; according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, response models of various flexible loads and storage battery models, an optimization model is constructed by using the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum electric power self-satisfaction rate, and the optimization model is solved to obtain the operation plans of various flexible loads and storage batteries on the prediction day; and correspondingly controlling the operation of various flexible loads and the storage battery on the prediction day according to the operation plans of the various flexible loads and the storage battery on the prediction day.
According to the technical scheme disclosed by the application, the generation power of the distributed photovoltaic system on the prediction day and the power consumption power of the inflexible load in the building where the distributed photovoltaic system is located on the prediction day are predicted, establishing response models and storage battery models of various flexible loads, establishing an optimization model based on the response models and the storage battery models of various flexible loads, and the optimization model is solved to obtain the operation plan of the storage battery and each flexible load on the prediction day, and controls the operation of various flexible loads and storage batteries on the forecast days according to the operation plan, the power load curve of the building where the distributed photovoltaic system is located is adjusted by changing the flexible load power utilization mode, distributed photovoltaic power generation on the user side is consumed as much as possible, and the power which is not consumed is stored in the storage battery, so that the installation capacity of the storage battery is reduced, and the consumption cost of the distributed photovoltaic power generation is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of an operation control method of a light storage flexible system according to an embodiment of the present disclosure;
fig. 2 is an architecture diagram of an operation control system according to an embodiment of the present application;
FIG. 3 is a block diagram of another exemplary operational control system according to an embodiment of the present disclosure;
FIG. 4 is a flowchart illustrating a solution of the multi-objective optimization method according to an embodiment of the present disclosure;
FIG. 5 is another operational control flow diagram provided by an embodiment of the present application;
fig. 6 is a schematic structural diagram of an operation control device of a light storing flexible system according to an embodiment of the present application.
Detailed Description
The core of the application is to provide a method and a device for controlling the operation of a light storage flexible system, which are used for reducing the capacity of a storage battery and the consumption cost of distributed photovoltaic power generation.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1 to fig. 3, in which fig. 1 shows a flowchart of an operation control method of an optical storage flexible system provided in an embodiment of the present application, fig. 2 shows an architecture diagram of an operation control system provided in an embodiment of the present application, and fig. 3 shows an architecture diagram of another operation control system provided in an embodiment of the present application. The operation control method for the light storage flexible system provided by the embodiment of the application can comprise the following steps:
s11: the power generation power of the distributed photovoltaic system on the prediction day is calculated according to the weather data of the distributed photovoltaic system on the prediction day, and the power utilization power of the inflexible load on the prediction day is determined according to the historical power utilization data of the inflexible load in the building where the distributed photovoltaic system is located.
It should be noted that the distributed photovoltaic system is simply understood to be a photovoltaic power generation system installed on a roof of a user (e.g., a residential building). For a building where the distributed photovoltaic system is located, loads inside the building can be divided into flexible loads and inflexible loads, wherein the flexible loads: on the premise of not damaging the benefit of a user, the user changes the electricity utilization curve of the building by reducing, transferring and improving the electricity utilization power of the flexible load, so that the distributed photovoltaic power generation is matched, the consumption of the distributed photovoltaic power generation is improved, and the influence of intermittence and fluctuation on a power grid is reduced. The user benefit means: thermal comfort (for example, in summer, a user is used to set the temperature of the air conditioner within a range of 24-26 ℃ and cannot say that the temperature of the air conditioner is set to 30 ℃ in order to reduce the power of the air conditioner and match distributed photovoltaic power generation), and convenience (for example, the user is used to wash clothes at 9:00-11:00 am and cannot say that the washing machine washes clothes at 2:00-4:00 pm in order to change the power utilization curve of a building in the period).
The buildings where the distributed photovoltaic system is located can be divided into different types, such as houses, businesses, public buildings and the like, the flexible loads of different buildings are different, the residential buildings and single users (which can be understood as villas or rural residential buildings) are taken as an example for explanation in the application, and the application can be applied to other types of buildings. The user load of the residential building is divided into a flexible load and an inflexible load. The residential building flexible load can be simply understood as: the household appliance can change the running power or the working time on the premise of not damaging the benefit of a user; a non-compliant load may be understood as a household appliance that cannot change its operating power or operating time without compromising the user's interest. The flexible load of a residential building comprises: temperature control loads (by adjusting temperature set values, the output power of household appliances such as air conditioners, refrigerators, electric water heaters, and the like is changed); transferable loads (by changing the running time of the household appliance, such as transferring the running time of a washing machine from 9:00-9:40 to 11:00-11:40, such flexible loads including washing machines, dryers, dishwashers, etc.); the load can be reduced (by reducing the power of the household appliance, for example, reducing the illumination of the lighting system, such flexible load includes lighting, etc.); battery load (by shifting the charging time of such appliances, including electric vehicles and other devices with portable batteries (e.g., notebook computers), etc.). The inflexible load mainly comprises a television, a range hood and the like.
Sources of building load flexibility: for temperature-controlled loads, such as air conditioners, refrigerators, electric water heaters, etc., by changing the temperature set point of such home appliances, the power or the operation time of the home appliances is changed accordingly. For example, the set temperature of an air conditioner of a certain user at a certain moment in summer is 24 ℃, the acceptable air conditioner temperature of the user is set to be in a range of 24-27 ℃, the distributed photovoltaic power generation amount is not enough in the next period of time through photovoltaic power generation prediction, the set temperature of the air conditioner can be properly increased, for example, the set temperature of the air conditioner is increased to 26 ℃ from the original 24 ℃, and the power consumption of the air conditioner is reduced in the next period of time so as to respond to the change of the photovoltaic power generation. It should be noted that changing the temperature set point also requires that certain conditions are met, which do not sacrifice the thermal comfort of the user, i.e. do not exceed the thermal comfort temperature range of the user, i.e. the set temperature for the air conditioner of the user can only be adjusted within the temperature range of 24-27 ℃, which is not allowed by the user, thus sacrificing the thermal comfort of the user. For transferable loads, such as washing machines, dishwashers, dryers, etc., changing the run time of such household appliances can divert their electrical load. For example, the using time of the washing machine used by a certain user is usually 8:00-12:00 in the morning, the proper running time of the washing machine can be selected according to the distributed photovoltaic power generation amount in the time period, for example, the photovoltaic power generation amount is found to be more in 11:00-11:40 through photovoltaic power generation prediction, and then the running time of the washing machine can be transferred from the originally planned 9:00-9:40 to 11:00-11: 40. It should be noted that the transfer time of the transferable load also needs to meet certain conditions, which cannot sacrifice the convenience of the user, that is, cannot exceed the time range allowed by the user, that is, the operation time of the washing machine for the user can only be between 8:00 and 12:00, and the operation in other time periods brings inconvenience to the user; similar flexibility is provided for the curtailable load and the battery load.
In addition, it should be noted that the execution subject in the present application may specifically be an optimizer.
In the application, the weather data of the distributed photovoltaic system on the prediction day may be acquired, where the prediction day mentioned herein may be specifically the second day, that is, the weather data of the distributed photovoltaic system on the second day is acquired one day in advance (the weather data of the second day mentioned herein is specifically the weather prediction data of the second day), so as to predict the power generation power of the distributed photovoltaic system on the second day, and of course, the prediction day mentioned herein may also be other times as long as the corresponding weather prediction data may be acquired. In addition, the weather data of the predicted day mentioned in the present application specifically refers to outdoor ambient temperature, solar radiation intensity, and the like. After acquiring the weather data of the distributed photovoltaic system on the prediction day, the generated power of the distributed photovoltaic system on the prediction day can be calculated by the following equations (1) and (2):
Figure 841728DEST_PATH_IMAGE127
（1）
Figure 676960DEST_PATH_IMAGE128
（2）
wherein the content of the first and second substances,
Figure 36178DEST_PATH_IMAGE129
for the generation power (kW) of the distributed photovoltaic system on the forecast day,
Figure 347074DEST_PATH_IMAGE130
is the intensity of solar radiation (W/m)2)，
Figure 945545DEST_PATH_IMAGE131
For the installation area (m) of photovoltaic panels in a distributed photovoltaic system2)，
Figure 900863DEST_PATH_IMAGE132
In order to achieve the power generation efficiency of the photovoltaic cell panel,
Figure 926588DEST_PATH_IMAGE133
、
Figure 724780DEST_PATH_IMAGE134
、
Figure 297581DEST_PATH_IMAGE135
、
Figure 169722DEST_PATH_IMAGE136
、
Figure 694245DEST_PATH_IMAGE137
are all constant and are all provided with the same power,
Figure 120678DEST_PATH_IMAGE138
is the outdoor ambient temperature (DEG C),
Figure 529794DEST_PATH_IMAGE139
the mass of the air (kg),
Figure 754976DEST_PATH_IMAGE140
、
Figure 184821DEST_PATH_IMAGE141
and
Figure 629708DEST_PATH_IMAGE142
respectively the solar radiation intensity, the outdoor environment temperature and the air quality under the standard test conditions, and the values are respectively 1000W/m225 ℃ and 1.5 kg.
Considering that the user load of the building where the distributed photovoltaic system is located is divided into a flexible load and an inflexible load (such as a television, a range hood and the like), and the inflexible load can also consume the power generation power of the distributed photovoltaic system, therefore, the optimizer can obtain historical power utilization data of each inflexible load in a section of historical length in the building where the distributed photovoltaic system is located, and then can respectively obtain power utilization curves of the inflexible load of the user (namely obtain the power of the inflexible load at different moments t) by adopting a k-means clustering method
Figure 311357DEST_PATH_IMAGE143
) And then, the power utilization power of the inflexible load in the building where the distributed photovoltaic system is located on the prediction day can be obtained according to the power utilization curve of the inflexible load obtained through clustering. For example, the power consumption curves corresponding to the working day, the weekend and the holiday can be obtained by a clustering method according to the historical power consumption data of a certain inflexible load in a section of historical length, and then the corresponding power consumption curve can be used as the inflexible load according to which of the working day, the weekend and the holiday belongs to the predicted dayAnd obtaining the power consumption of each time in the forecast day based on the power consumption curve of the inflexible load on the forecast day.
S12: determining energy utilization attribute information of various flexible loads in a building where the distributed photovoltaic system is located, correspondingly constructing response models of the various flexible loads according to the energy utilization attribute information, and constructing a storage battery model according to information of the storage battery.
The optimizer can also collect energy consumption data of each flexible load, perform statistical analysis on the energy consumption data, determine energy consumption attribute information of each flexible load (such as a set temperature range of an air conditioner, a set temperature range of an electric water heater, the service time of a household appliance, time sequence duration of each time, the service frequency and the like), use energy attribute information of the flexible load as a constraint condition of a flexible space of a subsequently established optimization model (for example, the set temperature range of a user air conditioner in summer is 24-28 ℃, the set temperature of the air conditioner can only be optimized in the range in the optimization process, and for example, the service time of a user washing machine is usually 9:00-11:00 in the morning, the starting time of the washing machine can only be optimized in the period in the optimization process), and the optimizer can also determine the correlation coefficient of the response model of each flexible load by using a machine learning method according to the energy consumption data.
After determining the energy utilization attribute information of various flexible loads in the building where the distributed photovoltaic system is located and the correlation coefficients of the response models, the optimizer can construct the response models of various flexible loads corresponding to the energy utilization attribute information and the correlation coefficients of the response models respectively according to the various flexible loads. According to the process, the response models of various flexible loads are established based on the actual operation data of the flexible loads, and the real energy utilization attribute information of the user is obtained from the actual operation data, so that the method is more suitable for actual conditions and has higher accuracy.
In addition, the optimizer can also acquire the information of the storage battery and construct a storage battery model according to the information of the storage battery, wherein the storage battery is used for representing the relation between the charge state and the charge-discharge speed of the storage battery.
In the present application, the sequence between step S11 and step S12 is not limited.
S13: according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, response models of various flexible loads and storage battery models, an optimization model is constructed according to the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum electric power self-satisfaction rate, the optimization model is solved, and the operation plans of various flexible loads and storage batteries on the prediction day are obtained.
Based on the steps S11 and S12, the optimizer may construct an optimization model according to the power generation power of the distributed photovoltaic system on the prediction day, the power consumption power of the inflexible load on the prediction day, the response models of various flexible loads, the storage battery model, and the power grid purchase power, with the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission, and maximum power self-satisfaction rate, and then may solve the constructed optimization model to obtain the operation plan of various flexible loads and storage batteries on the prediction day, and obtain the power grid purchase power on the prediction day.
According to the method and the device, the optimization model is constructed by using the goals of minimum operation and maintenance cost of the user, minimum carbon dioxide emission and maximum power self-satisfaction rate, so that the operation and maintenance cost of the user can be reduced, the carbon dioxide emission can be reduced, and the power self-satisfaction is realized.
S14: and correspondingly controlling the operation of various flexible loads and the storage battery on the prediction day according to the operation plans of the various flexible loads and the storage battery on the prediction day.
Based on step S13, the optimizer may send the operation plans of the various flexible loads and the storage batteries to the corresponding flexible loads and the storage batteries at the corresponding time of the prediction day to correspondingly control the operation of the various flexible loads and the storage batteries on the prediction day, and send an electricity purchase request or an electricity sale request to the power grid according to the obtained electricity purchase power of the power grid on the prediction day, so as to obtain the corresponding electricity purchase power based on the electricity purchase request/sell the corresponding power to the power grid based on the electricity sale request.
In addition, relevant users can check the running state of each load, the running state of the storage battery, the generating capacity of the distributed photovoltaic system and the like through the APP on the mobile terminal, so that relevant information can be obtained in time.
According to the process, the power generation of the distributed photovoltaic system is consumed by utilizing the flexibility of the building flexible load, so that dynamic response to a power grid is achieved, a distributed photovoltaic power generation-storage battery-flexible load (light-storage-flexible) cooperative optimization control system is constructed through the process, the power consumption load curve of the building is adjusted by changing the power consumption mode of the flexible load, the distributed photovoltaic power generation on the user side is consumed as much as possible, the power which is not consumed is stored in the storage battery, so that the setting capacity of the storage battery is reduced, and the consumption cost of the distributed photovoltaic power generation is reduced.
Through the process, the building light-storage-flexible optimization control system is constructed and comprises a distributed photovoltaic system, a power grid, a storage battery, household appliances (including flexible loads and non-flexible loads), an optimizer and a mobile terminal. The hardware part comprises distributed photovoltaic power generation, a power grid, a storage battery and household appliances, and the software part comprises an optimizer and a mobile terminal. For distributed photovoltaic power generation and a power grid, the light-storage-flexible cooperative optimization control system only collects relevant parameters of the distributed photovoltaic power generation and the power grid, such as the power of photovoltaic power generation, the purchased electric quantity of a user from the power grid and the like, and cannot control the photovoltaic power generation and the power grid. For the household electrical appliance and the storage battery at the bottom layer, the optimization control system can not only collect the operation data thereof, but also automatically control the operation of the household electrical appliance and the storage battery through the related communication protocol, and the specific implementation mode is as follows: taking a household appliance as an example, each household appliance comprises 4 functional modules: the device comprises a basic function module, a control module, a data acquisition module and a communication module. The basic function module ensures the normal operation of the household appliance; the control module controls the start and stop of the temperature control load, sets the temperature and the input power, can transfer the start and stop of the load, can reduce the power of the load and charge the battery load; the data acquisition module acquires data such as the running state, the real-time power, the parameter setting and the like of the equipment; the communication module is used as a bridge to be in charge of bidirectional communication between the bottom layer household appliance and the optimizer (data acquired by the data acquisition module is transmitted to the optimizer through the communication module for modeling of the flexible load, an operation scheme obtained by optimization calculation of the optimizer is transmitted to the operation of the automatic household appliance control equipment through the communication module, and in addition, communication between the mobile terminal and the household appliance is also realized through the communication module). Similarly, the battery also contains these 4 functional modules. Along with the continuous development of technologies such as the internet of things and 5G, the household appliance comprising the basic function module, the control module, the data acquisition module and the communication module can be automatically controlled through an optimization control system, the operation state of the household appliance can be monitored in real time, and meanwhile, communication between a user and the household appliance can be realized. The modeling of each hardware part of the system (the model of distributed photovoltaic power generation, storage battery and flexible load introduced above) and the optimization model and optimization algorithm are included, and all the models and algorithms are programmed into the optimizer. In addition, the solution of the optimization model is also completed in the optimizer, the day-ahead operation scheme obtained by the optimization calculation of the optimizer is sent to the control module of the corresponding device through the communication module of the storage battery and the flexible load, and the charging and discharging of the storage battery and the start and stop of the flexible coincidence and the setting of relevant operation parameters are automatically controlled, so that the consumption of the distributed photovoltaic power generation is improved, the impact on a power grid is reduced, and the external electricity purchasing cost of a user is reduced; the mobile terminal displays information such as photovoltaic power generation, power acquisition/transmission of a power grid, running states, power and related parameter settings of a storage battery and various household appliances in real time in an APP mode; meanwhile, the user can also remotely control each household appliance through the APP; in addition, the user can also send the information of the energy utilization (for example, the user wants to use the washing machine at 8 am) to the optimization model through the APP, so that the energy utilization requirement communicated by the user through the APP can be preferentially met in the optimization process.
According to the technical scheme disclosed by the application, the generation power of the distributed photovoltaic system on the prediction day and the power consumption power of the inflexible load in the building where the distributed photovoltaic system is located on the prediction day are predicted, establishing response models and storage battery models of various flexible loads, establishing an optimization model based on the response models and the storage battery models of various flexible loads, and the optimization model is solved to obtain the operation plan of the storage battery and each flexible load on the prediction day, and controls the operation of various flexible loads and storage batteries on the forecast days according to the operation plan, the power load curve of the building where the distributed photovoltaic system is located is adjusted by changing the flexible load power utilization mode, distributed photovoltaic power generation on the user side is consumed as much as possible, and the power which is not consumed is stored in the storage battery, so that the installation capacity of the storage battery is reduced, and the consumption cost of the distributed photovoltaic power generation is reduced.
The operation control method for the light storage flexible system provided by the embodiment of the application, which is used for constructing the response model of the temperature control load according to the energy consumption attribute information of the temperature control load, can include the following steps:
constructing a thermodynamic model of the temperature control load according to the energy consumption behavior information and the energy consumption mode information of the temperature control load:
Figure 626931DEST_PATH_IMAGE144
power model in cooling mode:
Figure 929474DEST_PATH_IMAGE145
binary variable in refrigeration mode
Figure 658396DEST_PATH_IMAGE146
Comprises the following steps:
Figure 715039DEST_PATH_IMAGE147
power model in heating mode:
Figure 619542DEST_PATH_IMAGE148
binary variable in heating mode
Figure 328872DEST_PATH_IMAGE149
Comprises the following steps:
Figure 184570DEST_PATH_IMAGE150
(ii) a Wherein the content of the first and second substances,
Figure 270338DEST_PATH_IMAGE151
，
Figure 560505DEST_PATH_IMAGE152
，
Figure 503053DEST_PATH_IMAGE153
，
Figure 347512DEST_PATH_IMAGE154
，
Figure 735506DEST_PATH_IMAGE155
the temperature inside the ith temperature controlled load at time t +1,
Figure 208075DEST_PATH_IMAGE156
is the temperature coefficient of the ith temperature controlled load,
Figure 524787DEST_PATH_IMAGE157
the temperature inside the ith temperature controlled load at time t,
Figure 590963DEST_PATH_IMAGE158
is the ambient temperature at which the ith load is located,
Figure 18534DEST_PATH_IMAGE159
for the mode of operation in which the temperature controlled load is located,
Figure 781828DEST_PATH_IMAGE160
for the output power of the ith temperature controlled load,
Figure 3862DEST_PATH_IMAGE161
the coefficient of refrigeration performance for the ith temperature controlled load,
Figure 416389DEST_PATH_IMAGE162
the input power at time t for the ith temperature controlled load,
Figure 647650DEST_PATH_IMAGE163
is a binary variable which represents the starting and stopping state of the temperature control load,
Figure 970178DEST_PATH_IMAGE164
as the heating performance coefficient of the ith temperature-controlled load,
Figure 867508DEST_PATH_IMAGE165
is the thermal resistance of the ith temperature controlled load,
Figure 298489DEST_PATH_IMAGE166
is the heat capacity of the ith temperature controlled load,
Figure 802283DEST_PATH_IMAGE167
in the form of a time interval,
Figure 41634DEST_PATH_IMAGE168
a temperature is set for the ith temperature controlled load,
Figure 339892DEST_PATH_IMAGE169
a temperature threshold is set for the ith temperature controlled load,
Figure 163229DEST_PATH_IMAGE170
and
Figure 939555DEST_PATH_IMAGE171
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
Figure 767834DEST_PATH_IMAGE172
and
Figure 768151DEST_PATH_IMAGE173
the operation starting time and the operation ending time of the ith temperature control load are respectively.
In the present application, for the temperature control load, a thermodynamic model (taking an air conditioner as an example, the thermodynamic model describes a dynamic heat transfer process of an indoor environment and an outdoor environment of a building) and an energy consumption model (taking an air conditioner as an example, the energy consumption model describes a relation between an input power and an output power of the air conditioner) of the temperature control load may be established based on a thermodynamic method.
Specifically, the energy consumption behavior information and the energy consumption mode information of the temperature-controlled load (that is, the energy consumption attribute information of the temperature-controlled load includes two types of the energy consumption behavior information and the energy consumption mode information)
Figure 642566DEST_PATH_IMAGE174
(wherein,
Figure 252276DEST_PATH_IMAGE175
representing the acquired data set, G representing a statistical method), and adopting an RC (Resistance-capacitance) model to construct a thermodynamic model of the temperature control load:
Figure 403903DEST_PATH_IMAGE176
（3）
Figure 309542DEST_PATH_IMAGE177
（4）
Figure 608937DEST_PATH_IMAGE178
（5）
and (3) power model:
1) the power model in the cooling mode is:
Figure 586120DEST_PATH_IMAGE179
（6）
Figure 418684DEST_PATH_IMAGE180
（7）
Figure 698487DEST_PATH_IMAGE181
（8）
Figure 219598DEST_PATH_IMAGE182
（9）
2) the power model in the heating mode is as follows:
Figure 473DEST_PATH_IMAGE183
（10）
Figure 189008DEST_PATH_IMAGE184
（11）
Figure 155826DEST_PATH_IMAGE185
（12）
Figure 164233DEST_PATH_IMAGE186
（13）
in the above-mentioned formula,
Figure 155323DEST_PATH_IMAGE187
indicating the temperature inside the temperature controlled load, i indicating the load, including the air conditioner, the parallel flow, the electric water heater, t indicating the time,
Figure 932786DEST_PATH_IMAGE188
indicating the ambient temperature at which the temperature controlled load is located,
Figure 679025DEST_PATH_IMAGE189
indicating the mode of operation (cooling or heating) in which the temperature-controlled load is operating,
Figure 938843DEST_PATH_IMAGE190
the output power representing the temperature controlled load,
Figure 671307DEST_PATH_IMAGE191
as a function of the number of the coefficients,
Figure 834435DEST_PATH_IMAGE192
is the thermal resistance of the ith temperature controlled load,
Figure 751575DEST_PATH_IMAGE193
is the heat capacity of the ith temperature controlled load,
Figure 734575DEST_PATH_IMAGE194
in the form of a time interval,
Figure 566002DEST_PATH_IMAGE195
for the input power of the ith temperature controlled load,
Figure 521320DEST_PATH_IMAGE196
is a binary variable which represents the starting and stopping state of the temperature control load,
Figure 874941DEST_PATH_IMAGE197
the coefficient of refrigeration performance for the ith temperature controlled load,
Figure 79657DEST_PATH_IMAGE198
as the heating performance coefficient of the ith temperature-controlled load,
Figure 481820DEST_PATH_IMAGE199
a temperature is set for the ith temperature controlled load,
Figure 790179DEST_PATH_IMAGE200
a temperature threshold is set for the ith temperature controlled load,
Figure 314701DEST_PATH_IMAGE201
and
Figure 6714DEST_PATH_IMAGE202
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load, wherein the magnitudes of the minimum value and the maximum value are determined by the user (namely the energy utilization behavior information of the user),
Figure 212567DEST_PATH_IMAGE203
and
Figure 142477DEST_PATH_IMAGE204
the start run time and the end run time of the ith temperature controlled load, respectively, are both user dependent (i.e., user mode information of the user as described above).
Through the process, a response model of the temperature control load in the building where the distributed photovoltaic system is located can be established, so that the establishment of an optimization model and the acquisition of a temperature load operation plan on a prediction day can be conveniently carried out based on the response model of the temperature control load.
The operation control method for the light storage flexible system, provided by the embodiment of the application, includes the following steps of constructing a response model of a transferable load according to energy utilization attribute information of the transferable load:
constructing an energy consumption model of the transferable load according to the energy consumption mode information of the transferable load:
Figure 572321DEST_PATH_IMAGE205
(ii) a Wherein the content of the first and second substances,
Figure 521604DEST_PATH_IMAGE206
，
Figure 999990DEST_PATH_IMAGE207
，
Figure 784406DEST_PATH_IMAGE208
the power at time t for the jth transferable load,
Figure 650731DEST_PATH_IMAGE209
for the jth transferable load's power in different phases of operation,
Figure 317335DEST_PATH_IMAGE210
for the time when the jth transferable load starts running,
Figure 301210DEST_PATH_IMAGE211
for the running time of the jth transferable load in the running phase w,
Figure 736870DEST_PATH_IMAGE212
the time frame for which the operation is allowed for the jth transferable load,
Figure 774096DEST_PATH_IMAGE213
the operation duration for the jth transferable load.
In the present application, transferable loads mainly include washing machines, dryers, dishwashers and the like, which have a common feature: the device has fixed operation periods, each operation period is composed of a series of continuous and uninterrupted processes, and the power of the device in each process can be approximately considered as a constant value. Therefore, it is possible to use the energy pattern information of the transferable load
Figure 927997DEST_PATH_IMAGE214
Constructing an energy consumption model capable of transferring loads:
Figure 951448DEST_PATH_IMAGE215
（14）
Figure 5729DEST_PATH_IMAGE216
（15）
Figure 213857DEST_PATH_IMAGE217
（16）
in the above formula，
Figure 855054DEST_PATH_IMAGE218
Representing the power of the transferable load j at time t,
Figure 478933DEST_PATH_IMAGE219
for the jth transferable load's power in different phases of operation,
Figure 826869DEST_PATH_IMAGE220
for the time when the jth transferable load starts running,
Figure 642116DEST_PATH_IMAGE221
for the running time of the jth transferable load in the running phase W (W denotes the transferable load to be supplied with the W running phases),
Figure 832926DEST_PATH_IMAGE222
the time frame for which the operation is allowed for the jth transferable load, the size of which depends on the user (i.e. the previously mentioned energy usage pattern information of the transferable load),
Figure 994917DEST_PATH_IMAGE223
for the operation duration of the jth transferable load, accordingly,
Figure 525255DEST_PATH_IMAGE224
i.e. the latest time at which the transferable load starts running.
Through the process, a response model of the transferable load in the building where the distributed photovoltaic system is located can be established, so that the establishment of an optimization model and the acquisition of an operation plan of the transferable load on a forecast day are facilitated based on the response model of the transferable load.
The method for controlling the operation of the light storage flexible system, which is provided by the embodiment of the application, includes the following steps of constructing a response model capable of reducing load according to energy attribute information capable of reducing load:
constructing adjustable lighting power based on energy pattern information of load capable of reducing adjustable lighting power in loadEnergy consumption model of load of (1):
Figure 684972DEST_PATH_IMAGE225
(ii) a Wherein the content of the first and second substances,
Figure 793437DEST_PATH_IMAGE226
，
Figure 87015DEST_PATH_IMAGE227
is as follows
Figure 471860DEST_PATH_IMAGE228
The power of the load capable of adjusting the lighting power at the moment t,
Figure 864796DEST_PATH_IMAGE229
is as follows
Figure 639985DEST_PATH_IMAGE230
The adjustment factor of the load of adjustable lighting power at time t,
Figure 907893DEST_PATH_IMAGE231
is as follows
Figure 475140DEST_PATH_IMAGE232
The rated power of the load for which the lighting power can be adjusted,
Figure 304556DEST_PATH_IMAGE233
and
Figure 567041DEST_PATH_IMAGE234
are respectively the first
Figure 468001DEST_PATH_IMAGE235
A load start operation time and an operation end time of the adjustable lighting power;
constructing an energy consumption model of the load with the adjustable working gear according to the energy consumption mode information of the load with the adjustable working gear in the reducible load:
Figure 827438DEST_PATH_IMAGE236
(ii) a Wherein the content of the first and second substances,
Figure 263974DEST_PATH_IMAGE237
，
Figure 76072DEST_PATH_IMAGE238
is as follows
Figure 249564DEST_PATH_IMAGE239
The load of the individual adjustable operating gears is at the power in the e gear at time t,
Figure 463508DEST_PATH_IMAGE240
is as follows
Figure 572409DEST_PATH_IMAGE241
The load of each adjustable working gear is at different gears
Figure 370339DEST_PATH_IMAGE242
The power of (a) is determined,
Figure 347522DEST_PATH_IMAGE243
and
Figure 415972DEST_PATH_IMAGE244
is as follows
Figure 695775DEST_PATH_IMAGE245
The load start running time and the running end time of each adjustable working gear are adjusted.
In the present application, the reducible load can be classified into two types: one type is a load that can adjust lighting power according to indoor illuminance (simply referred to as a load that can adjust lighting power); the other type is a load (simply referred to as a load with adjustable operation range) whose power can be changed by changing its operation range, such as an electric furnace and a fan. Therefore, the energy attribute information capable of reducing the load is used
Figure 482465DEST_PATH_IMAGE246
Are respectively carried outThe construction of two types of response models capable of transferring load, wherein,
Figure 528919DEST_PATH_IMAGE247
and
Figure 893953DEST_PATH_IMAGE248
respectively, representing the reducible load m start running time and end running time, the magnitudes of which depend on the user (i.e. the user's energy usage pattern as described above). Specifically, the energy consumption model of the load with adjustable lighting power can be represented by equations (17) and (18), and the energy consumption model of the load with adjustable operating range can be represented by equations (19) and (20):
Figure 406974DEST_PATH_IMAGE249
（17）
Figure 743277DEST_PATH_IMAGE250
（18）
Figure 999946DEST_PATH_IMAGE251
（19）
Figure 980672DEST_PATH_IMAGE252
（20）
in the above-mentioned formula,
Figure 163129DEST_PATH_IMAGE253
is as follows
Figure 986729DEST_PATH_IMAGE254
The power of the load capable of adjusting the lighting power at the moment t,
Figure 781509DEST_PATH_IMAGE255
is as follows
Figure 413479DEST_PATH_IMAGE256
The adjustment factor of the load of adjustable lighting power at time t,
Figure 471565DEST_PATH_IMAGE257
is as follows
Figure 687520DEST_PATH_IMAGE258
The rated power of the load for which the lighting power can be adjusted,
Figure 285992DEST_PATH_IMAGE259
and
Figure 38047DEST_PATH_IMAGE260
are respectively the first
Figure 126089DEST_PATH_IMAGE261
The starting operation time and the ending operation time of the load of the adjustable lighting power are the energy using mode information of the load of the adjustable lighting power, and the size of the two is determined by a user;
Figure 596385DEST_PATH_IMAGE262
is as follows
Figure 434765DEST_PATH_IMAGE263
The load of the individual adjustable operating gears is at the power in the e gear at time t,
Figure 634803DEST_PATH_IMAGE264
is as follows
Figure 565849DEST_PATH_IMAGE265
The load of each adjustable working gear is at different gears
Figure 726703DEST_PATH_IMAGE266
The power of (a) is determined,
Figure 666978DEST_PATH_IMAGE267
and
Figure 455942DEST_PATH_IMAGE268
is as follows
Figure 339583DEST_PATH_IMAGE269
The load start running time and the running end time of each adjustable working position are the energy utilization mode information of the load of the adjustable working position, and the size of the energy utilization mode information is determined by a user.
Through the process, a response model capable of reducing the load in the building where the distributed photovoltaic system is located can be established, so that the establishment of an optimization model and the acquisition of an operation plan on a forecast day capable of reducing the load are facilitated based on the response model capable of reducing the load.
According to the operation control method of the light storage flexible system, a response model of the battery load is constructed according to the energy utilization attribute information of the battery load, and the method can comprise the following steps:
constructing an energy consumption model of the battery load according to the energy consumption mode information of the battery load:
Figure 784471DEST_PATH_IMAGE270
(ii) a Wherein the content of the first and second substances,
Figure 200540DEST_PATH_IMAGE271
，
Figure 47273DEST_PATH_IMAGE272
，
Figure 648018DEST_PATH_IMAGE273
，
Figure 78738DEST_PATH_IMAGE274
，…，
Figure 298497DEST_PATH_IMAGE275
，
Figure 999737DEST_PATH_IMAGE276
，
Figure 36963DEST_PATH_IMAGE277
，
Figure 190864DEST_PATH_IMAGE278
，
Figure 712850DEST_PATH_IMAGE279
representing the amount of power stored by the nth battery load by time t,
Figure 268596DEST_PATH_IMAGE280
represents the state of charge of the nth battery load, which has a value of 1 when charged and a value of 0 when uncharged,
Figure 476724DEST_PATH_IMAGE281
the charging power for the nth battery load,
Figure 117921DEST_PATH_IMAGE282
in order to achieve a high charging efficiency,
Figure 679483DEST_PATH_IMAGE283
in the form of a time interval,
Figure 853850DEST_PATH_IMAGE284
representing the amount of power stored by the nth battery load by the time t-1,
Figure 232879DEST_PATH_IMAGE285
the charging power for the nth battery load in different charging phases,
Figure 95793DEST_PATH_IMAGE286
the time to start charging for the nth battery load,
Figure 523363DEST_PATH_IMAGE287
the charging period of phase 1 for charging the nth battery load,
Figure 725805DEST_PATH_IMAGE288
indicating the state of charge of the nth battery load during charging phase 1,
Figure 717813DEST_PATH_IMAGE289
indicating a charging phase 1 according to
Figure 395919DEST_PATH_IMAGE290
And
Figure 627180DEST_PATH_IMAGE291
the number of divided sub-charging nodes,
Figure 215288DEST_PATH_IMAGE292
the charging period for the nth battery load charging phase r,
Figure 670540DEST_PATH_IMAGE293
the time frame for which charging is allowed for the nth battery load,
Figure 773625DEST_PATH_IMAGE294
and
Figure 979216DEST_PATH_IMAGE295
respectively the minimum amount of electricity and the maximum amount of energy that the nth battery load can store,
Figure 484147DEST_PATH_IMAGE296
the total charge time period for the nth battery load.
In the application, the battery load mainly comprises equipment with an electricity storage function, such as a sweeping robot, a mobile phone and a notebook computer, the charging process of the equipment comprises different stages, and each stage is charged with constant power and can be charged intermittently. Therefore, when the response model of the battery load is constructed according to the energy use attribute information of the battery load, the energy use mode information of the battery load can be specifically used
Figure 110300DEST_PATH_IMAGE297
Constructing an energy consumption model of the battery load:
Figure 435102DEST_PATH_IMAGE298
（21）
Figure 8166DEST_PATH_IMAGE299
（22）
Figure 538242DEST_PATH_IMAGE300
（23）
Figure 538559DEST_PATH_IMAGE301
（24）
Figure 678554DEST_PATH_IMAGE302
（25）
…
Figure 992991DEST_PATH_IMAGE303
（26）
Figure 206935DEST_PATH_IMAGE304
（27）
Figure 706050DEST_PATH_IMAGE305
（28）
Figure 441662DEST_PATH_IMAGE306
（29）
in the above-mentioned formula,
Figure 356529DEST_PATH_IMAGE307
representing the amount of power stored by the nth battery load by time t,
Figure 487296DEST_PATH_IMAGE308
represents the state of charge of the nth battery load, which has a value of 1 when charged and a value of 0 when uncharged,
Figure 384408DEST_PATH_IMAGE309
the charging power for the nth battery load,
Figure 639940DEST_PATH_IMAGE310
in order to achieve a high charging efficiency,
Figure 92918DEST_PATH_IMAGE311
in the form of a time interval,
Figure 717672DEST_PATH_IMAGE312
representing the amount of power stored by the nth battery load by the time t-1,
Figure 965114DEST_PATH_IMAGE313
the charging power for the nth battery load in different charging phases,
Figure 239100DEST_PATH_IMAGE314
the time to start charging for the nth battery load,
Figure 558086DEST_PATH_IMAGE315
the charging period of phase 1 for charging the nth battery load,
Figure 601129DEST_PATH_IMAGE316
indicating the state of charge of the nth battery load during charging phase 1,
Figure 721269DEST_PATH_IMAGE317
indicating a charging phase 1 according to
Figure 216972DEST_PATH_IMAGE318
The number of divided sub-charging nodes (specifically, if the charging period of the charging node 1 is T1, the charging period is T1
Figure 339649DEST_PATH_IMAGE319
），
Figure 237198DEST_PATH_IMAGE320
The charging period of charging phase 2 for the nth battery load,
Figure 29705DEST_PATH_IMAGE321
indicating the state of charge of the nth battery load during charging phase 2,
Figure 782678DEST_PATH_IMAGE322
indicating a charging phase 2 according to
Figure 443466DEST_PATH_IMAGE323
The number … … r of divided sub-charging nodes represents the total number of charging stages of the nth battery load,
Figure 195522DEST_PATH_IMAGE324
for the total charging period of the nth battery load,
Figure 221247DEST_PATH_IMAGE325
the time range for which charging is allowed for the nth battery load, the size of which depends on the user (i.e. the aforementioned energy usage pattern information of the battery load),
Figure 629225DEST_PATH_IMAGE326
indicating the latest time at which the nth battery load is allowed to start charging,
Figure 264344DEST_PATH_IMAGE327
and
Figure 870906DEST_PATH_IMAGE328
respectively, the minimum amount of electricity and the maximum amount of energy that the nth battery load can store, in kWh.
Through the process, a response model of the battery load in the building where the distributed photovoltaic system is located can be established, so that the establishment of an optimization model and the acquisition of an operation plan of the battery load on a prediction day are facilitated based on the response model of the battery load.
The operation control method for the light-storage flexible system provided by the embodiment of the application constructs a storage battery model according to the information of the storage battery, and can comprise the following steps:
constructing a storage battery model:
Figure 395428DEST_PATH_IMAGE087
(ii) a Wherein the content of the first and second substances,
Figure 353020DEST_PATH_IMAGE088
，
Figure 27714DEST_PATH_IMAGE089
，
Figure 987318DEST_PATH_IMAGE090
，
Figure 558108DEST_PATH_IMAGE091
the internal electric quantity of the storage battery at the moment t,
Figure 737416DEST_PATH_IMAGE092
is a binary variable, with a charge of 1, a discharge of 0,
Figure 543698DEST_PATH_IMAGE093
and
Figure 390432DEST_PATH_IMAGE094
respectively representing the charging power and the discharging power of the storage battery,
Figure 365079DEST_PATH_IMAGE095
and
Figure 31683DEST_PATH_IMAGE096
respectively showing the charge efficiency and the discharge efficiency of the secondary battery,
Figure 313760DEST_PATH_IMAGE097
in the form of a time interval,
Figure 77317DEST_PATH_IMAGE098
and
Figure 786647DEST_PATH_IMAGE099
respectively represent the maximum charging power and the maximum discharging power of the storage battery,
Figure 659923DEST_PATH_IMAGE100
and
Figure 745691DEST_PATH_IMAGE101
respectively representing the minimum and maximum electric quantities that the accumulator can store.
In the present application, when the battery model is built according to the information of the battery, the battery model may be specifically built based on a physical model to represent the relationship between the state of charge and the charging and discharging speed of the battery, which is specifically as follows:
Figure 363754DEST_PATH_IMAGE087
（30）
Figure 509565DEST_PATH_IMAGE088
（31）
Figure 88445DEST_PATH_IMAGE089
（32）
Figure 210859DEST_PATH_IMAGE090
（33）
in the above-mentioned formula,
Figure 949008DEST_PATH_IMAGE091
the unit of the electric quantity of the storage battery at the time t is kWh;
Figure 141DEST_PATH_IMAGE092
is a binary variable, charge is 1, discharge is 0;
Figure 66317DEST_PATH_IMAGE093
and
Figure 493887DEST_PATH_IMAGE094
respectively representing the charging power and the discharging power of the storage battery, and the unit is kW;
Figure 352122DEST_PATH_IMAGE095
and
Figure 72691DEST_PATH_IMAGE096
respectively representing the charging efficiency and the discharging efficiency of the storage battery;
Figure 891742DEST_PATH_IMAGE097
in the form of a time interval,
Figure 919741DEST_PATH_IMAGE098
and
Figure 304586DEST_PATH_IMAGE099
respectively representing the maximum charging power and the maximum discharging power of the storage battery, wherein the unit is kW;
Figure 900783DEST_PATH_IMAGE100
and
Figure 502404DEST_PATH_IMAGE101
respectively, the minimum electric quantity and the maximum electric quantity which can be stored by the storage battery are expressed in kWh.
Through the process, the storage battery model can be established, so that the establishment of the optimization model and the acquisition of the operation plan of the storage battery model on the prediction day are facilitated based on the storage battery model response model.
According to the operation control method of the light-storage flexible system, an optimization model is constructed according to the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum electric power self-satisfaction rate according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery model, and the method can comprise the following steps:
constructing an optimization model:
Figure 599673DEST_PATH_IMAGE102
；
Figure 573445DEST_PATH_IMAGE103
represents a set of decision variables that are to be made,
Figure 606123DEST_PATH_IMAGE104
，
Figure 258821DEST_PATH_IMAGE105
and
Figure 566306DEST_PATH_IMAGE106
respectively representing the charging power and the discharging power of the storage battery,
Figure 367821DEST_PATH_IMAGE107
a temperature is set for the ith temperature controlled load,
Figure 368138DEST_PATH_IMAGE108
for the time when the jth transferable load starts running,
Figure 242553DEST_PATH_IMAGE109
for the mth one that can reduce the power of the load,
Figure 619308DEST_PATH_IMAGE110
indicating the state of charge of the nth battery load,
Figure 770934DEST_PATH_IMAGE111
to purchase or sell electric power for the grid,
Figure 440688DEST_PATH_IMAGE112
an objective function representing an optimization model is provided,
Figure 802399DEST_PATH_IMAGE113
the operation and maintenance cost of the user is shown,
Figure 186107DEST_PATH_IMAGE114
the amount of carbon dioxide emissions is expressed,
Figure 520136DEST_PATH_IMAGE115
，
Figure 799939DEST_PATH_IMAGE116
the self-satisfaction rate of the electric power is represented,
Figure 819585DEST_PATH_IMAGE117
representing the constraints of the inequality therein,
Figure 272563DEST_PATH_IMAGE118
expressing the equality constraint and the power balance, wherein the power balance is as follows:
Figure 195520DEST_PATH_IMAGE119
，
Figure 770858DEST_PATH_IMAGE120
a decision space is represented in the form of,
Figure 44844DEST_PATH_IMAGE121
for the generated power of the distributed photovoltaic system on the forecast day,
Figure 737732DEST_PATH_IMAGE122
is a binary variable, with a charge of 1, a discharge of 0,
Figure 780774DEST_PATH_IMAGE121
the total power usage for all non-compliant loads,
Figure 527013DEST_PATH_IMAGE124
the input power of the ith temperature control load at the moment t, I is the total amount of the temperature control loads,
Figure 22717DEST_PATH_IMAGE125
power of jth transferable load at time t, J total transferable load, M reducibleThe total amount of the load is,
Figure 551918DEST_PATH_IMAGE126
is the charging power of the nth battery load, and N is the total amount of the battery loads.
In the present application, the charge/discharge power of the storage battery, the set temperature of the temperature-controlled load, the time for starting the operation of the transferable load, the power for which the load can be reduced, the state of charge of the battery load, the power purchased by the grid, or the power sold by the grid may be used as the decision variables; the method comprises the following steps of (1) minimizing operation and maintenance cost of a user, minimizing carbon dioxide emission and maximizing the self-satisfaction rate of electric power as a target function; the method comprises the following steps of constructing a multi-objective optimization model by using constraint of a user flexible space, constraint of power balance (a prediction model of a distributed photovoltaic system, a storage battery model, a flexible load response model and power grid external power purchase jointly form energy balance constraint), constraint of storage battery charge and discharge, constraint of household appliance operation constraint and the like as constraint conditions, wherein the form of the multi-objective optimization model is as follows:
Figure 676563DEST_PATH_IMAGE102
（34）
in the above-mentioned formula,
Figure 531387DEST_PATH_IMAGE103
represents a set of decision variables that are to be made,
Figure 717649DEST_PATH_IMAGE104
，
Figure 50541DEST_PATH_IMAGE105
and
Figure 301132DEST_PATH_IMAGE106
respectively representing the charging power and the discharging power of the storage battery,
Figure 654753DEST_PATH_IMAGE107
a temperature is set for the ith temperature controlled load,
Figure 859469DEST_PATH_IMAGE108
for the time when the jth transferable load starts running,
Figure 996052DEST_PATH_IMAGE109
for the mth one that can reduce the power of the load,
Figure 805876DEST_PATH_IMAGE110
indicating the state of charge of the nth battery load,
Figure 766617DEST_PATH_IMAGE111
to purchase or sell electric power for the grid,
Figure 193050DEST_PATH_IMAGE112
an objective function representing an optimization model is provided,
Figure 726800DEST_PATH_IMAGE113
representing the user's operation and maintenance costs (calculated by equations (36) - (40)),
Figure 187868DEST_PATH_IMAGE114
indicates the amount of carbon dioxide emission (calculated by equation (41)),
Figure 493079DEST_PATH_IMAGE115
，
Figure 436502DEST_PATH_IMAGE116
shows the power self-satisfaction rate (the power self-satisfaction rate is calculated by the formula (43), and the power self-satisfaction rate is calculated by the formula (42)
Figure 242784DEST_PATH_IMAGE329
），
Figure 823938DEST_PATH_IMAGE117
Representing inequality constraints including all inequalities in the preceding predictive model, battery model, response model for flexible loads,
Figure 565629DEST_PATH_IMAGE118
representing the equality constraints (including all the equalities during the preceding predictive model, battery model, response model for flexible loads) and power balance,
Figure 232233DEST_PATH_IMAGE120
a decision space is represented.
Wherein the power balance in light-storage-flexible is as follows: the light-storage-flexible power is sourced from distributed photovoltaic power generation, storage batteries and power grid purchase power, the power is used for meeting the power consumption of non-flexible load users and flexible loads of user terminals, and the power balance of the system is as follows:
Figure 842206DEST_PATH_IMAGE330
Figure 47841DEST_PATH_IMAGE331
（35）
in the formula
Figure 694854DEST_PATH_IMAGE121
Generating power for the distributed photovoltaic system on a prediction day;
Figure 442230DEST_PATH_IMAGE122
is a binary variable, charge is 1, discharge is 0;
Figure 527997DEST_PATH_IMAGE123
total power usage for all non-compliant loads;
Figure 21427DEST_PATH_IMAGE124
the input power of the ith temperature control load at the moment t is shown, and I is the total amount of the temperature control loads;
Figure 400193DEST_PATH_IMAGE125
the power of the jth transferable load at the moment t, and J is the total amount of transferable loads; m is the total amount of the load that can be reduced,
Figure 103707DEST_PATH_IMAGE126
is the charging power of the nth battery load, and N is the total amount of the battery loads.
The above mentioned user operation and maintenance cost
Figure 993166DEST_PATH_IMAGE113
The method specifically comprises the following steps:
Figure 341102DEST_PATH_IMAGE332
（36）
Figure 985710DEST_PATH_IMAGE333
（37）
Figure 114203DEST_PATH_IMAGE334
（38）
Figure 977991DEST_PATH_IMAGE335
（39）
Figure 508330DEST_PATH_IMAGE336
（40）
in the formula
Figure 323839DEST_PATH_IMAGE337
Is the total number of time intervals,
Figure 611732DEST_PATH_IMAGE338
、
Figure 577414DEST_PATH_IMAGE339
、
Figure 290155DEST_PATH_IMAGE340
、
Figure 447204DEST_PATH_IMAGE341
respectively representing the operation and maintenance cost of the distributed photovoltaic system, the operation and maintenance cost of a storage battery, the electricity purchasing cost of the power grid and the income for selling electricity to the power grid;
Figure 487973DEST_PATH_IMAGE342
、
Figure 257346DEST_PATH_IMAGE343
respectively representing the power of a storage battery, the power purchased from a power grid and the power sold to the power grid;
Figure 559014DEST_PATH_IMAGE344
、
Figure 388430DEST_PATH_IMAGE345
、
Figure 167028DEST_PATH_IMAGE346
respectively representing the operation and maintenance cost of the unit power generation amount (kWh) of the distributed photovoltaic system, the operation and maintenance cost of the unit electric quantity (kWh) stored or released by the storage battery, the price of purchasing electricity from the power grid and the price of selling electricity to the power grid.
Figure 271250DEST_PATH_IMAGE347
（41）
In the formula
Figure 568371DEST_PATH_IMAGE348
Carbon dioxide emission factor, kgCO, representing electrical power2/kWh。
Figure 896584DEST_PATH_IMAGE349
（42）
Figure 708682DEST_PATH_IMAGE350
（43）
The SSR in the formula represents the power self-satisfaction rate.
The charge-discharge power of the storage battery, the set temperature of the temperature-controlled load, the time for starting operation of the transferable load, the power capable of reducing the load, the charging state of the battery load and the power purchasing power of the power grid when the operation and maintenance cost of the user, the carbon dioxide emission and the self-satisfaction rate of the electric power are all minimum can be determined through the established optimization model, so that the operation control of predicting the day can be performed based on the charge-discharge power, the set temperature of the temperature-controlled load, the time for starting operation of the transferable load, the power capable of reducing the load, the charging state of the battery load and the power purchasing power of the power grid.
The operation control method for the light-storage flexible system provided by the embodiment of the application solves the optimization model, and obtains the operation plans of various flexible loads and storage batteries on the prediction days, and comprises the following steps:
and solving the optimization model by using a non-dominated sorting genetic algorithm, a sorting method approaching an ideal value and an information entropy method to obtain the operation plan of various flexible loads and storage batteries on the prediction day.
In the present application, a multi-objective optimization method including two processes of search and decision may be specifically adopted to solve the optimization model, specifically, a Pareto solution set of the multi-objective optimization problem is obtained in a search stage, and then a final solution is selected from the Pareto solution set in a decision stage, where a solution flow is shown in fig. 4, which shows a solution flow chart of the multi-objective optimization method provided in the embodiment of the present application.
In the search phase, a non-dominated sorting genetic algorithm (NSGA-II) is adopted to obtain a Pareto solution set, and the detailed steps of the algorithm are described as follows:
1) initializing parameters: including population size (N), maximum genetic algebra (Gen), crossover probability (P)e) Probability of variation (P)c) Cross distribution coefficient (eta)e) And coefficient of variation distribution (η)c）；
2) Population initialization: let m =1, randomly generate an initial population P containing N individuals, in case the optimization model constraints are metm，
Figure 256076DEST_PATH_IMAGE351
，（
Figure 470020DEST_PATH_IMAGE352
Respectively represent decision variables X under different values in the optimization model,
Figure 703555DEST_PATH_IMAGE353
) The objective function of each individual in the initial population is calculated according to the equations (36), (41) and (42)
Figure 206211DEST_PATH_IMAGE354
Obtaining the individual fitness of the population;
3) non-dominant ordering: for population PmAll individuals are subjected to rapid non-dominated sorting, and the crowdedness of each individual is calculated at the same time;
4) selecting a championship game: each time slave population PmRandomly selecting two individuals, preferentially selecting the individuals with high non-dominant ranking level, and preferentially selecting the individuals with high crowding degree if the ranking levels are the same;
5) genetic manipulation: crossover and mutation operations on individuals selected by the tournament method to produce progeny populations QmEach individual in the filial generation population needs to satisfy the constraint condition, and then the objective function of each individual is calculated
Figure 121078DEST_PATH_IMAGE355
The value of (c). In the crossing and variation operation, a crossing algorithm adopts analog binary crossing, and a variation algorithm adopts polynomial variation;
6) and (3) recombination: merging and recombining parent population PmAnd progeny population QmGenerating a population RmAnd for population RmPerforming rapid non-dominated sorting and congestion degree calculation;
7) generating a new generation of population: according to the non-dominant sorting level and the crowdedness, the slave population RmSelecting N individuals with top rank as a new generation population Pm+1；
8) Judging whether the maximum genetic algebra is reached: if m is larger than or equal to Gen, outputting a Pareto solution set; otherwise, let m = m +1, return to step 4) until the maximum genetic algebra is satisfied.
In the decision stage, a final unique solution is determined from a Pareto solution set through a top order system (TOPSIS) and an information entropy method which approximate to an ideal value, and the specific steps are as follows:
1) establishing a decision matrix and normalizing
Figure 517424DEST_PATH_IMAGE356
（44）
In the formular αβ Representing the elements of the normalized decision matrix,f αβ represents the first in Pareto solution setαThe first of a solutionβThe value of each of the objective function values,Nis the number of solutions contained in the Pareto solution set, which is equal to the population number.
2) Determining weight of each target based on information entropy method
Figure 358079DEST_PATH_IMAGE357
（45）
Figure 816873DEST_PATH_IMAGE358
（46）
In the formula
Figure 863327DEST_PATH_IMAGE359
Representing objects
Figure 51862DEST_PATH_IMAGE360
The entropy value of (a) of the image,
Figure 502566DEST_PATH_IMAGE361
representing objects
Figure 9509DEST_PATH_IMAGE362
The weight of (c).
3) Constructing a weighted normalization matrix
Figure 328495DEST_PATH_IMAGE363
（47）
In the formula
Figure 371537DEST_PATH_IMAGE364
Representing the elements of the weighted normalization matrix.
4) Determining a positive ideal solution and a negative ideal solution
Figure 993142DEST_PATH_IMAGE365
（48）
Figure 488846DEST_PATH_IMAGE366
（49）
In the formulaJ 1 AndJ 2 respectively represent a cost type index and a benefit type index,
Figure 522442DEST_PATH_IMAGE367
the positive ideal solution is shown,
Figure 685570DEST_PATH_IMAGE368
representing a negative ideal solution.
5) Euclidean distance calculation
Figure 602710DEST_PATH_IMAGE369
（50）
Figure 585710DEST_PATH_IMAGE370
（51）
In the formula
Figure 823662DEST_PATH_IMAGE371
Representing the distance of the objective function value to the positive ideal solution,
Figure 903614DEST_PATH_IMAGE372
representing the distance of the value of the objective function to the negative ideal solution.
6) Relative closeness calculation
Figure 929338DEST_PATH_IMAGE373
（52）
In the formula
Figure 337317DEST_PATH_IMAGE374
Indicating relative proximity.
7) Relative closeness ranking
Relative degree of closeness
Figure 739480DEST_PATH_IMAGE374
Sorting according to the sequence from big to small;
8) optimal solution
Selecting relative closeness
Figure 673937DEST_PATH_IMAGE374
The largest set of solutions is the optimal solution.
The optimal decision variables can be automatically and intelligently calculated through the process, namely, the optimal decision variables are obtained
Figure 369099DEST_PATH_IMAGE375
So as to respond and control the flexible load and the storage battery based on the above.
The operation control method for the light storage flexible system provided by the embodiment of the application can further comprise the following steps:
and receiving energy utilization attribute information of the target flexible load in the building where the distributed photovoltaic system is located, which is sent by a user.
In the application, for some emergency situations, for example, a certain user usually washes clothes in the morning, but something is gone out in the morning on a certain day suddenly, the user can directly modify the laundry behavior in the day through the mobile terminal APP at that time, that is, the user can send the energy consumption attribute information of various flexible loads in the building where the distributed photovoltaic system is located to the optimizer through the mobile terminal, at this time, the optimizer correspondingly constructs a response model of the target flexible load according to the newly received energy consumption attribute information of the target flexible load, so as to meet new requirements of the user. It should be noted that, when the user sends the energy consumption attribute information through the mobile terminal, the optimizer may override the corresponding historical energy consumption attribute information, but after the next day is normal, the optimizer may perform automatic optimization according to the historical energy consumption attribute information.
Therefore, the mobile terminal APP can display information such as the running state, power and related parameter setting of the storage battery and each load in real time; meanwhile, the user can also remotely control each load through the APP; in addition, the user can also send energy utilization attribute information (for example, the user wants to use the washing machine at 8 am) to the optimization model through the APP, so that the energy utilization requirement conveyed by the user through the APP can be preferentially met in the optimization process.
Specifically, reference may be made to fig. 5, which shows another operation control flow chart provided in the embodiment of the present application.
1) Basic modeling: based on the proposed light-storage-flexible cooperative optimization control method, a prediction model, a flexible load response model, a storage battery model and an optimization model are established according to the modeling method introduced above, and the models and the optimization algorithm are written into an optimizer to establish a light-storage-flexible cooperative optimization control system. After the system is built, the method can be applied to actual engineering in the following, and how to use the method is described in detail below.
2) And (3) optimizing and solving: the prediction model predicts the power of photovoltaic power generation at each time in the tomorrow; and inputting the predicted photovoltaic power generation power into an optimizer, and then solving the optimization model by an optimization algorithm to obtain a storage battery charge-discharge strategy and a daily operation plan of each flexible household appliance. It should be noted that since the user's interest cannot be sacrificed when utilizing the flexibility of the building load, the optimization is constrained by the user's energy use behavior (e.g., set temperature range of air conditioner, set temperature range of electric water heater, etc.) and energy use mode (e.g., use time, duration of each time, use frequency, etc.) of the household appliance. For example, the temperature setting range of the air conditioner of the user in summer is 24-28 ℃, in the optimization process, the set temperature of the air conditioner can be optimized only in the range; and for example, the use time of the washing machine of the user is usually 9:00-11:00 in the morning, the starting time of the washing machine can be optimized only in the period in the optimization process. Obviously, different users have different energy consumption behaviors and energy consumption modes, so the energy consumption behaviors and the energy consumption modes of the users are obtained by directly acquiring energy consumption data through loads at the bottom layer of the users and adopting a statistical analysis method, the problem of difference of the energy consumption behaviors and the energy consumption modes of the users can be well solved, and the energy consumption behaviors and the energy consumption modes of the users are more real and more accord with actual conditions. In addition, because the energy consumption behaviors and the energy consumption modes of the user are obtained from the data collected by the data collection module at the bottom layer of the household appliance by the machine learning method, the energy consumption behaviors and the energy consumption modes of the user obtained by the big data mode can only represent the general condition, and under the normal condition, the energy consumption behaviors and the energy consumption modes of the user obtained by the machine learning can be used in the optimization process. When a user has a special requirement, for example, the user needs to start the washing machine at 10:30 am tomorrow, the user can transmit the requirement to the optimizer through the mobile terminal APP, and the optimizer gives priority to the energy requirement input into the APP by the user during optimization. In addition, for some emergency situations, the user is allowed to cover the user behavior or the energy utilization mode through the APP or the button of the household appliance (the appliance is running) and other approaches, for example, a certain user usually washes clothes in the morning, but suddenly a certain day goes out in the morning, her APP directly modifies the clothes washing behavior in the day, so as to cover the historical energy utilization behavior, but after the next day is normal, the system can be automatically optimized according to the historical energy utilization behavior.
3) The equipment operates: the storage battery, each flexible load and the optimizer are communicated through WiFi; in the next day, the optimizer sends the control instructions of the corresponding moments to the storage battery and each flexible load through WiFi in the user's home according to the operation plan obtained in the step 2); after the storage battery and each flexible load receive the instruction, the control module automatically controls the operation of the flexible load according to the instruction; (with the development of technologies such as internet of things and smart home, at present, many flexible loads can realize information transfer and automatic control through WiFi), it needs to be stated that each flexible load has three control modes, namely, a button of the flexible load (that is, manual control in general), an APP and an optimization control system, wherein the button of the device has the highest and most-prior authority, then the APP, and finally the optimization control system. Under normal conditions, the storage battery and the flexible load execute an optimization control system to send out instructions, and when special conditions occur, such as some conditions mentioned in 2), a user sends the instructions through a button (manual control) of the flexible load or an APP, and then the equipment preferentially executes the instructions at the time.
4) Human-computer interaction: the user can look over the running state of equipment through the APP, energy consumption data to and the setting of relevant parameter (like air conditioner, electric water heater set temperature, washing machine's laundry process etc.), also can remote control household electrical appliances simultaneously, also can carry out the information transfer between user and the optimizer through the APP in addition.
An embodiment of the present application further provides an operation control device of a light storage flexible system, refer to fig. 6, which shows a schematic structural diagram of the operation control device of the light storage flexible system provided in the embodiment of the present application, and the operation control device may include:
the calculation module 61 is used for calculating the power generation power of the distributed photovoltaic system on the prediction day according to the weather data of the distributed photovoltaic system on the prediction day, and determining the power consumption power of the inflexible load on the prediction day according to the historical power consumption data of the inflexible load in the building where the distributed photovoltaic system is located;
the first construction module 62 is configured to determine energy consumption attribute information of various flexible loads in a building in which the distributed photovoltaic system is located, correspondingly construct response models of the various flexible loads according to the energy consumption attribute information, and construct a storage battery model according to information of a storage battery;
the second construction module 63 is used for constructing an optimization model according to the goals of the minimum user operation and maintenance cost, the minimum carbon dioxide emission and the maximum electric power self-satisfaction rate according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery models, and solving the optimization model to obtain the operation plans of various flexible loads and storage batteries on the prediction day;
and the control module 64 is used for correspondingly controlling the operation of various flexible loads and the operation of the storage battery on the prediction day according to the operation plans of the flexible loads and the storage battery on the prediction day.
In an embodiment of the present application, the first building module 62 may include:
the first construction unit is used for constructing a thermodynamic model of the temperature control load according to the energy consumption behavior information and the energy consumption mode information of the temperature control load:
Figure 61111DEST_PATH_IMAGE001
power model in cooling mode:
Figure 735806DEST_PATH_IMAGE002
binary variable in refrigeration mode
Figure 462454DEST_PATH_IMAGE003
Comprises the following steps:
Figure 266199DEST_PATH_IMAGE004
power model in heating mode:
Figure 711087DEST_PATH_IMAGE005
binary variable in heating mode
Figure 517369DEST_PATH_IMAGE003
Comprises the following steps:
Figure 98523DEST_PATH_IMAGE006
(ii) a Wherein the content of the first and second substances,
Figure 840214DEST_PATH_IMAGE007
，
Figure 999495DEST_PATH_IMAGE008
，
Figure 609468DEST_PATH_IMAGE009
，
Figure 45128DEST_PATH_IMAGE010
，
Figure 957721DEST_PATH_IMAGE011
the temperature inside the ith temperature controlled load at time t +1,
Figure 111621DEST_PATH_IMAGE012
is the temperature coefficient of the ith temperature controlled load,
Figure 259706DEST_PATH_IMAGE013
the temperature inside the ith temperature controlled load at time t,
Figure 313987DEST_PATH_IMAGE014
is the ambient temperature at which the ith load is located,
Figure 397481DEST_PATH_IMAGE015
for the mode of operation in which the temperature controlled load is located,
Figure 773099DEST_PATH_IMAGE016
for the output power of the ith temperature controlled load,
Figure 662557DEST_PATH_IMAGE017
the coefficient of refrigeration performance for the ith temperature controlled load,
Figure 400706DEST_PATH_IMAGE018
the input power at time t for the ith temperature controlled load,
Figure 950374DEST_PATH_IMAGE019
is a binary variable which represents the starting and stopping state of the temperature control load,
Figure 16550DEST_PATH_IMAGE020
as the heating performance coefficient of the ith temperature-controlled load,
Figure 444120DEST_PATH_IMAGE021
is the thermal resistance of the ith temperature controlled load,
Figure 36776DEST_PATH_IMAGE022
is the heat capacity of the ith temperature controlled load,
Figure 258810DEST_PATH_IMAGE023
in the form of a time interval,
Figure 310817DEST_PATH_IMAGE024
a temperature is set for the ith temperature controlled load,
Figure 276499DEST_PATH_IMAGE025
a temperature threshold is set for the ith temperature controlled load,
Figure 661344DEST_PATH_IMAGE027
and
Figure 382175DEST_PATH_IMAGE027
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
Figure 219681DEST_PATH_IMAGE028
and
Figure 431132DEST_PATH_IMAGE029
the operation starting time and the operation ending time of the ith temperature control load are respectively.
In an embodiment of the present application, the first building module 62 may include:
the second construction unit is used for constructing an energy consumption model of the transferable load according to the energy consumption mode information of the transferable load:
Figure 936063DEST_PATH_IMAGE030
(ii) a Wherein the content of the first and second substances,
Figure 93374DEST_PATH_IMAGE031
，
Figure 355860DEST_PATH_IMAGE032
，
Figure 928923DEST_PATH_IMAGE033
the power at time t for the jth transferable load,
Figure 350677DEST_PATH_IMAGE034
for the jth transferable load's power in different phases of operation,
Figure 849530DEST_PATH_IMAGE035
for the time when the jth transferable load starts running,
Figure 599311DEST_PATH_IMAGE036
for the running time of the jth transferable load in the running phase w,
Figure 38383DEST_PATH_IMAGE037
the time frame for which the operation is allowed for the jth transferable load,
Figure 252326DEST_PATH_IMAGE038
the operation duration for the jth transferable load.
In an embodiment of the present application, the first building module 62 may include:
a third constructing unit, configured to construct an energy consumption model of the load with adjustable lighting power according to the energy usage pattern information of the load with adjustable lighting power, which is capable of reducing the load with adjustable lighting power:
Figure 361228DEST_PATH_IMAGE039
(ii) a Wherein the content of the first and second substances,
Figure 159157DEST_PATH_IMAGE040
，
Figure 808444DEST_PATH_IMAGE041
is as follows
Figure 939211DEST_PATH_IMAGE042
The power of the load capable of adjusting the lighting power at the moment t,
Figure 281331DEST_PATH_IMAGE043
is as follows
Figure 802442DEST_PATH_IMAGE044
The adjustment factor of the load of adjustable lighting power at time t,
Figure 957218DEST_PATH_IMAGE045
is as follows
Figure 880174DEST_PATH_IMAGE046
The rated power of the load for which the lighting power can be adjusted,
Figure 127616DEST_PATH_IMAGE047
and
Figure 463919DEST_PATH_IMAGE048
are respectively the first
Figure 720588DEST_PATH_IMAGE049
A load start operation time and an operation end time of the adjustable lighting power;
a fourth construction unit, configured to construct, according to the energy consumption mode information of the load capable of reducing the load at the adjustable operating range in the load, an energy consumption model of the load at the adjustable operating range:
Figure 14165DEST_PATH_IMAGE050
(ii) a Wherein the content of the first and second substances,
Figure 432508DEST_PATH_IMAGE051
，
Figure 928211DEST_PATH_IMAGE052
is as follows
Figure 457413DEST_PATH_IMAGE053
The load of the individual adjustable operating gears is at the power in the e gear at time t,
Figure 587918DEST_PATH_IMAGE054
is as follows
Figure 442741DEST_PATH_IMAGE055
The load of each adjustable working gear is at different gears
Figure 160161DEST_PATH_IMAGE056
The power of (a) is determined,
Figure 493054DEST_PATH_IMAGE057
and
Figure 743644DEST_PATH_IMAGE058
is as follows
Figure 707052DEST_PATH_IMAGE059
The load start running time and the running end time of each adjustable working gear are adjusted.
In an embodiment of the present application, the first building module 62 may include:
a fifth constructing unit, configured to construct, according to the energy usage pattern information of the battery load, an energy consumption model of the battery load:
Figure 911768DEST_PATH_IMAGE060
(ii) a Wherein the content of the first and second substances,
Figure 48352DEST_PATH_IMAGE061
，
Figure 153449DEST_PATH_IMAGE062
，
Figure 350075DEST_PATH_IMAGE376
，
Figure 42087DEST_PATH_IMAGE377
，
Figure 44678DEST_PATH_IMAGE378
representing the amount of power stored by the nth battery load by time t,
Figure 771326DEST_PATH_IMAGE379
represents the state of charge of the nth battery load, which has a value of 1 when charged and a value of 0 when uncharged,
Figure 377669DEST_PATH_IMAGE380
the charging power for the nth battery load,
Figure 822557DEST_PATH_IMAGE381
in order to achieve a high charging efficiency,
Figure 238626DEST_PATH_IMAGE382
in the form of a time interval,
Figure 819780DEST_PATH_IMAGE383
representing the amount of power stored by the nth battery load by the time t-1,
Figure 686104DEST_PATH_IMAGE384
the charging power for the nth battery load in different charging phases,
Figure 116823DEST_PATH_IMAGE385
the time to start charging for the nth battery load,
Figure 336583DEST_PATH_IMAGE386
the charging period of phase 1 for charging the nth battery load,
Figure 37823DEST_PATH_IMAGE387
indicating the state of charge of the nth battery load during charging phase 1,
Figure 75049DEST_PATH_IMAGE388
indicating a charging phase 1 according to
Figure 963371DEST_PATH_IMAGE389
And
Figure 547674DEST_PATH_IMAGE390
is divided intoThe number of sub-charging nodes is,
Figure 41103DEST_PATH_IMAGE391
the charging period for the nth battery load charging phase r,
Figure 186914DEST_PATH_IMAGE392
the time frame for which charging is allowed for the nth battery load,
Figure 890427DEST_PATH_IMAGE393
and
Figure 514307DEST_PATH_IMAGE394
respectively the minimum amount of electricity and the maximum amount of energy that the nth battery load can store,
Figure 626357DEST_PATH_IMAGE395
the total charge time period for the nth battery load.
In an embodiment of the present application, the first building module 62 may include:
a sixth construction unit configured to construct a battery model:
Figure 943069DEST_PATH_IMAGE087
(ii) a Wherein the content of the first and second substances,
Figure 133879DEST_PATH_IMAGE088
，
Figure 561449DEST_PATH_IMAGE089
，
Figure 763891DEST_PATH_IMAGE090
，
Figure 579401DEST_PATH_IMAGE091
the internal electric quantity of the storage battery at the moment t,
Figure 687866DEST_PATH_IMAGE092
is a binary variable, with a charge of 1, a discharge of 0,
Figure 856810DEST_PATH_IMAGE093
and
Figure 241655DEST_PATH_IMAGE094
respectively representing the charging power and the discharging power of the storage battery,
Figure 696907DEST_PATH_IMAGE095
and
Figure 799992DEST_PATH_IMAGE096
respectively showing the charge efficiency and the discharge efficiency of the secondary battery,
Figure 271163DEST_PATH_IMAGE097
in the form of a time interval,
Figure 838410DEST_PATH_IMAGE098
and
Figure 136667DEST_PATH_IMAGE099
respectively represent the maximum charging power and the maximum discharging power of the storage battery,
Figure 727049DEST_PATH_IMAGE100
and
Figure 237796DEST_PATH_IMAGE101
respectively representing the minimum and maximum electric quantities that the accumulator can store.
The embodiment of the application provides a gentle system operation controlling means is stored up to light, and second construction module 63 can include:
a seventh construction unit, configured to construct an optimization model:
Figure 564609DEST_PATH_IMAGE102
；
Figure 564927DEST_PATH_IMAGE103
represents a set of decision variables that are to be made,
Figure 439342DEST_PATH_IMAGE104
，
Figure 816096DEST_PATH_IMAGE105
and
Figure 967723DEST_PATH_IMAGE106
respectively representing the charging power and the discharging power of the storage battery,
Figure 371897DEST_PATH_IMAGE107
a temperature is set for the ith temperature controlled load,
Figure 405712DEST_PATH_IMAGE108
for the time when the jth transferable load starts running,
Figure 320579DEST_PATH_IMAGE109
for the mth one that can reduce the power of the load,
Figure 123450DEST_PATH_IMAGE110
indicating the state of charge of the nth battery load,
Figure 704385DEST_PATH_IMAGE111
to purchase or sell electric power for the grid,
Figure 553392DEST_PATH_IMAGE112
an objective function representing an optimization model is provided,
Figure 6370DEST_PATH_IMAGE113
the operation and maintenance cost of the user is shown,
Figure 132589DEST_PATH_IMAGE114
the amount of carbon dioxide emissions is expressed,
Figure 645610DEST_PATH_IMAGE115
，
Figure 981913DEST_PATH_IMAGE116
the self-satisfaction rate of the electric power is represented,
Figure 737118DEST_PATH_IMAGE117
representing the constraints of the inequality therein,
Figure 717843DEST_PATH_IMAGE118
expressing the equality constraint and the power balance, wherein the power balance is as follows:
Figure 729661DEST_PATH_IMAGE119
，
Figure 225365DEST_PATH_IMAGE120
a decision space is represented in the form of,
Figure 223408DEST_PATH_IMAGE121
for the generated power of the distributed photovoltaic system on the forecast day,
Figure 619492DEST_PATH_IMAGE122
is a binary variable, with a charge of 1, a discharge of 0,
Figure 536632DEST_PATH_IMAGE123
the total power usage for all non-compliant loads,
Figure 785211DEST_PATH_IMAGE124
the input power of the ith temperature control load at the moment t, I is the total amount of the temperature control loads,
Figure 321366DEST_PATH_IMAGE125
the power of the jth transferable load at time t, J the total amount of transferable loads, M the total amount of reducible loads,
Figure 73421DEST_PATH_IMAGE126
is the charging power of the nth battery load, and N is the total amount of the battery loads.
The embodiment of the application provides a gentle system operation controlling means is stored up to light, and second construction module 63 can include:
and the solving unit is used for solving the optimization model by utilizing a non-dominated sorting genetic algorithm, a sorting method approaching an ideal value and an information entropy method to obtain the operation plans of various flexible loads and storage batteries on the prediction days.
The operation control device of the light storage flexible system provided by the embodiment of the application can further comprise:
the receiving module is used for receiving the energy utilization attribute information of the target flexible load in the building where the distributed photovoltaic system is located, which is sent by the user.
For the description of the relevant parts in the operation control device of the light storing flexible system provided by the present application, reference may be made to the detailed description of the corresponding parts in the operation control method of the light storing flexible system provided by the embodiment of the present application, which is not described herein again.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include elements inherent in the list. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. In addition, parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of corresponding technical solutions in the prior art, are not described in detail so as to avoid redundant description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method for controlling the operation of a light storage flexible system is characterized by comprising the following steps:
calculating the power generation power of the distributed photovoltaic system on a prediction day according to weather data of the distributed photovoltaic system on the prediction day, and determining the power consumption power of the inflexible load on the prediction day according to historical power consumption data of the inflexible load in a building where the distributed photovoltaic system is located;
determining energy utilization attribute information of various flexible loads in a building where the distributed photovoltaic system is located, correspondingly constructing response models of the various flexible loads according to the energy utilization attribute information, and constructing a storage battery model according to information of a storage battery;
according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery model, constructing an optimization model with the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum power self-satisfaction rate, and solving the optimization model to obtain the operation plans of various flexible loads and storage batteries on the prediction day;
and correspondingly controlling the operation of each type of flexible load and the operation of the storage battery on the forecast day according to the flexible load and the operation plan of the storage battery on the forecast day.
2. The operation control method of the light-storing flexible system according to claim 1, wherein constructing the response model of the temperature-controlled load according to the energy-consumption property information of the temperature-controlled load comprises:
constructing a thermodynamic model of the temperature control load according to the energy consumption behavior information and the energy consumption mode information of the temperature control load:
Figure 329497DEST_PATH_IMAGE001
power model in cooling mode:
Figure 119599DEST_PATH_IMAGE002
the binary variable in the cooling mode is
Figure 906551DEST_PATH_IMAGE003
：
Figure 705880DEST_PATH_IMAGE004
Power model in heating mode:
Figure 760424DEST_PATH_IMAGE005
binary variable in heating mode
Figure 721426DEST_PATH_IMAGE003
Comprises the following steps:
Figure 494210DEST_PATH_IMAGE006
(ii) a Wherein the content of the first and second substances,
Figure 831651DEST_PATH_IMAGE007
，
Figure 507745DEST_PATH_IMAGE008
，
Figure 905228DEST_PATH_IMAGE009
，
Figure 899729DEST_PATH_IMAGE010
，
Figure 40860DEST_PATH_IMAGE011
the temperature inside the ith temperature controlled load at time t +1,
Figure 69996DEST_PATH_IMAGE012
is the temperature coefficient of the ith temperature controlled load,
Figure 107222DEST_PATH_IMAGE013
the temperature inside the ith temperature controlled load at time t,
Figure 621643DEST_PATH_IMAGE014
is the ambient temperature at which the ith load is located,
Figure 300886DEST_PATH_IMAGE015
for the mode of operation in which the temperature controlled load is located,
Figure 184528DEST_PATH_IMAGE016
for the output power of the ith temperature controlled load,
Figure 658235DEST_PATH_IMAGE017
the coefficient of refrigeration performance for the ith temperature controlled load,
Figure 892907DEST_PATH_IMAGE018
the input power at time t for the ith temperature controlled load,
Figure 375841DEST_PATH_IMAGE019
is a binary variable which represents the starting and stopping state of the temperature control load,
Figure 615455DEST_PATH_IMAGE020
as the heating performance coefficient of the ith temperature-controlled load,
Figure 525642DEST_PATH_IMAGE021
is the thermal resistance of the ith temperature controlled load,
Figure 247610DEST_PATH_IMAGE022
is the heat capacity of the ith temperature controlled load,
Figure 268656DEST_PATH_IMAGE023
in the form of a time interval,
Figure 126890DEST_PATH_IMAGE024
a temperature is set for the ith temperature controlled load,
Figure 207979DEST_PATH_IMAGE025
a temperature threshold is set for the ith temperature controlled load,
Figure 477348DEST_PATH_IMAGE026
and
Figure 36505DEST_PATH_IMAGE027
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
Figure 14825DEST_PATH_IMAGE028
and
Figure 266815DEST_PATH_IMAGE029
the operation starting time and the operation ending time of the ith temperature control load are respectively.
3. The operation control method of the light-storing flexible system according to claim 1, wherein constructing the response model of the transferable load according to the energy-using property information of the transferable load comprises:
constructing an energy consumption model of the transferable load according to the energy consumption mode information of the transferable load:
Figure 697796DEST_PATH_IMAGE030
(ii) a Wherein the content of the first and second substances,
Figure 60645DEST_PATH_IMAGE031
，
Figure 129357DEST_PATH_IMAGE032
，
Figure 286669DEST_PATH_IMAGE033
can be transferred for the jthThe power of the load at the time t,
Figure 939367DEST_PATH_IMAGE034
for the jth transferable load's power in different phases of operation,
Figure 105906DEST_PATH_IMAGE035
for the time when the jth transferable load starts running,
Figure 793240DEST_PATH_IMAGE036
for the running time of the jth transferable load in the running phase w,
Figure 121453DEST_PATH_IMAGE037
the time frame for which the operation is allowed for the jth transferable load,
Figure 261447DEST_PATH_IMAGE038
the operation duration for the jth transferable load.
4. The operation control method of the light-storing flexible system according to claim 1, wherein constructing the reducible load response model according to the reducible load use property information includes:
constructing an energy consumption model of the load with adjustable lighting power according to the energy consumption mode information of the load with adjustable lighting power, wherein the energy consumption model comprises the following steps:
Figure 467563DEST_PATH_IMAGE039
(ii) a Wherein the content of the first and second substances,
Figure 9402DEST_PATH_IMAGE040
，
Figure 508517DEST_PATH_IMAGE041
is as follows
Figure 135807DEST_PATH_IMAGE042
The power of the load capable of adjusting the lighting power at the moment t,
Figure 644149DEST_PATH_IMAGE043
is as follows
Figure 306075DEST_PATH_IMAGE044
The adjustment factor of the load of adjustable lighting power at time t,
Figure 976090DEST_PATH_IMAGE045
is as follows
Figure 825098DEST_PATH_IMAGE046
The rated power of the load for which the lighting power can be adjusted,
Figure 373016DEST_PATH_IMAGE047
and
Figure 155027DEST_PATH_IMAGE048
are respectively the first
Figure 995944DEST_PATH_IMAGE049
A load start operation time and an operation end time of the adjustable lighting power;
constructing an energy consumption model of the load of the adjustable working gear according to the energy consumption mode information of the load of the adjustable working gear in the reducible load:
Figure 332248DEST_PATH_IMAGE050
(ii) a Wherein the content of the first and second substances,
Figure 916813DEST_PATH_IMAGE051
，
Figure 553330DEST_PATH_IMAGE052
is as follows
Figure 332193DEST_PATH_IMAGE053
The load of the individual adjustable operating gears is at the power in the e gear at time t,
Figure 421372DEST_PATH_IMAGE054
is as follows
Figure 544048DEST_PATH_IMAGE055
The load of each adjustable working gear is at different gears
Figure 35073DEST_PATH_IMAGE056
The power of (a) is determined,
Figure 483371DEST_PATH_IMAGE057
and
Figure 794267DEST_PATH_IMAGE058
is as follows
Figure 720635DEST_PATH_IMAGE059
The load start running time and the running end time of each adjustable working gear are adjusted.
5. The operation control method of a light-storing flexible system according to claim 1, wherein constructing a response model of a battery load according to energy use attribute information of the battery load comprises:
constructing an energy consumption model of the battery load according to the energy consumption mode information of the battery load:
Figure 66165DEST_PATH_IMAGE060
(ii) a Wherein the content of the first and second substances,
Figure 186830DEST_PATH_IMAGE061
，
Figure 985022DEST_PATH_IMAGE062
，
Figure 715081DEST_PATH_IMAGE063
，
Figure 180697DEST_PATH_IMAGE064
，…，
Figure 705219DEST_PATH_IMAGE065
，
Figure 990707DEST_PATH_IMAGE066
，
Figure 524457DEST_PATH_IMAGE067
，
Figure 80465DEST_PATH_IMAGE068
，
Figure 41468DEST_PATH_IMAGE069
representing the amount of power stored by the nth battery load by time t,
Figure 814252DEST_PATH_IMAGE070
represents the state of charge of the nth battery load, which has a value of 1 when charged and a value of 0 when uncharged,
Figure 886113DEST_PATH_IMAGE071
the charging power for the nth battery load,
Figure 795163DEST_PATH_IMAGE072
in order to achieve a high charging efficiency,
Figure 192647DEST_PATH_IMAGE073
in the form of a time interval,
Figure 187147DEST_PATH_IMAGE074
representing the amount of power stored by the nth battery load by the time t-1,
Figure 564164DEST_PATH_IMAGE075
the charging power for the nth battery load in different charging phases,
Figure 327721DEST_PATH_IMAGE076
the time to start charging for the nth battery load,
Figure 896106DEST_PATH_IMAGE077
the charging period of phase 1 for charging the nth battery load,
Figure 377903DEST_PATH_IMAGE078
indicating the state of charge of the nth battery load during charging phase 1,
Figure 791566DEST_PATH_IMAGE079
indicating a charging phase 1 according to
Figure 675209DEST_PATH_IMAGE080
And
Figure 169820DEST_PATH_IMAGE081
the number of divided sub-charging nodes,
Figure 138913DEST_PATH_IMAGE082
the charging period for the nth battery load charging phase r,
Figure 621847DEST_PATH_IMAGE083
the time frame for which charging is allowed for the nth battery load,
Figure 359996DEST_PATH_IMAGE084
and
Figure 4604DEST_PATH_IMAGE085
respectively the minimum amount of electricity and the maximum amount of energy that the nth battery load can store,
Figure 726572DEST_PATH_IMAGE086
the total charge time period for the nth battery load.
6. The operation control method of a light-storing flexible system according to claim 1, wherein constructing a battery model according to information of a battery comprises:
constructing the storage battery model:
Figure DEST_PATH_IMAGE087
(ii) a Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE088
，
Figure DEST_PATH_IMAGE089
，
Figure DEST_PATH_IMAGE090
，
Figure DEST_PATH_IMAGE091
the internal electric quantity of the storage battery at the moment t,
Figure DEST_PATH_IMAGE092
is a binary variable, with a charge of 1, a discharge of 0,
Figure DEST_PATH_IMAGE093
and
Figure DEST_PATH_IMAGE094
respectively representing the charging power and the discharging power of the storage battery,
Figure DEST_PATH_IMAGE095
and
Figure DEST_PATH_IMAGE096
respectively representing accumulatorsThe efficiency of the charge and the efficiency of the discharge,
Figure DEST_PATH_IMAGE097
in the form of a time interval,
Figure DEST_PATH_IMAGE098
and
Figure DEST_PATH_IMAGE099
respectively represent the maximum charging power and the maximum discharging power of the storage battery,
Figure DEST_PATH_IMAGE100
and
Figure DEST_PATH_IMAGE101
respectively representing the minimum and maximum electric quantities that the accumulator can store.
7. The operation control method of the light-storing flexible system according to claim 1, wherein an optimization model is constructed according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery model, with the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum self-satisfaction rate of power, and comprises the following steps:
constructing the optimization model:
Figure DEST_PATH_IMAGE102
；
Figure DEST_PATH_IMAGE103
represents a set of decision variables that are to be made,
Figure DEST_PATH_IMAGE104
，
Figure DEST_PATH_IMAGE105
and
Figure DEST_PATH_IMAGE106
respectively representing the charging power and the discharging power of the storage battery,
Figure DEST_PATH_IMAGE107
a temperature is set for the ith temperature controlled load,
Figure DEST_PATH_IMAGE108
for the time when the jth transferable load starts running,
Figure DEST_PATH_IMAGE109
for the mth one that can reduce the power of the load,
Figure DEST_PATH_IMAGE110
indicating the state of charge of the nth battery load,
Figure DEST_PATH_IMAGE111
to purchase or sell electric power for the grid,
Figure DEST_PATH_IMAGE112
an objective function representing the optimization model,
Figure DEST_PATH_IMAGE113
the operation and maintenance cost of the user is shown,
Figure DEST_PATH_IMAGE114
the amount of carbon dioxide emissions is expressed,
Figure DEST_PATH_IMAGE115
，
Figure DEST_PATH_IMAGE116
the self-satisfaction rate of the electric power is represented,
Figure DEST_PATH_IMAGE117
representing the constraints of the inequality therein,
Figure DEST_PATH_IMAGE118
expressing the equality constraint and the power balance, wherein the power balance is as follows:
Figure DEST_PATH_IMAGE119
，
Figure DEST_PATH_IMAGE120
a decision space is represented in the form of,
Figure DEST_PATH_IMAGE121
generating power for the distributed photovoltaic system on a predicted day,
Figure DEST_PATH_IMAGE122
is a binary variable, with a charge of 1, a discharge of 0,
Figure DEST_PATH_IMAGE123
the total power usage for all non-compliant loads,
Figure DEST_PATH_IMAGE124
the input power of the ith temperature control load at the moment t, I is the total amount of the temperature control loads,
Figure DEST_PATH_IMAGE125
the power of the jth transferable load at time t, J the total amount of transferable loads, M the total amount of reducible loads,
Figure DEST_PATH_IMAGE126
is the charging power of the nth battery load, and N is the total amount of the battery loads.
8. The operation control method of the light-storing flexible system according to claim 7, wherein solving the optimization model to obtain the operation plan of the various flexible loads and the storage battery on the prediction day comprises:
and solving the optimization model by using a non-dominated sorting genetic algorithm, a sorting method approaching an ideal value and an information entropy method to obtain various flexible loads and an operation plan of the storage battery on a prediction day.
9. The operation control method of a light storing flexible system according to claim 1, further comprising:
and receiving energy utilization attribute information of the target flexible load in the building where the distributed photovoltaic system is located, which is sent by a user.
10. A light storing flexible system operation control device, comprising:
the calculation module is used for calculating the power generation power of the distributed photovoltaic system on the prediction day according to the weather data of the distributed photovoltaic system on the prediction day, and determining the power consumption power of the inflexible load on the prediction day according to the historical power consumption data of the inflexible load in the building where the distributed photovoltaic system is located;
the first construction module is used for determining energy utilization attribute information of various flexible loads in a building where the distributed photovoltaic system is located, correspondingly constructing response models of the various flexible loads according to the energy utilization attribute information, and constructing a storage battery model according to information of a storage battery;
the second construction module is used for constructing an optimization model according to the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum electric power self-satisfaction rate according to the generated power of the distributed photovoltaic system on the prediction day, the power consumption of the inflexible load on the prediction day, the response models of various flexible loads and the storage battery model, and solving the optimization model to obtain the operation plans of various flexible loads and the storage battery on the prediction day;
and the control module is used for correspondingly controlling the operation of the flexible loads and the operation of the storage battery on the prediction day according to the flexible loads and the operation plan of the storage battery on the prediction day.
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