CN114418249B - Operation control method and device for light storage flexible system - Google Patents
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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 utility model discloses a technical scheme 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 and absorb the cost.
Description
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 consumption 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:
power model in cooling mode:
binary variable in refrigeration mode
Comprises the following steps:
power model in heating mode:
binary variable in heating mode
Comprises the following steps:
(ii) a Wherein,
，
，
，
，
the temperature inside the ith temperature controlled load at time t +1,
is the temperature coefficient of the ith temperature controlled load,
the temperature inside the ith temperature controlled load at time t,
is the ambient temperature at which the ith load is located,
for the mode of operation in which the temperature controlled load is located,
for the output power of the ith temperature controlled load,
the coefficient of refrigeration performance for the ith temperature controlled load,
the input power at time t for the ith temperature controlled load,
is a binary variable which represents the starting and stopping state of the temperature control load,
as the heating performance coefficient of the ith temperature-controlled load,
is the thermal resistance of the ith temperature controlled load,
is the heat capacity of the ith temperature controlled load,
in the form of a time interval,
a temperature is set for the ith temperature controlled load,
a temperature threshold is set for the ith temperature controlled load,
and
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
and
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:
(ii) a Wherein,
，
，
the power at time t for the jth transferable load,
for the jth transferable load's power in different phases of operation,
for the time when the jth transferable load starts running,
for the running time of the jth transferable load in the running phase w,
the time frame for which the operation is allowed for the jth transferable load,
the operation duration for the jth transferable load.
Preferably, the constructing of the load reducible response model based on the load reducible capability 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 load with adjustable lighting power can be reduced:
(ii) a Wherein,
，
is as follows
The power of the load of the adjustable lighting power at the time t,
is as follows
The adjustment factor of the load of adjustable lighting power at time t,
is a first
The rated power of the load for which the lighting power can be adjusted,
and
are respectively the first
The starting operation time and the ending operation time of each load capable of adjusting the 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:
(ii) a Wherein,
，
is as follows
The load of the individual adjustable operating gears is at the power in the e gear at time t,
is as follows
The load of each adjustable working gear is at different gears
The power of (a) is determined,
and
is as follows
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:
(ii) a Wherein,
，
，
，
，…，
，
，
，
，
representing the amount of power stored by the nth battery load by time t,
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,
the charging power for the nth battery load,
in order to achieve a high charging efficiency,
the time interval is a time interval of,
indicating the amount of power stored by the nth battery load by time t-1,
the charging power in different charging phases for the nth battery load,
the time to start charging for the nth battery load,
the charging period of phase 1 for charging the nth battery load,
indicating the state of charge of the nth battery load during charging phase 1,
indicating a charging phase 1 according to
And
the number of divided sub-charging nodes,
the charging period of the charging phase r for the nth battery load,
the time frame for which charging is allowed for the nth battery load,
and
respectively the minimum amount of electricity and the maximum amount of energy that the nth battery load can store,
the total charging 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:
(ii) a Wherein,
，
，
，
the internal electric quantity of the storage battery at the moment t,
is a binary variable, with a charge of 1, a discharge of 0,
and
respectively representing the charging power and the discharging power of the storage battery,
and
respectively showing the charge efficiency and the discharge efficiency of the secondary battery,
in the form of a time interval,
and
respectively represent the maximum charging power and the maximum discharging power of the storage battery,
and
respectively representing the minimum and maximum electric quantities that the accumulator can store.
Preferably, 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 types of flexible loads and the storage battery model, the optimization model is constructed with the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum power self-satisfaction rate, and the optimization model comprises the following steps:
constructing the optimization model:
；
represents a set of decision variables that are to be made,
，
and
respectively representing the charging power and the discharging power of the storage battery,
a temperature is set for the ith temperature controlled load,
for the time when the jth transferable load starts running,
for the mth one that can reduce the power of the load,
indicating the state of charge of the nth battery load,
to purchase or sell electric power for the grid,
an objective function representing the optimization model,
the operation and maintenance cost of the user is shown,
indicating carbon dioxide emissions，
，
The self-satisfaction rate of the electric power is represented,
representing the constraints of the inequality therein,
the equation constraint and the power balance are expressed, wherein the power balance is as follows:
，
a decision space is represented in the form of,
generating power for the distributed photovoltaic system on a predicted day,
is a binary variable, with a charge of 1, a discharge of 0,
the total power usage for all non-compliant loads,
the input power of the ith temperature control load at the moment t, I is the total amount of the temperature control loads,
the power of the jth transferable load at time t, J the total amount of transferable loads, M the total amount of reducible loads,
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, and establishing response models of various flexible loads and storage battery models, establishing an optimization model based on the response models of various flexible loads and the storage battery models, 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 the distributed photovoltaic power generation), and convenience (for example, the user is used to wash clothes at 9:00-11:00 in the morning, and cannot say that the washing machine washes clothes at 2:00-4:00 in the afternoon 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, such as reducing the illumination of the lighting system, and the power of the lighting system, the flexible load comprises lighting and the like); 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 obtained, where the prediction day mentioned here may be specifically the second day, that is, the weather data of the distributed photovoltaic system on the second day is obtained one day in advance (the weather data of the second day mentioned here 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 here may also be other times as long as the corresponding weather prediction data may be obtained. 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 equations (1) and (2):
wherein,
to be distributedThe generated power (kW) of a photovoltaic system at a predicted day,
is the intensity of solar radiation (W/m)2)，
For the installation area (m) of photovoltaic panels in a distributed photovoltaic system2)，
In order to achieve the power generation efficiency of the photovoltaic cell panel,
、
、
、
、
are all constant and are all provided with the same power,
is the outdoor ambient temperature (DEG C),
the mass of the air (kg),
、
and
respectively, Tai under standard test conditionsThe solar radiation intensity, the outdoor ambient temperature and the air quality 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
) 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 curve corresponding to each of the working day, the weekend, and the holiday may be obtained by a clustering method based on the historical power consumption data of a certain inflexible load in a certain historical length, and then the power consumption curve corresponding to the predicted day of the inflexible load may be used as the power consumption curve of the inflexible load according to which of the working day, the weekend, and the holiday the predicted day, so as to obtain the power consumption at each time of the predicted day based on the power consumption curve of the inflexible load.
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 dissipated 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 utilization load curve of the building is adjusted by changing the power utilization mode of the flexible load, the distributed photovoltaic power generation on the user side is dissipated as much as possible, and the power which is not completely dissipated is stored in the storage battery, so that the setting capacity of the storage battery is reduced, and the dissipation 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. (the current home appliances usually include only a basic function module and a control module (such as an air conditioner, an electric water heater, a washing machine, a dryer, and the like), do not have a data acquisition and communication module, and need a user to manually turn on the device to excite the control module to execute corresponding instructions to control the operation of the home appliances. with the continuous development of technologies such as the internet of things, 5G, and the like, we propose home appliances including the basic function module, the control module, the data acquisition module, and the communication module, which can automatically control the home appliances through an optimization control system, monitor the operation states of the home appliances in real time, and simultaneously can realize communication between the user and the home appliances. 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:
power model in cooling mode:
binary variable in refrigeration mode
Comprises the following steps:
power model in heating mode:
binary variable in heating mode
Comprises the following steps:
(ii) a Wherein,
，
，
，
，
the temperature inside the ith temperature controlled load at time t +1,
is the temperature coefficient of the ith temperature controlled load,
the temperature inside the ith temperature controlled load at time t,
is the ambient temperature at which the ith load is located,
for the mode of operation in which the temperature controlled load is located,
for the output power of the ith temperature controlled load,
the coefficient of refrigeration performance for the ith temperature controlled load,
the input power at time t for the ith temperature controlled load,
is a binary variable which represents the starting and stopping state of the temperature control load,
as the heating performance coefficient of the ith temperature-controlled load,
is the thermal resistance of the ith temperature controlled load,
is the heat capacity of the ith temperature controlled load,
in the form of a time interval,
a temperature is set for the ith temperature controlled load,
a temperature threshold is set for the ith temperature controlled load,
and
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
and
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)
(wherein,
representing the acquired data set, G representing a statistical method, and constructing a thermodynamic model of the temperature control load by adopting an RC (Resistance-capacitance) model:
and (3) power model:
1) the power model in the cooling mode is:
2) the power model in the heating mode is as follows:
in the above-mentioned formula,
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,
indicating the ambient temperature at which the temperature controlled load is located,
indicating the mode of operation (cooling or heating) in which the temperature-controlled load is operating,
the output power representing the temperature controlled load,
as a function of the number of the coefficients,
is the thermal resistance of the ith temperature controlled load,
heat for the ith temperature-controlled loadThe volume of the liquid to be treated is,
the time interval is a time interval of,
for the input power of the ith temperature controlled load,
is a binary variable which represents the starting and stopping state of the temperature control load,
the coefficient of refrigeration performance for the ith temperature controlled load,
for the heating coefficient of performance of the ith temperature-controlled load,
a temperature is set for the ith temperature controlled load,
a temperature threshold is set for the ith temperature controlled load,
and
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),
and
the start running time and the end running time of the ith temperature-controlled load respectively, the magnitudes of the start running time and the end running time are determined by the user (namely the user mode of the userFormula information).
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 loads according to the energy consumption mode information of the transferable loads:
(ii) a Wherein,
，
，
the power at time t for the jth transferable load,
for the jth transferable load's power in different phases of operation,
for the time when the jth transferable load starts running,
for the running time of the jth transferable load in the running phase w,
the time frame for which the operation is allowed for the jth transferable load,
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. Thus, it is possible to use the information of the energy usage pattern according to the transferable loads
Constructing an energy consumption model capable of transferring loads:
in the above-mentioned formula,
representing the power of the transferable load j at time t,
for the jth transferable load's power in different phases of operation,
for the time when the jth transferable load starts running,
for the jth transferable load in the operating phase W (W stands forLoad can be transferred for W operating phases),
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),
for the operation duration of the jth transferable load, accordingly,
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, constructs a response model capable of reducing the load according to the energy utilization attribute information capable of reducing the load, and may include:
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 can reduce the load with adjustable lighting power:
(ii) a Wherein,
，
is as follows
The power of the load capable of adjusting the lighting power at the moment t,
is as follows
The adjustment factor of the load of adjustable lighting power at time t,
is as follows
The rated power of the load for which the lighting power can be adjusted,
and
are respectively the first
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:
(ii) a Wherein,
，
is as follows
The load of the individual adjustable operating gears is at the power in the e gear at time t,
is as follows
The load of each adjustable working gear is at different gears
The power of (a) is determined,
and
is as follows
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
And respectively constructing two types of response models capable of transferring load, wherein,
and
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):
in the above-mentioned formula,
is as follows
The power of the load of the adjustable lighting power at the time t,
is as follows
The adjustment factor of the load of adjustable lighting power at time t,
is as follows
The rated power of the load for which the lighting power can be adjusted,
and
are respectively provided withIs as follows
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;
is as follows
The load of the individual adjustable operating gears is at the power in the e gear at time t,
is as follows
The load of each adjustable working gear is at different gears
The power of (a) is set,
and
is as follows
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:
(ii) a Wherein,
，
，
，
，…，
，
，
，
，
representing the amount of power stored by the nth battery load by time t,
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,
is the nth cellThe charging power of the load is set to be,
in order to achieve a high charging efficiency,
in the form of a time interval,
indicating the amount of power stored by the nth battery load by time t-1,
the charging power for the nth battery load in different charging phases,
the time to start charging for the nth battery load,
the charging period of phase 1 for charging the nth battery load,
indicating the state of charge of the nth battery load during charging phase 1,
indicating a charging phase 1 according to
And
the number of divided sub-charging nodes,
the charging period for the nth battery load charging phase r,
the time frame for which charging is allowed for the nth battery load,
and
respectively the minimum amount of electricity and the maximum amount of energy that the nth battery load can store,
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
Constructing an energy consumption model of the battery load:
…
in the above-mentioned formula, the first and second,
representing the amount of power stored by the nth battery load by time t,
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,
the charging power for the nth battery load,
in order to achieve a high charging efficiency,
in the form of a time interval,
representing the amount of power stored by the nth battery load by the time t-1,
the charging power for the nth battery load in different charging phases,
the time to start charging for the nth battery load,
the charging period of phase 1 for charging the nth battery load,
indicating the state of charge of the nth battery load during charging phase 1,
indicating a charging phase 1 according to
The number of divided sub-charging nodes (specifically, if the charging period of the charging node 1 is T1, the charging period is T1
），
The charging period of charging phase 2 for the nth battery load,
indicating the state of charge of the nth battery load during charging phase 2,
indicating a charging phase 2 according to
Divided sonThe number of charge nodes … … r represents the total number of charge stages for the nth battery load,
for the total charging period of the nth battery load,
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),
indicating the latest time at which the nth battery load is allowed to start charging,
and
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:
(ii) a Wherein,
，
，
，
the internal charge of the storage battery at the time t,
is a binary variable, with a charge of 1, a discharge of 0,
and
respectively representing the charging power and the discharging power of the storage battery,
and
respectively showing the charge efficiency and the discharge efficiency of the secondary battery,
the time interval is a time interval of,
and
respectively represent the maximum charging power and the maximum discharging power of the storage battery,
and
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:
in the above-mentioned formula, the first and second,
the unit of the electric quantity of the storage battery at the moment t is kWh;
is a binary variable, charge is 1, discharge is 0;
and
respectively representing the charging power and the discharging power of the storage battery, and the unit is kW;
and
respectively representing the charging efficiency and the discharging efficiency of the storage battery;
the time interval is a time interval of,
and
respectively representing the maximum charging power and the maximum discharging power of the storage battery, wherein the unit is kW;
and
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:
；
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:
in the above-mentioned formula,
represents a set of decision variables that are to be made,
，
and
respectively representing the charging power and the discharging power of the storage battery,
a temperature is set for the ith temperature controlled load,
for the time when the jth transferable load starts running,
for the mth one that can reduce the power of the load,
indicating the state of charge of the nth battery load,
to purchase or sell electric power for the grid,
an objective function representing an optimization model is obtained,
representing the user's operation and maintenance costs (calculated by equations (36) - (40)),
indicates the amount of carbon dioxide emission (calculated by equation (41)),
，
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))
），
Representing inequality constraints including all inequalities in the preceding predictive model, battery model, response model for flexible loads,
representing the equality constraints (including all the equalities during the preceding predictive model, battery model, response model for flexible loads) and power balance,
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:
in the formula
Generating power for the distributed photovoltaic system on a prediction day;
is a binary variable, charge is 1, discharge is 0;
total power usage for all non-compliant loads;
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;
the power of the jth transferable load at the moment t, and J is the total amount of the transferable loads; m is the total amount of the reducible load,
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
The method specifically comprises the following steps:
in the formula
Is the total number of time intervals,
、
、
、
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;
、
respectively representing the power of a storage battery, the power purchased from a power grid and the power sold to the power grid;
、
、
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.
In the formula
Carbon dioxide emission factor, kgCO, representing electrical power2/kWh。
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 to obtain 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，
，（
Respectively represent decision variables X under different values in the optimization model,
) The objective function of each individual in the initial population is calculated according to equations (36), (41) and (42)
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
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: from the population R according to the non-dominated sorting level and the crowding degreemSelecting 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
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 functions is,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
In the formula
Representing objects
The entropy value of (a) of the image,
representing objects
The weight of (c).
3) Constructing a weighted normalization matrix
In the formula
Representing the elements of the weighted normalization matrix.
4) Determining a positive ideal solution and a negative ideal solution
In the formulaJ 1 AndJ 2 respectively represent a cost type index and a benefit type index,
the positive ideal solution is shown,
representing a negative ideal solution.
5) Euclidean distance calculation
In the formula
Representing the distance of the objective function value to the positive ideal solution,
representing the distance of the value of the objective function to the negative ideal solution.
6) Relative closeness calculation
In the formula
Indicating relative proximity.
7) Relative closeness ranking
Relative degree of closeness
Sorting according to the sequence from big to small;
8) optimal solution
Selecting relative closeness
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
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; on the next day, the optimizer sends the control instructions of the corresponding moments to the storage battery and each flexible load respectively 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 the optimization control system to send out commands, and when special conditions occur, such as some conditions mentioned in 2), a user sends the commands through a button (manual control) of the flexible load or an APP (application), and then the device executes the commands preferentially 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:
a first construction unit for constructing energy consumption behavior information and energy consumption pattern information according to the temperature-controlled loadConstructing a thermodynamic model of temperature control load:
power model in cooling mode:
binary variable in refrigeration mode
Comprises the following steps:
power model in heating mode:
binary variable in heating mode
Comprises the following steps:
(ii) a Wherein,
，
，
，
，
the temperature inside the ith temperature controlled load at time t +1,
is the temperature coefficient of the ith temperature controlled load,
the temperature inside the ith temperature controlled load at time t,
is the ambient temperature at which the ith load is located,
for the mode of operation in which the temperature controlled load is located,
for the output power of the ith temperature controlled load,
the coefficient of refrigeration performance for the ith temperature controlled load,
the input power at time t for the ith temperature controlled load,
is a binary variable which represents the starting and stopping state of the temperature control load,
as the heating performance coefficient of the ith temperature-controlled load,
is the thermal resistance of the ith temperature controlled load,
is the heat capacity of the ith temperature controlled load,
in the form of a time interval,
is the ithThe temperature control load is set to a temperature,
a temperature threshold is set for the ith temperature controlled load,
and
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
and
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:
(ii) a Wherein,
，
，
the power at time t for the jth transferable load,
for the jth transferable load's power in different phases of operation,
for the time when the jth transferable load starts running,
for the running time of the jth transferable load in the running phase w,
the time frame for which the operation is allowed for the jth transferable load,
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:
(ii) a Wherein,
，
is as follows
The power of the load capable of adjusting the lighting power at the moment t,
is as follows
The adjustment factor of the load of adjustable lighting power at time t,
is as follows
The rated power of the load for which the lighting power can be adjusted,
and
are respectively the first
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:
(ii) a Wherein,
，
is as follows
The load of the individual adjustable operating gears is at the power in the e gear at time t,
is as follows
The load of each adjustable working gear is at different gears
The power of (a) is determined,
and
is as follows
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:
(ii) a Wherein,
，
，
，
，
representing the amount of power stored by the nth battery load by time t,
represents the state of charge of the nth battery load, which has a value of 1 when charged, has a value of 0 when uncharged,
the charging power for the nth battery load,
in order to achieve a high charging efficiency,
in the form of a time interval,
representing the amount of power stored by the nth battery load by the time t-1,
the charging power for the nth battery load in different charging phases,
the time to start charging for the nth battery load,
the charging period of phase 1 for charging the nth battery load,
indicating the state of charge of the nth battery load during charging phase 1,
indicating a charging phase 1 according to
And
the number of divided sub-charging nodes,
the charging period for the nth battery load charging phase r,
the time frame for which charging is allowed for the nth battery load,
and
respectively the minimum amount of electricity and the maximum amount of energy that the nth battery load can store,
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:
(ii) a Wherein,
，
，
，
the internal electric quantity of the storage battery at the moment t,
is a binary variable, with charge of 1, discharge of 0,
and
respectively representing the charging power and the discharging power of the storage battery,
and
respectively showing the charge efficiency and the discharge efficiency of the secondary battery,
in the form of a time interval,
and
respectively representing the maximum charging power and the maximum discharging power of the storage battery,
and
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:
；
In the operation control device of a light storage flexible system provided in an embodiment of the present application, the second building module 63 may 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:
and 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 (9)
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; correspondingly constructing response models of various flexible loads according to the energy utilization attribute information, wherein the response models comprise: constructing a thermodynamic model of the temperature control load, a power model under a refrigeration mode and a power model under a heating model according to the energy consumption behavior information and the energy consumption mode information of the temperature control load; constructing an energy consumption model of the transferable load according to the energy consumption mode information of the transferable load; constructing an energy consumption model of the load with adjustable lighting power according to energy consumption mode information of the load with adjustable lighting power in the reducible load, and constructing an energy consumption model of the load with adjustable working gear according to energy consumption mode information of the load with adjustable working gear in the reducible load; constructing an energy consumption model of the battery load according to the energy consumption mode information of the battery load;
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;
correspondingly controlling the operation of each type of flexible load and the operation of the storage battery on the forecast day according to the operation plan of each type of flexible load and the storage battery on the forecast day;
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, an optimization model is constructed according to the goals of minimum user operation and maintenance cost, minimum carbon dioxide emission and maximum power self-satisfaction rate, and the optimization model comprises the following steps:
constructing the optimization model:
x represents a set of decision variables,
and
respectively representing the charging power and the discharging power of the storage battery,
a temperature is set for the ith temperature controlled load,
for the time when the jth transferable load starts running,
for the mth one that can reduce the power of the load,
indicating the state of charge of the nth battery load,
for the purchase of electric power or the sale of electric power from the grid, f (X) an objective function representing said optimization model, f1(X) represents the user's operation and maintenance cost, f2(X) represents the amount of carbon dioxide emission, f3(X) — SSR, SSR represents power self-satisfaction rate, h (X) represents inequality constraint therein, g (X) represents equality constraint and power balance therein, wherein power balance is:
2. The operation control method of the light-storing flexible system according to claim 1, wherein constructing a 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:
power model in cooling mode:
binary variable Z in cooling modei，tComprises the following steps:
power model in heating mode:
binary variable Z in heating modei，tComprises the following steps:
wherein,
θi，r+1the temperature inside the ith temperature-controlled load at time t +1, aiIs the temperature coefficient of the ith temperature-controlled load, thetai，tIs the temperature inside the ith temperature-controlled load at time t, thetaa，tIs the ambient temperature of the ith load, U is the operating mode of the temperature controlled load,
output power, EIR, for the ith temperature-controlled loadi，tThe refrigeration coefficient of performance for the ith temperature-controlled load,
input power for the ith temperature-controlled load at time t, Zi，tIs a binary variable representing the start-stop state, COP, of the temperature-controlled loadi，tCoefficient of heating Performance, R, for the ith temperature-controlled loadiThermal resistance of the ith temperature-controlled load, CiIs the heat capacity of the ith temperature-controlled load, at is the time interval,
setting temperature for the ith temperature-controlled load, delta is a threshold value of the temperature set for the ith temperature-controlled load,
and
respectively setting the minimum value and the maximum value of the temperature for the ith temperature control load,
and
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:
wherein,
the power at time t for the jth transferable load,
for the jth transferable load's power in different phases of operation,
for the time when the jth transferable load starts running,
for the running time of the jth transferable load in the running phase w,
time frame allowed for operation of jth transferable load, Δ hjThe operation duration for the jth transferable load.
4. The light-storing flexible system operation control method according to claim 1, wherein constructing the reducible load response model according to the energy attribute information of the reducible load comprises:
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 load with adjustable lighting power can be reduced:
wherein,
is m at the m1The power of the load capable of adjusting the lighting power at the moment t,
is m at the m1The adjustment factor of the load of adjustable lighting power at time t,
is m at the m1The rated power of the load for which the lighting power can be adjusted,
and
are respectively m < th >1A 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:
wherein,
is m at2The power, P, of the load of each adjustable operating gear at the time t in the e gear1，P2，……，PjIs m at the m2The load of each adjustable working gear is at different gears D1，D2，......，DjThe power of (a) is determined,
and
is m at the m2The 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 a response model of a battery load is constructed according to energy use attribute information of the battery load, including;
constructing an energy consumption model of the battery load according to the energy consumption mode information of the battery load:
wherein,
representing the amount of power stored by the nth battery load by time t,
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,
is the charging power of the nth battery load, eta is the charging efficiency, at is the time interval,
indicating the amount of power stored by the nth battery load by time t-1,
the charging power for the nth battery load in different charging phases,
the time to start charging for the nth battery load,
the charging period of phase 1 for charging the nth battery load,
indicating the state of charge of the nth battery load during charging phase 1, and S1 indicating the charging phase 1 based on
And the number of sub-charging nodes divided by deltat,
the charging period of the charging phase r for the nth battery load,
the time frame for which charging is allowed for the nth battery load,
and
respectively, the minimum and maximum amounts of electricity, Δ h, that the nth battery load can storenThe 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:
wherein,
7. The operation control method of the light-storing flexible system according to claim 1, 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.
8. The operation control method of the 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.
9. 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 first construction module is specifically used for constructing a thermodynamic model of the temperature control load, a power model in a refrigeration mode and a power model in a heating model according to the energy consumption behavior information and the energy consumption mode information of the temperature control load; constructing an energy consumption model of the transferable load according to the energy consumption mode information of the transferable load; constructing an energy consumption model of the load with adjustable lighting power according to energy consumption mode information of the load with adjustable lighting power in the reducible load, and constructing an energy consumption model of the load with adjustable working gear according to energy consumption mode information of the load with adjustable working gear in the reducible load; constructing an energy consumption model of the battery load according to the energy consumption mode information of the battery load;
the second construction module is used for constructing an 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 and 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 the storage battery on the prediction day;
the control module 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 various flexible loads and the operation plan of the storage battery on the prediction day;
the second building block comprises:
a seventh constructing unit, configured to construct the optimization model:
x represents a set of decision variables,
and
respectively representing the charging power and the discharging power of the storage battery,
a temperature is set for the ith temperature controlled load,
for the time when the jth transferable load starts running,
for the mth one that can reduce the power of the load,
indicating the state of charge of the nth battery load,
for the purchase of electric power or the sale of electric power from the grid, f (X) an objective function representing said optimization model, f1(X) represents the user's operation and maintenance cost, f2(X) represents the amount of carbon dioxide emission, f3(X) — SSR, SSR represents power self-satisfaction rate, h (X) represents inequality constraint therein, g (X) represents equality constraint and power balance therein, wherein power balance is:
omega represents the space of decision making,
generating power, mu, for the distributed photovoltaic system at the predicted daybatIs a binary variable, with a charge of 1, a discharge of 0,
the total power usage for all non-compliant loads,
the input power of the ith temperature control load at the moment t, I is the total amount of the temperature control loads,
the power of the jth transferable load at time t, J the total amount of transferable loads, M the total amount of reducible loads,
is the charging power of the nth battery load, and N is the total amount of the battery loads.
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