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CN111224393A - Intelligent household electric energy scheduling optimization method and device and storage medium - Google Patents

Intelligent household electric energy scheduling optimization method and device and storage medium Download PDF

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CN111224393A
CN111224393A CN201811425960.7A CN201811425960A CN111224393A CN 111224393 A CN111224393 A CN 111224393A CN 201811425960 A CN201811425960 A CN 201811425960A CN 111224393 A CN111224393 A CN 111224393A
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electric energy
power
photovoltaic
storage battery
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王堃
许升
俞国新
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Qingdao Haier Smart Technology R&D Co Ltd
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract

The invention belongs to the field of electric energy management, and discloses an intelligent household electric energy scheduling optimization method, which comprises the following steps: optimizing the load electricity consumption of the smart home by adopting a photovoltaic electric energy scheduling optimization method, and determining the optimized load distribution condition; establishing an electric energy scheduling multi-objective optimization model with the participation of storage batteries, which takes the maximization of the electric energy scheduling profit of the storage batteries and the maximization of the photovoltaic consumption rate as objective functions; and solving the multi-objective optimization model participated by the storage battery by using a non-dominated sorting genetic algorithm based on an elite strategy. By adopting the technical scheme, the result after the photovoltaic electric energy scheduling optimization strategy is optimized is used as the object for optimizing the electric energy scheduling optimization strategy of the storage battery, the household load distribution is determined firstly, and then the charging and discharging actions of the storage battery are determined by adopting an optimization method, so that the storage battery is different from the storage battery fixing strategy, the energy storage characteristic of the storage battery can be fully utilized according to different requirements of users in the environment of real-time electricity price, the electricity utilization benefit of the users is increased, and the photovoltaic consumption rate is increased.

Description

Intelligent household electric energy scheduling optimization method and device and storage medium
Technical Field
The invention relates to the technical field of electric energy management, in particular to an intelligent household electric energy scheduling optimization method, an intelligent household electric energy scheduling optimization device and a storage medium.
Background
The household energy management system is an effective mode for household users to participate in demand response, and is also a concrete embodiment of technologies such as intelligent power utilization, distributed power generation and the like on household users. The household energy management system can assist a user in responding to time-of-use electricity prices and real-time electricity prices, provides a system optimization decision scheme, and coordinates and controls the operation of equipment in the system. In order for a home energy management system to effectively manage a home power load and optimize the operation thereof, a power utilization strategy of the home appliance load needs to be optimized.
The storage battery has the characteristic of energy storage, can store electric energy when the power grid is low in price, releases electric energy when the power grid is high in price, stores redundant electric energy when photovoltaic output is sufficient, releases electric energy when photovoltaic output is insufficient, stores redundant electric energy when redundant electric energy feed is not cost-effective on line, and releases electric energy when cost-effective. The characteristics of the storage battery determine that the storage battery can store electric energy when participating in electric energy scheduling and release the electric energy to obtain electric energy transfer benefit when having low price. Meanwhile, the storage battery energy storage characteristic has strong time sequence, and the storage battery can only carry out the actions of charging first and then discharging according to the time sequence, the characteristic increases the difficulty of the storage battery electric energy scheduling, and because of the characteristic, the scheduling of the storage battery generally adopts a fixed strategy of judging and arranging the storage battery charging and discharging actions according to the electric energy state in the system, namely when the photovoltaic power generation power is greater than the load power utilization power, the storage battery is charged preferentially rather than the feed internet access operation, and when the photovoltaic power generation power is less than the load power utilization power, the storage battery is preferentially used for supplying power.
Disclosure of Invention
The embodiment of the invention provides an intelligent household electric energy scheduling optimization method, which aims to solve the problem of power utilization strategy optimization of household appliance loads. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to a first aspect of the embodiment of the invention, an intelligent household electric energy scheduling optimization method is provided.
In some optional embodiments, the intelligent household electric energy scheduling optimization method includes:
optimizing the load electricity consumption of the smart home by adopting a photovoltaic electric energy scheduling optimization method, and determining the optimized load distribution condition;
establishing an electric energy scheduling multi-objective optimization model with the participation of storage batteries, which takes the maximization of the electric energy scheduling profit of the storage batteries and the maximization of the photovoltaic consumption rate as objective functions;
and solving the multi-objective optimization model participated by the storage battery by using a non-dominated sorting genetic algorithm based on an elite strategy.
In some alternative embodiments, the user electric energy profit may be expressed as:
Figure BDA0001881616520000021
in the formula (f)BattThe benefits of the storage battery participating in the electric energy scheduling are obtained;
Figure BDA0001881616520000022
the discharge power of the storage battery at the moment k;
Figure BDA0001881616520000023
compensating the electricity price for the discharge of the storage battery at the moment k; p is a radical ofkFor the electricity price of the power grid at the moment k,
Figure BDA0001881616520000024
for the charging power of the storage battery at the moment k,
Figure BDA0001881616520000025
charging the storage battery at the time k; n isBattThe total charge and discharge switching times of the storage battery in the electric energy scheduling period are obtained; fscostThe cost is reduced for one-time switching of the storage battery,
Figure BDA0001881616520000026
is composed of
Figure BDA0001881616520000027
FscostExpressed as:
Figure BDA0001881616520000028
in the formula, FBCost for battery replacement; cBIs the rated charge and discharge times, SOC, of the storage battery under the standard conditionmin、SOCmaxRespectively, the minimum and maximum states of charge of the battery.
In some optional embodiments, when the storage battery participates in the electric energy scheduling, the photovoltaic consumption rate may be expressed as:
Figure BDA0001881616520000031
Figure BDA0001881616520000032
in the formula,
Figure BDA0001881616520000033
for photovoltaic output electric energy when the storage battery participates in electric energy scheduling at the moment k,
Figure BDA0001881616520000034
for the load power at the moment k,
Figure BDA0001881616520000035
the photovoltaic output power consumed by the storage battery at the moment k,
Figure BDA0001881616520000036
is time kPower of photovoltaic generation; Δ t is the minimum time fraction; u shapePV,BattThe photovoltaic absorption rate.
In some alternative embodiments, the constraint is:
(1) power balance constraint
At the moment k, the active power balance of the supply and utilization has the following relationship:
Figure BDA0001881616520000037
in the formula,
Figure BDA0001881616520000038
for the grid output power at time k,
Figure BDA0001881616520000039
is the power of the photovoltaic power generation at the moment k,
Figure BDA00018816165200000310
for the load power at the moment k,
Figure BDA00018816165200000311
the battery power at time k;
(2) battery restraint
SOCmin<SOC<SOCmax
PBatt,ch(t)≤Pch,max
PBatt,dch(t)≤Pdch,max
SOCstart＝SOCend
In the formula Pch,maxCharging the storage battery with maximum power; pdch,maxDischarging the storage battery with maximum power;
SOCstartscheduling the state of charge at the beginning of the cycle for the storage battery; SOCendThe state of charge at the end of the battery scheduling cycle.
In some optional embodiments, the photovoltaic power scheduling optimization method includes:
establishing an electric energy scheduling multi-objective optimization model taking user electric energy profit maximization and photovoltaic consumption rate maximization as objective functions;
and solving the multi-objective optimization model by using a non-dominated sorting genetic algorithm based on an elite strategy.
In some optional embodiments, the user electric energy profit is expressed as:
Figure BDA0001881616520000041
wherein:
Figure BDA0001881616520000042
in the formula (f)all kThe electric energy profit at the moment k; f. ofallThe electric energy is gained;
Figure BDA0001881616520000043
the power grid output power at the moment k is obtained;
Figure BDA0001881616520000044
is the power of the photovoltaic power generation at the moment k,
Figure BDA0001881616520000045
government compensation for photovoltaic power generation (yuan/kw hour);
Figure BDA0001881616520000046
the total power consumption at the moment k is the non-transferable load; c represents a non-transferable class load set, C represents a non-transferable class load,
Figure BDA0001881616520000047
the total power consumption at the moment k is the transferable load;
Figure BDA00018816165200000410
the total power consumption of the air-conditioning load at the moment k; p is a radical ofkThe electricity price of the power grid at the moment k; Δ t is the minimum time period from the time k; t represents a negative of the user settingThe load working time, A represents the air conditioner load set,
Figure BDA0001881616520000049
is a photovoltaic feed unit price.
In some optional embodiments, the photovoltaic absorption rate is expressed as:
Figure BDA0001881616520000051
wherein,
Figure BDA0001881616520000052
in the formula,
Figure BDA0001881616520000053
representing the photovoltaic near-absorption capability at time k, UPVThe photovoltaic absorption rate is represented by the photovoltaic absorption rate,
Figure BDA0001881616520000054
the load power at time k.
In some alternative embodiments, the constraint:
(1) power balance constraint
Under the condition that the photovoltaic power generation system works in a grid connection mode, at the moment k, the active power balance of the household power supply and utilization is as follows:
Figure BDA0001881616520000055
(2) satisfaction constraint
The transferable class load turn-on time constraint is:
tmi,s≤ton≤tmx,s-Ts
in the formula, tmi,sUser-set lower limit, t, of applicable time interval of transferable class loadmx,s-TsRepresenting the applicable time interval upper limit of the transferable load set by the user; t is tonThe working starting time of the transferable load is;
(3) mutually exclusive constraint of buying and feeding
ηk+μk＝1。
According to a second aspect of the embodiments of the present invention, there is provided an intelligent household electric energy scheduling optimization apparatus, including:
the photovoltaic electric energy scheduling optimization module is used for optimizing the intelligent household load electricity utilization by adopting a photovoltaic electric energy scheduling optimization method;
the system comprises an objective function construction module, a storage battery and a photovoltaic consumption rate optimization module, wherein the objective function construction module is used for establishing an electric energy scheduling multi-objective optimization model with the participation of the storage battery, which takes the maximization of the electric energy scheduling profit of the storage battery and the maximization of the photovoltaic consumption rate as objective functions;
and the computing module is used for solving the multi-objective optimization model in which the storage battery participates by utilizing a non-dominated sorting genetic algorithm based on the elite strategy.
According to a third aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the method for optimizing smart home electric energy scheduling is implemented.
The embodiment of the invention has the beneficial effects that: the method is different from a storage battery fixing strategy, and realizes that the energy storage characteristics of the storage battery can be fully utilized according to different requirements of users in the environment of real-time electricity price, the electricity utilization benefit of the users is increased, and the photovoltaic consumption rate is increased.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart illustrating an implementation process of an intelligent household electric energy scheduling optimization method according to an exemplary embodiment.
Fig. 2 is a schematic flow chart illustrating an implementation of the photovoltaic power scheduling optimization method according to an exemplary embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments herein includes the full ambit of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like, herein are used solely to distinguish one element from another without requiring or implying any actual such relationship or order between such elements. In practice, a first element can also be referred to as a second element, and vice versa. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a structure, device or apparatus that comprises the element. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein, as used herein, are defined as orientations or positional relationships based on the orientation or positional relationship shown in the drawings, and are used for convenience in describing and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may include, for example, mechanical or electrical connections, communications between two elements, direct connections, and indirect connections via intermediary media, where the specific meaning of the terms is understood by those skilled in the art as appropriate.
Herein, the term "plurality" means two or more, unless otherwise specified.
Herein, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an associative relationship describing objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
Example 1
An intelligent household electric energy scheduling optimization method comprises the following steps:
optimizing the load electricity consumption of the smart home by adopting a photovoltaic electric energy scheduling optimization method, and determining the optimized load distribution condition;
establishing an electric energy scheduling multi-objective optimization model with the participation of storage batteries, which takes the maximization of the electric energy scheduling profit of the storage batteries and the maximization of the photovoltaic consumption rate as objective functions;
and solving the multi-objective optimization model participated by the storage battery by using a non-dominated sorting genetic algorithm based on an elite strategy.
The photovoltaic power generation system working mode adopted by a user is a grid-connected mode, the principle of electric energy scheduling is 'self-generation and self-utilization, residual electricity is used for surfing the Internet' under the mode, the photovoltaic feed power supply online electric energy and the load use condition are arranged in a load transfer mode based on the principle, but a large number of loads are still in a state of not utilizing photovoltaic output due to the arrangement condition and actual requirements of the user on the loads. And because the photovoltaic feed internet-surfing electricity price is fixed, and the real-time electricity price change interval is larger, in the load peak period that the real-time electricity price is higher than the feed internet-surfing electricity price, partial electric energy of feed internet surfing is transferred to the period, so that larger economic benefit can be generated, and meanwhile, the photovoltaic nearby consumption capacity can be improved.
The storage battery has the energy storage capacity, so the storage battery reconstructs the photovoltaic output and the load distribution condition of the storage battery on the basis of the result after the photovoltaic electric energy scheduling optimization, solves the problem of low photovoltaic consumption rate caused by the asynchronous time of the photovoltaic output and the household electricity consumption, and simultaneously improves the household electric energy yield through reasonable planning.
When the storage battery electric energy scheduling strategy is designed, the depreciation cost caused by frequent switching of the charge-discharge state of the storage battery is fully considered, the depreciation cost represents the cost of one-time switching of charge and discharge of the storage battery, so that the frequent unfavorable switching of the storage battery is limited, and meanwhile, the depreciation cost is used as the use cost of the storage battery, so that the benefit of deducting the cost of the electric energy scheduling of the storage battery is calculated.
The cost of one-time switching of the storage battery is FscostThen F isscostCan be expressed as:
Figure BDA0001881616520000081
in the formula FBCost for battery replacement; cBIs the rated charge and discharge times, SOC, of the storage battery under the standard conditionmin、SOCmaxRespectively, the minimum and maximum states of charge of the battery.
The benefits of the storage battery participating in the electric energy scheduling mainly comprise two parts, namely electric energy transfer benefits and government subsidy income which are respectively obtained for low storage and high generation. At present, energy storage subsidies mainly comprise investment capacity subsidies, energy storage discharge electricity price subsidies, financial preferential policies and the like, and because no energy storage subsidy policy exists at present in China, the energy storage discharge electricity price subsidies are adopted in the text with reference to an electric vehicle battery subsidy policy and a photovoltaic subsidy policy. When the energy storage discharge electricity price subsidy is adopted, the benefit is obtained by adopting a feeding and networking mode for avoiding the storage battery, and the subsidy is not given to the discharge of the storage battery feeding and networking.
The storage battery participates in the household electric energy scheduling and mainly has two functions, photovoltaic is consumed nearby and the household economic benefit is improved, so that the benefit of the storage battery participating in the electric energy scheduling is improved when the photovoltaic is consumed nearby, the advantage of real-time electricity price is fully utilized, reasonable planning is carried out on the electric energy scheduling of the storage battery, and the multi-objective optimization model is constructed at present:
(1) maximizing the electric energy dispatching benefit of the storage battery; (2) the photovoltaic absorption rate is maximized.
Considering that the benefit of the storage battery participating in the electric energy scheduling is maximum and the depreciation cost of the storage battery should be considered, the optimization goal of the electric energy benefit can be expressed as:
Figure BDA0001881616520000091
in the formula (f)BattThe benefits of the storage battery participating in the electric energy scheduling are obtained;
Figure BDA0001881616520000092
the discharge power of the storage battery at the moment k;
Figure BDA0001881616520000093
compensating the electricity price for the discharge of the storage battery at the moment k; p is a radical ofkFor the electricity price of the power grid at the moment k,
Figure BDA0001881616520000094
for the charging power of the storage battery at the moment k,
Figure BDA0001881616520000095
charging the storage battery at the time k; n isBattThe total charge and discharge switching times of the storage battery in the electric energy scheduling period are obtained;
Figure BDA0001881616520000096
is composed of
Figure BDA0001881616520000097
When the storage battery participates in electric energy scheduling, the photovoltaic consumption rate can be expressed as:
Figure BDA0001881616520000098
Figure BDA0001881616520000099
in the formula,
Figure BDA0001881616520000101
for photovoltaic output electric energy when the storage battery participates in electric energy scheduling at the moment k,
Figure BDA0001881616520000102
for the load power at the moment k,
Figure BDA0001881616520000103
the photovoltaic output power consumed by the storage battery at the moment k,
Figure BDA0001881616520000104
the power of photovoltaic power generation at the moment k; Δ t is the minimum time fraction; u shapePV,BattThe photovoltaic absorption rate.
The objective function of the multi-objective optimization of the battery power scheduling can be expressed as:
max{fBatt,UPV,Batt}
constraint conditions are as follows:
(1) power balance constraint
At the moment k, the active power balance of the supply and utilization has the following relationship:
Figure BDA0001881616520000105
in the formula,
Figure BDA0001881616520000106
for the grid output power at time k,
Figure BDA0001881616520000107
is the power of the photovoltaic power generation at the moment k,
Figure BDA0001881616520000108
for the load power at the moment k,
Figure BDA0001881616520000109
battery power at time k.
(2) Battery restraint
In order to ensure that the storage battery can be normally used, the charge state constraint and the maximum charge-discharge power constraint of the storage battery need to be met.
In several technical indexes of the storage battery, the state of charge is related to the maximum charging and discharging depth of the storage battery, and in order to reduce the influence of excessive charging and excessive discharging of the storage battery on the service life of the storage battery, prolong the working life of the storage battery, and avoid the occurrence of overshoot or over discharge of the storage battery, the SOC of the storage battery needs to be limited, and the state of charge of the storage battery is constrained by:
SOCmin<SOC<SOCmax
the maximum charge and discharge power constraints are:
PBatt,ch(t)≤Pch,max
PBatt,dch(t)≤Pdch,max
in the formula Pch,maxCharging the storage battery with maximum power; pdch,maxDischarging the maximum power for the storage battery.
The storage battery has the characteristic of energy storage, if the stored electric energy cannot be completely consumed in one scheduling period, the calculation of the photovoltaic consumption rate is influenced, and meanwhile, the result of electric energy scheduling optimization of the next scheduling period can also be influenced, so that in order to ensure the balance of the charging and discharging electric energy of the storage battery in one scheduling period, the state of charge balance constraint is set for the storage battery, the state of charge of the initial time and the state of charge of the end time of one scheduling period are equal, namely, the storage battery realizes load transfer in a complete sense in one scheduling period, namely:
SOCstart＝SOCend
in the formula SOCstartScheduling the state of charge at the beginning of the cycle for the storage battery; SOCendScheduling cycles for batteriesEnd state of charge.
The control decision variable of the system in each scheduling period in the optimization decision is the charge-discharge state x of the storage batteryi B(when 1 is taken, the charging action is shown, when 0 is taken, the non-action is shown, and when-1 is taken, the discharging action is shown), the storage battery is controlled by adopting the distributed power controller, so that the charging and discharging actions of the storage battery are changed.
Fig. 1 is a schematic diagram illustrating an implementation flow of the intelligent household electric energy scheduling optimization method. Firstly, acquiring the load distribution condition after optimization by adopting a photovoltaic electric energy scheduling optimization method, and then establishing an electric energy scheduling multi-objective optimization model with the storage battery, which takes the maximization of the electric energy scheduling profit of the storage battery and the maximization of the photovoltaic consumption rate as objective functions, as a main function; solving the multi-objective optimization model participated by the storage battery by using a non-dominated sorting genetic algorithm (NSGA-II algorithm) based on an elite strategy; and providing suggested options for the user by using the Pareto solution set.
Specifically, the process of solving the multi-objective optimization model involving the storage battery by using the non-dominated sorting genetic algorithm (NSGA-II algorithm) based on the elite strategy comprises the following steps:
according to a heuristic rule, 288 variables are generated in a time period of 5 minutes, and the state of charge of the first variable (i is 1) is set as the minimum state of charge SOC of the storage batteryminAnd judges whether it is in a charged state or a discharged state.
If the charging state is reached, the power supply power delta P is judgediWhether the power supply is positive (the power supply is positive, which indicates that the battery supplies power to the outside); if the power supply power is negative, the battery absorbs electric energy from the outside, and the maximum charging power P is usedch,maxCharging the storage battery, and starting to judge the next variable; if the power supply power is positive, judging whether the state of charge of the storage battery exceeds the maximum state of charge (SOC)max. If the maximum state of charge has been exceeded, then the next variable is judged; if the maximum state of charge is not exceeded, the storage battery is charged by the net photovoltaic and the charging power P of the storage battery is judgedBatt,chWhether it is greater than its supply power; if the charging power is less than the power supply power, the maximum charging function is selectedRate Pch,maxCharging the storage battery, and starting to judge the next variable; and if the charging power is greater than the power supply power, maintaining the balance of charging and discharging of the storage battery by referring to the power supply power, and starting to judge the next variable.
If the battery is in a discharging state, firstly, the positive and negative of the power supply of the storage battery are judged. If the variable is positive, directly judging the next variable; and if the charge state of the storage battery is negative, judging whether the charge state of the storage battery is larger than the minimum charge state of the storage battery. If the state of charge is smaller than the minimum state of charge, judging the next variable; if the state of charge is greater than the minimum state of charge, discharging the net load by the storage battery; and judging the discharge power P of the storage batteryBatt,dchIf the discharge power of the storage battery is larger than the negative value of the power supply power, maintaining the balance of the charge and discharge of the storage battery by referring to the power supply power, and starting to judge the next variable; if the discharge power of the storage battery is less than the negative value of the power supply power, the maximum discharge power P is useddch,maxThe battery is discharged and the next variable is determined.
And if the storage battery is not in a charging state or a discharging state, skipping the current variable and directly judging the next variable.
And the 288 variables are judged one by one according to the method, and after the judgment, the energy yield and the photovoltaic consumption rate of the storage battery after the storage battery participates in the electric energy scheduling are calculated.
And obtaining a Pareto solution set according to the method.
By adopting the technical scheme, an electric energy scheduling multi-objective optimization model taking the maximization of the electric energy scheduling profit of the storage battery and the maximization of the photovoltaic consumption rate as objective functions is established; and after the control decision variables and the constraint conditions are determined, solving the multi-objective optimization model by using a non-dominated sorting genetic algorithm based on an elite strategy to determine the planned arrangement of the charging and discharging actions of the storage battery. The energy storage characteristic of the storage battery can be fully utilized according to different requirements of users under the environment of real-time electricity price, the electricity utilization benefit of the users is increased, and the photovoltaic consumption rate is increased.
According to a second aspect of the embodiments of the present invention, there is provided an intelligent household electric energy scheduling optimization apparatus, including:
the photovoltaic electric energy scheduling optimization module is used for optimizing the intelligent household load electricity utilization by adopting a photovoltaic electric energy scheduling optimization method;
the system comprises an objective function construction module, a storage battery and a photovoltaic consumption rate optimization module, wherein the objective function construction module is used for establishing an electric energy scheduling multi-objective optimization model with the participation of the storage battery, which takes the maximization of the electric energy scheduling profit of the storage battery and the maximization of the photovoltaic consumption rate as objective functions;
and the computing module is used for solving the multi-objective optimization model in which the storage battery participates by utilizing a non-dominated sorting genetic algorithm based on the elite strategy.
The photovoltaic electric energy scheduling optimization module comprises:
the system comprises an objective function construction module, a target function optimization module and a photovoltaic consumption rate optimization module, wherein the objective function construction module is used for establishing an electric energy scheduling multi-objective optimization model taking user electric energy profit maximization and photovoltaic consumption rate maximization as objective functions;
and the calculation module is used for solving the multi-objective optimization model by using a non-dominated sorting genetic algorithm based on the elite strategy.
According to a third aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the foregoing smart home electric energy scheduling optimization method. The computer-readable storage medium includes a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic tape, an optical storage device, and the like.
Example 2
The photovoltaic electric energy scheduling optimization method comprises the following steps:
establishing an electric energy scheduling multi-objective optimization model taking user electric energy profit maximization and photovoltaic consumption rate maximization as objective functions;
and solving the multi-objective optimization model by using a non-dominated sorting genetic algorithm based on an elite strategy.
The basic research objective of photovoltaic power dispatching optimization is to maximize the household user power income, and the household user power income comprises three parts of photovoltaic power generation compensation income, feed income and load power consumption expense, and is represented as follows:
fall＝fPV,sub+fPV,s+fload
in the formula fallThe electric energy is gained; f. ofPV,subSubsidizing revenue for the photovoltaic power generation government; f. ofPV,sRevenue obtained for photovoltaic feed; f. ofloadThe electricity is purchased from the grid for the load.
According to the formula, the electric energy profit of the household users is composed of photovoltaic income and load expenditure, and in order to obtain the maximum electric energy profit of the users, a specific household power supply and utilization model needs to be constructed. According to the household electricity model, the formula can also be expressed as:
Figure BDA0001881616520000141
in the formula,
Figure BDA0001881616520000142
is the power of the photovoltaic power generation at the moment k,
Figure BDA0001881616520000143
government compensation for photovoltaic power generation (yuan/kw hour);
Figure BDA0001881616520000144
the power at the moment k of photovoltaic feed is obtained;
Figure BDA0001881616520000145
eta for feeding photovoltaic power at k timekThe feeding state at the moment k is 1, and the feeding is carried out on the power grid; mu.skThe electricity purchasing state at the moment k is 1, and the electricity purchasing from the power grid is indicated;
Figure BDA0001881616520000146
is k atPhotovoltaic power consumption equivalent power;
Figure BDA0001881616520000147
the total power consumption at the moment k is the non-transferable load;
Figure BDA0001881616520000148
the total power consumption at the moment k is the transferable load;
Figure BDA0001881616520000149
the total power consumption of the air-conditioning load at the moment k; p is a radical ofkThe electricity price of the power grid at the moment k; Δ t is the minimum time period from time k.
The loads in the system are divided into non-transferable loads and transferable loads. The non-transferable loads have no regulation capability, such as the load of a washing machine, the running characteristic of the load is not allowed to be interrupted, and the use condition of the load can be directly set by a user. Due to the important application value of the non-transferable load, the load does not need to be regulated and controlled, the load is predicted according to the living habits of users or set by the users, and the start-stop time of the load is known. The starting time of the transferable load can be moved in a larger range, and whether electric energy consumption is carried out or not is selected according to the electricity cost at different times. And the air-conditioning load can arrange electricity in a model prediction mode. The flexibility of load control provides the possibility of maximizing the user's electrical energy revenue.
And at the moment k, when the output power of the photovoltaic power generation is greater than the sum of the electric power used by the load in the running state, the surplus power can be fed, and when the output power of the photovoltaic power generation is less than or equal to the sum of the electric power used by the load in the running state, the surplus power is consumed nearby.
The supply power profit at time k can be expressed as:
Figure BDA0001881616520000151
in the formula (f)all kThe electric energy profit at the moment k;
Figure BDA0001881616520000152
the power grid output power at the moment k is obtained; t represents the load working time length set by the user, and A represents the air conditioner class load set.
The photovoltaic power generation should follow the principle of nearby consumption as much as possible, and the objective of photovoltaic power scheduling optimization is as follows:
(1) maximizing the electric energy yield of the user;
(2) photovoltaic near-absorption capacity is maximized.
The user's electric energy gain may be expressed as:
Figure BDA0001881616520000153
the photovoltaic near-extinction capability can be expressed in terms of a photovoltaic extinction ratio, which can be expressed as:
Figure BDA0001881616520000154
wherein,
Figure BDA0001881616520000155
the objective function of the photovoltaic electric energy scheduling multi-objective optimization can be expressed as:
max{fall,UPV}
constraint conditions are as follows:
(1) power balance constraint
Under the condition that the photovoltaic power generation system works in a grid connection mode, at the moment k, the active power balance of the household power supply and utilization is as follows:
Figure BDA0001881616520000161
wherein,
Figure BDA0001881616520000162
load power at time k;
(2) satisfaction constraint
The transferable class load turn-on time constraint is:
tmi≤ton≤tmx-Ts
in the formula, tmi,sUser-set lower limit, t, of applicable time interval of transferable class loadmx,s-TsRepresenting the applicable time interval upper limit of the transferable load set by the user; t is tonThe time of the start of the work for the transferable class load.
(3) Mutually exclusive constraint of buying and feeding
ηk+μk＝1
The interaction between the family and the electric energy of the power grid at the same time is unidirectional, namely, only one condition can occur at the same time when the family feeds electricity to the power grid and purchases electricity from the power grid, and the family cannot simultaneously feed electricity to the power grid and purchase electricity from the power grid.
The household energy management system controls the photovoltaic power generation system through the distributed power controller, and in each power dispatching cycle, the power flow direction of photovoltaic output power can be controlled by the distributed power controller, and the power is fed to the internet or used for load. In a scene that photovoltaic participates in management of a household energy management system, control variables of the system mainly comprise photovoltaic output flow direction state x in unit time interval in each scheduling cyclePV,s(t) (when 1 is taken, power feed is on-line, and when 0 is taken, power no-feed), xPV,u(t) (when 1 is taken, the consumption of electricity by the load is shown, and when 0 is taken, the consumption is not shown), and the on-off state xl of the household loadoad(t) (when 1 is taken, the operation is indicated, and when 0 is taken, the off is indicated), and the like.
As shown in fig. 2, a schematic flow chart of an implementation of the photovoltaic power scheduling optimization method is shown, where an electric power company obtains a real-time electricity price of the next day (an update cycle of the electricity price is one day), the power consumption of the next day of non-transferable load can be predicted through historical data of power consumption behaviors of users, weather forecast information such as temperature information of the next day is obtained through a meteorological department, and the photovoltaic power generation amount of the next day per hour is predicted according to the weather forecast information; the power consumption of the next day of non-transferable loads can be predicted through historical data of the power consumption behaviors of the users; and then the user selects and sets the operation parameters of the load in advance, such as: time range used the next day, duration of operation, shortest continuous running time of load (shortest continuous running time of load of non-transferable class equals to its duration of operation). The load scheduling adopts a mode of scheduling according to time periods, 24h in a day is divided into time periods k, and the load scheduling is placed in discrete time periods. In the formula
max{fall,UPV}
For the optimization goal, a non-dominated sorting genetic algorithm (NSGA-II algorithm) based on an elite strategy is adopted for optimization processing, and a Pareto solution set is used for providing suggestion options for a user.
By adopting the technical scheme, an electric energy scheduling multi-objective optimization model taking the maximization of the user electric energy profit and the maximization of the photovoltaic consumption rate as objective functions is established; the multi-objective optimization model is solved by using the non-dominated sorting genetic algorithm based on the elite strategy, so that the optimal electric energy scheduling strategy is obtained according to different requirements of users in the environment of real-time electricity price, and the electricity consumption cost of the users is reduced.
The present invention is not limited to the structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (17)

1. The intelligent household electric energy scheduling optimization method is characterized by comprising the following steps:
optimizing the load electricity consumption of the smart home by adopting a photovoltaic electric energy scheduling optimization method, and determining the optimized load distribution condition;
establishing an electric energy scheduling multi-objective optimization model with the participation of storage batteries, which takes the maximization of the electric energy scheduling profit of the storage batteries and the maximization of the photovoltaic consumption rate as objective functions;
and solving the multi-objective optimization model participated by the storage battery by using a non-dominated sorting genetic algorithm based on an elite strategy.
2. The intelligent household electric energy scheduling optimization method according to claim 1, wherein the electric energy scheduling profit of the storage battery is expressed as:
Figure FDA0001881616510000011
in the formula (f)BattThe benefits of the storage battery participating in the electric energy scheduling are obtained;
Figure FDA0001881616510000012
the discharge power of the storage battery at the moment k;
Figure FDA0001881616510000013
compensating the electricity price for the discharge of the storage battery at the moment k; p is a radical ofkFor the electricity price of the power grid at the moment k,
Figure FDA0001881616510000014
for the charging power of the storage battery at the moment k,
Figure FDA0001881616510000015
charging the storage battery at the time k; n isBattThe total charge and discharge switching times of the storage battery in the electric energy scheduling period are obtained; fscostThe cost is reduced for one-time switching of the storage battery,
Figure FDA0001881616510000016
is composed of
Figure FDA0001881616510000017
FscostExpressed as:
Figure FDA0001881616510000018
in the formula, FBCost for battery replacement; cBIs the rated charge and discharge times, SOC, of the storage battery under the standard conditionmin、SOCmaxRespectively, the minimum and maximum states of charge of the battery.
3. The intelligent household electric energy scheduling optimization method according to claim 1, characterized in that: when the storage battery participates in electric energy scheduling, the photovoltaic consumption rate can be expressed as:
Figure FDA0001881616510000021
Figure FDA0001881616510000022
in the formula,
Figure FDA0001881616510000023
for photovoltaic output electric energy when the storage battery participates in electric energy scheduling at the moment k,
Figure FDA0001881616510000024
for the load power at the moment k,
Figure FDA0001881616510000025
the photovoltaic output power consumed by the storage battery at the moment k,
Figure FDA0001881616510000026
the power of photovoltaic power generation at the moment k; Δ t is the minimum time fraction; u shapePV,BattThe photovoltaic absorption rate.
4. The intelligent household electric energy scheduling optimization method according to any one of claims 2 or 3, characterized in that: the constraint conditions are as follows:
(1) power balance constraint
At the moment k, the active power balance of the supply and utilization has the following relationship:
Figure FDA0001881616510000027
in the formula,
Figure FDA0001881616510000028
for the grid output power at time k,
Figure FDA0001881616510000029
is the power of the photovoltaic power generation at the moment k,
Figure FDA00018816165100000210
for the load power at the moment k,
Figure FDA00018816165100000211
the battery power at time k;
(2) battery restraint
SOCmin<SOC<SOCmax
PBatt,ch(t)≤Pch,max
PBatt,dch(t)≤Pdch,max
SOCstart＝SOCend
In the formula Pch,maxCharging the storage battery with maximum power; pdch,maxDischarging the storage battery with maximum power; SOCstartScheduling the state of charge at the beginning of the cycle for the storage battery; SOCendThe state of charge at the end of the battery scheduling cycle.
5. The intelligent household electric energy scheduling optimization method according to claim 1, characterized in that: the photovoltaic electric energy scheduling optimization method comprises the following steps:
establishing an electric energy scheduling multi-objective optimization model taking user electric energy profit maximization and photovoltaic consumption rate maximization as objective functions;
and solving the multi-objective optimization model by using a non-dominated sorting genetic algorithm based on an elite strategy.
6. The intelligent household electric energy scheduling optimization method according to claim 5, characterized in that: the user electric energy profit is expressed as:
Figure FDA0001881616510000031
wherein:
Figure FDA0001881616510000032
in the formula (f)all kThe electric energy profit at the moment k; f. ofallThe electric energy is gained;
Figure FDA0001881616510000033
the power grid output power at the moment k is obtained;
Figure FDA0001881616510000034
is the power of the photovoltaic power generation at the moment k,
Figure FDA0001881616510000035
government compensation for photovoltaic power generation (yuan/kw hour);
Figure FDA0001881616510000036
the total power consumption at the moment k is the non-transferable load; c represents a non-transferable class load set, C represents a non-transferable class load,
Figure FDA0001881616510000037
the total power consumption at the moment k is the transferable load;
Figure FDA00018816165100000310
the total power consumption of the air-conditioning load at the moment k; p is a radical ofkThe electricity price of the power grid at the moment k; Δ t is the minimum time period from the time k; t represents the load working time set by a user, and A represents an air conditioner load set;
Figure FDA0001881616510000039
is a photovoltaic feed unit price.
7. The intelligent household electric energy scheduling optimization method according to claim 5, characterized in that: the photovoltaic absorption rate is expressed as:
Figure FDA0001881616510000041
wherein,
Figure FDA0001881616510000042
in the formula,
Figure FDA0001881616510000043
representing the photovoltaic near-absorption capability at time k, UPVThe photovoltaic absorption rate is represented by the photovoltaic absorption rate,
Figure FDA0001881616510000044
the load power at time k.
8. The intelligent household electric energy scheduling optimization method according to claim 6 or 7, characterized in that:
constraint conditions are as follows:
(1) power balance constraint
Under the condition that the photovoltaic power generation system works in a grid connection mode, at the moment k, the active power balance of the household power supply and utilization is as follows:
Figure FDA0001881616510000045
(2) satisfaction constraint
The transferable class load turn-on time constraint is:
tmi,s≤ton≤tmx,s-Ts
in the formula, tmi,sUser-set lower limit, t, of applicable time interval of transferable class loadmx,s-TsRepresenting the applicable time interval upper limit of the transferable load set by the user; t is tonThe working starting time of the transferable load is;
(3) mutually exclusive constraint of buying and feeding
ηk+μk＝1。
9. The utility model provides an intelligence house electric energy scheduling optimizing apparatus which characterized in that includes:
the photovoltaic electric energy scheduling optimization module is used for optimizing the intelligent household load electricity utilization by adopting a photovoltaic electric energy scheduling optimization method;
the system comprises an objective function construction module, a storage battery and a photovoltaic consumption rate optimization module, wherein the objective function construction module is used for establishing an electric energy scheduling multi-objective optimization model with the participation of the storage battery, which takes the maximization of the electric energy scheduling profit of the storage battery and the maximization of the photovoltaic consumption rate as objective functions;
and the computing module is used for solving the multi-objective optimization model in which the storage battery participates by utilizing a non-dominated sorting genetic algorithm based on the elite strategy.
10. The intelligent household electric energy scheduling optimization apparatus according to claim 9, wherein the battery electric energy scheduling benefit is expressed as:
Figure FDA0001881616510000051
in the formula (f)BattThe benefits of the storage battery participating in the electric energy scheduling are obtained;
Figure FDA0001881616510000052
the discharge power of the storage battery at the moment k;
Figure FDA0001881616510000053
compensating the electricity price for the discharge of the storage battery at the moment k; p is a radical ofkFor the electricity price of the power grid at the moment k,
Figure FDA0001881616510000054
for the charging power of the storage battery at the moment k,
Figure FDA0001881616510000055
charging the storage battery at the time k; n isBattFor storage batteries in electric energy scheduling periodTotal charge-discharge switching times; fscostThe cost is reduced for one-time switching of the storage battery,
Figure FDA0001881616510000056
is composed of
Figure FDA0001881616510000057
FscostExpressed as:
Figure FDA0001881616510000058
in the formula, FBCost for battery replacement; cBIs the rated charge and discharge times, SOC, of the storage battery under the standard conditionmin、SOCmaxRespectively, the minimum and maximum states of charge of the battery.
11. The intelligent household electric energy scheduling optimization device according to claim 9, wherein when the storage battery participates in electric energy scheduling, the photovoltaic consumption rate can be expressed as:
Figure FDA0001881616510000061
Figure FDA0001881616510000062
in the formula,
Figure FDA0001881616510000063
for photovoltaic output electric energy when the storage battery participates in electric energy scheduling at the moment k,
Figure FDA0001881616510000064
for the load power at the moment k,
Figure FDA0001881616510000065
photovoltaic output absorbed for storage battery at time kThe power of the electric motor is controlled by the power controller,
Figure FDA0001881616510000066
the power of photovoltaic power generation at the moment k; Δ t is the minimum time fraction; u shapePV,BattThe photovoltaic absorption rate.
12. The intelligent household electric energy scheduling optimization device according to claim 10 or 11, characterized in that: the constraint conditions are as follows:
(1) power balance constraint
At the moment k, the active power balance of the supply and utilization has the following relationship:
Figure FDA0001881616510000067
in the formula,
Figure FDA0001881616510000068
for the grid output power at time k,
Figure FDA0001881616510000069
is the power of the photovoltaic power generation at the moment k,
Figure FDA00018816165100000610
for the load power at the moment k,
Figure FDA00018816165100000611
the battery power at time k;
(2) battery restraint
SOCmin<SOC<SOCmax
PBatt,ch(t)≤Pch,max
PBatt,dch(t)≤Pdch,max
SOCstart＝SOCend
In the formula Pch,maxCharging the storage battery with maximum power; pdch,maxDischarging the storage battery with maximum power; SOCstartTo storeThe state of charge at the beginning of a battery scheduling cycle; SOCendThe state of charge at the end of the battery scheduling cycle.
13. The intelligent household electric energy scheduling optimization device according to claim 9, wherein the photovoltaic electric energy scheduling optimization module comprises:
the system comprises an objective function construction module, a target function optimization module and a photovoltaic consumption rate optimization module, wherein the objective function construction module is used for establishing an electric energy scheduling multi-objective optimization model taking user electric energy profit maximization and photovoltaic consumption rate maximization as objective functions;
and the calculation module is used for solving the multi-objective optimization model by using a non-dominated sorting genetic algorithm based on the elite strategy.
14. The intelligent home power scheduling optimization apparatus of claim 13, wherein the user power revenue is expressed as:
Figure FDA0001881616510000071
wherein:
Figure FDA0001881616510000072
in the formula (f)all kThe electric energy profit at the moment k; f. ofallThe electric energy is gained;
Figure FDA0001881616510000073
the power grid output power at the moment k is obtained;
Figure FDA0001881616510000074
is the power of the photovoltaic power generation at the moment k,
Figure FDA0001881616510000075
government compensation for photovoltaic power generation (yuan/kw hour);
Figure FDA0001881616510000076
the total power consumption at the moment k is the non-transferable load; c represents a non-transferable class load set, C represents a non-transferable class load,
Figure FDA0001881616510000077
the total power consumption at the moment k is the transferable load;
Figure FDA00018816165100000710
the total power consumption of the air-conditioning load at the moment k; p is a radical ofkThe electricity price of the power grid at the moment k; Δ t is the minimum time period from the time k; t represents the load working time length set by the user, A represents the air conditioner class load set,
Figure FDA0001881616510000079
is a photovoltaic feed unit price.
15. The smart home electrical energy scheduling optimization apparatus of claim 13, wherein the photovoltaic consumption rate is expressed as:
Figure FDA0001881616510000081
wherein,
Figure FDA0001881616510000082
in the formula,
Figure FDA0001881616510000083
representing the photovoltaic near-absorption capability at time k, UPVThe photovoltaic absorption rate is represented by the photovoltaic absorption rate,
Figure FDA0001881616510000084
the load power at time k.
16. The intelligent household electric energy scheduling optimization device according to claim 14 or 15, wherein the constraint condition is:
(1) power balance constraint
Under the condition that the photovoltaic power generation system works in a grid connection mode, at the moment k, the active power balance of the household power supply and utilization is as follows:
Figure FDA0001881616510000085
(2) satisfaction constraint
The transferable class load turn-on time constraint is:
tmi,s≤ton≤tmx,s-Ts
in the formula, tmi,sUser-set lower limit, t, of applicable time interval of transferable class loadmx,s-TsRepresenting the applicable time interval upper limit of the transferable load set by the user; t is tonThe working starting time of the transferable load is;
(3) mutually exclusive constraint of buying and feeding
ηk+μk＝1。
17. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the smart home electrical energy scheduling optimization method according to any one of claims 1 to 9.
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