CN108182487B - Family energy data optimization method based on particle swarm optimization and Bendel decomposition - Google Patents

Family energy data optimization method based on particle swarm optimization and Bendel decomposition Download PDF

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CN108182487B
CN108182487B CN201711309807.3A CN201711309807A CN108182487B CN 108182487 B CN108182487 B CN 108182487B CN 201711309807 A CN201711309807 A CN 201711309807A CN 108182487 B CN108182487 B CN 108182487B
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黄言态
康敏
梁博淼
郑玉珍
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Abstract

The invention discloses a family energy data optimization method based on particle swarm optimization and Bendel decomposition. Establishing an optimized mathematical model of the household energy management system, wherein the optimized mathematical model takes the minimum total daily electricity expense in the whole household energy management system as an optimized target, establishes an optimized target function and simultaneously establishes a constraint condition of the optimized target function; inputting parameters required by an optimized mathematical model, solving the optimized mathematical model by adopting a method combining particle swarm optimization and Bendel decomposition to obtain optimal variable parameters, optimally controlling equipment in the household energy management system according to the optimal variable parameters, and obtaining the minimum electricity expense. The method can effectively solve the mathematical model and obtain a better solution.

Description

Family energy data optimization method based on particle swarm optimization and Bendel decomposition
Technical Field
The invention relates to a family energy data optimization method based on Particle Swarm Optimization (PSO) and Bender decomposition (bender decomposition), in particular to an improved particle swarm optimization method and an improved Bender decomposition method, and belongs to the field of family energy management.
Background
In recent decades, energy crisis and environmental pollution have drawn great attention to energy utilization efficiency and energy conservation. Currently, the research is mainly focused on the industrial field, which has a large energy demand. However, in recent years, with the development of economy, the demand for electric power in the home field has also rapidly increased. Therefore, research on the optimization of household energy is an important task for the development of smart grids.
In the face of the increasing energy demand of the residents, the prior art lacks an intelligent household energy data optimization method capable of realizing optimal household energy distribution quickly.
Disclosure of Invention
With the development of new energy, various small photovoltaic cells are applied to residents, an accurate mathematical model needs to be established for realizing optimization of household energy, however, the complexity and the accuracy of a household energy optimization model are in direct proportion, and optimal household energy distribution is difficult to obtain.
In order to solve the optimization of family energy, the invention establishes a mixed integer nonlinear optimization model, and has the characteristic of difficult solution. Although the solving of the model is a difficult problem, in order to solve the model, the invention provides a family energy data optimization method based on particle swarm optimization and Bendel decomposition by combining ideas of two algorithms of particle swarm evolutionary computation and Bendel decomposition, a principle mixed integer nonlinear optimization problem is decomposed into a mixed integer linear and nonlinear optimization problem by utilizing the Bendel decomposition method, the mixed integer linear and nonlinear optimization problem is solved by utilizing evolutionary computation, and a better solution can be quickly obtained by the method. By utilizing the invention, a user can feed back redundant electric energy to the power grid.
The technical scheme adopted by the invention is as follows:
1) establishing an optimized mathematical model of the household energy management system, wherein the optimized mathematical model takes the minimum total daily electricity expense in the whole household energy management system as an optimized target and establishes an optimized target function, and simultaneously establishes constraint conditions of the optimized target function, wherein the constraint conditions comprise basic electrical appliance constraints, and the basic electrical appliance constraints comprise electric energy supply and demand balance constraints, heat energy supply and demand balance constraints, fuel cell constraints, storage battery constraints, air conditioning equipment constraints and switch control equipment constraints;
2) inputting parameters required by an optimized mathematical model, solving the optimized mathematical model by adopting a method combining particle swarm optimization and Bendel decomposition to obtain optimal variable parameters, optimally controlling a fuel cell, a storage battery, air conditioning equipment and development control equipment in a household energy management system according to the optimal variable parameters, and obtaining the minimum electricity expense.
The household energy management system comprises a fuel cell, a storage battery, air conditioning equipment and switch control equipment, wherein the air conditioning equipment and the switch control equipment are connected to a power grid mainly composed of the fuel cell, the storage battery, a main grid and solar photovoltaic, the fuel cell is also connected to heat energy equipment, the fuel cell provides heat energy for the heat energy equipment, natural gas is respectively connected with the fuel cell and the heat energy equipment, the natural gas provides natural gas energy for the fuel cell, so that the natural gas energy in the fuel cell is converted into heat energy and electric energy, and the natural gas provides the natural gas energy for the heat energy equipment so that the heat energy equipment converts the natural gas energy into self-required heat energy.
The household energy management system also comprises basic electric energy equipment, a fuel cell, a storage battery, a main network and a solar photovoltaic jointly supply power for the basic electric energy equipment, the air conditioning equipment and the switch control equipment, and the part with insufficient power supply is supplemented by the main network.
The expression of the optimization objective function is as follows:
Figure GDA0003210183750000021
Figure GDA0003210183750000022
in the formula, f (p)eFC,ptm,pbatt,pdefe) Indicating the electricity charge per day, peFCRepresenting the electrical output power, p, of the fuel celltmRepresenting the electricity consumption of the air-conditioning apparatus, pbattRepresenting the charge and discharge capacity of the battery, pdefeRepresenting the amount of power used by the switch control device; t isgrid(h) Represents the electricity price at time h, pgrid(h) The power exchange quantity between the moment h and the main network is represented, if the quantity is positive, the user purchases power from the power grid, and the negative number represents the user and returns to the power grid; t isgas(h) Indicating the price of natural gas, p, at time hgas(h) Representing the direct heat energy supply of the natural gas at the moment h; peFC(h) Representing the electrical output power, p, of the fuel cell at time hhFCRepresenting the thermal output of the fuel cell at time h, ηFC(h) Represents the efficiency of the fuel cell at time h, γFC(h) Representing the fuel cell electrical energy and thermal energy ratio at time h.
The optimized mathematical model is a mixed integer nonlinear programming mathematical model, and the optimized objective function takes one day, namely 24 hours, as a scheduling period.
The basic electrical constraints are as follows:
A. the electric energy supply and demand balance constraint is as follows:
pgrid(h)＝peFC(h)+pms(h)+ptm(h)+pdefe(h)+pbatt(h)-ppv(h)
Figure GDA0003210183750000031
in the formula, pgrid(h) Representing the amount of interaction power, p, between the moment h and the main networkeFC(h) Representing the electrical output power, p, of the fuel cell at time hms(h) Representing the basic energy demand in the h-th time energy management system, i.e. the power consumption of the electrical appliances cannot be scheduled, ptm(h) Representing the electricity consumption of the air-conditioning unit at the h-th moment, pdefe(h) Representing the power consumption of the switching control unit at moment h, pbatt(h) Representing the charge and discharge capacity of the battery at time h, ppv(h) Representing the power generation capacity of the solar photovoltaic at the h moment,
Figure GDA0003210183750000032
respectively representing the minimum electric quantity and the maximum electric quantity interacted with the power grid;
B. the heat energy supply and demand balance constraint is as follows:
pgas(h)+phFC(h)＝ph(h)
in the formula, pgas(h) Representing the direct heat energy supply of the natural gas at time h, phFC(h) Represents the amount of heat energy supplied to the fuel cell at time h, ph(h) Representing the heat energy demand of the household energy management system at the moment h;
the above-mentioned direct heat energy supply p of natural gasgas(h) The following natural gas direct heat supply energy constraints are also met:
Figure GDA0003210183750000033
in the formula (I), the compound is shown in the specification,
Figure GDA0003210183750000034
represents the maximum direct supply of natural gas;
C. the fuel cell constraints are:
peFC(h)-peFC(h-1)<peFC,U
peFC(h-1)-peFC(h)<peFC,D
Figure GDA0003210183750000035
in the formula, peFC(h) And peFC(h-1) represents the amount of electric power supplied to the fuel cell at the time h and h-1, respectively, and peFC,URepresents the upper limit value of the fuel cell power, peFC,DRepresents a lower limit value of the electric power of the fuel cell,
Figure GDA0003210183750000036
and
Figure GDA0003210183750000037
respectively representing the minimum power supply electric energy and the maximum power supply electric energy of the fuel cell;
D. the battery constraints are:
Figure GDA0003210183750000038
Figure GDA0003210183750000039
Figure GDA00032101837500000310
Figure GDA0003210183750000041
SOCmin≤SOC(h)≤SOCmax
in the formula:
Figure GDA0003210183750000042
represents the charge amount of the battery at the h-th time,
Figure GDA0003210183750000043
represents the discharge amount of the battery at the h-th time,
Figure GDA0003210183750000044
represents the maximum amount of discharge of the battery,
Figure GDA0003210183750000045
represents the maximum charge of the battery, ηchRepresenting the efficiency of charging the battery, etadchRepresenting the efficiency of the discharge of the accumulator, pbatt(h) Represents the battery capacity, EbattRepresenting the total capacity of the battery, SOC (h) representing the state of charge of the battery at time h, SOCminRepresenting the minimum charge ratio, SOC, of the batterymaxRepresenting the maximum charge ratio of the battery;
E. the air conditioning equipment constraints are as follows:
Figure GDA0003210183750000046
Figure GDA00032101837500000415
Figure GDA0003210183750000047
in the formula, Tin(h+1),Tin(h) Respectively representing the temperature (DEG C) of indoor air at h and h +1, wherein delta represents a time interval, the specific implementation time interval delta is one hour, RC represents a thermal resistance parameter, and RC is 9.45; p is a radical oftm(h) Represents the thermal power at hout(h) Which represents the outdoor temperature, is the temperature of the room,
Figure GDA0003210183750000048
representing the lowest and highest temperatures in the room,
Figure GDA0003210183750000049
represents the maximum thermal power;
F. the switch control device is constrained as follows:
Figure GDA00032101837500000410
Figure GDA00032101837500000411
Figure GDA00032101837500000412
in the formula: deltaa,hIndicating the operating state of the switching control device a at the moment h, deltaa,τRepresenting the operating state of the load a at time τ, αa,βaIndicating the operating area of the switch control device a,
Figure GDA00032101837500000413
indicating that the switch control device a is currently h0Working state at the moment HaIndicating the length of work that the switch control device a needs to perform,
Figure GDA00032101837500000414
representing the working state before the h moment; h is0Represents the current time, h represents h0Before time, τ represents h0At a later time.
In the optimization objective function, the efficiency eta of the fuel cell at the h momentFC(h) And h time fuel cell electric energy and heat energy ratio gammaFC(h) Respectively adopting the following formulas to calculate:
Figure GDA0003210183750000051
Figure GDA0003210183750000052
in the formula, plr (h) represents the power load factor of the fuel cell at the time h, that is, the ratio of the output electric power to the maximum power.
The step 2) is specifically as follows:
2.1) decomposing the optimized mathematical model into a main function (MP) and a sub-function (SP), and then solving a final solution through mutual iterative operation of the two functions, wherein the final solution is specifically as follows:
subfunction (SP):
Figure GDA0003210183750000053
constraint of the Subfunction (SP):
Figure GDA0003210183750000054
peFC(h)-peFC(h-1)<peFC,U+w3
peFC(h-1)-peFC(h)<peFC,D+w4
SOCmin-w5≤SOC(h)≤SOCmax+w6
Figure GDA0003210183750000055
wherein U represents the target value of the sub-function,
Figure GDA0003210183750000056
representing the power consumption p of the switching control unitdefeIs determined from the master function, w1,w2,w3,w4,w5,w6,w7,w8Represents the first, second, …, eighth relaxation factor, pgridRepresenting the amount of interaction power between the main network, pmsRepresenting the basic energy demand, p, in a domestic energy management systempvRepresenting the generated energy of solar photovoltaic, SOC representing the electric quantity state of the storage battery, TinRepresents the temperature of the indoor air;
master function (MP):
Figure GDA0003210183750000057
Figure GDA0003210183750000058
constraint of the master function (MP):
Figure GDA0003210183750000061
Figure GDA0003210183750000062
Figure GDA0003210183750000063
wherein z represents a target value of the main function,
Figure GDA0003210183750000064
respectively representing the electrical output power p of the fuel celleFCPower consumption p of air conditionertmCharge/discharge amount p of secondary batterybattIs determined by a subfunction of1，μ2Respectively representing the first and second Lagrange multipliers, delta, of each particlea,tIndicating the operating state of the switching control device a at time t, deltaa,t0Indicating that the switching control device a is at the present time t0Working state of the moment;
2.2) solving by adopting the following process:
2.2.1) random Generation of NpopA binary particle comprising three switch control devices p1_defe,p2_defe,p3_defeThe state of the switch, which changes in units of one hour within 24 hours, is used as the power consumption p of the switch control devicedefeValue, 72 values per binary particle, expressed as
Figure GDA0003210183750000065
Indicating the switch operating state of the first switch control device in the ith particle every hour 24 hours a day,
Figure GDA0003210183750000066
indicating the switch operating state of the second switch control device in the ith particle every hour 24 hours a day,
Figure GDA0003210183750000067
represents the switch working state of the third switch control device in the ith particle in each hour in 24 hours a day, i epsilon {1.. Npop}；
2.2.2)According to N in 2.2.1)popP of each particledefeValue of pdefeAssigned values to the Subfunctions (SP)
Figure GDA0003210183750000068
Calculating NpopThe solution of the sub-function (SP) of the individual particles is assigned to the set value in the main function (MP)
Figure GDA0003210183750000069
Expressed as:
Figure GDA00032101837500000610
simultaneously acquiring a first Lagrange multiplier mu and a second Lagrange multiplier mu corresponding to each particle1，μ2；
2.2.3) set values assigned according to 2.2.2)
Figure GDA00032101837500000611
The set value is compared with
Figure GDA00032101837500000612
Carry in the master function (MP), calculate NpopTarget value Z of a master function (MP) of an individual particlei，i∈NpopAnd a solution, which is to assign the solution of each particle to the set value in the Subfunction (SP)
Figure GDA00032101837500000613
Expressed as:
Figure GDA00032101837500000614
and according to the target value ZiUpdating the particle swarm;
in specific implementation, the updating process is a traditional particle swarm updating mode, and comprises the following steps:
vi(h+1)＝ωvi(h)+c1*rand*(pibest-xi(h))+c2*rand*(pgbest-xi(h))
xi(h+1)＝xi(h)+vi(h+1)
wherein v isi(h+1)、vi(h) Denotes the velocity, x, of the particle at the time h +1 and h, respectivelyi(h+1)、xi(h) Denotes the value of the particle at the time h +1 and h, respectively, omega being the inertia factor, c1And c2The first and the second weight values are the first and the second weight values,
Figure GDA0003210183750000071
represents the optimal particle of the particle i, represents the optimal particle which appears in the particle i in the calculation process, and pgbest denotes the optimal particle among all particles, and rand denotes random generation of [0,1 ]]A number in between;
2.2.4) repeating the steps 2.2.2) to 2.2.3) for iterative calculation until the change of the optimal value is less than 0.01, ending the iteration to finally obtain ZiThe solution of the smallest particle is the optimal variable parameter. I.e. taking the optimal solution as ZiAs the electrical energy output power p of the fuel cell in the optimal variable parametereFCPower consumption p of air conditionertmCharge/discharge amount p of secondary batterybattAnd the amount of electricity p used for the switch control apparatusdefe。
The invention divides household appliances into two types, one is controllable load and the other is uncontrollable load. For household appliances with uncontrolled load, the load cannot be adjusted. In the present invention, however, the devices involved in the optimization do not comprise an uncontrollable load, such as the corresponding basic household appliance. The switch control type is considered as a switch control type for general equipment, and only the on and off of the equipment can be controlled.
The storage battery can realize charging and discharging under different conditions in the optimization of household energy, for example, the battery can be discharged in the peak power utilization period, and the battery can be charged in the valley power utilization period, so that the peak power utilization period is avoided, and the aim of reducing the economic expenditure is finally realized.
The main purpose of the air conditioning equipment control of the invention is to control the indoor temperature so as to make the temperature comfortable for users.
The fuel cell of the present invention is a hybrid thermoelectric device through which both thermal and electrical energy can be provided.
The main network of the invention refers to a power supply network of a power supply plant, and the main network can purchase electric quantity through the power network or avoid power consumption peaks according to the power rates of the power network in different periods so as to reduce expenses.
Compared with the prior art, the invention has the following advantages:
the method converts the family energy optimization problem into a more complex and more accurate mixed integer nonlinear optimization problem, combines a Bendel decomposition method and a particle swarm evolutionary computation method on a solving method, decomposes a principle mixed integer nonlinear optimization problem into a mixed integer linear and nonlinear optimization problem by using the Bendel decomposition method, and solves the problem by using evolutionary computation.
The invention collects the current working situation of electrical appliances in the family and the consumption demand situation of users, realizes the optimal energy distribution of the family, realizes the purposes of saving energy and reducing cost, and is beneficial to the safe operation of a power grid.
The method can accelerate the convergence speed of the algorithm, quickly obtain a better solution and quickly realize household energy distribution.
Drawings
FIG. 1 is a block diagram of a home energy management system;
fig. 2 is a flow chart of the family energy optimization solution of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The embodiment and the implementation process according to the invention are as follows:
1) establishing an optimized mathematical model of the household energy management system, wherein the optimized mathematical model takes the minimum total daily electricity expense in the whole household energy management system as an optimized target and establishes an optimized target function, and simultaneously establishes constraint conditions of the optimized target function, wherein the constraint conditions comprise basic electrical appliance constraints, and the basic electrical appliance constraints comprise electric energy supply and demand balance constraints, heat energy supply and demand balance constraints, fuel cell constraints, storage battery constraints, air conditioning equipment constraints and switch control equipment constraints;
the household energy management system comprises a fuel cell, a storage battery, air conditioning equipment and switch control equipment, wherein the air conditioning equipment and the switch control equipment are connected to a power grid mainly composed of the fuel cell, the storage battery, a main grid and solar photovoltaic, the fuel cell is also connected to heat energy equipment, the fuel cell provides heat energy for the heat energy equipment, natural gas is respectively connected with the fuel cell and the heat energy equipment, the natural gas provides natural gas energy for the fuel cell, so that the natural gas energy in the fuel cell is converted into heat energy and electric energy, and the natural gas provides the natural gas energy for the heat energy equipment so that the heat energy equipment converts the natural gas energy into self-required heat energy.
The household energy management system also comprises basic electric energy equipment, a fuel cell, a storage battery, a main network and a solar photovoltaic jointly supply power for the basic electric energy equipment, the air conditioning equipment and the switch control equipment, and the part with insufficient power supply is supplemented by the main network.
2) Inputting parameters required by an optimized mathematical model, solving the optimized mathematical model by adopting an improved Bendel decomposition method to obtain optimal variable parameters, optimally controlling a fuel cell, a storage battery, air conditioning equipment and switch control equipment in a household energy management system according to the optimal variable parameters, and obtaining the minimum electricity expense.
And 2.1) decomposing the optimized mathematical model into a main function (MP) and a sub-function (SP), and then performing mutual iterative operation on the two functions to obtain a final solution.
2.2) solving by adopting the following process:
2.2.1) the embodiment initializes the parameters first, and the parameters are set as: n is a radical ofpop＝200，
Figure GDA0003210183750000081
ppv(h) The predicted value of a certain day at random is selected in the example to be [0,2KW ]]Taken in between, SOCmin＝0.3，SOCmax＝0.9，Ebatt＝6.86KW，ηch＝0.9,ηdch＝0.9，
Figure GDA0003210183750000082
peFC,U＝1.5，peFC,D＝1.5，
Figure GDA0003210183750000083
Figure GDA0003210183750000091
Tout(h) The data of a certain day is randomly selected,
Figure GDA0003210183750000092
Na＝3，αa,βais randomly generating a value of [1,24 ]]Internal random generation of PaThe size is [1KW,3KW ]]Is randomly generated, pms(h) Has a value of [0,2 ]]Between the analog power consumption peak randomly generated, ph(h) Randomly generating [0,2 ]]Number between, pgas(h)＝2。
Then randomly generate particles:
2.2.2) p according to 2.2.1)defeValue of pdefeAssigned values to the Subfunctions (SP)
Figure GDA0003210183750000093
Calculating NpopThe solution of the particles is given to the set value in the master function (MP)
Figure GDA0003210183750000094
2.2.3) set values assigned according to 2.2.2)
Figure GDA0003210183750000095
The set value is compared with
Figure GDA0003210183750000096
Carry in the master function (MP), calculate NpopTarget value Z of a master function (MP) of an individual particlei，i∈NpopAnd a solution, which is to assign the solution of each particle to the set value in the Subfunction (SP)
Figure GDA0003210183750000097
And updating the particle swarm, wherein parameters in the updated particles are as follows: ω 0.73, c1＝2，c2＝2；
2.2.4) repeating the steps 2.2.2) to 2.2.3) for iterative calculation until the change of the optimal value is less than 0.01, ending the iteration to finally obtain ZiThe solution of the smallest particle is the optimal variable parameter.
The resulting situation for this example is: the performance of the algorithm was compared with the commercial optimization software tool, knitro algorithm, and the experimental results are as follows:
optimizing software Operation result
knitro 44.89RMB
The algorithm 46.26RMB
From the result, the result of the algorithm is similar to that of commercial optimization software Knitro, and the mixed integer nonlinear optimization model can be well solved.

Claims (2)

1. A family energy data optimization method based on particle swarm optimization and Bendel decomposition is characterized in that PSO represents a particle swarm optimization algorithm and comprises the following steps:
1) establishing an optimized mathematical model of the household energy management system, wherein the optimized mathematical model takes the minimum total daily electricity expense in the whole household energy management system as an optimized target and establishes an optimized target function, and simultaneously establishes constraint conditions of the optimized target function, wherein the constraint conditions comprise basic electrical appliance constraints, and the basic electrical appliance constraints comprise electric energy supply and demand balance constraints, heat energy supply and demand balance constraints, fuel cell constraints, storage battery constraints, air conditioning equipment constraints and switch control equipment constraints;
2) inputting parameters required by an optimized mathematical model, solving the optimized mathematical model by adopting a method combining particle swarm optimization and Bendel decomposition to obtain optimal variable parameters, optimally controlling a fuel cell, a storage battery, air conditioning equipment and development control equipment in a household energy management system according to the optimal variable parameters, and obtaining the minimum electricity expense;
the household energy management system comprises a fuel cell, a storage battery, air conditioning equipment and switch control equipment, wherein the air conditioning equipment and the switch control equipment are connected to a power grid mainly composed of the fuel cell, the storage battery, a main grid and solar photovoltaic, the fuel cell is also connected to heat energy equipment, and natural gas is respectively connected with the fuel cell and the heat energy equipment;
the expression of the optimization objective function is as follows:
Figure FDA0003210183740000011
Figure FDA0003210183740000012
in the formula, f (p)eFC,ptm,pbatt,pdefe) Indicating the electricity charge per day, peFCRepresenting the electrical output power, p, of the fuel celltmRepresenting the electricity consumption of the air-conditioning apparatus, pbattRepresenting the charge and discharge capacity of the battery, pdefeRepresenting switch control devicesElectricity consumption; t isgrid(h) Represents the electricity price at time h, pgrid(h) Representing the power exchange amount between the h moment and the main network; t isgas(h) Indicating the price of natural gas, p, at time hgas(h) Representing the direct heat energy supply of the natural gas at the moment h; peFC(h) Representing the electrical output power, p, of the fuel cell at time hhFCRepresenting the thermal output of the fuel cell at time h, ηFC(h) Represents the efficiency of the fuel cell at time h, γFC(h) Representing the electric energy and heat energy ratio of the fuel cell at the h moment;
the basic electrical constraints are as follows:
A. the electric energy supply and demand balance constraint is as follows:
pgrid(h)＝peFC(h)+pms(h)+ptm(h)+pdefe(h)+pbatt(h)-ppv(h)
Figure FDA0003210183740000021
in the formula, pgrid(h) Representing the amount of interaction power, p, between the moment h and the main networkeFC(h) Representing the electrical output power, p, of the fuel cell at time hms(h) Representing the basic energy demand, p, in the household energy management system at time htm(h) Representing the electricity consumption of the air-conditioning unit at the h-th moment, pdefe(h) Representing the power consumption of the switching control unit at moment h, pbatt(h) Representing the charge and discharge capacity of the battery at time h, ppv(h) Representing the power generation capacity of the solar photovoltaic at the h moment,
Figure FDA0003210183740000022
respectively representing the minimum electric quantity and the maximum electric quantity interacted with the power grid;
B. the heat energy supply and demand balance constraint is as follows:
pgas(h)+phFC(h)＝ph(h)
in the formula, pgas(h) Representing the direct heat energy supply of the natural gas at time h, phFC(h) Indicating fuel cell at time hAmount of heat energy supply, ph(h) Representing the heat energy demand of the household energy management system at the moment h;
the above-mentioned direct heat energy supply p of natural gasgas(h) The following natural gas direct heat supply energy constraints are also met:
Figure FDA0003210183740000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003210183740000024
represents the maximum direct supply of natural gas;
C. the fuel cell constraints are:
peFC(h)-peFC(h-1)<peFC,U
peFC(h-1)-peFC(h)<peFC,D
Figure FDA0003210183740000025
in the formula, peFC(h) And peFC(h-1) represents the amount of electric power supplied to the fuel cell at the time h and h-1, respectively, and peFC,URepresents the upper limit value of the fuel cell power, peFC,DRepresents a lower limit value of the electric power of the fuel cell,
Figure FDA0003210183740000026
and
Figure FDA0003210183740000027
respectively representing the minimum power supply electric energy and the maximum power supply electric energy of the fuel cell;
D. the battery constraints are:
Figure FDA0003210183740000028
Figure FDA0003210183740000029
Figure FDA00032101837400000210
Figure FDA00032101837400000211
SOCmin≤SOC(h)≤SOCmax
in the formula:
Figure FDA00032101837400000212
represents the charge amount of the battery at the h-th time,
Figure FDA00032101837400000213
represents the discharge amount of the battery at the h-th time,
Figure FDA0003210183740000031
represents the maximum amount of discharge of the battery,
Figure FDA0003210183740000032
represents the maximum charge of the battery, ηchRepresenting the efficiency of charging the battery, etadchRepresenting the efficiency of the discharge of the accumulator, pbatt(h) Represents the battery capacity, EbattRepresenting the total capacity of the battery, SOC (h) representing the state of charge of the battery at time h, SOCminRepresenting the minimum charge ratio, SOC, of the batterymaxRepresenting the maximum charge ratio of the battery;
E. the air conditioning equipment constraints are as follows:
Figure FDA0003210183740000034
Figure FDA0003210183740000035
Figure FDA0003210183740000036
in the formula, Tin(h+1),Tin(h) Respectively representing the temperature (DEG C) of indoor air at h and h +1, wherein delta represents a time interval, RC represents a thermal resistance parameter, and RC is 9.45; p is a radical oftm(h) Represents the thermal power at hout(h) Which represents the outdoor temperature, is the temperature of the room,
Figure FDA0003210183740000037
representing the lowest and highest temperatures in the room,
Figure FDA0003210183740000038
represents the maximum thermal power;
F. the switch control device is constrained as follows:
Figure FDA0003210183740000039
Figure FDA00032101837400000310
Figure FDA00032101837400000311
in the formula: deltaa,hIndicating the operating state of the switching control device a at the moment h, deltaa,τRepresenting the operating state of the load a at time τ, αa,βaIndicating the operating area of the switch control device a,
Figure FDA00032101837400000312
indicating that the switch control device a is currently h0Working state at the moment HaIndicating the length of work that the switch control device a needs to perform,
Figure FDA00032101837400000313
representing the working state before the h moment; h is0Represents the current time, h represents h0Before time, τ represents h0The time thereafter;
the step 2) is specifically as follows:
2.1) decomposing the optimized mathematical model into a main function (MP) and a sub-function (SP) firstly, wherein the method comprises the following steps:
subfunction (SP):
Figure FDA00032101837400000314
constraint of the Subfunction (SP):
Figure FDA00032101837400000315
peFC(h)-peFC(h-1)<peFC,U+w3
peFC(h-1)-peFC(h)<peFC,D+w4
SOCmin-w5≤SOC(h)≤SOCmax+w6
Figure FDA0003210183740000041
wherein U represents the target value of the sub-function,
Figure FDA0003210183740000042
representing the power consumption p of the switching control unitdefeHas been setValue, w1,w2,w3,w4,w5,w6,w7,w8Represents the first, second, …, eighth relaxation factor, pgridRepresenting the amount of interaction power between the main network, pmsRepresenting the basic energy demand, p, in a domestic energy management systempvRepresenting the generated energy of solar photovoltaic, SOC representing the electric quantity state of the storage battery, TinRepresents the temperature of the indoor air;
master function (MP):
Figure FDA0003210183740000043
Figure FDA0003210183740000044
constraint of the master function (MP):
Figure FDA0003210183740000045
Figure FDA0003210183740000046
Figure FDA0003210183740000047
wherein z represents a target value of the main function,
Figure FDA0003210183740000048
respectively representing the electrical output power p of the fuel celleFCPower consumption p of air conditionertmCharge/discharge amount p of secondary batterybattSet value of (d), mu1，μ2Respectively representing the first and second Lagrange multipliers, delta, of each particlea,tIndicating the operating state of the switch control device a at time t,
Figure FDA0003210183740000049
indicating that the switching control device a is at the present time t0Working state of the moment;
2.2) solving by adopting the following process:
2.2.1) random Generation of NpopA binary particle comprising three switch control devices p1_defe,p2_defe,p3_defeThe state of the switch, which changes in units of one hour within 24 hours, is used as the power consumption p of the switch control devicedefeValue, 72 values per binary particle, expressed as
Figure FDA00032101837400000410
Figure FDA00032101837400000411
Indicating the switch operating state of the first switch control device in the ith particle every hour 24 hours a day,
Figure FDA00032101837400000412
indicating the switch operating state of the second switch control device in the ith particle every hour 24 hours a day,
Figure FDA0003210183740000051
represents the switch working state of the third switch control device in the ith particle in each hour in 24 hours a day, i epsilon {1.. Npop}；
2.2.2) according to N in 2.2.1)popP of each particledefeValue of pdefeAssigned values to the Subfunctions (SP)
Figure FDA0003210183740000052
Calculating NpopThe solution of the sub-function (SP) of the individual particles is assigned to the set value in the main function (MP)
Figure FDA0003210183740000053
Expressed as:
Figure FDA0003210183740000054
simultaneously acquiring a first Lagrange multiplier mu and a second Lagrange multiplier mu corresponding to each particle1，μ2；
2.2.3) set values assigned according to 2.2.2)
Figure FDA0003210183740000055
The set value is compared with
Figure FDA0003210183740000056
Carry in the master function (MP), calculate NpopTarget value Z of a master function (MP) of an individual particlei，i∈NpopAnd a solution, which is to assign the solution of each particle to the set value in the Subfunction (SP)
Figure FDA0003210183740000057
Expressed as:
Figure FDA0003210183740000058
and according to the target value ZiUpdating the population of particles.
2. The family energy data optimization method based on particle swarm optimization and Bendel's decomposition according to claim 1, characterized in that: in the optimization objective function, the efficiency eta of the fuel cell at the h momentFC(h) And h time fuel cell electric energy and heat energy ratio gammaFC(h) Respectively adopting the following formulas to calculate:
Figure FDA0003210183740000059
Figure FDA00032101837400000510
in the formula, plr (h) represents the power load factor of the fuel cell at the time h, that is, the ratio of the output electric power to the maximum power.
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