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 PDFInfo
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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
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:
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)
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,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:
in the formula (I), the compound is shown in the specification,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
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,andrespectively representing the minimum power supply electric energy and the maximum power supply electric energy of the fuel cell;
D. the battery constraints are:
SOCmin≤SOC(h)≤SOCmax
in the formula:represents the charge amount of the battery at the h-th time,represents the discharge amount of the battery at the h-th time,represents the maximum amount of discharge of the battery,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:
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,representing the lowest and highest temperatures in the room,represents the maximum thermal power;
F. the switch control device is constrained as follows:
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,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,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:
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):
constraint of the Subfunction (SP):
peFC(h)-peFC(h-1)<peFC,U+w3
peFC(h-1)-peFC(h)<peFC,D+w4
SOCmin-w5≤SOC(h)≤SOCmax+w6
wherein U represents the target value of the sub-function,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):
constraint of the master function (MP):
wherein z represents a target value of the main function,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 asIndicating the switch operating state of the first switch control device in the ith particle every hour 24 hours a day,indicating the switch operating state of the second switch control device in the ith particle every hour 24 hours a day,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)Calculating NpopThe solution of the sub-function (SP) of the individual particles is assigned to the set value in the main function (MP)Expressed as:
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)The set value is compared withCarry 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)Expressed as:
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,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,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,peFC,U=1.5,peFC,D=1.5, Tout(h) The data of a certain day is randomly selected,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)Calculating NpopThe solution of the particles is given to the set value in the master function (MP)
2.2.3) set values assigned according to 2.2.2)The set value is compared withCarry 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)
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:
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)
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,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:
in the formula (I), the compound is shown in the specification,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
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,andrespectively representing the minimum power supply electric energy and the maximum power supply electric energy of the fuel cell;
D. the battery constraints are:
SOCmin≤SOC(h)≤SOCmax
in the formula:represents the charge amount of the battery at the h-th time,represents the discharge amount of the battery at the h-th time,represents the maximum amount of discharge of the battery,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:
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,representing the lowest and highest temperatures in the room,represents the maximum thermal power;
F. the switch control device is constrained as follows:
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,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,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):
constraint of the Subfunction (SP):
peFC(h)-peFC(h-1)<peFC,U+w3
peFC(h-1)-peFC(h)<peFC,D+w4
SOCmin-w5≤SOC(h)≤SOCmax+w6
wherein U represents the target value of the sub-function,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):
constraint of the master function (MP):
wherein z represents a target value of the main function,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,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 Indicating the switch operating state of the first switch control device in the ith particle every hour 24 hours a day,indicating the switch operating state of the second switch control device in the ith particle every hour 24 hours a day,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)Calculating NpopThe solution of the sub-function (SP) of the individual particles is assigned to the set value in the main function (MP)Expressed as:
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)The set value is compared withCarry 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)Expressed as:
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:
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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