CN116231670B - Integrated building HVAC load group distribution network optimization method considering occupation influence - Google Patents

Integrated building HVAC load group distribution network optimization method considering occupation influence Download PDF

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CN116231670B
CN116231670B CN202310505228.5A CN202310505228A CN116231670B CN 116231670 B CN116231670 B CN 116231670B CN 202310505228 A CN202310505228 A CN 202310505228A CN 116231670 B CN116231670 B CN 116231670B
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张姝
周丽萍
肖先勇
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Sichuan University
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    • H—ELECTRICITY
    • 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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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
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Abstract

The invention discloses an integrated building HVAC load group distribution network optimization method considering occupation influence, which comprehensively considers demands of building HVAC load groups and distribution networks, establishes a unified building distribution network joint optimization mathematical model, characterizes occupation information of a building as a corresponding occupation state function, and constructs a relaxation function describing the constraint variation of the occupation state on the building state allowed by the building HVAC load; the building HVAC load group distribution network combined optimization mathematical model considering the influence of the building occupation state is constructed, and the building side state model is refined from the combined optimization regulation and control of the integrated building HVAC load group distribution network according to the building side consideration and occupation condition on the allowable building state constraint influence in the HVAC load regulation and control process. The invention seeks deeper energy-saving potential of building side HVAC load while the demand response side meets the basic thermal comfort requirement of the user; higher energy utilization and safer and more reliable power distribution network node voltage levels are sought at the network side.

Description

Integrated building HVAC load group distribution network optimization method considering occupation influence
Technical Field
The invention relates to the technical field of integrated building HVAC load distribution, in particular to an integrated building HVAC load group distribution network optimization method considering occupation influence.
Background
Integrated building HVAC (Heating, ventilation and Air Conditioning Heating ventilation and air conditioning) load group distribution network: the research object is a system formed by connecting a plurality of buildings into a power distribution network, wherein the regulation and control of building loads mainly controls the controllable load HVAC load in the building. The optimization method is different from the independent optimization method of the HVAC load and the distribution network in the building, and is a collaborative joint optimization method of the building HVAC load and the distribution network.
The current research on the control method of building HVAC load can be divided into energy consumption management of building side HVAC load, building side HVAC load and other flexible load demand response management methods in the building, and building HVAC load participating in interactive operation of a transmission network and a distribution network. Research on energy management of building-side HVAC loads combines model prediction and short-term control to minimize total energy consumption of a building while guaranteeing a user's temperature comfort level. However, the study is directed only to how to regulate HVAC load and does not relate to its demand side response method. While research on building-side flexible load demand response management methods relates to demand-side response techniques for HVAC loads, interactive operation of building HVAC loads with the grid is not considered. However, the research on the aspect of participating in the interactive operation of the power grid by the building HVAC load cannot realize the dynamic adjustment of the optimization target between the power distribution network side target and the building side target.
And none of the above studies have considered the HVAC operating environment within a building. In fact, occupancy of people within a building is a major contributing source to building energy consumption. When people in the building occupy the building, the temperature in the building area needs to meet the requirements of indoor people on thermal comfort. Meanwhile, as the number of occupied people increases, the indoor temperature needs to be maintained at a set value level; when no person occupies the building, the indoor person thermal comfort requirement is not required to be met, and the allowable HVAC temperature regulation and control range in the building is increased. It can be seen that the temperature range controlled by the building HVAC will be affected by the occupancy behavior of the person. At present, the existing literature shows that building control predicted by personnel occupation behaviors can realize remarkable energy conservation of a building by researching the influence of personnel occupation on the HVAC control in the building. Therefore, it is necessary to consider the operation influence of personnel occupation on building HVAC in the interactive operation process of building HVAC load and power distribution network, and by constructing an integrated building HVAC load group power distribution network optimization method considering the occupation influence, the joint optimization of building HVAC load and power distribution network is reasonably optimally controlled so as to realize the promotion of building energy conservation and power distribution network operation level.
Disclosure of Invention
Aiming at the problems, the invention aims to provide an integrated building HVAC load group distribution network optimization method considering occupation influence, which can meet the minimum requirement of building side HVAC load energy consumption and the minimum requirement of distribution network side line loss and node voltage amplitude fluctuation while meeting the indoor temperature comfort level of a building side, can well reduce the building side and distribution network side energy consumption, and can improve the distribution network side voltage level. The technical proposal is as follows:
an integrated building HVAC load group distribution network optimization method taking into account occupancy effects, comprising the steps of:
step 1: modeling building and distribution networks
Building a 3R-2C heat resistance and heat capacity model of a building, obtaining a continuous time state equation of the building, and performing Gear discretization to obtain a discrete state equation and a state quantity control quantity corresponding constraint; the power distribution network side establishes a single radial power distribution network branch power flow model; accessing a power flow constraint of a power distribution network according to building loads, and integrating the building into the power distribution network to obtain an integrated building power distribution network mathematical model;
step 2: building occupancy information is established with occupancy state expression
First by the number of people occupiednDividing the occupation into different grades, then obtaining the situation that the occupation changes along with time, and finally obtaining the comprehensive occupation situation of the building; finally, representing the constraint change of the building indoor temperature state variable which is controlled by the HVAC load tolerance in the building by the occupation condition to obtain a building state relaxation function based on the occupation state;
step 3: building HVAC load group power distribution network optimization model integrating occupation influence
According to a building HVAC load state equation and a power distribution network side power flow equation, building HVAC loads are connected in parallel into a power distribution network, then according to the building state relaxation function based on the occupied state, building side and power distribution network side requirements are considered to determine an objective function, constraint conditions on two sides are considered, and a mathematical model for solving the problem of optimizing the power distribution network of the building HVAC load group after occupied influence is established;
step 4: building HVAC load group power distribution network optimization scheme flow for building integration considering occupation influence
After a mathematical model of a building HVAC load group distribution network optimization problem accounting for occupation influence is obtained, a MPC algorithm is used for calling YALMIP under MATLAB to perform rolling optimization solving on the mathematical model; based on a 3R-2C thermal resistance capacity model of a building, a branch power flow model of a power distribution network, an occupied state and a building state relaxation constraint, the optimization scheme of the power distribution network of the building HVAC load group considering the occupied influence is obtained.
Further, the step 2 specifically includes:
step 2.1: defining occupancy levels
And selecting and dividing the difference of the number of people occupied in different time periods into different occupancy levels A (n) according to a trapezoidal membership function, wherein the following formula is as follows:
Figure SMS_1
（1）
in the method, in the process of the invention,nas an occupied number in a building,bpeak value of average occupied number of all buildingsn≥bAt/2, the occupancy levelA(n) =1; on the contrary, when no people occupy the building, namelynWhen=0, then consider as the occupancy levelA(n) =0; when the occupied condition exists in the building and the number of people is not more thanbAt/2, i.e. 0<n<b/2, then take up the gradeA(n)=2n/b；
Step 2.2: defining the change trend of occupancy
Figure SMS_2
（2）
In the method, in the process of the invention,S'(t) Characterization oftAn increase or decrease in the number of people occupied at the moment, whentWhen the number of people occupied at the moment is increased,S'(t) When =1tWhen the number of people occupied at the moment is reducedS'(t)=-1；
Step 2.3: defining occupancy states
The occupancy state within a building is represented as
Figure SMS_3
The following formula:
Figure SMS_4
（3）
in the method, in the process of the invention,
Figure SMS_5
characterizing building presencetThe time occupied state is a class function containing positive and negative signs and represents the change of building occupied states in a day in a plurality of time periods;
step 2.4: building state relaxation function based on occupied state
Using occupancy-based states
Figure SMS_6
Relaxation function of->
Figure SMS_7
Describing the change quantity of the state constraint, adding the change quantity to the lower bound of the building state aiming at the occupied state of the whole building layer to represent the influence of the occupied state on the change of the building state constraint, wherein the expression is as follows:
Figure SMS_8
（4）
in the method, in the process of the invention,
Figure SMS_9
the upper limit corresponding to the building state variable and the upper limit corresponding to the indoor temperature tolerance; />
Figure SMS_10
The lower limit corresponding to the building state variable and the lower limit corresponding to the indoor temperature tolerance; />
Figure SMS_11
Is thattBuilding at moment before momentlState variables of (2); variable(s)αIs thattA time-of-day predefined building state variable constraint is an up-tunable threshold that describes an amount by which the temperature within a building area will increase when the building is not occupied; variable(s)βIs a predefined threshold for which the building temperature state variable constraint can be adjusted down, describing the amount by which the temperature within a building area will decrease when the number of people in the building increases and does not exceed half of the peak average occupancy of all buildings.
Further, the step 3 specifically includes:
step 3.1: determining an objective function
The optimization control targets for determining building power distribution network combination considering occupation influence are as follows: prediction time domainT p The weighted sum of the average line loss cost of the internal distribution network, the average energy consumption cost of building HVAC load and the average temperature offset penalty cost is the smallest, namely:
Figure SMS_12
（5）
in the formula (5), the amino acid sequence of the compound,λ DN the weight coefficient of the power distribution network side target is set;C price indicating real-time electricity price and superscripttRepresentation correspondencetTime; vector i t =([i 1 ,...,i Nbranch ] T ) t Representation oftSet of all branch current amplitude squares in time distribution network, vector r= [r 1 ,...,r Nbranch ] T Representing a set of line resistances,Nbranchrepresenting a branch set of the power distribution network;λ HVAC a weighting factor for building side HVAC load energy consumption costs,
Figure SMS_13
is thattBuilding at momentlActive power of the medium HVAC load;T p a prediction time domain of a model predictive control algorithm is adopted;x，u，λthe state variable, the input variable and the weight variable of the building are respectively; />
Figure SMS_14
Is a set of all buildings;
step 3.2: determining constraints
1) Building status and input constraints accounting for occupancy effects are:
Figure SMS_15
（6）
Figure SMS_16
（7）
Figure SMS_17
（8）
equation (6) is a discrete building state space equation in whichdIs a differential form of the variable;
Figure SMS_19
input quantity for discrete equation state>
Figure SMS_25
Coefficient matrix of (a); />
Figure SMS_27
Control input quantity for discrete equation>
Figure SMS_20
Coefficient matrix of (a); />
Figure SMS_22
Disturbance input variable for discrete equation>
Figure SMS_26
Coefficient matrix of (a); wherein->
Figure SMS_29
，EIs a second-order identity matrix,TsSampling time intervals for an optimization method; />
Figure SMS_21
、/>
Figure SMS_23
，/>
Figure SMS_24
、/>
Figure SMS_28
And->
Figure SMS_18
A coefficient matrix for a building continuous time state equation; />
Figure SMS_30
Representation oftBuilding at momentlIs a state matrix of the (c) in the (c),T wall andT zone respectively, buildingslWall temperature and indoor temperature state variables; />
Figure SMS_31
Representation oftBuilding at momentlActive power of heating ventilation air conditioning system corresponding to buildinglInputting a state equation into a matrix;
Figure SMS_32
representation oftBuilding at momentlIs provided with an uncontrollable input matrix of (a),T out indicating the outdoor temperature of the vehicle,Q' sol andQ' int the total solar radiation absorbed by the outer wall and the internal heat source of the building are respectively;
equation (7) is a building state constraint that contains a building state relaxation function,
Figure SMS_33
and->
Figure SMS_34
Respectively building stateslLower and upper limits of variables;
equation (8) is the constraint of building state equation input matrix, namely heating ventilation air conditioner active power,
Figure SMS_35
、/>
Figure SMS_36
respectively isActive power of building heating ventilation air conditioner>
Figure SMS_37
Lower and upper limits of (2);
coefficient matrix of building continuous time state equation
Figure SMS_38
、/>
Figure SMS_39
And->
Figure SMS_40
The values of (2) are respectively:
Figure SMS_41
、/>
Figure SMS_42
and
Figure SMS_43
；
wherein,,Cfor the total equivalent heat capacity of building walls and roof heating areas,R 1 、R 2 、R win equivalent resistance is used for building heating areas;C zone is the equivalent heat capacity of the region;μ HVAC is the performance parameter of the heating ventilation air conditioner;
the continuous time state equation for a building is as follows:
Figure SMS_44
；
2) The power flow constraint of the distribution network is as follows:
Figure SMS_45
（9）
Figure SMS_46
（10）
Figure SMS_47
（11）
Figure SMS_48
（12）
Figure SMS_49
（13）
in the method, in the process of the invention,p k andq k is an injection nodekNet active and reactive power of (a);P k andQ k is a flow-through branchknActive power and reactive power;r k andx k is a branchknThe resistance and reactance of the upper layer,I k is a flow-through branchkIs the square of the current amplitude of (a),k∈N node 、n∈N node 、kn∈N branch ，N node andN branch respectively a node set and a branch set of the power distribution network;C j representing nodesnIs common to sub-branches of (2)mThe strip is provided with a plurality of grooves,j=1,2,...,m；P Cj andQ Cj the active power and the reactive power corresponding to the sub-branch;V k andV n representing nodes respectivelykAndnsquare of the magnitude of the voltage;
the power distribution network node is accessed for building load by the method (13)kWhen the power balance equation is used, the power balance equation is used with the power distribution network; in the method, in the process of the invention,
Figure SMS_50
、
Figure SMS_51
representation oftNode of time distribution networkkThe net active and reactive power absorbed; />
Figure SMS_52
Active power for HVAC loads in a building;p misc is the active power of uncontrollable loads inside the building,q misc corresponding to reactive power byq misc =p misc PfThe calculation is performed such that,Pfis a power factor; superscripttAnd subscriptslRepresentation oftTime of day and buildingl，/>
Figure SMS_53
、/>
Figure SMS_54
Representation oftTime nodekAn uncontrollable active and reactive base load;
3) Branch power capacity constraint
Figure SMS_55
（14）
In the method, in the process of the invention,
Figure SMS_56
and->
Figure SMS_57
Respectively represent the flow through branchesknUpper allowable lower and upper limits of active power;
Figure SMS_58
and->
Figure SMS_59
Respectively represent the flow through branchesknUpper allowable lower and upper limits of reactive power;
4) Node voltage and branch current constraints
Figure SMS_60
（15）
In the method, in the process of the invention,
Figure SMS_61
and->
Figure SMS_62
Representing nodes respectivelykAn allowable lower limit value and an upper limit value of the square of the voltage amplitude; />
Figure SMS_63
Representing the flow through the branchkAn allowable upper limit value of the square of the current amplitude of (a);
5) Total active loss constraint of power distribution network
Figure SMS_64
（16）
In the method, in the process of the invention,P Loss representing line loss active power on the distribution network;
Figure SMS_65
and->
Figure SMS_66
The allowable lower limit value and the upper limit value of the line loss active power on the distribution network are respectively indicated.
Further, the step 4 is a simulation start time of the rolling optimization solutiont=t0; number of scroll optimizationsk=1, in each optimization solving process, a set of solutions of the prediction time domain is obtained, solutions in the corresponding control time domain are reserved, and the control time domain is returnedTbThe final optimization state of the internal building is used as the initial value of the building state of the next optimization solving problem; and will simulate the starting timetPropulsion ofTbRepeating the next optimization untiltLonger than the final simulation durationTfinalAnd outputting the final regulation scheme.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the building power distribution network combined optimization mathematical model, the requirements of the building HVAC load group and the power distribution network are comprehensively considered, and a unified building power distribution network combined optimization mathematical model is established, so that the requirements of the lowest energy consumption of building side HVAC loads and the minimum consumption of side line loss and the minimum fluctuation of node voltage of the power distribution network can be met.
2. The invention characterizes the occupancy information of the building as a corresponding occupancy state and constructs a relaxation function describing the occupancy state versus building state variable constraint variation allowed by building HVAC load.
3. The invention builds a mathematical model of the joint optimization of the building HVAC load group distribution network considering the influence of the building occupation state, and aims to start from the joint optimization regulation and control of the integrated building HVAC load group distribution network, and meanwhile, the building side state model is more refined by considering the influence of occupation conditions on the allowable building state constraint in the HVAC load regulation and control process. A building HVAC load group distribution network joint scheduling method considering occupation influence is studied. While the demand response side meets the user's basic thermal comfort requirements, seeking the energy saving potential of deeper building side HVAC loads; on the network side, higher energy utilization and safer and more reliable node voltage levels of the distribution network are sought.
Drawings
FIG. 1 is a 3R-2C network model of a building.
Fig. 2 is a line tree diagram of a single feeder radial distribution network.
Fig. 3 is a building state relaxation constraint based on occupancy information.
FIG. 4 is a flow chart of an integrated building HVAC load group distribution network optimization scheme of the present invention that accounts for occupancy effects.
Detailed Description
The invention will now be described in further detail with reference to the drawings and to specific examples.
The present invention aims to construct a relaxation function describing changes in building state variable constraints of occupancy state versus building HVAC load tolerance. And a mathematical model for joint optimization of the distribution network of the building HVAC load group considering the influence of the occupied state of the building is established, and the effectiveness of the optimization method is verified.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
1. first step modeling building and distribution network
Building a 3R-2C thermal resistance and heat capacity model of the building, obtaining a state equation of the building and carrying out Geand (5) discretizing to obtain a discrete state equation and a state quantity control quantity corresponding constraint. The building 3R-2C network model is shown in figure 1,R 1 、R 2 、R win equivalent resistance for building heating area,CIs the total equivalent heat capacity of heating areas of building walls and roofs,C zone Is the regional equivalent heat capacity;T wall andT zone respectively, buildingslWall temperature and indoor temperature state variables;T out is the outdoor temperature of the building,Q' sol AndQ' int the total solar radiation absorbed by the outer wall and the internal heat source of the building are respectively;Q' HVAC is the energy consumption of a heating ventilation air conditioning system, and can be realized byQ' HVAC =μ HVAC P HVAC And (5) calculating. Wherein,,μ HVAC is a performance parameter of the heating ventilation air conditioner. And the distribution network side establishes a single radial distribution line power flow model.
The single feeder radial distribution network line tree diagram is shown in figure 2, and in figure 2V k AndV n representing nodes respectivelykAndnsquare of the magnitude of the voltage;p k andq k is an injection nodekNet active and reactive power of (a);p n andq n is an injection nodenNet active power and reactive power of (a).P k AndQ k is a flow-through branchknActive power and reactive power.r k Andx k is a branchknThe resistance and reactance of the upper layer,I k is a flow-through branchkIs the square of the current amplitude of (a).C j (j=1,2,...,m) Representing nodesnIs common to sub-branches of (2)mA strip.
And accessing the power flow constraint of the power distribution network according to the building load, and integrating the building into the power distribution network to obtain the mathematical model of the integrated building power distribution network.
2. The second step is to establish the occupied state expression of the building occupied information
First by the number of people occupiednDividing the occupation into different grades, then obtaining the situation that the occupation changes along with time, and finally obtaining the comprehensive occupation situation of the building; and finally, characterizing the constraint change of the building indoor temperature state variable which is subjected to the HVAC load allowable regulation in the building by the occupation condition to study the influence of the constraint change on the joint regulation of the building distribution network integrated system.
1) Class of people occupied
The difference of the number of people occupied in different time periods can be selectively divided into different occupancy levels A (n) according to a trapezoidal membership function, and the following formula is adopted:
Figure SMS_67
（1）
in the method, in the process of the invention,nas an occupied number in a building,bpeak value of average occupied number of all buildingsn≥bAt/2, the occupancy levelA(n) =1; on the contrary, when no people occupy the building, namelynWhen=0, then consider as the occupancy levelA(n) =0; when the occupied condition exists in the building and the number of people is not more thanbAt/2, i.e. 0<n<b/2, then take up the gradeA(n)=2n/b。
2) Trend of occupancy change
Figure SMS_68
（2）
In the method, in the process of the invention,S'(t) Characterization oftAn increase or decrease in the number of people occupied at the moment, whentWhen the number of people occupied at the moment is increased,S'(t) When =1tWhen the number of people occupied at the moment is reducedS'(t)=-1。
3) Occupancy state
The occupancy state in the building is related to time and the number of people occupied at the same time and can be expressed as
Figure SMS_69
The following formula:
Figure SMS_70
（3）
in the method, in the process of the invention,
Figure SMS_71
characterizing building presencetThe time of day occupancy state is a ranking function that contains positive and negative signs. The method can represent the change of a plurality of main time periods of the building occupation state in one day, namely the increase of the number of people occupied in the morning on duty, the decrease of the number of people occupied in the midday on lunch, the increase of the number of people occupied in the afternoon on duty and the decrease of the number of people occupied in the evening off duty.
4) Building state relaxation function
Different occupied states in the building can correspond to different temperature adjustment ranges of the heating ventilation air conditioner, so that the constraint of the building state variable is changed. We can use an occupancy-based state
Figure SMS_72
Temperature>
Figure SMS_73
Describing the change amount of the state constraint, adding the change amount to the lower bound of the building state for the occupied state of the whole building level to represent the influence of the occupied state on the change of the building state constraint, wherein the building state relaxation constraint based on the occupied information is shown in fig. 3, and the expression is as follows:
Figure SMS_74
（4）
in the method, in the process of the invention,
Figure SMS_75
the upper limit corresponding to the building state variable and the upper limit corresponding to the indoor temperature tolerance; />
Figure SMS_76
The lower limit corresponding to the building state variable and the lower limit corresponding to the indoor temperature tolerance; />
Figure SMS_77
Is thattBuilding at moment before momentlState variables of (2); variable(s)αIs thattA time-of-day predefined building state variable constraint is an up-tunable threshold that describes an amount by which the temperature within a building area will increase when the building is not occupied; variable(s)βIs a predefined threshold for which the building temperature state variable constraint can be adjusted down, describing the amount by which the temperature within a building area will decrease when the number of people in the building increases and does not exceed half of the peak average occupancy of all buildings.
3. Thirdly, building an integrated building HVAC load group distribution network optimization model considering occupation influence
And according to the building HVAC load state equation and the power distribution network side power flow equation, building HVAC loads are connected into the power distribution network in parallel, and according to the building state relaxation function based on the occupied state, building side and power distribution network side requirements and constraint conditions on two sides are considered, and finally an optimization method mathematical model is built.
(1) Objective function
The joint optimization of the building HVAC load group distribution network considering the occupation influence adopts a model prediction control strategy, and a building side demand target and a distribution network side demand target need to be considered at the same time. The energy consumption of building side HVAC load and the side line loss of the distribution network are reduced on the premise of meeting the comfort level of the residents, so that the occupied building distribution network joint optimization control target can be obtained: prediction time domainTpThe weighted sum of the average line loss cost of the internal distribution network, the average energy consumption cost of building HVAC load and the average temperature offset penalty cost is the smallest, namely:
Figure SMS_78
（5）
in the method, in the process of the invention,λ DN the weight coefficient of the power distribution network side target is set;C price representing the electricity price of the corresponding time period; vector i t =([i 1 ,...,i Nbranch ] T ) t Representation oftSet of all branch current amplitude squares in time distribution network, vector r= [r 1 ,...,r Nbranch ] T Representing a set of line resistances,Nbranchthe method comprises the steps of collecting branches of a power distribution network;λ HVAC a weighting factor for building side HVAC load energy consumption costs,
Figure SMS_79
is thattBuilding at momentlActive power of the HVAC load.TpA prediction time domain of a model predictive control algorithm is adopted;x，u，λthe state variable, the input variable and the weight variable of the building are respectively; />
Figure SMS_80
Is a set of all buildings;
(2) Constraint conditions
1) Building status and input constraints accounting for occupancy
Figure SMS_81
（6）
Figure SMS_82
（7）
Figure SMS_83
（8）
Equation (6) is a discrete building state space equation in whichdIs a differential form of the variable;
Figure SMS_85
input quantity for discrete equation state>
Figure SMS_89
Coefficient matrix of (a); />
Figure SMS_93
Control input quantity for discrete equation>
Figure SMS_86
Coefficient matrix of (a); />
Figure SMS_91
Disturbance input variable for discrete equation>
Figure SMS_94
Coefficient matrix of (a); wherein->
Figure SMS_97
，EIs a second-order identity matrix,TsSampling time intervals for an optimization method; />
Figure SMS_84
、/>
Figure SMS_88
，/>
Figure SMS_92
、/>
Figure SMS_96
And->
Figure SMS_87
A coefficient matrix for a building continuous time state equation; />
Figure SMS_90
Representation oftBuilding at momentlIs a state matrix of the (c) in the (c),T wall andT zone respectively, buildingslWall temperature and indoor temperature state variables; />
Figure SMS_95
Representation oftBuilding at momentlActive power of heating ventilation air conditioning system corresponding to buildinglInputting a state equation into a matrix;
Figure SMS_98
representation oftBuilding at momentlIs provided with an uncontrollable input matrix of (a),T out indicating the outdoor temperature of the vehicle,Q' sol andQ' int the total solar radiation absorbed by the outer wall and the internal heat source of the building are respectively;
equation (7) is a building state constraint that contains a building state relaxation function,
Figure SMS_99
and->
Figure SMS_100
Respectively building stateslLower and upper limits of variables;
equation (8) is the constraint of building state equation input matrix, namely heating ventilation air conditioner active power,
Figure SMS_101
、/>
Figure SMS_102
active power of building heating ventilation air conditioner respectively>
Figure SMS_103
Lower and upper limits of (2);
2) Power distribution network tide constraint
Figure SMS_104
（9）
Figure SMS_105
（10）
Figure SMS_106
（11）
Figure SMS_107
（12）
Figure SMS_108
（13）
3) Branch power capacity constraint
Figure SMS_109
（14）
4) Node voltage and branch current constraints
Figure SMS_110
（15）
5) Total active loss constraint of power distribution network
Figure SMS_111
（16）
The complete model is shown below:
Figure SMS_112
（17）
4. fourth step, building HVAC load group power distribution network optimization scheme flow integrating occupation influence
After a mathematical model of the optimization problem of the distribution network of the building HVAC load group after the occupation influence is obtained, the mathematical model is rolled and optimized by calling YALMIP under MATLAB by using an MPC algorithm. Based on the aforementioned building 3R-2C thermal resistance and heat capacity model, the power distribution network branch power flow model, the occupied state and the building state relaxation constraint, the present section further provides a flow of a building HVAC load group power distribution network optimization scheme taking the occupied influence into account, as shown in FIG. 4.
The detailed optimization scheme flow is as follows:
step 1: establishing a state equation of a building and a tide equation of a power distribution network, and then accessing building loads into the power distribution network to establish a building grid-connected model; and finally, establishing an occupancy state function based on occupancy.
Step 2: and establishing corresponding constraints on the building side and the power distribution network side, and then representing building state relaxation constraints based on the occupied states.
Step 3: and establishing an objective function of the optimization problem and unifying constraint conditions to establish an optimized mathematical model. And adopting a strategy of a model predictive control algorithm.
Step 4: and inputting building 3R-2C heat resistance and heat capacity model parameters, building initial state values, power distribution network parameters, building occupation information and MPC simulation parameters, and calling YAMIP in matlab to solve the optimized mathematical model. At this time, the simulation starts at the momentt=t0; number of scroll optimizationsk=1。
Step 5: in each optimization solving process, a group of solutions of a prediction time domain are obtained, solutions in a corresponding control time domain are reserved, and the control time domain is returnedTbThe final optimized state of the internal building is used as the initial value of the building state of the next optimized solving problem. And will simulate the starting timetPropulsion ofTb. Repeating the optimization untiltLonger than the final simulation durationTfinal。
The mathematical model for building HVAC load group distribution network joint optimization, which is constructed by the invention and takes the influence of the occupied state of the building into consideration, is designed to start from the joint optimization regulation and control of the integrated building HVAC load group distribution network, and meanwhile, the building side state model is more refined by taking the influence of the allowable building state constraint in the occupied condition HVAC load regulation and control process into consideration at the building side. A building HVAC load group distribution network joint scheduling method considering occupation influence is studied. While the demand response side meets the user's basic thermal comfort requirements, seeking the energy saving potential of deeper building side HVAC loads; on the network side, higher energy utilization and safer and more reliable node voltage levels of the distribution network are sought.

Claims (2)

1. An integrated building HVAC load group distribution network optimization method taking into account occupancy effects, HVAC being heating ventilation and air conditioning, comprising the steps of:
step 1: modeling building and distribution networks
Building a 3R-2C heat resistance and heat capacity model of the building, obtaining a continuous time state equation of the building, and performing Gear discretization to obtain a discrete state equation and a corresponding constraint of a state quantity control quantity; the power distribution network side establishes a single radial power distribution network branch power flow model; accessing a power flow constraint of a power distribution network according to building loads, and integrating the building into the power distribution network to obtain an integrated building power distribution network mathematical model;
step 2: building occupancy information is established with occupancy state expression
First by the number of people occupiednDividing the occupation into different grades, then obtaining the situation that the occupation changes along with time, and finally obtaining the comprehensive occupation situation of the building; finally, representing the constraint change of the building indoor temperature state variable which is controlled by the HVAC load tolerance in the building by the occupation condition to obtain a building state relaxation function based on the occupation state;
step 3: building HVAC load group power distribution network optimization model integrating occupation influence
According to a building HVAC load state equation and a power distribution network side power flow equation, building HVAC loads are connected in parallel into a power distribution network, then according to the building state relaxation function based on the occupied state, building side and power distribution network side requirements are considered to determine an objective function, constraint conditions on two sides are considered, and a mathematical model for solving the problem of optimizing the power distribution network of the building HVAC load group after occupied influence is established;
step 4: building HVAC load group power distribution network optimization scheme flow for building integration considering occupation influence
After a mathematical model of a building HVAC load group distribution network optimization problem accounting for occupation influence is obtained, a MPC algorithm is used for calling YALMIP under MATLAB to perform rolling optimization solving on the mathematical model; based on a building 3R-2C thermal resistance capacity model, a power distribution network branch power flow model, an occupied state and a building state relaxation constraint, obtaining a building HVAC load group power distribution network optimization scheme considering occupied influence;
the step 2 specifically includes:
step 2.1: defining occupancy levels
The difference of the number of people occupied in different time periods is selectively divided into different occupancy levels A according to a trapezoid membership functionn) The following formula:
Figure QLYQS_1
（1）
in the method, in the process of the invention,nas an occupied number in a building,bfor all buildingsPeak space average occupancy, whenn≥bAt/2, the occupancy levelA(n) =1; on the contrary, when no people occupy the building, namelynWhen=0, then consider as the occupancy levelA(n) =0; when the occupied condition exists in the building and the number of people is not more thanbAt/2, i.e. 0<n<b/2, then take up the gradeA(n)=2n/b；
Step 2.2: defining the change trend of occupancy
Figure QLYQS_2
（2）
In the method, in the process of the invention,S'(t) Characterization oftAn increase or decrease in the number of people occupied at the moment, whentWhen the number of people occupied at the moment is increased,S'(t) When =1tWhen the number of people occupied at the moment is reducedS'(t)=-1；
Step 2.3: defining occupancy states
The occupancy state within a building is represented as
Figure QLYQS_3
The following formula:
Figure QLYQS_4
（3）
in the method, in the process of the invention,
Figure QLYQS_5
characterizing building presencetThe time occupied state is a class function containing positive and negative signs and represents the change of building occupied states in a day in a plurality of time periods;
step 2.4: building state relaxation function based on occupied state
Using occupancy-based states
Figure QLYQS_6
Relaxation function of->
Figure QLYQS_7
Describing the change quantity of the state constraint, adding the change quantity to the lower bound of the building state aiming at the occupied state of the whole building layer to represent the influence of the occupied state on the change of the building state constraint, wherein the expression is as follows:
Figure QLYQS_8
（4）
in the method, in the process of the invention,
Figure QLYQS_9
the upper limit corresponding to the building state variable and the upper limit corresponding to the indoor temperature tolerance; />
Figure QLYQS_10
The lower limit corresponding to the building state variable and the lower limit corresponding to the indoor temperature tolerance; />
Figure QLYQS_11
Is thattBuilding at moment before momentlState variables of (2); variable(s)αIs thattA time-of-day predefined building state variable constraint is an up-tunable threshold that describes an amount by which the temperature within a building area will increase when the building is not occupied; variable(s)βIs a predefined threshold for which the building temperature state variable constraint can be adjusted down, describing the amount by which the temperature within a building area will decrease when the number of people in the building increases and does not exceed half of the peak average occupancy of all buildings;
the step 3 specifically includes:
step 3.1: determining an objective function
The optimization control targets for determining building power distribution network combination considering occupation influence are as follows: prediction time domainT p The weighted sum of the average line loss cost of the internal distribution network, the average energy consumption cost of building HVAC load and the average temperature offset penalty cost is the smallest, namely:
Figure QLYQS_12
（5）
in the formula (5), the amino acid sequence of the compound,λ DN the weight coefficient of the power distribution network side target is set;C price indicating real-time electricity price and superscripttRepresentation correspondencetTime; vector i t =([i 1 ,...,i Nbranch ] T ) t Representation oftSet of all branch current amplitude squares in time distribution network, vector r= [r 1 ,...,r Nbranch ] T Representing a set of line resistances,Nbranchrepresenting a branch set of the power distribution network;λ HVAC a weighting factor for building side HVAC load energy consumption costs,
Figure QLYQS_13
is thattBuilding at momentlActive power of the medium HVAC load;T p a prediction time domain of a model predictive control algorithm is adopted;x，u，λthe state variable, the input variable and the weight variable of the building are respectively; />
Figure QLYQS_14
Is a set of all buildings;
step 3.2: determining constraints
1) Building status and input constraints accounting for occupancy effects are:
Figure QLYQS_15
（6）
Figure QLYQS_16
（7）
Figure QLYQS_17
（8）
equation (6) is a discrete building state space equation in whichdIs a differential form of the variable;
Figure QLYQS_19
input quantity for discrete equation state>
Figure QLYQS_22
Coefficient matrix of (a); />
Figure QLYQS_26
Control input quantity for discrete equation>
Figure QLYQS_21
Coefficient matrix of (a); />
Figure QLYQS_24
Disturbance input variable for discrete equation>
Figure QLYQS_31
Coefficient matrix of (a); wherein->
Figure QLYQS_32
，EIs a second-order identity matrix,TsSampling time intervals for an optimization method; />
Figure QLYQS_18
、/>
Figure QLYQS_25
，/>
Figure QLYQS_28
、/>
Figure QLYQS_30
And->
Figure QLYQS_20
A coefficient matrix for a building continuous time state equation; />
Figure QLYQS_23
Representation oftBuilding at momentlState moment of (2)The array of which is arranged in a row,T wall andT zone respectively, buildingslWall temperature and indoor temperature state variables; />
Figure QLYQS_27
Representation oftBuilding at momentlActive power of heating ventilation air conditioning system corresponding to buildinglInputting a state equation into a matrix; />
Figure QLYQS_29
Representation oftBuilding at momentlIs provided with an uncontrollable input matrix of (a),T out indicating the outdoor temperature of the vehicle,Q' sol andQ' int the total solar radiation absorbed by the outer wall and the internal heat source of the building are respectively;
equation (7) is a building state constraint that contains a building state relaxation function,
Figure QLYQS_33
and->
Figure QLYQS_34
Respectively, buildingslA lower and upper state variable limit;
equation (8) is the constraint of building state equation input matrix, namely heating ventilation air conditioner active power,
Figure QLYQS_35
、/>
Figure QLYQS_36
active power of building heating ventilation air conditioner respectively>
Figure QLYQS_37
Lower and upper limits of (2);
coefficient matrix of building continuous time state equation
Figure QLYQS_38
、/>
Figure QLYQS_39
And->
Figure QLYQS_40
The values of (2) are respectively:
Figure QLYQS_41
、/>
Figure QLYQS_42
and
Figure QLYQS_43
；
wherein,,Cfor the total equivalent heat capacity of building walls and roof heating areas,R 1 、R 2 、R win equivalent resistance is used for building heating areas;C zone is the equivalent heat capacity of the region;μ HVAC is the performance parameter of the heating ventilation air conditioner;
the continuous time state equation for a building is as follows:
Figure QLYQS_44
；
2) The power flow constraint of the distribution network is as follows:
Figure QLYQS_45
（9）
Figure QLYQS_46
（10）
Figure QLYQS_47
（11）
Figure QLYQS_48
（12）
Figure QLYQS_49
（13）
in the method, in the process of the invention,p k andq k is an injection nodekNet active and reactive power of (a);P k andQ k is a flow-through branchknActive power and reactive power;r k andx k is a branchknThe resistance and reactance of the upper layer,I k is a flow-through branchkIs the square of the current amplitude of (a),k∈N node 、n∈N node 、kn∈N branch ，N node andN branch respectively a node set and a branch set of the power distribution network;C j representing nodesnIs common to sub-branches of (2)mThe strip is provided with a plurality of grooves,j=1,2,...,m；P Cj andQ Cj the active power and the reactive power corresponding to the sub-branch;V k andV n representing nodes respectivelykAndnsquare of the magnitude of the voltage;
the power distribution network node is accessed for building load by the method (13)kWhen the power balance equation is used, the power balance equation is used with the power distribution network; in the method, in the process of the invention,
Figure QLYQS_50
、/>
Figure QLYQS_51
representation oftNode of time distribution networkkThe net active and reactive power absorbed; />
Figure QLYQS_52
Active power for HVAC loads in a building;p misc is the active power of uncontrollable loads inside the building,q misc corresponding to reactive powerRate of byq misc =p misc PfThe calculation is performed such that,Pfis a power factor; superscripttAnd subscriptslRepresentation oftTime of day and buildingl，/>
Figure QLYQS_53
、/>
Figure QLYQS_54
Representation oftTime nodekAn uncontrollable active and reactive base load;
3) Branch power capacity constraint
Figure QLYQS_55
（14）
In the method, in the process of the invention,
Figure QLYQS_56
and->
Figure QLYQS_57
Respectively represent the flow through branchesknUpper allowable lower and upper limits of active power; />
Figure QLYQS_58
And
Figure QLYQS_59
respectively represent the flow through branchesknUpper allowable lower and upper limits of reactive power;
4) Node voltage and branch current constraints
Figure QLYQS_60
（15）
In the method, in the process of the invention,
Figure QLYQS_61
and->
Figure QLYQS_62
Representing nodes respectivelykAn allowable lower limit value and an upper limit value of the square of the voltage amplitude; />
Figure QLYQS_63
Representing the flow through the branchkAn allowable upper limit value of the square of the current amplitude of (a);
5) Total active loss constraint of power distribution network
Figure QLYQS_64
（16）
In the method, in the process of the invention,P Loss representing line loss active power on the distribution network;
Figure QLYQS_65
and->
Figure QLYQS_66
The allowable lower limit value and the upper limit value of the line loss active power on the distribution network are respectively indicated.
2. The method for optimizing an integrated building HVAC load group distribution network taking account of occupancy effects of claim 1, wherein the simulation start time of the rolling optimization solution in step 4t=t0; number of scroll optimizationsk=1, in each optimization solving process, a set of solutions of the prediction time domain is obtained, solutions in the corresponding control time domain are reserved, and the control time domain is returnedTbThe final optimization state of the internal building is used as the initial value of the building state of the next optimization solving problem; and will simulate the starting timetPropulsion ofTbRepeating the next optimization untiltLonger than the final simulation durationTfinalAnd outputting the final regulation scheme.
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