CN106765994A - A kind of multifarious air conditioner load clustered control strategy of hold mode - Google Patents

Abstract

The invention discloses a kind of multifarious air conditioner load clustered control strategy of hold mode, including：User's air-conditioning parameter is collected, state queue model is built；Judge whether to demand response；Determine the demand response time period and extract the load diversity index of air-conditioning cluster under demand response finish time baseline state；Response policy being formulated with reference to load diversity index and being implemented, air-conditioning design temperature is adjusted to new range；Air-conditioning design temperature is judged whether to recall to；Determine that design temperature is recalled to the time period, the diversity index under finish time time period baseline state is recalled in extraction, formulated with reference to index and recall to strategy and assign, air-conditioning design temperature is adjusted to initial range.The control strategy considers the load diversity of air-conditioning cluster while its regulation and control potentiality is excavated, and has ensured the comfort level of user, compensate for the deficiency of current air conditioner control strategy.

Description

A kind of multifarious air conditioner load clustered control strategy of hold mode

Technical field

The present invention relates to air conditioner control strategy, more particularly to a kind of multifarious air conditioner load clustered control plan of hold mode Slightly.

Background technology

With the development of economic society, air-conditioning equipment is widely used, even can in the big and medium-sized cities of China To reach 30%~40%, and there is the larger rising space.Air conditioner load as seaonal load, the time period for using compared with To concentrate, it is easily caused power network and load peak, or even the voltage security problem for jeopardizing power network occurs.It can thus be seen that air-conditioning The problem that load is brought in the construction of intelligent grid is very important.

If the electrical problem for solving air conditioner load only relies on increase, installed capacity pays larger cost meeting needs, And relatively low utilization rate of equipment and installations can be caused.Air conditioner load as a kind of temperature control load, with certain thermal inertia, by certain Demand response means can be effectively controlled to air-conditioning, and the related service (peak regulation etc.) such that it is able to response system is simultaneously solved The electrical problem of itself.The control strategy of current air conditioner load cluster is various for the regulation potentiality of air conditioner load cluster, load Property and users'comfort consideration it is not comprehensive, only caught wherein or 2 points are carried out when air conditioner load cluster regulates and controls Consider, cause air conditioner load cluster to give full play to its effect to power system stability operation.

The content of the invention

Goal of the invention：To solve the deficiencies in the prior art, there is provided a kind of abundant air conditioner load group that excavates regulates and controls the same of potentiality When ensure load diversity, devise recalling to strategy and using ensureing the normal of user for air-conditioning design temperature, and make it not The air conditioner load clustered control strategy of the load level close with baseline is kept in the controlled period.

Technical scheme：A kind of multifarious air conditioner load clustered control strategy of hold mode, comprises the following steps：

(1) air-conditioning parameter of user is collected, air-conditioning state queuing model is built；

(2) judge whether user carries out demand response, if carrying out demand response, perform step (3)；Conversely, performing step (8)；

(3) determine the air conditioner load cluster demand response time period, and extract under demand response finish time baseline state Air-conditioning cluster load diversity index, including air-conditioning group's indoor temperature average, air-conditioning group's indoor temperature variance and unlatching number of units；

(4) the air-conditioning cluster load diversity index and users'comfort under demand response finish time baseline state are combined Scope is formulated response policy and is implemented, and air-conditioning design temperature then is adjusted into new scope；

(5) judge whether to air-conditioning design temperature to recall to, if desired carry out air-conditioning design temperature readjustment, perform step (6)；Conversely, performing step (8)；

(6) determine that air-conditioning design temperature is recalled to the time period, Extracting temperature is recalled under finish time time period baseline state Air-conditioning cluster load diversity index；

(7) formulated with reference to load diversity index and users'comfort scope and recall to strategy and assign, then set air-conditioning Constant temperature degree recalls to setting range before response；

(8) terminate.

Further, the user's air-conditioning parameter collected in the step (1) includes：Air-conditioning design temperature scope, Yong Hukong Adjust total number of units, air-conditioning temperature rise period duration, air-conditioning temperature-reducing period duration, air-conditioner switch state, room temperature numerical value residing for air-conditioning；Together When, the air-conditioning state queuing model of structure is：

A cycle of operation in air-conditioning design temperature is divided into 1,2 altogether ..., n state；Wherein, temperature raised bench , there is n in section, i.e. air-conditioning dwell period₁Individual state；Temperature drop stage, i.e. air-conditioning open stage, there is n₂Individual state, wherein, n₁+n₂=n；The corresponding dead band temperature range of each state is respectively：

Wherein, T_{state_0}~T_{state_n}It is each state temperature terminal；The common N platforms of user's air-conditioning, each is in t The air-conditioning number of units of state is respectively：N₁,N₂,…,N_n。

Further, the air-conditioning cluster load diversity index extraction in the step (3) under baseline state is as follows：

Determine to need to carry out the time period of demand response in advance, the time period can be drawn by the load curve of similar day；Base Air-conditioning cluster load diversity index extraction formula under wire state is as follows：

(a) air-conditioning group's indoor temperature average

Wherein, T_averageT () is t air-conditioning group's indoor temperature average, T under baseline state_{state_i-1}For under baseline state I-th lower limit of state dead band temperature range, T_{state_i}It is i-th upper limit of state dead band temperature range, N under baseline state_iFor T is in i-th air-conditioning number of units of state under baseline state；

(b) air-conditioning group's indoor temperature variance

Wherein, T_invT () is t air-conditioning group's indoor temperature variance under baseline state；

C () air-conditioning group opens number of units

Wherein, S_openT () is t air-conditioning group's unlatching number of units under baseline state.

Further, the air-conditioning cluster load for being combined under demand response finish time baseline state in the step (4) is more It is as follows that sample index formulates response policy with users'comfort scope：

(I) object function is determined：Maximize load reduction

max(P_cut)=max (P_singleN_cut) t=T_{d_start},…,T_{d_end}-1 (4)

Wherein, P_cutIt is the minimum load reduction that can be reached in the demand response time period, P_singleIt is single air conditioner work( Rate, N_cutIt is decision variable to be optimized, as the air-conditioning number of units of the actual reduction in the demand response time period, T_{d_start}It is demand Carved at the beginning of response events, T_{d_end}It is the finish time of demand response event；

(II) constraints is set：

The adjustment constraint of (a) temperature：

Wherein, DB (t) is air-conditioning group's t dead band size, T_maxsetT () is on the air-conditioning design temperature of air-conditioning group's t Limit, T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t, T_maxIt is user's comfort temperature upper limit, INC rings for demand Answer finish time air-conditioning design temperature and the demand response start time air-conditioning design temperature amount of having a net increase of；

B () load diversity is constrained：

Wherein,

T(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The temperature at -1 moment,

T_average(T_{d_end}) it is T_{d_end}Indoor temperature average under moment baseline state,

T_inv(T_{d_end}) it is T_{d_end}Indoor temperature variance under moment baseline state,

S_open(T_{d_end}) it is T_{d_end}Air-conditioning group opens number of units under moment baseline state,

n₁(k, t) is in the status number of dwell period for the state queue model of kth platform air-conditioning t,

n₂(k, t) is in the status number of open stage for the state queue model of kth platform air-conditioning t,

T (k, t) for kth platform air-conditioning t temperature, k=1 ..., N,

T_{state_m-1}(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The lower limit of -1 moment status temperature,

T_{state_m}(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The upper limit of -1 moment status temperature,

r_down(k, t) is the rate of temperature fall of the kth platform air-conditioning t that state queue model draws,

r_up(k, t) is the heating rate of the kth platform air-conditioning t that state queue model draws,

T_maxsetT () is the air-conditioning design temperature upper limit of air-conditioning group's t,

T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t,

τ is the duration that each state is occupied,

S (k, t) is the on off state of kth platform air-conditioning t, wherein, S (k, t)=1 represents opens, and S (k, t)=0 represents Close,

T_{average_d}T () is t air-conditioning group indoor temperature average in the demand response time period,

T_{inv_d}T () is t air-conditioning group indoor temperature variance in the demand response time period,

S_{open_d}T () is that t air-conditioning group opens number of units in the demand response time period,

△₁It is the allowable error value of indoor temperature average under indoor temperature average under demand response state and baseline state,

△₂It is the allowable error value of indoor temperature variance under indoor temperature variance under demand response state and baseline state；

Load down constraint in (c) demand response time period：

Wherein, P_baseT () is that t air-conditioning cluster baseline runs power；

The change bound constraint of (d) room temperature：

Wherein, T_minIt is user's comfort temperature lower limit, T_maxIt is user's comfort temperature upper limit；

The minimum run time constraint of (e) air-conditioning：

Wherein, UT_kRepresent the minimum operation duration of kth platform air-conditioning, U_k ⁰Represent the initial launch duration of kth platform air-conditioning, G_k Initial time period of the air-conditioning after beginning is controlled is represented, in order to keep continuous with controlled preceding running status, operation is also at least needed Duration；

The minimum idle time constraint of (f) air-conditioning：

Wherein, DT_kRepresent the minimum stoppage in transit duration of kth platform air-conditioning；Represent the initial stoppage in transit duration of kth platform air-conditioning；L_kTable Show air-conditioning start it is controlled after initial time period, in order to keep continuous with controlled preceding running status, at least need to stop transport when It is long.

Further, air-conditioner temperature recalls to tactful as follows in the step (7)：

(I) object function is determined：Minimize load rebound amount

Wherein, P_realT () recalls to the power in the time period, the switch shape of M (k, t) kth platform air-conditioning t for design temperature State, wherein, M (k, t)=1 represents is opened, and M (k, t)=0 represents to be closed, T_{m_start}For air-conditioning design temperature recalls to the time period Start time, T_{m_end}The finish time of time period is recalled to for air-conditioning design temperature；

(I) constraints is set：

The adjustment constraint of (a) temperature：

Wherein,

T_{d_start}Carved at the beginning of for demand response event,

T_{d_end}It is the finish time of demand response event,

T_maxset(T_{m_end}) it is air-conditioning group T_{m_end}The air-conditioning design temperature upper limit at moment,

T_minset(T_{m_end}) it is air-conditioning group T_{m_end}The air-conditioning design temperature lower limit at moment,

T_maxset(T_{d_start}- 1) it is T_{d_start}The air-conditioning design temperature upper limit at -1 moment,

T_minset(T_{d_start}- 1) it is T_{d_start}The air-conditioning design temperature lower limit at -1 moment,

T_maxset(T_{d_end}) it is T_{d_end}The air-conditioning design temperature upper limit at moment,

INC is the finish time air-conditioning design temperature and demand response finish time sky that air-conditioning design temperature recalls to the time period Adjust the design temperature amount of having a net increase of；

The constraint constraint of (b) load diversity：

Wherein,

T_{average_m}T () is the air-conditioning cluster indoor temperature average that air-conditioning design temperature recalls to t in the time period,

T_{inv_m}T () is the air-conditioning cluster indoor temperature variance that air-conditioning design temperature recalls to t in the time period,

M_{open_m}T () is the air-conditioning cluster unlatching number of units that air-conditioning design temperature recalls to t in the time period,

T(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The temperature at -1 moment,

T_average(T_{m_end}) it is T_{m_end}Indoor temperature average under moment baseline state,

T_inv(T_{m_end}) it is T_{m_end}Indoor temperature variance under moment baseline state,

S_open(T_{m_end}) it is T_{m_end}Air-conditioning group opens number of units under moment baseline state,

n₁(k, t) is in the status number of dwell period for the state queue model of kth platform air-conditioning t,

n₂(k, t) is in the status number of open stage for the state queue model of kth platform air-conditioning t,

T (k, t) for kth platform air-conditioning t temperature, k=1 ..., N,

T_{state_m-1}(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The lower limit of -1 moment status temperature,

T_{state_m}(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The upper limit of -1 moment status temperature,

r_down(k, t) is the rate of temperature fall of the kth platform air-conditioning t that state queue model draws,

r_up(k, t) is the heating rate of the kth platform air-conditioning t that state queue model draws,

T_maxsetT () is the air-conditioning design temperature upper limit of air-conditioning group's t,

T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t,

τ is the duration that each state is occupied,

M (k, t) is the on off state of kth platform air-conditioning t, wherein, M (k, t)=1 represents opens, and M (k, t)=0 represents Close,

S_openT () is that t air-conditioning group opens number of units,

△₁The permission of time period air-conditioning cluster indoor temperature average and indoor temperature average under baseline state is recalled to for temperature Error amount,

△₂The permission of time period air-conditioning cluster indoor temperature variance and indoor temperature variance under baseline state is recalled to for temperature Error amount；

The change bound constraint of (c) room temperature：

Wherein, T_minIt is user's comfort temperature lower limit, T_maxIt is user's comfort temperature upper limit；

The minimum run time constraint of (d) air-conditioning：

Wherein, UT_kRepresent the minimum operation duration of kth platform air-conditioning, U_k ⁰Represent the initial launch duration of kth platform air-conditioning, G_k Initial time period of the air-conditioning after beginning is controlled is represented, in order to keep continuous with controlled preceding running status, operation is also at least needed Duration；

The minimum idle time constraint of (e) air-conditioning：

Wherein, DT_kRepresent the minimum stoppage in transit duration of kth platform air-conditioning；Represent the initial stoppage in transit duration of kth platform air-conditioning；L_k Initial time period of the air-conditioning after beginning is controlled is represented, in order to keep continuous with controlled preceding running status, at least needs what is stopped transport Duration.

Beneficial effect：Compared with prior art, control strategy of the invention fully excavated air conditioner load group regulation and control dive Power, load reduction as big as possible is realized in the demand response stage, is preferably power system service；Meanwhile, the method is filled Divide the comfort level for considering user, by the indoor temperature control of air-conditioning cluster in the range of user allows, in demand response The design temperature of user's air-conditioning is adjusted to former scope after end, it is to avoid generation influence is used on the normal of user；In addition, in demand Response policy and design temperature have taken into full account the load diversity of air-conditioning cluster in recalling to the design process of strategy, make its The uncontrolled stage can keep the load level close with baseline, reduce the impact to safe operation of power system.

Brief description of the drawings

Fig. 1 is the flow chart of the multifarious air conditioner load clustered control strategy of hold mode of the invention；

Fig. 2 is air-conditioning state queuing model schematic diagram；

Fig. 3 is the power curve comparison diagram that air conditioner load cluster control method is implemented；

Fig. 4 is the temperature variation that air conditioner load cluster control method is implemented.

Specific embodiment

The present invention program is described in further detail with specific embodiment below in conjunction with the accompanying drawings, so that this area Technical staff can be better understood from the present invention and can be practiced.

As illustrated in fig. 1 and 2, a kind of multifarious air conditioner load clustered control strategy of hold mode, comprises the following steps：

(1) air-conditioning to demand response (DR) user carries out parameter collection, builds air-conditioning state queuing model；

User's air-conditioning parameter of collection includes：Air-conditioning design temperature scope, the total number of units of user's air-conditioning, during the air-conditioning temperature rise period It is long, air-conditioning temperature-reducing period duration, air-conditioner switch state, room temperature numerical value residing for air-conditioning；

As shown in Fig. 2 abscissa represents the time, ordinate represents air-conditioner temperature, and the air-conditioning state queuing model of structure is：

It is (T that demand response sets temperature range_{m inset},T_{m axset}), a cycle of operation in air-conditioning design temperature is total to 1,2 are divided into ..., n state；Wherein, temperature ascent stage, i.e. air-conditioning dwell period, there is n₁Individual state；At a temperature of depression of order Section, i.e. air-conditioning open stage, there is n₂Individual state, wherein, n₁+n₂=n；τ is the duration that each state is occupied, air-conditioning closed mode When a length of n₁τ, when a length of n of opening₂τ, the corresponding dead band temperature range of each state is respectively：Wherein, T_{state_0}~T_{state_n}It is each state temperature terminal；The common N platforms of user's air-conditioning, the air-conditioning number of units of each state is in t Respectively：N₁,N₂,…,N_n。

Specifically, the setting temperature range of air-conditioning cluster is (23.5 DEG C, 24.5 DEG C), the one cycle of operation is divided It is 1,2 ..., 18 states, wherein temperature ascent stage, i.e. air-conditioning closed mode, including 10 states, the temperature drop stage, That is air-conditioning opening, including 8 states；Can be by the 1st to the 10th corresponding dead band of state in temperature ascent stage Temperature range is divided into：[23.5 DEG C, 23.6 DEG C), [23.6 DEG C, 23.7 DEG C), [23.7 DEG C, 23.8 DEG C) ..., [24.4 DEG C, 24.5℃]；In the temperature drop stage the 1st to the 8th corresponding temperature range of state is divided into：[24.375 DEG C, 24.5 DEG C), [24.25,24.375 DEG C), [24.125 DEG C, 24.25 DEG C) ..., [23.5 DEG C, 23.625 DEG C]；Each state duration τ is 1 minute.Assuming that air conditioner load group has 50 air-conditionings, it is randomly dispersed in this 18 states, according to the state queue set up Model carries out baseline simulations and therefrom extracts each moment air-conditioning group's indoor temperature average, air-conditioning group's indoor temperature variance and sky Tune to open and open number of units.

(2) judge whether user carries out demand accordingly, if it is not, directly performing step (8)；If so, performing step (3)；

(3) determine the air conditioner load cluster demand response time period, and extract under demand response finish time baseline state Air-conditioning cluster load diversity index, including air-conditioning group's indoor temperature average, air-conditioning group's indoor temperature variance and unlatching number of units； Specific formula for calculation is：

(a) air-conditioning group's indoor temperature average

Wherein, T_averageT () is t air-conditioning group's indoor temperature average, T under baseline state_{state_i-1}For under baseline state I-th lower limit of state dead band temperature range, T_{state_i}It is i-th upper limit of state dead band temperature range, N under baseline state_iFor T is in i-th air-conditioning number of units of state under baseline state；

(b) air-conditioning group's indoor temperature variance

Wherein, T_invT () is t air-conditioning group's indoor temperature variance under baseline state；

C () air-conditioning group opens number of units

Wherein, S_openFor t air-conditioning group opens number of units under (t) baseline state.

(4) the air-conditioning cluster load diversity index and users'comfort under demand response finish time baseline state are combined Scope is formulated response policy and is implemented, and air-conditioning design temperature then is adjusted into new scope；

The air-conditioning cluster demand response strategy of consideration air conditioner load group's load diversity index and users'comfort scope is such as Under：

(I) object function is determined：Maximize load reduction

max(P_cut)=max (P_singleN_cut) t=T_{d_start},…,T_{d_end}-1 (4)

Wherein, P_cutIt is the minimum load reduction that can be reached in the demand response time period, P_singleIt is single air conditioner work( Rate, N_cutIt is decision variable to be optimized, as the air-conditioning number of units of the actual reduction in the demand response time period, T_{d_start}It is demand Carved at the beginning of response events, T_{d_end}It is the finish time of demand response event；

(II) constraints is set：

The adjustment constraint of (a) temperature：

Wherein, DB (t) is air-conditioning group's t dead band size, T_maxsetT () is on the air-conditioning design temperature of air-conditioning group's t Limit, T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t, T_maxIt is user's comfort temperature upper limit, INC rings for demand Answer finish time air-conditioning design temperature and the demand response start time air-conditioning design temperature amount of having a net increase of；The purpose of the constraint be by The air-conditioning design temperature of finish time in demand response stage is adjusted to new temperature range.

B () load diversity is constrained：

Wherein,

T(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The temperature at -1 moment,

T_average(T_{d_end}) it is T_{d_end}Indoor temperature average under moment baseline state,

T_inv(T_{d_end}) it is T_{d_end}Indoor temperature variance under moment baseline state,

S_open(T_{d_end}) it is T_{d_end}Air-conditioning group opens number of units under moment baseline state,

n₁(k, t) is in the status number of dwell period for the state queue model of kth platform air-conditioning t,

n₂(k, t) is in the status number of open stage for the state queue model of kth platform air-conditioning t,

T (k, t) for kth platform air-conditioning t temperature, k=1 ..., N,

T_{state_m-1}(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The lower limit of -1 moment status temperature,

T_{state_m}(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The upper limit of -1 moment status temperature,

r_down(k, t) is the rate of temperature fall of the kth platform air-conditioning t that state queue model draws,

r_up(k, t) is the heating rate of the kth platform air-conditioning t that state queue model draws,

T_maxsetT () is the air-conditioning design temperature upper limit of air-conditioning group's t,

T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t,

τ is the duration that each state is occupied,

S (k, t) is the on off state of kth platform air-conditioning t, wherein, S (k, t)=1 represents opens, and S (k, t)=0 represents Close,

T_{average_d}T () is t air-conditioning group indoor temperature average in the demand response time period,

T_{inv_d}T () is t air-conditioning group indoor temperature variance in the demand response time period,

S_{open_d}T () is that t air-conditioning group opens number of units in the demand response time period,

△₁It is the allowable error value of indoor temperature average under indoor temperature average under demand response state and baseline state,

△₂It is the allowable error value of indoor temperature variance under indoor temperature variance under demand response state and baseline state；

The constraint purpose is that the load diversity index of demand response finish time is kept into or phase consistent with baseline state Closely.

Load down constraint in (c) demand response time period：

Wherein P_baseT () is that t air-conditioning cluster baseline runs power；The meaning of the formula is when ensureing demand response Between load reduction in section be greater than P all the time_cut。

The change bound constraint of (d) room temperature：

Wherein, T_minIt is user's comfort temperature lower limit, T_maxIt is user's comfort temperature upper limit；It should be noted that in T_{d_end}When Carve air-conditioning design temperature and re-start setting；The purpose of the constraint is to ensure the comfort level of user and setting temperature control In constant temperature degree dead band.

The minimum run time constraint of (e) air-conditioning：

Wherein, UT_kRepresent the minimum operation duration of kth platform air-conditioning, U_k ⁰Represent the initial launch duration of kth platform air-conditioning, G_k Initial time period of the air-conditioning after beginning is controlled is represented, in order to keep continuous with controlled preceding running status, operation is also at least needed Duration；The purpose of the constraint is the service life for preventing from reducing air-conditioning in order to reduce the start-stop time of air-conditioning, and air-conditioning minimum is stopped Fortune time-constrain purpose is same.

The minimum idle time constraint of (f) air-conditioning：

Wherein, DT_kRepresent the minimum stoppage in transit duration of kth platform air-conditioning；Represent the initial stoppage in transit duration of kth platform air-conditioning；L_kTable Show air-conditioning start it is controlled after initial time period, in order to keep continuous with controlled preceding running status, at least need to stop transport when It is long.

N in object function can be drawn according to above-mentioned object function and constraints_cutMaximum and air conditioner load Plan for start-up and shut-down S (k, t) of group, is capable of achieving load and cuts down and ensure load according to the plan for start-up and shut-down control air conditioner load group for formulating Diversity and demand response finish time air-conditioning group design temperature adjust to new temperature range.

Wherein, parameter setting is：Single air conditioner power P_single3.5kW is set to, demand response start time is 11：30, Finish time is 12:00, rate of temperature fall r_down(k, t) and heating rate r_up(k, t) be respectively -0.125 DEG C/min and 0.1 DEG C/ Min, user's comfort temperature lower limit T_minWith user's comfort temperature upper limit T_maxRespectively 23.5 DEG C and 27 DEG C, demand response state The allowable error value △ of indoor temperature average under lower indoor temperature average and baseline state₁With indoor temperature under demand response state The allowable error value △ of indoor temperature variance under variance and baseline state₂Sets itself as needed, during the minimum operation of air-conditioning UT long_kIt is 2 minutes, the minimum stoppage in transit duration DT of air-conditioning_kIt is 3 minutes.

The implementation result of air conditioning requirements response policy is as shown in Figure 3 and Figure 4.Be can be seen that from the power curve of Fig. 3 Demand response implementation phase, i.e., 11：30 to 12:00, the operation power of air conditioner load group is significantly reduced, and ensure that user Residing temperature is no more than comfort level setting range (see Fig. 4).After the demand response time period terminates on the design temperature of air-conditioning Lower limit is adjusted to 27 DEG C and 26 DEG C, 12:00 to 12：Air conditioner load group is in uncontrolled state in 30 time period, still can The load level close with baseline is kept, so as to avoid load vibration.It can thus be seen that the air conditioning requirements that the present invention is given Response policy can well keep the diversity of load and effectively realize load reduction.

(5) judge whether to air-conditioning design temperature to recall to, if it is not, directly performing step (8)；If so, performing step (6)；

(6) determine that air-conditioning design temperature is recalled to the time period, Extracting temperature is recalled under finish time time period baseline state Air-conditioning cluster load diversity index；

(7) formulated with reference to load diversity index and users'comfort scope and recall to strategy and assign, then set air-conditioning Constant temperature degree recalls to setting range before response；

After start-up and shut-down control being carried out to air-conditioning group and implements demand response, air conditioner load cluster is in order to ensure the various of load Property has simultaneously been brought up within a new temperature range design temperature of air-conditioning, and the load curve of air-conditioning cluster can have and base The close load level of load curve under wire state, does not result in load curve vibration.But, the air-conditioning after demand response terminates Design temperature is higher, and maintaining the design temperature to run for a long time may not adapt to the change of weather user's is comfortable so as to influence Degree, it is therefore desirable to which consider air-conditioning design temperature recalls to strategy, the design temperature of air-conditioning before recalling to demand response.It is empty formulating Tone group design temperature should determine the time period that design temperature is recalled to before recalling to strategy according to system loading situation.

It is as follows that air-conditioner temperature recalls to strategy：

(I) object function is determined：Minimize load rebound amount

Wherein, P_realT () recalls to the power in the time period, the switch shape of M (k, t) kth platform air-conditioning t for design temperature State, wherein, M (k, t)=1 represents is opened, and M (k, t)=0 represents to be closed, T_{m_start}For air-conditioning design temperature recalls to the time period Start time, T_{m_end}The finish time of time period is recalled to for air-conditioning design temperature；The purpose of the object function is to set air-conditioning The peak power that constant temperature degree was recalled in the time period is minimized.

(II) constraints is set：

The adjustment constraint of (a) temperature：

Wherein,

T_{d_start}Carved at the beginning of for demand response event,

T_{d_end}It is the finish time of demand response event,

T_maxset(T_{m_end}) it is air-conditioning group T_{m_end}The air-conditioning design temperature upper limit at moment,

T_minset(T_{m_end}) it is air-conditioning group T_{m_end}The air-conditioning design temperature lower limit at moment,

T_maxset(T_{d_start}- 1) it is T_{d_start}The air-conditioning design temperature upper limit at -1 moment,

T_minset(T_{d_start}- 1) it is T_{d_start}The air-conditioning design temperature lower limit at -1 moment,

T_maxset(T_{d_end}) it is T_{d_end}The air-conditioning design temperature upper limit at moment,

INC is the finish time air-conditioning design temperature and demand response finish time sky that air-conditioning design temperature recalls to the time period Adjust the design temperature amount of having a net increase of；

The purpose of the constraint is to be adjusted to and demand response rank the air-conditioning design temperature of finish time in demand response stage Design temperature scope before section starts is identical.

The constraint constraint of (b) load diversity：

Wherein,

T_{average_m}T () is the air-conditioning cluster indoor temperature average that air-conditioning design temperature recalls to t in the time period,

T_{inv_m}T () is the air-conditioning cluster indoor temperature variance that air-conditioning design temperature recalls to t in the time period,

M_{open_m}T () is the air-conditioning cluster unlatching number of units that air-conditioning design temperature recalls to t in the time period,

T(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The temperature at -1 moment,

T_average(T_{m_end}) it is T_{m_end}Indoor temperature average under moment baseline state,

T_inv(T_{m_end}) it is T_{m_end}Indoor temperature variance under moment baseline state,

S_open(T_{m_end}) it is T_{m_end}Air-conditioning group opens number of units under moment baseline state,

n₁(k, t) is in the status number of dwell period for the state queue model of kth platform air-conditioning t,

n₂(k, t) is in the status number of open stage for the state queue model of kth platform air-conditioning t,

T (k, t) for kth platform air-conditioning t temperature, k=1 ..., N,

T_{state_m-1}(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The lower limit of -1 moment status temperature,

T_{state_m}(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The upper limit of -1 moment status temperature,

r_down(k, t) is the rate of temperature fall of the kth platform air-conditioning t that state queue model draws,

r_up(k, t) is the heating rate of the kth platform air-conditioning t that state queue model draws,

T_maxsetT () is the air-conditioning design temperature upper limit of air-conditioning group's t,

T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t,

τ is the duration that each state is occupied,

M (k, t) is the on off state of kth platform air-conditioning t, wherein, M (k, t)=1 represents opens, and M (k, t)=0 represents Close,

S_openT () is that t air-conditioning group opens number of units,

△₁The permission of time period air-conditioning cluster indoor temperature average and indoor temperature average under baseline state is recalled to for temperature Error amount,

△₂The permission of time period air-conditioning cluster indoor temperature variance and indoor temperature variance under baseline state is recalled to for temperature Error amount；

The change bound constraint of (c) room temperature：

Wherein, T_minIt is user's comfort temperature lower limit, T_maxIt is user's comfort temperature upper limit；

The minimum run time constraint of (d) air-conditioning：

Wherein, UT_kRepresent the minimum operation duration of kth platform air-conditioning, U_k ⁰Represent the initial launch duration of kth platform air-conditioning, G_k Initial time period of the air-conditioning after beginning is controlled is represented, in order to keep continuous with controlled preceding running status, operation is also at least needed Duration；

The minimum idle time constraint of (e) air-conditioning：

Wherein, DT_kRepresent the minimum stoppage in transit duration of kth platform air-conditioning；Represent the initial stoppage in transit duration of kth platform air-conditioning；L_kTable Show air-conditioning start it is controlled after initial time period, in order to keep continuous with controlled preceding running status, at least need to stop transport when It is long.

Recalling to strategy by the air-conditioning design temperature can suppress to lower air-conditioner temperature while load diversity is ensured The load vibration that set point is brought, makes the design temperature scope of air conditioner load group return to the design temperature model before demand response Enclose and keep the load level close with baseline.

Wherein the design parameter of object function and constraints sets as follows：

Design temperature recalls to start time time period for 12:30, finish time is 13:00, in remaining parameter and step 3 Parameter setting is identical.

Air-conditioning design temperature recalls to implementation of strategies effect as shown in Figure 3 and Figure 4.Can from the power curve of Fig. 3 Go out, recalled to the stage in design temperature, i.e., 12:30 to 13：00, the stability of maximum load is effectively maintained, reduce peak valley Difference, is conducive to the safe operation of power system.Design temperature recall to the time period terminate after air-conditioning design temperature bound it is extensive It is again 24.5 DEG C and 23.5 DEG C (see Fig. 4), 13:00 to 13：Air conditioner load group is in uncontrolled state in 30 time period, protects The load level close with baseline is held, load vibration is efficiently avoid.It can thus be seen that the air-conditioning setting that the present invention is given Temperature recalls to the stabilization that strategy can well keep the diversity of load and effectively maintain maximum load, is conducive to power system Scheduling and operation.

Claims (5)

1. the multifarious air conditioner load clustered control strategy of a kind of hold mode, it is characterised in that comprise the following steps：

(1) air-conditioning parameter of user is collected, air-conditioning state queuing model is built；

(2) judge whether user carries out demand response, if carrying out demand response, perform step (3)；Conversely, performing step (8)；

(3) determine the air conditioner load cluster demand response time period, and extract the air-conditioning under demand response finish time baseline state Cluster load diversity index, including air-conditioning group's indoor temperature average, air-conditioning group's indoor temperature variance and unlatching number of units；

(4) the air-conditioning cluster load diversity index and users'comfort scope under demand response finish time baseline state are combined Formulate response policy and implement, air-conditioning design temperature is then adjusted to new scope；

(5) judge whether to air-conditioning design temperature to recall to, if desired carry out air-conditioning design temperature readjustment, perform step (6)；Instead It, performs step (8)；

(6) determine that air-conditioning design temperature is recalled to the time period, Extracting temperature recalls to the air-conditioning under finish time time period baseline state Cluster load diversity index；

(7) formulated with reference to load diversity index and users'comfort scope and recall to strategy and assign, air-conditioning is then set into temperature Degree recalls to setting range before response；

(8) terminate.

2. the multifarious air conditioner load clustered control strategy of a kind of hold mode according to claim 1, it is characterised in that The air-conditioning state queuing model of structure is in the step (1)：

Setting air conditioning requirements response temperature range, is divided into 1,2 ... altogether by a cycle of operation in air-conditioning design temperature, n State；Wherein, there is n in temperature ascent stage, i.e. air-conditioning dwell period₁Individual state；Temperature drop stage, i.e. air-conditioning open rank , there is n in section₂Individual state, wherein, n₁+n₂=n；The corresponding dead band temperature range of each state is respectively：Wherein, T_{state_0}~T_{state_n}It is each state temperature terminal；The common N platforms of user's air-conditioning, the air-conditioning number of units of each state is in t Respectively：N₁,N₂,…,N_n。

3. the multifarious air conditioner load clustered control strategy of a kind of hold mode according to claim 2, it is characterised in that Air-conditioning cluster load diversity index extraction in the step (3) under baseline state is as follows：

Determine to need to carry out the time period of demand response in advance, the time period can be drawn by the load curve of similar day；Baseline shape Air-conditioning cluster load diversity index extraction formula under state is as follows：

(a) air-conditioning group's indoor temperature average

$T_{average}\left(t\right)=\left(\underset{i=1}{\overset{n}{\Σ}}\right(\left(T_{state\_i-1}+T_{state\_i}\right)N_i/2\left)\right)/N---\left(1\right)$

Wherein, T_averageT () is t air-conditioning group's indoor temperature average, T under baseline state_{state_i-1}For i-th under baseline state The lower limit of state dead band temperature range, T_{state_i}It is i-th upper limit of state dead band temperature range, N under baseline state_iIt is baseline T is in i-th air-conditioning number of units of state under state；

(b) air-conditioning group's indoor temperature variance

$T_{inv}\left(t\right)=\left(\underset{i=1}{\overset{n}{\Σ}}{\left(\left(T_{state\_i-1}+T_{state\_i}\right)/2-T_{average}\right)}^2{N}_i\right)/N---\left(2\right)$

Wherein, T_invT () is t air-conditioning group's indoor temperature variance under baseline state；

C () air-conditioning group opens number of units

$S_{open}\left(t\right)=\underset{i=n_1+1}{\overset{n}{\Σ}}{N}_i---\left(3\right)$

Wherein, S_openT () is t air-conditioning group's unlatching number of units under baseline state.

4. the multifarious air conditioner load clustered control strategy of a kind of hold mode according to claim 3, it is characterised in that The air-conditioning cluster load diversity index combined under demand response finish time baseline state in the step (4) is comfortable with user It is as follows that degree scope formulates response policy：

(I) object function is determined：Maximize load reduction

max(P_cut)=max (P_singleN_cut) t=T_{d_start},…,T_{d_end}-1 (4)

Wherein, P_cutIt is the minimum load reduction that can be reached in the demand response time period, P_singleIt is single air conditioner power, N_cutIt is decision variable to be optimized, as the air-conditioning number of units of the actual reduction in the demand response time period, T_{d_start}It is demand response Carved at the beginning of event, T_{d_end}It is the finish time of demand response event；

(II) constraints is set：

The adjustment constraint of (a) temperature：

$\left\{\begin{array}{c}DB\left(t\right)=T_{\max set}\left(t\right)-T_{\min set}\left(t\right)\\ T_{\max set}\left(T_{d\_end}\right)=T_{\max }\\ T_{\min set}\left(T_{d\_end}\right)=T_{\max }-DB\left(T_{d\_start}\right)\\ INC=T_{\max }-T_{\max set}(T_{d\_start}-1)\end{array}\right.---\left(5\right)$

Wherein, DB (t) is air-conditioning group's t dead band size, T_maxsetT () is the air-conditioning design temperature upper limit of air-conditioning group's t, T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t, T_maxIt is user's comfort temperature upper limit, INC is demand response knot Beam moment air-conditioning design temperature and the demand response start time air-conditioning design temperature amount of having a net increase of；

B () load diversity is constrained：

$\left\{\begin{array}{c}T(k,T_{d\_start}-1)=\left(T_{state\_m-1}\right(k,T_{d\_start}-1)+T_{state\_m}(k,T_{d\_start}-1\left)\right)/2\\ r_{down}(k,t)=-\left(T_{\max set}\right(t)-T_{\min set}(t\left)\right)/\left(n_2\right(k,t\left)\mathrm{\τ}\right)\\ r_{up}(k,t)=\left(T_{\max set}\right(t)-T_{\min set}(t\left)\right)/\left(n_1\right(k,t\left)\mathrm{\τ}\right)\\ T(k,t)=T(k,T_{d\_start}-1)+\underset{i=T_{d\_start}}{\overset{t}{\Σ}}\left(r_{down}\right(k,i\left)S\right(k,i)+r_{up}(k,i\left)\right(1-S\left(k,i\right)\left)\right)\\ T_{average\_d}\left(t\right)=\left(\underset{k=1}{\overset{N}{\Σ}}T\right(k,t\left)\right)/N\\ T_{inv\_d}\left(t\right)=\left(\underset{k=1}{\overset{N}{\Σ}}{\left(T\left(k,t\right)-T_{average\_d}\left(t\right)\right)}^2\right)/N\\ S_{open\_d}\left(t\right)=\underset{k=1}{\overset{N}{\Σ}}S(k,t)\\ T_{average}\left(T_{d\_end}\right)+INC-{\mathrm{\Δ}}_1\≤T_{average\_d}\left(T_{d\_end}\right)\≤T_{average}\left(T_{d\_end}\right)+INC+{\mathrm{\Δ}}_1\\ T_{inv}\left(T_{d\_end}\right)-{\mathrm{\Δ}}_2\≤T_{inv\_d}\left(T_{d\_end}\right)\≤T_{inv}\left(T_{d\_end}\right)+{\mathrm{\Δ}}_2\\ S_{open\_d}\left(T_{d\_end}\right)=S_{open}\left(T_{d\_end}\right)\end{array}\right.---\left(6\right)$

Wherein,

T(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The temperature at -1 moment,

T_average(T_{d_end}) it is T_{d_end}Indoor temperature average under moment baseline state,

T_inv(T_{d_end}) it is T_{d_end}Indoor temperature variance under moment baseline state,

S_open(T_{d_end}) it is T_{d_end}Air-conditioning group opens number of units under moment baseline state,

n₁(k, t) is in the status number of dwell period for the state queue model of kth platform air-conditioning t,

n₂(k, t) is in the status number of open stage for the state queue model of kth platform air-conditioning t,

T (k, t) for kth platform air-conditioning t temperature, k=1 ..., N,

T_{state_m-1}(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The lower limit of -1 moment status temperature,

T_{state_m}(k,T_{d_start}- 1) it is kth platform air-conditioning T_{d_start}The upper limit of -1 moment status temperature,

r_down(k, t) is the rate of temperature fall of the kth platform air-conditioning t that state queue model draws,

r_up(k, t) is the heating rate of the kth platform air-conditioning t that state queue model draws,

T_maxsetT () is the air-conditioning design temperature upper limit of air-conditioning group's t,

T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t,

τ is the duration that each state is occupied,

S (k, t) is the on off state of kth platform air-conditioning t, wherein, S (k, t)=1 represents opens, and S (k, t)=0 represents closes Close,

T_{average_d}T () is t air-conditioning group indoor temperature average in the demand response time period,

T_{inv_d}T () is t air-conditioning group indoor temperature variance in the demand response time period,

S_{open_d}T () is that t air-conditioning group opens number of units in the demand response time period,

△₁It is the allowable error value of indoor temperature average under indoor temperature average under demand response state and baseline state,

△₂It is the allowable error value of indoor temperature variance under indoor temperature variance under demand response state and baseline state；

Load down constraint in (c) demand response time period：

$P_{base}\left(t\right)-\underset{k=1}{\overset{N}{\Σ}}{P}_{\sin gle}S(k,t)\≥P_{\sin gle}{N}_{cut}---\left(7\right)$

Wherein, P_baseT () is that t air-conditioning cluster baseline runs power；

The change bound constraint of (d) room temperature：

$\left\{\begin{array}{c}{T}_{\min }\≤T(k,t)\≤T_{\max }\\ T_{\min set}\left(T_{d\_end}\right)\≤T(k,T_{d\_end})\≤T_{\max set}\left(T_{d\_end}\right)\end{array}\right.---\left(8\right)$

Wherein, T_minIt is user's comfort temperature lower limit, T_maxIt is user's comfort temperature upper limit；

The minimum run time constraint of (e) air-conditioning：

$\left\{\begin{array}{c}\underset{t=T_{d\_start}}{\overset{T_{d\_start}+G_k-1}{\Σ}}\[1-S(k,t)\]=0\\ \begin{array}{cc}\underset{t=m}{\overset{m+{\mathrm{UT}}_k-1}{\Σ}}S(k,t)\≥{\mathrm{UT}}_k\[S(k,t)-S(k,t-1)\]& (\∀m=T_{d\_start}+G_k\mathrm{...},T_{d\_end}-{\mathrm{UT}}_k+1)\end{array}\\ \begin{array}{cc}\underset{t=m}{\overset{T_{d\_end}}{\Σ}}\{S(k,t)-\[S(k,n)-S(k,n-1)\]\}\≥0& (\∀n=T_{d\_end}-{\mathrm{UT}}_k+\mathrm{2...},T_{d\_end})\end{array}\\ G_k=\min \{(T_{d\_end}-T_{d\_start}+1),\[{\mathrm{UT}}_k-{U_k}^0\]S(k,0)\}\end{array}\right.---\left(9\right)$

Wherein, UT_kRepresent the minimum operation duration of kth platform air-conditioning, U_k ⁰Represent the initial launch duration of kth platform air-conditioning, G_kRepresent empty The initial time period after beginning is controlled is adjusted, in order to keep continuous with controlled preceding running status, the duration of operation is also at least needed；

The minimum idle time constraint of (f) air-conditioning：

$\left\{\begin{array}{c}\underset{t=T_{d\_start}}{\overset{T_{d\_start}+L_k-1}{\Σ}}S(k,t)=0\\ \begin{array}{cc}\underset{t=m}{\overset{m+{\mathrm{DT}}_k-1}{\Σ}}\[1-S(k,t)\]\≥{\mathrm{DT}}_k\[S(k,t-1)-S(k,t)\]& (\∀m=T_{d\_start}+L_k\mathrm{...},T_{d\_end}-{\mathrm{DT}}_k+1)\end{array}\\ \begin{array}{cc}\underset{t=m}{\overset{T_{d\_end}}{\Σ}}\{1-S(k,t)-\[S(k,n-1)-S(k,n)\]\}\≥0& (\∀n=T_{d\_end}-{\mathrm{DT}}_k+\mathrm{2...},T_{d\_end})\end{array}\\ L_k=\min \{(T_{d\_end}-T_{d\_start}+1),\[{\mathrm{DT}}_k-Q_k^0\]\[1-S(k,0)\]\}\end{array}\right.---\left(10\right)$

Wherein, DT_kThe minimum stoppage in transit duration of kth platform air-conditioning is represented,Represent the initial stoppage in transit duration of kth platform air-conditioning, L_kRepresent empty The initial time period after beginning is controlled is adjusted, in order to keep continuous with controlled preceding running status, the duration stopped transport at least is needed.

5. a kind of multifarious air conditioner load clustered control strategy of hold mode according to claim 4, its feature exists In air-conditioner temperature is recalled to tactful as follows in the step (7)：

(I) object function is determined：Minimize load rebound amount

$\min \left(\max \right(P_{real}\left(t\right)\left)\right)=\min \left(\max \right(P_{\sin gle}\underset{k=1}{\overset{N}{\Σ}}M\left(k,t\right)\left)\right),t=T_{m\_start},\mathrm{...},T_{m\_end}---\left(11\right)$

Wherein, P_realT () recalls to the power in the time period for design temperature, the on off state of M (k, t) kth platform air-conditioning t, its In, M (k, t)=1 represents is opened, and M (k, t)=0 represents to be closed, T_{m_start}At the beginning of the time period being recalled to for air-conditioning design temperature Carve, T_{m_end}The finish time of time period is recalled to for air-conditioning design temperature；

(II) constraints is set：

The adjustment constraint of (a) temperature：

$\left\{\begin{array}{c}{T}_{\max set}\left(T_{m\_end}\right)=T_{\max set}(T_{d\_start}-1)\\ T_{\min set}\left(T_{m\_end}\right)=T_{\min set}(T_{d\_start}-1)\\ INC=T_{\max set}\left(T_{m\_end}\right)-T_{\max set}\left(T_{d\_end}\right)\end{array}\right.---\left(12\right)$

Wherein,

T_{d_start}Carved at the beginning of for demand response event,

T_{d_end}It is the finish time of demand response event,

T_maxset(T_{m_end}) it is air-conditioning group T_{m_end}The air-conditioning design temperature upper limit at moment,

T_minset(T_{m_end}) it is air-conditioning group T_{m_end}The air-conditioning design temperature lower limit at moment,

T_maxset(T_{d_start}- 1) it is T_{d_start}The air-conditioning design temperature upper limit at -1 moment,

T_minset(T_{d_start}- 1) it is T_{d_start}The air-conditioning design temperature lower limit at -1 moment,

T_maxset(T_{d_end}) it is T_{d_end}The air-conditioning design temperature upper limit at moment,

INC recalls to the finish time air-conditioning design temperature of time period and demand response finish time air-conditioning sets for air-conditioning design temperature The constant temperature degree amount of having a net increase of；

The constraint constraint of (b) load diversity：

$\left\{\begin{array}{c}T(k,T_{m\_start}-1)=\left(T_{state\_m-1}\right(k,T_{m\_start}-1)+T_{state\_m}(k,T_{m\_start}-1\left)\right)/2\\ r_{down}(k,t)=-\left(T_{\max set}\right(t)-T_{\min set}(t\left)\right)/\left(n_2\right(k,t\left)\mathrm{\τ}\right)\\ r_{up}(k,t)=\left(T_{\max set}\right(t)-T_{\min set}(t\left)\right)/\left(n_1\right(k,t\left)\mathrm{\τ}\right)\\ T(k,t)=T(k,T_{m\_start}-1)+\underset{i=T_{m\_start}}{\overset{t}{\Σ}}\left(r_{down}\right(k,i\left)M\right(k,i)+r_{up}(k,i\left)\right(1-M\left(k,i\right)\left)\right)\\ T_{average\_m}\left(t\right)=\left(\underset{k=1}{\overset{N}{\Σ}}T\right(k,t\left)\right)/N\\ T_{inv\_m}\left(t\right)=\left(\underset{k=1}{\overset{N}{\Σ}}{\left(T\left(k,t\right)-T_{average\_m}\left(t\right)\right)}^2\right)/N\\ S_{open\_m}\left(t\right)=\underset{k=1}{\overset{N}{\Σ}}M(k,t)\\ T_{average}\left(T_{m\_end}\right)+INC-{\mathrm{\Δ}}_1\≤T_{average\_m}\left(T_{m\_end}\right)\≤T_{average}\left(T_{m\_end}\right)+INC+{\mathrm{\Δ}}_1\\ T_{inv}\left(T_{m\_end}\right)-{\mathrm{\Δ}}_2\≤T_{inv\_m}\left(T_{m\_end}\right)\≤T_{inv}\left(T_{m\_end}\right)+{\mathrm{\Δ}}_2\\ M_{open\_m}\left(T_{m\_end}\right)=S_{open}\left(T_{m\_end}\right)\end{array}\right.---\left(13\right)$

Wherein,

T_{average_m}T () is the air-conditioning cluster indoor temperature average that air-conditioning design temperature recalls to t in the time period,

T_{inv_m}T () is the air-conditioning cluster indoor temperature variance that air-conditioning design temperature recalls to t in the time period,

M_{open_m}T () is the air-conditioning cluster unlatching number of units that air-conditioning design temperature recalls to t in the time period,

T(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The temperature at -1 moment,

T_average(T_{m_end}) it is T_{m_end}Indoor temperature average under moment baseline state,

T_inv(T_{m_end}) it is T_{m_end}Indoor temperature variance under moment baseline state,

S_open(T_{m_end}) it is T_{m_end}Air-conditioning group opens number of units under moment baseline state,

n₁(k, t) is in the status number of dwell period for the state queue model of kth platform air-conditioning t,

n₂(k, t) is in the status number of open stage for the state queue model of kth platform air-conditioning t,

T (k, t) for kth platform air-conditioning t temperature, k=1 ..., N,

T_{state_m-1}(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The lower limit of -1 moment status temperature,

T_{state_m}(k,T_{m_start}- 1) it is kth platform air-conditioning T_{m_start}The upper limit of -1 moment status temperature,

r_down(k, t) is the rate of temperature fall of the kth platform air-conditioning t that state queue model draws,

r_up(k, t) is the heating rate of the kth platform air-conditioning t that state queue model draws,

T_maxsetT () is the air-conditioning design temperature upper limit of air-conditioning group's t,

T_minsetT () is the air-conditioning design temperature lower limit of air-conditioning group's t,

τ is the duration that each state is occupied,

M (k, t) is the on off state of kth platform air-conditioning t, wherein, M (k, t)=1 represents opens, and M (k, t)=0 represents closes Close,

S_openT () is that t air-conditioning group opens number of units,

△₁The allowable error of time period air-conditioning cluster indoor temperature average and indoor temperature average under baseline state is recalled to for temperature Value,

△₂The allowable error of time period air-conditioning cluster indoor temperature variance and indoor temperature variance under baseline state is recalled to for temperature Value；

The change bound constraint of (c) room temperature：

$\left\{\begin{array}{c}{T}_{\min }\≤T(k,t)\≤T_{\max }\\ T_{\min set}\left(T_{m\_end}\right)\≤T(k,T_{m\_end})\≤T_{\max set}\left(T_{m\_end}\right)\end{array}\right.---\left(14\right)$

Wherein, T_minIt is user's comfort temperature lower limit, T_maxIt is user's comfort temperature upper limit；

The minimum run time constraint of (d) air-conditioning：

$\left\{\begin{array}{c}\underset{t=T_{m\_start}}{\overset{T_{m\_start}+G_k-1}{\Σ}}\[1-M(k,t)\]=0\\ \begin{array}{cc}\underset{t=m}{\overset{m+{\mathrm{UT}}_k-1}{\Σ}}M(k,t)\≥{\mathrm{UT}}_k\[M(k,t)-M(k,t-1)\]& (\∀m=T_{m\_start}+G_k\mathrm{...},T_{m\_end}-{\mathrm{UT}}_k+1)\end{array}\\ \begin{array}{cc}\underset{t=m}{\overset{T_{m\_end}}{\Σ}}\{M(k,t)-\[M(k,n)-M(k,n-1)\]\}\≥0& (\∀n=T_{m\_end}-{\mathrm{UT}}_k+\mathrm{2...},T_{m\_end})\end{array}\\ G_k=\min \{(T_{m\_end}-T_{m\_start}+1),\[{\mathrm{UT}}_k-U_k^0\]M(k,0)\}\end{array}\right.---\left(15\right)$

Wherein, UT_kRepresent the minimum operation duration of kth platform air-conditioning, U_k ⁰Represent the initial launch duration of kth platform air-conditioning, G_kRepresent empty The initial time period after beginning is controlled is adjusted, in order to keep continuous with controlled preceding running status, the duration of operation is also at least needed；

The minimum idle time constraint of (e) air-conditioning：

$\left\{\begin{array}{c}\underset{t=T_{m\_start}}{\overset{T_{m\_start}+L_k-1}{\Σ}}M(k,t)=0\\ \begin{array}{cc}\underset{t=m}{\overset{m+{\mathrm{DT}}_k-1}{\Σ}}\[1-M(k,t)\]\≥{\mathrm{DT}}_k\[M(k,t-1)-M(k,t)\]& (\∀m=T_{m\_start}+L_k\mathrm{...},T_{m\_end}-{\mathrm{DT}}_k+1)\end{array}\\ \begin{array}{cc}\underset{t=m}{\overset{T_{d\_end}}{\Σ}}\{1-M(k,t)-\[M(k,n-1)-M(k,n)\]\}\≥0& (\∀n=T_{m\_end}-{\mathrm{DT}}_k+\mathrm{2...},T_{m\_end})\end{array}\\ L_k=\min \{(T_{m\_end}-T_{m\_start}+1),\[{\mathrm{DT}}_k-Q_k^0\]\[1-M(k,0)\]\}\end{array}\right.---\left(16\right)$

Wherein, DT_kThe minimum stoppage in transit duration of kth platform air-conditioning is represented,Represent the initial stoppage in transit duration of kth platform air-conditioning, L_kRepresent empty The initial time period after beginning is controlled is adjusted, in order to keep continuous with controlled preceding running status, the duration stopped transport at least is needed.