CN115344070B - Air conditioner energy optimization method based on combination control of temperature set value and main power switch - Google Patents
Air conditioner energy optimization method based on combination control of temperature set value and main power switch Download PDFInfo
- Publication number
- CN115344070B CN115344070B CN202210950456.9A CN202210950456A CN115344070B CN 115344070 B CN115344070 B CN 115344070B CN 202210950456 A CN202210950456 A CN 202210950456A CN 115344070 B CN115344070 B CN 115344070B
- Authority
- CN
- China
- Prior art keywords
- air conditioner
- temperature
- equation
- model
- indoor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000011217 control strategy Methods 0.000 claims abstract description 31
- 238000012937 correction Methods 0.000 claims abstract description 15
- 238000005265 energy consumption Methods 0.000 claims abstract description 9
- 238000004378 air conditioning Methods 0.000 claims description 19
- 230000014509 gene expression Effects 0.000 claims description 13
- 239000007787 solid Substances 0.000 claims description 12
- 230000009471 action Effects 0.000 claims description 9
- 230000005611 electricity Effects 0.000 claims description 9
- 238000009795 derivation Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000011160 research Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005338 heat storage Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention relates to an air conditioner energy optimizing method based on the combination control of a temperature set value and a main power switch, which comprises the following steps: step 1: establishing a thermodynamic characteristic model of the air conditioner load; step 2: temperature set point control and main power switch control strategy; step 3: a constant temperature control correction model; step 4: a linearized constant temperature control model; step 5: an energy consumption optimization model of the air conditioner load; step 6: and (5) optimizing and solving. According to the technical scheme, in order to obtain the energy-consumption optimization result aiming at the actual air conditioner, a discrete temperature set value and a main power supply on-off state are selected as decision variables, an air conditioner energy-consumption optimization model based on the combination control of the temperature set value and the main power supply on-off state is established, and the optimization model is solved, so that the optimization result capable of controlling the actual air conditioner is obtained.
Description
Technical Field
The invention relates to an optimization method, in particular to an air conditioner energy optimization method based on joint control of a temperature set value and a main power switch, and belongs to the field of electric power automation.
Background
As the permeability of renewable energy increases, the power fluctuation caused by renewable energy increases. In order to support active power balance of an electric power system, demand response technology is gaining increasing importance.
The demand response adjusts the power consumption of a user according to the operation requirement of a power grid, so that peak clipping and valley filling are realized, renewable energy power fluctuation is stabilized, electric equipment participating in the demand response has certain power transfer capacity, such as temperature control load (air conditioner and water heater), certain heat storage and cold storage capacity, and the load can be reduced while the stored heat/cold quantity is maintained, so that the load is adjusted. At present, there are various researches on the optimization of the air conditioner load energy at home and abroad, and the researches can be divided into power optimization and temperature set value optimization according to different optimization decision variables.
In terms of power optimization, the existing optimization method optimizes the compressor state of the air conditioner, the decision variable is the on-off state of the compressor, and the constant temperature control of the air conditioner is not affected by turning off the compressor (if the compressor is turned off, when the room temperature is higher than the threshold value, the compressor is turned on again). But in reality the on-off state of the main power supply is switched by the remote control that is on-load, not the compressor. If the air conditioner is set in an off state, the air conditioner is completely closed and is not controlled by the constant temperature.
In the aspect of optimizing the temperature set value, the existing optimizing method considers that the temperature set value of the air conditioner can be continuously adjusted, the temperature set value is used as a continuous variable for optimization, and the minimum temperature adjusting step length of the air conditioner remote controller is 1 ℃ in practice.
Aiming at the defects of the prior researches, the invention provides an air conditioner energy optimization model based on the combination control of a temperature set value and a main power supply on-off state.
Disclosure of Invention
The invention provides an energy optimizing method for an air conditioner based on joint control of a temperature set value and a main power switch, aiming at the problems existing in the prior art.
In order to achieve the above object, the technical scheme of the invention is as follows, an air conditioner energy optimizing method based on the combination control of a temperature set value and a main power switch, the method comprises the following steps:
Step 1: establishing a thermodynamic characteristic model of the air conditioner load;
step 2: temperature set point control and main power switch control strategy;
Step 3: a constant temperature control correction model;
Step 4: a linearized constant temperature control model;
step 5: an energy consumption optimization model of the air conditioner load;
step 6: and (5) optimizing and solving.
Wherein, step 1: the thermodynamic characteristic model of the air conditioner load is established, and the thermodynamic characteristic model is concretely as follows:
the thermodynamic model of air conditioning load can be described in terms of the following differential equation set:
Wherein:
T a (T) is the indoor air temperature;
t m (T) is the indoor solid temperature;
t o (T) is the outdoor air temperature;
R a is the equivalent thermal resistance of indoor air;
c a is the equivalent heat capacity of the indoor air;
r m is the equivalent thermal resistance of the indoor solid;
c m is the equivalent heat capacity of the indoor solids;
q (t) is the thermal power of the air conditioning load;
Discretized recursive calculation formula for T a (T):
Wherein:
Δt is the time step.
Wherein, step 2: the temperature set point control and main power switch control strategy is as follows:
Since the thermal power Q (t) cannot be continuously adjusted, the constant-frequency air conditioner needs to periodically switch the on-off state of the compressor to maintain the indoor temperature, and the module introduces the on-off state variable of the compressor based on the thermodynamic characteristic model, and the thermal power Q (t) of the constant-frequency air conditioner can be expressed as:
Q(t)＝s(t)·η·PN (3)
Wherein P N is the rated power of the air conditioner, η is the thermal efficiency, s (t) represents the on-off state of the compressor of the air conditioner, s (t) =1 represents the compressor in the "on" state, s (t) =0 represents the compressor in the "off state;
The on-off state S (T) of the compressor of the constant frequency air conditioner is jointly determined by the indoor temperature T a (T) and the on-off state S (T) of the main power supply, and the constant temperature control strategy can be expressed as follows:
Wherein T set is a temperature set value, deltaT db is a temperature regulation dead zone, when the indoor temperature T a (T) reaches an upper limit T set+ΔTdb/2 or a lower limit T set-ΔTdb/2, the air conditioning load is turned on or off, and the indoor temperature T a (T) of the air conditioning load can be controlled within the range of [ T set-ΔTdb/2,Tset+ΔTdb/2 ] through a constant temperature control strategy.
Wherein, step 3: the constant temperature control correction model is specifically as follows:
Equation (4) is a piecewise function, which is unfavorable for solving by adopting a convex optimization method, and the on-off switching period of the s (t) controlled by constant temperature is very short, if s (t) is used as a decision variable, the period of time is too long, and then the decision variable is too many, the solving is difficult, in order to facilitate the solving of the optimization problem, it is assumed that Q (t) is the average thermal power for a period of time, and when the on-off period is very small, the thermal power Q (t) can be approximately continuously adjusted in the constant temperature control.
For convenience of derivation, in the following derivation of the Q (t) expression, the main power supply is in an "on" state;
when the temperature set point T set (T) is given and the main power supply is in the "on" state (S (T) =1), in order to maintain the temperature set point at T set (T), the thermal power may be expressed as:
Q ex (T) is the steady state thermal power at room temperature T set (T). According to the principle of conservation of energy, the energy obtained in the room under steady-state conditions should be equal to the lost energy, so Q ex (t) is equal in value to the indoor and outdoor heat exchange power:
Under steady state conditions, there is T a(t)＝Tset (T), which is substituted into (6) to obtain:
Equations (5) and (7) constitute a constant temperature control strategy for air conditioning load at continuous power regulation. And the thermal power Q (T) is changed by controlling the T set (T), so that the T a (T) is regulated by the thermodynamic equation (2) of the system, and the T a(t)＝Tset (T) is finally realized. While equations (5) and (7) give the relationship of Q (T) and T set (T) in the continuous state, they are used only in the case where Q (T) and T a (T) continuously change.
Theoretically, if Q (T) and T a (T) can be continuously varied and Q ex (T) is within the adjustable capacity of the maximum power (Q ex(t)<ηPN), the air conditioner can adjust the indoor temperature T a (T) under the control strategies of (5) and (7) so that T a(t)＝Tset (T). However, there is a minimum step size (sampling period) Δt for any control or optimization, and due to Δt, under iterative action of the control strategies (5) and (7) and the thermodynamic characteristics (2) of the system, the indoor temperature T a (T) does not occur in the case of T a(t)＝Tset (T), but rather, switches back and forth between T a(t)>Tset (T) and T a(t)<Tset (T).
To obtain the relationship between Q (T) and T set (T) under discrete conditions, the module considers the difference between T a (T) and T a (t+1), modifies strategy (5),
The module considers the different strategies (5) of T a (T) and T a (t+1) to make corrections.
When T a(t)>Tset (T) and T a(t+1)>Tset (T), there are:
Q(t)＝ηPN (8)
When T a(t)＝Tset (T) and T a(t+1)＝Tset (T), there are:
Q(t)＝Qex(t) (9)
When T a(t)<Tset (T) and T a(t+1)<Tset (T), there are:
Q(t)＝0 (10)
Under the above three conditions, the strategy of Q (t) is consistent with (5).
However, when T a(t)<Tset (T) and T a(t+1)＝Tset (T), or when T a(t)>Tset (T) and T a(t+1)＝Tset (T), the value of Q (T) cannot be represented simply by 0, Q ex (T), or ηP N. The size of Q (T) should be such that T a(t+1)＝Tset (T), but not T a (t+1), appears beyond T set (T) when T a(t)<Tset (T) and T a(t+1)>Tset (T), or T a(t)>Tset (T) and T a(t+1)<Tset (T).
Substituting T a(t+1)＝Tset (T) into (2), and calculating to obtain the expression of Q (T) at the moment, wherein Q (T) at the moment is denoted as Q th (T):
The expressions (8) - (11) give the expressions of Q (t) under different conditions, and the thermal power Q (t) under the constant temperature control strategy is expressed as follows
Note that Q th (T) actually represents the required thermal power for temperature regulation from any T a (T) to T set (T) over Δt time, so the Q th (T) size can be used to determine whether T a (t+1) can reach T set (T) within Δt. Q th (t) satisfies 0<Q th(t)<ηPN when Q (t) =q th (t) and Q (t) =q ex (t). When T a(t)＝Tset (T), there is Q ex(t)＝Qth (T) according to (7) and (11). In addition, when Q th(t)≥ηPN is equal to or less than 0, T a(t)>Tset (T) is necessarily satisfied, and when Q th (T) is equal to or less than 0, T a(t)<Tset (T) is also necessarily satisfied. (12) can be converted into:
Equation (13) and equation (12) are equivalent, but greatly simplified, and (13) converts the condition of T set (T) into the condition of Q th (T), which is convenient for optimization solution.
The above derivation is based on the assumption that the main power on-off state S (t) =1. When considering the variation of S (t), equation (13) needs to be converted into the following form:
Wherein, step 4: the linearized constant temperature control model is specifically as follows:
for the piecewise function of Q (t) given by equation (14), a large M method can be used to translate into mixed integer linearization problems, by introducing 0-1 variables σ 1 and σ 2, and a sufficiently large positive number M, (14) can be translated into the following equivalent form.
As can be seen from (15), Q (t) =s (t) ηp N and Q th(t)≥S(t)ηPN are satisfied when σ 1 =1 and σ 2 =0, Q (t) =q th (t) and 0<Q th(t)<S(t)ηPN are satisfied when σ 1 =0 and σ 2 =0, and Q (t) =0 and Q th (t) +.0 are satisfied when σ 1 =0 and σ 2 =1. Corresponding to the three conditions in equation (14), respectively.
Equations (11) and (15) constitute a mixed integer linearized thermostatic control model of air conditioning load, i.e., the relationship of Q (T) to T set (T) under a thermostatic control strategy.
Wherein, step 5: the energy consumption optimizing model for the air conditioner load is specifically as follows:
The air conditioner load optimization model needs to consider the electricity fee cost and the comfort level cost, wherein the comfort level cost reflects the influence of the deviation of the indoor temperature from the temperature set value on the comfort level of a user. The objectives of the optimization are expressed as follows:
Min(Celec+Ccomfort)(16)
Wherein C elec and C comfort represent the electricity fee cost and the comfort cost, respectively, and can be calculated as follows:
Where N T is the number of optimization periods, p elec (t) is the electricity price at time t, and p comfort (t) is the comfort price at time t. To facilitate solution using convex optimization, equation (18) can be converted to the following equivalent:
Wherein α + (T) and α - (T) are two auxiliary variables respectively representing the degree to which the indoor temperature T a (T) deviates positively/negatively from the original temperature set value T set0;
the constraints of the model are as follows.
1) Relation constraint of electric power and thermal power:
2) Thermodynamic properties of air conditioner:
The thermodynamic characteristic model of the air conditioner adopts a second-order equivalent thermal parameter model shown in the step (2);
3) Constant temperature control model of air conditioner:
the constant temperature control model of the air conditioner is based on formulas (11) and (15);
in addition, in order to ensure that the temperature set point is adjusted within a certain range, a temperature set point adjustment limit value needs to be set:
Tsetmax≤Tset(t)≤Tsetmin
(22)
Wherein T setmin and T setmax are upper and lower allowable temperature set points;
4) Total power on-off times constraint:
Wherein x S(t),yS (t) is a variable of 0-1, x S(t)/yS (t) respectively represents on-off actions of the total power supply of the air conditioner, and the following conditions are satisfied:
xS(t)-yS(t)＝S(t)-S(t-1) (24)
xS(t)+yS(t)≤1 (25)
5) Temperature adjustment times state constraint:
wherein x TA(t),yTA (t) is a 0-1 variable, x TA(t)/yTA (t) represents a temperature up/down adjustment action, respectively, and satisfies:
-(xTA(t)-yTA(t))(Tsetmax-Tsetmin)≤Tset(t)-Tset(t-1)≤(xTA(t)-yTA(t))(Tsetmax-Tsetmin)(27)
xTA(t)+yTA(t)≤1
(28)
6) Initial value condition:
Initial values of the indoor air temperature T a (T) and the indoor solid temperature T m (T) need to be set:
Wherein T a1 and T m1 are the values of the starting times T a (T) and T m (T), respectively.
In summary, the constant temperature control correction optimization scheduling model of the air conditioner load comprises:
Objective function: (16), (17), (19);
constraint conditions: (2) (11), (15), (20), (21) and (29).
Wherein, step 6: and carrying out optimization solving on the established optimization model by utilizing a cplex tool box of MATLAB, wherein the optimization solving method comprises the following steps of:
it can be seen that the "energy-saving optimization model for air conditioner load" is a mixed integer linear optimization problem, and the optimization tool (for example cplex tool box) can be used to solve the model, so as to finally obtain an optimization result of the actual controllable air conditioner load, including an optimized temperature set value T set (T) and a main power supply on-off signal S (T).
Compared with the prior art, the invention has the following advantages that 1) the technical scheme takes the minimum step length of the temperature set value of the air conditioner load in the actual life scene into consideration, and the optimal temperature set value T set (T) indirectly adjusts the electric power of the air conditioner. Establishing a conditional relation model between T set (T) and air conditioner power, converting the conditional relation model into an expression of the air conditioner power through conditional judgment, and converting the expression into a mixed integer linear programming problem by using a large M method to solve the problem; 2) The optimization considers the on-off control signal S (t) of the main power supply of the air conditioner in practical application, rather than the on-off state S (t) of the compressor. 3) The combined optimization strategy of the temperature set point T set (T) and the main power on-off control signal S (T) is applicable to an actual air conditioner control system, and the optimized decision variable corresponds to an air conditioner control panel. The method is suitable for both fixed-frequency air conditioners and variable-frequency air conditioners.
Drawings
FIG. 1 is a schematic diagram of a thermodynamic model of an air conditioning load;
FIG. 2 is a schematic flow chart of an air conditioning energy optimization model based on a combination of a temperature set point and a main power switch; FIG. 3 compares (a) electricity price conditions with different thermostatic control models; (b) optimizing power results under different control strategies; (c) The optimized indoor temperature T a (T); (d) Optimized control signals S (T) and T set (T).
Detailed Description
In order to enhance the understanding of the present invention, the present embodiment will be described in detail with reference to the accompanying drawings.
Example 1: referring to fig. 1, in this embodiment, an air conditioner energy optimizing method based on a combination control of a temperature setting value and a main power switch,
The realization steps mainly include introducing a temperature set value control and a main power supply on-off control strategy into an air conditioner load thermodynamic characteristic model, obtaining a constant temperature control correction model through variable replacement and condition conversion, further linearizing to obtain a linearized constant temperature control model, establishing an optimization target on the basis and considering other constraint conditions, establishing an air conditioner load energy optimization model, and carrying out optimization solution through mixed integer linear programming.
An air conditioner energy optimizing method based on the combination control of a temperature set value and a main power switch, the method comprises the following steps:
Step 1: establishing a thermodynamic characteristic model of the air conditioner load;
step 2: temperature set point control and main power switch control strategy;
Step 3: a constant temperature control correction model;
Step 4: a linearized constant temperature control model;
step 5: an energy consumption optimization model of the air conditioner load;
step 6: and (5) optimizing and solving.
Wherein, step 1: the thermodynamic characteristic model of the air conditioner load is established, and the thermodynamic characteristic model is concretely as follows:
the thermodynamic model of air conditioning load is shown in fig. 1 and can be described in the form of the following differential equation set:
Wherein:
T a (T) is the indoor air temperature;
t m (T) is the indoor solid temperature;
t o (T) is the outdoor air temperature;
R a is the equivalent thermal resistance of indoor air;
c a is the equivalent heat capacity of the indoor air;
r m is the equivalent thermal resistance of the indoor solid;
c m is the equivalent heat capacity of the indoor solids;
q (t) is the thermal power of the air conditioning load;
Discretized recursive calculation formula for T a (T):
wherein, step 2: the temperature set point control and main power switch control strategy is as follows:
Since the thermal power Q (t) cannot be continuously adjusted, the constant-frequency air conditioner needs to periodically switch the on-off state of the compressor to maintain the indoor temperature, and the module introduces the on-off state variable of the compressor based on the thermodynamic characteristic model, and the thermal power Q (t) of the constant-frequency air conditioner can be expressed as:
Q(t)＝s(t)·η·PN (3)
Wherein P N is the rated power of the air conditioner, η is the thermal efficiency, s (t) represents the on-off state of the compressor of the air conditioner, s (t) =1 represents the compressor in the "on" state, s (t) =0 represents the compressor in the "off state;
The on-off state S (T) of the compressor of the constant frequency air conditioner is jointly determined by the indoor temperature T a (T) and the on-off state S (T) of the main power supply, and the constant temperature control strategy can be expressed as follows:
Wherein T set is a temperature set value, deltaT db is a temperature regulation dead zone, when the indoor temperature T a (T) reaches an upper limit T set+ΔTdb/2 or a lower limit T set-ΔTdb/2, the air conditioning load is turned on or off, and the indoor temperature T a (T) of the air conditioning load can be controlled within the range of [ T set-ΔTdb/2,Tset+ΔTdb/2 ] through a constant temperature control strategy.
Wherein, step 3: the constant temperature control correction model is specifically as follows:
Equation (4) is a piecewise function, which is unfavorable for solving by adopting a convex optimization method, and the on-off switching period of the s (t) controlled by constant temperature is very short, if s (t) is used as a decision variable, the period of time is too long, and then the decision variable is too many, the solving is difficult, in order to facilitate the solving of the optimization problem, it is assumed that Q (t) is the average thermal power for a period of time, and when the on-off period is very small, the thermal power Q (t) can be approximately continuously adjusted in the constant temperature control.
For convenience of derivation, in the following derivation of the Q (t) expression, the main power supply is in an "on" state;
when the temperature set point T set (T) is given and the main power supply is in the "on" state (S (T) =1), in order to maintain the temperature set point at T set (T), the thermal power may be expressed as:
Q ex (T) is the steady state thermal power at room temperature T set (T). According to the principle of conservation of energy, the energy obtained in the room under steady-state conditions should be equal to the lost energy, so Q ex (t) is equal in value to the indoor and outdoor heat exchange power:
Under steady state conditions, there is T a(t)＝Tset (T), which is substituted into (6) to obtain:
Equations (5) and (7) constitute a constant temperature control strategy for air conditioning load at continuous power regulation. And the thermal power Q (T) is changed by controlling the T set (T), so that the T a (T) is regulated by the thermodynamic equation (2) of the system, and the T a(t)＝Tset (T) is finally realized. While equations (5) and (7) give the relationship of Q (T) and T set (T) in the continuous state, they are used only in the case where Q (T) and T a (T) continuously change.
Theoretically, if Q (T) and T a (T) can be continuously varied and Q ex (T) is within the adjustable capacity of the maximum power (Q ex(t)<ηPN), the air conditioner can adjust the indoor temperature T a (T) under the control strategies of (5) and (7) so that T a(t)＝Tset (T). However, there is a minimum step size (sampling period) Δt for any control or optimization, and due to Δt, under iterative action of the control strategies (5) and (7) and the thermodynamic characteristics (2) of the system, the indoor temperature T a (T) does not occur in the case of T a(t)＝Tset (T), but rather, switches back and forth between T a(t)>Tset (T) and T a(t)<Tset (T).
To obtain the relationship between Q (T) and T set (T) under discrete conditions, the module considers the difference between T a (T) and T a (t+1), modifies strategy (5),
The module considers the different strategies (5) of T a (T) and T a (t+1) to make corrections.
When T a(t)>Tset (T) and T a(t+1)>Tset (T), there are:
Q(t)＝ηPN (8)
When T a(t)＝Tset (T) and T a(t+1)＝Tset (T), there are:
Q(t)＝Qex(t) (9)
When T a(t)<Tset (T) and T a(t+1)<Tset (T), there are:
Q(t)＝0 (10)
Under the above three conditions, the strategy of Q (t) is consistent with (5).
However, when T a(t)<Tset (T) and T a(t+1)＝Tset (T), or when T a(t)>Tset (T) and T a(t+1)＝Tset (T), the value of Q (T) cannot be represented simply by 0, Q ex (T), or ηP N. The size of Q (T) should be such that T a(t+1)＝Tset (T), but not T a (t+1), appears beyond T set (T) when T a(t)<Tset (T) and T a(t+1)>Tset (T), or T a(t)>Tset (T) and T a(t+1)<Tset (T).
Substituting T a(t+1)＝Tset (T) into (2), and calculating to obtain the expression of Q (T) at the moment, wherein Q (T) at the moment is denoted as Q th (T):
The expressions (8) - (11) give the expressions of Q (t) under different conditions, and the thermal power Q (t) under the constant temperature control strategy is expressed as follows
Note that Q th (T) actually represents the required thermal power for temperature regulation from any T a (T) to T set (T) over Δt time, so the Q th (T) size can be used to determine whether T a (t+1) can reach T set (T) within Δt. Q th (t) satisfies 0<Q th(t)<ηPN when Q (t) =q th (t) and Q (t) =q ex (t). When T a(t)＝Tset (T), there is Q ex(t)＝Qth (T) according to (7) and (11). In addition, when Q th(t)≥ηPN is equal to or less than 0, T a(t)>Tset (T) is necessarily satisfied, and when Q th (T) is equal to or less than 0, T a(t)<Tset (T) is also necessarily satisfied. (12) can be converted into:
Equation (13) and equation (12) are equivalent, but greatly simplified, and (13) converts the condition of T set (T) into the condition of Q th (T), which is convenient for optimization solution.
The above derivation is based on the assumption that the main power on-off state S (t) =1. When considering the variation of S (t), equation (13) needs to be converted into the following form:
Wherein, step 4: the linearized constant temperature control model is specifically as follows:
for the piecewise function of Q (t) given by equation (14), a large M method can be used to translate into mixed integer linearization problems, by introducing 0-1 variables σ 1 and σ 2, and a sufficiently large positive number M, (14) can be translated into the following equivalent form.
As can be seen from (15), Q (t) =s (t) ηp N and Q th(t)≥S(t)ηPN are satisfied when σ 1 =1 and σ 2 =0, Q (t) =q th (t) and 0<Q th(t)<S(t)ηPN are satisfied when σ 1 =0 and σ 2 =0, and Q (t) =0 and Q th (t) +.0 are satisfied when σ 1 =0 and σ 2 =1. Corresponding to the three conditions in equation (14), respectively.
Equations (11) and (15) constitute a mixed integer linearized thermostatic control model of air conditioning load, i.e., the relationship of Q (T) to T set (T) under a thermostatic control strategy.
Wherein, step 5: the energy consumption optimizing model for the air conditioner load is specifically as follows:
The air conditioner load optimization model needs to consider the electricity fee cost and the comfort level cost, wherein the comfort level cost reflects the influence of the deviation of the indoor temperature from the temperature set value on the comfort level of a user. The objectives of the optimization are expressed as follows:
Min(Celec+Ccomfort)(16)
Wherein C elec and C comfort represent the electricity fee cost and the comfort cost, respectively, and can be calculated as follows:
Where N T is the number of optimization periods, p elec (t) is the electricity price at time t, and p comfort (t) is the comfort price at time t. To facilitate solution using convex optimization, equation (18) can be converted to the following equivalent:
Wherein α + (T) and α - (T) are two auxiliary variables respectively representing the degree to which the indoor temperature T a (T) deviates positively/negatively from the original temperature set value T set0;
the constraints of the model are as follows.
1) Relation constraint of electric power and thermal power:
2) Thermodynamic properties of air conditioner:
The thermodynamic characteristic model of the air conditioner adopts a second-order equivalent thermal parameter model shown in the step (2);
3) Constant temperature control model of air conditioner:
the constant temperature control model of the air conditioner is based on formulas (11) and (15);
in addition, in order to ensure that the temperature set point is adjusted within a certain range, a temperature set point adjustment limit value needs to be set:
Tsetmax≤Tset(t)≤Tsetmin
(22)
Wherein T setmin and T setmax are upper and lower allowable temperature set points;
4) Total power on-off times constraint:
Wherein x S(t),yS (t) is a variable of 0-1, x S(t)/yS (t) respectively represents on-off actions of the total power supply of the air conditioner, and the following conditions are satisfied:
xS(t)-yS(t)＝S(t)-S(t-1) (24)
xS(t)+yS(t)≤1 (25)
5) Temperature adjustment times state constraint:
wherein x TA(t),yTA (t) is a 0-1 variable, x TA(t)/yTA (t) represents a temperature up/down adjustment action, respectively, and satisfies:
-(xTA(t)-yTA(t))(Tsetmax-Tsetmin)≤Tset(t)-Tset(t-1)≤(xTA(t)-yTA(t))(Tsetmax-Tsetmin)(27)
xTA(t)+yTA(t)≤1
(28)
6) Initial value condition:
Initial values of the indoor air temperature T a (T) and the indoor solid temperature T m (T) need to be set:
Wherein T a1 and T m1 are the values of the starting times T a (T) and T m (T), respectively.
In summary, the constant temperature control correction optimization scheduling model of the air conditioner load comprises:
Objective function: (16), (17), (19);
constraint conditions: (2) (11), (15), (20), (21) and (29).
Wherein, step 6: and carrying out optimization solving on the established optimization model by utilizing a cplex tool box of MATLAB, wherein the optimization solving method comprises the following steps of:
it can be seen that the "energy-saving optimization model for air conditioner load" is a mixed integer linear optimization problem, and the optimization tool (for example cplex tool box) can be used to solve the model, so as to finally obtain an optimization result of the actual controllable air conditioner load, including an optimized temperature set value T set (T) and a main power supply on-off signal S (T).
It should be noted that the above-mentioned embodiments are not intended to limit the scope of the present invention, and equivalent changes or substitutions made on the basis of the above-mentioned technical solutions fall within the scope of the present invention as defined in the claims.
Claims (1)
1. An air conditioner energy optimizing method based on the combination control of a temperature set value and a main power switch is characterized by comprising the following steps:
Step 1: establishing a thermodynamic characteristic model of the air conditioner load;
step 2: temperature set point control and main power switch control strategy;
Step 3: a constant temperature control correction model;
Step 4: a linearized constant temperature control model;
step 5: an energy consumption optimization model of the air conditioner load;
Step 6: the solution is optimized and carried out,
Wherein, step 1: the thermodynamic characteristic model of the air conditioner load is established, and the thermodynamic characteristic model is concretely as follows:
Described as a form of the following differential equation set:
Wherein:
T a (T) is the indoor air temperature;
t m (T) is the indoor solid temperature;
t o (T) is the outdoor air temperature;
R a is the equivalent thermal resistance of indoor air;
c a is the equivalent heat capacity of the indoor air;
r m is the equivalent thermal resistance of the indoor solid;
c m is the equivalent heat capacity of the indoor solids;
q (t) is the thermal power of the air conditioning load;
Discretized recursive calculation formula for T a (T):
Wherein:
Δt is the step of time in time,
Wherein, step 2: the temperature set point control and main power switch control strategy is as follows:
Since the thermal power Q (t) cannot be continuously adjusted, the constant-frequency air conditioner needs to periodically switch the on-off state of the compressor to maintain the indoor temperature, and the control strategy is based on a thermodynamic characteristic model, and introduces a compressor on-off state variable, and the thermal power Q (t) of the constant-frequency air conditioner is expressed as:
Q(t)＝s(t)·η·PN (3)
Wherein P N is the rated power of the air conditioner, η is the thermal efficiency, s (t) represents the on-off state of the compressor of the air conditioner, s (t) =1 represents the compressor in the "on" state, s (t) =0 represents the compressor in the "off state;
The on-off state S (T) of the compressor of the constant frequency air conditioner is jointly determined by the indoor temperature T a (T) and the on-off state S (T) of the main power supply, and the constant temperature control strategy is expressed as follows:
Wherein T set is a temperature set value, deltaT db is a temperature regulation dead zone, when the indoor temperature T a (T) reaches an upper limit T set+ΔTdb/2 or a lower limit T set-ΔTdb/2, the air conditioning load is turned on or off, the indoor temperature T a (T) of the air conditioning load can be controlled within the range of [ T set-ΔTdb/2,Tset+ΔTdb/2 ] through a constant temperature control strategy,
Wherein, step 3: the constant temperature control correction model is specifically as follows:
Assuming that Q (t) is the average thermal power for a period of time, when the on-off period is small, the thermal power Q (t) is approximately continuously adjusted in the thermostatic control;
When the temperature set point T set (T) is given and the main power supply is in the "on" state, i.e., S (T) =1, in order to maintain the temperature set point at T set (T), the thermal power is expressed as:
Q ex (T) is the steady-state thermal power of the indoor temperature at T set (T), and according to the principle of conservation of energy, the energy obtained in the indoor under steady-state conditions should be equal to the lost energy, so Q ex (T) is equal in value to the indoor-outdoor heat exchange power:
Under steady state conditions, there is T a(t)＝Tset (T), which is substituted into equation (6) to obtain:
Equations (5) and (7) form a constant temperature control strategy for continuous power adjustment of air conditioning load, the thermal power Q (T) is changed by controlling T set (T), then T a (T) is adjusted by equation (2), and finally T a(t)＝Tset (T) is realized,
Theoretically, if Q (T) and T a (T) can be continuously varied, and Q ex (T) is within the adjustable capacity of maximum power (Q ex(t)<ηPN), the air conditioner can adjust the indoor temperature T a (T) under the control strategy of equation (5) and equation (7), so that T a(t)＝Tset (T) exists, however, any control or optimization has a minimum step size Deltat, due to the existence of Deltat, under the iterative action of the control strategies equation (5), equation (7) and equation (2), the indoor temperature T a (T) will not occur in the case of T a(t)＝Tset (T), but will switch back and forth between T a(t)>Tset (T) and T a(t)<Tset (T),
In order to obtain the relation between Q (T) and T set (T) under discrete conditions, the constant temperature control correction model considers the difference between T a (T) and T a (t+1), corrects the formula (5),
The constant temperature control correction model corrects the formula (5) by considering the difference between T a (T) and T a (t+1),
When T a(t)>Tset (T) and T a(t+1)>Tset (T), there are:
Q(t)＝ηPN (8)
When T a(t)＝Tset (T) and T a(t+1)＝Tset (T), there are:
Q(t)＝Qex(t) (9)
When T a(t)<Tset (T) and T a(t+1)<Tset (T), there are:
Q(t)＝0 (10)
Under the above three conditions, Q (t) is consistent with equation (5),
However, when T a(t)<Tset (T) and T a(t+1)＝Tset (T), or when T a(t)>Tset (T) and T a(t+1)＝Tset (T), the value of Q (T) cannot be represented simply by 0, Q ex (T), or ηP N, the size of Q (T) should ensure that T a(t+1)＝Tset (T), rather than T a (t+1) is beyond T set (T) where T a(t)<Tset (T) and T a(t+1)>Tset (T), or T a(t)>Tset (T) and T a(t+1)<Tset (T),
Substituting T a(t+1)＝Tset (T) into formula (2), and calculating to obtain the expression of Q (T) at the moment, wherein Q (T) at the moment is denoted as Q th (T):
The expressions (8) - (11) give the expressions of Q (t) under different conditions, and the thermal power Q (t) under the constant temperature control strategy is expressed as follows
Note that Q th (T) actually represents the required thermal power for temperature adjustment from any T a (T) to T set (T) in Δt time, so that determination of whether T a (t+1) can reach T set (T) in Δt using Q th (T) size, Q th (T) satisfies 0<Q th(t)<ηPN in the case of Q (T) =q th (T) and Q (T) =q ex (T), Q ex(t)＝Qth (T) is present according to formula (7) and formula (11) in the case of T a(t)＝Tset (T), and T a(t)>Tset (T) is also satisfied in the case of Q th(t)≥ηPN, T a(t)<Tset (T) is also satisfied in the case of Q th (T) +.0, and formula (12) is convertible to:
Equation (13) and equation (12) are equivalent, simplified, and equation (13) converts the condition of T set (T) into the condition of Q th (T), which facilitates optimization solution,
The above derivation is based on the assumption that the main power on-off state S (t) =1, and when considering the variation of S (t), the equation (13) needs to be converted into the following form:
step 4: the linearized constant temperature control model is specifically as follows:
For the piecewise function of Q (t) given in equation (14), the large M method is used to translate into mixed integer linearization problem, by introducing 0-1 variables σ 1 and σ 2, and a sufficiently large positive number M, equation (14) is translated into the following equivalent form;
As seen from the formula (15), Q (t) =s (t) ηp N and Q th(t)≥S(t)ηPN are satisfied when σ 1 =1 and σ 2 =0, Q (t) =q th (t) and 0<Q th(t)<S(t)ηPN are satisfied when σ 1 =0 and σ 2 =0, Q (t) =0 and Q th (t) +.0 are satisfied when σ 1 =0 and σ 2 =1, respectively corresponding to three conditions in the formula (14),
Formulas (11) and (15) form a mixed integer linear constant temperature control model of air conditioning load, namely the relation between Q (T) and T set (T) under a constant temperature control strategy,
Step 5: the energy consumption optimizing model for the air conditioner load is specifically as follows:
The objectives of the optimization are expressed as follows:
Min(Celec+Ccomfort) (16)
Wherein C elec and C comfort represent the electricity fee cost and the comfort cost, respectively, and are calculated as follows:
Wherein N T is the number of optimization periods, p elec (t) is the electricity price at time t, p comfort (t) is the comfort price at time t, and for convenience, convex optimization is adopted for solving, the formula (18) is converted into the following equivalent form:
Wherein α + (T) and α - (T) are two auxiliary variables respectively representing the degree to which the indoor temperature T a (T) deviates positively/negatively from the original temperature set value T set0;
The constraint conditions of the air conditioner load energy optimization model are as follows,
1) Relation constraint of electric power and thermal power:
2) Thermodynamic properties of air conditioner:
the thermodynamic characteristic model of the air conditioner adopts a second-order equivalent thermal parameter model shown in a formula (2);
3) Constant temperature control model of air conditioner:
the constant temperature control model of the air conditioner is based on the formula (11) and the formula (15);
setting a temperature set point adjustment limit:
Tsetmax≤Tset(t)≤Tsetmin (22)
Wherein T setmin and T setmax are upper and lower allowable temperature set points;
4) Total power on-off times constraint:
Wherein x S(t),yS (t) is a variable of 0-1, x S(t)/yS (t) respectively represents on-off actions of the total power supply of the air conditioner, and the following conditions are satisfied:
xS(t)-yS(t)＝S(t)-S(t-1) (24)
xS(t)+yS(t)≤1 (25)
5) Temperature adjustment times state constraint:
wherein x TA(t),yTA (t) is a 0-1 variable, x TA(t)/yTA (t) represents a temperature up/down adjustment action, respectively, and satisfies:
-(xTA(t)-yTA(t))(Tsetmax-Tsetmin)≤Tset(t)-Tset(t-1)≤(xTA(t)-yTA(t))(Tsetmax-Tsetmin) (27)
xTA(t)+yTA(t)≤1 (28)
6) Initial value condition:
Initial values of the indoor air temperature T a (T) and the indoor solid temperature T m (T) need to be set:
Wherein T a1 and T m1 are the values of the starting times T a (T) and T m (T), respectively,
In summary, the constant temperature control correction optimization scheduling model of the air conditioner load comprises:
objective function: equation (16), equation (17), equation (19);
Constraint conditions: equation (2), equation (11), equation (15), equation (20), equation (21) -equation (29),
Step 6: the optimization solution is specifically as follows:
Finally, the optimization result of the actual controllable air conditioner load is obtained, wherein the optimization result comprises an optimized temperature set value T set (T) and a main power supply on-off signal S (T).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210950456.9A CN115344070B (en) | 2022-08-09 | 2022-08-09 | Air conditioner energy optimization method based on combination control of temperature set value and main power switch |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210950456.9A CN115344070B (en) | 2022-08-09 | 2022-08-09 | Air conditioner energy optimization method based on combination control of temperature set value and main power switch |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115344070A CN115344070A (en) | 2022-11-15 |
| CN115344070B true CN115344070B (en) | 2024-05-17 |
Family
ID=83952743
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210950456.9A Active CN115344070B (en) | 2022-08-09 | 2022-08-09 | Air conditioner energy optimization method based on combination control of temperature set value and main power switch |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115344070B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115730725B (en) * | 2022-11-24 | 2023-11-07 | 国网江苏省电力有限公司营销服务中心 | Energy consumption optimization method and system for air conditioner load and fresh air machine to jointly participate in room temperature adjustment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111555304A (en) * | 2019-04-11 | 2020-08-18 | 东南大学 | Air conditioner load virtual energy storage scheduling method for power grid frequency modulation service |
| CN114543275A (en) * | 2021-10-25 | 2022-05-27 | 国网浙江省电力有限公司湖州供电公司 | Air conditioner energy consumption optimization measuring and calculating method and system |
| CN114623569A (en) * | 2021-11-04 | 2022-06-14 | 国网浙江省电力有限公司湖州供电公司 | Cluster air conditioner load differentiation regulation and control method based on deep reinforcement learning |
| CN114665458A (en) * | 2021-12-17 | 2022-06-24 | 南京师范大学 | Air conditioner load multi-objective optimization scheduling method based on elastic constraint |
| CN114659254A (en) * | 2022-03-17 | 2022-06-24 | 南京师范大学 | Air conditioner load frequency control method based on discrete adjustment of temperature set value |
-
2022
- 2022-08-09 CN CN202210950456.9A patent/CN115344070B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111555304A (en) * | 2019-04-11 | 2020-08-18 | 东南大学 | Air conditioner load virtual energy storage scheduling method for power grid frequency modulation service |
| CN114543275A (en) * | 2021-10-25 | 2022-05-27 | 国网浙江省电力有限公司湖州供电公司 | Air conditioner energy consumption optimization measuring and calculating method and system |
| CN114623569A (en) * | 2021-11-04 | 2022-06-14 | 国网浙江省电力有限公司湖州供电公司 | Cluster air conditioner load differentiation regulation and control method based on deep reinforcement learning |
| CN114665458A (en) * | 2021-12-17 | 2022-06-24 | 南京师范大学 | Air conditioner load multi-objective optimization scheduling method based on elastic constraint |
| CN114659254A (en) * | 2022-03-17 | 2022-06-24 | 南京师范大学 | Air conditioner load frequency control method based on discrete adjustment of temperature set value |
Non-Patent Citations (2)
| Title |
|---|
| 参与需求侧响应的空调负荷群调节控制方法及优化调度策略;刘利兵 等;工程科学与技术;20170430(第S1期);全文 * |
| 空调负荷二阶等效热参数模型参数辨识方法;包宇庆;电力系统自动化;20210610;第45卷(第10期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115344070A (en) | 2022-11-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111555304B (en) | Air conditioner load virtual energy storage scheduling method for power grid frequency modulation service | |
| CN110794775B (en) | Multi-boiler load intelligent control system and method | |
| CN111555274B (en) | Dynamic assessment method for air conditioner load demand response capability | |
| US20120086273A1 (en) | Dynamic thermostatic control of small-scale electrical loads for matching variations in electric utility supply | |
| CN115344070B (en) | Air conditioner energy optimization method based on combination control of temperature set value and main power switch | |
| CN113757931B (en) | Air conditioner control method and system | |
| CN114580254A (en) | Building indoor temperature regulation and control method and system based on model predictive control | |
| CN109812946B (en) | Control method suitable for large-scale resident air conditioner load group demand response | |
| CN112556258A (en) | Heat pump intelligent control method for compensating time delay | |
| CN111442328A (en) | Electric heating equipment and control method thereof | |
| CN106595364A (en) | Intelligent control method for off-peak electricity solid heat storage equipment | |
| CN114659254B (en) | Air conditioner load frequency control method based on discrete adjustment of temperature set value | |
| CN116149401A (en) | System and method for controlling outlet temperature of heat exchanger of compressed air energy storage power station | |
| CN112994036B (en) | Temperature control load participation micro-grid regulation and control method and system based on model prediction | |
| CN111750507A (en) | Air conditioner tail end wind and water linkage adjusting system and adjusting method | |
| CN115079560A (en) | Oil gas temperature control method and system for compressed air energy storage system | |
| CN202382361U (en) | Intelligent air-conditioning power-saving device | |
| CN112539450B (en) | Multi-strategy intelligent heat supply method based on temperature and time offset control | |
| CN115328241A (en) | Intelligent greenhouse control method and system for intelligent small town | |
| JP6071474B2 (en) | Air conditioning equipment | |
| CN114172202A (en) | Load frequency control method of wind power interconnection-containing power system based on active response of demand side resources | |
| CN107273673A (en) | A kind of air conditioner cold water unit group control method for considering to stabilize wind-powered electricity generation fluctuation | |
| CN113420413A (en) | Flexible load adjustability quantification method and system based on load plasticity | |
| CN115823650B (en) | Indoor thermal comfort and energy regulation and control method based on model predictive control | |
| CN112967152B (en) | Water-loop heat pump load aggregation modeling and control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |